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Saucon Source (https://sauconsource.com/2022/11/03/bar-talk-the-eq-series-week-1-introduction-to-eq/)

Without any show of hands, if I were to ask if you were familiar with the term IQ, the answer would likely be yes. And if I were to ask you to define IQ, it would probably be something like: “IQ is a numeric score derived from a test designed to assess intelligence.” Or put simply, one could summarize, “IQ is a number that determines how smart someone is.” Even if we have never taken an IQ test and have no idea what our actual IQ score is, we have been conditioned throughout life to understand that being born with a high IQ is a good bit of luck. It’s widely recognized as a desirable, door-opening attribute associated with paving the way to success. Right or wrong, IQ has long been held as a sort of measuring stick for how much potential someone has to rise through the ranks of life, including professional careers.
This traditional line of thinking is coming into question as more attention is given to the significance of Emotional Intelligence, commonly referred to as EQ. One crucial distinction between EQ and IQ is that EQ is a score that can be increased over time, whereas IQ is considered to be a fixed point throughout our life.
I spend a fair amount of time speaking about EQ to individuals as well as groups. In this six-part series of Bar Talk, I will share some of the main components and approaches that you can easily incorporate into your life to elevate your personal and professional results along with your relationships with others. One of the reasons EQ is such a critical topic to learn more about is that it plays a central role in our lives on a daily basis, both in managing our thoughts and mindset and in our interactions with all of the people in our life. To underscore its level of importance, Dr.Daniel Goleman, who is widely considered the pioneering voice and researcher of EQ, considers these skillsets to be the defining factors in determining our level of health, wealth and happiness in life.
Whether or not you have heard the term ‘emotional intelligence,’ you see it (or a lack of it) continuously, even if you haven’t consciously recognized it as EQ. For example, maybe you work with someone who is incredibly bright and a subject matter expert in your industry. They are also a nightmare to deal with by being argumentative, condescending and hypercritical. Generally speaking, they could be described as having a toxic personality. On the other end of the spectrum is a co-worker who has a magnetic personality, seems to be known and loved by all and has every door and opportunity open to them. They can diffuse stress, effectively communicate with any audience, collaborate and move through the world with ease and a smile. Perhaps you know someone like this and have thought at some point, “they’re so lucky!” It’s not luck if it’s repeatable, and it’s likely that person has a high level of EQ and puts the work in to stay sharp in all of the areas that shine through. In other words: “do what others won’t, get results that others don’t.”
Before we dig into the various areas of EQ, let’s first pause to consider how our fantastic human brain is wired up. All of our senses (sight, sound, smell, taste and touch) enter our body as signals that travel through our spinal cord into our brain. Once the signals come into our brain, the first stop is our limbic system, where our emotions are created. Following that, those signals travel to our neocortex, where the rational part of our brain is, and logic prevails. Here’s the problem: before all of these input messages coming into our body have a chance to arrive at the rational part of our brain, we are already having an emotional reaction to them. This default brain setting served our ancient ancestors well when they were facing mortal dangers hunting for food and fending off predators, which required making instantaneous fight or flight choices, but in the present day we are most often best served by thinking before reacting. Our brain has not changed with the times, so it’s up to us to develop the practice of recognizing the fact that emotional responses are always occurring, but without being hijacked by them. Think of it this way: on the great road trip of life our emotions are always along for the ride. We choose whether to let them drive or keep them in the passenger seat.
EQ can be broken down into four distinct areas: Self-Awareness, Self-Management, Social-Awareness and Relationship Management. In next week’s’ Part 2 edition I’ll provide an overview of the first personal competency: Self-Awareness. In each of the sections we will cover some straightforward techniques that can be put into practice immediately if you start developing the habit. Like many other self-development concepts, these are easy to do and also easy not to do, but I’m confident the result you will start seeing early on will motivate you to want to continue developing these powerful skillsets.
Eric Bartosz is the founder of BAR40 and the author of the internationally acclaimed and bestselling book ‘BAR40: Achieving Personal Excellence.’ He lives in Center Valley with his wife Trish, daughter Riley and pug Piper, is an adjunct MBA professor at DeSales University and serves the community as an Upper Saucon firefighter, a board member of Big Brothers Big Sisters of the Lehigh Valley and a local race organizer. Eric is a 20+ year runner and racer and can often be found logging miles on the Saucon Rail Trail.

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Ellsworth Harvey ‘Skip’ Smith Jr. (1941 – 2022) Ellsworth Harvey “Skip” Smith Jr., 80, of Hellertown, passed away on Wednesday, Nov. 2, 2022 at Country Meadows.
James R. Davis Sr. (1948 – 2022) James R. Davis, Sr., 74, of Center Valley, died Wednesday, Nov. 2, 2022 at his home.
Rita Theresa Eckardt (1940 – 2022) Rita Theresa Eckardt, 82, of Hellertown, died Saturday, Oct. 29, 2022 at St.
Saucon Source is a member- and advertiser-supported independent local news site covering the Saucon Valley area, the Southern Lehigh area, Fountain Hill, South Bethlehem and parts of Upper Bucks County in eastern Pennsylvania. The site is updated daily and features stories we hope will help readers live better-informed lives. For more information about local news coverage or advertising on the site, please email [email protected].
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ADM Photo & Video, based in Fort Myers, FL, is proud to announce that have recently added family portraiture to their studio photography services. ADM Photo & Video is well known in Southwest Florida for their business headshots and passport photography, but by adding on family portraits it addresses a popular niche which is regularly requested by Fort Myers residents. Interested buyers of this service are encouraged to check the company website for further information on this service.
ADM Photo & Video operates out of their clean and comfortable studio which is situated in the Royal Palm Square shopping plaza in Fort Myers. This location is ideally situated to serve the needs of businesses and residents in both Fort Myers and Cape Coral.

“Our goal at ADM Photo & Video is to make sure anyone who comes into be photographer doesn’t feel like they are part of a conveyor belt where they are being photographed and moved along as quickly as possible,” says Earl Garner, founder and chief photographer of ADM Photo & Video. “Instead we try to make sure that every client feels like they have been treated as an individual, and as such their unique needs have been attended to. We want it to be as an enjoyable experience as possible, and that every client is happy to recommend us to their colleagues and friends,”
When it comes to family portraiture, the ADM Photo & Video style is what is known as fine-art photography. When you look at the photographs they tend to look more like paintings than actual photographs. This is achieved by a combination of the way the studio lights are positioned and how the photographs are edited.
All sessions are carried out by appointment, allowing ample time for clients to be photographed and to view all of the photos that were taken prior to leaving the studio.
ADM Photo & Photo are highly respected as photographers in Fort Myers and many businesses have made them their selected headshot photographer for their new employees. Those who are looking for more information about ADM Photo & Video’s portrait or headshot photography services should click here.
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For more information about ADM Photo & Video, contact the company here:
ADM Photo & Video
Earl Gardner
239-362-3842
[email protected]
1400 Colonial Blvd, Suite 41, Fort Myers, FL 33907

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Nov 03, 2022, 06:30 ET
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VANCOUVER, BC, Nov. 3, 2022 /PRNewswire/ – Luminex Resources Corp. (TSXV: LR) (OTCQX: LUMIF) (the “Company” or “Luminex”) is pleased to announce results from three holes drilled at the Camp deposit, part of the Condor North project area. Drill holes CC22-39, CC22-40 and CC22-41 have enabled the Company to delineate a thicker part of the Camp deposit. The three holes were drilled to infill an area of more widely spaced holes in the lower-central part of the deposit, which formed as composite sheets of mineralized material that dip steeply to the north. This thicker part of the deposit has been defined by intercepts measuring above 10 metres true thickness horizontally, using a 1.0 g/t gold cut-off grade. It comprises an irregular area measuring approximately 200 metres by 200 metres and remains open to expansion to the southeast, northwest and to depth (see Figure 1). Luminex expects these holes to add to the Camp mineral resource.

Luminex is continuing to drill infill and step out holes on the Camp deposit, as well as step out holes at the emerging, nearby Cuyes West and Ruiz structures with two active rigs at Condor North.
Table 1. Drill intercepts for Camp deposit holes.
Hole
Azimuth / Dip / Length
From
(m)
To
(m)
Interval
(m)
True
Thickness
(m)
Au
(g/t)
Ag
(g/t)
Au Eq
(g/t)
CC22-39
30° /
-52° / 753.3m
185.0
187.0
2.0
1.00
2.92
8.1
3.02
And
240.0
242.0
2.0
1.00
4.08
28.8
4.42
And
248.0
256.0
8.0
5.00
2.51
10.1
2.63
And
312.0
313.0
1.0
0.60
5.32
35.9
5.75
And
345.0
347.0
2.0
1.00
1.44
6.3
1.52
And
355.0
356.0
1.0
0.50
3.28
2.3
3.31
And
381.8
387.0
5.2
3.00
14.61
37.4
15.06
And
393.0
397.0
4.0
2.20
2.00
21.5
2.25
And
403.0
420.0
17.0
10.50
1.41
15.7
1.60
And
492.0
500.0
8.0
4.65
1.51
10.2
1.63
And
560.0
562.0
2.0
1.20
2.13
13.8
2.30
CC22-40
22° /
-38° / 528.2m
194.0
202.0
8.0
4.90
1.79
7.9
1.88
Incl
194.0
196.0
2.0
1.40
5.22
11.7
5.36
And
259.7
260.0
0.3
0.25
3.62
63.8
4.39
And
307.0
308.0
1.0
0.70
2.80
36.9
3.24
And
335.0
336.0
1.0
0.70
11.80
25.3
12.10
And
350.0
351.0
1.0
0.70
5.48
15.6
5.67
And
364.0
367.0
3.0
2.10
2.05
14.9
2.23
And
377.0
394.0
17.0
11.90
3.07
25.8
3.38
Incl
384.0
389.0
5.0
3.50
8.92
36.0
9.35
And
448.8
450.0
1.2
0.90
3.78
15.8
3.97
CC22-41
210° /
-70° / 720.0m
419.7
431.0
11.3
3.50
1.20
8.2
1.29
And
437.0
458.6
21.6
7.80
2.29
25.3
2.59
Incl
437.0
442.0
5.0
1.80
4.84
19.4
5.07
Incl
453.0
455.3
2.3
0.83
5.45
21.1
5.70
And
465.0
506.8
41.8
13.20
2.17
25.5
2.47
Incl
475.0
477.0
2.0
0.60
4.94
33.3
5.34
Incl
490.0
494.0
4.0
1.26
3.34
35.3
3.76
Incl
497.0
506.8
9.8
3.10
3.40
30.5
3.76
And
558.5
560.2
1.7
0.52
3.32
15.9
3.51
Note: Intervals, except those marked with an asterisk (*), are calculated using a lower limit of 1.00 g/t Au with a maximum inclusion of up to five continuous metres below cut-off and the highest gold value used in the reported weighted averages is 82.4 g/t Au. AuEq values assume $1,500 gold and $18.00 silver (AuEq= Au g/t + (Ag g/t *0.012). Equivalent values assume 100% recovery of all quoted metals.

Hole CC22-41 intersected the thickest zone of gold mineralisation, 41.8m grading 2.17 g/t gold, 25.5 g/t silver for a gold equivalent of 2.47 g/t, including 9.8m grading 3.40 g/t gold, 30.5 g/t silver for a gold equivalent of 3.76 g/t. The true thicknesses of these intercepts are interpreted to approximately 31% of the core length.
Holes CC22-39 and CC22-40 also contained notable intervals such as 5.2m grading 14.61 g/t gold, 37.4 g/t silver for a gold equivalent of 15.06 g/t in hole CC22-39 and 5.0m grading 8.92 g/t gold, 36.0 g/t silver for a gold equivalent of 9.35 g/t in hole CC22-40. The true thicknesses of these intervals are estimated to be 57% and 70% of the core length respectively.
Quality Assurance
All Luminex sample assay results have been independently monitored through a quality control / quality assurance (“QA/QC”) protocol which includes the insertion of blind standards, blanks as well as pulp and reject duplicate samples. Logging and sampling are completed at Luminex’s core handling facility located at the Condor property. Drill core is diamond sawn on site and half drill-core samples are securely transported to ALS Laboratories’ (“ALS”) sample preparation facility in Quito, Ecuador. Sample pulps are sent to ALS’s lab in Lima, Peru for analysis where gold content is determined by fire assay of a 50-gram charge with ICP finish.
Silver and other elements are also determined by ICP methods. Over-limit samples assaying greater than 10 g/t gold and 100 g/t silver are re-analyzed by ALS using fire assay with a gravimetric finish. Luminex is not aware of any drilling, sampling, recovery or other factors that could materially affect the accuracy or reliability of the data referred to herein. ALS Laboratories is independent of Luminex.
Qualified Persons
Leo Hathaway, P. Geo, Senior Vice President Exploration of Luminex and the Qualified Person as defined by National Instrument 43-101 Standards of Disclosure for Mineral Projects, has reviewed, verified and approved the scientific and technical information concerning the Condor Project in this news release and has verified the data underlying that scientific and technical information.
About Luminex Resources
Luminex Resources Corp. (TSXV:LR, OTCQX:LUMIF) is a Vancouver, Canada based precious and base metals exploration and development company focused on gold and copper projects in Ecuador. Luminex’s inferred and indicated mineral resources are located at the Condor Gold-Copper project in Zamora-Chinchipe Province, southeast Ecuador. Luminex also holds a large and highly prospective land package in Ecuador, including the Pegasus and Orquideas projects, which are being co-developed with Anglo American and JOGMEC respectively.
Further details are available on the Company’s website at https://luminexresources.com/.
To receive news releases please sign up at https://www.luminexresources.com/contact/contact-us/.
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LUMINEX RESOURCES CORP.
Signed: “Marshall Koval” 
Marshall Koval, CEO and Director 
Neither the TSX Venture Exchange nor its Regulation Services Provider (as that term is defined in the policies of the TSX Venture Exchange) accepts responsibility for the adequacy or accuracy of this news release.
Cautionary Note Regarding Forward-Looking Information
Certain statements and information herein, including all statements that are not historical facts, contain forward-looking statements and forward-looking information within the meaning of applicable securities laws. Such forward-looking statements or information include, but are not limited to, statements regarding future drilling and work programs at Condor and future changes to the mineral resource estimate at Camp. Often, but not always, forward-looking statements or information can be identified by the use of phrases or statements that certain actions, events or results “will” occur or be achieved.
With respect to forward-looking statements and information contained herein, the Company has made numerous assumptions including among other things, assumptions about general business and economic conditions, the prices of gold and copper, and anticipated costs and expenditures. The foregoing list of assumptions is not exhaustive.
Although management of the Company believes that the assumptions made and the expectations represented by such statements or information are reasonable, there can be no assurance that a forward-looking statement or information herein will prove to be accurate. Forward-looking statements and information by their nature are based on assumptions and involve known and unknown risks, uncertainties and other factors which may cause the Company’s actual results, performance or achievements, or industry results, to be materially different from any future results, performance or achievements expressed or implied by such forward-looking statements or information. These factors include, but are not limited to: risks associated with the business of the Company; business and economic conditions in the mining industry generally; the supply and demand for labour and other project inputs; changes in commodity prices; changes in interest and currency exchange rates; risks relating to inaccurate geological and engineering assumptions (including with respect to the tonnage, grade and recoverability of reserves and resources); risks relating to unanticipated operational difficulties (including failure of equipment or processes to operate in accordance with specifications or expectations, cost escalation, unavailability of materials and equipment, government action or delays in the receipt of government approvals, industrial disturbances or other job action, and unanticipated events related to health, safety and environmental matters); risks relating to adverse weather conditions; political risk and social unrest; changes in general economic conditions or conditions in the financial markets; changes in laws (including regulations respecting mining concessions); and other risk factors as detailed from time to time in the Company’s continuous disclosure documents filed with Canadian securities administrators. The Company does not undertake to update any forward-looking information, except in accordance with applicable securities laws.
SOURCE Luminex Resources Corp.
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Tucson residents Rolande Baker, left, and her friend Carla Roberts outside U.S. District Court in Washington, where Baker appeared after her arrest Wednesday for disrupting a Supreme Court hearing with an abortion-rights protest. (Photo by Haley Smilow/Cronkite News)
WASHINGTON – A Tucson woman was one of three people arrested Wednesday after they disrupted a Supreme Court hearing with a protest over the court’s decision to reverse its 1973 ruling that had recognized a right to an abortion.
Rolande Baker said the protest, which momentarily halted arguments in an unrelated banking case, was timed to fall right before the elections so that women and abortion-rights activists would be moved to express their anger at the ballot box.
“Things must change in Arizona,” said Carla Roberts, a friend and fellow activist from Tucson who greeted Baker after she was released from federal custody Thursday. “That’s why we flew all the way here from Arizona. to make sure that women in Arizona know that we must stand together, and we must fight.”
Baker was in the court Wednesday along with Emily Paterson and Nikki Enfield, both from Virginia, for arguments in Bittner v. United States, a case that asks how often bank regulators can apply penalties for failure to file required reports on foreign accounts under the Bank Secrecy Act.
Attorney Daniel Geyser had barely started speaking before he was interrupted by one of the women, who a witness in the court said stood up and urged American women to denounce the court’s abortion ruling in Dobbs v. Jackson Women’s Health, whcih overturned Roe v. Wade, and to remember to vote.
Court order brings abortions back, for now, but providers take it day by day
She did not resist as she was then quickly and quietly escorted from the courtroom by police.
Geyser started again but was interrupted less than a minute later by Baker. She said Thursday that she got up from her seat in the public section of the court and said, “Our rights must not be served. Vote pro-choice, just vote,” before she, too, was escorted from the court.
Before the first minute of arguments was done, the third protester had stood up and said her piece.
Amy Howe, a reporter for the SCOTUSblog website who was in the courtroom, said that after they spoke, all three women held out their wrists as if to be handcuffed, and each was lead peacefully from the courtroom by police.
All three were charged with “uttering loud, threatening or abusive language in the Supreme Court” and “demonstrating with the intent of interfering with the administration of justice or with the intent of influencing a judge in the discharge of his or her duty,” according to a statement from the court’s press office. They were held overnight in the D.C. Central Detention Facility and appeared Thursday afternoon in U.S. District Court before being released.
Even though it was peaceful, Howe called the protest “surprising” for the court, which rarely sees any sort of emotional display. The last time there were protests in the court building was 2015, protests that Howe said were not nearly as peaceful as Wednesday’s.
“That was very different because there were more protesters and they did not go easily,” Howe said. “The police really had to sort of grab them and pull them out.”
By contrast, Howe said Wednesday’s interruption barely disrupted the proceedings. “Geyser just kept speaking,” she said. “The justices did not acknowledge the interruption.”
Supporters greet the three women who disrupted a Supreme Court hearing with an abortion-rights protest. The protesters are Tucson resident Rolande Baker, with cane, next to Nikki Enfield and Emily Patterson. Holding a sign next to Patterson is Baker’s friend Carla Roberts. (Photo by Haley Smilow/Cronkite News)
The women’s statements were edited out of the audio recording of the arguments, and the transcript merely says “(Interruption)” in place of their actual words. Two of the protesters can barely be heard on a C-SPAN recording of the proceedings, but their statements are almost impossible to make out.
Even though their statements were edited out of the official court record, Baker said she still thinks the message got across.
“They (Supreme Court) don’t want it to be said,” Baker said outside the U.S. District Court Thursday evening. “They don’t want it to be told. They don’t want our voice to be heard, but we know.”
For Baker, a retired schoolteacher and great-grandmother, this was not her first arrest while protesting. She said her protesting days began during the Vietnam war, but that this issue hit close to home for her and friend, Roberts. That’s why they hope the protest will encourage people to vote.
“If you haven’t voted, vote, and when you vote, it’s blue all the way down the ballot,” she said.
Haley Smilow expects to graduate in spring 2023 with a bachelor’s degree in sports journalism. Smilow has interned with Phoenix Magazine, AZTV and Phoenix Rising.

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The Buccaneers and Rams met on Sunday in a rematch of the divisional round playoff game that sent Los Angeles to the NFC title game a year ago. Both teams have come a long way since then, and not in a good way, as both Tom Brady and Matthew Stafford entered Week 9 desperate for a turnaround. And both continued to struggle throughout their rematch, trading dismal drives all evening. That is, until Brady engineered a last-minute comeback to steal a 16-13 victory and snap the Bucs’ three-game losing streak, dropping the reigning champions two games below .500.
The win was not pretty, to be sure, with Mike Evans and Scotty Miller each struggling to hang on to the ball as Brady fought to elevate Tampa Bay’s slumping offense at home. But the “W” suddenly, inexplicably, pushes Brady and Co. back to first place in the NFC South. The Rams, meanwhile, have even more questions to answer after Sean McVay’s squad consistently failed to seal a one-score lead that came almost exclusively on the back of Cooper Kupp.
Here are additional takeaways from Sunday’s sloppy Buccaneers-Rams showdown:
The Rams are awful? Los Angeles’ refusal to put the ugly game away certainly helped. But let’s focus on the positive: Tom Brady still has it, even if the rest of his team does not. It was another day of amateurish play-calling from offensive coordinator Byron Leftwich, who’s never met a second-and-short running play he doesn’t love, and Julio Jones predictably limped off to leave Brady throwing to a drop-infected crew headlined by Mike Evans and Scotty Miller. Even so, Brady kept firing catchable bullets and saved his best for last, needing just 35 seconds to go 60 yards in six plays for the game-winning touchdown drive. On a night his staff and supporting cast tried their hardest to let him down, TB12 stood tall, briefly offsetting all the big-picture concern about this team.
Credit belongs to Todd Bowles’ defense, too. While Bowles may be cranking up the heat on his own seat, his “D” got after Stafford on the regular Sunday, taking advantage of L.A.’s weakened front to finish with four sacks and twice as many QB hits. Vita Vea, in particular, was a force on the interior, downing Stafford twice. Somehow, the Rams ended the game without throwing a pick, but the Bucs stymied their aerial attack throughout the contest, with the sole exception of Cooper Kupp.
Sean McVay’s offense is dead. Stafford can’t be blamed entirely for the shortcomings in front of him — the line remains a sieve, prompting the QB to either rush throws or prepare to be walloped — but he isn’t capable of elevating such a shoddy unit, either. The big-armed signal-caller was fortunate to escape without throwing his ninth or 10th picks of the year, but if he wasn’t airing it out to a wide-open Cooper Kupp downfield, he probably wasn’t on the mark. Allen Robinson did emerge on a couple of third downs, showcasing his hands, but it’s saying something that his “big day” featured all of 24 yards through the air. The ground game, meanwhile, was nonexistent as usual, even with the notable return of Cam Akers to the field.
First down Bucs, with 44 seconds left in the game: Brady hits Cade Otton over the middle for a 28-yard pickup, pushing the ball to the Rams’ 32-yard line. Up to that point, Tampa Bay had not proven capable of delivering in or near the red zone, but this connection was a clear indication that Brady wasn’t finished. The Rams were totally inept on their previous five possessions, totaling just a single first down in that span while trying to ice the game, and Brady’s Otton connection ensured Tampa Bay would at least get one final shot at putting the ball in the end zone.
Kupp had the splashiest plays, for sure, but we’re breaking the rules here and awarding this to Brady’s entire final series, which proved he — not the Bucs around him — is still capable of success in what’s been an otherwise rocky 2022:
44 seconds and no timeouts. @TomBrady‘s 55th career game-winning drive. #LARvsTB pic.twitter.com/mIdMRCMMpz
The Buccaneers (4-5) will head overseas for the first-ever NFL matchup in Germany, squaring off with the Seahawks (6-3), who routed their rival Cardinals on Sunday. The Rams (3-5), meanwhile, will return home in Week 10 for a showdown with those same Cardinals, who will be looking to snap a two-game slide.
44 seconds and no timeouts. @TomBrady‘s 55th career game-winning drive. #LARvsTB pic.twitter.com/mIdMRCMMpz
Six plays. 60 yards. 35 seconds. 🐐 pic.twitter.com/25dcH9ao4E
.@TomBrady & the @Buccaneers take the lead with nine seconds remaining!

📺: #LARvsTB on CBS
📱: Stream on NFL+ https://t.co/sjhHSVuY5P pic.twitter.com/x6gOGepZuP
The Bucs are gonna do it! Brady hits Cade Otton on the little roll-out and Tampa Bay is ahead with nine seconds to play!
Tom Brady delivers one to Scotty Miller along the left sideline to push the Bucs inside the red zone and stop the clock at :19. Does it again on the next play to creep closer to the end zone. Tampa Bay has a real chance to swipe this one, especially after a slant to Mike Evans draws a pass interference call that will push the ball closer to the goal line.
Jim Nantz on #Rams #Bucs: ‘The punting on both sides has been sensational.’

A matchup of the last two Super Bowl champions has been reduced to this.
Rams keep insisting on the Buccaneers stealing this game, and vice-versa. Up 13-9 still, they can’t get a single first down to put it away (again). Run: no gain. Run: 2 yards. Run: 1 yard. That’s an offensive line that cannot win. And so Tom Brady gets another shot to elevate his dysfunctional supporting cast.
Rams have no trouble batting down Tom Brady’s fourth-and-goal desperation throw, intended for Mike Evans but tossed into at least double coverage. The Buccaneers are playing exactly like a team with no idea what it wants to be.
Does anyone want Tom Brady to succeed? Seriously? Who is helping him right now? Mike Evans has struggled to hang onto the football all night, and then Scotty Miller drops a perfectly thrown pass to the back of the end zone. Cade Otton takes his turn on the next play, failing to secure a tight-window bullet. Bucs still hapless in the red zone, it seems, even with the game on the line.
Rams with a chance to basically put this thing away with a couple of first downs, and instead they get nowhere. Cam Akers is stuffed behind the line. Cooper Kupp loses yards on a reverse. And then Matthew Stafford avoids a sack only to run into another. Disgusting football from the reigning champions, continuing a trend we’ve witnessed all year. If they hang on to win this 13-6, it’ll only be because their opponents, the Buccaneers, are equally sapped of creativity, proper depth, etc.
Epitome of this game:
That Tom Brady milestone sequence was wild.

“This could be it… no! A 2-yard completion.”

“There it is! Oh hold on, a holding penalty.”

“He’s got it! 100,000! But the Bucs are short of the sticks.”
With that completion to Leonard Fournette, Tom Brady becomes the first player in NFL history to amass 100,000 career passing yards (regular season + postseason).
Tom Brady finally lets one fly down the right sidelines, putting the ball right on Mike Evans’ hands and beyond those of Jalen Ramsey, but Evans can’t hang on. Worse yet, he didn’t keep his feet in-bounds during the route. Wasted opportunity. No points. Bucs still trailing 13-6, and again, that one-score deficit almost feels insurmountable based on their inability to move the ball.
Another look at Cooper Kupp getting free:
.@CooperKupp down there somewhere.

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There it is: Cooper Kupp gets open downfield again, and Matthew Stafford steps up to hit him. Thirty-four yards that time, and Kupp continues to be the Rams’ MVP today. Up to 122 yards on six catches, putting L.A. in the red zone for the second time of the day. Right on cue, facing second-and-goal, Stafford gets swallowed up by Rakeem Nunez-Roches for the sack, and then Sean McVay dials up a short run to shorten the ensuing field goal. Conservative finale to one of their few explosive drives of the game. It’s now 13-6, and somehow that lead feels massive to a Bucs offense that’s been brutal to observe.
Rams extend the lead to 10-6 after a short Matt Gay field goal, but only after two straight Matthew Stafford incompletions put a sour cap on an otherwise promising drive that began at their own 25. Allen Robinson made one of the best plays of his season during the stretch, laying out beyond two defenders to secure a 15-yard gain. Let’s see what Byron Leftwich has in store next.
Julio Jones very slow to get up after Tom Brady can’t connect with the receiver on a downfield shot on third-and-long. The wideout slipped on the grass as he prepared to come back toward the ball. He hasn’t been able to stay healthy during his Buccaneers debut, and it wouldn’t be surprising if he’s limited from here on out. The clock keeps ticking on this mess of a matchup, and both sides keep refusing to move the ball. Bucs now 4 of 11 on third downs.
That’s a half in Tampa Bay. It’s a one-point game after Ryan Succop connects from 38, and both teams take to the locker rooms with their heads hardly held high. Seven three-and-outs between the two slumping clubs, and only God knows what they can tell themselves during the break to amend their offensive woes. This says it all: the Bucs are far and away the leaders in that category so far, and they’re averaging a whopping 4.9 yards per play, going 0-for-1 in the red zone. The Cooper Kupp splash TD was the only real highlight of the first two quarters, and it’s saving L.A. thus far.
Bobby Wagner times the jump to block the field goal! 💪 @Bwagz

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Starting to understand fan concerns about Byron Leftwich’s play-calling: what on Earth do the Bucs expect to do handing it off on second-and-five in perpetuity, let alone to a back other than Leonard Fournette? The line is not constructed to steamroll a D-line, let alone that of the Rams, and yet here they go, over and over, pounding it up the gut when you’ve got Tom Brady under center, Mike Evans out wide and Chris Godwin to boot. Ryan Succop comes on for a 52-yard field goal try after yet another stalled drive, and fittingly, it’s blocked. Rams retain their 7-3 lead in this ugly game of football.
Three plays. Punt. Three plays. Punt. Neither the Bucs nor the Rams appear ready to break out of their stagnant shells. Tampa Bay continues to run the same things and expect different results: runs or short throws to running backs on early downs. Doesn’t matter who’s touching the ball on their end; no one is finding an open hole. The best stretch they had was on their first drive, when Tom Brady actually had the opportunity to drop back and push the ball downfield to Mike Evans and Co.
Matthew Stafford nearly gets picked while trying to sling one out of bounds, with Mike Edwards getting his hands on the ball toward the sidelines. The Rams QB, now 7 of 14 on the afternoon, was tied for second in the NFL with eight picks coming into the game. L.A.’s passing game remains a crapshoot if Cooper Kupp isn’t the intended target in open space. Allen Robinson’s gotten his hands on a couple of third-down conversions, but he’s not creating anything extra, either.
Cooper. Kupp. Somehow the Buccaneers failed to keep a single man on the Rams’ top target, and Matthew Stafford zips one downfield after a bobbled snap. The receiver takes it the rest of the way, 69 yards, for the score, and L.A. is out in front, 7-3. That right there is the one way the Rams can win this game: keeping the ball in their play-maker’s hands.
Cooper Kupp go route. 🔥 #RamsHouse

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One quarter in, and it’s same old, same old with these teams: the offensive firepower may exist, but it only shows up in spurts. A combined five first downs for the Bucs and Rams through 15 minutes, with each side averaging fewer than six yards per play. Cam Akers quickly becoming a focal point of L.A.’s offense so far, which would’ve sounded wild a few weeks ago.
Cam Akers is back. Despite his apparent philosophical differences with the Rams, and both sides’ efforts to split ahead of the trade deadline, No. 3 is on the field and taking carries for Los Angeles now. Already out-touching Darrell Henderson Jr. early on.
Van Jefferson missed a ton of time to start the year while recovering from injury, and the lack of practice really showed at the start of the Rams’ second drive, with Matthew Stafford delivering a perfect downfield strike to the wide-open receiver, only for Jefferson to let the ball bounce right off his chest. Can’t have those kind of big-play mistakes and expect to win. But that’s nothing that hasn’t been heard already this year in L.A. The Bucs then kill the drive for good with a couple of sacks — one negated by penalty, then another for real by Vita Vea. A coward’s-draw dump-off to Cooper Kupp sets up the punt.
Three-and-out is exactly how the Rams did not want to start this game, but alas, the Buccaneers get good starting field position after a nice return from Jaelon Darden, and then Leonard Fournette rams through Jalen Ramsey for a key first to set up another big play courtesy of Julio Jones, finally healthy and on the receiving end of a precision throw from Tom Brady. Tampa’s QB, by the way, looks comfortable out of the gate, making quick decisions and well-timed strikes over the middle. The downside: Really don’t love feeding Fournette up the gut on consecutive red-zone plays after Brady’s arm helped drive the team into scoring range, forcing the Bucs to settle for three on their first drive. 3-0 ballgame, and it should be 7-0, Tampa Bay.
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Communications Biology volume 5, Article number: 1167 (2022) Cite this article
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Simulation is an efficient approach for the investigation of theoretical and applied issues in population and quantitative genetics, and animal and plant breeding. In this study, we report a multi-module simulation platform called Blib, that is able to handle more complicated genetic effects and models than existing tools. Two derived data types are first defined in Blib, one to hold the required information on genetic models, and the other one to represent the genetics and breeding populations. A number of subroutines are then developed to perform specific tasks. Four case studies are present as examples to show the applications of Blib, i.e., genetic drift of multiple alleles in randomly mating populations, joint effects of neutral mutation and genetic drift, comparison of mass versus family selection, and choice of testers in hybrid breeding. Blib together with its application modules, has great potential to benefit theoretical genetic studies and intelligent breeding by simulating and predicting outcomes in a large number of scenarios, and identifying the best optimum selection and crossing schemes.
Simplified assumptions are often made for reasons of tractability in many theoretical studies, however the real world is more complicated. Computer simulation provides an efficient tool that has been widely used in many scientific fields and disciplines, probably since computers were invented. Genetic studies and breeding applications allow us to investigate the implications of relaxing some of the assumptions made in population and quantitative genetics, and their effects on the activities of breeding programs¹. On the other hand, simulation can generate a large amount of data that is impossible or difficult to obtain from the empirical experiments or theoretical considerations, which can be used to compare different selection methods, and validate the proposed theories or models. In addition, simulation can help perform pilot research, accelerate research and development, and transfer discoveries from the laboratory or theory to practice^2,3. As an example, by using molecular data on parents and genomic prediction models, the segregating populations of virtual crosses can be generated and then compared, from which the most promising ones can be predicted before the crosses are actually made in the field⁴.
To use the simulation approach in genetic studies and breeding applications, the development of suitable software packages, platforms and/or modules is fundamental. QU-GENE is such a platform that was developed for the quantitative analysis of genetic models^5,6. Based on the QU-GENE platform, several application modules have been developed, and then used to investigate various issues in breeding. As an example module of QU-GENE, QuLine was designed to simulate the breeding programs for deriving inbred (or pure) lines and has been used to compare different breeding methods^7,8,9, predict cross performance using known gene information¹⁰, optimize marker assisted selection in the pyramiding of multiple genes¹¹, and investigate the efficiency of a single backcrossing breeding strategy¹². QuHybrid is another module designed to simulate breeding programs for selecting hybrids as the final products, and it has been used to calculate the probability of success of a biofortified breeding program for improving the provitamin A content in maize¹³. More recently, QuMARS was developed to simulate and optimize various recurrent selection strategies, including phenotypic selection, marker-assisted recurrent selection, and genomic selection (GS) for both short-term and long-term breeding procedures¹⁴.
Several other simulation platforms have been developed in past decades for various purposes. SimuPop provides a forward-time simulation environment, which may be used to manipulate genetic populations after many generations of random mating, and investigate the evolutionary mechanism of natural populations¹⁵. PedigreeSim simulates meiosis in both diploid and tetraploid species and can be used to generate pedigrees and cross populations¹⁶. Slim is a forward simulation tool designed to study the effects of linkage and selection and is capable of modeling the complex scenarios of demography and population substructure¹⁷. Forqs is a forward-in-time simulator of recombination, quantitative traits and selection designed to investigate haplotype patterns in scenarios where substantial evolutionary change has taken place¹⁸. By using progeny simulation and genomic prediction strategies, PopVar can predict the population mean, genetic variance, mean of superior progenies, and correlated responses of multiple traits¹⁹. AlphaSim is a package for simulating plant and animal breeding programs that is flexible in terms of historical population structure and diversity, pedigree structure, trait architecture and selection strategy²⁰. Similar to QU-GENE application modules, ADAM-plant can simulate breeding programs for self-pollinated and cross-pollinated crop plants, and for the application of new technologies such as speed breeding and GS²¹.
The QU-GENE platform together with its application modules represents one of the most mature and widely used simulation tools in genetics and breeding. The platform can handle a wide range of genetic models including molecular markers as well as genes underlying phenotypes, genetic linkage, multiple alleles, additive, dominance, epistasis, and gene-by-environment interactions. However, it was originally designed without considering more complicated effects and/or genetic phenomena, such as mutations, cytoplasm effects, and fertilities of female and male gametes, needless to say the interactions between cytoplasm and nuclear genes. These phenomena or effects are important in evolutionary biology, population genetics and breeding applications, but cannot easily be imbedded into the existing platform and its application modules.
In this study, we report a multi-module genetic simulation platform called Blib, which has the potential for a much wider range of genetic studies and intelligent breeding. First, we introduce the technical issues for derived data types of the generalized genetic models and genetics/breeding populations, together with major subroutines in Blib. We then present four case studies as examples to show some of the applications of Blib in genetic studies and breeding.
One Blib application module called DRIFT was developed to simulate genetic drift in randomly mating populations. First, two subroutines, i.e., Blib_ReadGmodel and Blib_ReadPopulation, were called to assign the global variable Gmodel and one initial population from two external files, respectively. Then, the subroutine Blib_Cross1Population was repeatedly called to conduct random mating in one parental population and generate the progeny population for a given number of generations. The whole procedure was repeated for a given number of runs or replications. Statistical parameters associated with the population were calculated by calling subroutine Blib_CalcPstatistics and outputted to external files for each generation of random mating and each run. One locus with multiple alleles but without mutation was considered in the genetic model. Two population sizes (N) were set at 10 and 50. For each population size, four initial allele numbers were considered in the base populations, i.e., 2, 0.5 N, N and 2 N. The alleles had equal frequency in the base populations. The drift procedure was repeated for 100 generations of random mating and 100 simulation runs. For each generation, allelic frequencies in the 100 subpopulations (generated from the 100 simulation runs) were recorded.
The inbreeding coefficient in limited-size populations during random drift has been well studied in population genetics^22,23,24. Starting from one base population, the theoretical inbreeding coefficient after t generations of random drift in populations with the size of N is given by Eq. (1). Assuming there is no inbreeding in the base population, i.e., F₀ = 0, we have Eq. (2).
Equations (1) and (2) are derived under some idealized conditions, such as fixed population size, equal fertility of female and male individuals, equal fertility of female and male gametes, and no generation overlap²³. Under the nonidealized conditions, Eqs. (1) and (2) are applicable when replacing N with the effective population size (i.e. N_e). Assuming there are a number of subpopulations under genetic drift starting from one base population, the observed inbreeding coefficient, i.e., F_Obs, can be estimated from the observed allelic frequencies in subpopulations, i.e.,
where p_ij is the observed frequency of the i^th allele (i = 1 to k, where k is the number of alleles) in the j^th subpopulation (j = 1 to m, where m is the number of subpopulations, or the number of simulation runs in this study), and ({bar{p}}_{i}) is the mean frequency of the i^th allele across the m subpopulations²³.
Figure 1a, b show the change in the inbreeding coefficient during the 100 generations of random drift for two population sizes, 10 and 50, respectively, where the theoretical values were calculated by Eq. (2) and the observed values were calculated by Eq. (3). Obviously, the observed inbreeding coefficients are independent of the number of alleles (k) occurring in the base population, even though k is included in Eq. (3). The inbreeding coefficients estimated from Eq. (3) are highly consistent with their theoretical values, as given by Eq. (2). For a population size of 10, the inbreeding coefficient approaches one after 60-70 generations of random mating; for a population size of 50, the inbreeding coefficient approaches ~0.5 after 60–70 generations. The inbreeding coefficient is expected to approach one after infinite generations of random drift in limited-size populations. When the inbreeding coefficient becomes one, each subpopulation is fixed for one homozygous genotype, which is normally called the fixed or pure line. A simulation experiment based on the Blib application module DRIFT confirms the theory on inbreeding coefficients and its validity in the case of multiple alleles at one genetic locus.
a Inbreeding coefficient when N is 10. b Inbreeding coefficient when N is 50. c Alleles retained when N is 10. d Alleles retained when N is 50.
Figure 1c, d show the change in allele number during the 100 generations of random drift for two population sizes. The observed number of alleles in one subpopulation was equal to the number of alleles with nonzero frequencies. Unlike the inbreeding coefficient, Fig. 1c, d indicate that the number of alleles retained in subpopulations depends on the number of alleles in base populations in earlier generations of random drift. The more alleles there are in the base population, the more alleles are retained, especially when the population size is large. It is expected that the allele number will also approach one after infinite generations of random drift in limited-size populations. The number of alleles located at one chromosomal locus is an important parameter for characterizing the genetic architecture of populations²². The change in allele number during random drift is difficult to study theoretically, but can be properly investigated through simulation.
Neutral theory plays an important role in modern population genetics and evolution theories^{24,25,26,27,28}. When mutation is included in the genetic model, the Blib application module as introduced and used in Case study I can also be used to simulate the simultaneous action of mutation and drift during random mating. Under mutation, the inbreeding coefficient between two succeeding generations is given in Eq. (4), where N is the population size, and u is the mutation rate^22,24. According to neutral theory, an equilibrium can be reached after infinite generations of neutral mutation and genetic drift in limited-size populations. The theoretical inbreeding coefficient at equilibrium is shown in Eq. (5), which can be acquired from Eq. (4) by letting ({F}_{t}={F}_{t-1}) and (1/{(1-u)}^{2}approx 1+2u). The number of alleles at mutation–drift equilibrium was investigated by Crow and Kimura²², Ewens²⁹, and Karlin and McGregor³⁰. Equation (6) is an approximate expression given by Ewens²⁹, where (theta =4Nu).
In practice, assuming there are a number of subpopulations under mutation and drift at generation t, the observed inbreeding coefficient can be estimated from the observed allelic frequencies in m subpopulations²⁴, i.e.,
where p_ij is the observed frequency of the i^th allele (i = 1 to k, where k is the number of alleles) in the j^th subpopulation (j = 1 to m, where m is the number of subpopulations, or the number of simulation runs in this study).
In the simulation using the Blib application module DRIFT, one locus with multiple alleles and mutations was considered in the genetic model. Two rates at the level of pairwise mutation were considered, i.e., 0.0001 and 0.0002. Two population sizes (N) were set at 10 and 50. For each population size, four initial allele numbers were considered in base populations, i.e., 2, 0.5 N, N and 2 N. All alleles had equal frequencies in the base populations. The mutation and drift procedure was repeated for 200 generations of random mating and 100 simulation runs. For each generation, allelic frequencies in the 100 subpopulations (generated from the 100 simulation runs) were recorded.
Table 1 shows the theoretical and observed inbreeding coefficients, together with the number of retained alleles in the population. Theoretical inbreeding coefficients at equilibrium were calculated by Eq. (5), and theoretical numbers of retained alleles after 200 generations were calculated by Eq. (6), where the total mutation rate was used. The total mutation rate listed in Table 1 is the pairwise mutation rate multiplied by k-1, where k is the number of alleles. Observed inbreeding coefficients after 200 generations were calculated by Eq. (7), and observed numbers of the retained alleles after 200 generations were acquired by counting the alleles with nonzero frequencies in subpopulations. The difference between the theoretical and observed values in Table 1 is marginal for both the inbreeding coefficient and the number of retained alleles. Even though neutral theory is built on the infinite-alleles model²⁴, simulation results indicate that the theory on inbreeding coefficient and retained alleles works well under the scenario of a limited number of alleles at a genetic locus, even for the two-allele model (Table 1). Simulation using the Blib application module DRIFT provides solid evidence to demonstrate the wide application of neutral theory in population genetics.
In fact, in Blib, mutation rates at any genetic locus are defined by one k by (k-1) matrix, where k is the number of alleles at the locus (Supplementary Fig. 1). For example, for one locus with three alleles, the first row of the matrix is mutation rates from allele 1 to alleles 2 and 3; the second row is mutation rates from allele 2 to alleles 1 and 3; and the third row is mutation rates from allele 3 to alleles 1 and 2. Therefore, nonuniform mutation rates can be defined in Blib, such as forward mutations having higher rates than reverse mutations or alleles at different loci having different mutation rates. It is anticipated that the effects of heterogeneous mutation rates on neutral theories, e.g. Equations (4) to (6), can be quantified as well by further simulation experiments using the Blib application module DRIFT.
One other Blib application module called PRS was developed to simulate the phenotypic recurrent selections that are typical in both animal and plant breeding. The selection in PRS can be based on phenotypic values at the individual level, or on phenotypic means of the progeny families. First, two subroutines Blib_ReadGmodel and Blib_ReadPopulation were called to assign the global variable Gmodel and one initial population from two external files, respectively. Then, the subroutine Blib_Cross1Population was called to conduct random mating in one parental population and generate a new progeny population. Blib_CalcPhenovalue was called to calculate phenotypic values for the progeny population. Blib_Select was called to conduct the selection. Finally, Blib_CalcPstatistics was called to calculate the statistical parameters associated with the population.
Four selection methods were considered in PRS, i.e., phenotypic or mass selection, which is based on the performance of individuals (PS); S1 family selection, which is based on the performance of one generation of selfed families (S1); S2 family selection, which is based on the performance of two generations of selfed families (S2); and half-sib selection, which is based on the performance of half-sib families (HS). Forty cycles of recurrent selection were conducted. Upward selection was conducted for 20 cycles, followed by 20 cycles of downward selection.
Two alleles were considered at each locus in the genetic model. Size of the initial population was set at 100. Models with three types of effects were simulated, i.e., pure additive effects (AD0), additive and dominant effects (AD1), and additive, dominant and epistatic effects (ADE). Ten independent genes were predefined to affect the trait of interest. For model ADE, five epistatic networks were considered. In each network, genes at two different loci interact with each other. Genotypic values at each individual locus or in each epistatic network are randomly assigned values between 0 and 1. Broad-sense heritability was set at 0.2 for each model. Mutation and no mutation were considered separately. When mutation was included, the rates of forward and reverse mutation were set at 0.02 and 0.01, respectively, for all genetic loci in consideration.
The simulation was repeated for 100 runs. Figure 2 shows the change in population mean during 40 cycles of recurrent selection for the three effect models under no mutation and with mutation. The values shown in Fig. 2 have been adjusted by the lowest and highest genotypic values in each genetic model, and therefore range from 0 to 1. Classical quantitative genetics theory indicates that genetic gain from selection depends only on additive variance in the population²³, where higher additive variance results in greater response to selection. Therefore, the population quickly moves to the peak trait value after a few cycles of selection under model AD0 with only the additive effects (Fig. 2a, b). At the peak trait value, the population is fixed for one homozygous genotype, leaving no genetic variation, and therefore, the reverse selection cannot act without mutations (Fig. 2a). This phenomenon has been well explained by the pure-line theory in breeding. However, when mutation occurs, the population quickly moves in the opposite direction as reverse selection is applied (Fig. 2b). Nonadditive variances are included in models AD1 and ADE, which reduce the efficiency of selection (Fig. 2c–f). Obviously, the population can never reach the highest genotypic value when nonadditive effects are present, and the individual with the highest performance is heterozygous. One peak trait value can still be reached, but diversity still occurs in the populations at the peak trait value. Therefore, when reverse selection is applied, populations can move in the opposite direction (Fig. 2c, e). This clearly indicates that mutation can make the selection more efficient under both models AD1 and ADE (Fig. 2d, f), which may be worth investigating further.
Three biallelic genetic models were considered, i.e., AD0 (additive model), AD1 (additive and dominant model) and ADE (additive, dominant and epistatic model). Four selection methods are simulated, i.e., phenotypic selection (PS), S1 family selection (S1), S2 family selection (S2), and half-sib selection (HS). a Model AD0 and no mutation was considered. b Model AD0 and mutation was considered. c Model AD1 and no mutation was considered. d Model AD1 and mutation was considered. e Model ADE and no mutation was considered. f Model ADE and mutation was considered.
In Fig. 2, efficiency can be compared for the four selection methods as well. In model AD0, PS, S1 and S2 are almost equally efficient, but HS is less efficient in changing the population mean. This can be explained by the quantitative genetic theory that 100% of the additive variance is included in PS, S1, and S2, but 25% of the additive variance is included in HS²³. For model AD1 without mutation, the difference among the four selection methods is much smaller during the 20 cycles of forward selection. However, PS is more efficient during the 20 cycles of reverse selection (Fig. 2c). For model AD1 with mutation, PS is more efficient than the other three methods during the 40 cycles of forward and reverse selections (Fig. 2d). For model ADE, S1 and S2 are more efficient during the 20 cycles of forward selection; HS is less efficient regardless of the presence of mutation (Fig. 2e, f).
Theories in classical quantitative genetics are mainly built on the multifactorial hypothesis. Some observations in Fig. 2 can be properly explained by the equation of genetic gain in quantitative genetics²³. There are also some interesting issues that may be worthy of further investigation, such as the efficiency of selection when multiple alleles are considered in genetic models. In addition to the change in population mean as shown in Fig. 2, allelic frequencies, components of genetic variance, narrow- and broad-sense heritability, correlations between phenotypic traits, and correlations between environments can be investigated as well during short or long term selection, which cannot easily be studied theoretically. In addition, Blib is able to simulate a large number of genetic models and the outcomes of these models after long-term selection. By comparing the simulation results with observations from artificial experiments, such as long-term selection on oil and protein contents in corn³¹, the genetic architecture of quantitative traits may be better understood.
As the major component and activity in hybrid breeding programs, a large number of newly developed inbred lines are evaluated by their testing crosses with a few testers from the alternative heterotic group. The elite inbred lines thus selected are expected to have high combining ability, and are then used to develop new hybrids. However, the choice of suitable testers is an open question in hybrid breeding due to the complicated nature of the breeding procedure³². One Blib application module, called ISB-B4H, was developed to simulate breeding programs for developing hybrids as the final product in plants. A detailed description of ISB-B4H is beyond the scope of this paper. Instead, we use this module to show how the breeding efficiency yielded by different testers can be investigated by simulation in a more holistic view, by which the most suitable tester may be predicted and chosen for a particular hybrid breeding program.
Assume one hybrid breeding program, such as that for maize, begins with two heterotic groups, each consisting of 50 inbred lines as parents. One target trait, such as yield, is controlled by 100 biallelic loci distributed on 10 chromosomes with known genetic effects, which may come from genotype-to-phenotype modeling of historical breeding data, training populations or any other genetic studies^4,6,14. In each breeding cycle, a total of 100 biparental crosses are made within one heterotic group, and from each cross a total of 100 doubled haploids (DH) are developed. Elite DH lines are selected through two stages of testcrossing evaluation. In stage one, the 10,000 DH lines are crossed with one tester from the other heterotic group, and 5% are selected on the basis of their performance in testing crosses. In stage two, the 500 DH lines retained from stage one are crossed again with the same tester, and 10% are selected, resulting in 50 lines which are then used to develop new hybrids and perform the next round of biparental crosses. The simulation was repeated for 100 runs.
During simulation, the 50 retained DH lines at the end of each breeding cycle are evaluated by the 50 crosses with the tester, 2500 single crosses with the 50 inbred lines in the other heterotic group, and the inbred lines per se. Figure 3 shows the change in genotypic means from four testers in 10 breeding cycles. Tester1 and Tester2 are randomly selected from the 50 inbred lines in the other heterotic group; Tester3 harbors all favorable alleles at the 100 genetic loci; Tester4 harbors all unfavorable alleles at the 100 genetic loci. In this breeding program, selection is conducted solely on testing cross performance. The genotypic mean of testing crosses is advanced in parallel for the four testers (Fig. 3a). Tester3 has the largest number of favorable alleles, and the genotypic mean of its testing crosses is also the highest; Tester4 has the fewest favorable alleles, and the genotypic mean of its testing crosses is also the lowest. Due to the equal intensity of selection, genetic gains in testing crosses are equal among the four testers.
a Genotypic means of 50 tester crosses. b Genotypic means of 2500 hybrids with one other heterotic group. c Genotypic means of 50 inbred lines per se. The ordinate axes of the three subfigures are different for a better illustration of the differences among the four testers.
In hybrid breeding, the intensity of selection mostly comes from the selection on testing cross performance. However, the improvement of testing cross performance is in fact not the most important objective. Only a higher genotypic mean of single crosses with the alternative heterotic group can give breeders a greater opportunity to select more elite hybrids. Regarding the genotypic mean of 2500 single crosses, Tester1 is identified to be the best, and Tester4 is the worst (Fig. 3b). Genetic advances in crosses with the other heterotic group cannot easily be evaluated in practice but can be properly performed by the Blib application module ISB-B4H. In addition, genetic advances in inbred lines per se can also be determined (Fig. 3c). Advances observed in Fig. 3b, c are also called the in-direct or correlated genetic gains according to quantitative genetics theory²³. In this sense, hybrid breeding actually provides an example where the in-direct gain outperforms the direct gain.
Predictions are expected at almost every stage during the rather long breeding procedure. When making crosses, breeders wish to know which parents should be chosen, and how many crosses should be made. In early segregating generations, breeders wish to know how many individuals should be planted in the field, how strong selection should be, and which phenotypic traits should be used as the selection criteria. At the stage of yield and adaptation testing, breeders wish to know the best locations for conducting the trials, how many locations should be used and how many replications should be arranged. Some issues of interest in breeding program design and analysis are beyond the reach of empirical experimental investigations. Simulation studies can be conducted to complement, extend, and improve the design and interpretation of empirical studies^2,7,10,33.
Generally, decisions can be made from predictions; predictions can be made from simulations. With the accumulation of our knowledge and understanding of the inheritance of breeding traits, it is anticipated that the simulation, prediction and decision-supported tools will be in high demand^3,33. Once properly designed and developed, these tools can help investigate many what-if crossing and selection scenarios, allow many scenarios to be tested and compared in silico in a short period of time, and finally help breeders make important decisions before conducting resource-demanding field experiments^7,11. Simulation and prediction tools are essential components especially for the incoming breeding 4.0³⁴, when breeding activities will be more driven by information technology in acquiring phenotypic data, molecular technology in acquiring genotypic data, and artificial intelligence in utilizing big data to make selection decisions.
We designed and developed Blib as reported in this study over our decades of research experience and advances in genetic analysis methodology^{35,36,37,38,39,40}, breeding modeling and simulation^7,10,11,12, and computer tool and software development^41,42,43,44. Blib is composed of one global variable to define the generalized genetic models, one derived data type to define the genetics/breeding populations, and a number of general subroutines for manipulation and calculation. Blib has been well-developed and widely tested in recent years, and it can be treated as a computing and programming library for the purpose of genetics and breeding simulation. By using the library as the underlying components, various application modules can be readily developed to complete specific tasks, which have been shown by the four case studies. The separation of application modules from the library can save a great deal of time and effort when other researchers wish to develop new simulation tools or modify the existing modules.
In addition to genetic effects and models that have been considered in QU-GENE^5,6, Blib can define and handle mutations (both for cytoplasm and nuclear genes), cytoplasm effects, cytoplasm by nuclear gene interactions, and fertilities of female and male gametes. By considering the additional genetic effects and models not included in QU-GENE, we expect that the application modules built on Blib will have greater potential for the theoretical genetic studies and breeding practice. For example, theories on population genetics seldom consider more than two factors simultaneously due to the intractability in mathematical deduction. By using the Blib platform, an application module can be easily developed to evaluate the joint effect of multiple factors affecting population structure, such as drift, mutation, migration, selection, stratification, and admixture. Such a module can also be used to generate a large number of genetic populations to evaluate the efficiency of statistical methods in genetic linkage mapping^40,41,44 and genome-wide association studies (GWASs), and evaluate the efficiency of various prediction models in GS^4,14,33.
We do not expect one or just a few application modules to be able to meet most purposes. In contrast, we expect that different application modules will need to be designed and developed for different purposes. However, one module can work with different models and simulate different genetic phenomena as defined in external input files. For demonstration and validation, we developed one Blib application module to simulate genetic drift in randomly mating populations (i.e., Case studies I and II), one module to simulate mass and family recurrent selection (i.e., Case study III) and one module to simulate breeding programs for developing hybrids (i.e., Case study IV). As shown in Case studies I and II, by modifying the input information, the module DRIFT can simulate not only the randomly mating populations under genetic drift but also the populations under the joint action of mutation and drift. It can also simulate a single locus with two alleles, a single locus with multiple alleles, and multiple loci with two or multiple alleles. When multiple loci are considered in the genetic model, linkage can be defined as well. A similar situation is true for the PRS module shown in Case study III, when different kinds of genetic effects are defined in input files. By adopting this philosophy, we believe that the need to develop new modules can be minimized. For most researchers, their efforts can be spent more effectively in building suitable genetic models, designing simulation experiments, running suitable application modules, and analyzing simulation results.
As another example, one specific module was developed to simulate the genetic recombination events between parents with the designed crossing schemes, which has been integrated with the genomic prediction tool to conduct genomic cross prediction in flax linseed breeding⁴⁵. More application modules are being developed or planned, such as the in silico simulation of general breeding programs for developing pure lines, hybrids (as shown in Case study IV), and clonal varieties in plants, cross performance prediction and parental selection given the training populations with genotypic and phenotypic values, and simulation of various mapping populations from the user-defined linkage maps and genetic models. These modules are applicable to genetic studies and breeding practices in both sexually and asexually propagated species. Once developed and validated, all Blib-based application modules will become freely available to scientific researches.
To run one application module of Blib, at least three kinds of external files are needed as the input information. The first contains the information used to define the generalized genetic model (i.e., Gmodel), the second defines one or several initial populations, and the third defines the parameters in the simulation experiment. Although many genetic effects and models can be handled in Blib, not all of them have to be included in each external file or each simulation experiment. Take the application module used to simulate recurrent selections (i.e. Case study III) as an example. Three genetic models (i.e., pure additive, both additive and dominant, and epistasis) are defined separately in three external files. To run the same module on the three genetic models sequentially, we can monitor the change in population mean due to phenotypic and family selection schemes, as shown in Case study III. By adopting this philosophy, we believe that the procedure used to develop the Blib application module can be greatly simplified and more straightforward. The development of application modules can only focus on the third kind of input information and organization of the Blib subroutines to complete more specific and complex tasks.
To facilitate access to Blib and its application modules, we are currently developing some user interfaces that will help prepare the external files of input information for the global variable Gmodel and initial populations, which are fundamental to the simulation studies based on Blib. Alternatively, the information needed for simulation may be extracted from the existing database systems that are affiliated with on-going breeding programs⁴⁶. By connecting with the information systems and ongoing breeding pipelines, the simulation platform Blib and its application modules can be used to conduct real-time simulation and prediction related to the outcomes of a large number of potential selection and crossing schemes. Thus, the optimum selection and crossing schemes can be identified and recommended to breeders before conducting field experiments. By doing so, simulation tools and approaches that are built on Blib are expected to make significant contributions by facilitating the change in conventional breeding from artificial selection to intelligent selection or helping move from the current stage to the incoming Breeding 4.0.
In Blib, a global variable named Gmodel was first defined, which holds all necessary information in a generalized genetic model. This information was packed up into 13 derived data types in Fortran (Fig. 4; Supplementary Table 1). General information in Gmodel includes the numbers of environments, traits, composite traits, chromosomes, genetic loci (can be either genes or markers) in the genome, markers, genes, epistasis networks, cytoplasm, cytoplasm actions, and fertility actions. Included in the environmental information are the environmental name and frequency of occurrence in the target population of environments (TPE). Included in the trait information are the trait name, heritability and error variance in each environment. In addition, genes, epistasis networks, and cytoplasm actions for each trait are also stored to assist in the calculation of genotypic values for traits during simulation. Composite traits are calculated by addition, subtraction, multiplication and division from two or more underlying traits defined in Gmodel%trt(:) (Supplementary Table 1). The inclusion of composite traits in Blib also provides the opportunity to acquire the phenotypic values externally, such as the prediction of breeding values by one genomic prediction model that has been previously established in a training population.
Shown in the upper left is the definition of global variable Gmodel, which holds the required information on genetic models, e.g. environments, traits, composite traits, chromosomes, markers, genes, epistasis networks, cytoplasm, cytoplasm actions, and fertility actions. These information is packed up into 13 derived data types in Fortran. Shown in the upper right is one derived data type in Fortran, which can be called to define the genetic and breeding populations whenever needed. It consists of two integer variables, and seven variables of other derived data types. Based on Gmodel and the derived type of populations, subroutines are then developed, which are indicated in the lower part of the figure. Four subroutines are designed to manipulate the global variable Gmodel, and nine are designed to manipulate individual populations. Five subroutines are developed to implement the overloading features of populations, three are developed for the purpose of generation advancement, and two are developed for the purpose of selection.
Given next is the information on chromosomes and genetic loci on chromosomes (Supplementary Table 1). For each chromosome, the variable ‘Gmodel%chr(:)’ defines the chromosome name, and number of loci on the chromosome. For each set of ordered genetic loci on chromosomes, the variable ‘Gmodel%locus(:)’ defines the locus name, chromosomal position in centi-Morgans (cM), recombination frequency with the prededing locus, number of alleles at the locus, whether the locus is a marker or a gene, whether to consider mutation at the locus, and mutation rates if mutation is considered. For each locus specified as a marker, variable ‘Gmodel%mrk(:)’ defines the locus identity, and scores of all genotypes at the marker locus, allowing selection based on marker scores. For each locus specified as a gene, ‘Gmodel%gene(:)’ defines the locus identity, the number of traits affected, which traits are affected, and the values of all genotypes at the gene locus for each affected trait in each environment. For each epistasis network, the variable ‘Gmodel%epn(:)’ defines the trait identity, the environment identity, the number of loci in the network, which loci are involved, and the values of all genotypic combinations in the network.
Included in the cytoplasm information are the cytoplasm name, whether to consider mutation, and mutation rates if mutation is considered (Supplementary Table 1). Cytoplasm may have independent effects on some traits, or may interact with some nuclear genes to affect one trait. The effects on traits involving the cytoplasm are called cytoplasm actions in Blib. For each cytoplasm action, the variable ‘Gmodel%cact(:)’ defines the cytoplasm identity, the trait identification, the environment identity, the number of loci in the action, which nuclear genetic loci are involved, and the values of all genetic combinations in the action (Supplementary Table 1). The fertility of female and male gametes may be affected by the cytoplasm alone, nuclear genes alone, or cytoplasm by nuclear gene interactions. These effects on female and male fertility are called fertility actions in Blib. For each fertility action, the variable ‘Gmodel%fert(:)’ defines the cytoplasm identification, the environment identity, the number of loci in the action, which loci are involved, and the values of all genotypic combinations in the action (Supplementary Table 1). Finally, the variable ‘Gmodel%range’ defines the lowest and highest marker scores and the lowest and highest values for each trait (Supplementary Table 1), by which the phenotypic traits may be adjusted to be located in the range of 0 to 1 for the convenience of comparison.
Suitable definition of the large number of highly varied and complicated genetic models is key in developing the genetic simulation tools or platforms. More technical details of the five selected variables are given below in Supplementary Table 1, which consist of the core contents of the global variable Gmodel. When loading the information from one external file, the pointer ‘Gmodel%locus(:)’ is first allocated, with the size being equal to the number of genetic loci defined in Gmodel%status. For each ordered genetic locus defined by TYPE(Gmodel_locus) (Supplementary Fig. 1), one character variable, i.e., ‘Gmodel%locus(:)%name’, represents name of the locus. Two real variables, i.e., ‘Gmodel%locus(:)%pos’ and ‘Gmodel%locus(:)%recfreq’, represent the chromosomal position in cM and the recombination frequency with the preceding locus. Three integer variables, i.e., ‘Gmodel%locus(:)%nallel’, ‘Gmodel%locus(:)%isgene’ and ‘Gmodel%locus(:)%mutate’, represent the number of alleles at the locus, whether the locus is a marker or a gene, and whether or not to consider mutation at the locus. The last real variable, i.e., ‘Gmodel%locus(:)%murate’, is defined as a real pointer of one-dimensional array, which is to be allocated and assigned with mutation rates when mutation is considered. Input information of seven loci is given in the lower part of Supplementary Fig. 1 as an illustration. For example for the second locus, we have Gmodel%locus(2)%name = ‘Locus2’ and Gmodel%locus(2)%pos = 10.0, and therefore, its genetic distance from the preceding locus is equal to 10 cM from which variable Gmodel%locus(2)%recfreq for recombination frequency can be assigned by Haldane’s mapping function. Gmodel%locus(2)%nallel = 3 indicates that there are three alleles at the locus; Gmodel%locus(2)%isgene = 0 indicates that the locus is a marker; and Gmodel%locus(2)%mutate = 1 indicates that mutation is considered at the locus. Then, Gmodel%locus(2)%murate is defined as an array with a size of 6, and assigned the six mutation rates from allele 1 to alleles 2 and 3 (i.e., 0.0001 and 0.0002), from allele 2 to alleles 1 and 3 (i.e., 0.0003 and 0.0004), and from allele 3 to alleles 1 and 2 (i.e., 0.0005 and 0.006) (Supplementary Fig. 1).
The pointer ‘Gmodel%gene(:)’ is first assigned with the size being equal to the number of genes defined in Gmodel%status. For each gene defined by ‘TYPE(Gmodel_gene)’ (Supplementary Fig. 2), two integer variables, i.e., ‘Gmodel%gene(:)%wlocus’ and ‘Gmodel%gene(:)%ntrt’, represent the locus identity and number of traits affected by the gene. The integer variable ‘Gmodel%locus(:)%wtrt’ is defined as a pointer, which is to be allocated and assigned with the trait identity. The real variable ‘Gmodel%locus(:)%gvalue(:,:,:)’ is defined as a pointer of three dimensions, which is to be allocated and assigned with the genotypic values of all genotypes at the gene locus for all affected traits in all environments. For example for the gene defined in the lower part of Supplementary Fig. 2, Gmodel%gene(1)%wlocus = 3, and Gmodel%gene(1)%ntrt = 2, indicating that the gene is located at the third locus in the genome and has effects on two traits. The real variable ‘Gmodel%locus(1)%gvalue’ is defined as an array of three dimensions with sizes of 2, 3, and 10, representing the number of traits affected by the gene, number of environments, and number of genotypic combinations at the gene locus. The 60 values shown on the lower right side in Supplementary Fig. 2 represent the 10 genotypic values for the second and third traits in the three environments which are defined by Gmodel%env(:).
The pointer ‘Gmodel%cyto(:)’ is first defined, with the size being equal to the number of cytoplasms defined in Gmodel%status. For each cytoplasm defined by ‘TYPE(Gmodel_cyto)’ (Supplementary Fig. 3), one character variable, i.e., ‘Gmodel%cyto(:)%name’, represents the name of the cytoplasm. One integer variable, i.e., ‘Gmodel%cyto(:)%mutate’, represents whether or not to consider mutation for the cytoplasm. One real variable, i.e., ‘Gmodel%locus(:)%murate’, is defined as a pointer that is to be allocated and assigned with mutation rates if mutation is considered. For example, for the fourth cytoplasm in the lower part of Supplementary Fig. 3, Gmodel%cyto(4)%name = ‘Cyto4’, and Gmodel%cyto(4)%mutate = 1, indicating that mutation is considered for the cytoplasm. Gmodel%cyto(4)%murate is defined as an array with a size of 4, and assigned the four mutation rates from cytoplasm 4 to cytoplasms 1, 2, 3 and 5 (i.e., 0.0090, 0.0050, 0.0000, and 0.0000) (Supplementary Fig. 3).
The pointer ‘Gmodel%cact(:)’ is first defined with the size being equal to the number of cytoplasm actions defined in Gmodel%status. For each cytoplasm action defined by ‘TYPE(Gmodel_cact)’ (Supplementary Fig. 4), five integer variables, i.e., ‘Gmodel%cact(:)%wcyto’, ‘Gmodel%cact(:)%wtrt’, ‘Gmodel%cact(:)%wenv’, ‘Gmodel%cact(:)%nvalue’, and ‘Gmodel%cact(:)%nlocus’ represent the cytoplasm identity, trait identity, environment identity, number of values, and number of loci included in the action, respectively. The number of values, i.e., ‘Gmodel%cact(:)%nvalue’, is automatically calculated from the number of alleles at each locus included in the action. The integer variable ‘Gmodel%cact(:)%wlocus’ is defined as an integer pointer, which is to be allocated and assigned the locus identity. The real variable ‘Gmodel%cact(:)%cvalue(:)’ is defined as a real array pointer, which is to be allocated and assigned the genotypic values in the action. For example for the first action defined in the lower part of Supplementary Fig. 4, Gmodel%cact(1)%wcyto = 2, Gmodel%cact(1)%wtrt = 4, Gmodel%cact(1)%wenv = 1, Gmodel%cact(1)%nvalue = 1, and Gmodel%cact(1)%nlocus = 0, indicating that the second cytoplasm has an independent effect on the fourth trait in the first environment. The independent effect is equal to 1.01, which is assigned to the allocated real array ‘Gmodel%cact(2)%cvalue(1:1)’. For the second action in Supplementary Fig. 4, the first cytoplasm and the tenth locus together affect the first trait in the first environment. There are two alleles at the tenth locus (Supplementary Fig. 1); therefore, three genotypic values are given in Supplementary Fig. 4 for the action, which are assigned to the allocated real array ‘Gmodel%cact(2)%cvalue(1:3)’. For the third action in Supplementary Fig. 4, the third cytoplasm and three loci together affect the fourth trait in the first environment. The three loci (i.e., 10, 11, and 22) have 2, 2, and 3 alleles (Supplementary Fig. 1), resulting in 3, 3, and 6 genotypes, respectively. Therefore, a total of 54 genotypic values are given in Supplementary Fig. 4 for the action, which are assigned to the allocated real array ‘Gmodel%cact(3)%cvalue(1:54)’.
We assume that fertility is independent of environment. Alternatively, fertility is mainly controlled by cytoplasm and/or nuclear genes under normal environmental conditions. The pointer ‘Gmodel%fert(:)’ is first defined, with the size being equal to the number of fertility actions defined in Gmodel%status. For each fertility action defined by ‘TYPE(Gmodel_fert)’ (Supplementary Fig. 5), three integer variables, i.e., ‘Gmodel%fert(:)%wcyto’, ‘Gmodel%fert(:)%nvalue’, and ‘Gmodel%fert(:)%nlocus’, represent the cytoplasm identity, number of values, and number of loci included in the action, respectively. The number of values, i.e., ‘Gmodel%fert(:)%nvalue’, is automatically calculated from the number of alleles at each locus included in the action. The integer variable ‘Gmodel%fert(:)%wlocus’ is defined as a pointer, which is to be allocated and assigned with the locus identity. Two real variables ‘Gmodel%cact(:)%fvalue(:)’ and ‘Gmodel%cact(:)%mvalue(:)’, are defined as real pointers of a one-dimensional array, which are to be allocated and assigned with the female and male fertility values. For example for the first action defined in the lower part of Supplementary Fig. 5, Gmodel%fert(1)%wcyto = 3, Gmodel%fert(1)%nvalue = 1, and Gmodel%fert(1)%nlocus = 0, indicating that the third cytoplasm has an independent effect on fertility. The female fertility is equal to 1.00 (i.e., normal), which is assigned to the allocated array ‘Gmodel%fert(1)%fvalue(1:1)’; the male fertility is equal to 0.90 (i.e., partial sterility), which is assigned to the allocated array ‘Gmodel%fert(1)%mvalue(1:1)’. For the second action in Supplementary Fig. 5, Gmodel%fert(2)%wcyto = 0, Gmodel%fert(2)%nvalue = 6, and Gmodel%fert(2)%nlocus = 1, indicating that there is one locus with independent effects on fertility, i.e., locus 22. There are three alleles at the locus (Supplementary Fig. 1); therefore, six values are given for female fertility, which are assigned to the allocated array ‘Gmodel%cact(2)%fvalue(1:6)’; and six values are given for male fertility, which are assigned to the allocated array ‘Gmodel%cact(2)%mvalue(1:6)’. For the third action in Supplementary Fig. 5, Gmodel%fert(3)%wcyto = 0, Gmodel%fert(3)%nvalue = 18, and Gmodel%fert(3)%nlocus = 2, indicating that two loci together affect fertility, i.e., locus 1 and locus 2. There are two alleles at locus 1 and three alleles at locus 2 (Supplementary Fig. 1), resulting in a total of 18 (i.e., 3×6) genotypes when the two loci are considered together. Therefore, 18 values are given for female fertility, which are assigned to the allocated array ‘Gmodel%cact(3)%fvalue(1:18)’, and 18 values are given for male fertility which are assigned to the allocated array ‘Gmodel%cact(3)%mvalue(1:18)’.
In Blib, ‘Population’ is one derived data type in Fortran, which can be called to define the generalized genetic and breeding populations whenever needed (Supplementary Fig. 6). For example, the code ‘TYPE(Population):: Popg, Popb, PopF1(3)’ defines two populations (i.e., Popg and Popb), and one array of populations with the size of three, i.e., PopF1(1:3), which can then be manipulated in Blib subroutines and application modules. Type ‘Population’ consists of two integer variables, and seven variables of other derived data types (Fig. 1; Supplementary Table 2). One integer variable, i.e., ‘…%psize’, saves the population size, and the other one, i.e., ‘…%pdefn’, saves the method to generate the individual genotypes, e.g., by allele frequencies or by allele combinations. The second integer variable is mainly used when assigning one population from one user-defined external input file (Supplementary Table 2).
The variable ‘…%pinput’ saves the frequencies of cytoplasm, and frequencies of alleles at each locus, or the specified allele combinations, which are loaded externally (Supplementary Table 2). The variable ‘…%pstruct’ saves the parental identity, cytoplasm type, and two alleles at two homologous chromosomes of every diploid individual in the population. This is the most important piece of information regarding the genetic and breeding populations. The variable ‘…%gpvalue’ holds the female and male gametic fertilities, marker score, and genotypic and phenotypic values of every diploid individual in the population, which are allocated and assigned whenever needed in application modules.
The other variables of derived data types record the genetic parameters at the population level, which are allocated and assigned upon request. The variable ‘…%stgv’ records population means, variances, genotype by environment interactions, broad-sense heritability, trait correlations, and environmental correlations. The variable ‘…%stvar’ records the additive and dominance variances, and broad-sense and narrow-sense heritabilities. The variable ‘…%stfre’ records the frequencies of cytoplasm, and frequencies of alleles at each marker or gene locus. The variable ‘…%stdiv’ records the diversities of markers and genes. Frequencies and diversities can also be calculated independently from genes located on each chromosome, from genes affecting each trait, or from genes in each epistasis network. All these parameters have been widely used in theoretical and applied population genetics.
Based on the previously described global variable Gmodel and the derived data type ‘Population’, a number of general subroutines were then developed for specific purposes (Fig. 1; Supplementary Table 3). Four subroutines were designed to manipulate the global variable Gmodel (1.1 to 1.4 in Supplementary Table 3), such as read the user-defined information to Gmodel from an external file (i.e., Blib_ReadGmodel), calculating and assigning the variables in Gmodel not directly given in external file (i.e., Blib_CalcGmodel), releasing the allocated memory in Gmodel (i.e., Blib_CloseGmodel), and writing the information in Gmodel to an external file (i.e., Blib_WriteGmodel).
Nine subroutines were designed to manipulate the population (Fig. 1; 2.1 to 2.9 in Supplementary Table 3). The first three were designed to allocate memory to the to-be-used variables in the population (i.e., Blib_AllocatePopulation), read one population from one user-defined external file (i.e., Blib_ReadPopulation), and create the genotypes of all individuals from the user-defined cytoplasm frequency and allele frequencies (i.e., Blib_CreatePopulation). The three subroutines in the middle were designed to calculate female and male fertility values for each individual in the population (i.e., Blib_CalcFertility), calculate genotypic values for each individual in the population for a specific trait in a specific environment (i.e., Blib_CalcGenovalue), and calculate phenotypic values for each individual in the population for a specific trait in a specific environment (i.e., Blib_CalcPhenovalue). Subroutine Blib_CalcPstatistics calculates the statistical parameters related to the genetic and breeding population, such as population means for marker score and traits, additive and dominance variances, frequencies of alleles at each locus, and genetic diversities. The last two can be used to release part or all of the allocated memory in the population (i.e., Blib_ClosePopulation), and write one population to an external file (i.e., Blib_WritePopulation).
Overloading is an important feature in modern programming languages. Five subroutines were designed to have the overloading features for populations (3.1–3.5 in Supplementary Table 3). By using these subroutines, we can define one population as an empty population (i.e., Blib_pop_to_pop), merge two populations to make a new population (i.e., Blib_pop_ad_pop), merge a one-dimensional array of populations to obtain a new population (i.e., Blib_pops_to_pop), split one population into a one-dimensional array of populations each with a size of one (i.e., Blib_pop_to_pops), and assign a one-dimensional array of populations to another one-dimensional array of populations (i.e., Blib_pops_to_pops). By calling the overloading subroutines, the development of Blib application modules can be greatly simplified, especially when considering that both among-family selection and within-family selection are frequently applied in most breeding programs.
Three subroutines were developed for the purpose of generation advancement (4.1 to 4.3 in Supplementary Table 3). The first one, i.e., Blib_Cross1Population, was designed to conduct hybridization using one population of parents, and then generate a new population of progenies. The second one, i.e., Blib_Cross2Population, was designed to conduct the hybridization between two populations (one used as female parents, and the other used as male parents), and then generate a new population of progenies. The third one, i.e., Blib_Mutation, was designed to conduct mutation (both cytoplasm and the alleles at each mutating locus) once a new population is formed. Notably, the third subroutine is always automatically called by the first two.
Two subroutines were developed for the purpose of selection (5.1 to 5.2 in Supplementary Table 3). One was designed to randomly select a given number of individuals from one population, and form a new population (i.e., Blib_SelectRandom). The other one was designed to select a given number of individuals by one specified selection mode from one population (i.e., Blib_Select), such as top selection (i.e., individuals with the highest trait values are selected), bottom selection (i.e., individuals with the lowest trait values are selected), and middle selection (i.e., individuals with medium trait values are selected). More advanced selection methods are sure to occur, which will be considered as Blib application modules are further developed.
All statistical analyses were performed using Microsoft Excel 2016. Case studies were conducted using the application modules and input files provided in Supplementary Software 1. In case studies I and II, two population sizes were set at 10 and 50. In case study III, size of the initial population was set at 100. In case study IV, each of the two heterotic groups consisted of 50 inbred lines. Each case study was repeated for 100 simulation runs as defined in the input files.
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Input and output files of the four case studies were provided as Supplementary Software 1. Source data underlying figures were presented in Supplementary Data 1.
Compiled codes of application modules used in the four case studies were provided as Supplementary Software 1.
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This work was supported by the National Natural Science Foundation of China (Project No. 31861143003), and The Agricultural Science and Technology Innovation Program of CAAS.
National Key Facility for Crop Gene Resources and Genetic Improvement, and Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100081, China
Luyan Zhang, Huihui Li & Jiankang Wang
National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences (CAAS), Sanya, Hainan, 572024, China
Huihui Li & Jiankang Wang
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L.Z. tested the Blib platform, developed the Blib application modules, conducted the case studies, and wrote the manuscript draft. H.L. tested the Blib application modules, and revised the manuscript draft. J.W. developed the Blib platform, developed the Blib application modules, and wrote and revised the manuscript draft.
Correspondence to Jiankang Wang.
The authors declare no competing interests.
Communications Biology thanks Xuehui Huang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Gene Chong.
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Zhang, L., Li, H. & Wang, J. Blib is a multi-module simulation platform for genetics studies and intelligent breeding. Commun Biol 5, 1167 (2022). https://doi.org/10.1038/s42003-022-04151-9
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Jordan Bobick is the owner and founder of Higher Ground Music & Video Production in Key Largo. Offering live playback and tracking inside and outside the studio, the technology allows for remote operation in which someone can record at home with Bobick’s expertise and ability to provide professional, quality sound.
This revolutionary technology, in conjunction with the actual studio, also offers the ability for a remote collaboration. Bobick said, “An example is a drummer is at my studio, but his group is scattered about the world. I worked with producers in Los Angeles, but the artist was here, and we worked together in real time.” 
Bobick said this technology came to market within the past few years. Bobick has a vast background that demonstrates his creativity and a passion for technology, business and people.
“I grew up in Melbourne but had been coming here my whole life as my grandparents lived in Lower Matecumbe. My brother has lived here for a while, and then I finally anchored here after the pandemic. I worked in hospitality before starting the studio in July of this year.”
Prior to opening the studio, Bobick served four years in the Marines, including time in Iraq. He has also been a scuba instructor, done maritime salvage and worked in social services using his undergraduate degree in psychology from University of Central Florida. He is now working on a doctorate in industrial organizational psychology-behavioral performance. He said he wanted to do something different, and he will graduate in May 2023.
“I didn’t want to work for someone else any more; I knew I had the ability.” Bobick said. “In the social services field, I got to work with young people who were amazingly talented. I feel that this is my job from the universe, and I’m now working with this passion.” It’s psychology infused with business at another level.
As for the studio, Bobick said, “I wanted the studio to feel like you’re sitting in a lounge—kind of chill and playing. So, I integrated all the hardware aspects right into the furniture. And yes, I built all this myself. I spend a lot of time planning in my mind.”
“Inputs from the studio go right into the computer—everything is digital versus a big mixing board.”
Bobick has been playing guitar since he was 10 years old. And he can play just about every instrument in the studio — piano, keyboard, bass, banjo, synthesizers. He started working on the studio idea about five years ago.
He added, chuckling, “I could talk about it for days. I drive my husband insane.”
Bobick and his husband, Michael Tomanio, have been together over 10 years and married for a few years. They met online but went to the same community college. “We started taking the bus together; it was really cute. He’s a great guy.”
Some of the services that Bobick can provide at Higher Ground are voice-over, live recording with their instruments, music lessons, audiobooks, mixing and mastering- remote and live, green screen full production—music videos, or creating a commercial.
“I also do live recording, video and audio and can layer that into a final production. Another trend is gaming, and game creators need music for their games as well as digitizing characters.”
When asked about his long-term vision, he said, “I built this studio as a prototype. We are in the digital age; it’s built to be accessible to the metaverse. I want to create this as a web of studios that can act as a network across the nation and the world. A system of smart studios that work with a remote engineer, and the studio can open and shut down with a one-time code. You don’t need the engineer present or special equipment, a fully digital studio with a streamlined design. This model could grow as a franchise.”
Higher Ground will host an outdoor event, Key Largo Review, on Saturday, Nov. 5, from 6 to 10 p.m. 
“I will go live on Facebook, Instagram and Twitch and stream to all these different social media platforms. So, if they say, ‘I want to get my performance out to my audience,’ right on the phone, we can log into their account, hit the live button and then the artist can perform.” Bobick said. “I’m focused on this as a series of Saturday night shows that have a lineup to feature artists of different genres.”
Bobick also aims to provide an outlet for younger artists, hip hop, Latin artists and more, and to make sure it’s an LBTQ-safe space where everyone can feel comfortable.
“I’m grateful I’ve had a lot of great mentors, including experienced recording artists, along the way. Word has been getting out locally, and I already have had some great referrals,” he said. 
He loves the Keys. “Everyone knows each other. I love the sense of community. I’m a scuba diver and do a lot on the water, so this is a waterman’s paradise. I am grateful for this beautiful place, wonderful people and to have a business.”
Connect with Higher Ground Music and Video Production on Instagram @higher_ground_keylargo or on Facebook at HigherGroundKeyLargo. To sign up for Higher Ground’s Key Largo Review, message on IG or text 845-282-6568.

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PINELLAS COUNTY – There will be three days of free panels during the upcoming Tampa Bay Afrofuturism Festival (TBAFF), featuring local and regional heavyweights and community stakeholders in discussion.
While many think of Afrofuturism as an arts movement, TBAFF festival organizer Jake-ann Jones of afroQuantum Experiences notes that, in reality, it is more of a social justice project.
“If you look at the history of how the conversation around Afrofuturism has developed, it’s always included discussions on the social and political realities that Black folks live in, even as we look forward and strive to thrive. There’s no way to talk about Black Futures without acknowledging the historical impact of others on our freedoms and how we’ve had to rework all of our own projects around those impacts,” Jones noted.
The festival will have three panels located in Largo and St. Pete. Full panel schedule is as follows:
DAY 1: “THE JOURNEY: The Future of Black Tampa Bay” on Friday, Nov. 11 at 2 p.m. at Creative Pinellas during the inaugural Tampa Bay Afrofuturism Festival! Creative Pinellas is located at 12211 Walsingham Rd, Largo.
Panelists:
DAY 2: “AFROTech Tampa Bay” on Saturday, Nov. 12 at 10 a.m. at The Woodson Museum during the inaugural Tampa Bay Afrofuturism Festival! The Woodson is located at 2240 9th Ave. S, St. Petersburg.
Panelists:
DAY 3: “Black Family Health Day” on Sunday, Nov. 13. The panel and audience will watch a 10:30 a.m. screening of the film “SIMI” by filmmaker, educator and healer Debbie Yati Garret, and then the panel will begin at 11 a.m., at The Studio@620. The Studio@620 is located at 620 First Ave. S, St. Petersburg.
Panelists:
Interested in learning more about Afrofuturism? Here are some links to explore:
For a full list of resources on Afrofuturism, visit readingblackfutures.com/defining-afrofuturism
For more information and a full schedule of festival activities, visit tampabayafrofuturismfestival.com.
The Tampa Bay Afrofuturism Festival is an initiative of Pinellas Diaspora Arts Project, Inc (PDAP). A registered 501c3, PDAP’s mission includes elevating Black and Brown art and artists in Tampa Bay, with a focus on youth and family health and wellness. Visit pinellasdap.org  or follow them on Facebook at facebook.com/PinellasDAP and Instagram instagram.com/pinellasdap.
‘The Weekly Challenger’s’ Jake-ann Jones delves into revolutionary Florence…
PINELLAS COUNTY — The Pinellas Diaspora Arts Project will host the inaugural Tampa…
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An intimate interview with the pioneering techno icon, whose first album in a decade, "Electronic Generations," is also his first body of work as a live electronic musician.
Mistakes are like scars. They're permanent, but precious if you're able to find the beauty in them.
Perhaps no one relates more than Carl Cox, one of the most iconic artists in electronic music history, who is tearing hell-for-leather into a creative reawakening he tells us was ignited by a serendipitous string of "mistakes and happy accidents." The byproduct is Electronic Generations, his first album in a decade—and his best yet.
On a rare rainy, dreary morning in Los Angeles when my fresh coffee tasted blander than usual, Cox is patching in from the radiant Spanish city of Torremolinos, where he's relaxing before a performance in a small ballroom. He'll be going live there, almost like a warmup for his sold-out show at Wembley Arena a few days later. Of course, with Carl there's no such thing as a warmup gig—every performance is a masterclass in dancefloor control for the "King of Techno."
"I love small parties," Cox says. "The money that I get from small parties is nothing, but I love it because it grounds me. It's where I came from. For me, you have to do the small ones to basically understand where you are as an artist today."
But who exactly is Carl Cox today, aside from your favorite DJ's favorite DJ?
"I’m not DJ Carl Cox anymore. I'm Carl Cox, the artist."
He's a man possessed, someone you wouldn't recognize. Not in the superficial way—Cox is still the same gregarious and effervescent techno teddy bear—but in terms of his artistry. In that regard, he's as different as the weather in Spain and California on the morning we spoke.
It's not that Cox went away and returned anew. The world's preeminent techno artist never left, remaining as prolific as ever even through the pandemic, performing 52 virtual "Cabin Fever" shows when people needed to dance more than ever. He headlined Glastonbury, where he threw down an epic set alongside Chase & Status. And he debuted his extraordinary "Hybrid Live" show at this year's Ultra Music Festival, where he poured gasoline on the flame of his reputation as a persevering pioneer, which has never even flickered.
That reputation is reflected in Electronic Generations, which releases on December 2nd. What makes the album so special, however, is the fact that it shows a side of Cox which his fans have never heard.
Carl Cox performing his "Hybrid Live" set at Ultra Music Festival 2022 in Miami.
Rudgrcom
Electronic Generations is Cox's first-ever body of work as a live artist. The album, which he equates to "a true electronic music experience," is a microcosm of a latent dream that had been dormant for over 40 years. It's the culmination of his push to rupture the inextricable link between his DJing and his original music.
"I've always wanted to go out as a live entity, outside of DJing," Cox explains. "I’m not DJ Carl Cox anymore. I'm Carl Cox, the artist. And this album is only the beginning of what I do next."
It all started after the onset of COVID-19. It'd been years since the legendary DJ experienced a semblance of stillness in life and he grew lonely, the exuberant camaraderie of rave culture replaced by a general air of malaise. He'd also tragically lost both his mother and father, who died following bouts with septicemia and dementia, respectively.
Just like the rest of us who became tethered to our devices during those despondent days, Cox spent his time "talking to machines."
But his machines were a little different. Cox had spent three years building a hi-tech recording studio on a farm in Melbourne, eventually settling into a bleeding edge bolt-hole where he says he could make music for the rest of his life.
A blasé Cox quickly found himself toiling through periods of stagnation, the kind that wallops you after years of vestigial wear-and-tear. He became disillusioned after releasing four albums, and he'd had enough.
"At the end of the day, it's just electronic music… It's the millions of people all around the world going out and listening to the art of the kick drum. So it depends what comes after the kick drum which defines the music. And I said to myself, 'I think I've done enough.'"
With nothing but time on his hands—the fingertips of which could tell the most remarkable DJing stories if only their nails could speak—Cox began to play around with the equipment in his studio. Drum machines, modular synthesizers, samplers, loop stations, Moogs—the list goes on. He dove into his arsenal of gear like a golden retriever after discovering a pile of autumn leaves.
Cox says it wasn't long before he got into a groove, fluidly using each machine in unison and creating live electronic music for the first time.
A few of the machines in Carl Cox's recording studio in Melbourne.
c/o Press
His mind became a tabula rasa. Yes, we all wish we could have a blank slate amidst the vicious throes of burnout, but that can only happen once we've put in the work. It's only natural to become jaded after decades of flying the flag of underground techno.
"My energy and my soul came back into something which had been missing for so many years," Cox rhapsodizes. "There was a spark here of something which I've ignited."
After an hour and a half of flinging headlong into this live music continuum, he had laid down roughly a dozen demos, which he says he'd never done before. It was poetry in motion.
"This was what was so exciting for me because I'm finding myself now making music without even thinking about making music. Because that’s what's been suppressed for so long," adds Cox, who says he "captured a moment" in the recording process of Electronic Generations that can only be heard in the finished product.
It's not easy working with gear like modular synthesizers, which are considered a lost art in today's music production community thanks to a surfeit of software and digital plugins. Cox pauses and leaves my frame for a moment before returning with a Pulsar-23 from Soma Laboratory, which looks more like a control deck on Han Solo's Millennium Falcon.
"This is one crazy ass machine," he says of the semi-modular drum machine, a gift from Joseph Capriati.
Cox also praises Pioneer DJ's DJM-V10 mixer, a "game-changer" because of its capability to record each channel live and print the resulting waveforms—mistakes and all—directly into his digital audio workstation, Ableton. His project files became the stuff of Jackson Pollock paintings, canvases full of chaotic musical brushstrokes which form a cohesive work of art when manipulated by the right craftsman.
Eventually he had written 20 ideas for new songs and had to stop for his own good. He was like a can of soda—too much action on the inside and it'll explode.
Carl Cox.
c/o Press
From there, Cox found himself at a crossroads. It seems the impact of COVID-19 deeply affected him, the scourge of the virus lighting the match of a samsara.
"I thought, 'If we get out of this pandemic, I don’t really want to come back as a DJ,'" he explains. "I want to come back and give you more, give you something else that I wasn’t thinking about doing."
Cox first tested his new music and direction in November 2020, when he recorded a live set for "Movement Selects," a virtual performance series launched by the organizers of Detroit's fabled Movement festival. Using a Moog module, DJM-V10 and more, Cox says he played an arcane set replete with "undefined" original music no one had ever heard. The only machine that couldn't talk that day? Shazam.
"I can’t suppress this music. It has to go somewhere."
For many fans, watching Cox perform an improvised show was like a baby seeing a mirror for the first time.
"They loved it," he gushes. "They were blown away."
The pioneers of the future rave genre are reinforcing its roots with their inventive new EP.
Adriatique, Atish, Chloé Caillet and more feature in the October 2022 edition of The House Brew.
"I'm not DJ Carl Cox anymore. I’m Carl Cox, the artist. And this album is only the beginning of what I do next."
So Cox decided to approach a few record companies. He wasn't itching to release the music on a major label, instead tentatively pursuing a "renegade" release plan via his own Awesome Soundwave imprint.
"'I can’t suppress this music,'" he remembers thinking at the time. "'It has to go somewhere.'"
An exec at BMG agreed. In fact, he said Electronic Generations is the best music Cox had ever made.
A nostalgic Cox brings up "I Want You (Forever)," the first record he ever produced. He released the track on Paul Oakenfold's Perfecto Records—a subsidiary of BMG—in 1991, only to be re-signed by the iconic music company over 30 years later. Never loquacious, he's now at a loss for words. His humility and appreciation for this "unreal" full-circle moment are palpable.
"I Want You (Forever)" is a relic of a bygone era, but all it takes is one spin for that vintage breakbeat serotonin rush to wash over and send shivers to your toes.
Things have changed dramatically since then, especially the life of a traveling DJ. Back when Cox started out, today's level of fame didn’t even exist for DJs, who toiled through an endless loop of underground raves and house parties. Cox cut his teeth in that DIY concrete jungle in the early '80s, when promoters didn’t want to pay DJs well because of their perceived lack of ability to sell tickets and fill out venues.
But by 1988, he was getting booked all around the U.K. and started to travel heavily, at one point "just going mad" after racking up 120,000 miles on his car in a single year. And as his fame grew, the same promoters started to plaster his name on their rave flyers.
With the help of his then-girlfriend, Cox began to send a contract to promoters that stated he'd perform for a price and timeframe he determined, a move he says no other DJs made at that time. If the promoter didn’t sign the agreement and send half the money upfront, he'd simply move on.
Electronic Generations pays homage to that influential work, and to the ensuing paradigm shift it helped trigger. It's a snapshot of that serpentine journey, which culminated in a world where DJs can perform for the kinds of crowds that only rockers and pop stars had seen.
“When I did that—probably in 1990—is the time that I knew there was a shift with DJs becoming a commodity based on business," Cox explains. "It took a while to get people to take DJs seriously, as performing artists. And as you can see, we are in a big, responsible position now, to be able to basically turn up and stand in front of all these thousands of people who have paid money to see us perform."
Carl Cox using a Roland TR-8 machine.
Dan Reid
Now, as Cox ascends on his new flight path, he knows there may be birds flying into the propellers. He admits that techno traditionalists may lash out after hearing Electronic Generations. After all, the decision to throw a wrench into such a time-tested career is as daring as it is dicey.
"My album doesn’t conform to anything," Cox asserts. "I’m not going to apologize that it’s too hard. I’m not going to apologize it’s got no vocals in… It doesn’t matter. It’s the way it went down is what that core energy is all about. And I really enjoy the fact that it doesn’t sit well with most people."
After four galvanizing decades of making people dance, it was time to do something for himself.
"I never made [Electronic Generations] for any reason, apart from my reason," Cox continues. "My reason is that this is the embodiment of what got me into techno music in the first place. The way how this music was made. Most of the records that you’re hearing are made for a purpose: 'This record will work on the dancefloor.' So you make and formulate it to do just that. I just can’t do that."
Cox believes his longtime fans—many of whom have stuck with him since the early '80s—can see a future in his new music and can readily understand his reasons for creating it. But the younger generation is another story.
When Cox released his memoir, Oh Yes, Oh Yes!, a slew of media outlets ran lurid, catchpenny headlines alluding to its most dramatic stories, like when gang members opened fire during one of his DJ sets. They largely ignored the one constant that has led him to become the apotheosis of techno: his music (duh). And if there's anyone to usher in a new era of the genre for today's young ravers, it's him.
"They don’t know what they’re looking for unless they’re exposed to it," Cox explains. "I think—more than anything—my life performances as a DJ and the tracks that I choose to play have all been about education. I will get someone who has no idea about the techno sound and present what I believe is techno of old and new."
The "techno of new" is experiencing quite a moment thanks to DJs like Charlotte de Witte and Amelie Lens, who Cox recognizes as today's preeminent artists. Beloved for their ferocious approach to the genre, they've been booked to perform at practically every major electronic music festival in the world, slamming tracks at a breakneck 140 beats per minute.
That acid-fueled sound isn't new to Cox. He rinsed it a lot, he says, about 20 years ago. But things have changed.
"It has that energy but it doesn’t have a soul to it," Cox says. "Because there's nothing soulful about electronic music when it’s coming at you that hard… [Electronic Generations] is techno, but it’s not strictly techno in the end of the day. If I was doing strictly techno, every record would sound the same—militant, straight-up and then you dance into the next one. I can't do that."
It’s important to note that Cox doesn't fault Lens, de Witte and his many other techno contemporaries, who are simply creating and sharing the music they love. But fans are mercurial and move onto new things quickly, he cautions, so it's important to be agile when pursuing a career as unpredictable as that of a DJ. It’s one of the key ingredients of the secret sauce behind his unbelievable longevity in the tortuous world of electronic dance music.
"If you listen to any of the albums I’ve made before, this is something else."
"It’s alright having that energy today, but there has to be a tomorrow," Cox adds. “And my journey has been all about tomorrow, based on why I’m still here and why I’m still relevant in such a way. Because people know from a historic point of view that I’ve been through everything. Why would I now—20 years later—play the same music that I played 20 years ago? I wouldn’t have progressed."
This brings up a simple yet contentious question. What even is techno nowadays?
Is it defined by the distorted kicks of hard dance? The frenetic cadence of gabber? "People see that as techno," explains Cox, who says he used to play out that kind of music back in the day as well. But it gave him headaches and became "soulless."
"Why would I play that music at the end of the day, when I want to give the premise of what I know is 'the' sound of what techno music is all about?" asks Cox. "So my album is a little bit of a window or showcase of what the sound of techno is from today’s point of view. And from my point of view, this is my offering." 
Ultimately Cox believes Electronic Generations, which features pre-mixes from the likes of Fatboy Slim, Nicole Moudaber, Franky Wah, Riton and Juan Atkins, will live up to its name and influence the techno lineage for years to come. The album will act like a lightning rod to his critics' emotional electricity at first, but he says it'll eventually serve as "the sound of tomorrow."
"If you listen to any of the albums I’ve made before, this is something else," explains Cox, who could only stop waxing poetic about his new music if you muzzled him. "This is completely cutting-edge compared to everything that I’ve created and what I’ve done, to the point that the album has led me towards doing a live electronic show at Wembley Arena."
Carl Cox performs at Wembley Arena on October 15th, 2022.
Dan Reid
Cox's historic performance at the famed Wembley Arena is quite an anomaly when thinking back to his isolation in the Land Down Under. At that time, he'd been aimlessly messing around with machines, making beats which had a snowball's chance in hell of escaping the four walls of his studio.
Fast-forward to 2022, when that music is rattling the very bones of Wembley, one of London's most beloved arenas. It's so wonderfully frightening to think how much can change in two years, let alone 40.
The real scary part? Cox maintains that he's only just scratching the surface. He says he hasn’t experimented with his gear as much as he'd like to and believes he can come up with even better music if he takes time to unlock his full potential.
Even at his age, Cox believes there's no limit to how far he can go. His fans can kind find solace in the notion that he's only getting started at the age of 60—and he's not going anywhere.
"I'm always here. I’m always around," Cox says with a chuckle. "Uncle Carl still plays the music that you can get behind."
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The legendary DJ and techno producer also released a new single with Nicole Moudaber, “How It Makes You Feel.”
The techno icon is the lynchpin of Ultra’s RESISTANCE Megastructure, one of the world’s top festival stages.
A candid interview with Hardwell, who is creating, performing and living on his own terms after over a decade in electronic dance music.
Techno legend Carl Cox talks about Avicii’s “shout for help” in the music industry.
Cox commandeered an epic setup of synthesizers and drum machines for the show, where he debuted his upcoming album and treated attendees to history in the making.
Cox and Wah have once again joined forces after collaborating on 2021’s scintillating “We Are One.”
You don’t need drugs to have a good time, just ask Carl Cox.
In one of two special Ibiza performance this summer, Carl Cox packed out the world’s largest nightclub.

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By Matt Grobar
Film Reporter
UTA has announced a sizable class of promotions, with 67 employees of the agency being elevated across 27 divisions. The move speaks to the company’s momentum and growth across numerous departments and business verticals that support its clients across entertainment, sports, culture and business, with the agency having announced over 120 promotions across multiple offices in the last year alone.
UTA’s most recent set of promotions, recognizing the contributions of colleagues at various levels of the company, come from departments including Motion Picture Literary, Music, News & Broadcasting, Production, Publishing, Talent, Theatre, Television Literary, Television Talent, Asian Business Development, Audio, Brand Studio, Comedy Touring,  Communications, Digital Talent, Endorsements & Voiceover, Finance & Accounting, Fine Arts, Gaming & Esports, Human Resources, Information Technology, IQ, Media Rights, MediaLink, UTA Foundation, Ventures and Web 3.0.

The group of promotees is a diverse one, with 60% identifying as women, and 35% as people of color. 80% of those promoted to Agent started their careers in UTA’s Agent Training Program.
“Over the past year, UTA has accelerated our momentum with new acquisitions, investments in our client capabilities, and key hires. This growth could not be possible without the hard work, ambition, and collaborative nature of the colleagues we promoted today,” said UTA President David Kramer. “Providing opportunities for advancement is core to our culture, and we are proud to share in this group’s success and excited to see what their next chapters at UTA will bring.”
UTA just this past May announced the formation of its largest ever partner class, elevating 26 to partner across 15 divisions, in a time of substantial development and growth for the company. The agency two months later announced a strategic partnership with EQT Private Equity, which made the global investment firm its largest outside shareholder.
UTA also recently announced the acquisition of Curtis Brown Group, one of the UK’s largest and most prestigious literary and talent groups, with the deal significantly expanding UTA’s global footprint. Also recently acquired was the software and data analytics company MediaHound, with that deal advancing UTA’s data analytics capabilities.

More information on those now elevated at UTA and the positions they’ll assume can be found below.
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Young RJ rode around with pre-fame Timbaland, learned from J. Dilla, and produced for Kurupt before he was able to legally buy alcohol. The 20-plus-year veteran has stories from the studio that are testaments to how long he’s been a student of the game. 
“Kurupt actually helped me with my homework. I had a project that had to be turned in, and Kurupt helped me finish it so we could get back to work,” Young RJ told REVOLT.
In this installment of “Studio Sessions,” one of Slum Village’s key members explains Dilla’s cryptic final message to him and what he learned from the late musician. He also discusses working on a new Slum Village album.
Your first major placement didn’t come with Slum Village or J. Dilla; it came with Kurupt while you were still in high school. How did that session happen?
That’s what made Slum take me seriously. My dad would pick me up from school, and there would be different people in the car. One time, he picked me up and had Timbaland in the car before he was Timbaland. We went to the studio, and Aaliyah was recording out of our studio at the time. They were working on the One In A Million album. I was in the session where they were doing the song “One In A Million.” From there, I started messing around with the drum machines and things of that nature. Then, Dilla started coming in and telling me, “I think you should do some of this and some of that.” He was teaching me. 
One day, Kurupt just so happened to come to Detroit. He was working on his album and was recorded on Fan-tas-tic Vol 2. My dad told him, “Well, I’m gonna let my son play you some stuff.” Then, Kurupt started going crazy over the joints and my dad was like, “Are you really feeling them, or are you just saying that?” Kurupt told him, “Nah, this is it.” We recorded about three or four songs in about a day-and-a-half. When Dilla heard what I did with Kurupt, he said, “Okay, now it is time to work.” We got it going from there.
How was it watching Kurupt record to your beats when you were still a teenager?
He was very quick. Once he heard something he liked, he was on it. He would go in the booth, he would lay what he had, chill for a second, and then he may take a nap. He’ll wake up like, “All right, I’m ready.” Then, he may record some more of the bars for it, and then he may be like, “Well, I’m gonna come back tomorrow.” He knew exactly what he wanted to do. Kurupt actually helped me with my homework. I had a project that had to be turned in, and Kurupt helped me finish it so we could get back to work.
 
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Taking it back further, how did you connect with J. Dilla?
I connected with D because my dad and John Salley had a studio at the time called Hoop Sound Studios, and they were looking for groups and auditions. We had a kid’s group called Kid You Not. We used to all hang out at the studio. One day, Baatin and T3 came in and auditioned, and then it went from there. I was about 10 years old when I met Dilla. 
You’ve said Dilla helped you with production tips. What were those sessions like learning from him?
Dilla would come to the house and make beats after we shut that studio down. He would come to the house and use the equipment. I would come home from school and see him regularly. When I was 15, I started taking production seriously, and Dilla showed me things on the drum machine.
Do you remember the last session you had with Dilla?
Yeah, it was right before he went out to LA. He and I were working on many different projects he had to complete and because he wasn’t 100 percent, he asked me to assist him with finishing up some of the work. I wasn’t credited on most of the stuff, but some of the things, I was. I remember asking him, “Why don’t you just start a production team?” He said, “Nah, I don’t want nobody having my sound when I’m gone.” That was the last time I seen him in person outside of coming to the “Selfish” music video shoot.
 
View this post on Instagram
 
A post shared by Young RJ (@youngrj313)
You’ve kept yourself ingrained in the Detroit hip hop space. How did you connect with Proof of D-12?
Proof and I connected from him coming up to the studio. Proof and I built a relationship in Europe when we toured the Detroit Deli album. When we got back, he was like, “I’m going to work on an album. I want you to do some music on there.” So, T3 was going to the studio to record the song he was on, and he gave him a CD we had that had “Gurls Wit Da Boom” and “Purple Gang” on it, and he ended up using them for his album. After that, he and I did a song for my record, and then we were supposed to link up because he wanted me to play some stuff for Eminem, but he ended up passing away. 
What is a memorable studio session you were in?
I used to always love going to the studio sessions they would do in LA. I was there when they recorded “Selfish.” I was able to see how Kanye [West] produced a song, and I talked with him about it. Then, you would see Common pop in. Then, you would see will.i.am pop in. Anybody you could think of would fall through these sessions. 
You put out a new album, World Tour, last month. One of the artists on it is a favorite of mine — Boldy James.
Boldy is the homie. Boldy and I met a little bit before the Blaq Royalty project. We got in the studio, and we’ve been cool ever since. We talk about things outside of music — like how the kids are doing. So, when you hear the songs, that’s pretty much what you’re hearing. You hear that relationship. As far as making music, Boldy will let you set the tone. He’ll say, “Play the beat.” When you play the beat, he’s like, “Okay, all right, so what are we talking about?” And then I’ll bounce the idea off him. Or, he may say, “This is what I got.” I’m not a street guy, so I always tell him whatever I’m writing will be from what I see and not from me doing it. He’s always respected that. Once we get in, I may already have the verse down, and then he’ll sit there and write and knock it out. He’s not a guy that’s going to take it home. We may do three or four songs in three or four hours.
 
View this post on Instagram
 
A post shared by Young RJ (@youngrj313)
How long did it take you to record your World Tour album?
I recorded this album in quarantine. I have a studio in my lower level. So, just being in there recording music and not having anything to do but work and make music, I was able to lock in and just get it knocked out. I wanted to say things with a message and make records that regular classic Slum Village fans would love. The main thing was putting together a solid album that the majority of it had a message [and] to get out everything that needed to be said before we moved on to work on the new Slum Village album.
So, a new Slum Village album is in the works?
Yeah. It should be out next year. We’ve been working on it for about a month. It’s always us first figuring out the direction we want to go sonically and then just making music that feels good. We’ve been locking in, and we’ll lock in for the next two or three months until we get it done. There may be some Dilla on there. We have to see what makes the final cut. 
What do you have coming for the rest of this year and 2023?
I have a whole other solo project done. It’s just a matter of when I drop it. I want to focus on getting this new Slum Village done. We have some shows coming up next month, and then we’re taking off and focusing entirely on getting this album where we want it to be. And then, of course, we got a lot in the works next year.
Eminem's daughter Hailie Jade supported her father as he was inducted into the Rock & Roll Hall of Fame on Nov. 5. Scroll on to see the photo from their father-daughter outing.
We've officially reached an all-time new low — sartorially speaking.
"I'm a rock star now!"
The legendary actor, who says he doesn't have any rights to the "Rocky" franchise he created, said he's against the "dark" tone in "Creed III."
Robert Downey Jr. let his son and daughter shave his head for his first TV role after leaving the MCU in "Avengers: Endgame."
Put this dress in a museum.
"My King @elliotpage took me out. 💜," Mae Martin captioned a photo gallery on Instagram alongside Elliot Page
The best of both worlds.
In an Instagram post, 'Saved By the Bell' cast member and '90210' star Tiffani Thiessen posted a steamy throwback picture. Fans and celebrities reacted to the picture.
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Selena Gomez discussed the mishap with Kelly Clarkson amid the release of her new documentary, "My Mind & Me."
"Hot take: not everything AOC says is accurate," Musk said after Rep. Alexandria Ocasio-Cortez accused him of messing with her Twitter account.
She's noticing just how helpful it is for Princesses Beatrice and Eugenie.
In an Instagram Story early Sunday morning, Kim Kardashian revealed she woke up to find Saint, Chicago and Psalm all sleeping next to her
Frontman Rob Halford also sang "Jolene" with fellow inductee Dolly Parton and others. Judas Priest Reunite with K.K. Downing for Rock & Roll Hall of Fame Performance: Watch Spencer Kaufman
Sylvester Stallone, 76, discussed once bowing out of a $34 million project before finishing "Rambo III," and is putting his focus on family now more than ever.
In a rare family photo shared to Instagram, Hallmark actress Lacey Chabert posed with 5-year-old daughter Julia that surprised fans.
UPDATE, with video The View‘s Whoopi Goldberg announced on the show today that she’s leaving Twitter and called Elon Musk’s week-long tenure “a mess.” Goldberg made the announcement after summarizing last week’s Twitter news – the staff firings (and some rehirings), the $8 blue check fee and, finally, the banning of comedian Kathy Griffin for […]
'Don't Worry Darling,' starring Harry Styles and Florence Pugh, is finally available to stream. Here's how to watch the Olivia Wilde-directed film on HBO Max.

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World’s Largest Pilates Brand Will Further Expand Its Presence in Europe
IRVINE, Calif.–(BUSINESS WIRE)–Xponential Fitness, Inc. (NYSE: XPOF) announced today it has signed a Master Franchise Agreement in Portugal for its Club Pilates brand. The agreement, signed with Porto-based Mitrinha & Libelinha, Lda., gives the Master Franchisee the opportunity to license a minimum of 20 Club Pilates studios in Portugal over the next 10 years. The Master Franchise partners bring extensive experience scaling globally-recognized consumer retail brands in Portugal.

With six Club Pilates studios currently operating in neighboring Spain and development agreements in place for the UK and Germany, the Portugal deal represents a further extension of Xponential’s reach across Europe. According to data from the International Health, Racquet & Sportsclub Association, the European fitness industry generates more than $30 billion annually, second only to North America, making it a priority region for Xponential’s international expansion. Recent findings from an Allied Market Research report showed that the European Pilates & yoga studios market size is projected to reach $49.4 million by 2027, growing at a CAGR of 13.05% from 2021 to 2027.
“The Portuguese fitness market is vibrant and growing, and Club Pilates is an ideal brand to launch in the market as interest in boutique fitness grows and as our business in Spain accelerates,” said John Kersh, Chief International Development Officer of Xponential Fitness. “We look forward to working with our partners in Portugal as we bring Club Pilates to even more customers across Europe.”
The agreement marks the continued expansion of Xponential Fitness outside North America. The company currently has multiple brands’ studios operating or in development in 16 countries across North America, Europe, Asia, and Australia. Most recently in 2022, Xponential signed master franchise agreements for Club Pilates in Mexico and the U.K., for CycleBar in Japan, and for Club Pilates, StretchLab, CycleBar, and Rumble in Kuwait.
Club Pilates is backed by Xponential Fitness, the largest global franchisor of boutique fitness brands which also include Pure Barre, Rumble, StretchLab, CycleBar, Row House, YogaSix, AKT, Body Fit Training, and STRIDE Fitness.
For more information about Xponential Fitness visit www.xponential.com.
ABOUT CLUB PILATES:
Founded in 2007, Club Pilates is the largest Pilates brand by number of studios, designed with the vision of making Pilates more accessible, approachable and welcoming to everyone. Based in Irvine, CA, Club Pilates has appeared in both Entrepreneur Magazine’s Franchise 500 and Fastest-Growing Franchises six years running, and Inc. Magazine’s Inc. 5000 lists multiple times. Club Pilates offers extensive training certification for its instructors. Its 500-hour training program includes instruction on Pilates, barre, Triggerpoint, and TRX Suspension Trainers. Club Pilates is headquartered in Irvine, CA and backed by Xponential Fitness, the largest franchisor of boutique fitness brands. To learn more about Club Pilates, visit https://www.clubpilates.com.
ABOUT XPONENTIAL FITNESS:
Xponential Fitness, Inc. (NYSE: XPOF) is the largest global franchisor of boutique fitness brands. Through its mission to make boutique fitness accessible to everyone, the Company operates a diversified platform of ten brands spanning across verticals including Pilates, indoor cycling, barre, stretching, rowing, dancing, boxing, running, functional training and yoga. In partnership with its franchisees, Xponential offers energetic, accessible, and personalized workout experiences led by highly qualified instructors in studio locations across 48 U.S. states and Canada, and through master franchise or international expansion agreements in 14 additional countries. Xponential Fitness’ portfolio of brands includes Club Pilates, the nation’s largest Pilates brand; CycleBar, the nation’s largest indoor cycling brand; StretchLab, the largest assisted stretching brand in the United States; Row House, a high-energy, low-impact indoor rowing workout; AKT, a dance-based cardio workout combining toning, interval and circuit training; YogaSix, the largest franchised yoga brand; Pure Barre, a total body workout that uses the ballet barre to perform small isometric movements; STRIDE, a treadmill-based cardio and strength training concept; Rumble, a boxing-inspired full-body workout; and BFT, a functional training, and strength-based program. For more information, please visit the Company’s website at https://xponential.com.
David Robertson, Fishman Public Relations, [email protected] or 847-945-1300
David Robertson, Fishman Public Relations, [email protected] or 847-945-1300

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MIAMI, FL – NOVEMBER 7, 2022 – Motorsport Games Inc. (NASDAQ: MSGM) (“Motorsport Games”), a leading racing game developer, publisher, and esports ecosystem provider of official motorsport racing series throughout the world, announced today a content update to rFactor 2, one of the most authentic sim racing platforms available to racers around the world. 
This quarter’s content update contains new cars for users to experience behind the wheel. Following the partnership between the British Touring Car Championship (BTCC), Motorsport Games, and Studio 397, a fifth real-world car will be part of the roster – the BMW 330i M Sport NGTC BTCC (trailer here). The rear-wheel drive car – developed by West Surrey Racing – marks the manufacturer’s official entry into the BTCC. Further, the Vanwall Vandervell LMH (trailer here) will be added, built to Le Mans Hypercar specifications, and created with data derived from the real car and the same engineering team. 
New tracks will be added to the platform as well, all of which marks the first time any of them have been laser (LIDAR) scanned into a commercially available product. British circuits Thruxton (Hampshire) and Croft (North Yorkshire) have been laser-scanned into rFactor 2 alongside the ExCeL London Circuit, featuring an innovative indoor/outdoor layout. Adding to the British circuits, the Bahrain International Circuit will make its debut in rFactor 2 and will feature a full day/night cycle and four distinct track layouts. More information about each track can be found here: 
“As rFactor 2 continues to expand and define what top simulation platforms should look like, we are excited for our fans to get behind the wheel and race with new cars and on new tracks with this update,” said Zach Griffin, Director of Technology at Motorsport Games. “Through our partnership with the BTCC and by being the official esports partner of Formula E, as well as rFactor 2 being the official simulation platform of Formula E, we are able to bring to life the elements of real-world racing into the simulation and provide an authentic experience for all. The new laser-scanned tracks, the first time they have been implemented into a product to this scale, also marks a new chapter for sim racing, and we are proud to be at the forefront of this innovation.”
Several quality-of-life updates to rFactor 2 will be launching today as well, including:
The new content and changes follow the addition of a native in-game store, which removed the need to exit the platform, showcasing bundle offers and previously purchased content which was implemented in October 2022. 
Fans have already gotten a taste of the latest BTCC content to be added at the four race activations this year (Snetterton, Thruxton, Silverstone National, and Brands Hatch GP). The latest car and track additions to rFactor 2 bring the totals to six layouts and five car types, respectively, from the BTCC represented in the simulation, including the two Brands Hatch layouts.
The pricing breakdown for the newly released content is as follows:
About Motorsport Games: 
Motorsport Games, a Motorsport Network company, is a leading racing game developer, publisher and esports ecosystem provider of official motorsport racing series throughout the world. Combining innovative and engaging video games with exciting esports competitions and content for racing fans and gamers, Motorsport Games strives to make the joy of racing accessible to everyone. The Company is the officially licensed video game developer and publisher for iconic motorsport racing series across PC, PlayStation, Xbox, Nintendo Switch and mobile, including NASCAR, INDYCAR, 24 Hours of Le Mans and the British Touring Car Championship (“BTCC”), as well as the industry leading rFactor 2 and KartKraft simulations. rFactor 2 also serves as the official sim racing platform of Formula E, while also powering F1 Arcade through a partnership with Kindred Concepts. Motorsport Games is an award-winning esports partner of choice for 24 Hours of Le Mans, Formula E, BTCC, the FIA World Rallycross Championship and the eNASCAR Heat Pro League, among others. Motorsport Games is building a virtual racing ecosystem where each product drives excitement, every esports event is an adventure and every story inspires.
Forward-Looking Statements:
Certain statements in this press release which are not historical facts are forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended, and are provided pursuant to the safe harbor provisions of the Private Securities Litigation Reform Act of 1995. Any statements in this press release that are not statements of historical fact may be deemed forward-looking statements. Words such as “continue,” “will,” “may,” “could,” “should,” “expect,” “expected,” “plans,” “intend,” “anticipate,” “believe,” “estimate,” “predict,” “potential,” and similar expressions are intended to identify such forward-looking statements. These forward-looking statements include, but are not limited to, statements concerning the expected benefits of the content updates to rFactor 2, related products and features and the positive attributes of the platform, such as the Company’s belief that rFactor 2 is one of the most authentic sim racing platforms available to racers around the world. All forward-looking statements involve significant risks and uncertainties that could cause actual results to differ materially from those expressed or implied in the forward-looking statements, many of which are generally outside the control of Motorsport Games and are difficult to predict. Examples of such risks and uncertainties include, without limitation: difficulties, delays in or unanticipated events that may impact the timing and expected benefits of the rFactor 2 updates and/or related products and features, such as due to unexpected release delays. Factors other than those referred to above could also cause Motorsport Games’ results to differ materially from expected results. Additional factors that could cause actual results to differ materially from those expressed or implied in the forward-looking statements can be found in Motorsport Games’ filings with the Securities and Exchange Commission (the “SEC”), including its Annual Report on Form 10-K for the fiscal year ended December 31, 2021, its Quarterly Reports on Form 10-Q filed with the SEC during 2022, as well as in its subsequent filings with the SEC. Motorsport Games anticipates that subsequent events and developments may cause its plans, intentions and expectations to change. Motorsport Games assumes no obligation, and it specifically disclaims any intention or obligation, to update any forward-looking statements, whether as a result of new information, future events or otherwise, except as expressly required by law. Forward-looking statements speak only as of the date they are made and should not be relied upon as representing Motorsport Games’ plans and expectations as of any subsequent date. Additionally, the business and financial materials and any other statement or disclosure on, or made available through, Motorsport Games’ website or other websites referenced or linked to this press release shall not be incorporated by reference into this press release.
Website and Social Media Disclosure: 
Investors and others should note that we announce material financial information to our investors using our investor relations website (ir.motorsportgames.com), SEC filings, press releases, public conference calls and webcasts. We use these channels, as well as social media and blogs, to communicate with our investors and the public about our company and our products. It is possible that the information we post on our websites, social media and blogs could be deemed to be material information. Therefore, we encourage investors, the media and others interested in our company to review the information we post on the websites, social media channels and blogs, including the following (which list we will update from time to time on our investor relations website): 
  Websites 
  Social Media  
  motorsportgames.com 
  Twitter: @msportgames & @traxiongg 
  traxion.gg 
  Instagram: msportgames & traxiongg 
  motorsport.com 
  Facebook: Motorsport Games & traxiongg 
 
  LinkedIn: Motorsport Games 
 
  Twitch: traxiongg 
 
  Reddit: traxiongg 
The contents of these websites and social media channels are not part of, nor will they be incorporated by reference into, this press release. 
Press:  
ASTRSK PR 
[email protected]
Motorsport.tv guide: What’s on 5-6 November weekend?
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Royal Court theatre, London
Martin Crimp’s show uses AI and deep-fake technology to question the definition of drama – and what it is to be human
There are circles within circles of inquiries in Martin Crimp’s drama. Or is it non-drama? It is certainly an inquisition of story, authorship, theatre and reality that seeks to expose, maybe dismantle, the means of production within storytelling: how a story is delivered, who delivers it and how it is received in this theatrical space.
Not One of These People is directed and designed by Christian Lapointe and performed by Crimp himself. We receive most of it through a screen surrounded by camera equipment – somewhat appropriate for a play conceived during lockdown.
Deep-fake technology is used to animate almost 300 photographed faces on the screen, which are not real but created by artificial intelligence. The images are first accompanied by a male voiceover – revealed to be Crimp, who casually walks on stage, reads from a script, walks off again – and then animated by technology that maps Crimp’s voice and facial movements on to theirs.
The central concerns are of authorship and appropriation: the static pictures blink awake creepily and the playwright is exposed as a technological puppet-master pulling their strings. The faces make comments, big and inconsequential, about the world around them. They discuss race and the patriarchy. There are secret confessions, talk of love, violence, incest, sex and identity. There is humour, intrigue and shock in their words; it could be a series of starting points for a story but deliberately resists the gathering force of a cohesive narrative. In the last phase we see Crimp behind the now transparent screen in what looks like a study. He reads, writes and listens to music, the faces speaking all the while as if they are fictional voices jostling inside his head.
While this point is effectively made, it is dramatised without much development, which comes to feel hypnotic and banal. The more interesting questions float around this central one. Is this film a piece of theatre, for instance? It is staged in a theatrical space, but it is also meta-theatre, turning over the concept of what a story is and exposing the playwright’s relationship to its staging.
Another question hovers over the characters and their “humanness”. The nuggets of thought and snatches of experiences are delivered by machines yet they sound and feel human. But this line of inquiry is not developed and is far more penetratingly explored in David Farr’s A Dead Body in Taos, currently on tour.
It seems, in style, like a video installation that might be found in a backroom of the Tate on a perpetual loop (Crimp has a passing resemblance to Warhol too), and there is a repetition to the exercise, which does not try to hide the fact that it is conceptual, deconstructive, cerebral and undramatic.
But it also offers a reset for the audience. Crimp does not allow us to passively receive a story and there are questions about what we expect of, and accept as, theatre. It shows too how we collaborate to make the story: I find myself trying to join the dots, find themes, connect with these bite-size comments to make a bigger whole. Ultimately, it is the most unusual thing I have seen in the theatre for a long time but its force, as a play of ideas, does not quite get under the skin.
At the Royal Court, London, until 5 November.

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