- ‘Bitcoin Senator’ Renews Push to Remedy Crypto’s ‘Erratic Regulatory Framework’ Decrypt
- New crypto regulations will protect consumers, keep industry from leaving U.S.: Senators Lummis & Gillibrand Yahoo Finance
- US senators reintroduce crypto bill aimed at comprehensive regulation Cointelegraph
- Sen. Cynthia Lummis on bipartisan crypto regulation bill: Lays out ‘rules of the road’ for companies CNBC Television
- McHenry Crypto Bill Would ‘Weaken Consumer and Investor Protections’, Say Lobbyists Decrypt
- View Full Coverage on Google News
Tag Archives: Regulatory
AstraZeneca dives into regulatory T cell therapies in deal with Quell Therapeutics – Endpoints News
- AstraZeneca dives into regulatory T cell therapies in deal with Quell Therapeutics Endpoints News
- AstraZeneca pays $85M to Quell Type 1 diabetes with ‘one and done’ cell therapy FierceBiotech
- AstraZeneca signs $2 billion agreement with Quell to develop cell therapies Reuters
- Quell Therapeutics Signs a Collaboration, Exclusive Option and License Agreement with AstraZeneca to Develop, Manufacture and Commercialize Engineered Treg Cell Therapies for Autoimmune Diseases GlobeNewswire
- Astra Enters Into $2 Billion-Plus Pact With UK Biotech Quell Bloomberg
- View Full Coverage on Google News
Rivian VP of Public Policy & Chief Regulatory Counsel leaves company
Rivian VP of Public Policy Jim Chen is departing the company. He is expected to leave the electric truck maker at the end of February.
Chen has a lot of experience lobbying for electric vehicle makers. Prior to his employment at Rivian in 2018, he worked for American electric car maker Tesla, where he led the company’s efforts to secure direct-to-consumer sales in various states. Such deals are extremely important for companies like Tesla and Rivian, as they do not utilize a traditional dealership model.
During his time at Rivian, Chen was responsible for advocating for changes to laws that require car companies to sell vehicles using franchised dealerships. He was also in charge of overseeing Rivian’s federal lobbying and regulatory affairs, as noted by The Wall Street Journal.
A statement from a Rivian representative noted that Chen’s departure from the company was a mutual agreement, and it was driven partly by the lobbyist’s desire to prioritize time with his family. “I am proud of the work we have done, the influence we have had, and the team that we have built,” Chen noted.
A look at Chen’s work over the years makes his decision to take a step back from the frontline of the EV movement, at least for now, understandable. During his time with Tesla, he successfully secured compromise deals that ultimately allowed the EV maker to operate Tesla-owned stores in areas where the dealership lobby is dominant. Among these areas is Georgia, which approved legislation in 2015 that allowed Tesla to sell cars without going through local dealers.
In previous comments, Chen mentioned that he departed from Tesla after over five years because he burned out due to the pace of work at the company. But even after coming over to Rivian, he met numerous challenges. In Georgia alone, Chen’s efforts to secure direct-to-consumer sales for Rivian were met with resistance from the state. This was despite the company’s plans to invest $5 billion in a Georgia factory.
Rivian has seen some changes in its top management, including the replacement of its head of manufacturing and chief operating officer in the past year. The company also announced last year that it was cutting off 6% of its workforce as a cost-saving measure.
The Teslarati team would appreciate hearing from you. If you have any tips, contact me at maria@teslarati.com or via Twitter @Writer_01001101.
Twitter Exodus Hits Teams Tasked With Regulatory, Content Issues Globally
Elon Musk’s
move to purge Twitter Inc. employees who don’t embrace his vision has led to a wave of departures among policy and safety-issue staffers around the globe, sparking questions from regulators in key jurisdictions about the site’s continued compliance efforts.
Scrutiny has been particularly close in Europe, where officials have in recent years assumed a greater role in regulating big tech companies.
Staff departures in recent days include dozens of people spread across units such as government policy, legal affairs and Twitter’s “trust and safety” division, which is responsible for functions like drafting content-moderation rules, according to current and former employees, postings on social media and emails sent to work addresses of people who had worked at Twitter that recently bounced back. They have left from hubs including Dublin, Singapore and San Francisco.
Many of the departures follow Mr. Musk’s ultimatum late last week that staffers pledge to work long hours and be “extremely hardcore” or take a buyout. Hundreds or more employees declined to commit to what Mr. Musk has called Twitter 2.0 and were locked out of company systems. That comes after layoffs in early November that cut roughly half of the company’s staff.
Twitter conducted another round of job cuts affecting engineers late Wednesday, before the Thanksgiving holiday in the U.S., people familiar with the matter said. The exact scope couldn’t be immediately learned, though some of the people estimated dozens of employees were let go.
Twitter sent fired engineers an email saying their code wasn’t satisfactory and offering four weeks of severance, some of the people said. Some other engineers received an email warning them to improve their performance to keep their jobs, the people said.
Ireland’s Data Protection Commission said this week it was asking Twitter whether it still had sufficient staff to assure compliance with the European Union’s privacy law, the General Data Protection Regulation, or GDPR. The company last week told the Irish data regulator that it did, but is still reviewing the impact of the staff departures, a spokesman for the Irish regulator said.
He said Twitter has appointed an interim chief data protection officer, an obligation under the GDPR, after the departure of Damien Kieran, who had served in the role but left shortly after the first round of layoffs.
In France, meanwhile, the country’s communications regulator said it sent a letter last Friday asking that Twitter explain by this week whether it has sufficient personnel on staff to moderate hate speech deemed illegal under French law—under which Twitter could face legal orders and fines.
SHARE YOUR THOUGHTS
What will be the impact on Twitter of having a reduced staff to oversee regulatory and content issues? Join the conversation below.
The staff departures come as Twitter holds talks with the EU about the bloc’s new social-media law, dubbed the Digital Services Act, which will apply tougher rules on bigger platforms like Twitter by the middle of next year.
Didier Reynders,
the EU’s justice commissioner, is slated to attend a previously scheduled meeting with Twitter executives in Ireland on Thursday. He plans to ask about the company’s ability to comply with the law and to meet its commitments on data protection and tackling online hate speech, according to an EU official familiar with the trip.
Věra Jourová, a vice president of the EU’s executive arm, said she was concerned about reports of the firing of vast amounts of Twitter staff in Europe. “European laws continue to apply to Twitter, regardless of who is the owner,” she said.
Mr. Musk has said that he would follow the laws of the countries where Twitter operates and that it “cannot become a free-for-all hellscape.”
Twitter didn’t respond to a request for comment.
Late Wednesday, Mr. Musk tweeted that the number of views of tweets he described as “hate speech” had fallen below levels seen before a spike in such views in late October.
“Congrats to the Twitter team!” Mr. Musk wrote.
Some of the people who either departed or declined to sign on to Twitter 2.0 appear to include Sinead McSweeney, the company’s Ireland-based vice president of global policy and philanthropy, who led government relations and compliance initiatives with regulations worldwide, as well as the two remaining staffers in Twitter’s Brussels office.
Ms. McSweeney and the two Brussels employees declined to comment, but emails to their work addresses started bouncing back undeliverable in recent days according to checks by The Wall Street Journal. Four other Brussels-based employees were earlier this month told they were being laid off, according to social-media posts and people familiar with the matter.
Damien Viel, Twitter’s country manager for France, was also among a wave of staffers who posted publicly this week that they had left the company. He declined to comment when reached by the Journal.
At least some of the departures occurred in teams that reported to
Yoel Roth,
Twitter’s former head of trust and safety, who resigned earlier this month. In an op-ed for the New York Times, Mr. Roth said he resigned because Mr. Musk made it clear that he alone would make decisions on policy and the platform’s rules and that he had little use for those at the company who were advising him on those issues.
The team included Ilana Rosenzweig, who worked as Twitter’s senior director and head of international trust and safety. She has left the company, according to her LinkedIn profile. Based in Singapore, Ms. Rosenzweig led Twitter’s trust and safety teams across Europe, the Middle East and Africa, along with Japan and other Asia-Pacific countries, according to her profile.
“I decided not to agree to Twitter 2.0,” Keith Yet, a Twitter trust and safety worker based in Singapore, wrote on LinkedIn on Monday. Mr. Yet worked on child sexual exploitation issues and handling legal escalations from Japan and other countries, according to his LinkedIn profile. Attempts to reach Ms. Rosenzweig and Mr. Yet were unsuccessful.
The departures come amid a wave of new tech regulation, particularly in Europe. The Digital Services Act, which will by the middle of next year require tech companies like Twitter with more than 45 million users in the EU to maintain robust systems for removing content that European national governments deem to be illegal.
The act also requires these companies to reduce risks associated with content that regulators consider harmful or hateful. It mandates regular outside audits of the companies’ processes and threatens noncompliance fines of up to 6% of a company’s annual revenue.
Political leaders had warned that Mr. Musk’s Twitter would have to comply with EU rules. “In Europe, the bird will fly by our rules,” tweeted the EU’s commissioner for the internal market,
Thierry Breton,
hours after Mr. Musk completed his Twitter deal in late October tweeting, “the bird is free.”
A spokesman for the European Commission, the EU’s executive arm, said this week that it had active contacts with the company regarding the regulation and tackling disinformation and illegal hate speech, but declined to comment on the substance of Twitter’s compliance plans.
Activists and researchers are also concerned that the departures could undermine Twitter’s ability to block state-backed information operations aimed at spreading propaganda and harassing adversaries. The wave of departures “raises questions about how Twitter will moderate tweets and comments in a professional and neutral manner,” said Patrick Poon, an activist turned scholar at Japan’s Meiji University, who analyzes free speech.
—Liza Lin, Alexa Corse and Sarah E. Needleman contributed to this article.
Write to Sam Schechner at Sam.Schechner@wsj.com, Kim Mackrael at kim.mackrael@wsj.com and Newley Purnell at newley.purnell@wsj.com
Copyright ©2022 Dow Jones & Company, Inc. All Rights Reserved. 87990cbe856818d5eddac44c7b1cdeb8
US Lawmaker Calls on SEC to Issue Crypto Regulations — Says ‘a Formal Regulatory Process Is Needed Now’ – Regulation Bitcoin News
A U.S. senator has called on the Securities and Exchange Commission (SEC) to issue crypto regulations now “through a transparent notice-and-comment regulatory process.” He stressed that “some digital assets are securities, others may be commodities, and others may subject to a completely different regulatory regime.”
US Senator Calls for ‘Transparent Notice-and-Comment Regulatory Process’ to Regulating Crypto Assets
U.S. Senator John Hickenlooper (D-CO) has sent a letter to the chairman of the Securities and Exchange Commission (SEC), Gary Gensler, regarding crypto regulations.
In his letter dated Oct. 13, the senator told Gensler, “Clear rules promote an environment where investors are protected,” adding:
I write to urge the SEC to issue regulations for digital asset securities through a transparent notice-and-comment regulatory process.
He stressed: “Currently, digital asset markets do not have a coordinated regulatory framework. This creates uneven enforcement, and deprives investors of a clear understanding of how they are protected from fraud, manipulation, and abuse.”
Noting that existing laws and regulations were not designed for digital assets, he explained: “Applying the old rules to the new market could inadvertently cause financial services to be more expensive, less accessible, and the SEC’s disclosure regime to be less useful to the American people.” The senator noted:
Given the complexity of these issues, and recognizing that some digital assets are securities, others may be commodities, and others may subject to a completely different regulatory regime, a formal regulatory process is needed now.
“This will significantly improve policy development and allow the SEC to collect views and understand concerns,” he said.
The senator proceeded to outline some of the key areas that the SEC should address, including clarifying what types of digital assets are securities, addressing how to issue and list digital securities, establishing a registration regime for digital asset security trading platforms, and setting rules on how trading and custody of digital assets should be carried out.
Hickenlooper opined:
I recognize these questions are complicated, but it is time for the SEC to engage.
What do you think about the letter from Senator Hickenlooper to SEC Chairman Gary Gensler? Let us know in the comments section below.
Image Credits: Shutterstock, Pixabay, Wiki Commons
Disclaimer: This article is for informational purposes only. It is not a direct offer or solicitation of an offer to buy or sell, or a recommendation or endorsement of any products, services, or companies. Bitcoin.com does not provide investment, tax, legal, or accounting advice. Neither the company nor the author is responsible, directly or indirectly, for any damage or loss caused or alleged to be caused by or in connection with the use of or reliance on any content, goods or services mentioned in this article.
Samsung’s Galaxy S23 Ultra has received its first regulatory approval
Last updated: September 23rd, 2022 at 17:29 UTC+02:00
A few hardware components that will be employed by the Galaxy S23 series, such as the battery, have popped up online previously. But now, the complete Galaxy S23 Ultra appears to have passed through China’s regulatory agency 3C.
Samsung is moving forward with the Galaxy S23 Ultra’s development, and the phone gained approval from 3C yesterday, September 22. The application popped up on the Chinese social media network Weibo (via RealMiCentral). It doesn’t reveal much about the phone beside the SM-S918 model number and the fact that the regulatory agency tested the device using a 25W EP-TA800 wall charger.
Galaxy S23 Ultra should hit the market early 2023
The 3C application also confirms that the Galaxy S23 Ultra is manufactured in Thai Nguyen, Vietnam. This isn’t surprising, considering that Samsung manufactures most of its smartphones in Vietnam, where it reaches an annual capacity of around 120 million units.
As for the mentions of the EP-TA800 charger, this doesn’t really say much about the phone’s battery specifications. Samsung no longer ships smartphones with wall chargers included in the box (with few exceptions), and the Galaxy S23 Ultra won’t be any different.
However, Samsung is working on a new wireless charging pad, or Charging Hub, and it carries model number EP-P9500. It wasn’t spotted at 3C, but the company will likely unveil and release this new Charging Hub at Unpacked next year to give Galaxy S23 customers a way to recharger their smartphones, earbuds, and smartwatches wirelessly, all at the same time.
Warren Buffett Hikes Occidental Petroleum Stake After Getting Regulatory OK To Buy 50%
Warren Buffett bought up more Occidental Petroleum (OXY) shares after his Berkshire Hathaway (BRKB) won regulatory approval to buy up to 50%. OXY stock rose slightly late Friday.
X
Berkshire Hathaway now owns 26.8% of Occidental Petroleum, according to a regulatory filing Friday night. That came exactly one month after Warren Buffett’s firm disclosed its OXY stock stake had reached 20.2%. Crossing the 20% ownership level means Berkshire Hathaway can record Occidental earnings on its books.
The Federal Energy Regulatory Commission has given Berkshire Hathaway permission to buy up to 50% of Occidental Petroleum. That’s according to an order made public on Aug. 19. Berkshire made the request on July 11.
There is speculation that Berkshire could choose to buy all of Occidental, though The Wall Street Journal, citing sources, reported last month that Warren Buffett was unlikely to do so.
Join IBD experts as they analyze actionable stocks in the stock market rally on IBD Live
OXY Stock Rises On Buffett Buys
OXY stock rose 1.6% in late trading Friday. For the week, Occidental Petroleum stock fell 4.6% to 65.61, though it did find support at its 50-day moving average.
OXY stock broke out of a cup-with-handle base powerfully on Aug. 19 on the news that Buffett had approval to buy 50% of the company. Shares ran up to a record 77.13 on Aug. 29, but have now round-tripped all of that gain from the 66.26 buy point.
Occidental stock had held up better than many oil producers during the summer, thanks to Warren Buffett steadily adding shares. But OXY stock has come under pressure with crude oil prices retreating significantly.
Meanwhile, BRKB stock rose 2.9% to 285.77 last week. That followed three weekly declines, starting a big reversal from the 200-day moving average.
Please follow Ed Carson on Twitter at @IBD_ECarson for stock market updates and more.
YOU MIGHT ALSO LIKE:
Market Rally Even Stronger Than It Looks; Five Stocks In Buy Areas
These Are Warren Buffett’s Top Stocks Now
Warren Buffett Stocks: MSFT, GOOGL Among 24 Names On This Screen
Why This IBD Tool Simplifies The Search For Top Stocks
Elon Musk Comes Up With Another Reason To Terminate $44 Billion Twitter Deal
You’ll Almost Have to Buy an Electric Vehicle if Climate Bill Passes
The attractions of electric vehicles have been magnetic in 2022.
Soaring gas prices, inflation not seen in four decades and a push for cleaner, environmentally friendly technology have all combined to make EVs more appealing than ever to consumers.
Now, there is legislation headed toward the home stretch that will give EVs not only new legitimacy in the eyes of the auto market, but potentially lucrative perks to automakers and consumers who decide to bet on electric vehicles as their chosen mode of transportation — and investment.
The new climate bill passed by the Senate on Aug. 7 is now headed to a vote in the House.
So What Can Consumers Expect in the Climate Bill?
If lawmakers make it official, the legislation is chock full of rebates, tax deductions, subsidies and incentives to move everything from their house’s power source to their toaster to a more climate-friendly energy source.
“This climate spending includes $60 billion for solar panel and wind turbine manufacturing (and $30 billion in credits for new projects), $60 billion for disadvantaged communities that bear the brunt of climate impacts, $27 billion for clean tech R&D, $20 billion to reduce agricultural emissions, $5 billion for forest conservation, $4 billion for drought funding in Western states, new battery manufacturing credits and many more climate-related priorities,” EV site Elektrek reports.
What Do You Get For Your Electric Vehicle in the Climate Bill?
It also includes a 30% tax deduction for homeowners who install solar energy at their homes, and a variety of financial perks for switching to the electric versions of household appliances, tech used for running a business and even cars.
Scroll to Continue
It is that last one that has investors sitting up to take notice.
Both new and used electric vehicles will be eligible for tax credits, up to $7,500 for new cars, and $4,000 for use ones. The bill also ends the 200,000 cap on the number of cars that can be sold by a manufacturer that qualify for rebates. That’s good news for Tesla (TSLA) , which reached the cap long ago. But the flip side is that the bill limits the rebates to lower priced vehicles and families with relatively low incomes, so many high-end cars and trucks won’t qualify.
While electric vehicles have been gaining popularity at a scorching pace as gas prices have pushed some drivers into the market – some estimates say interest in EVs has soared by as much as 60% since January 2022 – there is still a lack of inventory in the space.
That data comes from Recurrent, which took a deep dive look at a market that was worth $163 billion in 2020 and is projected to be worth $824 billion by 2030.
There Will Be A Lot More EV Competition Soon
That is slated to change. An earlier report from the Environmental Defense Fund outlined just how far automakers are looking ahead.
It found that over a dozen carmakers in America have rolled out plans to open EV manufacturing sites, spending $75 billion in six new states nationwide.
That is in addition to the $515 billion global auto companies are spending to build electric vehicles by 2030, when more than 100 new EV models are expected to be on the market, the report found.
Even tech darling Apple (AAPL) — which has fans who are arguably as rabid as Elon Musk’s Tesla (TSLA) — appears to be getting ready to debut its own electric vehicle.
The evolution, evolvability and engineering of gene regulatory DNA
Wittkopp, P. J. & Kalay, G. Cis-regulatory elements: molecular mechanisms and evolutionary processes underlying divergence. Nat. Rev. Genet. 13, 59–69 (2011).
Google Scholar
Hill, M. S., Vande Zande, P. & Wittkopp, P. J. Molecular and evolutionary processes generating variation in gene expression. Nat. Rev. Genet. 22, 203–215 (2021).
Google Scholar
Fuqua, T. et al. Dense and pleiotropic regulatory information in a developmental enhancer. Nature 587, 235–239 (2020).
Google Scholar
de Visser, J. A. G. M. & Krug, J. Empirical fitness landscapes and the predictability of evolution. Nat. Rev. Genet. 15, 480–490 (2014).
Google Scholar
Kondrashov, D. A. & Kondrashov, F. A. Topological features of rugged fitness landscapes in sequence space. Trends Genet. 31, 24–33 (2015).
Google Scholar
de Visser, J. A. G. M., Elena, S. F., Fragata, I. & Matuszewski, S. The utility of fitness landscapes and big data for predicting evolution. Heredity 121, 401–405 (2018).
Google Scholar
Weirauch, M. T. & Hughes, T. R. Conserved expression without conserved regulatory sequence: the more things change, the more they stay the same. Trends Genet. 26, 66–74 (2010).
Google Scholar
Orr, H. A. The genetic theory of adaptation: a brief history. Nat. Rev. Genet. 6, 119–127 (2005).
Google Scholar
Weinreich, D. M., Lan, Y., Wylie, C. S. & Heckendorn, R. B. Should evolutionary geneticists worry about higher-order epistasis? Curr. Opin. Genet. Dev. 23, 700–707 (2013).
Google Scholar
Venkataram, S. et al. Development of a comprehensive genotype-to-fitness map of adaptation-driving mutations in yeast. Cell 166, 1585–1596 (2016).
Google Scholar
Keren, L. et al. Massively parallel interrogation of the effects of gene expression levels on fitness. Cell 166, 1282–1294 (2016).
Google Scholar
Sarkisyan, K. S. et al. Local fitness landscape of the green fluorescent protein. Nature 533, 397–401 (2016).
Google Scholar
Ogden, P. J., Kelsic, E. D., Sinai, S. & Church, G. M. Comprehensive AAV capsid fitness landscape reveals a viral gene and enables machine-guided design. Science 366, 1139–1143 (2019).
Google Scholar
Pitt, J. N. & Ferré-D’Amaré, A. R. Rapid construction of empirical RNA fitness landscapes. Science 330, 376–379 (2010).
Google Scholar
Shultzaberger, R. K., Malashock, D. S., Kirsch, J. F. & Eisen, M. B. The fitness landscapes of cis-acting binding sites in different promoter and environmental contexts. PLoS Genet. 6, e1001042 (2010).
Google Scholar
Mustonen, V., Kinney, J., Callan, C. G. & Lässig, M. Energy-dependent fitness: a quantitative model for the evolution of yeast transcription factor binding sites. Proc. Natl Acad. Sci. USA 105, 12376–12381 (2008).
Google Scholar
Hartl, D. L. What can we learn from fitness landscapes? Curr. Opin. Microbiol. 0, 51–57 (2014).
Google Scholar
Otwinowski, J. & Nemenman, I. Genotype to phenotype mapping and the fitness landscape of the E. coli lac promoter. PLoS ONE 8, e61570 (2013).
Google Scholar
Sinai, S. & Kelsic, E. D. A primer on model-guided exploration of fitness landscapes for biological sequence design. Preprint at https://arxiv.org/abs/2010.10614 (2020).
Zhou, J. & Troyanskaya, O. G. Predicting effects of noncoding variants with deep learning-based sequence model. Nat. Methods 12, 931–934 (2015).
Google Scholar
Avsec, Ž. et al. Base-resolution models of transcription-factor binding reveal soft motif syntax. Nat. Genet. 53, 354–366 (2021).
Google Scholar
Shrikumar, A., Greenside, P. & Kundaje, A. Learning important features through propagating activation differences. Proc. 34th International Conference on Machine Learning 3145–3153 (2017).
Avsec, Ž. et al. Effective gene expression prediction from sequence by integrating long-range interactions. Nat. Methods 18, 1196–1203 (2021).
Google Scholar
Fragata, I., Blanckaert, A., Louro, M. A. D., Liberles, D. A. & Bank, C. Evolution in the light of fitness landscape theory. Trends Ecol. Evol. 34, 69–82 (2019).
Google Scholar
Payne, J. L. & Wagner, A. The causes of evolvability and their evolution. Nat. Rev. Genet. 20, 24–38 (2019).
Google Scholar
de Boer, C. G. et al. Deciphering eukaryotic gene-regulatory logic with 100 million random promoters. Nat. Biotechnol. 38, 56–65 (2020).
Google Scholar
Crocker, J. et al. Low affinity binding site clusters confer hox specificity and regulatory robustness. Cell 160, 191–203 (2015).
Google Scholar
Habib, N., Wapinski, I., Margalit, H., Regev, A. & Friedman, N. A functional selection model explains evolutionary robustness despite plasticity in regulatory networks. Mol. Syst. Biol. 8, 619 (2012).
Google Scholar
Gillespie, J. H. Molecular evolution over the mutational landscape. Evolution 38, 1116–1129 (1984).
Google Scholar
Jerison, E. R. & Desai, M. M. Genomic investigations of evolutionary dynamics and epistasis in microbial evolution experiments. Curr. Opin. Genet. Dev. 35, 33–39 (2015).
Google Scholar
Sæther, B.-E. & Engen, S. The concept of fitness in fluctuating environments. Trends Ecol. Evol. 30, 273–281 (2015).
Google Scholar
Vaswani, A. et al. in Advances in Neural Information Processing Systems 30 (eds. Guyon, I. et al.) 5998–6008 (Curran Associates, 2017).
Weirauch, M. T. et al. Evaluation of methods for modeling transcription factor sequence specificity. Nat. Biotechnol. 31, 126–134 (2013).
Google Scholar
Yang, N. & Bielawski, N. Statistical methods for detecting molecular adaptation. Trends Ecol. Evol. 15, 496–503 (2000).
Google Scholar
Moses, A. M. Statistical tests for natural selection on regulatory regions based on the strength of transcription factor binding sites. BMC Evol. Biol. 9, 286 (2009).
Google Scholar
Rifkin, S. A., Houle, D., Kim, J. & White, K. P. A mutation accumulation assay reveals a broad capacity for rapid evolution of gene expression. Nature 438, 220–223 (2005).
Google Scholar
Peter, J. et al. Genome evolution across 1,011 Saccharomyces cerevisiae isolates. Nature 556, 339–344 (2018).
Google Scholar
Erb, I. & van Nimwegen, E. Transcription factor binding site positioning in yeast: proximal promoter motifs characterize TATA-less promoters. PLoS One 6, e24279 (2011).
Google Scholar
Gilad, Y., Oshlack, A. & Rifkin, S. A. Natural selection on gene expression. Trends Genet. 22, 456–461 (2006).
Google Scholar
Alhusaini, N. & Coller, J. The deadenylase components Not2p, Not3p, and Not5p promote mRNA decapping. RNA 22, 709–721 (2016).
Google Scholar
Yang, J.-R., Maclean, C. J., Park, C., Zhao, H. & Zhang, J. Intra and interspecific variations of gene expression levels in yeast are largely neutral: (Nei Lecture, SMBE 2016, Gold Coast). Mol. Biol. Evol. 34, 2125–2139 (2017).
Google Scholar
Chen, J. et al. A quantitative framework for characterizing the evolutionary history of mammalian gene expression. Genome Res. 29, 53–63 (2019).
Google Scholar
Payne, J. L. & Wagner, A. Mechanisms of mutational robustness in transcriptional regulation. Front. Genet. 6, 322 (2015).
Google Scholar
Shoval, O. et al. Evolutionary trade-offs, Pareto optimality, and the geometry of phenotype space. Science 336, 1157–1160 (2012).
Google Scholar
van Dijk, D. et al. Finding archetypal spaces using neural networks. IEEE International Conference on Big Data 2634-2643 (2019).
He, X., Duque, T. S. P. C. & Sinha, S. Evolutionary origins of transcription factor binding site clusters. Mol. Biol. Evol. 29, 1059–1070 (2012).
Google Scholar
Cliften, P. F. et al. Surveying Saccharomyces genomes to identify functional elements by comparative DNA sequence analysis. Genome Res. 11, 1175–1186 (2001).
Google Scholar
Heinz, S., Romanoski, C. E., Benner, C. & Glass, C. K. The selection and function of cell type-specific enhancers. Nat. Rev. Mol. Cell Biol. 16, 144–154 (2015).
Google Scholar
Lehner, B. Selection to minimise noise in living systems and its implications for the evolution of gene expression. Mol. Syst. Biol. 4, 170 (2008).
Google Scholar
Metzger, B. P. H., Yuan, D. C., Gruber, J. D., Duveau, F. & Wittkopp, P. J. Selection on noise constrains variation in a eukaryotic promoter. Nature 521, 344–347 (2015).
Google Scholar
Kosuri, S. et al. Composability of regulatory sequences controlling transcription and translation in Escherichia coli. Proc. Natl Acad. Sci. USA. 110, 14024–14029 (2013).
Google Scholar
Shalem, O. et al. Systematic dissection of the sequence determinants of gene 3’ end mediated expression control. PLoS Genet. 11, e1005147 (2015).
Google Scholar
Kinney, J. B., Murugan, A., Callan, C. G. Jr & Cox, E. C. Using deep sequencing to characterize the biophysical mechanism of a transcriptional regulatory sequence. Proc. Natl Acad. Sci. USA. 107, 9158–9163 (2010).
Google Scholar
Sharon, E. et al. Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters. Nat. Biotechnol. 30, 521–530 (2012).
Google Scholar
Melnikov, A. et al. Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay. Nat. Biotechnol. 30, 271–277 (2012).
Google Scholar
Kwasnieski, J. C., Mogno, I., Myers, C. A., Corbo, J. C. & Cohen, B. A. Complex effects of nucleotide variants in a mammalian cis-regulatory element. Proc. Natl Acad. Sci. USA 109, 19498–19503 (2012).
Google Scholar
Kircher, M. et al. Saturation mutagenesis of twenty disease-associated regulatory elements at single base-pair resolution. Nat. Commun. 10, 3583 (2019).
Google Scholar
Townsley, K. G., Brennand, K. J. & Huckins, L. M. Massively parallel techniques for cataloguing the regulome of the human brain. Nat. Neurosci. 23, 1509–1521 (2020).
Google Scholar
Renganaath, K. et al. Systematic identification of cis-regulatory variants that cause gene expression differences in a yeast cross. eLife 9, e62669 (2020).
Google Scholar
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Google Scholar
Alipanahi, B., Delong, A., Weirauch, M. T. & Frey, B. J. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat. Biotechnol. 33, 831–838 (2015).
Google Scholar
Travers, C. et al. Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface 15, 20170387 (2018).
Avsec, Ž. et al. The Kipoi repository accelerates community exchange and reuse of predictive models for genomics. Nat. Biotechnol. 37, 592–600 (2019).
Google Scholar
Quang, D. & Xie, X. FactorNet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data. Methods 166, 40–47 (2019).
Google Scholar
Zhou H. et al. Towards a better understanding of reverse-complement equivariance for deep learning models in genomics. Proc. 16th Machine Learning in Computational Biology meeting 165, 1–33 (2022).
Morrow, A. et al. Convolutional kitchen sinks for transcription factor binding site prediction. Preprint at https://arxiv.org/abs/1706.00125 (2017).
Kelley, D. R., Snoek, J. & Rinn, J. L. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res. 26, 990–999 (2016).
Google Scholar
Koo, P. K., Majdandzic, A., Ploenzke, M., Anand, P. & Paul, S. B. Global importance analysis: an interpretability method to quantify importance of genomic features in deep neural networks. PLoS Comput. Biol. 17, e1008925 (2021).
Google Scholar
Quang, D. & Xie, X. DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Res. 44, e107 (2016).
Google Scholar
Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. International Conference on Learning Representations (Poster) (2015).
Abadi, M. et al. TensorFlow: large-scale machine learning on heterogenous systems. Software available from https://www.tensorflow.org/ (2015).
Jouppi, N. P. et al. In-datacenter performance analysis of a tensor processing unit. In Proc. 44th Annual International Symposium on Computer Architecture 1–12 (2017).
Li, J., Pu, Y., Tang, J., Zou, Q. & Guo, F. DeepATT: a hybrid category attention neural network for identifying functional effects of DNA sequences. Brief. Bioinform. 22, bbaa159 (2020).
Ullah, F. & Ben-Hur, A. A self-attention model for inferring cooperativity between regulatory features. Nucleic Acids Res. 49, e77 (2021).
Google Scholar
Clauwaert, J., Menschaert, G. & Waegeman, W. Explainability in transformer models for functional genomics. Brief. Bioinform. 22, bbab060 (2021).
Google Scholar
Hinton, G. & Tieleman, T. Lecture 6.5—RmsProp: divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning 4, 26–31 (2012).
Sinai, S. et al. AdaLead: a simple and robust adaptive greedy search algorithm for sequence design. Preprint at https://arxiv.org/abs/2010.02141 (2020).
Linder, J., Bogard, N., Rosenberg, A. B. & Seelig, G. A generative neural network for maximizing fitness and diversity of synthetic DNA and protein sequences. Cell Syst. 11, 49–62 (2020).
Google Scholar
Brookes, D., Park, H. & Listgarten, J. Conditioning by adaptive sampling for robust design. Proc. Mach. Learn. Res. 97, 773–782 (2019).
Killoran, N., Lee, L. J., Delong, A., Duvenaud, D. & Frey, B. J. Generating and designing DNA with deep generative models. Neurips Computational Biology Workshop (2017).
Fortin, F.-A., Rainville, F.-M. D., Gardner, M.-A., Parizeau, M. & Gagné, C. DEAP: evolutionary algorithms made easy. J. Mach. Learn. Res. 13, 2171–2175 (2012).
Google Scholar
Jaeger, S. A. et al. Conservation and regulatory associations of a wide affinity range of mouse transcription factor binding sites. Genomics 95, 185–195 (2010).
Google Scholar
Tanay, A. Extensive low-affinity transcriptional interactions in the yeast genome. Genome Res. 16, 962–972 (2006).
Google Scholar
Sniegowski, P. D. & Gerrish, P. J. Beneficial mutations and the dynamics of adaptation in asexual populations. Phil. Trans. R. Soc. B 365, 1255–1263 (2010).
Google Scholar
Szendro, I. G., Franke, J., de Visser, J. A. & Krug, J. Predictability of evolution depends nonmonotonically on population size. Proc. Natl Acad. Sci. USA 110, 571–576 (2013).
Google Scholar
Orr, H. A. The population genetics of adaptation: the adaptation of DNA Sequences. Evolution 56, 1317–1330 (2002).
Google Scholar
Bailey, T. L. DREME: motif discovery in transcription factor ChIP-seq data. Bioinformatics 27, 1653–1659 (2011).
Google Scholar
de Boer, C. G. & Hughes, T. R. YeTFaSCo: a database of evaluated yeast transcription factor sequence specificities. Nucleic Acids Res. 40, D169–D179 (2012).
Google Scholar
Kent, W. J. BLAT—the BLAST-Like Alignment Tool. Genome Res. 12, 656–664 (2002).
Google Scholar
Cherry, J. M. et al. Saccharomyces Genome Database: the genomics resource of budding yeast. Nucleic Acids Res. 40, D700–D705 (2012).
Google Scholar
Smith, J. D., McManus, K. F. & Fraser, H. B. A novel test for selection on cis-regulatory elements reveals positive and negative selection acting on mammalian transcriptional enhancers. Mol. Biol. Evol. 30, 2509–2518 (2013).
Google Scholar
Liu, J. & Robinson-Rechavi, M. Robust inference of positive selection on regulatory sequences in the human brain. Sci. Adv. 6, eabc9863 (2020).
Google Scholar
Rice, D. P. & Townsend, J. P. A test for selection employing quantitative trait locus and mutation accumulation data. Genetics 190, 1533–1545 (2012).
Google Scholar
Denver, D. R., Morris, K., Lynch, M. & Thomas, W. K. High mutation rate and predominance of insertions in the Caenorhabditis elegans nuclear genome. Nature 430, 679–682 (2004).
Google Scholar
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
Google Scholar
Thompson, D. A. et al. Evolutionary principles of modular gene regulation in yeasts. eLife 2, e00603 (2013).
Google Scholar
Yassour, M. et al. Strand-specific RNA sequencing reveals extensive regulated long antisense transcripts that are conserved across yeast species. Genome Biol. 11, R87 (2010).
Google Scholar
Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-seq data without a reference genome. Nat. Biotechnol. 29, 644–652 (2011).
Google Scholar
Wu, T. D. & Watanabe, C. K. GMAP: a genomic mapping and alignment program for mRNA and EST sequences. Bioinformatics 21, 1859–1875 (2005).
Google Scholar
Wapinski, I., Pfeffer, A., Friedman, N. & Regev, A. Natural history and evolutionary principles of gene duplication in fungi. Nature 449, 54–61 (2007).
Google Scholar
Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).
Google Scholar
Yates, A. D. et al. Ensembl 2020. Nucleic Acids Res. 48, D682–D688 (2020).
Google Scholar
DiCarlo, J. E. et al. Genome engineering in Saccharomyces cerevisiae using CRISPR–Cas systems. Nucleic Acids Res. 41, 4336–4343 (2013).
Google Scholar
Fleiss, A. et al. Reshuffling yeast chromosomes with CRISPR/Cas9. PLoS Genet. 15, e1008332 (2019).
Google Scholar
Horwitz, A. A. et al. Efficient multiplexed integration of synergistic alleles and metabolic pathways in yeasts via CRISPR–Cas. Cell Syst. 1, 88–96 (2015).
Google Scholar
Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 25, 402–408 (2001).
Google Scholar
Vandesompele, J. et al. Accurate normalization of real-time quantitative RT–PCR data by geometric averaging of multiple internal control genes. Genome Biol. 3, research0034.1 (2002).
Teste, M.-A., Duquenne, M., François, J. M. & Parrou, J.-L. Validation of reference genes for quantitative expression analysis by real-time RT–PCR in Saccharomyces cerevisiae. BMC Mol. Biol. 10, 99 (2009).
Google Scholar
Mardones, W. et al. Rapid selection response to ethanol in Saccharomyces eubayanus emulates the domestication process under brewing conditions. Microb. Biotechnol. https://doi.org/10.1111/1751-7915.13803 (2021).
Ibstedt, S. et al. Concerted evolution of life stage performances signals recent selection on yeast nitrogen use. Mol. Biol. Evol. 32, 153–161 (2015).
Google Scholar
Rich, M. S. et al. Comprehensive analysis of the SUL1 promoter of Saccharomyces cerevisiae. Genetics 203, 191–202 (2016).
Google Scholar
Rest, J. S. et al. Nonlinear fitness consequences of variation in expression level of a eukaryotic gene. Mol. Biol. Evol. 30, 448–456 (2013).
Google Scholar
Bergen, A. C., Olsen, G. M. & Fay, J. C. Divergent MLS1 promoters lie on a fitness plateau for gene expression. Mol. Biol. Evol. 33, 1270–1279 (2016).
Google Scholar
Alstott, J., Bullmore, E. & Plenz, D. Powerlaw: a Python package for analysis of heavy-tailed distributions. PLoS One 9, e85777 (2014).
Google Scholar
Lockheed scraps $4.4 bln deal to buy Aerojet amid regulatory roadblocks
Register now for FREE unlimited access to Reuters.com
Register
Feb 13 (Reuters) – U.S. arms maker Lockheed Martin Corp (LMT.N) called off plans on Sunday to acquire rocket engine maker Aerojet Rocketdyne Holdings Inc (AJRD.N) for $4.4 billion amid opposition from U.S. antitrust enforcers.
The Federal Trade Commission sued to block the deal in late-January on the grounds that it would allow Lockheed to use its control of Aerojet to hurt other defense contractors. Missile maker Raytheon Technologies (RTX.N) was an outspoken opponent of the proposed acquisition.
The merger, which was announced in late 2020, drew criticism as it would give Lockheed a dominant position over solid fuel rocket motors — a vital piece of the U.S. missile industry.
Register now for FREE unlimited access to Reuters.com
Register
Lockheed’s Chief Executive James Taiclet said the acquisition would have improved efficiency, speed and cut costs for the U.S. government, but that terminating the agreement was in its stakeholders’ best interest.
Aerojet, which reports fourth quarter earnings later this week, said in a separate statement that it still expects a strong “future performance,” despite the scrapped merger.
The companies’ merger agreement does not include a termination fee in the event that antitrust regulators opposed the deal, according to a regulatory filing. A Lockheed spokesman previously said the company did not plan to make any such payment to Aerojet.
If the deal had ended up in court, it would have been the first litigated defense merger challenge in decades, according to FTC.
Other critics of the deal included U.S. Democratic Senator Elizabeth Warren, whohad asked the FTC to examine the internal firewalls Lockheed said it would put in place to prevent it from gaining a competitive advantage over its peers.
Lockheed had said it accounted for 33% of Aerojet’s sales and argued that the deal would reduce costs for the Pentagon and the U.S. taxpayer.
Rocket motors like those made by Aerojet are used in everything from the homeland defensive missile system to Stinger missiles.
Aerojet develops and manufactures liquid and solid rocket propulsion, air-breathing hypersonic engines and electric power and propulsion for space, defense, civil and commercial applications. Its customers include the Pentagon, NASA, Boeing (BA.N), Lockheed Martin, Raytheon and the United Launch Alliance.
Register now for FREE unlimited access to Reuters.com
Register
Reporting by Anirudh Saligrama in Bengaluru and Diane Bartz and Mike Stone in Washington, D.C.; Editing by Diane Craft and Jacqueline Wong
Our Standards: The Thomson Reuters Trust Principles.