Monday, September 16, 2024









When one Chinese national recently petitioned the U.S. Citizenship and Immigration Services to become a permanent resident, he thought his chances were pretty good. As an accomplished biologist, he figured that news articles in top media outlets, including The New York Times, covering his research would demonstrate his "extraordinary ability" in the sciences, as called for by the EB-1A visa.

But when the immigration officers rejected his petition, they noted that his name did not appear anywhere in the news articles. News coverage of a paper he co-authored did not directly demonstrate his major contribution to the work.


As this biologist's close friend, I felt bad for him because I knew how much he had dedicated to the project. He even started the idea as one of his Ph.D. dissertation chapters. But as a scientist who studies topics related to scientific innovation, I understand the immigration officers' perspective: Research is increasingly done through teamwork, so it's hard to know individual contributions if a news article reports only the study findings.

This anecdote made me and my colleagues Misha Teplitskiy and David Jurgens curious about what affects journalists' decisions about which researchers to feature in their news stories.

There's a lot at stake for a scientist whose name is or isn't mentioned in journalistic coverage of their work. News media play a key role in disseminating new scientific findings to the public. The coverage of a particular study brings prestige to its research team and their institutions. The depth and quality of coverage then shapes public perception of who is doing good science. In some cases, as my friend's story suggests, the coverage can affect individual careers.

Do scientists' social identities, such as ethnicity or race, play a role in who gets named?

This question is not straightforward to answer. On the one hand, racial bias may exist, given the profound underrepresentation of minorities in U.S. mainstream media. On the other, science journalism is known for its high standard of objective reporting. We decided to investigate this question in a systematic fashion using large-scale observational data.
The least coverage? Chinese and African names


My colleagues and I analyzed 223,587 news stories from 288 U.S. media outlets, sourced from Altmetric.com, a website that monitors online posts about research papers. The news stories, published from 2011-2019, covered 100,486 scientific papers. For each paper, we focused on authors with the highest chance of being mentioned: the first author, last author and other designated corresponding authors. We calculated how often the authors were mentioned in the news articles reporting their research.

We used an algorithm to infer perceived ethnicity from authors' names. We figured that journalists may rely on such cues in the absence of scientists' self-reported information. We considered authors with Anglo names – like John Brown or Emily Taylor – as the majority group and then compared the average mention rates across nine broad ethnic groups.
Pragmatic factors and language choices


First and foremost, the underrepresentation of scientists with East Asian and African names may be due to pragmatic challenges faced by U.S.-based journalists in interviewing them. Factors like time zone differences for researchers based overseas and actual or perceived English fluency could be at play as a journalist works under deadline to produce the story.

We isolated these factors by focusing on researchers affiliated with American institutions. Among U.S.-based researchers, pragmatic difficulties should be minimized because they're in the same geographic region as the journalists and they're likely to be proficient in English, at least in writing. In addition, these scientists would presumably be equally likely to respond to journalists' interview requests, given that media attention is increasingly valued by U.S. institutions.

This study conceptualizes a quadrant model characterizing four profiles by contrasting university scientists’ multiple goals: research performance and commercialization. Since literature shows that these goals are conflicting but not mutually exclusive, social capital theory is drawn to test the influence of scientists’ bonding, bridging, and linking social capital on their profile affiliation.

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Friday, September 13, 2024

US scientists are leaving academia. That’s bad news for drug companies







New YorkCNN —

Scientists in the United States, especially those in the biomedical fields, are increasingly leaving the world of academia for better-paying industry jobs amid stagnant federal funding and low wages.

It’s a troubling sign for the future of US-based medical research and development at pharmaceutical and biotech companies, which rely on the experimental science housed at universities to develop cutting-edge commercial products.

According to the National Institutes of Health, the number of postdoctoral fellows supported by NIH grants has been steadily falling for more than 20 years, with a significant dip after 2020. The number of postdocs in the biological and biomedical fields has declined 9% between 2018 and 2022, and those in health-related fields have fallen by 8%, according to a survey published on March 20 by the National Science Foundation.


Why are postdoctoral researchers important?

The problem is that postdocs are a critical component of the research and development workforce.

“Science postdocs perform the science,” Donna Ginther, an economist who studies the science labor market at the University of Kansas, told CNN. “They’re actually in the lab doing the work, so they make very important contributions to new scientific discovery.”

Those contributions are part of a long game. Biomedical companies take scientific contributions and, over time, aggregate them into a commercial product. Building on the discovery of mRNA in the 1960’s, the technology behind an mRNA vaccine for humans was in development for decades before the Covid-19 vaccine was first administered in 2020.

“It takes a long time from that first discovery to turn it into a product,” said Ginther.

In 2005, researchers learned that RNA could have immense therapeutic potential, but “found there was no interest” in this discovery, Nobel Prize-winning biochemist Dr. Katalin Kariko told Wired Magazine in 2020.

By using that technology to develop their mRNA vaccines for Covid-19, pharmaceutical companies like Pfizer and Moderna made a windfall in profits. Between 2020 and 2021, Pfizer saw its revenue nearly double. In the same period, Moderna’s total revenue skyrocketed from $803 million to $18.5 billion, astounding growth the company said was “primarily due to commercial sales of (the) COVID-19 vaccine.”

Ginther said most biotech and biomedical companies aren’t concerned with funding basic science like universities.

Why are scientists leaving academic labs?

Interestingly, the number of graduate students — those pursuing master’s degrees and PhDs — in biomedical fields has grown by 22% between 2018 and 2022, according to the NSF survey. And the number of predoctoral students funded by NIH fellowships has increased steadily since 1998.

Experts say these students are increasingly seeking out better paying industry jobs. Unlike academic positions, these roles don’t require postdoctoral research experience.

“There’s no economic benefit (to doing a postdoctoral fellowship),” said Ginther. “If you spend four to six years in a postdoc and then jump into the industry, your starting salary is the same as if we would have just entered into industry automatically.”

The pay gap between academia and industry is also astronomical, which has the added effect of making corporate jobs much more financially attractive to students.

The NIH determines stipend amounts for US citizens in postdoctoral programs. The stipend for a first-year postdoc is roughly $56,000 per year, with marginal yearly wage increases. But in biotech, postdocs and PhD graduates can easily crack six figures.

Ginther served on an advisory committee to the NIH in December, where she and colleagues recommended the NIH increase their stipend amounts by 34% to $70,000 per year.

“The NIH was waiting for its budget and that budget was passed a week ago, so I’m sure they’re having discussions about it right now,” she said. Ginther noted that the federal budget granted to the NIH was flat for fiscal year 2024, which “in a time with rising .

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Thursday, September 12, 2024

A Cure for Multiple Sclerosis? Scientists Say Within Our Lifetime







A generation ago, most people with multiple sclerosis could expect to rely on walkers and wheelchairs or be limited to their bed within 15 years of diagnosis. Today, UC San Francisco’s discoveries are keeping millions with MS healthier for longer – many disability-free. Now, the university is paving the way for an MS cure in our lifetimes.

But the breakthrough almost didn’t happen. “Biologically implausible,” is what the National Institutes of Health’s rejection letter said. It was the late ’90s and UCSF Neurology Professor Stephen Hauser, MD, and team had applied for federal funding for the first clinical trial of the new medicine, rituximab, as a potential MS treatment.

The study wouldn’t just test a new drug, but a revolutionary new theory about MS’ inner workings. So outlandish was the idea that the largest public funder of scientific research said it couldn’t be done.

The rejection “hit like a ton of bricks,” Hauser remembered in his 2023 memoir, The Face Laughs While the Brain Cries. But if Hauser’s team proposed another trial, following conventional wisdom, the institutes assured him there was money.

The message was clear: “Just forget your last 15 years of work, kid, and do what we think.”
‘I decided right there that this would be my life’s work’


Two decades earlier as a young medical resident, Hauser treated 27-year-old Andrea. A recent Harvard Law graduate, Andrea was a rising star in President Jimmy Carter’s administration. Then, as sudden as a lightning strike, her behavior became erratic. By the time Hauser saw her, she was propped up in a hospital bed, the right side of her mouth drooping, saliva pooling at the corner.

MS is a disease of the brain and immune system. Our brains contain a network of billions of neurons, nerve cells that send messages telling our mouth to speak, our legs to move and our lungs to breathe. Each neuron is wrapped in myelin, a protective coating like the plastic sheath around a copper electrical wire. But with MS, our immune system – which normally protects us against germs – mistakes myelin for a foreign invader. This case of mistaken identity prompts an all-out assault. The immune system attacks the myelin, short-circuiting our neurons like frayed power cords.

“Over time, the connections between nerves are lost, nerve cells die. MS robs people of basic functions we take for granted – vision, sensation, motor strength … and sometimes, as with Andrea, even the ability to speak, eat or breathe independently,” Hauser said.

With no effective treatment, there was nothing Hauser could do for Andrea. “I decided right there that this common, crippling disease of young adults would be my life’s work.”

“The federal government has to rely on expert referees to guide funding decisions, and it’s very difficult to turn around entrenched ideas, even if there’s all sorts of wonderful biology behind the new idea,” said Hauser, explaining why he understood the NIH’s initial rejection. “And it’s important that the federal government not spend their resources funding new ideas that most experts feel are unlikely to succeed; this protects us all against the use of public funds to advance crackpot ideas.”

Pharmaceutical company Genentech, co-founded by UCSF professors, agreed to fund what would become a wildly successful trial of the drug as an MS treatment. Rituximab stopped the disease virtually in its tracks, changing the face of MS forever. The breakthrough led to the 2017 development of a second MS medicine, ocrelizumab and then a third, ofatumumab, in 2020, also pioneered by Hauser and his UCSF colleagues working closely with industry partners.

“Beautifully, this is a UCSF story but it also involves great colleagues at other centers across the globe, courageous industry leaders and fantastic philanthropists who believed in high-risk work at a time when traditional funding sources would never be able to support this research,” Hauser said. “And finally, without the thousands of patients who trusted in us and were courageous enough to volunteer for these clinical studies, this progress could never have been made.”


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Wednesday, September 11, 2024

'AI scientist' created to run its own experiments. What will this mean for scientific discoveries?







Researchers at Sakana.AI, a Tokyo-based company, have worked on developing a large language model (LLM) designed specifically for scientific research.


Researchers at Sakana.AI have developed an artificial intelligence (AI) model that may be able to automate the entire scientific research process.

The "AI Scientist" can identify a problem, develop hypotheses, implement ideas, run experiments, analyse results, and write reports.

The researchers also incorporated a secondary language model to peer review and evaluate the quality of these reports and validate the findings.

"We sort of think of this as a type of GPT-1 moment for generative scientific discovery," Robert Lange, research scientist and founding member at Sakana.AI, told Euronews Next, adding that much like AI's early stages in other fields, its true potential in science is only just beginning to be realised.
Integrating AI into scientific research

AI’s integration into science has faced some limitations due to the complexities of the field and ongoing issues with these tools, such as hallucinations and questions about ownership.

Yet, its influence in science may already be more widespread than many realise, often used without clear disclosure by researchers.

Earlier this year, a study that analysed writing patterns and specific word usage in academic papers following the release of the now well-known AI chatbot, ChatGPT, estimated that around 60,000 research papers may have been enhanced or polished using AI tools.

Although the use of AI in scientific research could raise some ethical concerns, it could also present an opportunity for new advancements in the field when done properly, with the European Commission saying that AI can act as a "catalyst for scientific breakthroughs and a key instrument in the scientific process".

The AI Scientist project is still in its early stages with researchers publishing a paper in pre-print last month, and the system has some notable limitations.

Some of the flaws, as detailed by the researchers, include incorrect implementation of ideas, unfair comparisons to baselines, and critical errors in writing and evaluating results.

Still, Lange sees these issues as crucial stepping stones and expects that the AI model will significantly improve with more resources and time.


"When you think about the history of machine learning models, like image generation models, chatbots right now, also and text-to-video models, they oftentimes start out with some flaws and some maybe images which are generated, which are not super visually pleasing," Lange said.

"But over time, as we put in more collective resources as a community, they become much more powerful and much more capable," he added.
Ethical use of AI in science

As the integration of AI into scientific research progresses, Lange emphasises that transparency is necessary.

One way to do that is to add watermarks to AI-generated papers, which could ensure that AI contributions are openly disclosed.

"I'm a big believer in sort of making sure that all of these things are developed collectively, as well as iteratively, so that we can make sure that they are safely deployed," Lange said.


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Tuesday, September 10, 2024

Career Chat: Directing a New Science Path







A professional detour veered Ludovic Desvignes into a high-maintenance career—director of a high-containment facility.

Scientists study pathogens that require added levels of security and researcher protection in dedicated laboratories, or high-containment facilities (HCFs), that involve extra coordination between research teams, biosafety officers, and medical staff. Ludovic Desvignes oversees these labs as the high-containment laboratories director at New York University Langone Health (NYULH).I support the ongoing research in our biosafety level 3 (BSL-3) laboratory, our germ-free animal facility, and our mosquito insectary by recruiting and training staff and other personnel, obtaining and maintaining the equipment for those labs, and developing online training modules. Because of the extra protection measures needed for entry into the BSL-3 lab, I’m also the person who fixes and replaces a lot of equipment. Some of my other responsibilities are akin to a liaison role, where I advise and communicate information regarding BSL-3 research between our researchers, biosafety officers, and medical staff. I often consult on researchers’ standard operating procedures to help them write their protocols involving these types of pathogens. Additionally, I’m developing a HCF core to facilitate high-containment research for groups that don’t have the means to conduct it themselves.

How did you come into this career?


While I was a postdoctoral researcher in Joel Ernst’s lab at NYULH, a lab manager position opened up. Since this was a more stable and better-paying job, I spoke to my advisor about taking on the role, and I transitioned into the position of a research scientist and lab manager for this group. Ernst was the de facto coordinator for NYULH’s HCF at the time, so I became the manager of that facility as well. Along the way, I realized that I liked helping scientists do research more than doing it myself, but at the time, I still intended to be a principal investigator; I secured a non-tenure track faculty position at NYULH and began teaching and submitting grants with the plan to start my own independent lab. Then, Hurricane Sandy hit, and I lost all of the mice that my research was centered around, so that option stagnated. I focused on teaching and managing the HCF. When Ernst left the institution, NYULH offered me a position to stay as the HCF director, and I accepted.

What advice do you have for trainees interested in this position?


You have a lot more skills than those that you use at the bench, so find what you’re good at and enjoy doing and tap into that. I was always an organized person but taking on the duties of a lab manager familiarized me with the different aspects involved in running a lab, such as stocking and ordering materials, overseeing the animal colony, and writing the research protocols. I also learned how to manage interpersonal relationships, which I now do a lot of in this position. Even if you pursue the faculty track, having these experiences and skills will prepare you for the administrative responsibilities, such as serving on various oversight committees, that are required of principal investigators. However, if you change your mind or something unexpected happens, like it did in my case, these experiences can be the off-ramps that guide you into a new career.

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Monday, September 9, 2024








Inside the human airway, a certain cell type reigns supreme: multiciliated cells, decorated with dozens of hair-like cilia all beating in tandem. These cells are responsible for clearing out foreign bacteria and viruses.

“They break the normal architecture since almost all cells in your body have zero or one cilia each,” said Jeremy Reiter, a developmental geneticist at the University of California, San Francisco. “And whenever an individual cell type does something cool or unexpected, it’s an interesting subject for figuring out how that happens.”

Scientists have long been curious about how cells become multiciliated. Other researchers had previously discovered that several genes that were strongly linked to the canonical cell cycle were reused during the differentiation process for multiciliated cells.1 Reiter and his team wanted to find out how exactly the cell cycle might be related to differentiation in these special cells. In work recently published in Nature, the team found that these multiciliated cells actually leveraged a previously unknown variant of the cell cycle that they named the “multiciliation cycle.”2 This research, scientists said, could be useful for better understanding processes like cancer in which the cell cycle also goes awry.


To better understand how the multiciliation cycle worked, the scientists first identified changes in gene expression as the cells differentiated from stem cells into multiciliated cells. “The classic mitotic kinases that regulate division normally were expressed really nicely and sequentially during differentiation of multiciliated cells,” said Semil Choksi, a coauthor and postdoctoral researcher in Reiter’s lab. This sequential expression followed the phases of the traditional cell cycle: G0/G1 (the cell grows larger), S (the cell replicates its DNA), and G2/M (the cell prepares for division and then splits).

While the multiciliation cycle followed the transcriptional phases of the traditional cell cycle, DNA replication did not occur and the cells did not end up dividing. Instead, they grew a bunch of cilia. To figure out what might be driving these differences, the scientists sifted through the genes that were differentially expressed between this variant cell cycle and the traditional cell cycle.


worked, the scientists first identified changes in gene expression as the cells differentiated from stem cells into multiciliated cells. “The classic mitotic kinases that regulate division normally were expressed really nicely and sequentially during differentiation of multiciliated cells,” said Semil Choksi, a coauthor and postdoctoral researcher in Reiter’s lab. This sequential expression followed the phases of the traditional cell cycle: G0/G1 (the cell grows larger), S (the cell replicates its DNA), and G2/M (the cell prepares for division and then splits).

While the multiciliation cycle followed the transcriptional phases of the traditional cell cycle, DNA replication did not occur and the cells did not end up dividing. Instead, they grew a bunch of cilia. To figure out what might be driving these differences, the scientists sifted through the genes that were differentially expressed between this variant cell cycle and the traditional cell cycle.


A super-resolution micrograph depicts two airway cells stained for centrioles (magenta) and cilia (cyan). The primary ciliated cell (left) is a precursor cell, in which the multiciliation cycle is initiated. The differentiated cell (right) has hundreds of centrioles and cilia as a result of the multiciliation cycle.


They found that one factor, called E2F7, was expressed at higher levels in the multiciliation cycle. When the team knocked out E2F7 in mice, they found that the multiciliated cells in these mice had significantly increased markers of DNA synthesis, indicating that the multiciliation cycle in these mutated cells had shifted to be more like the canonical cell cycle.


Interestingly, these E2F7 knockout mice also had hydrocephalus, an abnormal buildup of fluid in the brain. This was due to the dysfunction of multiciliated cells in the brain, which were unable to adequately clear out fluid. When the scientists examined all the multiciliated cells in more detail, they found that the cells had fewer cilia. And instead of the neat phases of sequential gene expression found in normal multiciliated cell differentiation, the cell cycle-related genes expressed by these mutated cells melded into each other, without a distinct transcriptional change going from the S phase into the G2/M phase.

These findings demonstrate the importance of E2F7 as one of the key factors driving the multiciliation cycle. Reiter analogizes these various versions of the cell cycle to clocks. “I think that the multiciliated cell cycle gives us a different type of clock, one where the gears—or the fundamental mechanism underlying the progression—is very similar to the canonical cell cycle,” he said. “But the output is different.”

“This is a really beautiful piece of work,” said Cayla Jewett, a multiciliated cell researcher at Johns Hopkins University. She is curious about whether, beyond E2F7, the other genes that differ between this variant and the canonical cell cycle are functionally relevant. Better understanding how multiciliated cells form, she said, could provide insight into diseases like hydrocephalus that might be caused by dysfunction of these cells.

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Friday, September 6, 2024

Plant Scientist Wins Prestigious European Research Grant









A plant scientist from the University of Nottingham has been awarded €2M for research to unlock the mysteries of how plant roots sense and respond to water availability.

Dr Poonam Mehra is a BBSRC Discovery Fellow in the School of Biosciences at the University of Nottingham and has been selected from over 3,000 applicants to receive a prestigious European Research Council (ERC) Starting grant for her research.

Her project titled 'Water Blind' will pioneer the first-of-its-kind screening of mutants to identify 'water-blind' roots, which are impaired in their ability to perceive water. Multidisciplinary approaches will be utilized to uncover the molecular networks responsible for water sensing and explore how these mechanisms influence soil exploration and water foraging in plants.

The funding is one of 494 Starting Grants awarded to young scientists and scholars across Europe. The finding – totalling nearly €780 million – supports cutting-edge research in a wide range of fields, from life sciences and physics to social sciences and humanities. It will help researchers at the beginning of their careers to launch their own projects, form their teams and pursue their most promising ideas.

"Water stress is one of the most significant challenges facing global agriculture, particularly as climate change disrupts traditional rainfall patterns," said Dr Mehra. "While we know that plant roots can adapt their shape to forage for water, the mechanisms behind how roots sense water availability remain largely unknown. The ERC funding will allow me to explore this question using innovative, real-world approaches.


I am excited about the translational impact of this research, as humankind faces increasing challenges from climate change and altered rainfall patterns. By understanding how roots sense water availability, this new knowledge can support efforts to develop climate-smart crops, making global food production more resilient to a changing world.

This competition attracted 3,474 proposals, which were evaluated by peer review panels of internationally renowned researchers. Overall, 14.2% of the proposals were selected for funding.

44% of these Starting Grants were awarded to female researchers, compared with 43% in 2023 and 39% in 2022. The Starting Grants calls have attracted nearly 62,000 submissions since 2007. During this time, the number of proposals submitted by women has increased from around 30% to around 40%.

Empowering researchers early on in their careers is at the heart of the mission of the ERC. I am particularly pleased to welcome UK researchers back to the ERC. They have been sorely missed over the past years. With fifty grants awarded to researchers based in the UK, this influx is good for the research community overall.

Starting Grants amount to €1.5 million per grant for a period of five years. However, additional funds can be made available to cover costs related to moving from a country outside the EU or associated countries, the purchase of major equipment, access to large facilities or major experimental and fieldwork-related costs.

The grants are estimated to create 3,160 jobs within the teams of new grantees.


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Thursday, September 5, 2024

Researchers built an ‘AI Scientist’ — what can it do?



The large language model does everything from reading the literature to writing and reviewing its own papers, but it has a limited range of applicability so far.










Could science be fully automated? A team of machine-learning researchers has now tried.

‘AI Scientist’, created by a team at Tokyo company Sakana AI and at academic labs in Canada and the United Kingdom, performs the full cycle of research from reading the existing literature on a problem and formulating hypothesis for new developments to trying out solutions and writing a paper. AI Scientist even does some of the job of peer reviewers and evaluates its own results.

AI Scientist joins a slew of efforts to create AI agents that have automated at least parts of the scientific process. “To my knowledge, no one has yet done the total scientific community, all in one system,” says AI Scientist co-creator Cong Lu, a machine-learning researcher at the University of British Columbia in Vancouver, Canada. The results1 were posted on the arXiv preprint server this month.

“It’s impressive that they’ve done this end-to-end,” says Jevin West, a computational social scientist at the University of Washington in Seattle. “And I think we should be playing around with these ideas, because there could be potential for helping science.”

The output is not earth-shattering so far, and the system can only do research in the field of machine learning itself. In particular, AI Scientist is lacking what most scientists would consider the crucial part of doing science — the ability to do laboratory work. “There’s still a lot of work to go from AI that makes a hypothesis to implementing that in a robot scientist,” says Gerbrand Ceder, a materials scientist at Lawrence Berkeley National Laboratory and the University of California, Berkeley. Still, Ceder adds, “If you look into the future, I have zero doubt in mind that this is where much of science will go.”
Automated experiments

AI Scientist is based on a large language model (LLM). Using a paper that describes a machine learning algorithm as template, it starts from searching the literature for similar work. The team then employed the technique called evolutionary computation, which is inspired by the mutations and natural selection of Darwinian evolution. It proceeds in steps, applying small, random changes to an algorithm and selecting the ones that provide an improvement in efficiency.

To do so, AI Scientist conducts its own ‘experiments’ by running the algorithms and measuring how they perform. At the end, it produces a paper, and evaluates it in a sort of automated peer review. After ‘augmenting the literature’ this way, the algorithm can then start the cycle again, building on its own results.

The authors admit that the papers AI Scientists produced contained only incremental developments. Some other researchers were scathing in their comments on social media. “As an editor of a journal, I would likely desk-reject them. As a reviewer, I would reject them,” said one commenter on the website Hacker News.

West also says that the authors took a reductive view of how researchers learn about the current state of their field. A lot of what they know comes from other forms of communication, such as going to conferences or chatting to colleagues at the water cooler. “Science is more than a pile of papers,” says West. “You can have a 5-minute conversation that will be better than a 5-hour study of the literature.”

West’s colleague Shahan Memon agrees — but both West and Memon praise the authors for having made their code and results fully open. This has enabled them to analyze the AI Scientist’s results. They’ve found, for example, that it has a “popularity bias” in the choice of earlier papers it lists as references, skirting towards those with high citation counts. Memon and West say they are also looking into measuring whether AI Scientist’s choices were the most relevant ones.
Repetitive tasks

AI Scientist is, of course, not the first attempt at automating at least various parts of the job of a researcher: the dream of automating scientific discovery is as old as artificial intelligence itself — dating back to the 1950s, says Tom Hope, a computer scientist at the Allen Institute for AI based in Jerusalem. Already a decade ago, for example, the Automatic Statistician2 was able to analyse sets of data and write up its own papers. And Ceder and his colleagues have even automated some bench work: the ‘robot chemist’ they unveiled last year can synthesize new materials and experiment with them3.

Hope says that current LLMs “are not able to formulate novel and useful scientific directions beyond basic superficial combinations of buzzwords”. Still, Ceder says that even if AI won’t able to do the more creative part of the work any time soon, it could still automate a lot of the more repetitive aspects of research. “At the low level, you’re trying to analyse what something is, how something responds. That’s not the creative part of science, but it’s 90% of what we do.” Lu says he got a similar feedback from a lot of other researchers, too. “People will say, I have 100 ideas that I don’t have time for. Get the AI Scientist to do those.”

Lu says that to broaden AI Scientist’s capabilities — even to abstract fields beyond machine learning, such as pure mathematics — it might need to include other techniques beyond language models. Recent results on solving maths problems by Google Deep Mind, for example, have shown the power of combining LLMs with techniques of ‘symbolic’ AI, which build logical rules into a system rather than merely relying on it learning from statistical patterns in data. But the current iteration is but a start, he says. “We really believe this is the GPT-1 of AI science,” he says, referring to an early large language model by OpenAI in San Francisco, California.

The results feed into a debate that is at the top of many researchers’ concerns these days, says West. “All my colleagues in different sciences are trying to figure out, where does AI fit in in what we do? It does force us to think what is science in the twenty-first century — what it could be, what it is, what it is not,” he says.

Website: International Research Scientist Awards


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Wednesday, September 4, 2024

A look at the top science stories and breakthroughs of 2023!






As 2023 rolls to a close, Knowable Magazine has looked back over its articles and canvassed editorial committee members from the 51 academic journals — covering analytical chemistry to vision science — published by Knowable’s parent company, Annual Reviews. From good news to bad, from novel vaccines to insect invaders, this year left us with much to ponder. Here we present 12 newsworthy developments from 2023.

Jabs for hope

Hot on the heels of the COVID-19 vaccine success story (including updated jabs that target Omicron subvariants of the rapidly shifting virus), 2023 saw the greenlighting of several new vital vaccines. Abrysvo and Arexvy, the first vaccines against respiratory syncytial virus (RSV), a cold-like virus that can be dangerous for the old or the young, are now available in the United States and elsewhere. And the World Health Organization has recommended a second malaria vaccine, R21, following RTS,S in 2021. RTS,S has already been given to nearly 2 million children in Africa; the new vaccine is about half the price.

This double hit against malaria is a “huge win” for kids, says Matthew Laurens, a pediatric infectious disease specialist at the University of Maryland School of Medicine in Baltimore, who wrote about malaria vaccines in a 2022 opinion article for Knowable. “Like COVID-19, we need multiple malaria vaccines if we’re to succeed in combating this deadly disease.”
Scary smarts

One of the biggest newsmakers of the year was artificial intelligence (AI). San Francisco tech company OpenAI’s conversational bot ChatGPT, first launched in November 2022, was estimated to have more than 100 million monthly users by January 2023. People were simultaneously impressed and appalled by the capacity of AI based on deep learning (a technique inspired by the human brain) to write everything from poetry to class essays and research papers.

“In terms of public interest, I have not seen anything like this in my 30-year career,” says Colin Phillips, a psycholinguist at the University of Maryland and co-editor of the Annual Review of Linguistics.

WATCH: ‘Godfather of AI’ discusses dangers the developing technologies pose to society

Rapidly improving AI has left governments, scientists and consumers alike wondering how best to harness its abilities and guard against its misuse, including the deepfakes now featuring in scams and propaganda. International leaders agreed to work together to guide the technology at the UK’s AI Safety Summit in November — hoping to get regulations in place before computers grow smarter than people.
Wild weather

News reports of broken heat records are starting to sound like, well, broken records. But 2023 really was a standout: The planet had its hottest year on record. As of October, it was about 1.4 degrees Celsius warmer than the 1850–1900 average, topping the previous greatest above-average heat bumps of about 1.3 degrees C in both 2020 and 2016.

This extreme heat of 2023 resulted from both long-term climate change trends and the year’s El Niño, a natural climate pattern that, overall, tends to make the world warmer. This was the hottest summer since recordkeeping began in 1880, and September was by far the most weirdly warm month ever seen.
Everything electric to end emissions

In December, delegates at the United Nations climate change convention discussed the first official inventory of our actions to combat global warming. The “global stocktake” concluded that while the world is making some progress and it will be possible to reach the Paris goal of limiting global warming to 2 degrees Celsius, leaders are going to have to accelerate action to get there.

For now, fossil fuel production remains too high for climate targets. But a Climate Analytics report says that there’s a 70 percent chance that greenhouse gas emissions will fall in 2024, making 2023 the “peak” year. Of course, getting away from fossil fuels means ramping up alternative energy sources. Renewables are soaring — particularly solar, and particularly in China.

“Climate change is no longer about our grandchildren or polar bears — it is here, and now affecting everyone, everywhere on the planet, but especially devastating for the poor.”

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Tuesday, September 3, 2024

A new ‘AI scientist’ can write science papers without any human input. Here’s why that’s a problem









Scientific discovery is one of the most sophisticated human activities. First, scientists must understand the existing knowledge and identify a significant gap. Next, they must formulate a research question and design and conduct an experiment in pursuit of an answer. Then, they must analyse and interpret the results of the experiment, which may raise yet another research question.

Can a process this complex be automated? Last week, Sakana AI Labs announced the creation of an “AI scientist” – an artificial intelligence system they claim can make scientific discoveries in the area of machine learning in a fully automated way.

Using generative large language models (LLMs) like those behind ChatGPT and other AI chatbots, the system can brainstorm, select a promising idea, code new algorithms, plot results, and write a paper summarising the experiment and its findings, complete with references. Sakana claims the AI tool can undertake the complete lifecycle of a scientific experiment at a cost of just US$15 per paper – less than the cost of a scientist’s lunch.

These are some big claims. Do they stack up? And even if they do, would an army of AI scientists churning out research papers with inhuman speed really be good news for science?
How a computer can ‘do science’

A lot of science is done in the open, and almost all scientific knowledge has been written down somewhere (or we wouldn’t have a way to “know” it). Millions of scientific papers are freely available online in repositories such as arXiv and PubMed.

LLMs trained with this data capture the language of science and its patterns. It is therefore perhaps not at all surprising that a generative LLM can produce something that looks like a good scientific paper – it has ingested many examples that it can copy.

What is less clear is whether an AI system can produce an interesting scientific paper. Crucially, good science requires novelty.
But is it interesting?


Scientists don’t want to be told about things that are already known. Rather, they want to learn new things, especially new things that are significantly different from what is already known. This requires judgement about the scope and value of a contribution.

The Sakana system tries to address interestingness in two ways. First, it “scores” new paper ideas for similarity to existing research (indexed in the Semantic Scholar repository). Anything too similar is discarded.

Second, Sakana’s system introduces a “peer review” step – using another LLM to judge the quality and novelty of the generated paper. Here again, there are plenty of examples of peer review online on sites such as openreview.net that can guide how to critique a paper. LLMs have ingested these, too.
AI may be a poor judge of AI output

Feedback is mixed on Sakana AI’s output. Some have described it as producing “endless scientific slop”.

Even the system’s own review of its outputs judges the papers weak at best. This is likely to improve as the technology evolves, but the question of whether automated scientific papers are valuable remains.

The ability of LLMs to judge the quality of research is also an open question. My own work (soon to be published in Research Synthesis Methods) shows LLMs are not great at judging the risk of bias in medical research studies, though this too may improve over time.
AI tools to support scientists, not replace them


Specialised search tools make use of AI to help scientists find and synthesise existing work. These include the above-mentioned Semantic Scholar, but also newer systems such as Elicit, Research Rabbit, scite and Consensus.

Text mining tools such as PubTator dig deeper into papers to identify key points of focus, such as specific genetic mutations and diseases, and their established relationships. This is especially useful for curating and organising scientific information.

Machine learning has also been used to support the synthesis and analysis of medical evidence, in tools such as Robot Reviewer. Summaries that compare and contrast claims in papers from Scholarcy help to perform literature reviews.
AI research may exacerbate existing problems

While Sakana AI states it doesn’t see the role of human scientists diminishing, the company’s vision of “a fully AI-driven scientific ecosystem” would have major implications for science.

One concern is that, if AI-generated papers flood the scientific literature, future AI systems may be trained on AI output and undergo model collapse. This means they may become increasingly ineffectual at innovating.

However, the implications for science go well beyond impacts on AI science systems themselves.

There are already bad actors in science, including “paper mills” churning out fake papers. This problem will only get worse when a scientific paper can be produced with US$15 and a vague initial prompt.

The need to check for errors in a mountain of automatically generated research could rapidly overwhelm the capacity of actual scientists. The peer review system is arguably already broken, and dumping more research of questionable quality into the system won’t fix it.

Website: International Research Scientist Awards
 
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Monday, September 2, 2024

Your Guide to Becoming a Successful Research Scientist 2024!

 



Becoming a research scientist is quite the dream role for many individuals passionate about discovering new things and pushing the boundaries of what is known. They’ve also become essential in advancing science, technology, and our understanding of the world.

If you’re wondering, “How can I become a research scientist?”, this guide will explain what a research scientist does, how to become a research scientist, their importance, the skills required, the different types of research scientists, their average salary, career opportunities, and the future of this exciting field.

What Does a Research Scientist Do?

In summary, research scientists conduct experiments, analyze data, and develop new theories or products. Their work is often performed in laboratories through fieldwork and theoretical research. They specialize in specific study areas, mainly biology, chemistry, physics, environmental and climate, or social sciences. They design and conduct experiments, collect and analyze data, publish their findings in journals, and present their work at conferences. They often also collaborate with other scientists, engineers, and professionals to solve complex problems.

Tasks can vary greatly depending on the researcher's field and specific role in terms of day-to-day responsibilities. For instance, a research scientist in the pharmaceutical industry might spend their day developing new medications, conducting trials, and ensuring regulatory compliance. On the other hand, an environmental scientist analyzing the climate conditions might be out in the field collecting samples and data and developing conservation plans for climate change. Despite these differences, all research scientists share a common goal: expanding human knowledge to solve real-world problems.
How to Become a Research Scientist?
The Educational Route:Bachelor's Degree: One of the first major steps in how to become a research scientist typically begins with earning a bachelor's degree in a relevant field. This foundational step usually takes about four years and provides essential knowledge in biology, chemistry, physics, or psychology. Students should focus on building a solid foundation in their chosen field and seek research opportunities, such as working in a laboratory or completing a research project.
Master's Degree: While not always required, a master's degree can provide additional specialization and research experience. It typically takes one or two years to complete and can benefit those looking to gain more in-depth knowledge or pivot to a slightly different area of expertise.

Future of Research Scientists

The future for research scientists looks promising, driven by technological advancements and increasing global challenges that require innovative solutions. Here are some trends shaping the future of this profession:Interdisciplinary Research: The complexity of modern problems often requires collaboration across different scientific disciplines. Interdisciplinary research is becoming more common, leading to impossible breakthroughs within a single field. For example, combining insights from biology, chemistry, and computer science can accelerate the development of new medicines.
Big Data and AI: The ability to analyze large datasets using artificial intelligence and machine learning transforms how research is conducted. These technologies enable researchers to uncover patterns and insights that were previously unattainable. For instance, AI can help predict climate change impacts with greater accuracy. If you want to leverage data science’s power, you could enroll in a top course here.

Becoming a research scientist is quite the dream role for many individuals passionate about discovering new things and pushing the boundaries of what is known. They’ve also become essential in advancing science, technology, and our understanding of the world.

If you’re wondering, “How can I become a research scientist?”, this guide will explain what a research scientist does, how to become a research scientist, their importance, the skills required, the different types of research scientists, their average salary, career opportunities, and the future of this exciting field.

What Does a Research Scientist Do?

In summary, research scientists conduct experiments, analyze data, and develop new theories or products. Their work is often performed in laboratories through fieldwork and theoretical research. They specialize in specific study areas, mainly biology, chemistry, physics, environmental and climate, or social sciences. They design and conduct experiments, collect and analyze data, publish their findings in journals, and present their work at conferences. They often also collaborate with other scientists, engineers, and professionals to solve complex problems.

Tasks can vary greatly depending on the researcher's field and specific role in terms of day-to-day responsibilities. For instance, a research scientist in the pharmaceutical industry might spend their day developing new medications, conducting trials, and ensuring regulatory compliance. On the other hand, an environmental scientist analyzing the climate conditions might be out in the field collecting samples and data and developing conservation plans for climate change. Despite these differences, all research scientists share a common goal: expanding human knowledge to solve real-world problems.

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Certifications and Licenses:

Depending on your field, obtaining specific certifications or licenses may be necessary. For example, clinical research scientists may need particular certifications to conduct medical research. Additionally, certifications in specialized techniques or methodologies can enhance your qualifications and career prospects.

Importance of Research Scientists

Research scientists are the backbone of scientific advancement. Their work leads to discoveries and innovations that can profoundly impact society. For instance, medical research scientists develop new treatments and vaccines, environmental scientists study climate change and its effects, and computer scientists analyze new technologies to develop programming theories to enhance processes.

The importance of research scientists extends beyond the lab. Their work informs policy decisions, drives technological innovation, and contributes to economic growth. For example, research in renewable energy technologies helps combat climate change and creates new industries and job opportunities. In healthcare, research scientists develop new treatments and technologies that improve patient outcomes and quality of life.
Skills Required for Research Scientists

Being a successful research scientist requires a combination of technical skills and soft skills. Here are some essential skills:
Technical Skills:Analytical Skills: Ability to analyze complex data and interpret results accurately. This includes proficiency in statistical analysis and data visualization tools.
Technical Proficiency: Expertise using scientific equipment, software, and methodologies specific to your field. Familiarity with advanced technologies and techniques, such as molecular biology methods or computational modeling, can be crucial.
Statistical Analysis: Knowledge of statistical methods to evaluate research data. This includes understanding experimental design, data collection, and the application of statistical tests.
Soft Skills:Critical Thinking: The ability to think critically and solve problems creatively. Research scientists must be able to develop hypotheses, design experiments, and troubleshoot issues that arise during their work.
Effective Communication: Bearing strong written and verbal communication skills for publishing papers and presenting findings. Communicating complex scientific concepts to a non-expert audience is also essential, particularly when applying for funding or presenting to stakeholders.
Attention to Detail: Precision and accuracy are essential in experiments and data analysis. Even small errors can result in major discrepancies in research outcomes.
Teamwork: Ability to collaborate effectively with other scientists and professionals. Research is often collaborative, and strong interpersonal skills are essential for working in multidisciplinary teams.
Time Management: Managing time efficiently to balance multiple research projects and deadlines. Effective time management skills are crucial for meeting grant deadlines, publishing schedules, and completing experiments.

Types of Research Scientists

When assessing how to become research scientist, it’s good to understand that the types can be categorized into various types based on their fields of study. Here are some common types:Biological Scientists: They focus on understanding living organisms, their functions, and interactions. This category includes microbiologists, geneticists, and ecologists. Their work can lead to advancements in medicine, agriculture, and environmental conservation.
Physical Scientists: They explore the fundamental principles of the physical universe. This category typically includes physicists and astronomers. Their discoveries can lead to new technologies like quantum computing and advanced materials.
Environmental Scientists: They investigate environmental issues such as pollution, climate change, and natural resource management. Their work is critical for developing sustainable practices and policies to protect the planet.
Medical Scientists: They conduct research to improve human health. This includes epidemiologists, pharmacologists, and biomedical scientists. Their research can often lead to discovering new treatments, vaccines, and medical devices.
Social Scientists: They study human behavior, societies, and social relationships. This includes psychologists, sociologists, and anthropologists. Their insights can inform public policy, education, and community development initiatives.
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Average Research Scientist Salary

Research scientists enjoy competitive salaries that reflect their expertise and contributions to their fields. According to Payscale, the average salary for a research scientist in the United States is approximately $87,800 annually. This figure can vary based on factors such as industry, location, level of education, and years of experience.

Career Opportunities for Research Scientists

The career opportunities for research scientists are vast and varied. When deciding how to become a research scientist, it’s important to understand their scope of work which includes:Academia: Research scientists in academia typically work at universities or colleges, where they conduct research, teach, and mentor students. These positions often involve securing grant funding for research projects. Academic positions also provide opportunities for interdisciplinary collaboration and access to cutting-edge research facilities.
Industries: Many research scientists work in sectors such as pharmaceuticals, biotechnology, energy, and manufacturing. They develop new products, improve existing ones, and ensure quality control. Industry roles often offer competitive salaries and the opportunity to work on applied research projects with immediate practical applications.
Government Institutes: Research scientists can work for government agencies, conducting research that informs public policy and regulatory decisions. Examples include the Environmental Protection Agency (EPA) and the National Institutes of Health (NIH). Government roles provide stability and the chance to contribute to public health and safety initiatives.
Nonprofit Organizations: Some research scientists work for nonprofit organizations focused on specific issues, such as environmental conservation, public health, or social justice. These positions can be highly fulfilling, allowing scientists to align their work with their values and contribute to meaningful causes.
Private Research Institutes: These institutes often focus on specialized research areas and may collaborate with academia, industry, and government entities. Working at a private research institute can provide access to unique resources and the opportunity to work on innovative projects.

Future of Research Scientists

The future for research scientists looks promising, driven by technological advancements and increasing global challenges that require innovative solutions. Here are some trends shaping the future of this profession:Interdisciplinary Research: The complexity of modern problems often requires collaboration across different scientific disciplines. Interdisciplinary research is becoming more common, leading to impossible breakthroughs within a single field. For example, combining insights from biology, chemistry, and computer science can accelerate the development of new medicines.
Big Data and AI: The ability to analyze large datasets using artificial intelligence and machine learning transforms how research is conducted. These technologies enable researchers to uncover patterns and insights that were previously unattainable. For instance, AI can help predict climate change impacts with greater accuracy. If you want to leverage data science’s power, you could enroll in a top course here.
Environmental Sustainability: With growing concerns about climate change and environmental degradation, research scientists are focusing on sustainable solutions. This includes developing renewable energy sources, improving waste management, and conserving biodiversity. Research in these areas is critical for achieving long-term environmental sustainability and mitigating the impacts of climate change.
Global Health Initiatives: Research scientists are crucial in addressing global health issues, from combating infectious diseases to improving healthcare delivery in underserved regions. Collaborative international research efforts are essential for tackling global health challenges and ensuring equitable access to healthcare advancements.

Conclusion

When considering how to become a research scientist or how long does it take to become a research scientist, it’s important to factor in the required dedication, hard work, and passion to recognize scientific research's importance and training, but the rewards are significant. Research scientists contribute critically to our understanding of the world and help solve some of humanity's most pressing challenges. With a wide range of career opportunities and a bright future, now is an excellent time to pursue a career as a research scientist.

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Prof. Dr. Gulsen Meral | Genetics and Genomics | Innovative Research Award

Congratulations to Gulsen Meral on receiving the Innovative Research Award! Your pioneering contributions to genetics, genomics, and epigene...