Public Trust in Scientists and Views on Their Role in Policymaking

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A majority of Americans say they have confidence in scientists to act in the public’s best interests. Confidence ratings have moved slightly higher in the last year, marking a shift away from the decline in trust seen during the COVID-19 pandemic.
A new Pew Research Center survey of 9,593 U.S. adults conducted Oct. 21-27, 2024, takes a close look at the public image of scientists, who serve as one potential source of information for Americans navigating complex policy debates and everyday decisions around things like their personal health and wellness.

Key findings
76% of Americans express a great deal or fair amount of confidence in scientists to act in the public’s best interests.

This is up slightly from 73% in October 2023 and represents a halt to the decline seen during the COVID-19 pandemic. Scientists continue to enjoy strong relative standing compared with the ratings Americans give to many other prominent groups, including elected officials, journalists and business leaders.
Majorities view research scientists as intelligent (89%) and focused on solving real-world problems (65%).

In addition, about two-thirds (65%) view research scientists as honest and 71% view them as skilled at working in teams.Communication is seen as an area of relative weakness for scientists.

Overall, 45% of U.S. adults describe research scientists as good communicators. A slightly larger share (52%) say this phrase does not describe research scientists well.

Another critique many Americans hold is the sense that research scientists feel superior to others; 47% say this phrase describes them well.
Americans are split over scientists’ role in policymaking.

Overall, 51% say scientists should take an active role in public policy debates about scientific issues. By contrast, nearly as many (48%) say they should focus on establishing sound scientific facts and stay out of public policy debates.

Americans also aren’t convinced scientists make better policy decisions on science issues than other people – just 43% think this is the case.


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Though the partisan gap in trust remains sizable, Republicans’ overall level of confidence in scientists is up 5 percentage points compared with a year ago – the first uptick in trust among Republicans since the start of the pandemic.

Partisans also differ over scientists’ role in policy debates, with Democrats far more supportive than Republicans of active engagement in making policy on scientific issues.
Trends in trust in scientists

In recent years, the scientific community has engaged with the public’s declining trust directly, and there are multiple organizations working on ways to support trust in science and improve communication with wider audiences.

About three-quarters of Americans say they have either a great deal (26%) or a fair amount (51%) of confidence in scientists to act in the best interests of the public. This share is up slightly since last year. Still, levels of confidence in scientists remain lower than in April 2020 – at the outset of the COVID-19 pandemic. At that time, 87% expressed at least a fair amount of confidence in scientists, including 39% who said they had a great deal of confidence.
Trust in scientists compared with trust in other groups

In an era of low public trust in institutions, scientists continue to be held in higher regard than several other prominent groups we’ve asked about, including journalists, elected officials, business leaders and religious leaders. Confidence ratings for scientists are even slightly higher than those for public school principals and police officers – two groups that receive positive overall ratings.

Go to the Appendix for more detailed views of these groups. The Appendix also includes views of medical scientists, whose ratings are very similar to those for scientists generally.

Scientists Want 'Mirror Life' Microbe Research To Halt. Here's Why

Leading scientists from across the globe have suggested halting the research aimed at creating "mirror life" microbes. The reason? Their concern is that the synthetic organism may pose an "unprecedented risk" to life on the planet.

The international group of 38 people, including Nobel laureates and other experts, has warned that mirror bacteria, constructed through mirror images of molecules found in nature, might become established in the environment and slip past the immune defences of natural organisms, putting the lives of humans, animals and plants at risk of lethal infections, The Guardian reported.

The latest concerns over the technology have been revealed in a 299-page report, along with commentary in the Science journal.

Professor Vaughn Cooper, an evolutionary biologist at the University of Pittsburgh, said the threat the group talked about was "unprecedented".

"Mirror bacteria would likely evade many human, animal and plant immune system responses and in each case would cause lethal infections that would spread without check," Cooper said.

Others part of the expert group include Nobel laureates -- Professor Greg Winter at the University of Cambridge and Professor Jack Szostak of the University of Chicago -- as well as Dr Craig Venter, the scientist, who earlier led the private effort to sequence the human genome during the 1990s.Though enthusiastic about the research on mirror molecules, the report highlights substantial risks in mirror microbes and has called for a worldwide debate.

It will take at least 10 years to build a viable mirror microbe. However, a new risk assessment has raised major concerns about the organisms, forcing the group to urge scientists to halt their work, besides asking those funding to make clear they shall no longer support the research.

Professor Vaughn Cooper, an evolutionary biologist at the University of Pittsburgh, said the threat the group talked about was "unprecedented".


"Mirror bacteria would likely evade many human, animal and plant immune system responses and in each case would cause lethal infections that would spread without check," Cooper said.

Others part of the expert group include Nobel laureates -- Professor Greg Winter at the University of Cambridge and Professor Jack Szostak of the University of Chicago -- as well as Dr Craig Venter, the scientist, who earlier led the private effort to sequence the human genome during the 1990s.

Usually, molecules for life exist in two distinct forms -- each being the mirror image of the other. All living organism's DNA is made from their "right-handed" nucleotides. The "left-handed" amino acids make proteins, the building blocks of cells.

Researchers have already manufactured large, functional mirror molecules for an in-depth study. Some have even made progress towards building mirror microbes, but constructing a whole organism from the mirror molecules is not a reality yet.

These mirror molecules, once built, can be turned into therapies to treat chronic and hard-to-treat diseases. The mirror microbes could make bioproduction facilities that utilise bugs to churn out chemicals.

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How to boost diversity in UK STEM careers

Diverse teams are more innovative, solve problems faster and offer perspectives and ideas.

When Carla Aldington was young, life was tough. Growing up in Cornwall, she was one of a handful of people from an ethnic minority in a town of over 20,000. The racism she encountered was relentless. Then she found support from an unlikely source – her chemistry teacher. Aldington recalls thinking: “Wow, I want to be just like her”. With this teacher’s encouragement, Aldington excelled through college and later graduated top of her class with a master’s degree in chemistry.

Many young people from minority backgrounds are not so fortunate. Despite positive change in recent years, the fields of science, technology, engineering and mathematicos, or STEM, still have a significant diversity problem. There is an under-representation of women, peple who identify as LGBTQ+, people with disabilities, people belonging to a racial or ethnic minority, or who come from lower socio-economic backgrounds. This has resulted in a STEM ecosystem that fails to reflect the diverse society it aims to serve and fails to benefit from the increasingly clear advantages that diversity brings.

Change is afoot, however. Mentoring schemes, collaborations and an increasing awareness among stakeholders across different sectors, including industry, government and NGOs, about the benefits of an inclusive workforce, are all having a positive effect. GSK, the global biopharma company, has invested £6 million over ten years into UK STEM-focused initiatives that aim to boost STEM career progression for young people from under-represented groups, including through high-quality mentoring that is delivered in collaboration with grassroots partner organisations. “We know how much diversity, equity and inclusion matters for our own organisation — from fuelling our ability to discover and develop ground-breaking medicines and vaccines to understanding the needs of diverse patients – so we wanted to do more working with partners to support the broader ecosystem of diverse innovators of the future,” says Sally Jackson, senior vice president, Global Communications and Government Affairs and CEO Office at GSK.

Understanding that collaboration is key to making a difference, GSK recently brought together STEM experts to discuss the barriers that persist for under-represented groups entering STEM careers, what solutions are working best, and what steps are needed for a more inclusive future for STEM.
Role Models

The major challenge for minority groups is a lack of role models, says Ollie Folayan, co-founder of the Association for Black and Minority Ethnic Engineers. Many people don’t come from families of scientists, and they don’t see figures who look like them in the curriculum, either. Anne-Marie Imafidon, chief executive of Stemettes, a social enterprise that connects women and non-binary people with STEM, says the STEM curriculum is currently failing to tell the whole story and needs to include many more people from under-represented groups who have successfully contributed to scientific understanding and progress.

A second barrier is exposure. People in deprived areas have fewer opportunities to study triple science at GCSE, for instance, which is often a precursor for further study in STEM. They also have less exposure to science activities in schools and clubs. Séverine Trouillet, chief executive of STEM Learning, argues that more student-facing activities and opportunities for work experience are needed to expose children to STEM and nurture their interest.

Challenges also exist at later career stages, says Stephen Hendry at the Royal Society of Chemistry (RSC). For instance, research funding applications for women reduce with each career stage, from 71 per cent for pre-doctoral programmes to 37 per cent at senior investigator level. “We’ve got a leaky pipeline,” says Hendry.

Some of that attrition may be due to another barrier: bullying and victimisation. Part of the lack of diversity in STEM has been attributed to hostile working environments. For instance, a report from the RSC, Institute of Physics and Royal Astronomical Society showed that some people who identify as LGBTQ+ do not find sciences like physics and chemistry to be comfortable working environments, and have thoughts about leaving.

Collective action is needed to address these challenges. That might involve STEM organisations linking up with industry to create mentorships that provide valuable guidance at critical career transitions, or businesses going into schools and providing work experience to give young people a foot in the door and then supporting people throughout their careers. Organisations need to come together to provide role models, share data to gain a better understanding of which groups are being missed and pursue policies that promote more systemic change. “When collaboration happens, it can be a really powerful thing,” says Jackson.


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Researchers identify key mechanism linking brain stress to Alzheimer's disease

Researchers with the Advanced Science Research Center at the CUNY Graduate Center (CUNY ASRC) have unveiled a critical mechanism that links cellular stress in the brain to the progression of Alzheimer's disease (AD). The study, published in the journal Neuron, highlights microglia, the brain's primary immune cells, as central players in both the protective and harmful responses associated with the disease.

Microglia, often dubbed the brain's first responders, are now recognized as a significant causal cell type in Alzheimer's pathology. However, these cells play a double-edged role: some protect brain health, while others worsen neurodegeneration. Understanding the functional differences between these microglial populations has been a research focus for Pinar Ayata, the study's principal investigator and a professor with the CUNY ASRC Neuroscience Initiative and the CUNY Graduate Center's Biology and Biochemistry programs.

We set out to answer what are the harmful microglia in Alzheimer's disease and how can we therapeutically target them. We pinpointed a novel neurodegenerative microglia phenotype in Alzheimer's disease characterized by a stress-related signaling pathway."

Pinar Ayata, study's principal investigator and professor with the CUNY ASRC Neuroscience Initiative and the CUNY Graduate Center's Biology and Biochemistry programs

The research team discovered that activation of this stress pathway, known as the integrated stress response (ISR), prompts microglia to produce and release toxic lipids. These lipids damage neurons and oligodendrocyte progenitor cells-;two cell types essential for brain function and most impacted in Alzheimer's disease. Blocking this stress response or the lipid synthesis pathway reversed symptoms of Alzheimer's disease in preclinical models.

Key findingsDark microglia and Alzheimer's disease: Using electron microscopy, the researchers identified an accumulation of "dark microglia," a subset of microglia associated with cellular stress and neurodegeneration, in postmortem brain tissues from Alzheimer's patients. These cells were present at twice the levels seen in healthy-aged individuals.
Toxic lipid secretion: The ISR pathway in microglia was shown to drive the synthesis and release of harmful lipids that contribute to synapse loss, a hallmark of Alzheimer's disease.
Therapeutic potential: In mouse models, inhibiting ISR activation or lipid synthesis prevented synapse loss and accumulation of neurodegenerative tau proteins, offering a promising pathway for therapeutic intervention.
"These findings reveal a critical link between cellular stress and the neurotoxic effects of microglia in Alzheimer's disease," said the study's co-lead author Anna Flury, a member of Ayata's lab and a Ph.D. student with the CUNY Graduate Center's Biology Program. "Targeting this pathway may open up new avenues for treatment by either halting the toxic lipid production or preventing the activation of harmful microglial phenotypes."

Implications for Alzheimer's patients

This research underscores the potential of developing drugs that target specific microglial populations or their stress-induced mechanisms. "Such treatments could significantly slow or even reverse the progression of Alzheimer's disease, offering hope to millions of patients and their families," explained co-lead author Leen Aljayousi, a member of Ayata's lab and a Ph.D. student with the CUNY Graduate Center's Biology Program.

The study represents a major leap forward in understanding the cellular underpinnings of Alzheimer's and emphasizes the importance of microglial health in maintaining overall brain function.

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Robots May Soon Detect Emotions by Measuring Your Sweat, Scientists Say

n a study published in IEEE Access on October 15, researchers have highlighted the potential of using skin conductance to detect human emotions. The study examined how variations in sweat levels, which alter the skin's ability to conduct electricity, could provide insights into emotional states. According to the findings, these physiological responses, triggered by emotions like fear, humour, or familial bonding, could pave the way for more emotionally intelligent technology in the future.
Skin Conductance and Emotion Analysis

The research was carried out by scientists from Tokyo Metropolitan University. During the study, 33 participants were shown videos designed to evoke specific emotions, ranging from horror scenes to family reunion clips. Measurements were taken using probes attached to their fingers. These probes recorded how quickly skin conductance peaked and returned to baseline. Distinct patterns were identified, with fear responses persisting the longest, while humour elicited quicker but shorter-lived reactions.



The team explained in their report that fear's prolonged response might be tied to evolutionary survival mechanisms, while the mixed nature of family bonding emotions appeared to create slower, overlapping reactions. They also noted that limited studies have explored the dynamics of skin conductance associated with humour and fear.
Potential Applications and Challenges

As per the report, combining skin conductance data with other physiological signals, such as heart rate or brain activity, could significantly enhance the accuracy of emotion detection. While this research does not directly involve robotics, the findings are considered foundational for integrating emotion-detection capabilities into future technologies. Hypothetical applications include stress-responsive smart devices or media platforms that adapt to user moods.

Conventional methods of emotion detection often rely on facial recognition or voice analysis, which can be prone to errors and raise privacy concerns. The researchers suggest that skin conductance may offer a more reliable and less invasive alternative.

For the study, the team highlighted a growing interest in leveraging physiological signals for emotionally intelligent services, indicating potential advancements in personalised technologies.

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Splicing Fungal Genes Help Cells Change Shape

The opportunistic pathogen Candida albicans grows in two forms: yeast and filament. The latter state contributes to the severity of infections, and elevated temperature promotes this morphological switch. However, the mechanisms that drive this transition are poorly understood.

In a study published in mBio, a research team identified alternative splicing—the selective inclusion or excision of introns in a gene—as a contributor to filament formation in fever-like temperatures.1 Elucidating these pathways could offer novel strategies to target fungi during disease.

The team cultured a collection of mutants at 39 degrees Celsius and used microscopy to identify genes important to filamentation. They found that strains lacking genes relating to mRNA splicing failed to undergo this transition. Alternative splicing promotes adaptation to environmental changes; in fungi, the most common example of alternative splicing is intron retention.

To explore the relationship between splicing and filamentation, the researchers performed RNA sequencing on wild type C. albicans grown at 30 or 39°C. They noted that filamentous fungi induced by higher temperatures retained more introns. They also observed that intron retention decreased gene expression.

The researchers investigated the effect of a splicing mutant on intron retention and gene expression. They observed that while elevated temperatures increased intron retention in wild type cells, the mutant strain retained more introns in genes. However, unlike in wild type cells, splicing mutants with more retained introns lost their gene regulatory ability.

“Understanding why this is the case, understanding how these fluctuations in temperature are sensed and how those signals are transduced into sort of spliceosome function is certainly something that's interesting,” said Nicole Robbins, a mycologist and study author at the University of Toronto.

“[The study] really added to this growing body of evidence that we have a very complex system of different layers of regulation which enable Candida albicans to react in a plastic or adaptable way to its environment,” said Sascha Brunke, a fungal microbiologist at the Leibniz Institute for Natural Product Research and Infection Biology.

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Discovery of "tipped over" black hole surprises NASA scientists

NASA researchers combined years of data and new imaging techniques to learn more about a "tipped over" black hole that is moving in an unexpected way.

The black hole is located in a galaxy called NGC 5084. Researchers have been aware of the galaxy for years, NASA said in a news release.

New analysis techniques developed at NASA's Ames Research Center in California helped researchers see four long plumes of plasma emanating from the galaxy. Most galaxies don't have plumes, and when they do, only one or two are present. The plumes suggested the galaxy might house a supermassive black hole, NASA said. Spotting both pairs, which formed an "X" shape, led researchers to focus more on the area.

Using archived data from NASA's Hubble Space Telescope and the Atacama Large Millimeter/submillimeter Array Telescope based in Chile, researchers found that the galaxy also had a "small, dusty inner disk" rotating at the center of the galaxy, again suggesting a black hole there. Even more surprisingly, both the disk and black hole were rotating at a 90-degree angle relative to the rest of the galaxy, meaning both features are essentially "lying on their sides," NASA said.

It's not clear what caused the change in the galaxy. It may have collided with another galaxy and formed a chimney of superheated gas, creating the X-shaped plasma plumes. Further research will have to be conducted to learn more about the circumstances.

"Detecting two pairs of X-ray plumes in one galaxy is exceptional," said Pamela Marcum, an astrophysicist at Ames and co-author on the discovery, in the news release. "The combination of their unusual, cross-shaped structure and the 'tipped-over,' dusty disk gives us unique insights into this galaxy's history."

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India’s market research industry valued at $3.2bn

INDIA – India’s market research industry grew 12.6% to a total of $3.2bn in the 2024 financial year, according to figures from the Market Research Society of India (MRSI).

MRSI also projected that the Indian market would grow to $3.4bn in 2025, with customer and marketing analytics accounting for 58% of the total market.

Analytics grew 17% from the previous year, which MRSI put down to the adoption of AI, machine learning and big data in retail, finance and media.

Custom market research grew 9% from the previous year and accounted for 29% of the Indian market research sector, with sectors such as e-commerce and automotive increasingly focusing on personalised insights.

International clients remain the top revenue source for the Indian research and insights industry, accounting for an 80% share. On the supply side, analytics remains a major driver of international demand.

The research and insights sector employed around 145,000 full-time professionals in2024, with a growing demand for skills in AI, machine learning and data science.

Nitin Kamat, president at MRSI, said: “The Indian research and insights industry is experiencing robust growth, driven by the integration of big data and AI with traditional analytics, particularly as digitalisation accelerates in sectors like retail, e-commerce, finance and media.

“The growing trend of organisations investing in internal capabilities is reshaping the ecosystem, with smaller, faster and more localised research increasingly relying on client data, social media and behavioural panels. This shift toward flexible research solutions and strategic B2B insights highlights the industry’s adaptability to evolving client needs, reaffirming India’s position as a pivotal market for delivering high-quality insights.”

Mitali Chowhan, director general at MRSI, added: “The growth of India’s research and insights industry is a testament to its resilience and adaptability.

“As businesses increasingly rely on data-driven decision-making, India is uniquely positioned to deliver actionable insights with precision.”

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Need a research hypothesis? Ask AI

Crafting a unique and promising research hypothesis is a fundamental skill for any scientist. It can also be time consuming: New PhD candidates might spend the first year of their program trying to decide exactly what to explore in their experiments. What if artificial intelligence could help?


MIT researchers have created a way to autonomously generate and evaluate promising research hypotheses across fields, through human-AI collaboration. In a new paper, they describe how they used this framework to create evidence-driven hypotheses that align with unmet research needs in the field of biologically inspired materials.

Published Wednesday in Advanced Materials, the study was co-authored by Alireza Ghafarollahi, a postdoc in the Laboratory for Atomistic and Molecular Mechanics (LAMM), and Markus Buehler, the Jerry McAfee Professor in Engineering in MIT’s departments of Civil and Environmental Engineering and of Mechanical Engineering and director of LAMM.

The framework, which the researchers call SciAgents, consists of multiple AI agents, each with specific capabilities and access to data, that leverage “graph reasoning” methods, where AI models utilize a knowledge graph that organizes and defines relationships between diverse scientific concepts. The multi-agent approach mimics the way biological systems organize themselves as groups of elementary building blocks. Buehler notes that this “divide and conquer” principle is a prominent paradigm in biology at many levels, from materials to swarms of insects to civilizations — all examples where the total intelligence is much greater than the sum of individuals’ abilities.

“By using multiple AI agents, we’re trying to simulate the process by which communities of scientists make discoveries,” says Buehler. “At MIT, we do that by having a bunch of people with different backgrounds working together and bumping into each other at coffee shops or in MIT’s Infinite Corridor. But that's very coincidental and slow. Our quest is to simulate the process of discovery by exploring whether AI systems can be creative and make discoveries.”

Automating good ideas

As recent developments have demonstrated, large language models (LLMs) have shown an impressive ability to answer questions, summarize information, and execute simple tasks. But they are quite limited when it comes to generating new ideas from scratch. The MIT researchers wanted to design a system that enabled AI models to perform a more sophisticated, multistep process that goes beyond recalling information learned during training, to extrapolate and create new knowledge.

The foundation of their approach is an ontological knowledge graph, which organizes and makes connections between diverse scientific concepts. To make the graphs, the researchers feed a set of scientific papers into a generative AI model. In previous work, Buehler used a field of math known as category theory to help the AI model develop abstractions of scientific concepts as graphs, rooted in defining relationships between components, in a way that could be analyzed by other models through a process called graph reasoning. This focuses AI models on developing a more principled way to understand concepts; it also allows them to generalize better across domains.

“This is really important for us to create science-focused AI models, as scientific theories are typically rooted in generalizable principles rather than just knowledge recall,” Buehler says. “By focusing AI models on ‘thinking’ in such a manner, we can leapfrog beyond conventional methods and explore more creative uses of AI.”

For the most recent paper, the researchers used about 1,000 scientific studies on biological materials, but Buehler says the knowledge graphs could be generated using far more or fewer research papers from any field.

With the graph established, the researchers developed an AI system for scientific discovery, with multiple models specialized to play specific roles in the system. Most of the components were built off of OpenAI’s ChatGPT-4 series models and made use of a technique known as in-context learning, in which prompts provide contextual information about the model’s role in the system while allowing it to learn from data provided.

The individual agents in the framework interact with each other to collectively solve a complex problem that none of them would be able to do alone. The first task they are given is to generate the research hypothesis. The LLM interactions start after a subgraph has been defined from the knowledge graph, which can happen randomly or by manually entering a pair of keywords discussed in the papers.

In the framework, a language model the researchers named the “Ontologist” is tasked with defining scientific terms in the papers and examining the connections between them, fleshing out the knowledge graph. A model named “Scientist 1” then crafts a research proposal based on factors like its ability to uncover unexpected properties and novelty. The proposal includes a discussion of potential findings, the impact of the research, and a guess at the underlying mechanisms of action. A “Scientist 2” model expands on the idea, suggesting specific experimental and simulation approaches and making other improvements. Finally, a “Critic” model highlights its strengths and weaknesses and suggests further improvements.

“It’s about building a team of experts that are not all thinking the same way,” Buehler says. “They have to think differently and have different capabilities. The Critic agent is deliberately programmed to critique the others, so you don't have everybody agreeing and saying it’s a great idea. You have an agent saying, ‘There’s a weakness here, can you explain it better?’ That makes the output much different from single models.”

Other agents in the system are able to search existing literature, which provides the system with a way to not only assess feasibility but also create and assess the novelty of each idea.

Making the system stronger

To validate their approach, Buehler and Ghafarollahi built a knowledge graph based on the words “silk” and “energy intensive.” Using the framework, the “Scientist 1” model proposed integrating silk with dandelion-based pigments to create biomaterials with enhanced optical and mechanical properties. The model predicted the material would be significantly stronger than traditional silk materials and require less energy to process.

Scientist 2 then made suggestions, such as using specific molecular dynamic simulation tools to explore how the proposed materials would interact, adding that a good application for the material would be a bioinspired adhesive. The Critic model then highlighted several strengths of the proposed material and areas for improvement, such as its scalability, long-term stability, and the environmental impacts of solvent use. To address those concerns, the Critic suggested conducting pilot studies for process validation and performing rigorous analyses of material durability.

The researchers also conducted other experiments with randomly chosen keywords, which produced various original hypotheses about more efficient biomimetic microfluidic chips, enhancing the mechanical properties of collagen-based scaffolds, and the interaction between graphene and amyloid fibrils to create bioelectronic devices.

“The system was able to come up with these new, rigorous ideas based on the path from the knowledge graph,” Ghafarollahi says. “In terms of novelty and applicability, the materials seemed robust and novel. In future work, we’re going to generate thousands, or tens of thousands, of new research ideas, and then we can categorize them, try to understand better how these materials are generated and how they could be improved further.”

Going forward, the researchers hope to incorporate new tools for retrieving information and running simulations into their frameworks. They can also easily swap out the foundation models in their frameworks for more advanced models, allowing the system to adapt with the latest innovations in AI.

“Because of the way these agents interact, an improvement in one model, even if it’s slight, has a huge impact on the overall behaviors and output of the system,” Buehler says.

Since releasing a preprint with open-source details of their approach, the researchers have been contacted by hundreds of people interested in using the frameworks in diverse scientific fields and even areas like finance and cybersecurity.

“There’s a lot of stuff you can do without having to go to the lab,” Buehler says. “You want to basically go to the lab at the very end of the process. The lab is expensive and takes a long time, so you want a system that can drill very deep into the best ideas, formulating the best hypotheses and accurately predicting emergent behaviors. Our vision is to make this easy to use, so you can use an app to bring in other ideas or drag in datasets to really challenge the model to make new discoveries.”

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Scientists want to send endangered species’ cells to the moon

As more and more species near extinction, scientists have been collecting samples from animals, plants and other creatures and storing them in biorepositories across the globe (SN: 5/8/19). But climate change, environmental disasters and wars threaten these modern Noah’s arks (SN: 2/28/22). Now, a team of researchers is brainstorming an out-of-this-world solution: building one of these vaults on the moon.



A biorepository in a permanently shadowed region at the moon’s south pole could be far more stable than those on Earth. Those areas usually remain around –196° Celsius, the minimum temperature required to store most animal cells long-term, research scientist Mary Hagedorn and colleagues report July 31 in BioScience.

“It’s very good to have as many plans as possible, especially when it comes to saving our biodiversity and life on Earth,” says Hagedorn, of the Smithsonian National Zoo and Conservation Biology Institute in Washington D.C.
The need for a biobank on the moon

Hagedorn and colleagues were inspired by the Svalbard Global Seed Vault in Norway, which takes advantage of freezing Arctic temperatures to preserve millions of seeds from around the world.

But in 2017, melting permafrost flooded the vault and put its precious seeds at risk. That event and others like it underscore the need for a backup plan, researchers say.

A few years later, a different team proposed building a lunar ark in lava tubes that run beneath the moon’s surface (SN: 12/15/16), but that design requires a solar-powered cooling system; any loss of the power and the samples would be destroyed. In the moon’s forever-frozen shadowed regions, a lunar vault wouldn’t need energy or constant human maintenance, Hagedorn’s team says.

Given the shadowy south pole’s low temperatures, Hagedorn says, a vault there could store “one of the most powerful cells that we have today” — fibroblasts. Scientists can transform these animal cells into stem cells, “and then those stem cells can be used for cloning,” she says. The cells could be valuable for regenerating populations of threatened or extinct species and for building ecosystems in future human colonies on the moon or Mars (SN: 11/18/20).
What will it take to build one?

The new proposal has its share of hurdles, including what to do about radiation and the long-term effects of microgravity on the samples. Hagedorn and colleagues are designing radiation-proof sample storage containers. The next step would be to test out prototypes on a future moon mission.

“The authors do a good job laying out many of the challenges,” says lunar scientist Benjamin Greenhagen of the Johns Hopkins Applied Physics Laboratory in Laurel, Md. Another problem could be dust. “Dust is everywhere and gets in everything,” he says. “If their storage requires mechanisms or seals, they will want to consider dust mitigation from the very earliest stages.”

Some of the moon’s permanently dark regions also aren’t immune from temperature swings, as more or less reflected light shines into the shadows, Greenhagen says. “They are still cold but perhaps not always cold enough for this project without some level of thermal management.”

By far, the biggest challenge will be getting buy-in from the scientific community and other stakeholders, and to get nations to work together on the plan, Hagedorn says.


Furthermore, “there are communities on Earth to whom the moon is sacred,” Greenhagen says. “The authors should proactively engage these communities and look for an inclusive path forward to store biologic materials on the moon.”

Samples that should be deposited first in the lunar vault include those from endangered species, pollinators, ecological engineers and species that have the potential to help humans during space exploration, the team says. But because the project is still in early stages, “nothing’s set in stone at this point, other than we would probably go to the moon,” Hagedorn says. The team welcomes feedback on the proposal.

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How To Conduct Research On Blockchain?

Blockchain technology has revolutionized various industries worldwide, leading to innovations in information handling, security, and decentralization. Whether you are a developer, investor, or enthusiast, it is important to know how to conduct research on blockchain to make informed decisions.

Bitcoin was launched in 2008, and since then, it has changed and developed significantly, including its blockchain development and the price spike of more than $100,000. Like any other digital currency, it was first conceived as decentralized. Still, over the years, it has evolved into a platform that can reshape many sectors.

When smart contracts were introduced in 2015, this brought in new opportunities, like increasing supply chain efficiency or the security of healthcare records. While this is potential, Gartner’s prediction that 30% of large companies will use blockchain for supply chain tracking by 2023 hasn’t come to fruition yet.

Last year, the cryptocurrency market experienced a serious slump, leading to significant financial losses and exposing a volatile industry. Governments are now regulating these emerging assets.

Although these advancements have been made, regulatory and technical obstacles remain. One frontier of integrating artificial intelligence with blockchain looks promising, with markets expecting a $16.4 billion market size by 2026. Yet, integrating such complex technologies is still a challenge.

The environmental impact of blockchain and mining is also an important subject of attention. If blockchain platforms adopt sustainable consensus mechanisms, the World Economic Forum predicts that by 2025, up to 10% of global GDP can be stored on them.

This guide will walk you through the essential steps to navigate the blockchain ecosystem effectively.
Understanding Blockchain Fundamentals

Before diving into blockchain, it’s essential to understand the basic principles to grasp the mechanisms behind this technology effectively. If you’re wondering how to conduct research on blockchain, start by exploring its core concept: a decentralized ledger that securely and transparently records transactions across multiple computers.
Key Terms to Know:Ledger: A digitized list of all the transactions.
Nodes: Blockchain nodes are the computers that keep the blockchain network running.
Smart Contracts: Smart contracts have conditions set ahead and then carry out themselves.
Cryptographic Hash: This ensures that the blockchain data is correct.
Blocks: Units that store transaction info are called blocks.
Consensus Mechanisms: Consensus mechanisms are protocols that ensure all nodes agree on the blockchain’s state.
Define Your Research Goals

First, find out what the goal of your research is. Here are the different reasons why you are investigating blockchain:Cryptocurrencies: Study of digital currencies like Bitcoin and Ethereum.
Smart Contracts: Self-executing contracts with the terms directly written into code.
Consensus Algorithms: Mechanisms like Proof of Work (PoW) and Proof of Stake (PoS) that validate transactions.
Blockchain Interoperability: Techniques to enable different blockchains to communicate.
Decentralized Finance (DeFi): Financial services using blockchain technology.
Blockchain in Supply Chain: Tracking goods and verifying authenticity.

A clear objective streamlines your research process and helps you accomplish a specific task. To research blockchain, we need to understand how to conduct research on blockchain so we can quickly find the right resources and insights.
Reviewing Literature

A literature review is also needed to understand the current state of research and where there are research gaps. You can browse relevant papers using academic databases such as Google Scholar, IEEE Xplore, and SpringerLink. Keyword Search: Select keywords linked clearly to your research area.
Reading Abstracts: It gives a quick idea of what the papers are related to.
Identifying Trends: Record recurring themes and major findings.
Leverage Reliable Sources

Misinformation is a common thing in the world of blockchain. Use trustworthy sources to ensure accuracy:Official Whitepapers: These documents explain a blockchain project’s visions and technical details. Bitcoin’s whitepaper by Satoshi Nakamoto is an essential read, for instance.
Blockchain News Platforms: There are websites like Blockchain News that give a proper update about blockchain industry news.
Research Reports: Data-driven insights come from Messari, Glassnode, and Chainalysis.
GitHub Repositories: GitHub is a gold mine for developers, digging into project codebases and developments and seeing who is doing what.
Evaluate Market Trends

Knowing the development trends in the market can tell you whether a blockchain project is relevant and viable. Study the following:Market Capitalization: This means a higher market cap and generally speaks of a project’s credibility.
Adoption Rate: Projects will likely succeed where there are widespread use cases and partnerships.
Community Support: A successful project almost always means a strong and active community. Platforms like Discord, Telegram, and Twitter reveal the community sentiment.
Dive Into Use Cases

It is not only about the cryptocurrency. Its applications span multiple industries:

Finance: Uniswap and Aave are good examples of the Decentralized Finance (DeFi) platforms.

Supply Chain: The benefit of transparency and tracking through the VeChain platform.

Healthcare: Secure patient data sharing.

Gaming includes play-to-earn models and NFTs in games such as Axie Infinity.

Find out the projects in the industry in which you’re interested to see how blockchain could be innovatively done. This provides a deeper understanding of how to conduct research on blockchain on specific use cases.
Assess Team and Roadmap

A project’s team and roadmap are indicators of its potential success:Team Expertise: Find out the background of those making it up. If they are experienced in blockchain development, how?
Roadmap Clarity: Does the project have a realistic, transparent development and implementation plan?
Partnerships: A project can be validated by collaboration with a recognized company.
Test the Ecosystem

Theoretical research, though, cannot compete with hands-on experience. Here’s how you can interact with blockchain ecosystems:Create a Wallet: Storing digital assets requires wallets such as MetaMask or TrustW
Interact with dApps: Try to understand the actual functionality of decentralized applications.
Run a Node: Participate in a blockchain network through a node so you can technically learn something deeper.
Understand Legal and Regulatory Aspects

Blockchain is moving through a fast-evolving regulatory landscape. Research the following:Regulations in Your Region: Countries have different laws related to blockchain and cryptocurrencies.
Compliance: Ensure the project follows the local regulations, such as KYC/AML requirements.
Taxation: Know how you will be taxed on your crypto earnings in your country.
Stay Updated

Blockchain technology is a very fast-changing technology. To keep your research current, you need to keep up. Follow these strategies:Subscribe to Newsletters: Since there is no central authoritative source of this information, they provide regular updates on platforms like Messari and The Defiant.
Join Online Communities: Get involved in Reddit, Telegram, or LinkedIn communities.
Attend Webinars and Conferences: These are where we get events like Consensus and Blockchain Expo, and we get great insights from people who have been allowed to speak at that stage.
Common Mistakes to Avoid

When conducting blockchain research, steer clear of these pitfalls:Ignoring Red Flags: An unclear white paper or anonymous team can sometimes be risky.
FOMO (Fear of Missing Out): Do not make sudden decisions solely on hype.
Overlooking Security: Losing the importance of security audits can be disastrous.

According to research from Statista, there are now 10,309 cryptocurrencies as of Dec. 2024.

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ICMR sets up India's first diabetes biobank in Chennai for research

The country's first diabetes biobank, a repository of population-based biological samples aimed at supporting scientific research, has been established in Chennai by the Indian Council of Medical Research (ICMR) in collaboration with the Madras Diabetes Research Foundation (MDRF).

The biobank set up at the MDRF, Chennai aims to gather, process, store and distribute biospecimens to assist scientific studies with the permission of the ICMR.

The biobank will facilitate advanced research on the causes of diabetes, the variations of the Indian type of diabetes and related disorders, said Dr V Mohan, chairman of the MDRF and Dr Mohan's Diabetes Specialities Centre.

The biobank has blood samples from two ICMR-funded studies -- the ICMR-?India Diabetes (ICMR-?INDIAB) study conducted in all states and Union territories in phases from 2008 to 2020 and the "Registry of people with diabetes in India at a young age at the onset", which was launched in 2006 and is still ongoing.

A plethora of blood samples of different types of diabetes in the young, such as Type 1, Type 2 and gestational diabetes, have been stored for future studies and research, Mohan said.

The process of setting up the biobank started around two years ago.

The details of the biobank and the purpose behind setting it up have been explained in detail in an article published in the "Indian Journal of Medical Research" last week.


Biobanks are critical in biomedical research, collecting, processing, storing and distributing biospecimens to support scientific research, the article said.

"This diabetes biobank could help in the identification of novel biomarkers for early diagnosis and the development of personalised treatment strategies," it added.

Additionally, it would support longitudinal studies to track the progression of diabetes and its complications over time, leading to better management and prevention strategies.

"By fostering collaborative research efforts, a diabetes biobank in India could significantly advance our understanding of the disease and contribute to the global fight against the diabetes epidemic," the article said.

Involving private agencies in developing biobanks and research is essential for translating research into practice. Adopting new technologies for specimen storage, preservation, data management and sharing is crucial for creating a cost-effective, long-lasting disease-specific biobank in India.


Of the various biobanks available globally, the most well-known is the UK Biobank, supported by the National Health Service (NHS), which is a vast biomedical database with genetic, lifestyle and health information from five lakh participants in the United Kingdom.

The collected information comprises phenotypic, genomic and imaging data derived from direct assessments, verbal interviews, online questionnaires and electronic health records.

This dataset continues to expand as new biomedical data are added through ongoing assessments and longitudinal follow up.

The ICMR-INDIAB study is one of the large epidemiological studies on diabetes with a sample size of 1.2 lakh nationally-representative individuals, covering every state of India.

This cross-sectional, community-based study was done in adults of either sex aged 20 years in phases from 2008 to 2020, and sampled 33,537 urban and 79,506 rural residents in 31 states and Union territories.

The study reported that the prevalence of diabetes and metabolic NCDs in India is greater than the earlier estimates, with 10.1 crore individuals diagnosed with diabetes and 13.6 crore with prediabetes.

Hypertension, generalised and abdominal obesity affected 31.5 crore, 25.4 crore and 35.1 crore people respectively. Additionally, 21.3 crore people had hypercholesterolemia and 18.5 crore had high LDL cholesterol. The study also indicated that the diabetes epidemic is stabilising in more socio-economically advanced states but rising in less-developed states.

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Photonic processor could enable ultrafast AI computations with extreme energy efficiency

The deep neural network models that power today’s most demanding machine-learning applications have grown so large and complex that they are pushing the limits of traditional electronic computing hardware.

Photonic hardware, which can perform machine-learning computations with light, offers a faster and more energy-efficient alternative. However, there are some types of neural network computations that a photonic device can’t perform, requiring the use of off-chip electronics or other techniques that hamper speed and efficiency.

Building on a decade of research, scientists from MIT and elsewhere have developed a new photonic chip that overcomes these roadblocks. They demonstrated a fully integrated photonic processor that can perform all the key computations of a deep neural network optically on the chip.

The optical device was able to complete the key computations for a machine-learning classification task in less than half a nanosecond while achieving more than 92 percent accuracy — performance that is on par with traditional hardware.

The chip, composed of interconnected modules that form an optical neural network, is fabricated using commercial foundry processes, which could enable the scaling of the technology and its integration into electronics.

In the long run, the photonic processor could lead to faster and more energy-efficient deep learning for computationally demanding applications like lidar, scientific research in astronomy and particle physics, or high-speed telecommunications.

“There are a lot of cases where how well the model performs isn’t the only thing that matters, but also how fast you can get an answer. Now that we have an end-to-end system that can run a neural network in optics, at a nanosecond time scale, we can start thinking at a higher level about applications and algorithms,” says Saumil Bandyopadhyay ’17, MEng ’18, PhD ’23, a visiting scientist in the Quantum Photonics and AI Group within the Research Laboratory of Electronics (RLE) and a postdoc at NTT Research, Inc., who is the lead author of a paper on the new chip.

Bandyopadhyay is joined on the paper by Alexander Sludds ’18, MEng ’19, PhD ’23; Nicholas Harris PhD ’17; Darius Bunandar PhD ’19; Stefan Krastanov, a former RLE research scientist who is now an assistant professor at the University of Massachusetts at Amherst; Ryan Hamerly, a visiting scientist at RLE and senior scientist at NTT Research; Matthew Streshinsky, a former silicon photonics lead at Nokia who is now co-founder and CEO of Enosemi; Michael Hochberg, president of Periplous, LLC; and Dirk Englund, a professor in the Department of Electrical Engineering and Computer Science, principal investigator of the Quantum Photonics and Artificial Intelligence Group and of RLE, and senior author of the paper. The research appears today in Nature Photonics.

Machine learning with light

Deep neural networks are composed of many interconnected layers of nodes, or neurons, that operate on input data to produce an output. One key operation in a deep neural network involves the use of linear algebra to perform matrix multiplication, which transforms data as it is passed from layer to layer.

But in addition to these linear operations, deep neural networks perform nonlinear operations that help the model learn more intricate patterns. Nonlinear operations, like activation functions, give deep neural networks the power to solve complex problems.

In 2017, Englund’s group, along with researchers in the lab of Marin Soljačić, the Cecil and Ida Green Professor of Physics, demonstrated an optical neural network on a single photonic chip that could perform matrix multiplication with light.

But at the time, the device couldn’t perform nonlinear operations on the chip. Optical data had to be converted into electrical signals and sent to a digital processor to perform nonlinear operations.

“Nonlinearity in optics is quite challenging because photons don’t interact with each other very easily. That makes it very power consuming to trigger optical nonlinearities, so it becomes challenging to build a system that can do it in a scalable way,” Bandyopadhyay explains.

They overcame that challenge by designing devices called nonlinear optical function units (NOFUs), which combine electronics and optics to implement nonlinear operations on the chip.

The researchers built an optical deep neural network on a photonic chip using three layers of devices that perform linear and nonlinear operations.

A fully-integrated network

At the outset, their system encodes the parameters of a deep neural network into light. Then, an array of programmable beamsplitters, which was demonstrated in the 2017 paper, performs matrix multiplication on those inputs.

The data then pass to programmable NOFUs, which implement nonlinear functions by siphoning off a small amount of light to photodiodes that convert optical signals to electric current. This process, which eliminates the need for an external amplifier, consumes very little energy.

“We stay in the optical domain the whole time, until the end when we want to read out the answer. This enables us to achieve ultra-low latency,” Bandyopadhyay says.

Achieving such low latency enabled them to efficiently train a deep neural network on the chip, a process known as in situ training that typically consumes a huge amount of energy in digital hardware.

“This is especially useful for systems where you are doing in-domain processing of optical signals, like navigation or telecommunications, but also in systems that you want to learn in real time,” he says.

The photonic system achieved more than 96 percent accuracy during training tests and more than 92 percent accuracy during inference, which is comparable to traditional hardware. In addition, the chip performs key computations in less than half a nanosecond.

“This work demonstrates that computing — at its essence, the mapping of inputs to outputs — can be compiled onto new architectures of linear and nonlinear physics that enable a fundamentally different scaling law of computation versus effort needed,” says Englund.

The entire circuit was fabricated using the same infrastructure and foundry processes that produce CMOS computer chips. This could enable the chip to be manufactured at scale, using tried-and-true techniques that introduce very little error into the fabrication process.

Scaling up their device and integrating it with real-world electronics like cameras or telecommunications systems will be a major focus of future work, Bandyopadhyay says. In addition, the researchers want to explore algorithms that can leverage the advantages of optics to train systems faster and with better energy efficiency.

This research was funded, in part, by the U.S. National Science Foundation, the U.S. Air Force Office of Scientific Research, and NTT Research.

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