This was what Dr. Dirk Wulff and Professor Rui Mata, researchers in the Faculty of Psychology at the University of Basel, set out to discover. “There is a lot of academic interest in the phenomenon of risk,” explains Dirk Wulff. “But disciplines such as psychology, sociology and economics define it in different ways.”

According to Wulff, little attention has been paid until now to the fact that the meaning of risk can differ from individual to individual depending on goals and life experience. He feels it is important to understand how different people think about risk in order, for example, to gauge attitudes to new technologies or societal challenges.

Risk links polar ends of the sentiment spectrum

To investigate this, the researchers have developed a new method based on word associations and an algorithmic process that maps the representation of risk for different groups and individuals. The researchers adopted a new approach, employing a snowball word association method. Participants were asked to name five things they associated with the term risk and then, in turn, five things they associated with these associations. Using this method, researchers surveyed a nationally representative sample of 1205 people, with equal representation of men and women and different age ranges.

An algorithm was used to generate a semantic network of risk from the 36,100 associations. It identified the following components: threat, fortune, investment, activity and analysis. The semantic cluster “threat” (danger, accident, loss, etc.) was the component most prominently associated with risk, closely followed by “fortune” (profit, game, adventure). “Up until now, studies have mostly focused on the negative components of risk and ignored the fact that it can also have positive associations,” Wulff comments.

The method is designed to map both individual and group-specific differences in risk perception. The psychologists investigated the differences between men and women and between different age groups. Overall, women and men and people of different ages appeared to share similar thoughts about risk. Nevertheless, there were some differences: a higher proportion of older people than younger people and a higher proportion of women than men associated risk more closely with threat and less with fortune.

Small differences between languages

The researchers also posed the question: Do people from different language regions think about risk in a similar way? To investigate this, they compared the semantic network of risk that emerged from the German survey group with those that resulted in two other languages — Dutch and English. There were some small differences in the frequency of associations. For example, in Dutch the term risk tended to be more closely associated with threat and in English more with fortune and finance. Overall, however, the results indicate that there are some universal correlations in risk representation that transcend language boundaries.

“Our study lays new foundations for examining the question of how people think about risk,” says Wulff. “It could play an important role in helping to provide a better understanding of how different social groups interpret risk, enabling risk communication strategies to be improved to combat social polarization.”

Story Source:

Materials provided by University of Basel. Note: Content may be edited for style and length.

Researchers have developed a two-step approach using whole exome sequencing to zero in on genes and pathways that predict whether cancer patients will respond to immunotherapy. The study, published in Nature Communications and conducted by researchers at New York University, Weill Cornell Medicine, and the New York Genome Center, illustrates how the use of whole exome sequencing can better predict treatment response than current laboratory tests.

“Can we better predict who will benefit from immunotherapy? Scientists have developed various biomarkers that help anticipate immunotherapy treatment response, but there’s still an unmet need for a robust, clinically practical predictive model,” said Neville Sanjana, assistant professor of biology at NYU, assistant professor of neuroscience and physiology at NYU Grossman School of Medicine, a core faculty member at New York Genome Center, and the study’s co-senior author.

Several biomarkers — including age, tumor type, and the number of mutations found in cancer cells, known as tumor mutational burden — are already known to correlate with responses to immunotherapy. Tumor mutational burden, which is calculated by analyzing a few hundred genes, is the most well-established predictor and is often used to determine a patient’s eligibility for immune checkpoint inhibitors.

If scientists look at a much larger portion of our genes, could that help to better predict which patients will respond to immunotherapy? Whole exome sequencing is a method for sequencing the part of the genome that codes for proteins — around 20,000 genes, or two percent of the genome — to look for mutations that may be involved in disease.

While whole exome sequencing is not widely used in cancer treatment, some recent studies of immunotherapies have started to include sequencing. These studies are small, but together can help illuminate the relationship between genomic factors and how patients respond to immunotherapy.

The researchers combined data from six previous immunotherapy studies of patients with melanoma, lung cancer, bladder cancer, and head and neck cancer. Whole exome sequencing was available for all participants, who were treated with an immune checkpoint inhibitor (either anti-PD-1 or anti-CTLA-4).

But even after combining the six studies, the number of patients — 319 in total — was still relatively small.

“The problem with a small study of only a few hundred people is a mismatch between the number of patients and the vast number of genes sequenced in whole exome sequencing. We’d ideally have a dataset with more patients than genes,” said Zoran Gajic, a graduate student in the Sanjana Lab, and the study’s first author.

To get around this problem, the researchers turned to a model called fishHook which distinguishes mutations that drive cancer from background mutations, or mutations that occur by chance but are not involved in cancer. The model corrects for a range of factors that affect the rates of background mutations — for instance, adjusting for the size of a gene, since larger genes are more likely to have mutations.

Using this model, the researchers employed a two-step approach: first, they looked at the sequencing from all patients to find any genes with a higher mutational burden than they would expect, adjusting for genomic factors like gene size or whether a particular piece of DNA is a known hotspot that tends to accumulate more mutations. This yielded six genes with suspiciously high mutational burdens.

Next, the researchers determined if any of these six genes were enriched in people who responded or did not respond to immunotherapy. Two of the genes — KRAS, a gene often mutated in lung cancer, and BRAF, the most commonly mutated gene in melanoma — were enriched in patients who responded to immunotherapy. In contrast, two other genes — TP53 and BCLAF1 — were enriched in those who did not respond to immunotherapy. BCLAF1 is not well studied, but these findings suggest that patients with BCLAF1 mutations are less likely to respond to immune checkpoint inhibitors.

Using the same two-step approach on collections of genes called pathways, the researchers determined that certain pathways (MAPK signaling, p53 associated, and immunomodulatory) also predicted immune checkpoint inhibitor response.

They then combined the four genes and three pathways with other predictive variables such as age, tumor type, and tumor mutational burden to create a tool they named the Cancer Immunotherapy Response CLassifiEr (CIRCLE). CIRCLE was able to better predict immunotherapy response by approximately 11% than tumor mutational burden alone. CIRCLE was also able to accurately predict cancer survival after immunotherapy.

“These results suggest that the use of broader diagnostics such as whole exome or even whole genome sequencing may significantly improve our ability to predict who will respond to immunotherapy — essentially, showing that more data does help to better predict treatment response,” said Marcin Imieliński, associate professor of computational genomics and associate professor of pathology and laboratory medicine at Weill Cornell Medicine, a core faculty member at the New York Genome Center, and the study’s co-senior author.

To validate their approach, the researchers tested CIRCLE on data from 165 additional cancer patients with whole exome sequencing who underwent treatment with immunotherapy and found that CIRCLE captured predictive information beyond that obtained from tumor mutational burden alone.

Future research will involve testing CIRCLE on larger cohorts of patient data, as the researchers anticipate that the model will improve with data from thousands of patients rather than hundreds. They also hope that with larger cohorts, they can begin to tease out which patients are likely to respond to different immunotherapies, given the growing number of treatments available.

“We envision that this two-step approach and use of whole exome sequencing will pave the way for better prognostic tools for cancer immunotherapy,” said Sanjana.

Additional authors include Aditya Deshpande of NYGC and Weill Cornell Medicine and Mateusz Legut of NYGC and NYU. The research was funded by the National Institutes of Health (U24-CA15020, DP2HG010099, R01CA218668, and GM136573), Sidney Kimmel Foundation, Brain and Behavior Foundation, Burroughs Wellcome Fund, Doris Duke Clinical Foundation, Starr Cancer Consortium, Melanoma Research Alliance, Hope Funds for Cancer Research, and startup funds from NYU, Weill Cornell Medicine, and the New York Genome Center.

Though engineering of biological systems is truly indispensable in biotechnology and synthetic biology, today machine learning has become useful in all fields of biology. However, it is obvious that application and improvement of algorithms, computational procedures made of lists of instructions, is not easily accessible. Not only are they limited by programming skills but often also insufficient experimentally-labeled data. At the intersection of computational and experimental works, there is a need for efficient approaches to bridge the gap between machine learning algorithms and their applications for biological systems.

Now a team at the Max Planck Institute for Terrestrial Microbiology led by Tobias Erb has succeeded in democratizing machine learning. In their recent publication in “Nature Communications,” the team presented together with collaboration partners from the INRAe Institute in Paris, their tool METIS. The application is built in such a versatile and modular architecture that it does not require computational skills and can be applied on different biological systems and with different lab equipment. METIS is short from Machine-learning guided Experimental Trials for Improvement of Systems and also named after the ancient goddess of wisdom and crafts Μῆτις, lit. “wise counsel.”

Less data required

Active learning, also known as optimal experimental design, uses machine learning algorithms to interactively suggest the next set of experiments after being trained on previous results, a valuable approach for wet-lab scientists, especially when working with a limited number of experimentally-labeled data. But one of the main bottlenecks is the experimentally-labeled data generated in the lab that are not always high enough to train machine learning models. “While active learning already reduces the need for experimental data, we went further and examined various machine learning algorithms. Encouragingly, we found a model that is even less dependent on data,” says Amir Pandi, one of the lead authors of the study.

To show the versatility of METIS, the team used it for a variety of applications, including optimization of protein production, genetic constructs, combinatorial engineering of the enzyme activity, and a complex CO₂ fixation metabolic cycle named CETCH. For the CETCH cycle, they explored a combinatorial space of 1025 conditions with only 1,000 experimental conditions and reported the most efficient CO₂ fixation cascade described to date.

Optimizing biological systems

In application, the study provides novel tools to democratize and advance current efforts in biotechnology, synthetic biology, genetic circuit design, and metabolic engineering. “METIS allows researchers to either optimize their already discovered or synthesized biological systems,” says Christoph Diehl, Co-lead author of the study. “But it is also a combinatorial guide for understanding complex interactions and hypothesis-driven optimization. And what is probably the most exciting benefit: it can be a very helpful system for prototyping new-to-nature systems.”

METIS is a modular tool running as Google Colab Python notebooks and can be used via a personal copy of the notebook on a web browser, without installation, registration, or the need for local computational power. The materials provided in this work can guide users to customize METIS for their applications.

Story Source:

Materials provided by Max-Planck-Gesellschaft. Note: Content may be edited for style and length.

According to co-author Dr Bjorn Sturmberg, the results show the use of electric vehicles in remote communities is more feasible than might be expected.

“We analysed the distances between people’s homes and the nearest “service hub” towns — where they might go to the do the shopping, for example,” Dr Sturmberg, said.

“The vast majority of residents, or 93 per cent, could do those trips with even the lower-range of electric vehicles currently available on the Australian market. That’s without needing to recharge en route.”

Dr Sturmberg said given this, there’s no excuse for leaving our remote communities out of the discussion.

“We need to do better — electric vehicles shouldn’t be left in the too hard basket. It’s an unequitable and unfair path forward if remote and regional communities are the last ones left driving diesel vehicles, especially as they will be some of the most impacted by catastrophic climate change,” Dr Sturmberg said.

“Yes the barriers are obvious — large distances, unsealed roads. But the benefits are equally obvious. It’s difficult and expensive to get diesel out to these communities, and electric engines are simpler and more robust than fuel engines.”

Co-author Dr Francis Markham added there are some limits to what we know, and aspects that need further investigation.

“For example, we still don’t have clear data on the impact of unsealed roads or different conditions on the effective range of electric vehicles,” Dr Markham said.

“And information on the performance of electric vehicles in very hot conditions is still lacking. However, we are confident that electric vehicles do have a place in regional and remote Australia.”

According to the researchers, transport is one of the key issues we need to tackle to limit the worst effects of climate change.

“The transport sector is responsible for 25 per cent of global emissions and more than 18 per cent of Australia’s greenhouse gas pollution,” Dr Sturmberg said.

“It must rapidly decarbonise — and electric vehicles are going to be a crucial part of that decarbonisation.”

Story Source:

Materials provided by Australian National University. Note: Content may be edited for style and length.

Water is certainly the best-known liquid in the world. Water plays a crucial role in all biological and many chemical processes. The water molecules themselves hardly hold any secrets. In school already we learn that water consists of one oxygen atom and two hydrogen atoms. We even know the typical obtuse angle that the two O-H legs form with each other. In addition, we know when water boils or freezes and how these phase transitions are related to pressure. But between facts on individual molecules and a deeper understanding of the macroscopic phenomena, there is a wide area of uncertainty: Only statistical information is known about the behaviour of the individual molecules in normal liquid water: the water molecules in the liquid phase form a fluctuating network of hydrogen bonds, disordered and dense, and their interactions are not at all as well understood as in the gaseous state.

Pure liquid water examined

Now, a team led by HZB physicist Dr. Annette Pietzsch has taken a closer look at pure liquid water at room temperature and normal pressure. Using X-ray analysis at the Swiss Light Source of the Paul Scherrer Institute and statistical modelling, the scientists have succeeded in mapping the so-called potential energy surfaces of the individual water molecules in the ground state, which come in a large variety of shapes depending on their environment.

Oscillations and vibrations measured

“The special thing here is the method: we studied the water molecules on the ADRESS beamline using resonant inelastic X-ray scattering. Simply put, we nudged individual molecules very carefully and then measured how they fell back into the ground state,” says Pietzsch. The low-energy excitations led to stretching oscillations and other vibrations, which — combined with model calculations — produced a detailed picture of the potential surfaces.

“This gives us a method to experimentally determine the energy of a molecule as a function of its structure,” explains Pietzsch. “The results help to enlighten the chemistry in water, for example to understand better how water behaves as a solvent.”

Outlook: METRIXS at BESSY II

The next experiments are already planned at the BESSY II X-ray source at HZB. There, Annette Pietzsch and her team have set up the METRIXS measuring station, which is designed precisely for investigating liquid samples with RIXS experiments. “After the summer shutdown due to maintenance work on BESSY II, we will start with the first tests of our instruments. And then we can move on.”

Story Source:

Materials provided by Helmholtz-Zentrum Berlin für Materialien und Energie. Note: Content may be edited for style and length.

A citizen scientist has searched NSF’s NOIRLab’s catalog of 4 billion celestial objects, known as NOIRLab Source Catalog DR2, to reveal brown dwarfs with companions. His intensive investigation led to the discovery of 34 ultracool dwarf binary systems, nearly doubling previously known samples [1].

Brown dwarfs lie somewhere between the most massive planets and the smallest stars. Lacking the mass needed to sustain nuclear reactions in their core, brown dwarfs loosely resemble cooling embers on a huge scale. Their faintness and relatively small sizes make them difficult to identify. Data from sensitive telescopes have enabled the discovery of several thousand objects but just a small subset have been identified as binaries. The difficulty in observing these faint embers also means that astronomers are still unsure how often brown dwarfs have companions.

To help find brown dwarfs, the astronomers of the Backyard Worlds: Planet 9 citizen science project have previously turned to a worldwide network of more than 100,000 volunteer citizen scientists who scrutinized telescope images to identify the subtle motion of brown dwarfs against background stars. Despite the abilities of machine learning and supercomputers, the human eye is still a unique resource when it comes to scouring telescope images for moving objects.

“The Backyard Worlds project has fostered a diverse community of talented volunteers,” commented Aaron Meisner, an astronomer at NSF’s NOIRLab and co-founder of Backyard Worlds. “150,000 volunteers across the globe have participated in Backyard Worlds, among which a few hundred ‘super users’ perform ambitious self-directed research projects.”

One such ‘super sleuth — citizen scientist Frank Kiwy — embarked on a research project involving the NOIRLab Source Catalog DR2, a catalog of nearly 4 billion unique celestial objects that contains all of the public imaging data in NOIRLab’s Astro Data Archive. By searching the data for objects with the color of brown dwarfs, Kiwy was able to find more than 2500 potential ultracool dwarfs lurking in the archive. These were then scrutinized for hints of comoving companions, yielding a total of 34 systems comprising a white dwarf or low-mass star with an ultracool dwarf companion [2]. Kiwy then led a team of professional astrophysicists in publishing these discoveries in a scientific paper.

“I love the Backyard Worlds: Planet 9 project! Once you master the regular workflow you can dive much deeper into the subject,” commented Kiwy. “If you’re a person who is curious and not afraid to learn something new, this might be the right thing for you.”

“This amazing result clearly demonstrates that NOIRLab’s data archive has a reach far beyond that of professional astronomers,” notes Chris Davis, NSF’s Program Director for NOIRLab. “Keen members of the public can also participate in cutting-edge research and directly share in the joy of cosmic discovery!”

As well as being an inspiring story of citizen science, these discoveries could help astronomers determine if brown dwarfs are more akin to oversized planets or undersized stars, as well as providing insights into how star systems evolve over time. It also demonstrates the continued exceptional contribution to astronomy made by scientists using astronomical archives and science platforms such as NOIRLab’s Astro Data Archive and Astro Data Lab at the Community Science and Data Center (CSDC).

“These discoveries were made by an amateur astronomer who conquered astronomical big data,” concluded Aaron Meisner. “Modern astronomy archives contain an immense treasure trove of data and often harbor major discoveries just waiting to be noticed.”

Notes

[1] Previous samples include white dwarf plus ultracool dwarf (L dwarf) pairs separated by more than 150 astronomical units (au), and red dwarf plus L dwarf pairs with separations between 700 and 1800 au. An astronomical unit (au) is a unit used by astronomers that was originally chosen to represent the average distance between the Earth and the Sun: roughly 150 million kilometers or 93 million miles.

[2] The closest-together pair of dwarfs had a physical separation of only ~170 au, and the furthest apart were about 8500 au from one another.

“We need more effective methods of managing acute and chronic pain, and that starts with gaining a better understanding of the basic neural processes that regulate pain,” said NIDCR Director Rena D’Souza, D.D.S., Ph.D. “By uncovering the circuitry that mediates the pain-reducing effects of sound in mice, this study adds critical knowledge that could ultimately inform new approaches for pain therapy.”

Dating back to 1960, studies in humans have shown that music and other kinds of sound can help alleviate acute and chronic pain, including pain from dental and medical surgery, labor and delivery, and cancer. However, how the brain produces this pain reduction, or analgesia, was less clear.

“Human brain imaging studies have implicated certain areas of the brain in music-induced analgesia, but these are only associations,” said co-senior author Yuanyuan (Kevin) Liu, Ph.D., a Stadtman tenure-track investigator at NIDCR. “In animals, we can more fully explore and manipulate the circuitry to identify the neural substrates involved.”

The researchers first exposed mice with inflamed paws to three types of sound: a pleasant piece of classical music, an unpleasant rearrangement of the same piece, and white noise. Surprisingly, all three types of sound, when played at a low intensity relative to background noise (about the level of a whisper) reduced pain sensitivity in the mice. Higher intensities of the same sounds had no effect on animals’ pain responses.

“We were really surprised that the intensity of sound, and not the category or perceived pleasantness of sound would matter,” Liu said.

To explore the brain circuitry underlying this effect, the researchers used non-infectious viruses coupled with fluorescent proteins to trace connections between brain regions. They identified a route from the auditory cortex, which receives and processes information about sound, to the thalamus, which acts as a relay station for sensory signals, including pain, from the body. In freely moving mice, low-intensity white noise reduced the activity of neurons at the receiving end of the pathway in the thalamus.

In the absence of sound, suppressing the pathway with light- and small molecule-based techniques mimicked the pain-blunting effects of low-intensity noise, while turning on the pathway restored animals’ sensitivity to pain.

Liu said it is unclear if similar brain processes are involved in humans, or whether other aspects of sound, such as its perceived harmony or pleasantness, are important for human pain relief.

“We don’t know if human music means anything to rodents, but it has many different meanings to humans — you have a lot of emotional components,” he said.

The results could give scientists a starting point for studies to determine whether the animal findings apply to humans, and ultimately could inform development of safer alternatives to opioids for treating pain.

This research was supported by the NIDCR Division of Intramural Research. Support also came from the National Key Research and Development Program of China Brain Science and Brain-Like Intelligence Technology, National Natural Science Foundation of China, Science Fund for Creative Research Groups of the National Natural Science Foundation of China, CAS Project for Young Scientists in Basic Research, Natural Science Foundation of Anhui Province, and the University of Science and Technology of China Research Funds of the Double First-Class Initiative.