Culture

Safe and effective vaccines offer hope for an end to the COVID-19 pandemic. However, the possible emergence of vaccine-resistant SARS-CoV-2 variants, as well as novel coronaviruses, make finding treatments that work against all coronaviruses as important as ever. Now, researchers reporting in ACS' Journal of Proteome Research have analyzed viral proteins across 27 coronavirus species and thousands of samples from COVID-19 patients, identifying highly conserved sequences that could make the best drug targets.

Drugs often bind inside "pockets" on proteins that hold the drug snugly, causing it to interfere with the protein's function. Scientists can identify potential drug-binding pockets from the 3D structures of viral proteins. Over time, however, viruses can mutate their protein pockets so that drugs no longer fit. But some drug-binding pockets are so essential to the protein's function that they can't be mutated, and these sequences are generally conserved over time in the same and related viruses. Matthieu Schapira and colleagues wanted to find the most highly conserved drug-binding pockets in viral proteins from COVID-19 patient samples and from other coronaviruses, revealing the most promising targets for pan-coronavirus drugs.

The team used a computer algorithm to identify drug-binding pockets in the 3D structures of 15 SARS-CoV-2 proteins. The researchers then found corresponding proteins in 27 coronavirus species and compared their sequences in the drug-binding pockets. The two most conserved druggable sites were a pocket overlapping the RNA binding site of the helicase nsp13, and a binding pocket containing the catalytic site of the RNA-dependent RNA polymerase nsp12. Both of these proteins are involved in viral RNA replication and transcription. The drug-binding pocket on nsp13 was also the most highly conserved across thousands of SARS-CoV-2 samples taken from COVID-19 patients, with not a single mutation. The researchers say that novel antiviral drugs targeting the catalytic site of nsp12 are currently in phase II and III clinical trials for COVID-19, and that the RNA binding site of nsp13 is a previously underexplored target that should be a high priority for drug development.

UT Southwestern researchers have identified an immune protein tied to the rare neurodegenerative condition known as Niemann-Pick disease type C. The finding, made in mouse models and published online in Nature, could offer a powerful new therapeutic target for Niemann-Pick disease type C, a condition that was identified more than a century ago but still lacks effective treatments.

"Niemann-Pick disease has never been considered an immune disorder," says study leader Nan Yan, Ph.D., associate professor of immunology and microbiology. "These findings put it in a whole new light."

Niemann-Pick disease type C, which affects about 1 in every 150,000 people worldwide, has long been considered a disease of cholesterol metabolism and distribution, a topic well-studied at UT Southwestern, where faculty members Michael Brown, M.D., and Joseph Goldstein, M.D., won the Nobel Prize in 1985 for their discovery of low-density lipoprotein (LDL) receptors, which led to the development of statin drugs.

When the Npc1 gene is mutated, cholesterol isn't sent where it's needed in cells, causing the progressive decline in motor and intellectual abilities that characterize Niemann-Pick. Yan's lab, which doesn't study cholesterol metabolism, made its discovery by chance while researching an immune protein known as STING, short for stimulator of interferon genes.

STING is a critical part of the body's defense against viruses, typically relying on another protein known as cyclic GMP-AMP synthase (cGAS) to sense DNA and turn on immune genes to fight off viral invaders. The cGAS enzyme was identified at UT Southwestern.

STING journeys to different organelles to perform various tasks before it ends up in lysosomes, which serve as cellular garbage dumps. Disposal of STING is critical for an appropriate immune response, explains Yan; research from his lab and others has shown that when STING isn't properly discarded, it continues to signal immune cells, leading to a variety of autoimmune conditions.

To determine what proteins interact with STING as it travels through cells, Yan and his colleagues used a technique called proximity labeling, which causes other proteins around a protein of interest to glow. After analyzing their data, Yan's team was surprised to find that STING interacts with a protein that's located on the surface of lysosomes and is produced by the Npc1 gene.

Because STING had never been implicated in Niemann-Pick disease type C, Yan and his team investigated whether it might play a role. The researchers removed the gene for STING from mice in which the Npc1 gene had also been deleted. Deleting Npc1 typically causes progressive problems in motor function, but animals with both the Npc1 and Sting genes deleted remained healthy.

Further research suggested that the protein produced by Npc1 has a binding site for STING that allows it to enter lysosomes for disposal. When the protein produced by Npc1 is missing, STING remains in cells, propagating Niemann-Pick disease type C. When Yan and his colleagues analyzed cells from human Niemann-Pick disease type C patients, they found that several immune-stimulating genes were overactive, as would be expected if STING disposal was defective.

In addition, Yan found that STING signaling is activated independently of cGAS in Niemann-Pick disease. This expands STING biology beyond its conventional role in host defense against infection.

Yan says that his lab and others are investigating the use of experimental drugs that inhibit STING to treat various autoimmune conditions. These compounds may also be useful for Niemann-Pick disease type C.

"If we can demonstrate that these compounds are effective in our animal models," Yan says, "we may be able to offer an effective therapy to Niemann-Pick disease patients."

Although Enterococcus faecalis is usually an innocuous member of the bacterial community in the human gut, it can also cause several infections, including liver disorders. The bacteria produce cytolysins, which are molecules that destroy cells. In a new study, researchers have uncovered how they do so.

"Your chances of dying increase by 5-fold when you get infected by E. faecalis that can make cytolysin compared to those that cannot," said Wilfred van der Donk (MMG), a professor of chemistry and investigator of the Howard Hughes Medical Institute. "Cytolysin is an important molecule and it has been known since the 1930s, our lab determined the cytolysin structure only in 2013."

Concerningly, E. faecalis is resistant to vancomycin, which is used as a last resort to treat bacterial infections. By understanding how cytolysins affect cells, the researchers hope to prevent its production and reduce the number of lethal infections.

Cytolysin is made up of two subunits, CylLL" and CylLS", which have been previously shown to kill both mammalian and bacterial cells. The structure of the subunits is stabilized with the help of rings, called macrocycles, that staple the ends and prevent the structure from unfolding. To understand how they work, the researchers replaced each amino acid in both these subunits to determine which amino acids are important.

"After mutating the amino acid residues, we purified and tested each of the mutants to see whether they had anti-bacterial activity or if they could lyse rabbit blood cells," said Imran Rahman, a graduate student in the van der Donk lab and the first author of the paper. "We found that the macrocyclizations in both subunits are important for both activities."

Additionally, the researchers discovered that CylLL" contains a hinge region which is also important. "The hinge contains three consecutive glycine residues and if we delete them, CylLL" becomes so unstable that we can no longer purify it," Rahman said.

The residues help CylLL" switch between two different shapes: a jackknife and an elongated form that can span across the bacterial membrane. "When it's longer, the subunit can make holes in the membrane and we think that's why the glycine residues are required for its activity," van der Donk said.

The researchers are interested in identifying the targets of cytolysin. "These molecules are unusual because unlike our current antibiotics, which bind to big cellular targets, these bind to small molecules and seem to use them to make holes in the membrane," van der Donk said. "We don't know what their targets are and we're working to find them."

Researchers from the University of Southern California (USC) Department of Computer Science and NVIDIA have unveiled a new simulator for robotic cutting that can accurately reproduce the forces acting on a knife as it slices through common foodstuffs, such as fruit and vegetables. The system could also simulate cutting through human tissue, offering potential applications in surgical robotics. The paper was presented at the Robotics: Science and Systems (RSS) Conference 2021 on July 16, where it received the Best Student Paper Award.

In the past, researchers have had trouble creating intelligent robots that replicate cutting. One challenge: in the real world, no two objects are the same, and current robotic cutting systems struggle with variation. To overcome this, the team devised a unique approach to simulate cutting by introducing springs between the two halves of the object being cut, represented by a mesh. These springs are weakened over time in proportion to the force exerted by the knife on the mesh.

"What makes ours a special kind of simulator is that it is 'differentiable,' which means that it can help us automatically tune these simulation parameters from real-world measurements," said lead author Eric Heiden, a PhD in computer science student at USC. "That's important because closing this reality gap is a significant challenge for roboticists today. Without this, robots may never break out of simulation into the real world."

To transfer skills from simulation to reality, the simulator must be able to model a real system. In one of the experiments, the researchers used a dataset of force profiles from a physical robot to produce highly accurate predictions of how the knife would move in real life. In addition to applications in the food processing industry, where robots could take over dangerous tasks like repetitive cutting, the simulator could improve force haptic feedback accuracy in surgical robots, helping to guide surgeons and prevent injury.

"Here, it is important to have an accurate model of the cutting process and to be able to realistically reproduce the forces acting on the cutting tool as different kinds of tissue are being cut," said Heiden. "With our approach, we are able to automatically tune our simulator to match such different types of material and achieve highly accurate simulations of the force profile." In ongoing research, the team is applying the system to real-world robots.

Northwestern University researchers have developed a new framework using machine learning that improves the accuracy of interatomic potentials -- the guiding rules describing how atoms interact -- in new materials design. The findings could lead to more accurate predictions of how new materials transfer heat, deform, and fail at the atomic scale.

Designing new nanomaterials is an important aspect of developing next-generation devices used in electronics, sensors, energy harvesting and storage, optical detectors, and structural materials. To design these materials, researchers create interatomic potentials through atomistic modeling, a computational approach that predicts how these materials behave by accounting for their properties at the smallest level. The process to establish materials' interatomic potential -- called parameterization -- has required significant chemical and physical intuition, leading to less accurate prediction of new materials design.

The researchers' platform minimizes user intervention by employing multi-objective genetic algorithm optimization and statistical analysis techniques, and screens promising interatomic potentials and parameter sets.

"The computational algorithms we developed provide analysts with a methodology to assess and avoid traditional shortcomings," said Horacio Espinosa, James N. and Nancy J. Farley Professor in Manufacturing and Entrepreneurship and professor of mechanical engineering and (by courtesy) biomedical engineering and civil and environmental engineering, who led the research. "They also provide the means to tailor the parameterization to applications of interest."

The findings were published in a study titled "Parametrization of Interatomic Potentials for Accurate Large Deformation Pathways Using Multi-Objective Genetic Algorithms and Statistical Analyses: A Case Study on Two-Dimensional Materials" on July 21 in Nature Partner Journals - Computational Materials.

Xu Zhang and Hoang Nguyen, both students in Northwestern Engineering's Theoretical and Applied Mechanics (TAM) graduate program, were co-first authors of the study. Other co-authors included Jeffrey T. Paci of the University of Victoria, Canada, Subramanian Sankaranarayanan of Argonne National Laboratory, and Jose Mendoza of Michigan State University.

The researchers' framework uses training and screening datasets obtained from density functional theory simulation results, followed by an evaluation step that includes principal component analysis and correlation analysis.

"We defined a sequence of steps to reach an iterative learning approach given specific optimization objectives," said Espinosa, who directs the TAM program. "Our statistical approach enables users to realize conflicting optimization goals that are important in setting limits of applicability and transferability to the parametrized potentials." These relations can reveal underlying physics behind some phenomena that seem to be irrelevant to each other.

The team identified a positive correlation between the accuracy of interatomic potential and the complexity and number of the stated parameters -- a phenomenon believed to be true in the field, but previously unproven using quantitative methods. This level of complexity must be met by a commensurate amount of training data. Failure to do so, especially data carrying critical information, leads to reduced accuracy.

The researchers found, for example, that to improve the fidelity of interatomic potentials, non-equilibrium properties and force-matching data are required.

"This included a better description of large deformation pathways and failure in materials," Nguyen said.

"While these are not conventional properties that people target during parametrization, they are critical in understanding the reliability and functionality of materials and devices," Zhang said.

The new approach also helps remove the barrier of user experience to enter this research field. "Through this work, we hope to make a step forward by making the simulation techniques more accurately reflect the property of materials. That knowledge can be expanded upon and eventually impact the design of devices and technology we all use," Zhang said.

Next, the researchers will use their models to expand their investigation to study fracture and deformation in 2D materials, as well as the role of defect engineering in toughness enhancements. They are also developing in situ electron microscopy experiments that will reveal atomistic failure modes, providing a way to assess the predictive capabilities of the parameterized potentials.

In a recently published paper, a research team, led by University of Miami (UM) Rosenstiel School of Marine and Atmospheric Science Professor Emeritus Joseph M. Prospero, chronicles the history of African dust transport, including three independent "first" discoveries of African dust in the Caribbean Basin in the 1950s and 1960s.

Every year, mineral-rich dust from North Africa's Sahara Desert is lifted into the atmosphere by winds and carried on a 5,000-mile journey across the North Atlantic to the Americas. African dust contains iron, phosphorus and other important nutrients that are essential for life in marine and terrestrial ecosystems, including the Amazon Basin. Wind-borne mineral dust also plays an important role in climate by modulating solar radiation and cloud properties.

The researchers also discuss the discovery in the 1970s and 1980s of the link between dust transport and African climate following an increase in dust transport to the Caribbean due to the onset of severe drought in the Sahel. Much of today's dust research is focused on North Africa as it is Earth's largest and most persistent source of dust.

Today, Prospero, nicknamed the "father of dust," is using a system of ground stations and satellites to study the effect that the global transport from the Sahara has on the atmospheric composition above the Caribbean.

BROOKLYN, New York, Wednesday, July 21, 2021 - This week, at the 38th International Conference on Machine Learning (ICML 21), researchers at the NYU Center for Cyber Security at the NYU Tandon School of Engineering are revealing new insights into the basic functions that drive the ability of neural networks to make inferences on encrypted data.

In the paper, "DeepReDuce: ReLU Reduction for Fast Private Inference," the team focuses on linear and non-linear operators, key features of neural network frameworks that, depending on the operation, introduce a heavy toll in time and computational resources. When neural networks compute on encrypted data, many of these costs are incurred by rectified linear activation function (ReLU), a non-linear operation.

Brandon Reagen, professor of computer science and engineering and electrical and computer engineering and a team of collaborators including Nandan Kumar Jha, a Ph.D. student, and Zahra Ghodsi, a former doctoral student under the guidance of Siddharth Garg, developed a framework called DeepReDuce. It offers a solution through rearrangement and reduction of ReLUs in neural networks.

Reagen explained that this shift requires a fundamental reassessment of where and how many components are distributed in neural networks systems.

"What we are trying to do is rethink how neural nets are designed in the first place," he explained. "You can skip a lot of these time- and computationally-expensive ReLU operations and still get high performing networks at 2 to 4 times faster run time."

The team found that, compared to the state-of-the-art for private inference, DeepReDuce improved accuracy and reduced ReLU count by up to 3.5% and 3.5×, respectively.

The inquiry is not merely academic. As the use of AI grows in concert with concerns about the security of personal, corporate, and government data security, neural networks are increasingly making computations on encrypted data. In such scenarios involving neural networks generating private inferences (PI's) on hidden data without disclosing inputs, it is the non-linear functions that exert the highest "cost" in time and power. Because these costs increase the difficulty and time it takes for learning machines to do PI, researchers have struggled to lighten the load ReLUs exert on such computations.

The team's work builds on innovative technology called CryptoNAS. Described in an earlier paper whose authors include Ghodsi and a third Ph.D. student, Akshaj Veldanda, CryptoNAS optimizes the use of ReLUs as one might rearrange how rocks are arranged in a stream to optimize the flow of water: it rebalances the distribution of ReLUS in the network and removes redundant ReLUs.

DeepReDuce expands on CryptoNAS by streamlining the process further. It comprises a set of optimizations for the judicious removal of ReLUs after CryptoNAS reorganization functions. The researchers tested DeepReDuce by using it to remove ReLUs from classic networks, finding that they were able to significantly reduce inference latency while maintaining high accuracy.

Reagan, with Mihalis Maniatakos, research assistant professor of electrical and computer engineering, is also part of a collaboration with data security company Duality to design a new microchip designed to handle computation on fully encrypted data.

Efforts to understand cardiac disease progression and develop therapeutic tissues that can repair the human heart are just a few areas of focus for the Feinberg research group at Carnegie Mellon University. The group's latest dynamic model, created in partnership with collaborators in the Netherlands, mimics physiologic loads on engineering heart muscle tissues, yielding an unprecedented view of how genetics and mechanical forces contribute to heart muscle function.

"Our lab has been working for a long time on engineering and building human heart muscle tissue, so we can better track how disease manifests and also, create therapeutic tissues to one day repair and replace heart damage," explains Adam Feinberg, a professor of biomedical engineering and materials science and engineering. "One of the challenges is that we have to build these small pieces of heart muscle in a petri dish, and we've been doing that for many years. What we've realized is that these in-vitro systems do not accurately recreate the mechanical loading we see in the real heart due to blood pressure."

Hemodynamic loads, or the preload (stretch on heart muscle during chamber filling) and afterload (when the heart muscle contracts), are important not only for healthy heart muscle function, but can also contribute to cardiac disease progression. Preload and afterload can lead to maladaptive changes in heart muscle, as is the case of hypertension, myocardial infarction, and cardiomyopathies.

In new research published in Science Translational Medicine, the group introduces a system comprised of engineered heart muscle tissue (EHT) that is attached to an elastic strip designed to mimic physiologic preloads and afterloads. This first-of-its-kind model shows that recreating exercise-like loading drives formation of more functional heart muscle that is better organized and generates more force each time it contracts. However, using cells from patients with certain types of heart disease, these same exercise-like loads can result in heart muscle dysfunction.

"One of the really important things about this work is that it's a collaborative effort between our lab and collaborators in the Netherlands, including Cardiologist Peter van der Meer," says Feinberg. "Peter treats patients that have genetically-linked cardiovascular disease, including a type called arrhythmogenic cardiomyopathy (ACM) that often becomes worse with exercise. We have been able to get patient-specific induced pluripotent stem cells, differentiate these into heart muscle cells, and then use these in our new EHT model to recreate ACM in a petri dish, so we can better understand it."

Jacqueline Bliley, a biomedical engineering graduate student and co-first author of the recently published paper, adds, "The collaborative nature of this work is so important, to be able to ensure reproducibility of the research and compare findings across the world."

Looking to the future, the collaborators aim to use their model and findings to study a wide range of other heart diseases with genetic mutations, develop new therapeutic treatments and test drugs to gauge their effectiveness.

"We can take lessons learned from building the EHT in a dish to create larger pieces of heart muscle that could be used therapeutically. By combining these new results with our previous work involving 3D bioprinting heart muscle (published in Science in 2019), we hope to one day engineer tissues large and functional enough to implant, and repair the human heart," projects Feinberg.

Evidence of the sustained benefits of an investigational antipsychotic treatment for people with dementia-related psychosis has been published.

Up to half of the 45 million people worldwide who are living with Alzheimer's disease will experience psychotic episodes, a figure that is even higher in some other forms of dementia. Psychosis is linked to a faster deterioration in dementia.

Despite this, there is no approved safe and effective treatment for these particularly distressing symptoms. In people with dementia, widely-used antipsychotics lead to sedation, falls and increased risk of deaths.

Pimavanserin works by blocking serotonin 5HT2A receptors, and doesn't interact with the dopamine receptors. It is licensed in the US to treat hallucinations and delusions in people with Parkinson's disease psychosis.

A new paper published in the New England Journal of Medicine outlines a clinical trial, conducted in 392 people with psychosis associated with Alzheimer's disease, Parkinson's disease, Lewy body, frontotemporal, or vascular dementia. All participants were given pimavanserin for 12 weeks. Those who met a threshold of symptom improvement were then assigned to pimavanserin or placebo for up to 26 weeks.

The trial was stopped early for positive efficacy results. Of the 351 participants, 217 (61.8%) had a sustained initial treatment benefit, of whom 112 were assigned to placebo and 105 to pimavanserin. Relapse occurred in 28/99 (28.3%) of the placebo group, compared to 12/95 (12.6%) of the pimvanserin group, with pimvanserin more than halving the relapse rate and significantly improving the sustained benefit.

Professor Clive Ballard, Executive Dean of the University of Exeter Medical School, said: "Psychosis affects up to half of all people with dementia, and it's a particularly distressing symptom - yet there's currently no safe and effective treatment. Currently used antipsychotics are known to cause harms, and best practice guidelines recommend prescribing for no longer than 12 weeks for people with dementia as a result. We urgently need alternatives. It's exciting that the relapse rate in the pimavanserin group was lower than the placebo group, indicating that the treatment benefits may be sustained over time. We now need longer and larger scale trials to explore this further."

The trial found headache, urinary tract infection and constipation occurred more frequently in the pimavanserin group, but there was no increase in mortality or the other serious events, such as stroke, which are known to increase with other antipsychotics.

Every day, people are exposed to a variety of synthetic chemicals through the products they use or the food they eat. For many of these chemicals, the health effects are unknown. Now a new study shows that several hundred common chemicals, including pesticides, ingredients in consumer products, food additives, and drinking water contaminants, could increase the risk of breast cancer by causing cells in breast tissue to produce more of the hormones estrogen or progesterone.

"The connection between estrogen and progesterone and breast cancer is well established," says co-author Ruthann Rudel, a toxicologist and research director at Silent Spring Institute. "So, we should be extremely cautious about chemicals in products that increase levels of these hormones in the body."

For instance, in 2002, when the Women's Health Initiative study found combination hormone replacement therapy to be associated with an increased risk of breast cancer, women stopped taking the drugs and incidence rates went down. "Not surprisingly, one of the most common therapies for treating breast cancer is a class of drugs called aromatase inhibitors that lower levels of estrogen in the body, depriving breast cancer cells of the hormones they need to grow," adds Rudel.

To identify these chemical risk factors, Rudel and Silent Spring scientist Bethsaida Cardona combed through data on more than 2000 chemicals generated by the U.S. Environmental Protection Agency (EPA)'s ToxCast program. The goal of ToxCast is to improve the ability of scientists to predict whether a chemical will be harmful or not. The program uses automated chemical screening technologies to expose living cells to chemicals and then examine the different biological changes they cause.

Reporting in the journal Environmental Health Perspectives, Rudel and Cardona identified 296 chemicals that were found to increase estradiol (a form of estrogen) or progesterone in cells in the laboratory. Seventy-one chemicals were found to increase levels of both hormones. The chemicals included ingredients in personal care products such as hair dye, chemical flame retardants in building materials and furnishings, and a number of pesticides.

The researchers don't yet know how these chemicals are causing cells to produce more hormones. It could be the chemicals are acting as aromatase activators, for instance, which would lead to higher levels of estrogen, says Cardona. "What we do know is that women are exposed to multiple chemicals from multiple sources on a daily basis, and that these exposures add up."

The Silent Spring researchers hope this study will be a wakeup call for regulators and manufacturers in how they test chemicals for safety. For instance, current safety tests in animals fail to look at changes in hormone levels in the animal's mammary glands in response to a chemical exposure. And, although high throughput testing in cells has been used to identify chemicals that activate the estrogen receptor, mimicking estrogen, the testing has not been used to identify chemicals that increase estrogen or progesterone synthesis.

"This study shows that a number of chemicals currently in use have the ability to manipulate hormones known to adversely affect breast cancer risk," says Dr. Sue Fenton, associate editor for the study and an expert in mammary gland development at the National Institute of Environmental Health Sciences. "Especially concerning is the number of chemicals that alter progesterone, the potential bad actor in hormone replacement therapy. Chemicals that elevate progesterone levels in the breast should be minimized."

The researchers outlined a number of recommendations in their study for improving chemical safety testing to help identify potential breast carcinogens before they end up in products, and suggest finding ways to reduce people's exposures, particularly during critical periods of development, such as during puberty or pregnancy when the breast undergoes important changes.

The project is part of Silent Spring Institute's Safer Chemicals Program which is developing new cost-effective ways of screening chemicals for their effects on the breast. Knowledge generated by this effort will help government agencies regulate chemicals more effectively and assist companies in developing safer products.

New research published in the European Journal of Clinical Investigation identifies cardiovascular test results that might help to identify patients hospitalized with COVID-19 who face an especially high risk of dying.

Out of 1,401 patients with a confirmed diagnosis of COVID-19 admitted to seven Italian centers, 226 (16.1%) underwent transthoracic echocardiography within 48 hours of admission. In-hospital death occurred in 68 patients (30.1%). Low left ventricular ejection fraction, low tricuspid annular plane systolic excursion, and acute respiratory distress syndrome were independently associated with in-hospital mortality.

"Clinical and echocardiographic parameters of disease severity might help to determine which patients with COVID-19 are at higher risk for in-hospital mortality," said lead author Angelo Silverio, MD, of the University of Salerno, in Italy.

The COVID-19 pandemic has strongly impacted our sleep and dream activity. In a recent study published in the Journal of Sleep Research, people had a higher number of awakenings, a harder time falling asleep, higher dream recall, and more lucid dreams during lockdown than after lockdown.

People also reported more dreams related to "being in crowded places" during post-lockdown than lockdown.

For the study, 90 adults in Italy recorded their dream experiences and completed a sleep-dream diary each morning.

"Our results... confirmed that both sleep and dream measures showed critical differences between lockdown and post-lockdown periods," the authors wrote.

New research published in the Journal of Bone and Mineral Research has found that cognitive decline is linked with accelerated bone loss and an increased fracture risk in women.

In the study of 1,741 women and 620 men aged ?65 years without dementia who were followed from 1997 through 2013, both genders experienced similar declines in cognitive function and bone mass.

After adjustments, cognitive decline was associated with bone loss in women but not men. Also, significant and clinical important cognitive decline in women was associated with a 1.7-fold higher risk of bone fractures over the subsequent 10 years.

Notably, the relationship between bone loss and cognitive decline was bidirectional with no evidence of one preceding the other. The relationship between cognitive decline, bone loss, and fracture risk in women may be driven by shared risk factors.

"These findings may help refine clinical practices guidelines regarding how bone loss and cognitive decline can be monitored in older age, to ensure appropriate and effective treatment," said lead author Dana Bliuc, PhD, MD, of the Garvan Institute of Medical Research, in Australia. "This is particularly important because both bone loss and cognitive decline are 'silent conditions' that can go undetected for long periods of time, often until the conditions have severely progressed."

When researchers evaluated consumers' understanding of allergy information on food labels, less than half of individuals found the information to be clear.

The study, which is published in Clinical & Experimental Allergy, involved two separate experiments with a total of 96 consumers with food allergies and 105 without. Investigators first randomly presented 18 different food products with labels suggesting peanut was, may be, or was not an ingredient, and then they presented three different formats of information: 'Produced in a Factory' and 'May contain' or 'Traces of'. Precautionary allergen labels (PALs) were especially problematic, with consumers attributing anything between 2% and 99% risk of a reaction and anything between 1% and 98% comprehensibility assessments. This suggests that precautionary statements such as 'may contain peanut' have little value for consumers and may lead to inappropriate dietary restrictions or risk-taking behavior.

"Also, many consumers interpret 'Produced in a factory' to reflect a weaker warning than 'May contain,'" said lead author Bregje Holleman, PhD, of Utrecht University, in The Netherlands. "From a communication perspective, it's logical for consumers to attribute different risk levels to warnings worded differently. But since producers probably mean to communicate the exact same level of risk with each of these different warnings, we advise to use only PAL wording."

A systematic review and meta-analysis published in Campbell Systematic Reviews identified and examined more than 100 risk and protective factors for radical attitudes, intentions, and behaviors (including terrorism) in democratic countries.

The factors can be grouped into five domains:

socio-demographic and background factors,

psychological and personality trait factors,

attitudinal and subjective belief related factors,

experiential factors, and

traditional criminogenic factors

While there is great variation, the most significant factors are traditional criminogenic and social-psychological factors.

"Our results suggest that some of the factors most commonly targeted by counter violent extremism interventions, such as social integration, have only small relationships with radicalization. On the other hand, traditional criminogenic factors, such as low self-control, have far more robust relationships," said lead author Michael Wolfowicz, PhD, of The Hebrew University of Jerusalem. He noted that the findings suggest that interventions commonly used to combat criminological outcomes may also be useful for fighting radicalization.

"Additionally, our results suggest a need to revamp the way that risk assessment tools are constructed, as not all factors included in such tools should be given the same weight," said Dr. Wolfowicz. "We hope that our results will contribute to the development of more evidence-based practice in this field."