Lifeboat Foundation

There has been a continuously increasing volume of data which has demonstrated that victimization, the clinical term for bullying, affects hundreds of millions of children and adolescents which can sometimes last for years and even decades. This is seen as a global health challenge by the World Health Organization and the United Nations. However, researchers maintain there is still a limited understanding of how this act can affect the developing brain physically.

Most of the research into the neurobiological processes that might contribute to these negative health outcomes has occurred in the past decade, much of it focused on bullying’s impact on the body’s stress response system. A paper published last December in the journal Molecular Psychiatry sheds some light on a different area: brain architecture. The trauma stemming from chronic bullying can affect the structure of the brain, according to longitudinal magnetic resonance imaging (MRI) data collected by an international team based at King’s College London. The findings echo previous research, which has demonstrated similar changes in children and adults who experienced what’s known as “child maltreatment” — neglect or abuse by adult caregivers.

Long-term changes to the brain’s structure and chemistry are an indicator “of how sinister bullying is” says Tracy Vaillancourt, a developmental psychologist at the University of Ottawa. Along with others in the field, she is hopeful that studies like the one from King’s College will be a catalyst for further research which could ultimately be used to inform policy decisions and support anti-bullying interventions.

A study led by researchers at Indiana University is the first to find similarities between the organization of chromosomes in humans and archaea. The discovery could support the use of archaea in research to understand human diseases related to errors in cellular gene expression, such as cancer.

The lead author on the study is Stephen Bell, a professor of biology and chair of the Department of Molecular and Cellular Biochemistry in the College of Arts and Sciences at IU Bloomington. The study will publish Sept. 19 in the journal Cell.

The similar clustering of DNA in humans and archaeal chromosomes is significant because certain genes activate or deactivate based upon how they’re folded.

A Ph.D. student at the University of Alberta has discovered a new and curious mineral inside a diamond unearthed from a mine in South Africa.

The mineral—named goldschmidtite in honor of Victor Moritz Goldschmidt, the founder of modern geochemistry—has an unusual chemical signature for a mineral from Earth’s mantle, explained Nicole Meyer, a graduate student in the Diamond Exploration Research and Training School.

“Goldschmidtite has high concentrations of niobium, potassium and the rare earth elements lanthanum and cerium, whereas the rest of the mantle is dominated by other elements, such as magnesium and iron,” said Meyer.

A new class of chemical instrumentation seeks to alleviate the tedium and complexity of organic syntheses.

Wave function represents the quantum state of an atom, including the position and movement states of the nucleus and electrons. For decades researchers have struggled to determine the exact wave function when analyzing a normal chemical molecule system, which has its nuclear position fixed and electrons spinning. Fixing wave function has proven problematic even with help from the Schrödinger equation.

Previous research in this field used a Slater-Jastrow Ansatz application of quantum Monte Carlo (QMC) methods, which takes a linear combination of Slater determinants and adds the Jastrow multiplicative term to capture the close-range correlations.

Now, a group of DeepMind researchers have brought QMC to a higher level with the Fermionic Neural Network — or Fermi Net — a neural network with more flexibility and higher accuracy. Fermi Net takes the electron information of the molecules or chemical systems as inputs and outputs their estimated wave functions, which can then be used to determine the energy states of the input chemical systems.

Jeff Dahn works with Tesla alongside his individual research pursuits. He’s discovered a chemistry that might make robo-taxis and longer-range EVs a reality.

Researchers have designed a machine learning algorithm that predicts the outcome of chemical reactions with much higher accuracy than trained chemists and suggests ways to make complex molecules, removing a significant hurdle in drug discovery.

University of Cambridge researchers have shown that an algorithm can predict the outcomes of complex chemical reactions with over 90% accuracy, outperforming trained chemists. The algorithm also shows chemists how to make target compounds, providing the chemical “map” to the desired destination. The results are reported in two studies in the journals ACS Central Science and Chemical Communications.

A central challenge in drug discovery and materials science is finding ways to make complicated organic molecules by chemically joining together simpler building blocks. The problem is that those building blocks often react in unexpected ways.

Tesla’s battery research group led by Jeff Dahn in Halifax has applied for a patent that describes a new battery cell chemistry that would result in faster charging and discharging, better longevity, and even lower cost.

Jeff Dahn is considered a pioneer in Li-ion battery cells. He has been working on the Li-ion batteries pretty much since they were invented. He is credited for helping increase the life cycle of the cells, which helped their commercialization. His work now focuses mainly on a potential increase in energy density and durability.

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The future of technology relies, to a great extent, on new materials, but the work of developing those materials begins years before any specific application for them is known. Stephen Wilson, a professor of materials in UC Santa Barbara’s College of Engineering, works in that “long before” realm, seeking to create new materials that exhibit desirable new states.

In the paper “Field-tunable quantum disordered ground state in the triangular-lattice antiferromagnet NaYbO₂,” published in the journal Nature Physics, Wilson and colleagues Leon Balents, of the campus’s Kavli Institute for Theoretical Physics, and Mark Sherwin, a professor in the Department of Physics, describe their discovery of a long-sought “quantum spin liquid state” in the material NaYbO₂ (sodium ytterbium oxide). The study was led by materials student Mitchell Bordelon and also involved physics students Chunxiao Liu, Marzieh Kavand and Yuanqi Lyu, and undergraduate chemistry student Lorenzo Posthuma, as well as collaborators at Boston College and at the U.S. National Institute of Standards and Technology.

At the atomic level, electrons in one material’s lattice structure behave differently, both individually and collectively, from those in another material. Specifically, the “spin,” or the electron’s intrinsic magnetic moment (akin to an innate bar magnet) and its tendency to communicate and coordinate with the magnetic moments of nearby electrons differs by material. Various types of spin systems and collective patterns of ordering of these moments are known to occur, and materials scientists are ever seeking new ones, including those that have been hypothesized but not yet shown to exist.