Lifeboat Foundation

CePO enables Llama3.3 70B to outperform flagship Llama 3.1 405B model and leading closed source models.

Neuroscientific research on human behavior and cognition has methodologically moved from unimodal explanatory approaches to machine learning-based predictive modeling (1). This implies a shift from standard approaches testing for associations between behavior and single neurobiological variables within one sample (unimodal explanatory research) to the identification of relationships between behavior and multiple neurobiological variables to forecast behavior of unseen individuals across samples (multimodal predictive research) (2). Modern machine learning techniques can learn such general relations in neural data (2, 3) and have consequently become increasingly prominent also in research on fundamental psychological constructs like intelligence (4).

Intelligence captures the general cognitive ability level of an individual person and predicts crucial life outcomes, such as academic achievement, health, and longevity (5, 6). Multiple psychometrical theories about the underlying conceptual structure of intelligence have been proposed. For example, Spearman (7) noticed that a person’s performance on different cognitive tasks is positively correlated and suggested that this “positive manifold” results from an underlying common factor—general intelligence (g). A decomposition of the g-factor into fluid (gF) and crystallized (gC) components was later proposed by Cattell (8, 9). While fluid intelligence is assumed to mainly consist of inductive and deductive reasoning abilities that are rather independent of prior knowledge and cultural influences, crystallized intelligence reflects the ability to apply acquired knowledge and thus depends on experience and culture (10).

Neurobiological correlates of intelligence differences were identified in brain structure (11) and brain function (12). However, rather than disclose a single “intelligence brain region”, meta-analyses and systematic reviews suggest the involvement of a distributed brain network (13–15), thus paving the way for proposals of whole-brain structural and functional connectivity (FC) underlying intelligence (16, 17). While the great majority of such studies used an explanatory approach, recently, an increasing number of machine learning-based techniques were developed and applied to predict intelligence from brain features (4, 18, 19). Although intrinsic FC measured during the (task-free) resting state has enabled robust prediction of intelligence (19), prediction performance can be boosted by measuring connectivity during task performance (18, 20).

Li-ion batteries that last beyond the life cycle of the EV can be bundled into energy storage solution for renewable energy projects.

A specialized technology can restore and preserve microcirculation and cellular functions hours post-mortem in an isolated pig brain.

A team of engineers is reimagining one of the essential processes in modern manufacturing. Their goal? To transform how a chemical called acrylonitrile (ACN) is made—not by building world-scale manufacturing sites, but by using smaller-scale, modular reactors that can work if they let the catalyst, in a sense, “breathe.”

Their article, titled “Propene Ammoxidation over an Industrial Bismuth Molybdate-Based Catalyst Using Forced Dynamic Operation,” is published in Applied Catalysis A: General.

ACN is everywhere, from carbon fibers in sports equipment to acrylics in car parts and textiles. Traditionally, producing it requires a continuous, energy-intensive process. But now, researchers at the University of Virginia and the University of Houston have shown that by pausing to “inhale” fresh oxygen, a chemical catalyst can produce ACN more efficiently. This discovery could open the door to smaller, versatile production facilities that adapt to fluctuating needs.

New research looking at the stem cells of ‘superagers’ — people aged 100 or more — could make it possible for more people to live a longer and healthier life.

Finding a reasonable hypothesis can pose a challenge when there are thousands of possibilities. This is why Dr. Joseph Sang-II Kwon is trying to make hypotheses in a generalizable and systematic manner.

Kwon, an associate professor in the Artie McFerrin Department of Chemical Engineering at Texas A&M University, published his work on blending traditional physics-based scientific models with experimental data to accurately predict hypotheses in the journal Nature Chemical Engineering.

Kwon’s research extends beyond the realm of traditional chemical engineering. By connecting physical laws with machine learning, his work could impact renewable energy, smart manufacturing, and health care, outlined in his recent paper, “Adding big data into the equation.”

The Call is still open on senescence in brain aging and Alzheimers disease!

Submit your paper today! 📩

Understanding Senescence in Brain Aging and Alzheimer’s Disease

Guest Editors Drs. Julie Andersen and Darren Baker, Associate Editor Dr. Anna Csiszar and Editor-in-Chief Dr. Zoltan Ungvari, and the editorial team of GeroScience (Journal of the American Aging Association; 2018 Impact Factor: 6.44) invite submission of original research articles, opinion papers and review articles related to research focused on understanding the role of senescence in brain aging and in Alzheimer’s disease. Senescent cells accumulate in aging and pathological conditions associated with accelerated aging. While earlier investigations focused on cellular senescence in tissues and cells outside of the brain (e.g. adipose tissue, dermal fibroblasts, cells of the cardiovascular system), more recent studies started to explore the role of senescent cells in age-related decline of brain function and the pathogenesis of neurodegenerative disease and vascular cognitive impairment. This call-for-papers is aimed at providing a platform for the dissemination of critical novel ideas related to the functional and physiological consequences of senescence in diverse brain cell types (e.g., oligodendrocytes, pericytes, astrocytes, endothelial cells, microglia, neural stem cells), with the ultimate goal to identify novel targets for treatment and prevention Alzheimer’s disease, Parkinson’s disease and vascular cognitive impairment. We welcome manuscripts focusing on senescent-cell-targeting mouse models, the role of paracrine senescence, senescence pathways in terminally differentiated neurons, the pleiotropic effects of systemic senescence, the role of senescence in neuroinflammation and the protective effects of senolytic therapies. We are especially interested in manuscripts exploring the causal role of molecular mechanisms of aging in induction of cellular senescence as well as links between lifestyle (e.g., diet, exercise, smoking), medical treatments (e.g. cancer treatments), exposure environmental toxicants and cellular senescence in the brain. We encourage submission of manuscripts on developing innovative strategies to identify and target senescent cells for prevention/treatment of age-related diseases of the brain. Authors are also encouraged to submit manuscripts focusing on translational aspects of senescence research.

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- is focusing on the role of molecular mechanisms of aging in the pathogenesis of cardiovascular diseases, COVID19, hypertension, obesity and vascularhomeostasis. ‘ + Read more in the comments and submit📧 at the link⬇

Cell Biology of Vascular Aging.

Guest Editors: Prof. Zoltan Arany, Prof. Jalees Rehman and Prof. Gabor Csanyi.

Deputy Editor Dr. Stefano Tarantini and Editor-in-Chief Dr. Zoltan Ungvari, and the editorial team of GeroScience (Official Journal of the American Aging Association, published by Springer) invite submission of original research articles, opinion papers and review articles related to research focused on understanding the mechanisms involved in vascular aging, the factors promoting accelerated aging in vascular cells and the role of vascular cells in the pathogenesis of age-related diseases. This call-for-papers is aimed at providing a platform for the dissemination of critical novel ideas related to the mechanisms of vascular aging as well as mechanisms related to key phenotypes of vascular aging including.

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