Articles tagged with Biological Models
Comprehensive exploration of living organisms, biological systems, and life processes across all scales from molecules to ecosystems. Encompasses cutting-edge research in biology, genetics, molecular biology, ecology, biochemistry, microbiology, botany, zoology, evolutionary biology, genomics, and biotechnology. Investigates cellular mechanisms, organism development, genetic inheritance, biodiversity conservation, metabolic processes, protein synthesis, DNA sequencing, CRISPR gene editing, stem cell research, and the fundamental principles governing all forms of life on Earth.
Comprehensive medical research, clinical studies, and healthcare sciences focused on disease prevention, diagnosis, and treatment. Encompasses clinical medicine, public health, pharmacology, epidemiology, medical specialties, disease mechanisms, therapeutic interventions, healthcare innovation, precision medicine, telemedicine, medical devices, drug development, clinical trials, patient care, mental health, nutrition science, health policy, and the application of medical science to improve human health, wellbeing, and quality of life across diverse populations.
Comprehensive investigation of human society, behavior, relationships, and social structures through systematic research and analysis. Encompasses psychology, sociology, anthropology, economics, political science, linguistics, education, demography, communications, and social research methodologies. Examines human cognition, social interactions, cultural phenomena, economic systems, political institutions, language and communication, educational processes, population dynamics, and the complex social, cultural, economic, and political forces shaping human societies, communities, and civilizations throughout history and across the contemporary world.
Fundamental study of the non-living natural world, matter, energy, and physical phenomena governing the universe. Encompasses physics, chemistry, earth sciences, atmospheric sciences, oceanography, materials science, and the investigation of physical laws, chemical reactions, geological processes, climate systems, and planetary dynamics. Explores everything from subatomic particles and quantum mechanics to planetary systems and cosmic phenomena, including energy transformations, molecular interactions, elemental properties, weather patterns, tectonic activity, and the fundamental forces and principles underlying the physical nature of reality.
Practical application of scientific knowledge and engineering principles to solve real-world problems and develop innovative technologies. Encompasses all engineering disciplines, technology development, computer science, artificial intelligence, environmental sciences, agriculture, materials applications, energy systems, and industrial innovation. Bridges theoretical research with tangible solutions for infrastructure, manufacturing, computing, communications, transportation, construction, sustainable development, and emerging technologies that advance human capabilities, improve quality of life, and address societal challenges through scientific innovation and technological progress.
Study of the practice, culture, infrastructure, and social dimensions of science itself. Addresses how science is conducted, organized, communicated, and integrated into society. Encompasses research funding mechanisms, scientific publishing systems, peer review processes, academic ethics, science policy, research institutions, scientific collaboration networks, science education, career development, research programs, scientific methods, science communication, and the sociology of scientific discovery. Examines the human, institutional, and cultural aspects of scientific enterprise, knowledge production, and the translation of research into societal benefit.
Comprehensive study of the universe beyond Earth, encompassing celestial objects, cosmic phenomena, and space exploration. Includes astronomy, astrophysics, planetary science, cosmology, space physics, astrobiology, and space technology. Investigates stars, galaxies, planets, moons, asteroids, comets, black holes, nebulae, exoplanets, dark matter, dark energy, cosmic microwave background, stellar evolution, planetary formation, space weather, solar system dynamics, the search for extraterrestrial life, and humanity's efforts to explore, understand, and unlock the mysteries of the cosmos through observation, theory, and space missions.
Comprehensive examination of tools, techniques, methodologies, and approaches used across scientific disciplines to conduct research, collect data, and analyze results. Encompasses experimental procedures, analytical methods, measurement techniques, instrumentation, imaging technologies, spectroscopic methods, laboratory protocols, observational studies, statistical analysis, computational methods, data visualization, quality control, and methodological innovations. Addresses the practical techniques and theoretical frameworks enabling scientists to investigate phenomena, test hypotheses, gather evidence, ensure reproducibility, and generate reliable knowledge through systematic, rigorous investigation across all areas of scientific inquiry.
Study of abstract structures, patterns, quantities, relationships, and logical reasoning through pure and applied mathematical disciplines. Encompasses algebra, calculus, geometry, topology, number theory, analysis, discrete mathematics, mathematical logic, set theory, probability, statistics, and computational mathematics. Investigates mathematical structures, theorems, proofs, algorithms, functions, equations, and the rigorous logical frameworks underlying quantitative reasoning. Provides the foundational language and tools for all scientific fields, enabling precise description of natural phenomena, modeling of complex systems, and the development of technologies across physics, engineering, computer science, economics, and all quantitative sciences.
A deep learning model, CGMformer, leverages large-scale continuous glucose monitoring (CGM) data to extract individual glucose dynamics. The model captures a continuous picture of glucose fluctuations, identifying patterns that may indicate early metabolic dysfunction.
A new test called EpiAgePublic estimates biological age using only three DNA sites in the ELOVL2 gene, simplifying traditional methods while maintaining accuracy. The study found that EpiAgePublic accurately tracks aging patterns and can identify factors accelerating the aging process.
Researchers identified recent advancements in bioinformatics foundation models, enhancing understanding of molecular landscapes and providing practical foundations for innovation in molecular biology. The models are versatile and essential tools for various downstream tasks, including genomics and drug discovery.
The 3D lung model can replicate realistic breathing maneuvers and offer personalized evaluation of aerosol therapeutics under various breathing conditions. The researchers detail in the paper how they built the 3D structure and what they’ve learned so far.
Research advances higher-order networks to capture multi-agent interactions, enabling accurate modeling of biological, social, and physical systems. The Dirac-Bianconi operator provides a powerful generalization of the graph Laplacian, encoding local and global interactions across different topological dimensions.
Researchers developed a 3D microscopic version of the human intestines on a chip, allowing for real-time examination of gut microbes' interactions with the human intestine. The 'Gut-Microbiome on a chip' model enables the study of complex interplay between gut microbes and health, facilitating targeted microbiome-based interventions.
Cardiac arrhythmia disrupts heart rhythm due to malfunctioning electrical impulses. Rui Zhu plans to use deep-learning and computational simulations to model diseased hearts and understand multi-physical factors contributing to arrhythmic conditions.
A study by University of Toronto researchers links polar bear population decline to extended energy deficits caused by a lack of food on dwindling sea ice. The model, tracking the bears' energy balance, shows reduced reproduction and cub survival rates due to shorter hunting seasons.
Researchers at the University of Surrey propose a new approach to treating inflammatory diseases using personalized probiotic therapies. By analyzing large-scale computer models and computational methodologies, they can identify potential targets and design tailored treatments.
Proteins form complexes to fulfill their functions, with assembly often beginning during synthesis. Misfolded proteins can lead to cellular dysfunction and diseases; understanding co-translational assembly may help develop new therapeutic approaches.
Researchers at McGill University discovered a novel brain mechanism that explains why bipolar patients alternate between mania and depression. A dopamine-based 'second brain clock' controls mood shifts, operating in tandem with the body's natural sleep-wake cycle.
The Global Conference on Gerophysics aims to bridge statistical mechanics, complex systems theory, and dynamical modeling with biological mechanisms of aging. The conference will bring together leading scientists to promote healthy longevity and reshape our understanding of the aging process.
Researchers developed an AI-powered technology that transforms low-resolution, label-free images into high-resolution, virtually stained ones without fluorescent dyes. This innovation delivers stable and accurate cell visualization, overcoming limitations of traditional imaging methods.
Researchers at UVA have developed computer models to target specific bacteria in specific parts of the body, reducing the chance of antibiotic resistance. This approach could lead to more effective treatments and reduce the need for broad-spectrum antibiotics.
The study reveals how the Balbiani body transforms from liquid droplets into a stable core, guiding early embryonic development. The team uncovered the role of microtubules in regulating Bucky ball protein granule movement and organization.
Organoids, derived from stem cells, closely mimic human tissue for biomedical research and drug testing. Standardization is crucial for generating reliable results in organoid construction, requiring approved operating procedures and informed consent from donors.
A team from the University of Cambridge has developed a model to predict desert locust swarms, enabling national agencies to respond quickly. The model uses weather forecast data and computational models to forecast locust swarm movements both short and long-term.
Researchers create SciAgents framework to autonomously generate and evaluate promising research hypotheses in biologically inspired materials. The framework uses graph reasoning methods to organize relationships between scientific concepts, mimicking biological systems.
A consensus platform for antibody characterization has been developed to evaluate antibody specificity and tackle the challenge of non-specific antibodies in life sciences. The Open Science approach supports large-scale collaboration among competitors in the antibody industry, resulting in an estimated $1 billion annual savings.
Researchers at MIT have developed Boltz-1, an open-source AI model that achieves state-of-the-art performance in predicting biomolecular structures. The model surpasses AlphaFold3, which is limited to academic research and commercial use, by incorporating new algorithms and improving prediction efficiency.
Researchers found that soil contains antibiotic resistance genes that can be transmitted to humans, making it a pressing public health threat. The study reveals how these genes spread through the environment and highlights the importance of understanding soil ecosystems to control antibiotic resistance.
Gene expression in cells occurs in short, unpredictable bursts due to transcriptional bursting. Researchers found that the folding and movement of DNA, as well as protein accumulation, changes depending on gene activity, with enhancers playing a crucial role in amplifying gene activity.
Researchers developed a new tool called SigRM to analyze single-cell epitranscriptomics data, enabling the study of RNA modifications in individual cells. This can provide valuable insights into gene regulation and its impact on health and disease, particularly in complex conditions like cancer.
Insilico Medicine has successfully integrated cutting-edge generative AI models into its platform, achieving significant increases in accuracy and speed. The company's AI-powered solutions have shown promising results in preclinical candidates and Phase II clinical trials.
A new study assesses the sustainability of varying Pacific walrus harvest rates under different climate and human disturbance scenarios. The research finds that current harvest levels are within a sustainable range if assessed regularly and adjusted accordingly.
A recent study suggests coral heat tolerance adaptation may not keep pace with ocean warming, and some sensitive species may face extinction. The research modelled different climate scenarios, revealing that natural selection may be insufficient to ensure coral survival under expected warming levels.
A new study suggests that coral heat tolerance adaptation via natural selection may be insufficient to overcome the impacts of ocean warming, unless Paris Agreement commitments are realized.
Researchers found a strong link between depression and menstrual pain in a new study published in Briefings in Bioinformatics. Depression may be a cause of dysmenorrhea, rather than a consequence, according to the findings.
Pharma.AI Week will showcase the latest advancements in Insilico's generative AI platform, including PandaOmics and Science42, with expert speakers and hands-on demos. The event aims to empower researchers and scientists with tools for faster and more accurate discoveries.
A new mathematical model of prostate cancer has been developed, revealing key findings on genetic changes and tumour growth. The study shows that strong genetic changes are necessary for aggressive tumours to develop early in the course of tumour development.
Acute interstitial nephritis (AIN) is a common cause of acute kidney injury, often linked to medication use. Johns Hopkins researchers developed an electronic diagnostic model using machine learning to predict AIN in patients, showing improved accuracy in diagnosis and potential benefits for treatment decisions.
Researchers developed a novel machine-learning model to identify and measure causal interactions that vary over time. The Temporal Autoencoders for Causal Inference (TACI) model performed well on synthetic and real-world datasets, detecting changes in strength and direction of causal relationships.
Scientists discovered that cyanobacteria align along inner edges of illuminated surfaces to create stable structures. This collective behavior arises from individual filament movement, enabling the formation of complex structures and curves.
A new version of the MESA heart disease risk score that doesn't include race predicts heart disease risk just as well as the original score. The study aims to assess the implications of including race in clinical risk prediction models and broaden their potential use.
The Macrosystems EDDIE modules have been effective in building student and instructor quantitative literacy and data science skills in ecological forecasting, reaching over 35,000 students globally. The modules aim to introduce students to core concepts of forecasting and complement educators' work teaching ecological concepts.
Researchers at MD Anderson Cancer Center have made significant advancements in understanding tissue regeneration, with a focus on epigenetic regulation and retrotransposon suppression. MicroRNAs have also been identified as potential biomarkers for COVID-19 severity in cancer patients, while a novel protein complex drives lung regenera...
Researchers developed an in vitro model of murine peritoneal macrophage aging to study molecular mechanisms and develop innovative strategies. Chronic treatment with CB3 completely prevented the increase of p21CIP1 and maintained proliferative activity in day 14 macrophages.
A new AI-powered model has been developed to predict kidney transplant outcomes with high accuracy, offering hope for more efficient organ allocation and improved patient outcomes. The tool, UK-DTOP, outperforms existing methods in predicting outcomes for deceased-donor kidney transplants.
Scientists estimate a 31% increase in global photosynthesis due to rising CO2 levels, with pan-tropical rainforests accounting for the largest difference. This improvement can enhance climate predictions and highlight the importance of natural carbon sequestration.
A new study reveals that AI-driven chatbots may perpetuate racial and ethnic biases in pain assessment, leading to further inequalities in healthcare. Researchers found that Black patients were consistently underassessed for their pain compared to white patients, regardless of whether the rater was human or AI.
Researchers are creating microphysiological systems to simulate infection and treatment in vitro, linking human lung and brain tissue models. This project aims to explore the relationship between respiratory diseases and neurological symptoms, potentially leading to new treatments.
Researchers have developed a computer simulation of brain neuron growth, using Approximate Bayesian Computation. The model successfully mimicked the growth patterns of real hippocampal neurons, showing promise for understanding neurodegenerative diseases and potential treatments.
A new study from the University of Waterloo suggests that men and women should eat different breakfasts to lose weight. The research found that men's metabolisms respond better to high-carbohydrate meals after fasting, while women are better served by higher-fat meals.
The study reveals substantial gaps in compensating for lost protected areas, risking global efforts to conserve biodiversity. Protected areas play a crucial role in conserving species and ecosystems, but PADDD events expose them to extinction risks.
In transport networks, competing branches change dynamics drastically when reaching the system's boundary, forming loops. This process leads to increased stability and reduced damage susceptibility. Various systems exhibit similar dynamics, supporting a simple physical explanation for loop formation.
Scientists have developed a strategy for optimizing light intensity to minimize electricity costs while maintaining plant growth. The study found that varying light intensity can cut electricity costs by 12% without compromising plants' carbon fixation.
Purdue researchers have developed an AI model that can predict maize yield using remote sensing data and environmental factors. The model, which combines hyperspectral cameras, LiDAR instruments, and genetic markers, can categorize healthy and stressed crops before farmers or scouts can spot a difference.
Researchers at Mizzou have developed Cryo2Struct, a computer program that uses AI to build the three-dimensional atomic structure of large protein complexes from cryo-electron microscopy images. This breakthrough enables scientists to better understand protein interactions, critical for developing effective treatments for diseases like...
Scientists used artificial intelligence and molecular dynamics simulations to understand how enzymes fold lasso peptides into a unique structure. They identified key residues important for interaction with the substrate, enabling the design of new cyclase variants that can produce potent lasso peptides.
Researchers at Ohio State University developed a ventilator-on-a-chip model that simulates lung injury during mechanical ventilation. The device detects real-time cellular changes, revealing shear stress from air sac collapse and reopening as the most injurious type of damage.
Researchers report significant strides in enhancing early diagnosis of bipolar disorder in adolescents by combining multimodal MRI with behavioral assessments. This approach reveals specific changes in brain networks signaling early-stage bipolar disorder, potentially leading to better and more personalized treatments. The study's find...
A new AI model called Crystalyze can analyze X-ray crystallography data to determine the structure of powdered crystals. The model was trained on a database of over 150,000 materials and successfully predicted structures for over 100 previously unsolved patterns.
Researchers have developed a new technique to study anisotropic materials, capturing full complexity of light behavior in these materials. The method revealed detailed insights into how light scatters differently along various directions within materials, allowing retrieval of scattering tensor coefficients.
Researchers at the University of Cambridge found that flowers like hibiscus use an invisible blueprint to dictate the size of their bullseyes, which can significantly impact their ability to attract pollinating bees. Larger bullseyes are preferred by bees and can potentially boost efficiency for both bees and blossoms.
A recent study identified three key DNA repair mechanisms that protect the genome from formaldehyde-induced damage during development, adulthood, and ageing. The researchers found that N-acetyl-L-cysteine can reverse most of the toxicity in animal models and human cells with altered DNA repair systems.
Researchers Gavin Cornwell, Sneha Couvillion, and Bo Peng will study bioparticles, soil microbes, and their impact on climate models. They aim to improve representations of ice nucleating particles and understand lipid exchange in soil ecosystems.
Researchers propose a new approach to understanding cancer evolution, acknowledging the importance of environmental influences and epigenetic changes. By refining the clonal evolution model, they aim to develop more effective cancer therapies that consider the full complexity of cancer cell evolution.
The study utilizes near-infrared (NIR) spectroscopy and machine learning to provide quick, accurate, and cost-effective product analysis. The researchers created a global model for corn kernel analysis, which can predict moisture and protein content with high accuracy across different locations.
Researchers at Carnegie Mellon University propose guidelines for using interpretable machine learning methods in computational biology to tackle complex problems. The guidelines address pitfalls such as relying on a single method and cherry-picking results, emphasizing the need for multiple approaches and human-centric considerations.
Researchers developed a novel clustering technique that considers both basic characteristics and target material properties, enabling the categorization of over 1,000 oxides into material groups. This approach uses machine learning to predict target properties and incorporates basic feature information into the analysis.