Articles tagged with Biological Models
A Lehigh University researcher is developing a new platform to enhance flu forecasting through a human judgment temporal forecast system, which aims to improve evidence-based public health decision making. The system will collect unbiased opinions and forecasts from public health experts and the general public.
The researchers propose a novel defense algorithm, Wavelet-Based Adversarial Training (WBAD), to protect medical digital twins. The two-stage defense mechanism achieves 98% accuracy in breast cancer prediction, even under adversarial attacks, providing a comprehensive and effective defense against cyberattacks.
A novel approach combines large language models and quantum computing to predict Salmonella antimicrobial resistance. The SARPLLM algorithm outperforms other models in prediction accuracy.
Marine turtles' shells exhibit complex trade-offs between toughness, stiffness and flexibility to protect against predators while optimizing movement. The shell's design accommodates demands of both survival and efficient locomotion in aquatic habitats.
Researchers discovered that mammalian membranes have drastically different phospholipid abundances between their two leaflets, contradicting a major assumption of cell biology. The asymmetry is enabled by cholesterol's unique properties, which act as a buffer to redistribute between the leaflets and maintain robust barriers.
Researchers warn of misunderstandings in handling AI models, highlighting conditions for confidence in predictions. Explainability methods are crucial to understand algorithmic decisions, but interpreting results requires caution due to AI limitations.
The conference gathered international researchers to discuss AI's role in drug discovery and development, including generative AI strategies for designing chemical compounds. The speakers emphasized the significance of personalized medicine, where therapies will be tailored to each patient's unique molecular profile.
Researchers have successfully reprogrammed part of the large intestine to function like the nutrient-absorbing small intestine, reversing malnutrition in a preclinical study. The technique, which deletes the colon gene SATB2, restored nutrient absorption and improved survival rates in mice with short bowel syndrome.
Researchers developed a new viscoelastic model of enzymes, elucidating the intertwined effects of elastic forces and friction forces on enzyme function. This breakthrough allows proteins to be perceived as soft robots or programmable active matter, revolutionizing our understanding of enzymatic catalysis.
Researchers at Weill Cornell Medicine developed a new AI model that harnesses whole-slide tumor imaging data and gene expression analyses to predict how patients with muscle-invasive bladder cancer will respond to chemotherapy. The model outperforms previous models using a single data type, identifying key genes and tumor characteristi...
Researchers develop AI model to predict novel mutations in protein sequences, combining grammatical and semantic changes. The method uses all available information about the sequence and mutations to create a more accurate prediction model.
A study using NHANES data found that inflammation, rather than diet and exercise, has the strongest association with telomere shortening. Managing chronic inflammation may be key to preserving telomere length and promoting healthy aging.
A team of researchers from Osaka University has demonstrated that human tissue can be used to solve complex equations and process information, outperforming traditional computing methods. This breakthrough uses the concept of reservoir computing, where data is input into a complex 'reservoir' that encodes rich patterns.
A new study presents a proof-of-concept leptomeningeal neural organoid (LMNO) fusion model to study meninges-brain signaling. The co-culture system of neural organoids fused with fetal leptomeninges from mice demonstrates stability and interface characteristics.
A new model predicts how bacteria navigate obstacles to spread, informing strategies for curbing infections or designing better drug delivery. The model focuses on three surface states: uninterrupted movement, sliding along surfaces, and getting stuck in corners.
Researchers found that inhibiting the sonic hedgehog pathway restricts feather bud outgrowth and branching in chickens. Temporarily modified Shh expression resulted in proto-featherlike structures, highlighting the pathway's importance in feather development.
The Open Brain Institute launches a groundbreaking platform to simulate and study digital brains, empowering researchers to explore brain complexity and diseases. With its virtual neuroscience laboratories, the OBI enables global collaboration and access to cutting-edge virtual labs.
A study found that maternal depression can negatively influence children's executive function and lead to emotional overeating. Mothers with postpartum depression may model unhealthy coping mechanisms for their children, affecting their ability to regulate emotions and develop healthy eating habits.
A recent USF study found that strong ocean currents and wind pushed sargassum into the tropics, where it thrived in ideal growing conditions. Nutrients were supplied via vertical mixing, fueling massive blooms that end up on Caribbean beaches.
Developing heart cells use filopodia to probe and grab onto potential partners, seeking stability through energy equilibrium. The model predicts how cells match and rearrange, mirroring real embryo outcomes.
Researchers develop a 'colocatome' to study the interactions between cancer cells and their surrounding non-cancerous cells, revealing how these interactions impact tumor growth and treatment resistance. The study aims to provide insights into universal rules of tumor behavior and guide the design of more effective treatments.
A new brain-mapping technique identified memory-related brain cells vulnerable to protein buildup, a key factor in Alzheimer's disease. The study found that certain cell types in the hippocampus and cortex were more affected by tau buildup.
A comprehensive analysis of butterfly data in the US found that butterfly abundance fell by 22% between 2000 and 2020. The study, led by Eliza Grames at Binghamton University, examined data from over 12.6 million butterflies across the continental US.
The new AI model leverages hypergraphs to quickly and accurately identify therapeutic gene targets for diseases. HIT outperformed existing models in all tested metrics, demonstrating its accuracy in classifying therapeutic gene targets with great precision.
A study by the University of Göttingen found that bridging structures, regional coordinators, and addressing stakeholder expectations are crucial for long-term success. The project promoted habitat connectivity and biodiversity conservation in agricultural landscapes.
An international research team developed a user-friendly software method called Segment Anything for Microscopy, which can precisely segment images of tissues, cells, and similar structures. The new model improved performance for cell segmentation, enabling researchers to automate tasks that previously took weeks of manual effort.
Camille Bilodeau's project uses AI and molecular simulations to design peptide-covered surfaces for targeted applications, including new medicines, water desalination, and semiconductor manufacturing. Her research group aims to develop a rapid predictive tool to understand surface-water interactions of tethered peptides.
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.
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 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.
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.
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.
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.
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 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.
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.
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.