Articles tagged with Biological Models
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.
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.
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 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...
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.