Articles tagged with Biological Models
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.
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.
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.
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.
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.
Researchers found that dopamine treatment increased neprilysin levels and reduced beta-amyloid plaques in mouse brains, improving memory function. Long-term L-DOPA treatment also showed improved cognitive performance in mice.
Researchers investigated the effects of initial microbiota on microbial succession during eggplant fermentation, finding two distinct patterns of LAB dominance. They also identified Lactiplantibacillus plantarum as a primary contributor to lactic acid production and nutrient content.
Eötvös Loránd University researchers develop first large-scale autonomous drone traffic solution, combining route planning and bio-inspired flocking models to avoid conflicts and manage remaining issues. The system can handle up to 5000 drones in two dimensions with varying speeds and priorities.
Scientists developed customized AI tools, including ChatGPT, to provide accurate responses on digital pathology. The tools help pathologists without extensive coding experience analyze tissue samples, bridging the gap between pathology and digital pathology skills.
Researchers developed a machine learning approach to identify potential subtypes in diseases, significantly enhancing disease classification and treatment strategies. The model uncovered 515 previously unannotated disease subtypes.
Researchers at Linköping University created a tool to predict risk of persistent side effects in breast cancer patients treated with taxane drugs. The model uses genetic characteristics to forecast the risk of nerve damage, which can be used to adapt treatment and improve patient outcomes.
Researchers developed a flexible-yet-sturdy morphing structure inspired by the starfish skeleton with 4D morphing features. The structure exhibits self-locking, continuous bending, self-healing, and shape memory features, making it suitable for industry applications in robotics, aviation, and biomedical devices.
Researchers at the University of Houston have introduced a new method for sleep stage classification that can be performed at home and uses only two leads. This approach achieves expert-level agreement with the gold-standard polysomnography without expensive equipment, paving the way for more accessible and cost-effective sleep studies.
A new radiative transfer modeling framework using Helios 3D software simulates RGB, multispectral, thermal, and depth images of plants with high accuracy. The framework reduces the need for manual data collection, enabling efficient training of deep learning models for high-throughput plant phenotyping and advancing agricultural research.
A 246 million-year-old nothosaur vertebra was discovered on New Zealand's South Island, shedding new light on early sea reptiles from the Southern Hemisphere. The find reveals that these marine reptiles originated near the equator and rapidly spread to other regions, challenging long-standing hypotheses about their migration patterns.
The study found that existing corn varieties are not ideal for future climates and that new crops with specific traits will be necessary. The research suggests that warmer temperatures, drier air, and increased CO2 will lead to decreased yields unless adaptations are made.
A team of researchers at Penn has developed an artificial intelligence tool that can mine the vast and largely unexplored biological data from over 10 million molecules to discover new candidates for antibiotics. The deep learning approach identified thousands of candidates in just a few hours, with many showing preclinical potential.
The new law recognizes personalized medicine as a fundamental patient right, emphasizing individualized treatment and care. It sets a precedent for future health care innovations worldwide, integrating genomics, proteomics, and AI technologies.
A study by Dr. Edwin Dalmaijer found that pigeons' desire for social proximity leads to improved flight paths as younger birds learn from older ones. This generational improvement in route efficiency is similar to those seen in real-life data, suggesting a key role for social factors in navigation.
A holistic approach to mental health management involves integrating medication with lifestyle changes, social support, and community engagement. This approach recognizes the interconnectedness of physical and mental factors affecting mental health, emphasizing individual rights and dignity.
A team of researchers from the University of Pennsylvania developed a model that incorporates two forms of gossip to study indirect reciprocity. They found that there is a mathematical relationship between these forms of gossip, allowing them to understand how much gossip is required to foster cooperation and how incorrect information ...
A research team at Mass General Brigham developed a foundation model that identified patterns predicting anatomical site, malignancy, and prognosis from radiological images. The approach remained powerful even with limited data, outperforming existing methods in specialized tasks.
A new screening algorithm combining maternal history, ultrasound data and several tests for blood markers may predict most preeclampsia cases in the first trimester of pregnancy. The study found that personalized risk-based treatment decisions may improve adherence to preventative measures.
A new model predicts that most of North America will be suitable for the establishment of the box tree moth, a invasive species from Asia. The Ecoclimatic CLIMEX model shows the box tree moth's distribution in Asia is likely incomplete, suggesting further expansion possible in its introduced range.
A recent study by University of Technology Sydney has modelled the transfer and deposition of plastic particles in the human respiratory system. The results have identified hotspots where plastic particles can accumulate, particularly in the nasal cavity and lungs, posing a risk to respiratory health.
The American Heart Association has funded four new grants to evaluate the use of race in predicting heart disease risk and develop unbiased clinical algorithms. The research projects will test various risk models to remove racial bias from care decisions, improving patient outcomes.
Researchers at UNC Lineberger Comprehensive Cancer Center have developed a mouse model of Kaposi sarcoma, which could facilitate the development of new drugs to treat the disease. The model provides a better understanding of angiogenesis and its potential targets for therapy.
Researchers at Insilico Medicine developed COSMIC, a new framework for molecular conformation space modeling that provides accurate insights into molecule positioning and activity. This enables faster and more efficient drug design decisions.
A new case report uses longitudinal multi-omics monitoring to detect a precancerous pancreatic tumor in a patient. The study highlights the potential of blood-based LMOM for early detection and personalized medicine, warranting further translational research.
A novel machine learning model has been developed to characterize material surfaces, accurately predicting key electronic properties. The model, which employs artificial neural networks and transfer learning, shows great promise for exploring new materials with superior properties.
A researcher at West Virginia University is using 'reverse engineering' to study the structural characteristics of slot machines and their impact on players. The study aims to understand what makes these games immersive and how they interact with individual vulnerabilities, ultimately informing the development of safer gambling products.
A recent study suggests that Canada lynx had a broader past range in the US, potentially including parts of Utah, central Idaho, and Yellowstone National Park. The researchers used a validated model to estimate the species' historic distribution, taking into account factors such as climate change and land use.
A new model integrating soil microbes and large perennial grasses into the DayCent framework improves its representation of ecosystem dynamics. The updated model includes a live microbial biomass pool and dead microbial biomass pool to simulate carbon storage in soils, enhancing the evaluation of bioenergy crop sustainability.
A team of scientists discovered new fusion sites in protein evolution that enable faster and more targeted drug development. By combining evolutionary processes with synthetic biology, they created customized biological drugs with improved therapeutic properties.
A new method for correcting glare in plant phenotyping has been developed, using polarized light to improve accuracy and reduce complexity. The technique has been validated in field trials, showing significant improvements in image data accuracy and reduction of error and variance.