Articles tagged with Data Sets
A new study found that valbenazine, a VMAT2 inhibitor, is safe and effective in treating chorea in patients with Huntington's disease. Chorea is a common symptom of the disease, causing involuntary and irregular movements.
A recent study by the University of Rochester found that mobility patterns can be predicted with surprising accuracy based on data collected from acquaintances, even if individual users turn off their own location tracking. The researchers discovered that up to 95% of an individual's movement pattern can be inferred from people they ar...
Researchers at NYU Tandon School of Engineering propose a paradigm to solve the problem of inferring collective size from individual behaviors. By observing self-propelled Vicsek particles, they show that the time rate of growth of mean square heading is sufficient to predict the number of particles under particular parameters.
A new study by University of Warwick researchers finds that the adoption of cereal crops is the key factor in the emergence of complex hierarchies and states. Contrary to conventional theory, high land productivity does not lead to the development of tax-levying states.
A recent study combining climate data with fossil records of large mammals in Africa found that times of erratic climate change do not lead to major evolutionary changes. The research, published in the Proceedings of the National Academy of Sciences, suggests that environmental variability and species turnover may not be closely related.
Researchers develop a new method to reconstruct the states of complex nonlinear systems based on time series data. The approach optimizes reconstruction parameters by focusing on the geometric structure of attractors, resulting in improved accuracy.
A machine learning model has been trained to accurately identify individuals with post-traumatic stress disorder (PTSD) by analyzing text data. The model achieved an 80% accuracy rate in distinguishing between those with and without PTSD. This breakthrough could lead to the development of a cost-effective screening tool for health prof...
Researchers created brain charts spanning human lifespan, revealing rapid brain growth in early life and slow decline with age. The charts use aggregated MRI datasets, covering a range of ages from fetus to 100-year-old adult, and aim to create a common language for describing brain development and maturation.
The BrainChart platform benchmarks brain development based on MRI data from over 100,000 individuals, creating a standardized chart like those for height and weight. The tool identifies previously unreported neurodevelopmental milestones and provides a common language to understand brain images from different sources.
Researchers found that ocean reanalysis data sets exhibit differing evolutions during El Niño events, particularly as they develop and decay. The disparity among datasets grows with the event's peak phase and lingers even after neutralization.
A crowdsourcing campaign has compiled data on the drivers of tropical forest loss between 2008 and 2019, resulting in a high-resolution dataset. The analysis found that agriculture expansion, road construction, and wood extraction are major contributors to deforestation.
Researchers from North Carolina State University have released a dataset providing detailed information on COVID-19 vaccine shipments and wastage across the US. The data aims to inform modeling and decision-making to optimize vaccine supply chains.
Researchers analyzed health records of nearly half a million patients post-heart attack and found that depression was associated with a nearly 50% higher stroke risk compared to those without depression. The study highlights the need for greater attention to mental health in research and practice.
A new study using subsurface imaging sheds light on the geological connection between Yellowstone's iconic hydrothermal features and deeper heat sources. The research team detected hydrothermal alteration and found a remarkable similarity in deep structure beneath areas such as Norris Geyser Basin and Lower Geyser Basin.
A study has shown that wind variations over the southern Red Sea are the main drivers of sea-level extremes, driving levels up and down depending on wind direction. This understanding is crucial for coastal planning and management to mitigate the impact of storm surges and coastal erosion.
Researchers warn of machine learning bias when data published for one task is used to train algorithms for a different one. This can lead to compromised integrity and 'overly optimistic' results in medical imaging applications.
A new visual leaf library, developed by a Penn State-led team, provides a resource to help scientists recognize and classify plant leaves. The library contains 30,252 high-resolution images of cleared and fossilized leaves, allowing for rapid searching and comparison.
Researchers at Kaunas University of Technology improved an algorithm to detect Alzheimer's disease from MRI images, achieving over 98% accuracy. The new model uses a modified neural network and adapts to variations in data, such as differences in hospital equipment and patient positions.
Researchers from the Netherlands Institute for Neuroscience have published a dataset of electrophysiology data recorded from two monkeys' visual cortex during resting state. The dataset provides high-density receptive field coverage and can be used to yield new insights into background activity influencing visual information processing.
The collaboration aims to accelerate addressing unmet needs in the neonatal population by applying quantitative modeling, biomarkers, and regulatory science. Patients and families will benefit from improved understanding of bronchopulmonary dysplasia and better patient enrollment for trials.
A new mathematical framework has been created to study fitness landscapes of regulatory DNA, enabling the prediction of gene expression changes. The framework uses a neural network model trained on millions of experimental measurements to decipher the evolutionary past and future of non-coding sequences.
Researchers at Texas A&M University developed a novel error estimator using transfer learning principles to evaluate machine-learning model performance. The technique enables fast screening of source data sets, improving the accuracy of diagnoses in complex medical issues like schizophrenia.
Researchers at Duke University developed EyeSyn, a virtual eyes system that simulates human eye movement to train metaverse platforms. The system reduces privacy concerns and allows smaller companies to access the metaverse with minimal resources.
Researchers created a comprehensive genomic regulatory map of a 24-hour-old zebrafish embryo, identifying millions of regulatory segments that control gene transcription. The study used single-cell technologies and machine learning algorithms to analyze genome data from over 23,000 nuclei.
Researchers developed a machine-learning technique that can pinpoint anomalies in large datasets, such as power grid failures and traffic bottlenecks. The model uses advanced probability distributions to identify low-density values, allowing for faster and more accurate anomaly detection.
Researchers analyzed 280 confirmed sea turtle entanglements in Massachusetts waters and found that quickly reporting incidents can improve survival rates. The study highlights the importance of complete disentanglement to minimize injury and promote survival, with many turtles being alive weeks to years after being disentangled.
GIST researchers propose a new strategy for crime prevention using artificial intelligence, trained on a large-scale dataset of deviant incident reports and corresponding images. The model, called DevianceNet, can accurately classify and detect deviant places, making it a useful tool in urban safety development.
A new analysis of human remains provides the earliest DNA from sub-Saharan Africa, outlining major demographic shifts between 80,000 and 20,000 years ago. The study reveals people moved and settled in other areas, developed alliances and networks to trade and share information.
Researchers used AI to predict flood damage in the US, finding a high probability of flood damage for more than 1.01 million square miles across the country. The study suggests that recent FEMA maps do not capture the full extent of flood risk, with 84.5% of reported damage not within high-risk flood areas.
Researchers studied how diverse neural network training datasets impact generalization. They found that data diversity is key to overcoming bias, but also degrade performance when neural networks are trained for multiple tasks simultaneously. The study highlights the importance of designing diverse and controlled datasets in machine le...
A review by Nathan et al. showcases big-data revolution in movement ecology, revealing new insights into animal behavior and habitat use. Reverse-GPS systems, such as ATLAS, track animals with high accuracy, while acoustic telemetry tracks aquatic life, providing crucial data for conservation.
A UCI team uncovered key brain mechanisms by which the hippocampus organizes memories into sequences, enabling decision-making. The finding may help understand memory failures in Alzheimer's disease and other forms of dementia.
A new dataset from NYU Tandon School of Engineering and Woven Planet Holdings promises to help visually impaired pedestrians and autonomous vehicles navigate complex urban settings. The robust dataset uses over 200,000 outdoor images to test visual place recognition technologies that can improve navigation accuracy.
Researchers from Argonne National Laboratory have created a set of new practices to guide the curation of high energy physics datasets, making them more FAIR and reusable. The goal is to automate the finding and use of data for humans and streamline the development of AI tools for scientific discovery.
KAUST researchers have developed a flexible statistical model to analyze environmental data, revealing niches where existing methods fall short. The study provides a new approach to modeling dependence structures, highlighting the importance of understanding model limitations and extrapolation beyond observed data.
Researchers from Universidad Carlos III de Madrid developed an open-source development kit (PDK) to create solutions for businesses using personal data. The tool allows companies to exploit user data in a respectful way, promoting individual control over their data.
Researchers at NYU Abu Dhabi have published a comprehensive review of 50 fundamental traffic models using an extensive data set of 2.3 billion vehicle observations from 25 cities worldwide. The study found that a non-parametric model outperformed other traffic flow models, regardless of road type and congestion level.
The collaboration aims to create an interoperable global data ecosystem for rare diseases, accelerating the development of new therapies. This partnership benefits patients, regulators, advocacy stakeholders, researchers, and industry, while expanding C-Path's global capabilities in collaborating on methodologies and technologies.
A new global dataset reveals approximately 73,000 tree species, highlighting the vulnerability of global forest biodiversity to climate change and land use. The research, led by Professor Andy Marshall, also identifies a 'hot spot' of likely undiscovered species in northeast Australia and the Pacific Islands.
Researchers at the University of Groningen have developed an AI system that can recognize indoor spaces with high accuracy by combining image and audio data. The system achieved a 70% accuracy rate in recognizing nine different types of indoor spaces, surpassing previous results.
The study reveals that the region within Io's orbit is dominated by oxygen and sulfur ions, with oxygen prevailing among the two. Further inward, within Amalthea's orbit, oxygen ion concentration increases unexpectedly.
A new study reveals that medieval warhorses were bred for success in various functions, including tournaments and long-distance raiding campaigns. The research, published in the International Journal of Osteoarchaeology, found that breeding and training were influenced by biological and cultural factors.
A new study by the University of Exeter found that countries with high levels of trust among their citizens experienced a faster decline in COVID-19 cases and deaths. This is because behaviors like mask wearing and social distancing rely on mutual trust to be effective.
Researchers developed an AI model that can diagnose COVID-19 with high accuracy, using federated learning to preserve patient data privacy. The model was trained on over 9,000 CT scans from 23 hospitals in the UK and China, and validated against a panel of radiologists.
Researchers have published an extensive 7T fMRI dataset to study how humans perceive and interpret naturalistic photographs. The Natural Scenes Dataset provides a massive scale of brain data for training complex deep-learning models that predict brain activity.
The MDI Biological Laboratory has been awarded a grant to promote cloud computing among researchers in Maine, aiming to level the playing field by providing access to sophisticated computing resources. The program will provide training on Google Cloud Platform and assist institutions in implementing cloud computing services.
Researchers developed an algorithm to differentiate life-threatening gunshot events from non-life-threatening plastic bag explosion events. The study found that 75% of plastic bag pop sounds were misclassified as gunshot sounds, highlighting the need for a diverse dataset of similar sounds.
A new database has been launched to systematically record findings on perovskite semiconductors, featuring over 42,000 individual data sets and analysis tools for interactive exploration. The FAIR principles guide the preparation of the data, enabling easy searching with modern algorithms and artificial intelligence.
Researchers from Kaunas University of Technology have developed an AI-based approach for contactless machine failure detection, using sound data from existing equipment. The solution is sustainable and relatively cheap, with no need for new sensors or equipment installation.
A team of University of Pennsylvania biologists used citizen science data to create a comprehensive abundance map of the black-legged tick, responsible for transmitting Lyme disease. By correcting biases in the data, they were able to increase its value and provide insights into tick distribution across the Northeast US.
A new dataset from Canterbury earthquakes provides over 15,000 case histories for liquefaction, significantly augmenting model training and testing. The dataset enhances hazard assessments and improves engineering solutions in earthquake recovery, benefiting society as a whole.
A new AI application called DeepMReye uses MRI signals to read eye movements and infer thoughts, memories, and goals. It can also diagnose brain diseases by analyzing characteristic eye movement patterns.
Convolutional neural networks trained to identify abnormalities on upper extremity radiographs are susceptible to a ubiquitous confounding image feature: radiograph labels. Covering these labels increases accuracy, while using them alone leads to decreased performance.
Research by Binghamton University economists found that COVID-19 lockdowns led to a significant improvement in air quality for minority neighborhoods in rural New York, narrowing the existing gap with majority white neighborhoods. The study suggests stronger regulation can improve air quality in polluted areas.
Researchers at Children's Hospital of Philadelphia have developed a novel therapy that targets proteins essential for tumor growth and survival. Using a multi-omics approach, they identified peptides unique to neuroblastoma tumors, which are then targeted by peptide-centric chimeric antigen receptors (PC-CARs).
Researchers identified a gene family called MEF2 that controls a genetic program promoting resistance to cognitive decline. Enriching activities appear to activate MEF2, which may help prevent age-related dementia.
A new study explores the problem of shortcuts in a popular machine learning method and proposes a solution that can prevent shortcuts by forcing the model to use more data. By removing simpler characteristics and asking the model to solve the task two ways, researchers reduce the tendency for shortcut solutions and boost performance.
A new study improves AI diagnoses by penalizing algorithms for false negatives, which can be more urgent than accuracy. Researchers achieved significant improvements in precision and recall for chronic kidney disease and other conditions using cost sensitivity techniques.
A study has created a massive database of academic papers documenting global adaptation actions to climate change. The research found that people are taking action, but these efforts tend to be fragmented and may not be enough to deal with the expected effects of climate change.
The project aims to address rising healthcare costs, health disparities, and expands digital health through advances in AI. It also trains students from different disciplines to develop and apply data science techniques.