Articles tagged with Data Sets
A comprehensive, user-friendly repository has been created to help study Alzheimer's disease. The ssREAD database encompasses 277 integrated datasets from 67 scRNA-seq & snRNA-seq studies, totaling 7,332,202 cells, and includes interactive visualizations for comprehensive analysis interpretations.
Researchers developed a reliable iris recognition method by applying statistical limits to the spatial domain zero crossing technique, reducing errors to 0.022%. The algorithm uses a neural network to recognize unique features of each person's iris, achieving over 99.78% accuracy.
Portland State University has secured a nearly $1 million grant from the National Science Foundation's Campus Cyberinfrastructure program to establish the Oregon Regional Computing Accelerator (Orca) cluster. The cluster will provide free-of-cost computing resources and cyberinfrastructure to colleges in rural, regional, and minority-s...
A novel approach to training AI systems uses information about spatial position to identify objects and navigate surroundings, inspired by children's visual development. The method improves contrastive learning models' effectiveness by incorporating simulated spatial context information, outperforming base models in various tasks.
A new study from the University of Notre Dame shows that repeated trips to the grocery store can be a reliable indicator of creditworthiness. The research found that buying healthier groceries and consistent shopping habits are linked to on-time credit card payments.
A new dataset integrates global Health AI research, providing a structured resource for researchers, policymakers, and practitioners. The dataset includes 96,332 Health AI documents, covering publications, open research datasets, patents, grants, and clinical trials.
A new transit station in Japan significantly reduced average healthcare expenditures per capita over four years, with savings of approximately $929.99. The study used a causal impact algorithm and time series data to analyze the medical expenditure data gathered from a suburban city on the West Japan Railway line.
A recent study by Kyoto University reveals that expatriates' boundary-spanning activities can lead to role stress, emotional exhaustion, and a sense of being an outsider among local employees. The study highlights the need for careful management to mitigate these negative impacts.
A team at the University of Münster developed an improved method for explaining machine predictions of chemical reactions, using mechanisms such as reproduction, mutation and selection. The algorithm creates customised molecular fingerprints that predict chemical reactions with surprising accuracy, suitable for predicting quantum chemi...
Research suggests that four proposed AI search engines for automating search and retrieval of digital histopathology slides have inadequate performance for routine clinical care. The algorithms showed less than 50% accuracy in some cases, highlighting the need for rigorous external validations and standardization before clinical adoption.
A study by Washington State University found ChatGPT's generative AI system provided inconsistent heart risk assessments for patients with chest pain. The AI failed to match traditional methods used by physicians and returned different results for the same patient data, highlighting its limitations in high-stakes clinical situations.
The new 'scLENS' tool overcomes challenges in single-cell transcriptomics by automatically differentiating signals from noise using Random Matrix Theory and Signal robustness test. This innovation significantly improves analysis accuracy and efficiency, enabling researchers to extract biological signals conveniently and automatically.
A new AI model generates realistic images of single cells, which are used as synthetic data to train an AI model for better cell segmentation. The researchers found that providing a more diverse dataset during training improves performance.
DynGAN detects and resolves mode collapse by establishing thresholds on discriminator outputs and training dynamic conditional generative models. This improves the diversity of generated samples, surpassing existing GANs and their variants.
Contraceptive services experienced downward trends from an initial increase in the month after Dobbs v. Jackson, indicating growing challenges for access. The study found decreasing workforce numbers providing contraception methods.
Mayo Clinic researchers have invented a new class of artificial intelligence (AI) algorithms called hypothesis-driven AI, which can help discover the complex causes of diseases like cancer and improve treatment strategies. This emerging class of AI offers an innovative way to use massive datasets to guide individualized medicine.
A new study found that non-medical cannabis use is significantly associated with a 96% decrease in odds of subjective cognitive decline. However, medical and dual-use were not significant in reducing the risk of SCD. The study highlights the potential protective effects of cannabis on cognition, but more longitudinal research is needed...
A new dataset has been released that combines molecular information about the poplar tree microbiome with ecosystem-level processes. The dataset provides detailed information on 27 genetically distinct variants of Populus trichocarpa, a bioenergy crop, and includes data on gene expression, soil chemistry, and microbial diversity.
A new satellite dataset provides unprecedented insights into global plant growth, derived from TROPOMI satellite observations. The Comprehensive Mechanistic Light Response (CMLR) gross primary production (GPP) dataset offers a more accurate measurement of plant productivity on a global scale.
Researchers found that doctors can confidently skip 50% of biopsies by combining MRI-based prostate imaging reporting and data system scores with prostate-specific antigen density testing. The study suggests that this approach can decrease patient harm and healthcare costs associated with unnecessary biopsies.
Researchers from the University of Washington created an AI algorithm to analyze infant poses using limited training data. By leveraging generative AI, they were able to produce high-quality results, enabling parents to monitor their babies' daily activities and detect potential health issues early.
Researchers investigate how different VAE model architectures, latent space configurations, and training datasets impact the performance of generative music models with explainable features. They find that measureVAE has higher reconstruction accuracy but lower musical attribute independence.
A recent study found that most Americans support democratic norms, with 17.2% of Democrats and 21.6% of Republicans supporting at least one norm violation. However, the researchers also discovered a divide between everyday citizens and elected officials who are pushing against democratic governance.
A study in São Paulo's central area found that poor wood condition, sidewalk root constriction, and drastic pruning are major predictors of urban tree failure. The researchers propose guidelines for stakeholders to reduce the number of failures, which average 2,000 per year.
Researchers introduced a novel method to augment in situ root datasets through an improved CycleGAN generator, achieving significant enhancements in speed, accuracy, and stability. The approach also boosts dataset versatility by including diverse culture mediums.
A minimal metadata set (MNMS) is established to enable in vivo data reuse and improve the sharing and reproducibility of research data. The MNMS is designed to contribute to making data from living animals compliant with the FAIR data concept, which emphasizes Easy-to-Find, Accessible, Interoperable, and Reusable data.
Researchers developed a machine-learning model to assess prostate cancer biopsy samples, overcoming limitations of traditional methods. The new model, nnU-Net, provides accurate 3D segmentation of glandular tissue structures, leading to better prognostic analyses and potential improvements in patient outcomes.
DomAda-FruitDet is a domain-adaptive anchor-free fruit detection model that achieves impressive average precision scores of up to 94.0% across various fruit datasets. The model effectively bridges the foreground and background domain gaps, enabling accurate and efficient auto-labeling in smart orchards.
A new study warns that rural pupils in England are facing underachievement in education due to socioeconomic factors. Despite better exam results, rural pupils from disadvantaged homes perform worse than urban pupils from similar backgrounds.
A new study refutes previous research suggesting Spinosaurus was an aquatic pursuit predator, highlighting methodological flaws and low accuracy of phylogenetic flexible discriminant analysis. The researchers provide guidelines for future studies to use with caution when applying this methodology to limited datasets.
A team of Rice University researchers has developed a platform for integrating DNA and RNA data from single-cell sequencing with greater speed and precision. The method, MaCroDNA, relies on a classical algorithm to identify matching pairs of data and outperformed state-of-the-art technologies in accuracy measurements.
Researchers developed a novel machine learning-based approach to analyze diffuse reflectance spectroscopy data, achieving higher accuracies and speeds than existing methods. The 'wavelength-independent regressor' model overcomes use-error limitations by incorporating diverse datasets, making it suitable for clinical settings.
A new study finds that biodiversity is becoming more similar through time, but an almost equal number of communities are becoming more distinct. The analysis of 527 datasets shows a balanced trend between homogenisation and differentiation across landscapes.
Researchers optimized polygenic risk scores using ancestrally diverse genomic data to improve accuracy across diverse populations. The recalibrated tests provided a more accurate assessment of disease risk for individuals with varied ancestral backgrounds.
Researchers found that widely used machine learning tools produce biased results for immunotherapy research, as they rely heavily on datasets from higher-income communities. This can lead to ineffective treatments for lower-income populations. The study highlights the need for accurate and unbiased data in machine learning models.
A new study found that 17% of young mothers in Wales experienced at least one child being taken into care between 2014 and 2021. The study also revealed that many care-experienced mothers experience early motherhood and placement instability, leading to concerns about family separation.
Researchers use machine learning to combine mismatched datasets and reduce variation by over 95%, retaining meaningful differences. The approach has potential to provide deeper understanding of normal metabolism and identify biomarkers for disease.
The study found a 4-fold increase in ASD diagnoses among 2-3 year-old children and a 2-fold increase in older ages. The research highlights the need for education and healthcare services to keep up with the growing demand, and emphasizes the importance of early intervention to improve communication and social skills.
The study found that different instrumental systems in the Global Ocean Observing System (GOOS) complement each other and are essential for accurately monitoring ocean warming. The GOOS, which includes platforms such as free drifting instrumented floats, mooring buoys, and autonomous pinniped data, provides a comprehensive view of ocea...
Researchers developed a predictive model to detect users and content related to Islamic State extremists on social media, identifying potential propaganda messages and their characteristics. The study's findings can help social media companies and law enforcement agencies track and prevent the spread of extremist propaganda.
A new study from the Max Planck Institute of Geoanthropology highlights the importance of ancient cities in understanding contemporary urban challenges. The research provides numerical datasets on road lengths, building types, population sizes, economic output, and environmental impacts to inform urban planning and policy.
Researchers have identified over 700 plant genera named for women, representing a significant increase in the number of genera linked to women. The dataset provides valuable information on the contributions of women to botany and can be used by other researchers to build upon this work.
A new study finds that complex and unfamiliar sentences generate stronger responses in the brain's language processing centers. Sentences with higher surprisal and linguistic complexity evoke more activation, while extremely simple or nonsensical sequences elicit little response.
A new tool, CellHint, has been developed to unify different single-cell data, creating harmonized datasets. Researchers applied CellHint to reveal underexplored connections between healthy and diseased lung cell states and identified potential interests for future research in adult human hippocampus.
A recent study using AI to analyze registry data on people's residence, education, income, health, and working conditions can predict life events such as personality and time of death. The model outperforms other advanced neural networks and provides precise answers despite ethical concerns about sensitive data and bias.
Researchers at Lancaster University find that the 'r' sound is becoming weaker in England, with most young speakers softening it to an elongation of the vowel. This decline is predicted to continue, potentially erasing traditional dialects and linguistic homogenization.
A NSF-funded project, MABLE, is developing a digital app using crowdsensing, AI, and robotics to empower individuals with responsive maps and turn-by-turn instructions. The app aims to improve accessibility and navigation for persons with visual or mobility impairments, such as those with low vision and wheelchair users.
The EXPLORE toolkit offers interactive visual analytics and machine learning to analyze galaxy data, identify unusual stars, and visualize the lunar surface. Users can create immersive experiences, including 3D models of the Moon and interactive sky maps.
Researchers have developed an AI algorithm that uses people's flavor impressions to make accurate predictions of individual wine preferences. The algorithm combines data from wine labels, user reviews, and sensory tastings to provide personalized recommendations.
The EUDTO-BioFlow project aims to establish a digital twin of the ocean by making critical marine biodiversity data publicly available. This will enable the simulation and study of 'what if' scenarios for effective conservation and management.
Research reviews 98 articles worldwide, finding suicide rates among men increase during and after economic crises. The study highlights the need for governments to fund health services and social support systems to mitigate mental health impact.
Researchers have combined 14 studies on piglet gut bacteria to uncover common patterns in microbiome development over time. The analysis revealed predictable trajectories and important details at a fine time scale, with an accuracy of 70% in predicting animal age based on microbiomes.
Sydney researchers have identified new structural relationships in complex networks like Twitter and political blogs. A 'source-basin' structure plays a crucial role in organizing the flow of information, with influential nodes serving as sources and densely connected active nodes forming basins.
Researchers developed three diffractive deep neural networks using orbital angular momentum to recognize objects in images, achieving accuracy comparable to wavelength and polarization-based models. The technology has potential for real-time processing applications like image recognition and data-intensive tasks.
Researchers created a large-scale remote sensing annotation dataset to support Earth observation research and monitor global land cover changes. The Globe230k dataset provides new insights into the dynamic monitoring of global land cover, enabling high-level semantic understanding of land use.
Virginia Tech researchers analyzed partisan media sentiment toward AI and found that liberal-leaning media tend to have a more negative tone than conservative media. The study suggests that this opposition can be attributed to concerns over AI amplifying existing social biases, such as racial and income disparities.
Researchers developed DIRFA, an AI-based program that generates realistic videos with facial animations synchronized to spoken audio, showcasing improvements over existing approaches. The tool has potential applications in healthcare, education, and entertainment, enhancing user experiences.
Researchers developed a deep convolutional neural network to pinpoint cardiac catheter tip locations in photoacoustic images, achieving high precision and recall. The approach has the potential to replace fluoroscopy during cardiac interventions, leading to safer procedures.
Researchers found that dyadic bicultural competence, the ability to navigate both cultural contexts, is associated with relationship quality. Couples with higher levels of this competence tend to experience fewer relationship challenges.
Researchers found that green spaces alleviate extreme heat's negative impacts on human health, while densely packed buildings increase mortality risk. Urban design strategies incorporating different types of greenery are recommended to mitigate heatwave-associated mortality.