Articles tagged with Data Sets
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.
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 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.
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.
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 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.
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.
A team of researchers applied an unsupervised data-driven analysis method to multiomics dataset, identifying patterns shared across gene expression, DNA methylation, and genetic variation. The study found that these patterns are associated with various diseases and can be used to predict disease onset.
A Dartmouth-led study found that tweets featuring K-pop group BTS generated 111 times more virality than similar tweets without the artist's name. This highlights the power of entertainment in driving public health messages and bridging cultural divides.
A new task group initiative bridges the gap between biodiversity and omics data standards, enabling sustainable interoperability and collaboration. The effort aims to facilitate data reuse, integration, and knowledge generation in biodiversity research.
Researchers from Imperial College London and the University of Nottingham used machine learning to identify 'atomic shapes' that form basic pieces of geometry in higher dimensions. The findings reveal unexpected patterns in these shapes and demonstrate the potential for machine learning to accelerate mathematical discoveries.
Researchers developed a precise historical reconstruction of the Red Sea circulation using fine-grained regional data. The new analysis reveals new characteristics of current circulation, temperature, salinity, and oceanic behavior, improving decision-making for megadevelopments like those in Saudi Arabia.
Using data from Mass General Brigham’s electronic health records, researchers found that SARS-CoV-2 accounted for approximately 1 in 6 cases of sepsis during the COVID-19 pandemic. The mortality rate for patients with SARS-CoV-2-associated sepsis was initially high but declined over time.
The Keck School of Medicine of USC has launched a five-year, $50.3 million multi-omics study to better understand the causes and prevention of various diseases, including NAFLD, in underrepresented racial and ethnic groups.
A study analyzed millions of tweets by Republican and Democratic US politicians over a decade, finding that Republicans were more likely to share untrustworthy information. The researchers identified linguistic signals associated with low-quality information and suggested potential solutions for the public to recognize these signals
Researchers analyzed data from 2005-2020 and found that 10-year conditional survival was similar between biventricular and most single-ventricle CHD patients. Biventricular CHD patients also showed better 10-year survival compared to non-CHD heart transplant recipients.
A new study published in Nature Human Behaviour shows that socio-economic factors play a larger role than climate in driving net-migration patterns worldwide. The researchers created a high-resolution dataset of net-migration over the past two decades to inform policy-making and fuel further research.
Researchers created a comprehensive molecular tree of camel spiders, revealing two main groups in the Americas and their relationships. The study also found that camel spiders began evolving around 250-300 million years ago during the Permian period.
A study by Brigham researchers reveals a 37% increase in out-of-state residents seeking abortion care in Massachusetts after Dobbs, with an estimated 45 additional abortions. Non-profit funding for out-of-state residents increased by nearly ten percent, while in-state residents' use of funding remained relatively stable.
A new study examines racial and socioeconomic differences in US preterm birth and mortality rates over 25 years. Despite improvements, disparities persist, with Black infants 1.4 times more likely to die from preterm birth than White and Hispanic infants.
A comprehensive study reveals a stagnation in European freshwater biodiversity recovery, highlighting the need for intensified mitigation strategies. The analysis of 1,816 time series of freshwater invertebrate communities between 1968 and 2020 shows promising increases until the 2010s, but a significant slowdown since then.
Augusta University researcher Arni S.R. Srinivasa Rao calls for updating the UN's traditional approach to measuring population replacement levels. He proposes a new formula that allows for more timely and accurate measurement, which is essential for understanding emerging global demographics.
A new study by the University of Illinois and USDA-Agricultural Research Service has identified the key factors influencing sweet corn yield. The analysis found that seed source is a significant variable, with processors having a choice over which hybrids to use, and high nighttime temperatures also impact yield.
A new dictionary provides a comprehensive overview of Shakespeare's language, revealing words with reduced mental ability and plant hybrids. The Arden Encyclopedia of Shakespeare's Language offers insights into the linguistic thumbprints of plays and characters, as well as the networks of character interaction.
The University of Missouri is launching a five-year, $3 million doctoral training program to prepare the next generation of scientists and engineers for emerging fields like materials science and data science. The program aims to empower future workers with both technical expertise and data-driven insights.
A new R package, lydemapr, has been developed to track the spread of the invasive Spotted Lanternfly in the US. The dataset contains detailed information on the pest's presence, establishment status, and population density across over 650,000 observations.