Articles tagged with Data Sets
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.
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.
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.
Researchers developed a deep-learning model to assess CXR images for probable COVID-19 severity. The model achieved an area under the receiver operating characteristic curve of 0.78 when predicting intensive care need within 24 hours.
Researchers from the University of Kansas have created a powerful dataset to facilitate drug development against gram-negative bacteria. The dataset reveals over 270,000 previously unidentified outer-membrane proteins with potential as vaccine targets.
Biased AI can limit climate predictions and misguide governments due to missing information from under-represented communities. Human-in-the-loop design can fill these 'data holes' by offering a sense check on used data and context.
A new study by University of East Anglia reveals ChatGPT's systematic left-wing bias, favoring Democrats in the US and Labour Party in the UK. The platform's responses also lean towards President Lula da Silva of Brazil's Workers' Party.
Research identifies key molecular signatures and pathways contributing to skeletal muscle strength loss in females with estrogen deficiency. The study found parallel patterns of inhibition and activation across various signaling pathways, including AMPK and calcium signaling.
A team of researchers developed a novel method that leverages temporal characteristics of blood pulse to estimate heart rates with improved accuracy, especially in scenes with ambient light fluctuations. The proposed method showed a 36.5% improvement in estimation accuracy compared to conventional methods.
Researchers developed an innovative optical tool, the Schistoscope, to capture microscopy images of urine samples for efficient detection of Schistosoma haematobium eggs. A two-stage diagnostic framework using deep learning accurately identified and counted eggs in field settings with high sensitivity, specificity, and precision.
A Swansea University-led study found that Welsh breastfeeding rates increased during the pandemic, with a significant correlation between mothers' intention to breastfeed and exclusive breastfeeding duration. The study proposes targeted interventions during pregnancy and policies to support families to improve breastfeeding duration.
A sociological study by the University of Zurich confirms that many professionals consider their work to be socially useless. Office jobs were found to be more than twice as likely to feel pointlessness compared to other occupations. The study suggests that factors such as routine work, job autonomy, and management quality also contrib...
Researchers combined linguistics and genetics to propose a new origin theory for the Indo-European languages, suggesting an ultimate homeland south of the Caucasus. The study estimated the family to be approximately 8100 years old, with five main branches split off by around 7000 years ago.
A new study uses machine learning to analyze data from DrugAge, a database of chemical compounds modulating lifespan in model organisms. The researchers create four types of datasets to predict whether or not a compound extends the lifespan of C. elegans, using features such as compound-protein interactions and Gene Ontology terms.
Researchers have developed a computational tool to compare large datasets and predict immune responses to disease, potentially leading to better vaccines. The new algorithm, designed by La Jolla Institute for Immunology scientists, uses machine learning to identify underlying patterns in immune system data.
A new study suggests capping the energy use of the top 20% of consumers could reduce carbon emissions by 11.4%. The strategy would allow those with lower incomes to increase their consumption levels, promoting fairness and delivering climate justice.
A new AI technology has been developed to generate artificial scientific data, allowing for faster and more efficient detection of material features. The AI uses generative adversarial networks to incorporate background noise and experimental imperfections into the generated data, making it virtually indistinguishable from real data.
Researchers propose a novel vehicle color recognition method based on Smooth Modulation Neural Network with Multi-Scale Feature Fusion, achieving high accuracy and overcoming class imbalance issues. The proposed method outperforms state-of-the-art VCR methods and meets the requirements for fine classification of vehicle colors.
A recent study by Kyoto University has raised concerns about the authenticity of Big Oil's net-zero emissions claims. The research team found that oil majors are not making sufficient progress in phasing out fossil fuels and transitioning to clean energy, despite their pledges to achieve net-zero by 2050.
BioAutoMATED is an all-in-one AutoML platform designed for biologists, enabling easy analysis and interpretation of biological sequences. The platform uses three existing AutoML tools to generate models that can predict biological functions from sequence information.
A retrospective analysis of national data found that over 20 million Americans experienced loss of smell or taste after COVID infection, with a large portion never fully recovering these senses. The study estimated that almost 28 million Americans may be left with decreased sense of smell after COVID infection.
Researchers analyzed 692,534 race times to find genetic improvement accounts for 60% of speed increase in short-distance races, while heritability is low across all distances. The study suggests weaker selection or other factors limiting genetic progress, particularly over long distances.
Researchers identified five subtypes of heart failure using machine learning, including early onset and atrial fibrillation related. These subtypes have different mortality risks, with some patients at higher risk of dying within a year after diagnosis.
A deep learning-based framework called EMGSense enables accurate wearable EMG device usage through AI self-training techniques. It achieves average accuracy of 91.9% in gesture recognition and 81.2% in activity recognition, outperforming state-of-the-art approaches.
A team of scientists has developed an automated algorithm to reconstruct the shape of each neuron inside a light microscopy image using deep learning. This breakthrough addresses the challenge of generalizing algorithms across diverse species, brain locations, developmental stages, and microscopy image sets.
Researchers tracked immune cell clusters in the aging mouse prostate using highly multiplexed immune profiling. Early adulthood sees myeloid cells, while between 6-12 months old, there's a profound shift to T and B lymphocyte-dominance. The study reveals new insight into prostatic inflammaging and the window for interventions.
A research group led by NCKU professor I-Non Chiu conducted the first cosmological study on galaxy clusters identified by eROSITA, analyzing 550 galaxy clusters. The results suggest that Dark Energy occupies up to 76% of the total energy density in the Universe.
Researchers used a multiomics approach to analyze changes in transposable elements after influenza A virus infection, identifying transcription factors contributing to individual responses. The study provides insights into the variable severity of illness among individuals infected with the same virus.
Online radiologists choose studies based on financial attractiveness, leading to delays in high-priority cases. This study found that expedited priority class contained the highest percentage of delayed studies.
A new guide has been created to standardize fossil pollen datasets, enabling researchers to compile and analyze large-scale syntheses of palaeoecological data. The FOSSILPOL workflow and R-package provide a step-by-step process for handling data preparation, ensuring good data quality and minimizing erroneous interpretations.
Researchers found that machine-learning models trained with descriptive data label rule violations more harshly than humans, leading to potential serious implications in the real world. This study highlights the need for careful consideration of data labeling and training methods to ensure fairness and accuracy in AI decision-making.
Researchers used cancer proteomics data to identify gene candidates for therapeutic targeting, focusing on protein kinases in uterine endometrial cancer cells. Public molecular resources and multi-omics data analysis can prioritize genes of interest for future studies.
Researchers have developed a new method called EvoAug that uses artificial DNA sequences inspired by evolution to train deep neural networks for genome analysis. This approach enables the model to recognize regulatory motifs more accurately, leading to better performance and potential breakthroughs in understanding human health.
A deep learning model has been developed to classify cancer cells into distinct types, enabling accurate prediction of metastatic potential. The tool achieves high accuracy and is simple to use, making it a promising solution for medical practitioners.
A mobile application utilizing Python and a single-element ultrasound transducer has been developed for photoacoustic tomography (PAT) image reconstruction. The application successfully reconstructs high-quality images with signal-to-noise ratio values above 30 decibels, making it suitable for point-of-care diagnosis in low-resource se...
More than 3 million sq km of Asian elephant habitat has been lost in 300 years, with suitable habitats cut by nearly two-thirds. The study suggests that the remaining elephant populations may not have adequate habitat areas, setting up a high potential for conflicts with people living in those areas.
A team of researchers from Carnegie Mellon University has developed an AI-based system to help clinicians make decisions quickly and precisely in the ICU. The system, called the AI Clinician Explorer, provides recommendations for treating sepsis based on data from over 18,000 patients.
A study by Drexel University and Vanderbilt University analyzed 82 relevant conversations on Instagram direct messages where teens asked for help, revealing that most disclosures were about mental health concerns. Support was offered in most cases, but specific sets of circumstances led to denial.
A team of experts identified 29 sources of bias in AI/ML models for medical imaging, including data collection, preparation, and deployment. The study provides a comprehensive roadmap for mitigating these biases and ensuring fairness, equity, and trust in AI/ML models.
GPMeta accelerates pathogen detection in metagenomic sequencing (mNGS) tests, achieving higher accuracy while significantly reducing processing time. The approach uses a succinct hash index scheme and multi-GPU support to handle massive data sets.
A study found that high-quality labeling of images boosts perceptions of training data credibility, leading to increased trust in AI systems. However, biases in the data can reduce trust in certain aspects.
A recent study from Aarhus University found that music used for studying and sleeping share similar characteristics, such as slow tempo and repetitive patterns. The study suggests that these similarities can be attributed to the calming effects of the music on the brain, creating a conducive environment for both tasks.
A machine learning program can spot risky conversations on Instagram by analyzing metadata clues, such as conversation length and participant engagement. The system was 87% accurate in identifying risky chats using sparse and anonymous details from over 17,000 private chats.
A new deep learning-based model estimates breast density with high precision, correlating it to cancer risk. The model's performance is comparable to that of human experts, but it can be trained faster and on smaller datasets.