Articles tagged with Data Sets
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 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 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 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 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 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.
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 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.
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.
Max Planck scientists explore the possibilities of artificial intelligence in materials science, discussing how combining physics-based modeling with AI can unlock complex material designs. The research focuses on overcoming limitations of traditional methods and handling sparse, noisy data.
A team of researchers has developed a new human-in-the-loop system to improve the accuracy and interpretability of deep neural networks. The system uses an interactive one-click method for annotating images, reducing the co-occurrence bias inherent in training datasets.
The new dataset provides a 'ranking' of countries contributing most to global warming, with CO2 emissions driving the most warming. Countries like Brazil and Indonesia are rising in their contribution, while industrialised nations see slight declines.
Researchers developed a model to track COVID-19 data, predicting transmission and informing health surveillance systems. The model successfully predicted the spread of COVID-19 in Cali, Colombia, highlighting the importance of high-resolution data in understanding virus dynamics.
A national analysis of pediatric hospitalizations from 2009 to 2019 found a significant increase in mental health diagnoses, with attempted suicide being the leading cause. The study highlights the growing importance of addressing mental health concerns in children and adolescents.
Researchers have developed a non-invasive method to track human aging using retinal scans, which are less expensive and more accurate than other aging clocks. The study found that changes in the eye can provide an actionable evaluation of gero-protective therapeutics, offering a new tool for tracking aging.
Researchers developed an AI tool called SILIC to identify 169 species, including 137 birds, from bird sounds in Yushan National Park. The dataset provides detailed acoustic activity patterns of wildlife across short and long temporal scales.
A machine-learning model was trained on 10 million tweets to infer users' subjective wellbeing. The study found that New Year's Day is the most popular holiday, followed by Defender of the Fatherland Day and International Women's Day. The researchers also discovered gender differences in attitudes towards certain holidays.
Researchers developed AI models based on UNet and MobileNet architectures to analyze standardized abnormalities in CT images, accurately identifying object presence and confidence. These models achieved an absolute percentage error of less than 5 percent, comparable to human professionals.
Researchers found that COVID-19 infections are linked to an increased risk of developing liver problems, acute pancreatitis, and other GI disorders. The study analyzed over 14 million medical records and estimated that SARS-CoV-2 infections have contributed to over 6 million new cases of GI disorders in the US.
A novel AI architecture, relational reasoning network, accurately identifies anatomical landmarks in CT scans for orthodontic treatments. The model learns spatial relationships between landmarks without explicit image segmentation, achieving accuracy comparable to conventional methods.
Researchers seek to develop algorithms providing meaningful explanations for AI decision-making, enabling higher human trust and adoption in fields like science. The project focuses on symbolic reasoning and estimating explanation accuracy, addressing the need for transparent AI systems.
Researchers at MIT developed a technique to improve machine-learning models' reliability without requiring additional data or extensive computing resources. The method uses a simpler companion model to estimate uncertainty, enabling more effective uncertainty quantification.
Researchers tested three common techniques to make algorithms fairer and found that one approach didn't reduce social norm bias at all. They proposed a new technique: a formula to directly measure social norm bias in an algorithm so it can be corrected. This bias can persist even after overt discrimination is removed.
Researchers found that electric car adoption in California was associated with real-world reductions in air pollution and asthma-related emergency room visits. The study also highlighted an 'adoption gap' between low-resource zip codes, pointing to opportunities for environmental justice.
Dr. Nico Spiller to develop new analysis methods using machine learning to analyze complex brain data related to memory, decision making, and movement. The fellowship aims to provide insights into neurodegenerative diseases such as Alzheimer's and Parkinson's disease.
Researchers identified 7 key symptoms of long COVID, including heart issues and joint pain, in a study of 52,461 patients. The findings could help healthcare providers diagnose and treat the condition more effectively.
A new measure called c-value helps researchers choose between techniques based on the chance that a new method is more accurate for a specific dataset. The tool answers questions like whether to use alternative estimation methods despite potential costs and effort.
A new study used satellite data and public registry information to track the changing identities of commercial fishing vessels, revealing that nearly 20% of high seas fishing is carried out by unregulated or unauthorized vessels. The study found hotspots of potential IUU fishing in the Southwest Atlantic Ocean and western Indian Ocean.
A recent study published in Nature has discovered several new disease genes and provided new insights into the effects of known genetic factors on disease. The study highlights an underappreciated complexity in dosage effects of genetic variants, challenging traditional Mendelian inheritance laws.
A team of scientists reviewed the effectiveness of reanalysis data products for studying the West African climate. They found that ERA5 achieved considerable progress in reducing biases and improving representation compared to its predecessor, ERA-interim.
Researchers identified a common brain network underlying several psychiatric illnesses, including schizophrenia and depression. The transdiagnostic network shows gray matter decreases in specific brain regions across most studies.
Researchers at Drexel University used GPT-3 to spot early signs of Alzheimer's in spontaneous speech, achieving 80% accuracy. The program analyzed word-use, sentence structure and meaning from transcripts to identify characteristic profiles of Alzheimer's speech.
A recent study has identified common and unique cellular processes in six neurodegenerative diseases, providing new insights into the underlying causes of these conditions. The research used machine learning analysis to compare RNA markers in whole blood samples from patients with distinct diseases, revealing eight shared themes across...
A team of ecologists is calling for the designation of World Heritage Environmental Datasets to secure funding and ensure their long-term accessibility. These datasets, which include vital information on climate change adaptation, resource management, and environmental policy, are essential to understanding global change.
Researchers analyzed data from over 80,000 primary care physicians and found no clear connection between MIPS scores and clinical performance. Doctors with low MIPS scores performed worse on some process measures but better on others, suggesting that the system may prioritize paperwork over patient outcomes.
The Washington D.C. metro area's dashboard is being developed at PSU, allowing users to see all the data together in one place. The project aims to improve nonmotorized planning by providing clean, quality-checked biking and walking count data.
A new study maps the global landscape of antimicrobial resistance, revealing surprising transmissions in Sub-Saharan Africa and highlighting the need for tailored strategies to combat resistance. The research, which analyzed sewage samples from 243 cities in 101 countries, found that resistance genes are more frequently transmitted acr...
Scientists used AI-driven PandaOmics platform to analyze gene expression datasets from DNA repair diseases, identifying biomarkers associated with treatment response. The study focused on genes that stratify cancer patients by survival outcomes, providing potential targets for personalized therapies.
Researchers have discovered that oligodendrocyte precursor cells (OPCs) play a crucial role in synaptic pruning, cleaning up unwanted connections between neurons. By analyzing a massive dataset of 3D brain cell structures, the team found OPCs digesting parts of neighboring neurons.
A global dataset reveals widespread witchcraft beliefs varying substantially between countries and world regions. Higher education and economic security are associated with lower belief in witchcraft. Weak institutions, low social trust, and conformist culture also correlate with higher witchcraft prevalence.
Chung-Ang University researchers propose a new algorithm, MR-UCB and MR-APE, to tackle stochastic multi-armed bandit problems with heavy-tailed noise distributions. The methods guarantee minimal loss for worst-case scenarios with minimal prior information.
Researchers created synthetic knee x-ray images to complement real images in osteoarthritis classification. Medical experts were unable to distinguish between authentic and synthetic images, highlighting the potential of synthetic data for collaboration and testing.
Researchers modelled relationship between plant diversity and environmental conditions, capturing how diversity varies along environmental gradients. The models predict highest concentrations of plant diversity in environmentally heterogeneous tropical areas like Central America and the Amazonia.
The Julich Brain Atlas provides detailed maps of brain cells and receptors, enabling better understanding of brain connectivity and function. The atlas allows for correlation between brain network activity and underlying anatomy, aiding in the diagnosis of psychiatric disorders.
Researchers at KAUST develop a novel multivariate skew-elliptical link model to address the challenges of highly imbalanced health data. The new model provides a better fit to COVID-19 datasets and offers flexibility over existing models.