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.
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.
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 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.
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).
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.
A new MIT study suggests that pedestrians choose routes that point most directly toward their destination, even if those routes are longer. This strategy, known as vector-based navigation, may have evolved to allow the brain to devote more power to other tasks.
The StEER Network's post-event reconnaissance helped assess building damage from Hurricane Michael, revealing widespread wind- and surge-induced damage. The dataset has been used to develop data-driven fragilities, train machine learning applications, and inform policy and practice improvements for coastal communities.
Researchers have developed a federated analytics system, FAMHE, that enables healthcare providers to collaborate on statistical analyses and machine learning models without exchanging underlying datasets. The system has been proven mathematically secure and accurately reproduced published studies in multi-centric settings.
A Texas A&M team led by Dr. Byung-Jun Yoon has received $2.4 million to develop new computational techniques for reducing the size of large scientific data sets. The goal is to preserve quantities of interest while minimizing data storage and processing needs.
Researchers at GlaxoSmithKline and CCDC combined proprietary and published datasets to train machine learning models for predicting stable polymorphs in new drug candidates. The approach leverages the large volume and variety of data in the Cambridge Structural Database, resulting in more confident predictions and improved model accuracy.
Researchers analyzed over 120 million English-language tweets to find a decrease in negative posts about COVID-19, particularly in countries with high vaccination rates. The study suggests that increased vaccination may have contributed to the drop in negativity, but further analysis is needed to fully understand this phenomenon.
A team of Harvard researchers created an integrated pipeline, STAMPScreen, to help genetic engineers identify target genes and perform screening studies. The protocol combines computational tools with lab experiments to quickly and efficiently test gene function in living cells.
Researchers used extensive 2D and 3D broadband seismic reflection data to visualize and understand the subsurface structures of the Orphan Basin. The study provides context for future assessments of source rock, reservoir, and seal strata in oil and gas exploration.
The COVID-19 pandemic has triggered unprecedented life expectancy losses across 29 countries, with a decline of over half a year observed in most nations. The largest declines were seen among males, particularly in the US, where a 2.2-year loss was reported.
Researchers at PSU and UT Arlington will assess how crowdsourced data can help establish pedestrian activity on streets. The study aims to provide a more accurate alternative to traditional counting methods, enabling safer studies and better urban planning.
Researchers at Lawrence Livermore National Laboratory calculated moment tensors for 130 underground nuclear and 10 chemical tests to aid explosion monitoring. The study's database of carefully documented explosions will be useful for researchers, allowing them to distinguish explosions from earthquakes and estimate yield.
Researchers analyzed 1,785 ancient human genomes to determine parental relatedness, revealing that cousin marriages occurred only 3% of the time. The new method allowed for more efficient screening of ancient DNA, also providing insights into population dynamics and demographic impact of agriculture.
Scientists compiled a 71-year potential risk index dataset to estimate TC impact severity on the Chinese mainland, showing increased damage over time. Recent upward trends in precipitation and wind values contribute to larger TC impacts along coastal China.
The world's largest repository of raw genomic sequences is missing critical data necessary for monitoring and preserving biodiversity. Only 14% of archived datasets contain information about when and where organisms were sampled, highlighting the need for community standards to preserve metadata.
Researchers analyzed 1.4 million field observations and 73,000 museum records to find a strong correlation between species abundance in nature and their presence in museum collections. This method enables scientists to study species decline and estimate past abundances, providing insights into conservation efforts.
A European project developed standard solutions for energy refurbishment, considering various climate zones. These packages include prefabricated façades, decentralized ventilation systems, and smart ceiling fans to reduce energy consumption and improve comfort. Pilot sites showed significant savings and improvements in tenant well-being.
A new study reveals that climate change will affect Antarctic seals, such as crabeater and Weddell seals, in distinct ways. Crabeater seals are more vulnerable due to their specialized diet and breeding on unstable pack-ice, while Weddell seals are less affected.
A new analytics platform, RDCA-DAP, will be launched to accelerate rare disease treatment innovation by hosting and standardizing rare diseases data. The platform is expected to empower patients and families to drive innovation in the field.
A new dataset combines demographic and financial information to identify communities at risk of climate-related gentrification. The Socio-Economic Physical Housing Eviction Risk (SEPHER) dataset provides insights into the impact of climate hazards on vulnerable populations, including increased evictions and rising housing costs.
A new study showcases the COVIDome Explorer, a public online portal for real-time COVID-19 data analysis, visualization and sharing. The platform enables rapid hypotheses testing, hypothesis generation and discoveries by experts and non-experts.
A new study found that larger cities produce more income inequality, with the poor experiencing a decline in quality of life. The researchers analyzed data from municipal areas across the US and found that the top 10% of earners gain an increasingly large portion of wealth as cities grow.
Earthquakes generated by controlled fluid injection at the Rangely oil field were caused by destabilizing fault pore pressure changes, according to a new mechanistic model. The study revisits data from the decades-old project in light of increased seismicity due to fluid injection.
A database of 3,000 lead isotope analyses has been compiled for the Iberian Peninsula, providing a comprehensive resource for geological and archaeological research. The IBERLID database includes standardized data on minerals, rocks, and metallic objects, facilitating comparison and analysis.
Researchers developed a novel evidence-based material recommender system that predicts high entropy alloy formation without data descriptors, overcomes data bias and poor availability. The method recommends an FeMnCoNi alloy as the most probable HEA and successfully synthesizes it, confirming its validity.