BACKGROUND: Chronic kidney disease (CKD) is associated with increased mortality. Individual mortality prediction could be of interest to improve individual clinical outcomes. Using an independent regional dataset, the aim of the present study was to ...
Cyanobacteria blooms in fishponds, driven by climate change and anthropogenic activities, have become a critical concern for aquatic ecosystems worldwide. The diversity in fishpond sizes and fish densities further complicates their monitoring. This s...
Soil aggregates are crucial for soil organic carbon (OC) accumulation. This study, utilizing a 32-year fertilization experiment, investigates whether the core microbiome can elucidate variations in carbon content and decomposition across different ag...
State-of-the-art deep learning models often fail to generalize in the presence of distribution shifts between training (source) data and test (target) data. Domain adaptation methods are designed to address this issue using labeled samples (supervise...
Speeding, a risky act of driving a vehicle at a speed exceeding the posted limit, has consistently emerged as a leading contributor to traffic fatalities. Identifying the risk factors associated with injury severity in speeding-related crashes is ess...
Electrochemical immunosensors, surpassing conventional diagnostics, exhibit significant potential for cancer biomarker detection. However, achieving a delicate balance between signal sensitivity and operational stability, especially at the heterostru...
BACKGROUND: Prolonged levodopa treatment in Parkinson's disease (PD) often leads to motor complications, including levodopa-induced dyskinesia (LID). Despite continuous levodopa treatment, some patients do not develop LID symptoms, even in later stag...
Multilocus variable number tandem repeat analysis (MLVA) is a molecular subtyping technique that remains useful for those without the resources to access whole genome sequencing for the tracking and tracing of bacterial contaminants. Unlike technique...
Journal of assisted reproduction and genetics
Jul 4, 2024
PURPOSE: To determine if an explainable artificial intelligence (XAI) model enhances the accuracy and transparency of predicting embryo ploidy status based on embryonic characteristics and clinical data.
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