Browse Articles

Induction of flowering in Arabidopsis through functional peptide-mediated FT mRNA delivery

Molecular characterization and information entropies of chevron-like graphene nanoribbons with chemical applications

XAI-XGBoost: an innovative explainable intrusion detection approach for securing internet of medical things systems

Comparative study of tools for copy number variation detection using next-generation sequencing data

The effects of Tai Chi exercise on body composition and cardiorespiratory fitness in Chinese university students with obesity: A quasi experiment study

CFD technology in innovative spur dike design inspired by the Eğri (curved) Bridge

Differential impact of lipoprotein(a) on subclinical coronary atherosclerosis in asymptomatic individuals with and without diabetes mellitus

Left atrioventricular coupling index measured by echocardiography in heart failure with preserved ejection fraction

Characterization of the activity of KTX-1001, a small molecule inhibitor of multiple myeloma SET domain using surface plasmon resonance

Dual therapeutic potential of Scoparia dulcis in combating hyperglycemia and inflammation in diabetes through network pharmacology and in silico analysis

Global estimate of mesopelagic mesozooplankton biomass

U-shaped relationship between the non-HDL to HDL cholesterol ratio and mortality in hypertensive individuals in the study from NHANES database

Enhanced YOLOv8 with lightweight and efficient detection head for for detecting rice leaf diseases

A qualitative study of facilitators and barriers to healthy eating among older adults in China based on nutritional literacy and the capability opportunity motivation behaviour model

Regulatory roles of long non-coding RNAs in minipigs revealed by cross-breed and cross-tissue transcriptomic analyses

Depressive symptoms as a mediator between sedentary behavior and social frailty among Chinese older adults in nursing home

Optimizing lightweight neural networks for efficient mobile edge computing

Deep learning-based automated detection and multiclass classification of soil-transmitted helminths and Schistosoma mansoni eggs in fecal smear images

Abstract In this work, we developed an automated system for the detection and classification of soil-transmitted helminths (STH) and Schistosoma (S.) mansoni eggs in microscopic images of fecal smears. We assembled an STH and S. mansoni dataset comprising over 3,000 field-of-view (FOV) images containing parasite eggs, extracted from more than 300 fecal smear prepared using the Kato-Katz technique. These images were acquired using Schistoscope—a cost-effective automated digital microscope. After annotating the STH and S. mansoni eggs, we employed a transfer learning approach to train an EfficientDet deep learning model, using 70% of the dataset for training, 20% for validation, and 10% for testing. The developed model successfully identified STH and S. mansoni eggs in the FOV images, achieving weighted average scores of $$95.9\%(\pm 1.1\%)$$ Precision, $$92.1\%(\pm 3.5\%)$$ Sensitivity, $$98.0\%(\pm 0.76\%)$$ Specificity, and $$94.0\%(\pm 1.98\%)$$ F-Score across four classes of helminths (A. lumbricoides, T. trichiura, hookworm, and S. mansoni). Our system highlights the potential of the Schistoscope, enhanced with artificial intelligence, for detecting STH and S. mansoni infections in remote, resource-limited settings and for supporting the monitoring and evaluation of neglected tropical disease (NTD) control programs.

Redefining text-to-SQL metrics by incorporating semantic and structural similarity

Abstract The rapid advancements in text-to-SQL systems have driven the scientific community to create increasingly complex benchmarks for this task. However, evaluation metrics often rely on simplistic or binary approaches that fail to capture the similarities and differences between equivalent SQL queries. Current metrics overlook critical aspects such as partial correctness, structural differences, and semantic equivalence. To address these limitations, we propose a novel metric for SQL query comparison, designed to offer a more precise assessment of the similarity between SQL queries at both the semantic (string) and execution result (resultant table) levels. This new metric allows for a granular evaluation of SQL query similarity, supporting a more accurate assessment and ranking of text-to-SQL tools and models. The proposed approach could have a meaningful impact on text-to-SQL research and development. It might improve evaluation by distinguishing between models that handle simple queries and those capable of tackling more complex ones. The metric could also help to identify where the differences between two queries lie. Additionally, it may support the development of more accurate language models by offering precise training signals to help the model recognize query similarities. The experimental results highlight the metric’s effectiveness over existing evaluation methodologies, allowing us to identify the current best text-to-SQL models through distribution analysis. In some cases, the metric allows the detection of missing aggregation operators or variations in query ordering operators.

Differential expression of PPARγ by pioglitazone and telmisartan causes effect of disparity on mTORC2-mediated cell proliferation and migration