Browse Articles

The application of suitable sports games for junior high school students based on deep learning and artificial intelligence

Using the Language of elite athletes to predict their personality and on court transgressions

A pilot study of PSMA-targeted F-18-DCFPyL PET imaging of patients with adenoid cystic carcinoma

Examining the severity of disability and vulnerability among older adults and its impact on elder abuse: a cross-sectional study

Lightweight hybrid transformers-based dyslexia detection using cross-modality data

Identification of natural killer cell-characteristic genes in atherosclerosis based on bioinformatics analysis

Heat stress induces specific methylation, transcriptomic and metabolic pattern in dairy cows and their female progeny

Abstract A heat stress (HS) cattle research design was implemented to study HS effects on the three different “omics features” methylations, gene expressions and metabolic pattern from a direct perspective in pregnant cows and from an indirect time-lagged intergenerational perspective in offspring (the respective F1 and as F1 offspring before calving). In this regard, a total number of 88 German Holstein dairy cows and their 93 female calves were blood sampled for DNA and RNA extraction and for metabolic phenotyping, and allocated to HS and respective control groups (the cows (dams) as well as their calves) according to a temperature–humidity threshold of 60. Separate principal component analyses for all “omics-tiers” revealed clear separations of HS from respective control groups, as well as dam—offspring separations according to gene expressions and metabolic pattern. The GO enrichment analyses based on the differentially expressed genes contributed to the detection of 10 significantly overrepresented biological processes in heat stressed dams, and of 95 overrepresented biological processes due to indirect maternal heat stress in calves. With regard to direct HS in dams and the first PCs of the different “omics” features, the correlation coefficient was 0.45 between methylation and gene expression data, 0.62 between expression and metabolites, and 0.38 between methylation and metabolite data. The separation of HS from the control group was very obvious when using the average and weighted average of the first and second components from the three multi-omics datasets. The present study provides extended insights into the complex genetic and physiological mechanisms of HS response in dam and calf groups from different generations, contributing to a deeper understanding of the interplay of prompt and time lagged HS effects between different omics-tiers.

Geoelectrical evidence of fluid controlling slow and regular earthquakes along a plate interface

Numerical investigations of hockey groynes performance on hydrodynamic of open channels flow by using computational fluid dynamics (CFD)

Research on operation strategy of multiple channels pharmaceutical supply chain based on blockchain technology

Effect of intrinsic heat treatment on the microstructure and mechanical properties of Al-Cu alloy fabricated by wire arc directed energy deposition

Origin of the solar-cycle imprint on global sea level change

Design and experiment of key components for self-propelled harvester for Chinese cabbage

A novel YOLO LSTM approach for enhanced human action recognition in video sequences

Abstract Human Action Recognition (HAR) is a critical task in computer vision with applications in surveillance, healthcare, and human–computer interaction. This paper introduces a novel approach combining the strengths of You Only Look Once (YOLO) for feature extraction and Long Short-Term Memory (LSTM) networks for temporal modeling to achieve robust and accurate action recognition in video sequences. The YOLO model efficiently identifies key features from individual frames, enabling real-time processing, while the LSTM network captures temporal dependencies to understand sequential dynamics in human movements. The proposed YOLO–LSTM framework is evaluated on multiple publicly available HAR datasets, achieving an accuracy of 96%, precision of 96%, recall of 97%, and F1-score of 96% on the UCF101 dataset; 99% across all metrics on the KTH dataset; 100% on the WEIZMANN dataset; and 98% on the IXMAS dataset. These results demonstrate the superior performance of our approach compared to existing methods in terms of both accuracy and processing speed. Additionally, this approach effectively handles challenges such as occlusions, varying illumination, and complex backgrounds, making it suitable for real-world applications. The results highlight the potential of combining object detection and recurrent architectures for advancing state-of-the-art HAR systems.

Prospective evaluation of soluble CD14 as a biomarker following five aflibercept treatments in diabetic macular edema

Sex differences in disease burden, utilization, and expenditure on primary health care services in Kerala, India

Multicenter development of a deep learning radiomics and dosiomics nomogram to predict radiation pneumonia risk in non-small cell lung cancer

Establish operating conditions for optimal output characteristics in reactivity controlled combustion engine

Abstract This work aims to determine the optimal engine operating conditions for balanced combustion, performance and emissions characteristics with considerable reduction in smoke and nitrogen oxides (NOx). This work examined direct injected (DI) 30% mahua biodiesel-diesel as high reactive fuel (HRF) and port fuel injected (PFI) ethanol as low reactive fuel (LRF) in reactivity controlled compression ignition (RCCI) combustion at different engine loads and ethanol energy shares (EES) (0, 10, 15, 20, 25, and 30%). The RCCI engine was able to access low temperature combustion (LTC) with improved brake thermal efficiency (BTE), lower smoke and NOx with a trade-off in carbon monoxide (CO) and hydrocarbon (HC). The duty conditions of modern engines require single optimal operating condition to suit applications such as hybrid powertrain, generators and irrigation pumps. Using response surface methodology (RSM) it was established that 28.43% EES at 83.4% engine load resulted in optimal output responses for their due weightages assigned. This was validated by experimental results. In RCCI mode BTE of 32.54%, brake specific energy consumption (BSEC) of 10.79 MJ/kWh were realized. Also, smoke and NOx were reduced by 34.8% and 29.3%, with a compromise in CO and HC increase by 36.4% and 34.9% compared to DI mode. All the engine output parameters reported were within acceptable range. HC and CO can be mitigated with conventional catalytic convertors.

FISH unveils a unified method for multi-marker biodose assessment

Reasons for non-acceptance of statin therapy by patients at high cardiovascular risk

Abstract Statins are a cornerstone of cardiovascular risk reduction. Nevertheless, non-acceptance of statin therapy recommendations by patients at high cardiovascular risk is common. The reasons for statin non-acceptance have not been well established. We conducted a manual record review of a randomly selected set of patients who did not accept statin therapy recommendations to identify (a) documented reasons for statin non-acceptance and (b) patients’ demographic characteristics, comorbidities and current treatment. We analyzed the relationships between patients’ characteristics and reasons for statin non-acceptance. The most common reasons for statin non-acceptance were preference for lifestyle modifications (51.5%), general aversion to medications (19.1%), polypharmacy burden (17.1%) and fear of adverse reactions (10.9%). Patients taking more medications were more likely to express a concern about polypharmacy burden (OR 1.09; 95% CI 1.005–1.18). Patients who previously had adverse reactions to non-cholesterol lowering medications were more likely to fear adverse reactions to statins (OR 1.13; 95% CI 1.001–1.28). Patients who expressed preference for lifestyle modifications had time to low density lipoprotein cholesterol (LDL-C) < 100 mg/dL similar to patients who did not accept statin therapy for other reasons (1935 vs. 1777 days, p = 0.26). Patients’ reasons for non-acceptance of statin therapy are often linked to their past and present medical experience. Appropriately addressing these concerns is important to maximizing cardiovascular risk reduction in individuals who may be reluctant to initiate statin therapy.