Browse Articles
Discover research articles across all indexed journals
Quantum annealing enhanced Markov-Chain Monte Carlo
Clinical and histopathological characteristics of melanomas in Asians under age 40
Primary sjögren’s syndrome among patients with cerebral infarction
Engineered microRNA scaffolds for potent gene silencing in vivo
Abstract RNA interference (RNAi) is emerging as a powerful strategy for therapeutic targeting of “undruggable” targets. However, efficacy of currently used siRNA-based therapies is often hindered by transient effects and limited modeling possibilities. Artificial microRNAs (amiRNAs or miRNA scaffolds) present a durable and precise approach to gene silencing, opening new avenues for developing long lasting targeted therapies. In this study, we engineered highly expressed primary miRNAs (pri-miRNAs) with sequence determinants known to enhance processing efficacy and precision. The resulting amiRNAs were extensively tested both in vitro and in vivo and proved to efficiently silence a target gene when virally delivered via adeno-associated virus (AAV) into mice brains. This study provides a set of novel amiRNAs with potential therapeutic application as well as a pipeline to generate and validate novel amiRNAs from endogenous pri-miRNAs.
Lowering pathological α-synuclein
Estradiol (E2) concentration shapes the chromatin binding landscape of estrogen receptor alpha
Exploring the relationship between self-care agency and quality of life in adults with diabetes: A cross-sectional study
Self-care agency is the ability to perform self-care. Clarifying the factors of self-care agency that are related to quality of life can help determine the most effective nursing support. This cross-sectional study of patients with type 1 or type 2 diabetes aimed to explore the relationship between self-care agency and quality of life in adults with diabetes. Using a selective sampling method, we conducted a questionnaire survey using the Instrument of Diabetes Self-Care Agency and the SF-12 Health Survey. After identifying items related to quality of life from single regression and correlation analyses, multiple regression analyses were performed. There were 139 respondents, with an average age of 62.8 ± 11.7 years, of whom 71 were men (51.0%) and 117 had type 2 diabetes (84.1%). The average self-care agency score was 153.6 ± 22.5 points. Based on the results of the single regression analysis, age, sex, HbA1c, and BMI were selected as adjustment factors. Multiple regression analyses showed that the “ability to cope with stress” was related to the role/social component summary of health-related quality of life (β = 0.40, p < 0.01). The association between self-care agency and the mental component summary differed by age and sex, while “ability to cope with stress” was commonly related to this component across all groups (β = 0.39–0.70, p < 0.05). The “ability to make the most of the support available” (β = 0.37–0.52, p < 0.05) and the “ability to self-manage” (β = 0.51–0.56, p < 0.01) were also related to this component in the 65-and-over group. No factors of self-care agency were related to the physical component summary of health-related quality of life. The results suggest that nurses can clarify the type of support that will lead to improved quality of life by evaluating patients’ self-care agency.
Triglyceride-glucose index combined with body roundness index predicts cardiovascular risk in middle-aged and elderly individuals: a 10-year cohort study
Improving spatiotemporal data fusion method in multiband images by distributing variates
Predictors for quantitative flow ratio loss in patients with de novo coronary artery disease treated with drug-coated balloons
Prediction of cardiovascular diseases based on GBDT+LR
Development and validation of a novel classification for characterizing degenerative spondylolisthesis of lumbar spine
Tanshinone IIA suppresses the proliferation and fibrosis of mesangial cell in diabetic nephropathy though WTAP-mediated m6A methylation
Seismic performance of multistory frames with novel self-centering friction SMA dampers
Adaptive VSS-EWMA control chart for monitoring the process dispersion
Prolonging the antidepressant effects of ketamine
Rag GTPases control lysosomal acidification by regulating v-ATPase assembly in Drosophila
Masked face matching benefits from isolated facial features
Verifying the identity of an unfamiliar person is a difficult task, especially when targets wear masks that cover most of their faces. This presents a major challenge for law enforcement in border control, security, and criminal investigations. Therefore, we aim to explore ways to improve face-matching performance when a face is heavily masked. In two experiments, we investigated whether face-matching performance can benefit from the presentation of isolated facial features, namely the eyes (Experiment 1) and the mouth (Experiment 2), when a target face is masked. Participants viewed pairs of faces and determined whether they belonged to the same person or different people. In congruent pairs, participants matched a full-face image to another full-face image or a masked image to an isolated facial feature. In incongruent pairs, participants matched a full-face image to an image of the eyes or the mouth only or to a masked image. Matching accuracy was significantly better in congruent than incongruent pairs. Interestingly, the benefit of showing an isolated facial feature was even present when that single feature was the mouth. Overall, the findings showed that focusing on isolated facial features, such as the eyes or mouth, can be a valuable strategy for enhancing identity matching with masked perpetrators.
Enhanced wind power forecasting using machine learning, deep learning models and ensemble integration
Abstract The inherent variability of wind and solar energy introduces fluctuations in power generation, making accurate forecasting essential for maintaining the grid’s stability. This study addresses key research gaps in wind energy forecasting, including the inability of traditional statistical models to capture complex, nonlinear temporal patterns, the underutilization of real-time, location-specific data, the lack of comparative analyses across diverse models and datasets, and the absence of systematic model selection strategies for future forecasting. To overcome these limitations, this study applies advanced machine learning (ML) and deep learning (DL) techniques with systematic hyperparameter tuning to enhance predictive performance. Three scenarios were examined: Case 1 used a Kaggle wind turbine SCADA dataset; Case 2 employed real-time wind data from Aralvaimozhi, Tamil Nadu, India; and Case 3 focused on future forecasting using the best performing models from the earlier cases. A wide range of ML models—Random Forest (RF), Decision Trees, Linear Regression, K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Gradient Boosting—alongside DL models such as Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) were evaluated. Weather features, particularly wind speed, were incorporated to improve the prediction accuracy. A Stacking Ensemble model was also constructed from the top-performing models to boost robustness and forecast reliability. The performance was evaluated using the Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) metrics. The results showed that Random Forest excelled in Case 1, while Case 2 saw outstanding performance from Random Forest, XGBoost, and the Stacking Ensemble, achieving R2 values of 0.995, 0.997, and 0.998; MAE of 0.027, 0.035, and 0.014; MSE of 0.026, 0.014, and 0.0016; and RMSE of 0.16, 0.119, and 0.04, respectively. By directly addressing the forecasting challenges of wind energy, this study supports improved resource management, grid reliability, and operational planning. The findings highlight the effectiveness of hyperparameter-tuned ensemble models, particularly stacking ensembles, in enhancing renewable energy forecasting and advancing global sustainability goals in the future.