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Predictive athlete performance modeling with machine learning and biometric data integration
Computational investigation of natural compounds as inhibitors against macrolide-resistant protein using virtual screening, molecular docking and MD simulations
The association between lipid accumulation products and bone mineral density in U.S. Adults, a nationally representative cross-sectional study
Artificial intelligence voice gender, gender role congruity, and trust in automated vehicles
Associations of circulating omentin-1 levels and long noncoding RNA MALAT1 expression with coronary heart disease in patients with type 2 diabetes mellitus
Abstract Coronary heart disease (CHD) is a severe diabetic vascular complication and the main cause of mortality among diabetes patients. Early diagnosis of CHD could prevent its development. Both omentin-1 (Oment-1) and the long noncoding RNA MALAT1 (lncRNA MALAT1) can be detected in peripheral blood and exhibit protective or detrimental effects on CHD. However, whether these two factors could be predictive of CHD in T2DM patients remains unclear. Therefore, this study aimed to investigate the associations of circulating Oment-1 levels and the expression of MALAT1 with CHD in T2DM patients and to assess their predictive efficacy. A total of 137 T2DM patients were enrolled, including 68 patients without CHD (T2DM group) and 69 patients with CHD (T2DM + CHD group). Clinical parameters were collected, and plasma Oment-1 was measured by enzyme-linked immunosorbent assay (ELISA). RNA was isolated from peripheral monocytes, and the expression of MALAT1 was determined by quantitative PCR. Cardiac function was measured by echocardiography. Compared with that in T2DM patients, the plasma Oment-1 level was significantly lower, while the expression of MALAT1 was significantly greater in T2DM + CHD patients (all P values < 0.01). Bivariate correlation analysis indicated that Oment-1 was positively correlated with the left ventricular ejection fraction (LVEF) ( P < 0.01). MALAT1 expression was negatively correlated with LVEF but positively correlated with age and DM duration ( P < 0.05). Binary logistic regression suggested that Oment-1 and MALAT1 were significantly associated with the presence of CHD. Receiver operating characteristic (ROC) curve analysis demonstrated that both Oment-1 (AUC = 0.663, sensitivity = 75%, specificity = 49%) and MALAT1 (AUC = 0.749, sensitivity = 73%, specificity = 66%) had significant diagnostic value for CHD among T2DM patients. Notably, the combination of Oment-1 and MALAT1 exhibited better diagnostic efficiency (AUC = 0.771, sensitivity = 66.7%, specificity = 75.3%). In conclusion, decreased circulating Oment-1 levels and increased MALAT1 expression are closely associated with CHD in T2DM patients, and their combination offers superior diagnostic efficiency, suggesting Oment-1 and MALAT1 may serve as a non-invasive tool for the early CHD detection and risk stratification in high-risk T2DM patients. Further studies are warranted to explore the pathophysiological mechanisms of Omentin-1 and MALAT1 in the pathogenesis of CHD in T2DM and to validate their clinical utility as potential biomarkers in large cohort studies.
Hormonal and transcriptomic regulation of drought adaptation in barley roots and leaves
Abstract Drought poses a significant threat to global crop productivity and food security. In this study, we aimed to elucidate the impact of drought on transcriptional regulation and alternative splicing in barley (Hordeum vulgare), and to determine whether these transcriptomic alterations correlate with changes in hormonal profiles. We hypothesized that drought stress induces extensive reprogramming of gene expression, including alternative splicing events, and that these molecular responses are accompanied by tissue-specific shifts in hormone levels, ultimately underpinning adaptive responses in both leaves and roots. To test this, we performed RNA-seq and comprehensive hormone profiling on leaves and roots sampled at 25 days after planting under both optimal and drought conditions. Our analysis identified over 6,655 differentially expressed genes, with a substantial subset exhibiting differential alternative splicing. In leaves, drought primarily downregulated photosynthesis-related genes while upregulating pathways involved in water stress and abscisic acid (ABA) signaling. In contrast, roots displayed broader metabolic adjustments and significant isoform switching. Hormone analysis revealed marked ABA accumulation, particularly in roots, alongside organ-specific modulation of jasmonates and auxins. A limited assessment of the rhizosphere microbial community revealed low transcript abundance, underscoring the primacy of intrinsic plant responses. Collectively, these findings provide valuable insights into the multilayered adaptive strategies of barley under drought stress.
Real time intravascular ultrasound evaluation and stent selection for cerebral venous sinus stenosis associated with idiopathic intracranial hypertension
The prediction of the effects of non-isothermal molding/reprocessing on the crystallinity and mechanical properties of PEEK and CF/PEEK
Brain structural alterations and cognitive dysfunction in lung cancer patients without brain metastasis
Comparative analysis of different PV technologies under the tropical environments
Abstract In this paper, six different types of solar PV technologies are compared in terms of their performances under tropical conditions, using three years of performance data from a 1.2 MW experimental solar farm. The technologies considered include single-crystalline silicon, polycrystalline silicon, microcrystalline silicon, amorphous silicon, copper indium selenium (CIS), and hetero-junction with intrinsic thin layer (HIT). The field performances of these cells were initially assessed using standard performance indices such as Array Yield, Reference Yield, Capture Loss, Performance Ratio, and Efficiency Ratio. Among the technologies studied, amorphous silicon and HIT-based systems demonstrated better performance, showing higher Performance and Efficiency Ratios, along with lower capture losses. This study also modelled the fluctuations in power production from these panels. Under probabilistic modeling, the ramping behavior of the systems was characterized using the Generalized Logistic Distribution. Based on this analysis, CIS PV systems were found to have minimum power ramps, where as the HIT based systems showed the highest power fluctuations. To predict minute-wise and hourly ramping of the PV systems under varying levels of solar insolation, machine learning methods based on Artificial Neural Networks (ANN), Support Vector Machines (SVM), and k-Nearest Neighbors (kNN) were developed. With a Normalized Root Mean Square Error (NRMSE) of over 96%, these models demonstrated high accuracy in capturing the ramping characteristics of the studied PV systems. The results of this study offer valuable insights into the performance of different PV systems under tropical regions, which can be used in efficiently designing and managing solar PV projects.
Different morphology and function of hip extensor muscles between sprint runners and sprint cyclists
Effect of biochar and cyanobacteria crust incorporation on soil wind erosion in arid mining area under freeze-thaw action
Design and optimization of honeycomb structure in dual-link mechanism for revolving two-wing doors
Microenvironments on individual sand grains enhance nitrogen loss in coastal sediments
Abstract The permeable silicate sediments which cover more than 50% of the continental shelves are a major, but poorly constrained sink for the vast amount of anthropogenic nitrogen (N) that enters the ocean. Surface-attached microbial communities on sand grains remove fixed-N via denitrification, a process generally restricted to anoxic or low oxygen (O2) environments. Yet, in sands, denitrification also occurs in the centimeters thick well-oxygenated surface layer, which leads to additional and substantial N-loss. So far however, the underlying mechanisms that drive denitrification in oxic sands are poorly resolved. In this study, we applied a non-invasive microfluidic technique to visualize and quantify how sediment-attached microorganisms shape O2 availability on the surface of silicate sand grains. This revealed a remarkable heterogeneity in rates; with colonies of O2 consuming and producing microorganisms situated within micrometers of each other. Using a mechanistic approach to model respiration on the surface of a single silicate sand grain we showed that the high rates of O2 consumption within the microbial colonies on the sand-grain surface outpace O2 supply from the surrounding pore water. As a result anoxic microenvironments develop on the sand grain surface, which so far have been invisible to conventional techniques. The model results indicate that anaerobic denitrification occurring in these anoxic microenvironments can account for up to 74% of denitrification in oxygenated sands, with the remainder occurring in the presence of oxygen. In a preliminary upscaling approach, using a global dataset we estimated that anoxic microenvironments in oxygenated surface layers could be responsible for up to a third of the total N-loss that occurs in silicate shelf sands. Consequently, denitrification in anoxic microenvironments drives substantial anthropogenic-N removal from continental silicate shelf sands.