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Context Previous research evaluating the effects in B-vitamins on the prevention and treatment of cardiovascular disease (CVD) has substantial limitations and lacks recently published large prospective studies; hence, conducting an updated meta-analysis is needed. Objective We investigated the association between vitamin B status and human CVD development in order to provide more specific advice about vitamin B intake for those at risk of CVD. Data Sources Relevant articles were identified by JSTOR, PubMed, and ProQuest databases. Data Extraction Key words used to identify the studies included the different combinations of B-vitamins, folate, folic acid, vitamin B6, vitamin B12, homocysteine, cardiovascular disease, stroke, coronary disease, myocardial infarction, and cerebrovascular and transient ischemic attack. The database search was supplemented by hand-searching of reference lists of selected articles. Data Analysis Pooled estimates were calculated from the mean differences using a random-effects model. Results Supplementation with folic acid was reported to have a clinical benefit of significantly reducing carotid intima-media thickness. Higher intakes of folic acid, vitamin B6, and vitamin B12 were generally associated with a lower risk of CVD in the general population, except in those without normal renal function and those with unstable angina or past non–ST-elevation myocardial infarction. Conclusion Vitamin B supplementation resulted in the greatest cardiovascular benefit in those with normal renal function and without unstable angina or non–ST-elevation myocardial infarction recently. Factors such as age, gender, and genetic polymorphisms contribute to varying effects. [ABSTRACT FROM AUTHOR]

The research of electric vehicle power battery fault diagnosis technology is turning to machine learning methods. However, during operation, the time of occurrence of faults is much smaller than the normal driving time, resulting in too small a proportion of fault data as well as a single fault characteristic in the collected data. This has hindered the research progress in this field. To address this problem, this paper proposes a data enhancement method using Least Squares Generative Adversarial Networks (LSGAN). The method consists of training the original power battery fault dataset using LSGAN models to generate diverse sample data representing various fault states. The augmented dataset is then used to develop a fault diagnosis framework called LSGAN-RF-GWO, which combines a random forest (RF) model with a Gray Wolf Optimization (GWO) model for effective fault diagnosis. The performance of the framework is evaluated on the original and enhanced datasets and compared with other commonly used models such as Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Naïve Bayes (NB). The results show that the proposed fault diagnosis scheme improves the evaluation metrics and accuracy level, proving that the LSGAN-RF-GWO framework can utilize limited data resources to effectively diagnose power battery faults. [ABSTRACT FROM AUTHOR]

Simple Summary: The small intestine of ruminants is an important organ in the digestion and absorption of rumen indigestible nutrients while it is prone to oxidative stress, causing barrier damage, thus intestinal absorption and metabolic disorders. Nicotinamide was previously found to have antioxidant properties. The aims of this study were to investigate the effect of nicotinamide on intestinal oxidative damage and explore its potential mechanism. The study revealed that nicotinamide effectively reduced oxidative damage in bovine intestinal epithelial cells through autophagy. This protective effect was evidenced by enhanced antioxidant capacity and increased expression of tight junction proteins, suggesting nicotinamide's role in improving cellular defense and intestinal barrier integrity. The small intestine is important to the digestion and absorption of rumen undegradable nutrients, as well as the barrier functionality and immunological responses in ruminants. Oxidative stress induces a spectrum of pathophysiological symptoms and nutritional deficits, causing various gastrointestinal ailments. Previous studies have shown that nicotinamide (NAM) has antioxidant properties, but the potential mechanism has not been elucidated. The aim of this study was to explore the effects of NAM on hydrogen peroxide (H2O2)-induced oxidative injury in bovine intestinal epithelial cells (BIECs) and its potential mechanism. The results showed that NAM increased the cell viability and total antioxidant capacity (T-AOC) and decreased the release of lactate dehydrogenase (LDH) in BIECs challenged by H2O2. The NAM exhibited increased expression of catalase, superoxide dismutase 2, and tight junction proteins. The expression of autophagy-related proteins was increased in BIECs challenged by H2O2, and NAM significantly decreased the expression of autophagy-related proteins. When an autophagy-specific inhibitor was used, the oxidative injury in BIECs was not alleviated by NAM, and the T-AOC and the release of LDH were not affected. Collectively, these results indicated that NAM could alleviate oxidative injury in BIECs by enhancing antioxidant capacity and increasing the expression of tight junction proteins, and autophagy played a crucial role in the alleviation. [ABSTRACT FROM AUTHOR]

Positioning based on Global Navigation Satellite Systems (GNSSs) in urban environments always suffers from multipath and Non-Line-of-Sight (NLoS) effects. In such conditions, the GNSS pseudorange measurements can be affected by biases disrupting the GNSS-based applications. Many efforts have been devoted to detecting and mitigating the effects of multipath/NLoS, but the identification and classification of such events are still challenging. This research proposes a method for the post-processing estimation of pseudorange biases resulting from multipath/NLoS effects. Providing estimated pseudorange biases due to multipath/NLoS effects serves two main purposes. Firstly, machine learning-based techniques can leverage accurately estimated pseudorange biases as training data to detect and mitigate multipath/NLoS effects. Secondly, these accurately estimated pseudorange biases can serve as a benchmark for evaluating the effectiveness of the methods proposed to detect multipath/NLoS effects. The estimation is achieved by extracting the multipath/NLoS biases from pseudoranges using a clustering algorithm named Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The performance is demonstrated using two real-world data collections in multipath/NLoS scenarios for both static and dynamic conditions. Since there is no ground truth for the pseudorange biases due to the multipath/NLoS scenarios, the proposed method is validated based on the positioning performance. Positioning solutions are computed by subtracting the estimated biases from the raw pseudoranges and comparing them to the ground truth. [ABSTRACT FROM AUTHOR]

Meibomian gland dysfunction (MGD)‐related dry eye disease (DED) is a significant subtype of DED. In this research, we investigate the effectiveness of far infrared (FIR) functional glasses in the treatment of MGD‐related DED. According to the TFO DEWS II diagnostic criteria, 61 eyes with MGD‐related DED were included. All participants wore functional FIR glasses throughout the day for a period of 4 weeks and were followed up three times during the treatment. All subjects were followed up thoroughly in accordance with the DED clinical examination procedure. Ultimately, the treatment's impact was assessed. We found the Visual Analogue Scale and Ocular Surface Disease Index scores after FIR treatment were significantly lower than the baseline values (p < 0.05). Compared with the baseline, fluorescein tear breakup time and corneal fluorescein staining score after FIR treatment were significantly improved (p < 0.05). The eyelid margin signs, meibum quality, and meibomian gland expressibility after the 4‐week treatment were significantly better than those at baseline (p < 0.05). We can see that wearing the FIR functional glasses significantly relieves the symptoms and signs of patients. We believe FIR therapy could be considered as a new method of MGD‐related DED. [ABSTRACT FROM AUTHOR]

The Category Switching Test (CaST) is a verbal fluency test with active semantic category switching. This study aimed to explore the association between CaST performance and brain amyloid-β (Aβ) burden in patients with mild cognitive impairment (MCI) and the neurofunctional mechanisms. A total of 112 participants with MCI underwent Florbetapir positron emission tomography, resting-state functional magnetic resonance imaging, and a neuropsychological test battery. The high Aβ burden group had worse CaST performance than the low-burden group. CaST score and left middle temporal gyrus fractional amplitude of low-frequency fluctuations (fALFF) related inversely to the global Florbetapir standardized uptake value rate. Functional connectivity between the left middle temporal gyrus and frontal lobe decreased widely and correlated with CaST score in the high Aβ burden group. Thus, CaST score and left middle temporal gyrus fALFF were valuable in discriminating high Aβ burden. CaST might be useful in screening for MCI with high Aβ burden. [ABSTRACT FROM AUTHOR]

The electricity consumption behavior of the inhabitants is a major contributor to the uncertainty of the residential load system. Human-caused uncertainty may have a distributional component, but it is not well understood, which limits further understanding the stochastic component of load forecasting. This study proposes a short-term load-interval forecasting method considering the stochastic features caused by users' electricity consumption behavior. The proposed method is composed of two parts: load-point forecasting using singular spectrum analysis and long short-term memory (SSA-LSTM), and load boundaries forecasting using statistical analysis. Firstly, the load sequence is decomposed and recombined using SSA to obtain regular and stochastic subsequences. Then, the load-point forecasting LSTM network model is trained from the regular subsequence. Subsequently, the load boundaries related to load consumption consistency are forecasted by statistical analysis. Finally, the forecasting results are combined to obtain the load-interval forecasting result. The case study reveals that compared with other common methods, the proposed method can forecast the load interval more accurately and stably based on the load time series. By using the proposed method, the evaluation index coverage rates (CRs) are (17.50%, 1.95%, 1.05%, 0.97%, 7.80%, 4.55%, 9.52%, 1.11%), (17.95%, 3.02%, 1.49%, 5.49%, 5.03%, 1.66%, 1.49%), (19.79%, 2.79%, 1.43%, 1.18%, 3.37%, 1.42%) higher than the compared methods, and the interval average convergences (IACs) are (−18.19%, −8.15%, 3.97%), (36.97%, 21.92%, 22.59%), (12.31%, 21.59%, 7.22%) compared to the existing methods in three different counties, respectively, which shows that the proposed method has better overall performance and applicability through our discussion. [ABSTRACT FROM AUTHOR]