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Multi-terminal flexible DC grid circuit breaker withstands multi-lightning strike analysis

The effects of the SARS-CoV-2 pandemic on self-reported interoception and mental health

Background Atypical interoception has been observed across multiple mental health conditions, including anxiety disorders and depression. Evidence suggests that not only pathological anxiety, but also heightened levels of state anxiety and stress are associated with interoceptive functioning. This study aimed to investigate the effects of the recent Coronavirus SARS-CoV-2 pandemic on self-reported interoception and mental health, and their relationship. Methods Self-report measures of interoceptive attention and accuracy, anxiety, stress and depression taken during the pandemic (at three time points) were compared to the same measures taken from comparable samples prior to the pandemic. In the sample collected during the pandemic, the relationship between interoceptive and mental health measures and focus on COVID-19-related news and information, propensity to take objective measures of COVID-19 symptoms, and subjective beliefs concerning COVID-19 symptoms was assessed. Finally, a cross-lagged panel model (CLPM) was used to test directional relationships between self-reported interoceptive and mental health measures across three time points. Results Higher self-reported anxiety was associated with a) increased self-reported attention to bodily signals, b) increased focus on COVID-19-related news and information, c) propensity to take objective measures of COVID-19 symptoms, and d) reduced self-reported interoceptive accuracy for bodily signals participants believed were associated with COVID-19. The CLPM revealed a mutual and comparable directional effect from T1 to T2 between interoceptive attention and measures of mental health. Conclusions Implications of these findings are discussed in the light of existing models and newly proposed accounts of the relationship between interoception and mental health.

Studies on the effects of avocado extract on carbon steel resistance to corrosion in salty formation water

Key barriers and decision supports in revitalizing heritage buildings from investors’ perspective in China: A case study in Guangzhou, China

In the decision-making process for investing in heritage buildings (HBs), various factors such as costs, interests, and tenancy terms influence investors decisions. Understanding the motivations of these investors can facilitate the involvement of social forces with diverse interests in adaptive reuse projects. This paper examines the primary barriers to revitalizing heritage buildings through adaptive reuse decision-making. The specific objectives of this study are as follows: i) To explore the barriers faced by investors in the adaptive reuse of heritage buildings. ii) To provide a comprehensive review of the factors influencing the adaptive reuse of heritage buildings, with a particular focus on developing countries. Through structured interviews with 19 investors and field research, three typical categories in the adaptive reuse decision-making process were identified: i) tenancy terms, ii) function, and iii) fire control problems. These findings indicate that vacancy is detrimental to the protection of heritage buildings, while rent plays a significant role in safeguarding them, as revealed by a correlation analysis using quantitative methods. Policymakers should better understand the expectations and needs of the public investors better to enhance support for optimal adaptive reuse decisions.

The kinetics of uracil-N-glycosylase distribution inside replication foci

Research on the evolution of college online public opinion risk based on improved Grey Wolf Optimizer combined with LSTM model

Since the dissemination of information is more rapid and the scale of users on online platforms is enormous, the public opinion risk is more visible and harder to tackle for universities and authorities. Improving the accuracy of predictions regarding online public opinion crises, especially those related to campuses, is crucial for maintaining social stability. This research proposes a public opinion crisis prediction model that applies the Grey Wolf Optimizer (GWO) algorithm combined with long short-term memory (LSTM) and implements it to analyze a trending topic on Sina Weibo to validate its prediction accuracy. A full-chain analytical framework for online public opinion prediction is established in this study, which enables the model to illustrate the level of risk related to public opinion and its variation trend by introducing the public opinion risk index. The prediction accuracy of the model is validated through several evaluation criteria, and a comparison between real and predicted results, and the simulation of the intervention on this incident indicates that the proposed model is competent for both trend prediction and assisting in intervention. The study also demonstrates the importance of immediate response and intervention to public opinion crises.

Uncovering the molecular mechanisms of Acer fabri in adjusting to low-temperature stress through integrated physiological and transcriptomic analysis

Correction: Smoking is associated with increased risk of cardiovascular events, disease severity, and mortality among patients hospitalized for SARS-CoV-2 infections

Facial dominance overrides gender in children’s stereotypes about intelligence

Correction: Validity and reliability International Classification of Diseases-10 codes for all forms of injury: A systematic review

Genome-wide identification and characterization of cation-proton antiporter (CPA) gene family in rice (Oryza sativa L.) and their expression profiles in response to phytohormones

The cation-proton antiporter (CPA) superfamily plays pivotal roles in regulating cellular ion and pH homeostasis in plants. To date, the regulatory functions of CPA family members in rice (Oryza sativa L.) have not been elucidated. In this study, we use rice public data and information techniques, 29 OsCPA candidate genes were identified in the rice japonica variety (referred to as OsCPA) and phylogenetically categorized into K+ efflux antiporter (KEA), Na+/H+ exchanger (NHX), and cation/H+ exchanger (CHX) groups containing 4, 7, and 18 OsCPA genes. The OsCPA proteins were predominantly localized in the plasma membrane and unevenly scattered on 11 chromosomes. The structural analysis of OsCPA proteins revealed higher similarities within groups. Prediction of selection pressure, collinearity, and synteny analysis indicated that all duplicated OsCPA genes had undergone strong purifying selection throughout their evolution. The cis-acting regulatory elements (CAREs) analysis identified 56 CARE motifs responsive to light, tissue, hormones, and stresses. Additionally, 124 miRNA families were identified in the gene promoters, and OsNHX7 was targeted by the highest number of miRNAs (43 miRNAs). Gene Ontology analysis demonstrated the numerous functions of OsCPA genes associated with biological processes (57.14%), cellular components (7.94%), and molecular functions (34.92%). A total of 12 transcription factor families (TFFs), including 40 TFs were identified in gene promoters, with the highest numbers of TFFs (5TFFs) linked to OsCHX13, and OsCHX15. Protein-protein interaction analysis suggested maximum functional similarities between rice and Arabidopsis CPA proteins. Based on expression analysis, OsKEA1, OsKEA2, OsNHX3, and OsNHX7 were frequently expressed in rice tissues. Furthermore, OsNHX3, OsNHX4, OsNHX6, OsNHX7, OsCHX8, and OsCHX17 in abscisic acid, OsKEA1, OsNHX3, and OsCHX8 in gibberellic acid, OsKEA1, OsKEA3, OsNHX1, and OsNHX3 in indole-3-acetic acid treatment were demonstrated as potential candidates in response to hormone. These findings highlight potential candidates for further characterization of OsCPA genes, which may aid in the development of rice varieties.

MiRNA-223-5p inhibits hypoxia-induced apoptosis of BMSCs and promotes repair in Legg-Calvé-Perthes disease by targeting CHAC2 and activating the Wnt/β-catenin signaling pathway

Legg-Calvé-Perthes disease (LCPD) involves femoral head osteonecrosis caused by disrupted blood supply, leading to joint deformity and early osteoarthritis. This study investigates the role of miRNA-223-5p in regulating hypoxia-induced apoptosis and enhancing osteogenesis in bone marrow mesenchymal stem cells (BMSCs). Utilizing a juvenile New Zealand white rabbit model of LCPD established through femoral neck ligation, we transfected BMSCs with miR-223-5p mimics, inhibitors, and controls, followed by hypoxic exposure. The impact of miR-223-5p on BMSC apoptosis was assessed using qPCR, Western blotting, and dual-luciferase reporter assays, focusing on the Wnt/β-catenin signaling pathway. In vivo, we evaluated the effects of transplanting miR-223-5p-overexpressing BMSCs into the LCPD model. Our results indicate that miR-223-5p is downregulated under hypoxic conditions. Overexpression of miR-223-5p in BMSCs inhibited hypoxia-induced apoptosis and activated the Wnt/β-catenin pathway by directly targeting CHAC2. In vivo, miR-223-5p-overexpressing BMSCs enhanced femoral head osteogenesis and reduced necrosis in the LCPD model. These findings suggest that miR-223-5p inhibits hypoxia-induced apoptosis in BMSCs by targeting CHAC2 and activating the Wnt/β-catenin pathway, proposing miR-223-5p as a promising target for improving bone repair in ischemic conditions.

Pedestrian POSE estimation using multi-branched deep learning pose net

In human activity-recognition scenarios, including head and entire body pose and orientations, recognizing the pose and direction of a pedestrian is considered a complex problem. A person may be traveling in one sideway while focusing his attention on another side. It is occasionally desirable to analyze such orientation estimates using computer-vision tools for automated analysis of pedestrian behavior and intention. This article uses a deep-learning method to demonstrate the pedestrian full-body pose estimation approach. A deep-learning-based pre-trained supervised model multi-branched deep learning pose net (MBDLP-Net) is proposed for estimation and classification. For full-body pose and orientation estimation, three independent datasets, an extensive dataset for body orientation (BDBO), PKU-Reid, and TUD Multiview Pedestrians, are used. Independently, the proposed technique is trained on dataset CIFAR-100 with 100 classes. The proposed approach is meticulously tested using publicly accessible BDBO, PKU-Reid, and TUD datasets. The results show that the mean accuracy for full-body pose estimation with BDBO and PKU-Reid is 0.95%, and with TUD multiview pedestrians is 0.97%. The performance results show that the proposed technique efficiently distinguishes full-body poses and orientations in various configurations. The efficacy of the provided approach is compared with existing pretrained, robust, and state-of-the-art methodologies, providing a comprehensive understanding of its advantages.

The extent of western lowland gorilla social relationships within and between groups

The nature of western lowland gorilla social relationships within and between groups is largely understudied, partly due to the challenges of monitoring associations between individuals who live in neighboring groups. In this study, we examined the social relationships of four western lowland gorilla groups in the Ndoki landscape of northern Republic of Congo. To do so, we compiled all-occurrence social interaction and silverback nearest neighbor social networks from data collected during daily group follows conducted over several years. We observed a total of 5,923 dyadic all-occurrence social interactions (1,350 ± 489 per group, 138 intergroup interactions) and 54,989 dyadic silverback nearest neighbor associations (13,747 ± 3,963 observations per group, 105 nearest neighbor observations of intergroup partners during group scans). For all groups, we found that males were more social than females, younger individuals were more social than older gorillas, and slightly greater rates of social behaviors were observed during periods of higher fruit availability. While there was a considerable amount of interindividual variation in social behavior, the network of social interactions demonstrated a large extent of social relationships within and between groups. Additionally, we performed simulated network removals to assess the impact on social dynamics. Across all groups and the total population, the removal of blackback and immature individuals markedly decreased the number of intra- and intergroup relationships (>60% decrease). The documented extent of western lowland gorilla social relationships has direct implications for the conservation of species with multi-level social dynamics. Gaining clarity on the ways in which western lowland gorilla groups naturally occur in the wild, not only provides a greater understanding for their conservation, but also offers insights for managing their social dynamics within captive environments.

Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques

Mold defects pose a significant risk to the preservation of valuable fine art paintings, typically arising from fungal growth in humid environments. This paper presents a novel approach for detecting and categorizing mold defects in fine art paintings. The technique leverages a feature extraction method called Derivative Level Thresholding to pinpoint suspicious regions within an image. Subsequently, these regions are classified as mold defects using either morphological filtering or machine learning models such as Classification and Regression Trees (CART) and Linear Discriminant Analysis (LDA). The efficacy of these methods was evaluated using the Mold Features Dataset (MFD) and a separate set of test images. Results indicate that both methods improve the accuracy and precision of mold defect detection compared to no classifier. However, the CART algorithm exhibits superior performance, increasing precision by 32% to 53% while maintaining high accuracy (96%) even with an imbalanced dataset. This innovative method has the potential to transform the approach to managing mold defects in fine art paintings by offering a more precise and efficient means of identification. By enabling early detection of mold defects, this method can play a crucial role in safeguarding these invaluable artworks for future generations.

An infodemiologic review of internet resources on dental hypersensitivity: A quality and readability assessment

Background This study aimed to investigate the quality and readability of online English health information about dental sensitivity and how patients evaluate and utilize these web-based information. Methods The credibility and readability of health information was obtained from three search engines. We conducted searches in "incognito" mode to reduce the possibility of biases. Quality assessment utilized JAMA benchmarks, the DISCERN tool, and HONcode. Readability was analyzed using the SMOG, FRE, and FKGL indices. Results Out of 600 websites, 90 were included, with 62.2% affiliated with dental or medical centers, among these websites, 80% exclusively related to dental implant treatments. Regarding JAMA benchmarks, currency was the most commonly achieved and 87.8% of websites fell into the "moderate quality" category. Word and sentence counts ranged widely with a mean of 815.7 (±435.4) and 60.2 (±33.3), respectively. FKGL averaging 8.6 (±1.6), SMOG scores averaging 7.6 (±1.1), and FRE scale showed a mean of 58.28 (±9.1), with "fair difficult" being the most common category. Conclusion The overall evaluation using DISCERN indicated a moderate quality level, with a notable absence of referencing. JAMA benchmarks revealed a general non-adherence among websites, as none of the websites met all of the four criteria. Only one website was HON code certified, suggesting a lack of reliable sources for web-based health information accuracy. Readability assessments showed varying results, with the majority being "fair difficult". Although readability did not significantly differ across affiliations, a wide range of the number of words and sentences count was observed between them.

Genetic diversity and population structure analyses of tropical maize inbred lines using Single Nucleotide Polymorphism markers

Analyses of the genetic distance and composition of inbred lines are a prerequisite for parental selection and to exploit heterosis in plant breeding programs. The study aimed to assess genetic diversity and population structure of a maize germplasm panel comprising 182 founder lines and 866 derived inbred lines using Single Nucleotide Polymorphism (SNP) markers to identify genetically unique lines for hybrid breeding. The founder lines were genotyped with 1201 SNPs, and the derived lines with 1484 SNPs. Moderate genetic variation, with genetic diversity ranging from 0.004 to 0.44 with a mean of 0.25, was recorded for the founder lines, while corresponding values of 0.004 to 0.34 with a mean of 0.13 were recorded for the derived lines. Heterozygosity values ranging from 0.00 to 0.24 and a mean of 0.08 were recorded for both lines. Of the SNP markers used, 82% of the 1201 markers and 84% of the 1484 markers exhibited polymorphism information content ranging from 0.25 to 0.50. Analysis of molecular variance revealed significant genetic differences (P ≤ 0.001) among and within populations in the founder and derived lines. Most detected variations, i.e., 97% and 88.38%, were attributed to within populations in the founder and derived lines, respectively. Population structure analysis identified three distinct subpopulations among founder lines and two among derived lines. Cluster analysis supported the population structure The following genetically distant founder and derived inbred lines were selected: G15NL337 and G15NL312 (Cluster 1), 15ARG152 and RGS-PL44 (Cluster 2), RGS-PL44 and 15ARG149 (Cluster 2), and RGS-PL33 and RGS-PL44 (Cluster 2), respectively. The selected lines are genetically distinct and recommended for marker-assisted hybrid maize breeding to exploit the frequency of beneficial alleles. This study provides valuable insights for maize breeding programs, enabling the exploitation of beneficial alleles and contributing to improved crop yields and food security through hybrid breeding.

Evolutionary analysis of landscape ecological risk in Baili Rhododendron National Forest Park

It is significant to research the ecological risk of land use landscape to promote ecological conservation and restoration. The characteristics of land use dynamic change in Baili Rhododendron National Forest Park were analyzed based on GlobeLand30 data for three periods in 2000, 2010 and 2020. With the support of the landscape ecological risk evaluation model and spatial analysis methods, the features of spatial and temporal differentiation of ecological risk and its spatial correlation in the study area were evaluated. The results show that (1) the landscape pattern changed visibly from 2000 to 2020, the comprehensive land-use dynamics increased from 8.53% to 9.66%, and the land use dynamics are greatest and change most dramatically on construction sites; (2) the landscape ecological risk level of the study area as a whole showed a declining trend, and 96.82% of the ecological risk plot index decreased, and the risk distribution was dominated by lower risk, medium risk and higher risk areas; (3) landscape ecological risk was spatially positively correlated across all time periods, and the spatial aggregation was gradually weakened, with the distribution of LH and HL areas dispersed and the distribution of HH and LL areas concentrated. The overall landscape ecological risk index has witnessed a decline, indicating a positive trajectory in the development of the ecological environment.

BMI-mediated association between glyphosate exposure and increased risk of atherosclerotic heart disease: A large-scale cross-sectional study

Background Glyphosate, as the main component of glyphosate pesticides, has been shown to have toxic effects on multiple human systems. However, the association between glyphosate and atherosclerotic cardiovascular disease (ASCVD) remains unclear. This study aims to explore the effect of glyphosate exposure on ASCVD. Methods This study involved 1,602 participants and employed various statistical techniques, such as multivariable logistic regression, linear fitting, mediation analysis, subgroup analysis, and sensitivity analysis, to elucidate the association between glyphosate exposure and cardiovascular diseases. Results Compared with the low level of glyphosate exposure, the risk of ASCVD increased significantly with high level of glyphosate exposure (OR = 2.05, 95%CI = 1.17–3.58, p<0.05, p for trend<0.05), which showed a linear upward trend. Further analysis found that exposure to high levels of glyphosate and angina pectoris (OR = 2.84, 95%CI = 1.00–8.04, p<0.05, p for trend<0.05), coronary heart disease (OR = 3.76, 95%CI = 1.80–7.83, p<0.01, p for trend<0.05), heart attack (OR = 2.65, 95%CI = 1.35–5.23, p<0.01, p for trend = 0.09) were associated. Mediating analysis found that BMI mediated the association between glyphosate exposure and ASCVD, with an indirect effect of 0.002405(95%CI: 0.000182–0.01, p = 0.02) and a direct effect of 0.039336(95%CI: 0.000391–0.09), total effect of 0.041741(95%CI: 0.002112–0.10). Conclusion Increased exposure to glyphosate is associated with an increased risk of ASCVD, and BMI plays a mediating role in this association. In addition, glyphosate exposure is associated with a higher risk of angina, coronary heart disease, and heart attack.

Computational design of multi-epitope vaccine against Hepatitis C Virus infection using immunoinformatics techniques

Hepatitis C Virus (HCV) is a blood borne pathogen that affects around 200 million individuals worldwide. Immunizations against the Hepatitis C Virus are intended to enhance T-cell responses and have been identified as a crucial component of successful antiviral therapy. Nevertheless, attempts to mediate clinically relevant anti-HCV activity in people have mainly failed, despite the vaccines present satisfactory progress. In this study, we used an array of immunoinformatics approaches to design a multiepitope peptide-based vaccine against HCV by emphasizing 6 conserved epitopes from viral protein NS5B. The potential epitopes were examined for their possible antigenic combination with each other along with GPGPG linkers using structural modeling and epitope-epitope interaction analysis. An adjuvant (β-defensin) was introduced to the N-terminus to increase the immunogenicity of the vaccine construct. Molecular dynamics simulation discloses the most stable structure of the proposed vaccine. The designed vaccine is potentially antigenic in nature and can form stable and significant interaction with both receptors TLR2 and TLR3. The vaccine construct was also subjected to In-Silico cloning which confirmed its expression efficiency in a vector. The findings indicate that the designed multi-epitope vaccine have a great potential for preclinical and clinical research, which is an important step in addressing the problems related to HCV infection.