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This research comprehensively investigates the epidemiological features and pathogen profile of acute respiratory infections (ARI) in Shihezi City, Xinjiang. A pivotal aspect of this study is the construction of a Bayes discriminant function for principal pathogen infections. This innovative methodology aims to furnish a robust scientific basis for the prevention and clinical management of ARI, potentially guiding more effective strategies in both public health and clinical settings. We compiled and examined data from January 2020 to June 2023, pertaining to patients admitted with acute respiratory infections at the First Affiliated Hospital of Shihezi University. This investigation focused on discerning patterns in epidemiology and pathogen etiology. Among 2110 cases of acute respiratory infections (ARI), 1736 underwent pathogenetic testing. Of these, 595 cases tested positive for at least one pathogen, marking a positivity rate of 34.27%. Viral detections, at a rate of 27.47%, were notably higher than bacterial detections, which stood at 6.51%. The most prevalent viruses identified were Human respiratory syncytial virus (hRSV), Severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐CoV‐2), and Human adenovirus (HAdV), while the dominant bacterial pathogens included Klebsiella pneumoniae, Haemophilus influenzae, and Staphylococcus aureus. Co‐infections were observed in 76 cases, accounting for 12.77% of positive diagnoses, predominantly involving hRSV in conjunction with other pathogens. In cases of acute bronchiolitis, hRSV was the most frequent pathogen, contributing to 23.10% of such cases. Similarly, in severe pneumonia cases, SARS‐CoV‐2 was predominant, accounting for 25.4% of these infections. The group with bacterial positivity exhibited elevated levels of C‐reactive protein (CRP, 19.17 mg/L) and neutrophilic granulocyte percentage (NE%, 54.7%). The Bayes discriminant function demonstrated an initial validation accuracy of 74.9% and a cross‐validation accuracy of 63.7%. The study underscores that hRSV, SARS‐CoV‐2, and HAdV are the primary pathogens in acute respiratory infections in the Shihezi region. Pathogen susceptibility exhibits variation across different age groups, with a higher pathogen detection rate in children compared to adults. The Bayes discriminant function shows significant promise in the classification and diagnosis of major pathogenic infections. [ABSTRACT FROM AUTHOR]

The translated protein undergoes a specific modification process, which involves the formation of covalent bonds on lysine residues and the attachment of small chemical moieties. The protein's fundamental physicochemical properties undergo a significant alteration. The change significantly alters the proteins' 3D structure and activity, enabling them to modulate key physiological processes. The modulation encompasses inhibiting cancer cell growth, delaying ovarian aging, regulating metabolic diseases, and ameliorating depression. Consequently, the identification and comprehension of post-translational lysine modifications hold substantial value in the realms of biological research and drug development. Post-translational modifications (PTMs) at lysine (K) sites are among the most common protein modifications. However, research on K-PTMs has been largely centered on identifying individual modification types, with a relative scarcity of balanced data analysis techniques. In this study, a classification system is developed for the prediction of concurrent multiple modifications at a single lysine residue. Initially, a well-established multi-label position-specific triad amino acid propensity algorithm is utilized for feature encoding. Subsequently, PreMLS: a novel ClusterCentroids undersampling algorithm based on MiniBatchKmeans was introduced to eliminate redundant or similar major class samples, thereby mitigating the issue of class imbalance. A convolutional neural network architecture was specifically constructed for the analysis of biological sequences to predict multiple lysine modification sites. The model, evaluated through five-fold cross-validation and independent testing, was found to significantly outperform existing models such as iMul-kSite and predML-Site. The results presented here aid in prioritizing potential lysine modification sites, facilitating subsequent biological assays and advancing pharmaceutical research. To enhance accessibility, an open-access predictive script has been crafted for the multi-label predictive model developed in this study. Author summary: Proteins undergo a variety of post-translational modifications (PTMs) after synthesis, such as lysine modifications, which significantly influence their structure and function. These modifications of lysine are known to regulate physiological processes, including the inhibition of cancer cell growth, the delay of aging, the regulation of metabolic diseases, and the improvement of depressive disorders. Abnormal modifications are closely associated with the occurrence and progression of a multitude of diseases. Therefore, the identification and comprehension of these modifications are of paramount importance for biological research and drug development. A multitude of studies have focused on a single type of lysine modification, with prediction methods for multiple lysine modification sites being relatively scarce. In this research, a novel multi-label prediction model named PreMLS has been developed for the simultaneous identification of four lysine modifications: methylation, acetylation, crotonylation, and succinylation. The imbalance issue in the dataset was addressed utilizing the ClusterCentroids undersampling algorithm, following which a predictive model, PreMLS, was constructed using a CNN to forecast multiple lysine modification sites. Compared to existing models, this new approach has significantly enhanced the accuracy and reliability of the predictions. [ABSTRACT FROM AUTHOR]

Leaves play a crucial role as ornamental organs in Spathiphyllum, exhibiting distinct differences across various Spathiphyllum varieties. Leaf development is intricately linked to processes of cell proliferation and expansion, with cell morphology often regulated by plant cell walls, primarily composed of cellulose. Alterations in cellulose content can impact cell morphology, subsequently influencing the overall shape of plant organs. Although cellulases have been shown to affect cellulose levels in plant cells, genetic evidence linking them to the regulation of leaf shape remains limited. This study took the leaves of Spathiphyllum 'Mojo' and its somatic variants as the research objects. We screened four cellulase gene family members from the transcriptome and then measured the leaf cellulose content, cellulase activity, and expression levels of cellulase-related genes. Correlation analysis pinpointed the gene SpGH9A3 as closely associated with leaf shape variations in the mutant. Green fluorescent fusion protein assays revealed that the SpGH9A3 protein was localized to the cell membrane. Notably, the expression of the SpGH9A3 gene in mutant leaves peaked during the early spread stage, resulting in smaller overall leaf size and reduced cellulose content upon overexpression in Arabidopsis. [ABSTRACT FROM AUTHOR]

Microwave surface emissivity is complex and variable, leading to increased difficulty in accurately retrieving atmospheric parameters and assimilating satellite microwave observations over land. The Community Radiative Transfer Model (CRTM) land emissivity model is a useful tool for providing microwave emissivity over complex surfaces. By combining the model with ground measurements from a mobile multi-surface observing system at the Xianghe site, China, the performance of the land emissivity model is evaluated over grass and sand surfaces. The simulated and measured emissivity agrees at both polarizations over the grassland surface but a more significant difference is observed at the horizontal polarization over the sand surface. To solve this problem, the Q/H module for soil reflectance roughness correction in the CRTM emissivity model was replaced with the Qp module for the sand surface. This results in a significant improvement in the horizontal polarization simulation, with the corresponding mean bias error (MBE) reducing from 0.08 in the Q/H module to less than 0.03. The adjustment demonstrates that the Qp module more effectively corrects the roughness effect on horizontally polarized emissivity for bare soil surfaces. For grassland, the CRTM emissivity model with the Q/H module demonstrates accurate simulations, showing its suitability for vegetated land surfaces. [ABSTRACT FROM AUTHOR]

The mobile ocean weather observation system, named Marine Weather Observer (MWO), developed by the Institute of Atmospheric Physics (IAP), consists of a fully solar-powered, unoccupied vehicle and meteorological and hydrological instruments. One of the MWOs completed a long-term continuous observation, actively approaching the center of Typhoon Sinlaku from 24 July to 2 August 2020, over the South China Sea. The in situ and high-temporal-resolution (1 min) observations obtained from MWO were analyzed and evaluated through comparison with the observations made by two types of buoys during the evolution of Typhoon Sinlaku. First, the air pressure and wind speed measured by MWO are in good agreement with those measured by the buoys before the typhoon, reflecting the equivalent measurement capabilities of the two methods under normal sea conditions. The sea surface temperature (SST) between MWO and the mooring buoys is highly consistent throughout the observation period, indicating the high stability and accuracy of SST measurements from MWO during the typhoon evolution. The air temperature and relative humidity measured by MWO have significant diurnal variations, generally lower than those measured by the buoys, which may be related to the mounting height and sensitivity of sensors. When actively approaching the typhoon center, the air pressure from MWO can reflect some drastic and subtle changes, such as a sudden drop to 980 hPa, which is difficult to obtain by other observation methods. As a mobile meteorological and oceanographic observation station, MWO has shown its unique advantages over traditional observation methods, and the results preliminarily demonstrate the reliable observation capability of MWO in this paper. [ABSTRACT FROM AUTHOR]

As an important teaching method in the era of education informatization 2.0, the smart classroom has become a hot topic in terms of whether it can really improve students' learning effectiveness. Many scholars have carried out empirical studies in this field, but the conclusions are not the same. By the use of meta-analysis, this study quantitatively analyzes 48 experimental and quasi-experimental studies in China and other countries to explore the smart classroom's effect and influencing factors. The results show that, on the whole, the smart classroom has a better-than-average promotion effect on students' learning effectiveness; from the perspective of moderating variables, the learning outcome of smart classrooms is influenced by the factors of learning stage, knowledge type, teaching cycle, class scale, and equipment condition, but is not moderated by the factors of subject, teaching strategy, and evaluation tool. Specifically, smart classrooms can become a suitable partner for large-class teaching, short-term courses, and experimental courses. However, the misuse of technology and equipment should be avoided in the application process. [ABSTRACT FROM AUTHOR]