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Two kinds of near-stoichiometric SiC fibers were prepared through the pyrolysis of oxygen-free cured polycarbosilane fibers at 1,000 °C within a controlled hydrogen atmosphere, followed by sintering at 1,500 °C under a nitrogen atmosphere by one-step and two-step firing processes. The compositions, structures, and properties of the obtained SiC fibers were characterized using elemental analysis, mechanical performance testing, X-ray diffraction analysis, X-ray photoelectron spectroscopy, scanning electron microscopy, and transmission electron microscopy. Silicon radicals were generated in the process of hydrogen firing, which readily reacted with oxygen to form SiO2 or SiCxOy phases on the fiber surface, resulting in the obtained fibers exhibited a gradient in compositional distribution across the radial direction, with higher oxygen content at their surfaces and higher carbon content at their cores. The strength of one-step-fired SiC fiber exceeded 1 GPa after treatment at 1,800℃ in argon and 1,500℃ in air for 1 h.

Efficient mobile detection equipment plays a vital role in ensuring maritime safety, and accurate ship identification is crucial for maritime traffic. Recently, the most advanced learning-based methods have markedly improved the accuracy of ship detection, but these models often face huge challenges on resource limited mobile devices due to their large size and high computational requirements. Thus, we propose a lightweight ship detection network based on attentional feature fusion, called AFF-LightNet. To enhance the fusion of multi-scale features and semantically inconsistent features, we introduce iterative attentional feature fusion (IAFF) into the proposed neck network, improving the efficiency of feature fusion by introducing a multi-scale channel attention module. Also, deep and cross network version 2 (DCNv2) is replaced by Convolution (Conv) in the backbone network to further improve the detection accuracy of the proposed network. It enhances the spatial sampling position in convolution and Region of Interest (Rol) pooling by introducing offsets. Moreover, a lightweight convolution GhostConv was introduced into the head network to reduce the number of parameters and computation cost. Last, Scalable Intersection over Union (SIOU) loss was leveraged to improve the convergence speed of the model. We conduct extensive experiments on the publicly available dataset SeaShips and compare it with existing methods. The experimental results show that compared with the standard YOLOv8n, the improved network has an average accuracy of 98.8%, an increase of 0.4%, a reduction of 1.9 G in computational complexity, and a reduction of 0.19 M in parameter count.

Abstract Aims HNF1B syndrome is caused by defects in the hepatocyte nuclear factor 1B (HNF1B) gene, which leads to maturity‐onset diabetes of the young type 5 and congenital organ malformations. This study aimed to identify a gene defect in a patient presenting with diabetes and severe diarrhea, while also analyzing the prevalence of hypomagnesemia and its correlation with the HNF1B genotype. Materials and Methods Whole exome sequencing was used to identify responsible point mutations and small indels in the proband and their family members. Multiplex ligation‐dependent probe amplification was carried out to identify HNF1B deletions. Furthermore, an analysis of published data on 539 cumulative HNF1B cases, from 29 literature sources, was carried out to determine the correlation between the HNF1B genotype and the phenotype of serum magnesium status. Results Using multiplex ligation‐dependent probe amplification, we identified a de novo heterozygous HNF1B deletion in the patient, who showed dorsal pancreas agenesis and multiple kidney cysts, as detected by magnetic resonance imaging. Magnesium supplementation effectively alleviated the symptoms of diarrhea. Hypomagnesemia was highly prevalent in 192 out of 354 (54.2%) patients with HNF1B syndrome. Compared with patients with intragenic mutations, those with HNF1B deletions were more likely to suffer from hypomagnesemia, with an odds ratio of 3.1 (95% confidence interval 1.8–5.4). Conclusions Hypomagnesemia is highly prevalent in individuals with HNF1B syndrome, and those with HNF1B deletion are more susceptible to developing hypomagnesemia compared with those with intragenic mutations. The genotype–phenotype associations in HNF1B syndrome have significant implications for endocrinologists in terms of genotype detection, treatment decisions and prognosis assessment.

Abstract Background High-altitude exposure can cause oxidative stress damage in the intestine, which leads to increased intestinal permeability and bacterial translocation, resulting in local and systemic inflammation. Control of infection is critically dependent on the host’s ability to kill pathogens with reactive oxygen species (ROS). Myeloperoxidase (MPO) targets ROS in pathogens. This study aimed to investigate the effects of hypoxia on the colonic mucosal barrier and myeloperoxidase (MPO)-mediated innate immune response in the colon. Methods and Results Genetically engineered mice were exposed to a hypobaric oxygen chamber for 3 days and an inflammation model was established using Salmonella Typhimurium infection. We found that hypoxic exposure caused the development of exacerbated bacterial colitis and enhanced bacterial dissemination in MPO-deficient mice. Infection and disease severity were associated with significantly increased Ly6G+ neutrophil and F4/80+ macrophage counts in infected tissues, which is consistent with elevated proinflammatory cytokines and chemoattractant molecules. Hypoxia restrained antioxidant ability and MPO deficiency aggravated the respiratory burst in the colon. Conclusion Hypoxia can damage the colonic mucosa. MPO mediates the innate immune response and regulates the mucosal and systemic inflammatory responses to Salmonella infection during hypoxia.

Antimicrobials are extensively utilized in dairy farms to prevent and control diseases in cattle. However, their use contributes to the emergence of antimicrobial-resistant bacteria (ARB) and antimicrobial-resistant genes (ARG), and these can be transmitted to the environment. Regular monitoring of antimicrobial resistance (AMR) is crucial for implementing effective mitigation strategies. This research aimed to assess the environmental microbial species present on dairy farms in Shandong Province and characterize the antimicrobial resistance profiles of the isolates. Five dairy farms located in Shandong Province were selected, representing the prevalent large-scale farming patterns in the area. Sampling took place from April to June 2022, with a total of 223 isolates collected from various environmental locations within each farm (bedding, sports field, and milking parlor). Matrix-assisted laser desorption/ionization—time-of-flight mass spectrometry (MALDI-TOF MS) was employed to identify the species of the clinical isolates. The main pathogens isolated were Aerococcus viridans (5.38%, n = 12), Corynebacterium xerosis (4.93%, n = 11), and Acinetobacter lwoffii (4.03%, n = 9). Among the bacterial isolates, resistance to lincomycin was highest at 91%, and 88% were resistant to sulfadiazine. Antimicrobial resistance genes were detected in only a small proportion of the isolates, the most common of which was sul1. These findings highlight the necessity for careful evaluation of antimicrobial usage in maintaining their effectiveness in human medicine. Understanding the microbial species present and their antimicrobial resistance profiles aids in focusing efforts toward sustainable antimicrobial use and safeguarding human health.

The rapid emergence of massive datasets in various fields poses a serious challenge to traditional statistical methods. Meanwhile, it provides opportunities for researchers to develop novel algorithms. Inspired by the idea of divide-and-conquer, various distributed frameworks for statistical estimation and inference have been proposed. They were developed to deal with large-scale statistical optimization problems. This paper aims to provide a comprehensive review for related literature. It includes parametric models, nonparametric models, and other frequently used models. Their key ideas and theoretical properties are summarized. The trade-off between communication cost and estimate precision together with other concerns is discussed.

Abstract The collectors of electronic transformers has the problem of electromagnetic incompatibility under the operation of disconnecting switches, which limits their wide application in smart grids. To evaluate the maximum level of disturbance that collectors are able to withstand in the stochastic process of switching operation, this work introduces a quantitative approach based on limited test measurements, which can quantify the level of disturbance in the signal acquisition ports of electronic voltage transformers (EVT) in different gas‐insulated switchgears (GIS). Based on large numbers of experimental statistical data of the true type simulation experimental platform, the Spearman rank correlation coefficient is used to prove that the amplitude of the disturbance voltage pulse at the secondary side port and the breakdown voltage between the primary side fracture approximately satisfy a linear relationship. Subsequently, the quantile regression model is used to quantify and verify the maximum disturbance level of the secondary side port. Moreover, a solution is proposed to tackle the difficulty in obtaining the breakdown voltage at breaks in the GIS substation field applicatvion, under which the quantified maximum disturbance voltage deviation of secondary ports is only −4.34% by comparison. The research work can further explore the practical value of the measured data in a few tests under GIS substation field conditions and provide references for the subsequent revision of EVT‐related standards.

Abstract Cell death and inflammation are intimately linked during mastitis due to Staphylococcus aureus (S. aureus). Pyroptosis, a programmed necrosis triggered by gasdermin protein family, often occurs after inflammatory caspase activation. Many pathogens invade host cells and activate cell-intrinsic death mechanisms, including pyroptosis, apoptosis, and necroptosis. We reported that bovine mammary epithelial cells (MAC-T) respond to S. aureus by NOD-like receptor protein 3 (NLRP3) inflammasome activation through K+ efflux, leading to the recruitment of apoptosis-associated speck-like protein (ASC) and the activation of caspase-1. The activated caspase-1 cleaves gasdermin D (GSDMD) and forms a N-terminal pore forming domain that drives swelling and membrane rupture. Membrane rupture results in the release of the pro-inflammatory cytokines IL-18 and IL-1β, which are activated by caspase-1. Can modulate GSDMD activation by NLRP3-dependent caspase-1 activation and then cause pyroptosis of bovine mammary epithelial cells.

IntroductionHexavalent chromium or Cr(VI) is essential to various industries, such as leather manufacturing and stainless steel production. Given that inevitable leakage from industries pollutes the soil and thereby affects the soil environment. Microbial communities could improve the quality of the soil. Abundant bacterial communities would significantly enhance the soil richness and resist external pressure, benefiting agriculture. But the pollution of heavy metal broke the balance and decrease the abundance of bacterial communities, which weak the self-adjust ability of soil. This study aimed to explore changes in the diversity of soil bacterial communities and to identify the influences of soil bacterial communities on enzymes in soil polluted by Cr(VI).MethodsThe target soils were sampled quickly and aseptically. Their chromium content was detected through inductively coupled plasma-mass spectrometry, and bacterial microbiome communities were explored through MiSeq high-throughput sequencing. Then, the content of nitrite reductase and catalases were investigated through enzyme-linked immunosorbent assay (ELISA).ResultsChromium content in polluted soils was higher than that in the control soils at all depths. Sobs, Chao1, Ace, and Shannon diversity estimators in the control were higher, whereas Simpson's diversity estimators in the control soils were lower than those of contaminated samples at all depths. Contaminants affected the composition of the bacterial community. The soil microbial species were relatively single and inhomogeneous in the polluted soils. The bacterial phyla in polluted and controlled soils include Proteobacteria, Actinobacteria, Chloroflexi, and Acidobacteria, which differ markedly in abundance.DiscussionThe results of these observations provide insights into the ecotoxicological effects of Cr(VI) exposure to soil microorganisms. To sum up these results are critical for evaluating the stabilized state of microbial community structures, contributing to the assessment of the potential risk of metal accumulation in soils.

This study establishes the applicability of various machine learning (ML) algorithms for creating in silico models based on data from historical bioprocess development and manufacturing activities. We demonstrate this by developing ML-based models for a particular case of predicting viral clearance performance of anion exchange (AEX) chromatography operated in flow-through (FT) mode, a processing step that is essential for ensuring viral safety of biotherapeutics and that requires significant resources due to a large number of expensive wet lab design and characterization experiments. Using these ML-based models, we then evaluate the impact of various feed-, virus-, and process-related parameters (such as the molecular attributes of the product, the type of chromatography matrix, loading capacity, equilibration/wash buffer pH and conductivity, virus type and load, etc.) on the viral clearance performance and hence generate process understanding essential for developing FT-AEX chromatography processes with robust viral clearance performance. Finally, we show that ML-based optimization methods can be used to determine process operating ranges for improved viral clearance performance. These results are significant as they provide approaches and understanding necessary for faster and cheaper development of robust FT-AEX chromatography-based viral clearance processes and generally provide guidance on the implementation of ML-based prediction and optimization approaches in bioprocessing - crucial to reducing costs and time associated with the design and manufacturing of biotherapeutics and making these treatments widely accessible.

Bovine mastitis is an important disease affecting dairy farming, and it causes large economic losses to the dairy industry. Escherichia coli (E. coli) is considered to be a causative environmental pathogen and frequently enters into mammary glands, causing inflammation. Artemisinin is a highly effective malaria remedy and is not easy to develop drug resistance to. In recent years, other effects of artemisinin (including antitumor, anti-inflammatory, antifungal, etc.) have been increasingly discovered and applied. The current study aimed to investigate whether artemisinin could attenuate E. coli–induced inflammation. Through the E. coli mastitis model in MAC-T cells and mice, the protective effects of artemisinin were analyzed by CCK-8 (Cell Counting Kit-8), Western blot, and RT-qPCR. The results showed that artemisinin reversed the decrease of cell viability and upregulated TLR4 (toll-like receptor 4)/NF-κB (nuclear factor κB) and MAPK (mitogen activated protein kinase)/p38 signaling pathways, as well as restrained the expression of TNF-α, IL-6, and IL-1β mRNA caused by E. coli. Meanwhile, artemisinin also alleviated mammary tissue damage, reduced inflammatory cells’ infiltration, and decreased the levels of inflammatory factors in a mice mastitis model. This study demonstrated that artemisinin alleviated the inflammatory response of mouse mastitis and MAC-T cells induced by E. coli, thus providing a practical approach for the clinical control of mastitis.

Staphylococcus aureus (S. aureus), a common mastitis pathogen widespread in the natural environment of dairy farms, is capable of invading mammary epithelial cells making treatment difficult. However, the mechanism of the response of bovine mammary epithelial cell to S. aureus invasion remains elusive. In this study, transcriptomic analysis and bioinformatics tools were applied to explore the differentially expressed RNAs in bovine mammary epithelial cells (bMECs) between the control and S. aureus-treated group. A total of 259 differentially expressed mRNAs (DEmRNAs), 27 differentially expressed microRNAs (DEmiRNAs), and 21 differentially expressed long non-coding RNAs (DElncRNAs) were found. These RNAs mainly enrich the inflammatory response, immune response, endocytosis, and cytokine-cytokine receptor interaction. qRT-PCR was used to analyze the quality of the RNA-seq results. In particular, to the defense mechanism of bovine mammary epithelial cells against intracellular S. aureus, the PPAR signaling pathway and the genes (ACOX2, CROT, and NUDT12) were found to be up-regulated to promote the production of peroxisomes and ROS, DRAM1 expression was also up-regulated to facilitate the activation of autophagy, indicating that the above mechanisms were involved in the elimination of intracellular S. aureus in bovine mammary epithelial cells.

In dairy herds, mastitis caused by Staphylococcus aureus is difficult to completely cure on the account that S. aureus can invade bovine mammary epithelial cells (BMECs) and result in persistent infection in the mammary gland. Recent studies have demonstrated that autophagy can participate in cell homeostasis by eliminating intracellular microorganisms. The aim of the study was to investigate why S. aureus can evade autophagy clearance and survive in BMECs. The intracellular infection model was first constructed; then, the bacteria in autophagosome was detected by transmission electron microscopy. The autophagy flux induced by the S. aureus was also evaluated by immunoblot analysis and fluorescent labeling method for autophagy marker protein LC3. In addition, lysosomal alkalization and degradation ability were assessed using confocal microscopy. Results showed that, after infection, a double-layer membrane structure around the S. aureus was observed in BMECs, indicating that autophagy occurred. The change in autophagy marker protein and fluorescent labeling of autophagosome also confirmed autophagy. However, as time prolonged, the autophagy flux was markedly inhibited, leading to obvious autophagosome accumulation. At the same time, the lysosomal alkalization and degradation ability of BMECs were impaired. Collectively, these results indicated that S. aureus could escape autophagic degradation by inhibiting autophagy flux and damaging lysosomal function after invading BMECs.

It is a huge market space for narrowband-ineternet of things (NB-IoT),and NB-IoT is the focus of future development direction.The global telecommunications operators have begun to distributed actively.In order to better determine the network deployment of the construction program and resource allocation,from the business scene of NB-IoT,the NB-IoT service characteristics were analyzed,and the NB-IoT core network business model parameters and networking solutions were given,which was of great significance to the follow-up NB-IoT core network resource allocation and planning scheme.

Recommender systems suggest items to users based on their potential interests, and they are important to alleviate the search and selection pressures induced by the increasing item information. Classical recommender systems mainly focus on the accuracy of recommendation. However, with the increase of the diversified demands of users, multiple metrics which may conflict with each other have to be considered in modern recommender systems, especially for the personalized recommender system. In this paper, we design a personalized recommendation system considering the three conflicting objectives, i.e., the accuracy, diversity, and novelty. Then, to let the system provide more comprehensive recommended items, we present a multiobjective personalized recommendation algorithm using extreme point guided evolutionary computation (called MOEA-EPG). The proposed MOEA-EPG is guided by three extreme points and its crossover operator is designed for better satisfying the demands of users. The experimental results validate the effectiveness of MOEA-EPG when compared to some state-of-the-art recommendation algorithms in terms of accuracy, diversity, and novelty on recommendation.