Immunoproteomic analysis of outer membrane proteins and extracellular proteins of Actinobacillus pleuropneumoniae JL03 serotype 3

<p>Abstract</p> <p>Background</p> <p><it>Actinobacillus pleuropneumoniae </it>is the causative agent of porcine contagious pleuropneumonia, a highly contagious respiratory infection in pigs, and all the 15 serotypes are able to cause disease. Current vaccines including subunit vaccines could not provide satisfactory protection against <it>A. pleuropneumoniae</it>. In this study, the immunoproteomic approach was applied to the analysis of extracellular and outer membrane proteins of <it>A. pleuropneumoniae </it>JL03 serotype 3 for the identification of novel immunogenic proteins for <it>A. pleuropneumoniae</it>.</p> <p>Results</p> <p>A total of 30 immunogenic proteins were identified from outer membrane and extracellular proteins of JL03 serotype 3, of which 6 were known antigens and 24 were novel immunogenic proteins for <it>A. pleuropneumoniae</it>.</p> <p>Conclusion</p> <p>These data provide information about novel immunogenic proteins for <it>A. pleuropneumoniae </it>serotype 3, and are expected to aid in development of novel vaccines against <it>A. pleuropneumoniae</it>.</p

Million-scale Object Detection with Large Vision Model

Over the past few years, there has been growing interest in developing a broad, universal, and general-purpose computer vision system. Such a system would have the potential to solve a wide range of vision tasks simultaneously, without being restricted to a specific problem or data domain. This is crucial for practical, real-world computer vision applications. In this study, we focus on the million-scale multi-domain universal object detection problem, which presents several challenges, including cross-dataset category label duplication, label conflicts, and the need to handle hierarchical taxonomies. Furthermore, there is an ongoing challenge in the field to find a resource-efficient way to leverage large pre-trained vision models for million-scale cross-dataset object detection. To address these challenges, we introduce our approach to label handling, hierarchy-aware loss design, and resource-efficient model training using a pre-trained large model. Our method was ranked second in the object detection track of the Robust Vision Challenge 2022 (RVC 2022). We hope that our detailed study will serve as a useful reference and alternative approach for similar problems in the computer vision community. The code is available at https://github.com/linfeng93/Large-UniDet.Comment: This paper is revised by ChatGP

Large-scale Multi-Modal Pre-trained Models: A Comprehensive Survey

With the urgent demand for generalized deep models, many pre-trained big models are proposed, such as BERT, ViT, GPT, etc. Inspired by the success of these models in single domains (like computer vision and natural language processing), the multi-modal pre-trained big models have also drawn more and more attention in recent years. In this work, we give a comprehensive survey of these models and hope this paper could provide new insights and helps fresh researchers to track the most cutting-edge works. Specifically, we firstly introduce the background of multi-modal pre-training by reviewing the conventional deep learning, pre-training works in natural language process, computer vision, and speech. Then, we introduce the task definition, key challenges, and advantages of multi-modal pre-training models (MM-PTMs), and discuss the MM-PTMs with a focus on data, objectives, network architectures, and knowledge enhanced pre-training. After that, we introduce the downstream tasks used for the validation of large-scale MM-PTMs, including generative, classification, and regression tasks. We also give visualization and analysis of the model parameters and results on representative downstream tasks. Finally, we point out possible research directions for this topic that may benefit future works. In addition, we maintain a continuously updated paper list for large-scale pre-trained multi-modal big models: https://github.com/wangxiao5791509/MultiModal_BigModels_SurveyComment: Accepted by Machine Intelligence Researc

Feasibility of peaking carbon emissions of the power sector in China’s eight regions: decomposition, decoupling, and prediction analysis

Abstract(#br)Carbon emissions in the power sector are an important part of China’s total carbon emissions and have a significant impact on whether China can achieve the 2030 carbon peak target. Based on the three perspectives of decomposition, decoupling, and prediction, this paper studies the feasibility of carbon emission peaks in eight major regional power sectors in China. First, the generalized Divisia index model (GDIM) is used to decompose the carbon emissions of the eight regional power sectors, and the driving factors and their effects on carbon emissions in the power sector of each region are compared. Then, the decoupling index based on the generalized Divisia index model (GDIM-D) is used to study the decoupling relationship between the carbon emissions of the eight regional..

Shock control of a low-sweep transonic laminar flow wing

This paper presents a combined experimental and computational study of a low-sweep transonic natural laminar flow (NLF) wing with shock-control bumps (SCBs). A transonic NLF wing with a relatively low sweep angle of 20 deg was chosen for this study. To avoid the complexity of the flow introduced by perforated/slotted walls commonly used for transonic wind-tunnel tests for reducing the wall interference, both experimental tests and computational simulations were conducted with solid wind-tunnel wall conditions. This allows for like-to-like validation of the computational simulation. Optimization of the shock-control bumps was first conducted to design the wind-tunnel test model with bumps. Two critical parameters of the three-dimensional SCBs for shock control (i.e., bump crest position and bump height) were optimized in terms of total drag reduction at the given design point in the wind tunnel. We show that the strong shock wave on the low-sweep NLF wing can be effective controlled by well-designed SCBs deployed along the wing span. The optimized SCBs result in 18.5% pressure drag reduction with 5% viscous drag penalty, and the SCBs also bring some benefits at off-design conditions. The wind-tunnel tests include pressure measurement, particle image velocimetry, and temperature-sensitive paint to provide detailed insight into the shock-control flowfield and to validate the computational simulations. Comparisons include surface pressure profile, velocity distribution, and transition location

Non-destructive 3D Microtomography of Cerebral Angioarchitecture Changes Following Ischemic Stroke in Rats Using Synchrotron Radiation

A better understanding of functional changes in the cerebral microvasculature following ischemic injury is essential to elucidate the pathogenesis of stroke. Up to now, the simultaneous depiction and stereological analysis of 3D micro-architectural changes of brain vasculature with network disorders remains a technical challenge. We aimed to explore the three dimensional (3D) microstructural changes of microvasculature in the rat brain on 4, 6 hours, 3 and 18 days post-ischemia using synchrotron radiation micro-computed tomography (SRμCT) with a per pixel size of 5.2 μm. The plasticity of angioarchitecture was distinctly visualized. Quantitative assessments of time-related trends after focal ischemia, including number of branches, number of nodes, and frequency distribution of vessel diameter, reached a peak at 6 h and significantly decreased at 3 days and initiated to form cavities. The detected pathological changes were also proven by histological tests. We depicted a novel methodology for the 3D analysis of vascular repair in ischemic injury, both qualitatively and quantitatively. Cerebral angioarchitecture sustained 3D remodeling and modification during the healing process. The results might provide a deeper insight into the compensatory mechanisms of microvasculature after injury, suggesting that SRμCT is able to provide a potential new platform for deepening imaging pathological changes in complicated angioarchitecture and evaluating potential therapeutic targets for stroke

Macrolide resistance and genotypic characterization of Streptococcus pneumoniae in Asian countries: a study of the Asian Network for Surveillance of Resistant Pathogens (ANSORP)

Objectives: To characterize mechanisms of macrolide resistance among Streptococcus pneumoniae from 10 Asian countries during 1998-2001. Methods: Phenotypic and genotypic characterization of the isolates and their resistance mechanisms. Results: Of 555 isolates studied, 216 (38.9%) were susceptible, 10 (1.8%) were intermediate and 329 (59.3%) were resistant to erythromycin. Vietnam had the highest prevalence of erythromycin resistance (88.3%), followed by Taiwan (87.2%), Korea (85.1%), Hong Kong (76.5%) and China (75.6%). Ribosomal methylation encoded by erm(B) was the most common mechanism of erythromycin resistance in China, Taiwan, Sri Lanka and Korea. In Hong Kong, Singapore, Thailand and Malaysia, efflux encoded by mef(A) was the more common in erythromycin-resistant isolates. In most Asian countries except Hong Kong, Malaysia and Singapore, erm(B) was found in >50% of pneumococcal isolates either alone or in combination with mef(A). The level of erythromycin resistance among pneumococcal isolates in most Asian countries except Thailand and India was very high with MIC90s of >128 mg/L. Molecular epidemiological studies suggest the horizontal transfer of the erm(B) gene and clonal dissemination of resistant strains in the Asian region. Conclusion: Data confirm that macrolide resistance in pneumococci is a serious problem in many Asian countries