Characterization of nonstructural protein 3 of a neurovirulent Japanese encephalitis virus strain isolated from a pig

<p>Abstract</p> <p>Background</p> <p>Japanese encephalitis virus (JEV), as a re-emerging virus that causes 10,000-15,000 human deaths from encephalitis in the world each year, has had a significant impact on public health. Pigs are the natural reservoirs of JEV and play an important role in the amplification, dispersal and epidemiology of JEV. The nonstructural protein 3 (NS3) of JEV possesses enzymatic activities of serine protease, helicase and nucleoside 5'-triphosphatase, and plays important roles in viral replication and pathogenesis.</p> <p>Results</p> <p>We characterized the NS3 protein of a neurovirulent strain of JEV (SH-JEV01) isolated from a field-infected pig. The NS3 gene of the JEV SH-JEV01 strain is 1857 bp in length and encodes protein of approximately 72 kDa with 99% amino acid sequence identity to that of the representative immunotype strain JaGAr 01. The NS3 protein was detectable 12 h post-infection in a mouse neuroblastoma cell line, Neuro-2a, and was distributed in the cytoplasm of cells infected with the SH-JEV01 strain of JEV. In the brain of mice infected with the SH-JEV01 strain of JEV, NS3 was detected in the cytoplasm of neuronal cells, including pyramidal neurons of the cerebrum, granule cells, small cells and Purkinje cells of the cerebellum.</p> <p>Conclusions</p> <p>The NS3 protein of a neurovirulent strain of JEV isolated from a pig was characterized. It is an approximately 72 kDa protein and distributed in the cytoplasm of infected cells. The Purkinje cell of the cerebellum is one of the target cells of JEV infection. Our data should provide some basic information for the study of the role of NS3 in the pathogenesis of JEV and the immune response.</p

Large Trajectory Models are Scalable Motion Predictors and Planners

Motion prediction and planning are vital tasks in autonomous driving, and recent efforts have shifted to machine learning-based approaches. The challenges include understanding diverse road topologies, reasoning traffic dynamics over a long time horizon, interpreting heterogeneous behaviors, and generating policies in a large continuous state space. Inspired by the success of large language models in addressing similar complexities through model scaling, we introduce a scalable trajectory model called State Transformer (STR). STR reformulates the motion prediction and motion planning problems by arranging observations, states, and actions into one unified sequence modeling task. With a simple model design, STR consistently outperforms baseline approaches in both problems. Remarkably, experimental results reveal that large trajectory models (LTMs), such as STR, adhere to the scaling laws by presenting outstanding adaptability and learning efficiency. Qualitative results further demonstrate that LTMs are capable of making plausible predictions in scenarios that diverge significantly from the training data distribution. LTMs also learn to make complex reasonings for long-term planning, without explicit loss designs or costly high-level annotations

Variations in growth traits and wood physicochemical properties among Pinus koraiensis families in Northeast China

This study aimed to explore and improve the different economic values of Pinus koraiensis (Siebold and Zucc.) by examining the variations in 6 growth traits and 9 physicochemical wood properties among 53 P. koraiensis half-sib families. Growth traits assessed included height, diameter at breast height, volume, degree of stem straightness, stem form, and branch number per node, while wood properties assessed included density, fiber length and width, fiber length to width ratio, and cellulose, hemicellulose, holocellulose, lignin, and ash contents. Except for degree of stem straightness and branch number per node, all other traits exhibited highly significant variations (P < 0.01) among families. The coefficients of variation ranged from 5.3 (stem form) to 66.7% (ash content), whereas, the heritability ranged from 0.136 (degree of stem straightness) to 0.962 (ash content). Significant correlations were observed among growth traits and wood physicochemical properties. Principal component analysis identified four distinct groups representing growth traits, wood chemical and physical properties, and stem form traits. Multi-trait comprehensive evaluation identified three groups of elite families based on breeding objectives, including rapid growth, improved timber production for building and furniture materials, and pulpwood production. These specific families should be used to establish new plantations

Temporal, geographical and demographic trends of stroke prevalence in China: a systematic review and meta-analysis.

China has made large efforts to reduce stroke prevalence. We aimed to systematically examine the prevalence of stroke in China over the past two decades. Databases, including China National Knowledge Infrastructure, Wanfang, VIP, and PubMed, were systematically searched for studies published in English or Chinese that reported stroke prevalence in China during 2000-2017. Meta-analysis was conducted to estimate the pooled stroke prevalence and the variations in stroke prevalence subgroups stratified by age, gender, time period, and region. In total, 96 papers met the inclusion criteria. Meta-analysis showed that the overall estimated national prevalence was 5.1% (5.0-5.3%) with large variations across regions: 3.1% (2.5-3.6%) in south China, 3.4% (3.0-3.8%) in southwest China, 3.6% (3.3-3.8%) in east China, 5.0% (4.7-5.4%) in central China, 5.8% (4.6-7.1%) in northwest China, 6.0% (5.0-7.0%) in northeast China, and 8.0% (7.4-8.5%) in north China. Men had a higher prevalence than women [7.3% (6.9-7.7%) . 5.6% (5.2-6.0%)]. Stroke prevalence increased with age, was 1.2% (1.0-1.3%), 2.9% (2.6-3.2%), 5.9% (5.2-6.5%), and 8.7% (8.0-9.5%) in the 40-49, 50-59, 60-69, and ≥70 years old groups, respectively. Men, people being older, or living in northern China had higher stroke prevalence. More vigorous efforts are needed in China to prevent stroke.Funding: The study was supported in part by research grants from the China Medical Board (Grant No. 16-262), and the National Key Research and Development Program of China (Grant Number: 2017YFC0907200 & 2017YFC0907201), the National Natural Science Foundation of China (Grant Number: NSFC 81703220)

High-altitude cerebral hypoxia promotes mitochondrial dysfunction and apoptosis of mouse neurons

IntroductionNeuronal cell death is an important factor in the pathogenesis of acute high-altitude cerebral hypoxia; however, the underlying molecular mechanism remains unclear. In this study, we tested if high-altitude hypoxia (HAH) causes neuronal death and mitochondrial dysfunction using various in vivo and in vitro approaches.MethodsAcute high-altitude cerebral hypoxia was induced by hypobaric hypoxia chamber in male mice. we explored the mechanisms of neuronal cell death using immunofluorescence, western blotting, transmission electron microscopy, and flow cytometry. Next, mitochondrial function and morphology were observed using Jc-1 staining, seahorse assay, western blotting, MitoTracker staining, and transmission electron microscopy. Moreover, open field test, elevated plus test, and Morris water maze were applied for animal behavior.ResultsResults revealed that HAH disrupted mitochondrial function and promoted neuronal apoptosis and necroptosis both in HT-22 cells and in mouse hippocampal neurons. Moreover, the mitochondrial membrane potential and adenosine triphosphate production decreased in neurons after HAH, while oxidative stress and mitochondrial fission increased. Behavioral studies suggested that HAH induced anxiety-like behavior and impaired spatial memory, while it had no effect on athletic ability.DiscussionThese findings demonstrated that HAH promotes mitochondrial dysfunction and apoptosis of mouse neurons, thus providing new insights into the role of mitochondrial function and neuronal cell death in acute high-altitude cerebral hypoxia

TorchAudio 2.1: Advancing speech recognition, self-supervised learning, and audio processing components for PyTorch

TorchAudio is an open-source audio and speech processing library built for PyTorch. It aims to accelerate the research and development of audio and speech technologies by providing well-designed, easy-to-use, and performant PyTorch components. Its contributors routinely engage with users to understand their needs and fulfill them by developing impactful features. Here, we survey TorchAudio's development principles and contents and highlight key features we include in its latest version (2.1): self-supervised learning pre-trained pipelines and training recipes, high-performance CTC decoders, speech recognition models and training recipes, advanced media I/O capabilities, and tools for performing forced alignment, multi-channel speech enhancement, and reference-less speech assessment. For a selection of these features, through empirical studies, we demonstrate their efficacy and show that they achieve competitive or state-of-the-art performance

Application of Bee Evolutionary Genetic Algorithm to Maximum Likelihood Direction-of-Arrival Estimation

The maximum likelihood (ML) method achieves an excellent performance for DOA estimation. However, its computational complexity is too high for a multidimensional nonlinear solution search. To address this issue, an improved bee evolutionary genetic algorithm (IBEGA) is applied to maximize the likelihood function for DOA estimation. First, an opposition-based reinforcement learning method is utilized to achieve a better initial population for the BEGA. Second, an improved arithmetic crossover operator is proposed to improve the global searching performance. The experimental results show that the proposed algorithm can reduce the computational complexity of ML DOA estimation significantly without sacrificing the estimation accuracy

Dyes Pigment.

A simulated wastewater containing the monoazo dye C.I. Acid Orange 7 was electrolytically treated using a three-dimensional electrode reactor equipped with granular activated carbon as particle electrode. The activated carbon fiber cathode was more effective than either a graphite or a stainless steel cathode due to its larger surface area which was beneficial to the electrogeneration of H(2)O(2). Under the reaction conditions of 20 V and 3.0 g L(-1) Na(2)SO(4) at pH 3.0, decrease in the COD of up to 80%, and in the TOC by up to 72% were achieved and almost the complete decolorization of the dye was secured after 180 min electrolysis. Furthermore, decay of the dye followed a pseudo-first-order reaction in the first 60 min treatment. The three-dimensional electrode system generated more hydroxyl radicals than a two-dimensional system due to the formation of microelectrodes under applied high potential. (C) 2007 Elsevier Ltd. All rights reserved.A simulated wastewater containing the monoazo dye C.I. Acid Orange 7 was electrolytically treated using a three-dimensional electrode reactor equipped with granular activated carbon as particle electrode. The activated carbon fiber cathode was more effective than either a graphite or a stainless steel cathode due to its larger surface area which was beneficial to the electrogeneration of H(2)O(2). Under the reaction conditions of 20 V and 3.0 g L(-1) Na(2)SO(4) at pH 3.0, decrease in the COD of up to 80%, and in the TOC by up to 72% were achieved and almost the complete decolorization of the dye was secured after 180 min electrolysis. Furthermore, decay of the dye followed a pseudo-first-order reaction in the first 60 min treatment. The three-dimensional electrode system generated more hydroxyl radicals than a two-dimensional system due to the formation of microelectrodes under applied high potential. (C) 2007 Elsevier Ltd. All rights reserved

A Simple Method of Evaluating the Thermal Properties of Metallurgical Cokes under High Temperature

The reactivity index of weight loss (RI) and tumbling strength after the reaction (I10600) of manufacturing coke were first tested at a temperature series of 1100, 1200, and 1300 °C under CO2 atmosphere with different compositions and duration times to study the effects of temperature, time, and gas composition on coke hot strength. Then the RI/I10600, carbon structure, and optical texture of the cokes prepared from different single coals were mainly studied after a solution reaction with CO2 under a high temperature of 1300 °C and a standard temperature of 1100 °C. It was found that temperature greatly affects the RI/I10600 of coke, especially at high temperatures up to 1300 °C. Compared with standard tests under 1100 °C, the changes of RI/I10600 for different cokes are very different at 1300 °C, and the changes are greatly related to coke optical texture. Under a high temperature in the testing method, the tumbling strength of cokes with more isotropy increased, whereas it decreased for those with less isotropy. This simple method of using high temperature could yield the same results when compared with complicated simulated blast furnace conditions