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This paper investigates the problem of synthesizing network attacks against fault diagnosis in the context of discrete event systems (DESs). It is assumed that the sensor observations sent to the operator that monitors a system are tampered with by an active attacker. We first formulate the process of online fault diagnosis under attack. Then, from the attack viewpoint, we define a sensor network attacker as successful if it can degrade the fault diagnosis in the case of maintaining itself as undiscovered by the operator. To verify such an attacker, an information structure called a joint diagnoser (JD) is proposed, which describes all possible attacks in a given attack scenario. Based on the refined JD, i.e., stealthy joint diagnoser (SJD), we present an algorithmic procedure for synthesizing a successful attacker if it exists. [ABSTRACT FROM AUTHOR]

Traditional magnetic levitation planar micromotors suffer from poor controllability, short travel range, low interference resistance, and low precision. To address these issues, a distributed coil magnetically levitated planar micromotor with a gated recurrent unit (GRU)-extended state observer (ESO) control strategy is proposed in this paper. First, the structural design of the distributed coil magnetically levitated planar micromotor employs a separation of levitation and displacement, reducing system coupling and increasing controllability and displacement range. Then, theoretical analysis and model establishment of the system are conducted based on the designed distributed coil magnetically levitated planar micromotor and its working principles, followed by simulation verification. Finally, based on the established system model, a GRU-ESO controller is designed. An ESO feedback control term is introduced to enhance the system's anti-interference capability, and the GRU feedforward compensation control term is used to improve the system's tracking control accuracy. The experimental results demonstrate the reliability of the designed distributed coil magnetic levitation planar micromotor and the effectiveness of the controller. [ABSTRACT FROM AUTHOR]

Lamination is the predominant and widely developed sedimentary structure in mudstones. Similar to organic pores in shale gas reservoirs, the inorganic pores in the laminae of shale oil reservoirs are equivalently important high-quality reservoir spaces and flow channels. The laminae characteristics are strongly heterogeneous, being controlled by both deposition and diagenesis. However, the origin of this diversity is poorly understood. A detailed examination of cores, thin sections, and scanning electron microscopy analyses were conducted on the lacustrine mudstone of the Qingshankou Formation in the Songliao Basin to study the influence of deposition and diagenesis on laminae characteristics and their relationship to reservoir quality. Three types of laminae are mainly developed, namely thick siliceous laminae, thin siliceous laminae, and thin siliceous and argillaceous mixed laminae. Deposition controls the type and distribution of laminae. The thin siliceous and argillaceous mixed laminae are controlled by climate-driven seasonal flux variations. The thick siliceous laminae and thin siliceous laminae are controlled by bottom current or gravity-driven transport processes due to increased terrestrial input. The thin siliceous laminae have the optimum reservoir properties, followed by the thin siliceous and argillaceous mixed laminae, while the thick siliceous laminae have the worst properties. Diagenesis controls the pore evolution of the laminae. Different laminae have different paths of diagenesis. The thin siliceous laminae are mainly cemented by chlorite, preserving some primary porosity. The clay mineral content of the thin siliceous and argillaceous mixed laminae is high, and the primary pores are mainly destroyed by the strong deformation of the clay minerals during compaction. The thick siliceous laminae are intensely cemented by calcite, losing most of the porosity. The present study enhances the understanding of reservoir characteristics in laminae and provides a reference for shale oil exploration. [ABSTRACT FROM AUTHOR]

The unique zigzag-patterned tea plant is a rare germplasm resource. However, the molecular mechanism behind the formation of zigzag stems remains unclear. To address this, a BC1 genetic population of tea plants with zigzag stems was studied using histological observation and bulked segregant RNA-seq. The analysis revealed 1494 differentially expressed genes (DEGs) between the upright and zigzag stem groups. These DEGs may regulate the transduction and biosynthesis of plant hormones, and the effects on the phenylpropane biosynthesis pathways may cause the accumulation of lignin. Tissue sections further supported this finding, showing differences in cell wall thickness between upright and curved stems, potentially due to lignin accumulation. Additionally, 262 single-nucleotide polymorphisms (SNPs) across 38 genes were identified as key SNPs, and 5 genes related to zigzag stems were identified through homologous gene function annotation. Mutations in these genes may impact auxin distribution and content, resulting in the asymmetric development of vascular bundles in curved stems. In summary, we identified the key genes associated with the tortuous phenotype by using BSR-seq on a BC1 population to minimize genetic background noise. [ABSTRACT FROM AUTHOR]

Apostasioideae, the early divergent subfamily of Orchidaceae, comprises Apostasia and Neuwiedia genera with approximately 20 species. Despite extensive research on Apostasioideae, previous studies have struggled to resolve taxonomic issues, particularly concerning the position of species within this subfamily. Here, we sequenced and annotated plastomes of Apostasia fujianica and Neuwiedia malipoensis, unveiling their phylogenetic relationships and shared plastome features with the other five published plastomes. We identified and analyzed the length, GC content, repeat sequences, and RSCU values of the chloroplast genomes. It is noteworthy that the chloroplast genome of N. malipoensis stands out as the largest among all known chloroplast genomes within the Apostasioideae subfamily, primarily due to contributions from both the LSC and SSC regions. Furthermore, our analysis revealed three unique structural rearrangements located approximately 10k–47k bp (ycf3–trnS-GCU) and 58k–59k bp(accD) in the LSC region and 118k–119k (ndhI) bp in the SSC region of the chloroplast genomes across all five species within the Apostasia genus, which presents a potential avenue for identifying distinctive chloroplast genetic markers, setting them apart from other orchid plants. And a total of four mutational hotspots (rpoC2, atpH, rps4, ndhK, and clpP) were identified. Moreover, our study suggested that Apostasia and Neuwiedia formed a monophyletic group, with Apostasia being sister to Neuwiedia. Within the Apostasia genus, five species were classified into two major clades, represented as follows: (A. odorata (A. shenzhenica and A. fujianica) (A. ramifera and A. wallichii)). These findings hold significance in developing DNA barcoding of Apostasioideae and contribute to the further phylogenetic understanding of Apostasioideae species. [ABSTRACT FROM AUTHOR]

Simple Summary: The kinesin superfamily is an important member of motor proteins, playing important roles in cell growth and development. Studies on KIF2A of the kinesin-13 family have mainly focused on cell division, while the relationship between KIF2A and signaling pathways remains unclear. The purpose of the present study was to determine whether KIF2A has a regulatory effect on the PI3K/AKT pathway, thus affecting spermatogenic cell proliferation and apoptosis during spermatogenesis, and whether its interacting protein PLK1 is involved in this process. RNAi experiments were performed in our study. Our results showed that KIF2A upregulates the PI3K/AKT signaling pathway through its interacting protein PLK1 during the spermatogenesis of Eriocheir sinensis. E. sinensis is an animal model for studying the reproduction and development of crustaceans. In this study, we knocked down the Es-Kif2a gene by injecting dsRNA into E. sinensis and inhibited Es-Plk1 gene expression by injecting PLK1 inhibitor BI6727 into E. sinensis. Then, the cell proliferation level, apoptosis level, and PI3K/AKT signaling expression level were detected. Our results showed that the proliferation level of spermatogenic cells decreased, while the apoptosis level increased after Es-Kif2a knockdown or Es-Plk1 inhibition. In order to verify whether these changes are caused by regulating the PI3K/AKT pathway, we detected the expression of PI3K and AKT proteins after Es-Kif2a knockdown or Es-Plk1 inhibition. Western Blot showed that in both the Es-Kif2a knockdown group and the Es-Plk1 inhibition group, the expression of PI3K and AKT proteins decreased. In addition, immunofluorescence showed that Es-KIF2A and Es-PLK1 proteins were co-localized during E. sinensis spermatogenesis. To further explore the upstream and downstream relationship between Es-KIF2A and Es-PLK1, we detected the expression level of Es-PLK1 after Es-Kif2a knockdown as well as the expression level of Es-KIF2A after Es-Plk1 inhibition. Western Blot showed that the expression of Es-PLK1 decreased after Es-Kif2a knockdown, while there was no significant change of Es-KIF2A after Es-Plk1 inhibition, indicating that Es-PLK1 may be a downstream factor of Es-KIF2A. Taken together, these results suggest that Es-KIF2A upregulates the PI3K/AKT signaling pathway through Es-PLK1 during the spermatogenesis of E. sinensis, thereby affecting the proliferation and apoptosis levels of spermatogenic cells. [ABSTRACT FROM AUTHOR]

Phosphoenolpyruvate carboxylase (PEPC) gene family plays a crucial role in both plant growth and response to abiotic stress. Approximately half of the Orchidaceae species are estimated to perform CAM pathway, and the availability of sequenced orchid genomes makes them ideal subjects for investigating the PEPC gene family in CAM plants. In this study, a total of 33 PEPC genes were identified across 15 orchids. Specifically, one PEPC gene was found in Cymbidium goeringii and Platanthera guangdongensis; two in Apostasia shenzhenica, Dendrobium chrysotoxum, D. huoshanense, Gastrodia elata, G. menghaiensis, Phalaenopsis aphrodite, Ph. equestris, and Pl. zijinensis; three in C. ensifolium, C. sinense, D. catenatum, D. nobile, and Vanilla planifolia. These PEPC genes were categorized into four subgroups, namely PEPC-i, PEPC-ii, and PEPC-iii (PTPC), and PEPC-iv (BTPC), supported by the comprehensive analyses of their physicochemical properties, motif, and gene structures. Remarkably, PEPC-iv contained a heretofore unreported orchid PEPC gene, identified as VpPEPC4. Differences in the number of PEPC homolog genes among these species were attributed to segmental duplication, whole-genome duplication (WGD), or gene loss events. Cis-elements identified in promoter regions were predominantly associated with light responsiveness, and circadian-related elements were observed in each PEPC-i and PEPC-ii gene. The expression levels of recruited BTPC, VpPEPC4, exhibited a lower expression level than other VpPEPCs in the tested tissues. The expression analyses and RT-qPCR results revealed diverse expression patterns in orchid PEPC genes. Duplicated genes exhibited distinct expression patterns, suggesting functional divergence. This study offered a comprehensive analysis to unveil the evolution and function of PEPC genes in Orchidaceae. [ABSTRACT FROM AUTHOR]

In the process of silicon single-crystal preparation, the timely identification and adjustment of abnormal conditions are crucial. Failure to promptly detect and resolve issues may result in a substandard silicon crystal product quality or even crystal pulling failure. Therefore, the early identification of abnormal furnace conditions is essential for ensuring the preparation of perfect silicon single crystals. Additionally, since the thermal field is the fundamental driving force for stable crystal growth and the primary assurance of crystal quality, this paper proposes a silicon single-crystal growth temperature gradient trend classification algorithm based on multi-level feature fusion. The aim is to accurately identify temperature gradient changes during silicon crystal growth, in order to promptly react to early growth failures and ensure the stable growth of high-quality silicon single crystals to meet industrial production requirements. The algorithm first divides the temperature gradient trend into reasonable categories based on expert knowledge and qualitative analysis methods. Then, it fuses the original features of actual production data, shallow features extracted based on statistical information, and deep features extracted through deep learning. During the fusion process, the algorithm considers the impact of different features on the target variable and calculates mutual information based on the difference between information entropy and conditional entropy, ultimately using mutual information for feature weighting. Subsequently, the fused multi-level feature vectors and their corresponding trend labels are input into a Deep Belief Network (DBN) model to capture process dynamics and classify trend changes. Finally, the experimental results demonstrate that the proposed algorithm can effectively predict the changing trend of thermal field temperature gradients. The introduction of this algorithm will help improve the accuracy of fault trend prediction in silicon single-crystal preparation, thereby minimizing product quality issues and production interruptions caused by abnormal conditions. [ABSTRACT FROM AUTHOR]

The effects of selenium supplementation in Tachysurus fulvidraco (T. fulvidraco) on low-temperature stress are not known. In this study, 280 healthy T. fulvidraco were divided into two groups, the G0 group (a control group) and the T0 group (a selenium treatment group on a 0.22 mg/kg diet), for a 6-week feeding time. Then, low-temperature stress (water temperature dropped from 26 to 13 °C, with a rate of 1 °C/h) was administered after that. The feeding results showed that selenium increased the percent weight gain (PWG), specific growth rate (SGR), and survival rate (SR) of T. fulvidraco and decreased the feed conversion rate (FCR), but these differences were not significant (p > 0.05). Under low temperatures, selenium still has no significant effects on antioxidant indexes such as glutathione peroxidase (GSH-Px) activity, superoxide dismutase (SOD) activity, and malondialdehyde (MDA) content in serum (p > 0.05). However, metabolomic analysis revealed that selenium caused changes in lipids and lipid-like molles, organic acids and their derivatives, and fatty acyls. Choline, linoleic acid, and glycerophospholipid metabolism pathways; d-arginine and d-ornithine metabolism; valine, leucine, and isoleucine degradation; and biosynthesis pathways, as well as pyrimidine metabolism pathways, were activated to produce these metabolites to combat against this stress. In addition, selenium increased the diversity of intestinal microbes in T. fulvidraco and decreased the relative abundance of Plesiomonas. However, the combined analysis showed the intestinal microbe changes did not affect metabolite production. In summary, selenium activated lipid, carbohydrate, and amino acid metabolism for energy substance provision, reduced the oxidation and production of other harmful substances, and increased the intestinal microbe diversity of T. fulvidraco to improve resistance to low-temperature stress. [ABSTRACT FROM AUTHOR]

Luisia, a genus of the subtribe Aeridinae of Orchidaceae, comprises ca. 40 species. Members of Luisia exhibit unique morphological characteristics and represent a valuable ornamental orchid genus. However, due to the scarcity of distinct morphological characters, species identification within this genus is ambiguous and controversial. In the present study, next-generation sequencing (NGS) methods were used to assemble the plastomes of five Luisia species and compare them with one publicly available Luisia plastid genome data. The plastomes of Luisia possessed a quadripartite structure, with sizes ranging from 146,243 bp to 147,430 bp. The plastomes of six Luisia species contained a total of 120 genes, comprising 74 protein-coding genes, 38 tRNA genes and eight rRNA genes. Notably, all ndh genes were pseudogenized or lost. An analysis of codon usage bias showed that leucine (Leu) exhibited the highest frequency, while cysteine (Cys) exhibited the lowest frequency. A total of 57 to 64 SSRs and 42 to 49 long repeats were identified. Five regions and five coding sequences were identified for DNA barcodes, based on the nucleotide diversity (Pi) analysis. The species of Luisia constituted a monophyletic group and were sister to Paraphalaenopsis with strong support. Our study deepens the understanding of species identification, plastome evolution and the phylogenetic positions of Luisia. [ABSTRACT FROM AUTHOR]

Actively responding to climate change and accelerating the realization of carbon emission reduction are the common goals of global sustainable development. Hydrogen energy, as a "zero carbon emission" secondary energy, is playing an increasingly important role on the world energy stage. Hydrogen energy infrastructure (focusing on hydrogen refueling stations) is a key infrastructure in the transition to a hydrogen economy. However, the indicators that influence the location selection of hydrogen refueling stations (HRSs) are specific and have complex relationships, and current research still lacks a comprehensive analysis of its location selection decision-making indicators. Based on this research gap, this study proposes a hybrid fuzzy decision-making approach to determine the relationship and degree of influence between indicators. Firstly, this study identified 18 decision indicators that influence location selection from four dimensions: policy, economy, society, and environment. Subsequently, semi-structured interviews were used to initially assess the relationship between indicators. Finally, by using interpretive structural modeling and the fuzzy decision-making trial and evaluation laboratory (ISM-FDEMATEL), a 7-level structural model is established, which clarifies the complex relationship between indicators and the individual attributes of each indicator. The research results show that the indicators of the policy dimension have a more significant influencing degree than the other three dimensions and have a greater influence on the entire indicator system; the indicators of the economic dimension have a higher influencing degree and are easily influenced by other indicators. These findings will benefit policymakers and practitioners to prioritize and manage their resources effectively and efficiently and provide a valuable reference for advancing the sustainable development of the infrastructure for hydrogen energy. [ABSTRACT FROM AUTHOR]

Monocrystalline silicon is an important raw material in the semiconductor and photovoltaic industries. In the Czochralski (CZ) method of growing monocrystalline silicon, various factors may cause node loss and lead to the failure of crystal growth. Currently, there is no efficient method to detect the node loss of monocrystalline silicon at industrial sites. Therefore, this paper proposed a monocrystalline silicon node-loss detection method based on multimodal data fusion. The aim was to explore a new data-driven approach for the study of monocrystalline silicon growth. This article first collected the diameter, temperature, and pulling speed signals as well as two-dimensional images of the meniscus. Later, the continuous wavelet transform was used to preprocess the one-dimensional signals. Finally, convolutional neural networks and attention mechanisms were used to analyze and recognize the features of multimodal data. In the article, a convolutional neural network based on an improved channel attention mechanism (ICAM-CNN) for one-dimensional signal fusion as well as a multimodal fusion network (MMFN) for multimodal data fusion was proposed, which could automatically detect node loss in the CZ silicon single-crystal growth process. The experimental results showed that the proposed methods effectively detected node-loss defects in the growth process of monocrystalline silicon with high accuracy, robustness, and real-time performance. The methods could provide effective technical support to improve efficiency and quality control in the CZ silicon single-crystal growth process. [ABSTRACT FROM AUTHOR]

For the unsteady multi-boundary inverse heat conduction problem, a real-time solution method for boundary heat flux based on dynamic matrix control is proposed in the paper. The method solves the heat flux at the boundary in real-time by measuring the temperature information at the measurement points of the heat transfer system. A two-dimensional direct heat conduction model of the heat transfer system is established in the paper, and is solved by the finite difference method to obtain the temperature information of the measurement points under any heat flux boundary. Then, the correspondence between the heat flux of boundary and the temperature information is presented by means of a step-response model. The regularization parameters are introduced into the method to improve the stability of the inversion process, and the effect of real-time inversion on the heat flux of the boundary is achieved through rolling optimization. The experimental results show that the proposed method can achieve real-time inversion of the heat fluxes of the two-dimensional boundary with good accuracy. [ABSTRACT FROM AUTHOR]

Apostasia shenzhenica belongs to the subfamily Apostasioideae and is a primitive group located at the base of the Orchidaceae phylogenetic tree. However, the A. shenzhenica mitochondrial genome (mitogenome) is still unexplored, and the phylogenetic relationships between monocots mitogenomes remain unexplored. In this study, we discussed the genetic diversity of A. shenzhenica and the phylogenetic relationships within its monocotyledon mitogenome. We sequenced and assembled the complete mitogenome of A. shenzhenica, resulting in a circular mitochondrial draft of 672,872 bp, with an average read coverage of 122× and a GC content of 44.4%. A. shenzhenica mitogenome contained 36 protein-coding genes, 16 tRNAs, two rRNAs, and two copies of nad4L. Repeat sequence analysis revealed a large number of medium and small repeats, accounting for 1.28% of the mitogenome sequence. Selection pressure analysis indicated high mitogenome conservation in related species. RNA editing identified 416 sites in the protein-coding region. Furthermore, we found 44 chloroplast genomic DNA fragments that were transferred from the chloroplast to the mitogenome of A. shenzhenica, with five plastid-derived genes remaining intact in the mitogenome. Finally, the phylogenetic analysis of the mitogenomes from A. shenzhenica and 28 other monocots showed that the evolution and classification of most monocots were well determined. These findings enrich the genetic resources of orchids and provide valuable information on the taxonomic classification and molecular evolution of monocots. [ABSTRACT FROM AUTHOR]

Silicon single crystal (SSC) quality monitoring and control has been a hot research topic in the field of the Czochralski crystal growth process. Considering that the traditional SSC control method ignores the crystal quality factor, this paper proposes a hierarchical predictive control strategy based on a soft sensor model for online control of SSC diameter and crystal quality. First, the proposed control strategy considers the V/G variable (V is the crystal pulling rate, and G is the axial temperature gradient at the solid–liquid interface), a factor related to crystal quality. Aiming at the problem that the V/G variable is difficult to measure directly, a soft sensor model based on SAE-RF is established to realize the online monitoring of the V/G variable and then complete hierarchical prediction control of SSC quality. Second, in the hierarchical control process, PID control of the inner layer is used to quickly stabilize the system. Model predictive control (MPC) of the outer layer is used to handle system constraints and enhance the control performance of the inner layer. In addition, the SAE-RF-based soft sensor model is used to monitor the crystal quality V/G variable online, thereby ensuring that the output of the controlled system meets the desired crystal diameter and V/G requirements. Finally, based on the industrial data of the actual Czochralski SSC growth process, the effectiveness of the proposed crystal quality hierarchical predictive control method is verified. [ABSTRACT FROM AUTHOR]

The rapid development of electric vehicle (EV) technology and the consequent charging demand have brought challenges to the stable operation of distribution networks (DNs). The problem of the collaborative optimization of the charging scheduling of EVs and voltage control of the DN is intractable because the uncertainties of both EVs and the DN need to be considered. In this paper, we propose a deep reinforcement learning (DRL) approach to coordinate EV charging scheduling and distribution network voltage control. The DRL-based strategy contains two layers, the upper layer aims to reduce the operating costs of power generation of distributed generators and power consumption of EVs, and the lower layer controls the Volt/Var devices to maintain the voltage stability of the distribution network. We model the coordinate EV charging scheduling and voltage control problem in the distribution network as a Markov decision process (MDP). The model considers uncertainties of charging process caused by the charging behavior of EV users, as well as the uncertainty of uncontrollable load, system dynamic electricity price and renewable energy generation. Since the model has a dynamic state space and mixed action outputs, a framework of deep deterministic policy gradient (DDPG) is adopted to train the two-layer agent and the policy network is designed to output discrete and continuous control actions. Simulation and numerical results on the IEEE-33 bus test system demonstrate the effectiveness of the proposed method in collaborative EV charging scheduling and distribution network voltage stabilization. [ABSTRACT FROM AUTHOR]

The diameter prediction of silicon ingots in the Czochralski process is a complex problem because the process is highly nonlinear, time-varying, and time-delay. To address this problem, this paper presents a novel hybrid deep learning model, which combines the deep belief network (DBN), support vector regression (SVR), and the ant lion optimizer (ALO). Continuous restricted Boltzmann machines (CRBMs) are used in DBN for working with continuous industrial data. The feature aggregates the outputs from various DBNs through an SVR model. Additionally, the ALO algorithm is used for the parameter's optimization of SVR. The newly developed model is verified with the actual production data and compared with the back propagation neural network (BPNN) and the SVR model. The simulation results demonstrate the availability and accuracy of the CRBM-DBN-ALO-SVR hybrid deep learning model. [ABSTRACT FROM AUTHOR]

To solve the problem of the common unsteady inverse heat conduction problem in the industrial field, a real-time solution method of improving the whale optimization algorithm (IWOA) and parameter-adaptive proportional-integral-differential (PID) is proposed in the paper. A feedback control system with IWOA-PID, which can inversely solve the boundary heat flux, is established. The deviation between the calculated temperature and the measured temperature of the measured point obtained by solving the direct heat conduction problem (DHCP) is used as the system input. The heat flux which is iteration-solved by IWOA-PID is used as system output. The method improves the initial solution distribution, global search capability and population diversity generalization of the traditional whale optimization algorithm (WOA), which effectively improves the parameter-adaptive capability of PID. The experimental results show that the solution method of inverse heat transfer proposed in the paper can accurately retrieve the variation of the boundary heat flux in real time and has good resistance and self-adaptability. [ABSTRACT FROM AUTHOR]

Epilepsy is one of the most serious nervous system diseases; it can be diagnosed accurately by video electroencephalogram. In this study, we analyzed microstate epileptic electroencephalogram (EEG) to aid in the diagnosis and identification of epilepsy. We recruited patients with focal epilepsy and healthy participants from the Third Xiangya Hospital and recorded their resting EEG data. In this study, the EEG data were analyzed by microstate analysis, and the support vector machine (SVM) classifier was used for automatic epileptic EEG classification based on features of the EEG microstate series, including microstate parameters (duration, occurrence, and coverage), linear features (median, second quartile, mean, kurtosis, and skewness) and non-linear features (Petrosian fractal dimension, approximate entropy, sample entropy, fuzzy entropy, and Lempel–Ziv complexity). In the gamma sub-band, the microstate parameters as a model were the best for interictal epilepsy recognition, with an accuracy of 87.18%, recall of 70.59%, and an area under the curve of 94.52%. There was a recognition effect of interictal epilepsy through the features extracted from the EEG microstate, which varied within the 4~45 Hz band with an accuracy of 79.55%. Based on the SVM classifier, microstate parameters and EEG features can be effectively used to classify epileptic EEG, and microstate parameters can better classify epileptic EEG compared with EEG features. [ABSTRACT FROM AUTHOR]

Medical optical imaging, with the aid of the "terahertz tomography", is a novel medical imaging technique based on the electromagnetic waves. Such advanced imaging techniques strive for the detailed theoretical and computational analysis for better verification and validation. Two important aspects, the analytic approach for the understanding of the Schrodinger transforms and machine learning approaches for the understanding of the medical images segmentation, are presented in this manuscript. While developing an AI algorithm for complex datasets, the computational speed and accuracy cannot be overlooked. With the passage of time, machine learning approaches have been further modified using the Bayesian, genetic and quantum approaches. These strategies have boosted the efficiency of the machine learning, and specifically the deep learning tools, by taking into account the probabilistic, evolutionary and quantum qubits hypothesis and operations, respectively. The current research encompasses the detailed analysis of image segmentation algorithms based on the evolutionary approach. The image segmentation algorithm that converts the color model from RGB to HSI and the image segmentation algorithm that uses the clustering technique are discussed in detail, and further extensions of these genetic algorithms to quantum algorithms are proposed. Based on the genetic algorithm, the optimal selection of parameters is realized so as to achieve a better segmentation effect. [ABSTRACT FROM AUTHOR]

Modern electronics not only require the thermal management ability of polymer packaging materials but also need anti-voltage and mechanical properties. Boron nitride nanosheets (BNNS), an ideal thermally conductive and high withstand voltage (800 kV/mm) filler, can meet application needs, but the complex and low-yield process limits their large-scale fabrication. Herein, in this work, we prepare sucrose-assisted ball-milled BN(SABM-BN)/polyetherimide (PEI) composite films by a casting-hot pressing method. SABM-BN, as a pre-ball-milled filler, contains BNNS and BN thick sheets. We mainly investigated the thermal conductivity (TC), breakdown strength, and mechanical properties of composites. After pre-ball milling, the in-plane TC of the composite film is reduced. It decreases from 2.69 to 2.31 W/mK for BN/PEI composite film at 30 wt% content; however, the through-plane TC of composites is improved, and the breakdown strength and tensile strength of the composite film reach the maximum of 54.6 kV/mm and 102.7 MPa at 5 wt% content, respectively. Moreover, the composite film is used as a flexible circuit substrate, and the working surface temperature is 20 ℃, which is lower than that of pure PEI film. This study provides an effective strategy for polymer composites for electronic packaging. [ABSTRACT FROM AUTHOR]

Cerebral visual impairments (CVIs) is an umbrella term that categorizes miscellaneous visual defects with parallel genetic brain disorders. While the manifestations of CVIs are diverse and ambiguous, molecular diagnostics stand out as a powerful approach for understanding pathomechanisms in CVIs. Nevertheless, the characterization of CVI disease cohorts has been fragmented and lacks integration. By revisiting the genome-wide and phenome-wide association studies (GWAS and PheWAS), we clustered a handful of renowned CVIs into five ontology groups, namely ciliopathies (Joubert syndrome, Bardet–Biedl syndrome, Alstrom syndrome), demyelination diseases (multiple sclerosis, Alexander disease, Pelizaeus–Merzbacher disease), transcriptional deregulation diseases (Mowat–Wilson disease, Pitt–Hopkins disease, Rett syndrome, Cockayne syndrome, X-linked alpha-thalassaemia mental retardation), compromised peroxisome disorders (Zellweger spectrum disorder, Refsum disease), and channelopathies (neuromyelitis optica spectrum disorder), and reviewed several mutation hotspots currently found to be associated with the CVIs. Moreover, we discussed the common manifestations in the brain and the eye, and collated animal study findings to discuss plausible gene editing strategies for future CVI correction. [ABSTRACT FROM AUTHOR]

The roll eccentricity signal is a weak and complex periodic signal that is difficult to be identified. To improve the detection accuracy of the roll eccentricity signal and to compensate effectively, this study proposed a roll eccentricity signal detection method by combining the sparse fast Fourier transform (SFFT) and the iterative adaptive approach (IAA). The proposed method can rapidly determine the frequency range of the roll eccentricity signal by using the SFFT. Then, it divides the frequency range into several small frequency bands. In each small frequency band, the frequency, amplitude, and phase angle of each harmonic component of the roll eccentricity signal were estimated by using IAA. The simulation results show that the proposed method can find all frequency components, and the frequency estimation accuracy is higher than 99.88%. Finally, the engineering application of this method and the eccentricity compensation control were investigated. In engineering applications, the proposed method can reduce the thickness fluctuation of the finished strip by 89.2%, and the product quality is improved significantly. The simulation results and engineering experiments show that the proposed method has an excellent effect on detecting and compensating roll eccentricity signals. [ABSTRACT FROM AUTHOR]

Background: Epilepsy (Ep) is a chronic neural disease. The diagnosis of epilepsy depends on detailed seizure history and scalp electroencephalogram (EEG) examinations. The automatic recognition of epileptic EEG is an artificial intelligence application developed from machine learning (ML). Purpose: This study compares the classification effects of two kinds of classifiers by controlling the EEG data source and characteristic values. Method: All EEG data were collected by GSN HydroCel 256 leads and high-density EEG from Xiangya Third Hospital. This study used time-domain features (mean, kurtosis and skewness processed by empirical mode decomposition (EMD) and three IMFs), a frequency-domain feature (power spectrum density, PSD) and a non-linear feature (Shannon entropy). Support vector machine (SVM) and gradient-boosting decision tree (GBDT) classifiers were used to recognize epileptic EEG. Result: The result of the SVM classifier showed an accuracy of 72.00%, precision of 73.98%, and an F1_score of 82.28%. Meanwhile, the result of the GBDT classifier showed a sensitivity of 98.57%, precision of 89.13%, F1_score of 93.40%, and an AUC of 0.9119. Conclusion: The comparison of GBDT and SVM by controlling the variables of the feature values and parameters of a classifier is presented. GBDT obtained the better classification accuracy (90.00%) and F1_score (93.40%). [ABSTRACT FROM AUTHOR]

Deep geothermal energy is a renewable and environmentally friendly resource, and the hot dry rock in a geothermal reservoir is subjected to thermal cycling treatment. Thermal cycling treatment can cause thermal stresses in the rock matrix and result in thermal cracking, which significantly influence the physical and mechanical properties of a rock. To investigate the influence of thermal cycling treatment on the microcrack propagation and mechanical behavior of a granite rock, a series of physical and mechanical tests were performed on nontreated and treated granite samples. The testing results show that the mass, density, and P-wave velocity of granite decrease with heating temperature and cycling time increase, while the volume of the samples increases significantly. The UCS and elastic modulus of the granite declined from 178.65 MPa and 20.09 GPa to 24.58 MPa and 3.81 GPa after treatment at 500 °C for 30 thermal cycling times, respectively. The degradation trends of the UCS and the elastic modulus of the granite can be characterized by the heating temperature and the thermal cycling times. High temperature and frequent thermal cycling treatment can induce microcrack propagation within the granite, which causes the failure of the samples and leads a transformation of granite from brittleness to ductility. [ABSTRACT FROM AUTHOR]

In this paper, the nonlinear effects of the built environment on bus–metro-transfer ridership are explored, based on Shanghai metro data, with an extreme gradient-boosting decision-trees (XGBoost) model. It was found that the bus-network density had the largest influence on transfer ridership, contributing 27.56% predictive power for transfer ridership, followed by closeness centrality and bus-stop density, and their contribution rates are 21.6% and 17.27%, respectively. Local explanations for the model reveal the following conclusions: most built-environment variables have nonlinear and threshold effects on bus–metro ridership. The suggested values for the dominant contributors to bus–metro-transfer ridership are obtained. For example, bus-network density, bus-stop density, and closeness centrality were 12.8 km/sq. km, 11 counts/sq. km, and 0.18 km/sq. km, respectively, for maximizing bus–metro-transfer ridership. The interaction impacts of the bus–metro connection characteristics and the closeness centrality of metro stations on transfer ridership were, also, examined. The result showed that the setting of bus–metro-transfer facilities depended on the location of metro stations. It was necessary to improve the bus–metro-connection system, in metro stations with high closeness centrality. [ABSTRACT FROM AUTHOR]

Simple Summary: This study first determined the 18S rDNA, ITS-1, ITS-2, and COI sequences of E. christenseni and E. arloingi. The lengths of the 18S rDNA, ITS-1, ITS-2, and COI were 1790 bp, 403 bp, 584 bp, and 1268 bp for E. arloingi and 1796 bp, 386 bp, 565 bp, and 1268 bp for E. christenseni, respectively. Furthermore, phylogenetic analyses based on the 18S rDNA, ITS-1, ITS-2, and COI loci were conducted to assess the relationship of E. christenseni, E. arloingi, and other Eimeria species in ruminants. Coccidiosis caused by Eimeria is one of the most common and significant diseases in goats, leading to serious economic losses in the development of the goat industry. Although several genetic loci, such as 18S rDNA, ITS-1, ITS-2, and COI, have been applied in the molecular characterization of Eimeria in chicken, rabbits, turkey, and wildlife, little is known about these molecular markers of Eimeria in goats. In the present study, we isolated purified oocysts of highly pathogenic Eimeriaarloingi and Eimeria christenseni from fecal samples of goats in Shaanxi province, China, and then subjected these purified oocysts to genomic DNA isolation, PCR amplification, and sequencing of 18S rDNA, ITS-1, ITS-2, and COI loci of Eimeria arloingi and Eimeria christenseni. Finally, the obtained sequences were used for phylogenetic analysis of Eimeria species in goats and other livestock. The lengths of the 18S rDNA, ITS-1, ITS-2, and COI were 1790 bp, 403 bp, 584 bp, and 1268 bp for E. arloingi and 1796 bp, 386 bp, 565 bp, and 1268 bp for E. christenseni, respectively. The phylogenetical analysis based on 18S rDNA indicated that E. christenseni and E. arloingi were the most closely related to ovine Eimeria, followed by E. bovis, E. ellipsoidalis, and E. zuernii from cattle. The phylogenetical analysis based on ITS-1 and ITS-2 could not effectively distinguish ovine Eimeria from caprine Eimeria. The phylogenetical analysis based on the COI locus could effectively distinguish between Eimeria species from goats and cattle, but it was ineffective in distinguishing between Eimeria species from sheep and goats. To the best of our knowledge, this is the first characterization of 18S rDNA, ITS-1, ITS-2, and COI in E. arloingi and E. christenseni; it can provide useful genetic markers for molecular epidemiological and population genetic studies on E. arloingi and E. christenseni in goats and contribute to the prevention and control of goat coccidiosis. [ABSTRACT FROM AUTHOR]

The equivalent pipe network (EPN) model is an effective way to model fluid flow in large-scale fractured rock masses with a complex fracture network due to its straightforwardness and computational efficiency. This study presents the EPN model for characterizing fluid flow through three-dimensional fracture networks using the Monte-Carlo method. The EPN model is extracted from an original three-dimensional discrete fracture network (DFN) model and is used to simulate the fluid flow processes. The validity of the proposed EPN modeling approach is verified via the comparisons of permeability (k) with analytical solutions and simulation results reported in the literature. The results show that the numerically calculated k using EPN models agrees well with the analytical values of simplified DFN models and the simulation results of complex DFN models. The k increases following an exponential function with the increment of mean length of exponentially distributed fractures (u), which is strongly correlated with fracture density (P32) and average intersection length (Li). The P32 increases in an exponential way with the increment of u. The Li increases as u increases, following a power-law function. The increment of u leads to the increment of a number of long fractures in three-dimensional DFN models. A larger u results in a denser fracture network and a stronger conductivity when the number and length distribution range of fractures remain the same. The representative elementary volumes (REVs) of three-dimensional DFN models with u = 9 m and P32 = 0.4 m2/m3 are determined as 2.36 × 104 m3, 9.16 × 103 m3, and 1.26 × 104 m3 in 3 flow directions, respectively. [ABSTRACT FROM AUTHOR]

Citrus canker, caused by the bacterium Xanthomonas citri subsp. citri (Xcc), seriously affects fruit quality and yield, leading to significant economic losses around the world. Understanding the mechanism of Xcc virulence is important for the effective control of Xcc infection. In this report, we investigate the role of a protein named HemK in the regulation of the virulence traits of Xcc. The hemK gene was deleted in the Xcc jx-6 background, and the ΔhemK mutant phenotypically displayed significantly decreased motility, biofilm formation, extracellular enzymes, and polysaccharides production, as well as increased sensitivity to oxidative stress and high temperatures. In accordance with the role of HemK in the regulation of a variety of virulence-associated phenotypes, the deletion of hemK resulted in reduced virulence on citrus plants as well as a compromised hypersensitive response on a non-host plant, Nicotiana benthamiana. These results indicated that HemK is required for the virulence of Xcc. To characterize the regulatory effect of hemK deletion on gene expression, RNA sequencing analysis was conducted using the wild-type Xcc jx-6 strain and its isogenic ΔhemK mutant strain, grown in XVM2 medium. Comparative transcriptome analysis of these two strains revealed that hemK deletion specifically changed the expression of several virulence-related genes associated with the bacterial secretion system, chemotaxis, and quorum sensing, and the expression of various genes related to nutrient utilization including amino acid metabolism, carbohydrate metabolism, and energy metabolism. In conclusion, our results indicate that HemK plays an essential role in virulence, the regulation of virulence factor synthesis, and the nutrient utilization of Xcc. [ABSTRACT FROM AUTHOR]

Modal parameter monitoring is a widely used structural health monitoring method. However, among other limitations, this method cannot effectively identify slight damage under ambient conditions. This study proposed a novel strain expansion–reduction approach for identifying damage. To verify the feasibility of the proposed method, we numerically and experimentally tested the method using a rigid acrylic frame. The frame was artificially damaged at various depths to reflect various damage scenarios. The increase in the damage index provided an accurate estimation of damage severity. For the case with merely 0.5% damage zone in one slat, the index is increased by 259% of the intact case. When the damage zone was doubled, the index increases significantly by 467% of the intact case, demonstrating excellent sensitivity of the proposed method. To guarantee practical use, the numerical model of the proposed method was applied to an offshore wind turbine jacket substructure and successfully identified multiple damage sites and the damage severity with extremely high (>10) damage index. [ABSTRACT FROM AUTHOR]

Infection induces the production of proinflammatory cytokines and chemokines such as interleukin-8 (IL-8) and IL-6. Although they facilitate local antiviral immunity, their excessive release leads to life-threatening cytokine release syndrome, exemplified by the severe cases of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. In this study, we investigated the roles of the integrated stress response (ISR) and activator protein-1 (AP-1) family proteins in regulating coronavirus-induced IL-8 and IL-6 upregulation. The mRNA expression of IL-8 and IL-6 was significantly induced in cells infected with infectious bronchitis virus (IBV), a gammacoronavirus, and porcine epidemic diarrhea virus, an alphacoronavirus. Overexpression of a constitutively active phosphomimetic mutant of eukaryotic translation initiation factor 2α (eIF2α), chemical inhibition of its dephosphorylation, or overexpression of its upstream double-stranded RNA-dependent protein kinase (PKR) significantly enhanced IL-8 mRNA expression in IBV-infected cells. Overexpression of the AP-1 protein cJUN or its upstream kinase also increased the IBV-induced IL-8 mRNA expression, which was synergistically enhanced by overexpression of cFOS. Taken together, this study demonstrated the important regulatory roles of ISR and AP-1 proteins in IL-8 production during coronavirus infection, highlighting the complex interactions between cellular stress pathways and the innate immune response. [ABSTRACT FROM AUTHOR]

Well-designed composite photocatalysts are of increasing concern due to their enhanced catalytic performance compared to a single component. Here, a photocatalyst composed of PbMoO4 (PMO) and poly-benzothiadiazole (BBT, a D-A-conjugated polymer) was successfully synthesized by BBT polymerization on the surface of the PMO. The resultant BBT-PMO with a heterojunction structure represented an enhanced ability to reduce highly toxic heavy metal Cr(VI) from water under visible light irradiation. The 16.7% BBT-PMO(N, nanoscale) showed the best performance. The corresponding kobs over the 16.7% BBT-PMO(N) was 26-fold (or 53-fold) of that over the pure BBT (or pristine PMO(N)), and this activity was maintained after four cycles. The reasons for its good performance are discussed in detail based on the experimental results. Moreover, the synthesis of the BBT in situ of the PMO also altered the morphology of the BBT component, increasing the specific surface area of the BBT-PMO(N) and endowing it with the ability to adsorb Cr(VI). Additionally, the photocatalyst was also environmentally friendly as such a wrapped structure could sustain the high stability of the PMO without dissociation. This work provides a good strategy for efficient photocatalytic Cr(VI) reduction by designing an organic–inorganic hybrid system with high redox capacity. [ABSTRACT FROM AUTHOR]

In this study, infrared thermography is used for vibration-based structural health monitoring (SHM). Heat sources are employed as sensors. An acrylic frame structure was experimentally investigated using the heat sources as structural marker points to record the vibration response. The effectiveness of the infrared thermography measurement system was verified by comparing the results obtained using an infrared thermal imager with those obtained using accelerometers. The average error in natural frequency was between only 0.64% and 3.84%. To guarantee the applicability of the system, this study employed the mode shape curvature method to locate damage on a structure under harsh environments, for instance, in dark, hindered, and hazy conditions. Moreover, we propose the mode shape recombination method (MSRM) to realize large-scale structural measurement. The partial mode shapes of the 3D frame structure are combined using the MSRM to obtain the entire mode shape with a satisfactory model assurance criterion. Experimental results confirmed the feasibility of using heat sources as sensors and indicated that the proposed methods are suitable for overcoming the numerous inherent limitations associated with SHM in harsh or remote environments as well as the limitations associated with the SHM of large-scale structures. [ABSTRACT FROM AUTHOR]

When solving real-world problems with complex simulations, utilizing stochastic algorithms integrated with a simulation model appears inefficient. In this study, we compare several hybrid algorithms for optimizing an offshore jacket substructure (JSS). Moreover, we propose a novel hybrid algorithm called the divisional model genetic algorithm (DMGA) to improve efficiency. By adding different methods, namely particle swarm optimization (PSO), pattern search (PS) and targeted mutation (TM) in three subpopulations to become "divisions," each division has unique functionalities. With the collaboration of these three divisions, this method is considerably more efficient in solving multiple benchmark problems compared with other hybrid algorithms. These results reveal the superiority of DMGA in solving structural optimization problems. [ABSTRACT FROM AUTHOR]

Ultra-wideband (UWB) sensors have been widely used in multi-robot systems for cooperative tracking and positioning purposes due to their advantages such as high ranging accuracy and good real-time performance. In order to reduce the influence of non-line-of-sight (NLOS) UWB communication caused by the presence of obstacles on ranging accuracy in indoor environments, the paper proposes a novel Bayesian filtering approach for UWB ranging error mitigation. Nonparametric UWB sensor models, namely received signal strength (RSS) model and time of arrival (TOA) model, are constructed to capture the probabilistic noise characteristics under the influence of different obstruction conditions and materials within a typical indoor environment. The proposed Bayesian filtering approach can be used either as a standalone error mitigation approach for peer-to-peer (P2P) ranging, or as a part of a higher level Bayesian state estimation framework. Experiments were conducted to validate and evaluate the proposed approach in two configurations, i.e., inter-robot ranging, and mobile robot tracking in a wireless sensor network. The experimental results show that the proposed method can accurately identify the line-of-sight (LOS) and NLOS scenarios with wood and metal obstacles in a probabilistic representation and effectively improve the ranging/tracking accuracy. In addition, the low computational overhead of the approach makes it attractive in real-time systems. [ABSTRACT FROM AUTHOR]

Doping of CeO2 on activated carbon (AC) can promote its performance for mercury abatement in flue gas, while the Hg0 removal mechanism on the AC surface has been rarely reported. In this research, density functional theory (DFT) calculations were implemented to unveil the mechanism of mercury removal on plain AC and CeO2 modified AC (CeO2-AC) sorbents. Calculation results indicate that Hg0, HCl, HgCl and HgCl2 are all chemisorbed on the adsorbent. Strong interaction and charge transfer are shown by partial density of states (PDOS) analysis of the Hg0 adsorption configuration. HCl, HgCl and HgCl2 can be dissociatively adsorbed on the AC model and subsequently generate HgCl or HgCl2 released to the gas phase. The adsorption energies of HgCl and HgCl2 on the CeO2-AC model are relatively high, indicating a great capacity for removing HgCl and HgCl2 in flue gas. DFT calculations suggest that AC sorbents exhibit a certain catalytic effect on mercury oxidation, the doping of CeO2 enhances the catalytic ability of Hg0 oxidation on the AC surface and the reactions follow the Langmuir–Hinshelwood mechanism. [ABSTRACT FROM AUTHOR]

The seepage action of underground water accelerates the deformation of roadway surrounding rock in deep mines. Therefore, the study of creep characteristics of surrounding rock under seepage action is the basis for the stability control of roadway surrounding rock in deep water-rich areas. In this paper, a seepage-creep coupling test system for complete rock samples was established. Combined with a scanning electron microscopy (SEM) test system, the seepage-creep law of coal measures sandstone and the damage mechanism were revealed. The study results showed that the maximum creep deformation of sandstone under natural and saturation state decreased gradually with the increase of confining pressure, and the maximum creep deformation under saturation state was greater than the corresponding value under natural state when the confining pressure was same. When the confining pressure was constant, the creep deformation, the constant creep deformation rate and the accelerated creep deformation rate of sandstone increased rapidly with the increase of infiltration pressure. With the change of time, the change of permeability parameters went through three cycles; each cycle was divided into two stages, slow change stage and rapid change stage, and the rate of variation increased with the increase of the seepage pressure. Based on the macroscopic and microscopic characteristics of sandstone rupture, the connection between macroscopic and microscopic mechanism on sandstone rupture was established. The results in this paper can provide a theoretical basis for stability control of roadway surrounding rock in water-rich areas. [ABSTRACT FROM AUTHOR]

Melatonin is a bioactive, edible ingredient that promotes human health and exists widely in plants, but little is known about its biosynthetic routes and underlying molecular mechanisms in the herbaceous peony. In this contribution, we found that herbaceous peony flowers are rich in melatonin that is found in the greatest quantities in the white series, followed by the ink series, the red series and then the pink series. On this basis, the melatonin content fluctuates during flower development and peaks during the bloom stage. Moreover, it is apparent that sun exposure and blue light induce melatonin production whereas green light restrains it during a 24-h light/dark cycle of melatonin content, as there were ‘dual peaks’ at 2 p.m. and 2 a.m. Additionally, the corresponding expression pattern of the herbaceous peony tryptophan decarboxylase gene (TDC) was positively related with melatonin production. These results suggest that color series, development stage and light play an important role in melatonin accumulation, and that TDC is a rate-limiting gene in melatonin biosynthesis. [ABSTRACT FROM AUTHOR]

Luisia , a genus of the subtribe Aeridinae of Orchidaceae, comprises ca. 40 species. Members of Luisia exhibit unique morphological characteristics and represent a valuable ornamental orchid genus. However, due to the scarcity of distinct morphological characters, species identification within this genus is ambiguous and controversial. In the present study, next-generation sequencing (NGS) methods were used to assemble the plastomes of five Luisia species and compare them with one publicly available Luisia plastid genome data. The plastomes of Luisia possessed a quadripartite structure, with sizes ranging from 146,243 bp to 147,430 bp. The plastomes of six Luisia species contained a total of 120 genes, comprising 74 protein-coding genes, 38 tRNA genes and eight rRNA genes. Notably, all ndh genes were pseudogenized or lost. An analysis of codon usage bias showed that leucine (Leu) exhibited the highest frequency, while cysteine (Cys) exhibited the lowest frequency. A total of 57 to 64 SSRs and 42 to 49 long repeats were identified. Five regions and five coding sequences were identified for DNA barcodes, based on the nucleotide diversity (Pi) analysis. The species of Luisia constituted a monophyletic group and were sister to Paraphalaenopsis with strong support. Our study deepens the understanding of species identification, plastome evolution and the phylogenetic positions of Luisia .

Similar to other viruses, coronavirus infection triggers cellular stress responses in infected host cells. The close association of coronavirus replication with the endoplasmic reticulum (ER) results in the ER stress responses, which impose a challenge to the viruses. Viruses, in turn, have come up with various mechanisms to block or subvert these responses. One of the ER stress responses is inhibition of the global protein synthesis to reduce the amount of unfolded proteins inside the ER lumen. Viruses have evolved the capacity to overcome the protein translation shutoff to ensure viral protein production. Here, we review the strategies exploited by coronavirus to modulate cellular stress response pathways. The involvement of coronavirus-induced stress responses and translational control in viral pathogenesis will also be briefly discussed.

Viroporins are members of a rapidly growing family of channel-forming small polypeptides found in viruses. The present review will be focused on recent structural and protein-protein interaction information involving two viroporins found in enveloped viruses that target the respiratory tract; (i) the envelope protein in coronaviruses and (ii) the small hydrophobic protein in paramyxoviruses. Deletion of these two viroporins leads to viral attenuation in vivo, whereas data from cell culture shows involvement in the regulation of stress and inflammation. The channel activity and structure of some representative members of these viroporins have been recently characterized in some detail. In addition, searches for protein-protein interactions using yeast-two hybrid techniques have shed light on possible functional roles for their exposed cytoplasmic domains. A deeper analysis of these interactions should not only provide a more complete overview of the multiple functions of these viroporins, but also suggest novel strategies that target protein-protein interactions as much needed antivirals. These should complement current efforts to block viroporin channel activity.

Viroporins are small hydrophobic viral proteins that oligomerize to form aqueous pores on cellular membranes. Studies in recent years have demonstrated that viroporins serve important functions during virus replication and contribute to viral pathogenicity. A number of viroporins have also been shown to localize to the endoplasmic reticulum (ER) and/or its associated membranous organelles. In fact, replication of most RNA viruses is closely linked to the ER, and has been found to cause ER stress in the infected cells. On the other hand, autophagy is an evolutionarily conserved "self-eating" mechanism that is also observed in cells infected with RNA viruses. Both ER stress and autophagy are also known to modulate a wide variety of signaling pathways including pro-inflammatory and innate immune response, thereby constituting a major aspect of host-virus interactions. In this review, the potential involvement of viroporins in virus-induced ER stress and autophagy will be discussed.

Animal coronaviruses, such as infectious bronchitis virus (IBV), and arteriviruses, such as porcine reproductive and respiratory syndrome virus (PRRSV), are able to manifest highly contagious infections in their specific native hosts, thereby arising in critical economic damage to animal industries. This review discusses recent progress in studies of virus-host interactions during animal and human coronavirus and arterivirus infections, with emphasis on IBV-host cell interactions. These interactions may be directly involved in viral replication or lead to the alteration of certain signaling pathways, such as cell stress response and innate immunity, to facilitate viral replication and pathogenesis.