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Infectious bursal disease (IBD) is an acute, highly contagious, immunosuppressive, and fatal infectious disease of young chickens caused by infectious bursal disease virus (IBDV). Since 2017, a new trend has been discovered in the IBDV epidemic, with very virulent IBDV (vvIBDV) and novel variant IBDV (nVarIBDV) becoming the two current dominant strains in East Asia including China. In this study, we compared the biological characteristics of the vvIBDV (HLJ0504 strain), nVarIBDV (SHG19 strain), and attenuated IBDV (attIBDV, Gt strain) using specific-pathogen-free (SPF) chicken infection model. The results showed that vvIBDV distributed in multiple tissues, replicated the fastest in lymphoid organs such as bursa of Fabricius, induced significant viremia and virus excretion, and is the most pathogenic virus with a mortality of more than 80%. The nVarIBDV had a weaker replication capability and did not kill the chickens but caused severe damage to the central immune organ bursa of Fabricius and B lymphocytes and induced significant viremia and virus excretion. The attIBDV strain was found not to be pathogenic. Further studies preliminarily suggested that the expression level of inflammatory factors triggered by HLJ0504 was the highest, followed by the SHG19 group. This study is the first to systematically compare the pathogenic characteristics of three IBDVs closely related to poultry industry from the perspectives of clinical signs, micro-pathology, virus replication, and distribution. It is of great importance to obtain an extensive knowledge of epidemiology, pathogenicity, and comprehensive prevention, and control of various IBDV strains. [ABSTRACT FROM AUTHOR]

The high activity and reliability of bifunctional oxygen catalysts are imperative for rechargeable metal–air batteries. However, the preparation of bifunctional non–noble metal electrocatalysts with multiple active sites remains a great challenge. Herein, an MOF–derived N–doped C–loaded uniformly dispersed CoO/MoC heterojunction catalyst for high–performance dual function was prepared by a simple "codeposition–pyrolysis" method. Experimental investigations revealed that the formation of the heterojunction can tailor the valence of Co and Mo sites, which impressively modulates the electronic properties of the active sites and promotes the electrocatalytic processes. The optimal catalyst reveals a high–wave half potential (E1/2 = 0.841 V) for ORR and a low overpotential (E10 = 348 mV) for OER. The NCCM–600–based Zn–air battery displays a high peak power density of 133.36 mW cm−2 and a prolonged cycling life of more than 650 h. This work provides avenues for the development of functional materials with enhanced properties in a variety of practical energy applications. [ABSTRACT FROM AUTHOR]

Shilling attacks have been a significant vulnerability of collaborative filtering (CF) recommender systems, and trust in CF recommender algorithms has been proven to be helpful for improving the accuracy of system recommendations. As a few studies have been devoted to trust in this area, we explore the benefits of using trust to resist shilling attacks. Rather than simply using user-generated trust values, we propose the genre trust degree, which differ in terms of the genres of items and take both trust value and user credibility into consideration. This paper introduces different types of shilling attack methods in an attempt to study the impact of users' trust values and behavior features on defending against shilling attacks. Meanwhile, it improves the approach used to calculate user similarities to form a recommendation model based on genre trust degrees. The performance of the genre trust-based recommender system is evaluated on the Ciao dataset. Experimental results demonstrated the superior and comparable genre trust degrees recommended for defending against different types of shilling attacks. [ABSTRACT FROM AUTHOR]

Urea electrolysis is regarded as a prospective method for energy-saving hydrogen production. However, the practical application of this technology is limited by the lack of high-performance bifunctional catalysts for hydrogen evolution reaction (HER) and urea oxidation reaction (UOR). Herein, a heterostructure catalyst composed of NiFe layered double hydroxide (LDH) and sulfides (NiFe LDH-NiFeSx/NF) catalysts is prepared via a simple one-step hydrothermal approach. Remarkably, the prepared NiFe LDH-NiFeSx/NF required 138 mV and 1.34 V to achieve 10 mA cm−2 for HER and UOR in 1 M KOH and 0.33 M urea, respectively. Furthermore, when NiFe LDH-NiFeSx/NF is used as a cathode for urea electrolysis, only 1.44 V is required at 10 mA cm−2, which is much lower than the 1.53 V needed for overall water splitting. [ABSTRACT FROM AUTHOR]

Artificial intelligence is developing rapidly in the direction of intellectualization and humanization. Recent studies have shown the vulnerability of many deep learning models to adversarial examples, but there are fewer studies on adversarial examples attacking facial expression recognition systems. Human–computer interaction requires facial expression recognition, so the security demands of artificial intelligence humanization should be considered. Inspired by facial expression recognition, we want to explore the characteristics of facial expression recognition adversarial examples. In this paper, we are the first to study facial expression adversarial examples (FEAEs) and propose an adversarial attack method on facial expression recognition systems, a novel measurement method on the adversarial hardness of FEAEs, and two evaluation metrics on FEAE transferability. The experimental results illustrate that our approach is superior to other gradient-based attack methods. Finding FEAEs can attack not only facial expression recognition systems but also face recognition systems. The transferability and adversarial hardness of FEAEs can be measured effectively and accurately. [ABSTRACT FROM AUTHOR]

With the continuous development of network technology, an intrusion detection system needs to face detection efficiency and storage requirement when dealing with large data. A reasonable way of alleviating this problem is instance selection, which can reduce the storage space and improve intrusion detection efficiency by selecting representative instances. An instance is representative not only in its class but also in different classes. This representativeness reflects the importance of an instance. Since the existing instance selection algorithm does not take into account the above situations, some selected instances are redundant and some important instances are removed, increasing storage space and reducing efficiency. Therefore, a new representativeness of instance is proposed and considers not only the influence of all instances of the same class on the selected instance but also the influence of instances of different classes on the selected instance. Moreover, it considers the influence of instances of different classes as an advantageous factor. Based on this representativeness, two instance selection algorithms are proposed to handle balanced and imbalanced data problems for intrusion detection. One is a representative-based instance selection for balanced data, which is named RBIS and selects the same proportion of instances from each class. The other is a representative-based instance selection for imbalanced data, which is named RBIS-IM and selects important majority instances according to the number of instances of the minority class. Compared with other algorithms on the benchmark data sets of intrusion detection, experimental results verify the effectiveness of the proposed RBIS and RBIS-IM algorithms and demonstrate that the proposed algorithms can achieve a better balance between accuracy and reduction rate or between balanced accuracy and reduction rate. [ABSTRACT FROM AUTHOR]

Online social networks (OSNs) suffer from forged messages. Current studies have typically been focused on the detection of forged messages and do not provide the analysis of the behaviors of message publishers and network strategies to suppress forged messages. This paper carries out the analysis by taking a game theoretic approach, where infinitely repeated games are constructed to capture the interactions between a publisher and a network administrator and suppress forged messages in OSNs. Critical conditions, under which the publisher is disincentivized to publish any forged messages, are identified in the absence and presence of misclassification on genuine messages. Closed-form expressions are established for the maximum number of forged messages that a malicious publisher could publish. Confirmed by the numerical results, the proposed infinitely repeated games reveal that forged messages can be suppressed by improving the payoffs for genuine messages, increasing the cost of bots, and/or reducing the payoffs for forged messages. The increasing detection probability of forged messages or decreasing misclassification probability of genuine messages also has a strong impact on the suppression of forged messages. [ABSTRACT FROM AUTHOR]

Chicken anemia virus (CAV) infection has been reported in various poultry industries worldwide. Since CAV infection is becoming increasingly prevalent, especially in local chickens of China, rapid CAV detection has become essential. The conventional diagnostic methods are time consuming and need special expertise. Therefore, in this study, we developed a specific and sensitive loop-mediated isothermal amplification (LAMP) assay for CAV detection by using multiple sequence alignment of VP2. This assay was performed at 61°C for 1 h, and there was no non-specific reaction to common avian disease viruses. The detection limit was 65 copies of viral DNA; thus, this assay showed similar sensitivity to quantitative polymerase chain reaction (qPCR) but it was more sensitive than conventional PCR. Moreover, this assay was performed using clinical samples. The LAMP assay results were 83.6% correlated to the PCR results of the clinical samples, indicating that this method is an effective tool for the rapid detection of CAV. [ABSTRACT FROM AUTHOR]

Featured Application: The model proposed in this paper can be deployed to the enterprise gateway, dynamically monitor network activities, and connect with the firewall to protect the company network from attacks. It can be deployed in cloud computing environment or in software-defined networks to monitor network behavior and alerts in real time. Machine learning plays an important role in building intrusion detection systems. However, with the increase of data capacity and data dimension, the ability of shallow machine learning is becoming more limited. In this paper, we propose a fuzzy aggregation approach using the modified density peak clustering algorithm (MDPCA) and deep belief networks (DBNs). To reduce the size of the training set and the imbalance of the samples, MDPCA is used to divide the training set into several subsets with similar sets of attributes. Each subset is used to train its own sub-DBNs classifier. These sub-DBN classifiers can learn and explore high-level abstract features, automatically reduce data dimensions, and perform classification well. According to the nearest neighbor criterion, the fuzzy membership weights of each test sample in each sub-DBNs classifier are calculated. The output of all sub-DBNs classifiers is aggregated based on fuzzy membership weights. Experimental results on the NSL-KDD and UNSW-NB15 datasets show that our proposed model has higher overall accuracy, recall, precision and F1-score than other well-known classification methods. Furthermore, the proposed model achieves better performance in terms of accuracy, detection rate and false positive rate compared to the state-of-the-art intrusion detection methods. [ABSTRACT FROM AUTHOR]

Epidemic models trade the modeling accuracy for complexity reduction. This paper proposes to group vertices in directed graphs based on connectivity and carries out epidemic spread analysis on the group basis, thereby substantially reducing the modeling complexity while preserving the modeling accuracy. A group-based continuous-time Markov SIS model is developed. The adjacency matrix of the network is also collapsed according to the grouping, to evaluate the Jacobian matrix of the group-based continuous-time Markov model. By adopting the mean-field approximation on the groups of nodes and links, the model complexity is significantly reduced as compared with previous topological epidemic models. An epidemic threshold is deduced based on the spectral radius of the collapsed adjacency matrix. The epidemic threshold is proved to be dependent on network structure and interdependent of the network scale. Simulation results validate the analytical epidemic threshold and confirm the asymptotical accuracy of the proposed epidemic model. [ABSTRACT FROM AUTHOR]

Recently, the number of people who are members of multiple online social networks simultaneously has increased. However, if these people share everything with others, they risk their privacy. Users may be unaware of the privacy risks involved with sharing their sensitive information on a network. Currently, there are many research efforts focused on social identity linkage (SIL) on multiple online social networks for commercial services, which exacerbates privacy issues. Many existing studies consider methods of encrypting or deleting sensitive information without considering if this is unreasonable for social networks. Meanwhile, these studies ignore privacy awareness, which is rudimentary and critical. To enhance privacy awareness, we discuss a user privacy exposure measure for users who are members of multiple online social networks. With this measure, users can be aware of the state of their privacy and their position on a privacy measurement scale. Additionally, we propose a straightforward method through our framework to reduce information loss and foster user privacy awareness by using spurious content for required fields. [ABSTRACT FROM AUTHOR]

For a large number of network attacks, feature selection is used to improve intrusion detection efficiency. A new mutual information algorithm of the redundant penalty between features (RPFMI) algorithm with the ability to select optimal features is proposed in this paper. Three factors are considered in this new algorithm: the redundancy between features, the impact between selected features and classes and the relationship between candidate features and classes. An experiment is conducted using the proposed algorithm for intrusion detection on the KDD Cup 99 intrusion dataset and the Kyoto 2006+ dataset. Compared with other algorithms, the proposed algorithm has a much higher accuracy rate (i.e., 99.772%) on the DOS data and can achieve better performance on remote-to-login (R2L) data and user-to-root (U2R) data. For the Kyoto 2006+ dataset, the proposed algorithm possesses the highest accuracy rate (i.e., 97.749%) among the other algorithms. The experiment results demonstrate that the proposed algorithm is a highly effective feature selection method in the intrusion detection. [ABSTRACT FROM AUTHOR]

Although the policy-gradient methods for reinforcement learning have shown significant improvement in image captioning, how to achieve high performance during the reinforcement optimizing process is still not a simple task. There are at least two difficulties: (1) The large size of vocabulary leads to a large action space, which makes it difficult for the model to accurately predict the current word. (2) The large variance of gradient estimation in reinforcement learning usually causes severe instabilities in the training process. In this paper, we propose two innovations to boost the performance of self-critical sequence training (SCST). First, we modify the standard long short-term memory (LSTM)based decoder by introducing a gate function to reduce the search scope of the vocabulary for any given image, which is termed the word gate decoder. Second, instead of only considering current maximum actions greedily, we propose a stabilized gradient estimation method whose gradient variance is controlled by the difference between the sampling reward from the current model and the expectation of the historical reward. We conducted extensive experiments, and results showed that our method could accelerate the training process and increase the prediction accuracy. Our method was validated on MS COCO datasets and yielded state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Connected vehicular cloud computing (CVCC) and the VANET can realize real-time monitoring and intelligent adjustment of traffic conditions. With the data collected by vehicles and the guidance provided by cloud computing platforms, the current traffic is facing new opportunities. CVCC is a mobile computing model, which extends the fixed nodes of traditional cloud infrastructure into mobile nodes composed of vehicles. Thus, compared to the traditional cloud infrastructures, CVCC requires a more complex mechanism to ensure the secure and efficient information transmission in both cloud platform-to-vehicle communication and vehicle-to-vehicle communication. Channel capacity, as the key parameter to measure the channel utilization, plays an important role in ensuring the reliability of CVCC service and the integrity of transmission data. In recent decades, the existing calculation methods could not solve the channel capacities problem in multi-participant VANETs. Different from the traditional calculation methods, we propose a novel calculation method in this article, which combines the core concepts of game theory and information theory, to calculate the channel capacities of multiple vehicular networks. The proposed method refreshes the concept of communication, which has potential applications in different services of CVCC. [ABSTRACT FROM AUTHOR]

Image captioning is a challenging task. Meanwhile, it is important for the machine to understand the meaning of an image better. In recent years, the image captioning usually use the long-short-term-memory (LSTM) as the decoder to generate the sentence, and these models show excellent performance. Although the LSTM can memorize dependencies, the LSTM structure has complicated and inherently sequential across time problems. To address these issues, recent works have shown benefits of the Transformer for machine translation. Inspired by their success, we develop a Captioning Transformer (CT) model with stacked attention modules. We attempt to introduce the Transformer to the image captioning task. The CT model contains only attention modules without the dependencies of the time. It not only can memorize dependencies between the sequence but also can be trained in parallel. Moreover, we propose the multi-level supervision to make the Transformer achieve better performance. Extensive experiments are carried out on the challenging MSCOCO dataset and the proposed Captioning Transformer achieves competitive performance compared with some state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Security confrontation is the second cornerstone of the General Theory of Security. And it can be divided into two categories: blind confrontation and nonblind confrontation between attackers and defenders. In this paper, we study the nonblind confrontation by some well-known games. We show the probability of winning and losing between the attackers and defenders from the perspective of channel capacity. We establish channel models and find that the attacker or the defender wining one time is equivalent to one bit transmitted successfully in the channel. This paper also gives unified solutions for all the nonblind confrontations. [ABSTRACT FROM AUTHOR]

This paper first presents a new generally perturbed compressed sensing (CS) model y=(A+E)(x+u)+e, which incorporated a general nonzero perturbation E into sensing matrix A and a noise u into signal x simultaneously based on the standard CS model y=Ax+e and is called noise folding in completely perturbed CS model. Our construction mainly will whiten the new proposed CS model and explore in restricted isometry property (RIP) and coherence of the new CS model under some conditions. Finally, we use OMP to give a numerical simulation which shows that our model is feasible although the recovered value of signal is not exact compared with original signal because of measurement noise e, signal noise u, and perturbation E involved. [ABSTRACT FROM AUTHOR]

A novel approach for the parameter identification of coupled map lattice (CML) based on compressed sensing is presented in this paper. We establish a meaningful connection between these two seemingly unrelated study topics and identify the weighted parameters using the relevant recovery algorithms in compressed sensing. Specifically, we first transform the parameter identification problem of CML into the sparse recovery problem of underdetermined linear system. In fact, compressed sensing provides a feasible method to solve underdetermined linear system if the sensing matrix satisfies some suitable conditions, such as restricted isometry property (RIP) and mutual coherence. Then we give a low bound on the mutual coherence of the coefficient matrix generated by the observed values of CML and also prove that it satisfies the RIP from a theoretical point of view. If the weighted vector of each element is sparse in the CML system, our proposed approach can recover all the weighted parameters using only about M samplings, which is far less than the number of the lattice elements N. Another important and significant advantage is that if the observed data are contaminated with some types of noises, our approach is still effective. In the simulations, we mainly show the effects of coupling parameter and noise on the recovery rate. [ABSTRACT FROM AUTHOR]

How to verify the integrity of outsourced data is an important problem in cloud storage. Most of previous work focuses on three aspects, which are providing data dynamics, public verifiability, and privacy against verifiers with the help of a third party auditor. In this paper, we propose an identity-based data storage and integrity verification protocol on untrusted cloud. And the proposed protocol can guarantee fair results without any third verifying auditor. The theoretical analysis and simulation results show that our protocols are secure and efficient. [ABSTRACT FROM AUTHOR]

Infectious bursal disease virus (IBDV) is a non-enveloped, bi-segmented double-stranded RNA virus and the causative agent of a poultry immunosuppressive disease known as infectious bursal disease (IBD). The novel variant IBDV (nVarIBDV) recently posed a great threat to the development of the poultry industry. In this study, we identified a novel segment-reassortant IBDV strain, IBDV-JS19-14701 (Genotype A2dB3). Phylogenic analysis showed that Segments A and B of IBDV-JS19-14701 were derived from emerging nVarIBDV (Genotype A2dB1) and long-prevalent HLJ0504-like strains (Genotype A3B3) in China, respectively. The pathogenicity of IBDV-JS19-14701 was further evaluated via animal experiments. IBDV-JS19-14701 exhibited a similar virulence to chickens with the nVarIBDV. The identification of this reassortment event is beneficial for understanding the epidemiology of nVarIBDV and will contribute to the efficient prevention and control of IBD. [ABSTRACT FROM AUTHOR]

Infectious bursal disease (IBD), an immunosuppressive disease of young chickens, is caused by infectious bursal disease virus (IBDV). Novel variant IBDV (nVarIBDV), a virus that can evade immune protection against very virulent IBDV (vvIBDV), is becoming a threat to the poultry industry. Therefore, nVarIBDV-specific vaccine is much needed for nVarIBDV control. In this study, the VP2 protein of SHG19 (a representative strain of nVarIBDV) was successfully expressed using an Escherichia coli expression system and further purified via ammonium sulfate precipitation and size-exclusion chromatography. The purified protein SHG19-VP2-466 could self-assemble into 25-nm virus-like particle (VLP). Subsequently, the immunogenicity and protective effect of the SHG19-VLP vaccine were evaluated using animal experiments, which indicated that the SHG19-VLP vaccine elicited neutralization antibodies and provided 100% protection against the nVarIBDV. Furthermore, the protective efficacy of the SHG19-VLP vaccine against the vvIBDV was evaluated. Although the SHG19-VLP vaccine induced a comparatively lower vvIBDV-specific neutralization antibody titer, it provided good protection against the lethal vvIBDV. In summary, the SHG19-VLP candidate vaccine could provide complete immune protection against the homologous nVarIBDV as well as the heterologous vvIBDV. This study is of significance to the comprehensive prevention and control of the recent atypical IBD epidemic. [ABSTRACT FROM AUTHOR]

The avian leukosis virus (ALV) strain DL00766 was isolated from a farm in China. The phylogenetic analysis showed that env had the highest homology with the E subgroup reference strain, ranging from 94.5% to 94.9%, whereas gp85 had the highest homology with the B and E subgroups, which were 89.0% to 91.3% and 91.3% to 91.8%. In addition, point mutation analysis of gp85 showed that a 400 bp long fragment in gp85 of DL00766 had the highest homology with subgroup B, ranging from 90.1% to 97.5%, and only 82.7% to 83.1% with E subgroup. These results indicate, DL00766 may be an AVL subgroup E isolate with a subgroup B-like gp85 region. This is also the first finding that the E subgroup is used as a recombinant subject, and the subgroup B provides a recombinant virus of an exogenous gene. [ABSTRACT FROM AUTHOR]

In previous work, imbalanced datasets composed of more benign samples (the majority class) than the malicious one (the minority class) have been widely adopted in Android malware detection. These imbalanced datasets bias learning toward the majority class, so that the minority class examples are more likely to be misclassified. To solve the problem, we propose a new oversampling method called fuzzy–synthetic minority oversampling technique, which is based on fuzzy set theory and the synthetic minority oversampling technique method. As the sample size of the majority class increases relative to that of the minority class, fuzzy–synthetic minority oversampling technique generates more synthetic examples for each minority class examples in the fuzzy region, where the minority examples have a low degree of membership to the minority class and are more likely to be misclassified. Using the new synthetic examples, the classifiers build larger decision regions that contain more minority examples, and they are no longer biased to the majority class. Compared with synthetic minority oversampling technique and Borderline–synthetic minority oversampling technique methods, fuzzy–synthetic minority oversampling technique achieves higher accuracy on both the minority class and the entire datasets. [ABSTRACT FROM AUTHOR]

More and more organizations outsource their data to remote cloud servers (RCSs). Data owners rent the cloud service providers (CSPs) infrastructure to store their unlimited resources by paying fees metered in month or gigabyte. For increasing the availability and scalability of data, the data owners store their data replicas on multiple servers across multiple data centers. Data owners should ensure that the CSPs actually store all their copies according to the service contract. In this paper, we present an efficient dynamic multicopies possession checking scheme that has the following properties: (1) the data owner uses fully homomorphic encryption (FHE) algorithm to generate multiple copies; (2) the scheme supports data block dynamic operation; (3) the scheme supports third-party auditor’s public validation. Finally, security analysis and experimental results show that our scheme can resist forgery, replacement, and replay attack and perform better than some other related scheme published recently. [ABSTRACT FROM AUTHOR]

The concept of public key encryption with equality test was introduced at CT-RSA 2010. It has been used in many fields, especially in cloud storage. However, the previous schemes do not provide an effective authorization mechanism. To fill this gap, Ma et al. presented a public key encryption with equality test supporting flexible authorization based on the bilinear pairings. Recently, Lin et al. presented a pairing-free scheme that employs quadratic curve to perform the equality tests, which can achieve a trade-off between computational cost and storage space. In this article, we show that the equality test can be better performed by using a straight line, rather than a quadratic curve. Moreover, we simplify the encryption algorithm, as well as reduce the ciphertext storage space. [ABSTRACT FROM AUTHOR]

Background: Invasive fungal infection (IFI) has become an increasing problem in NICU neonates, and end-organ damage (EOD) from IFI is one of the leading causes of morbidity and mortality in neonates. This study was conducted to summarize clinical data on epidemiology, risk factors, causative pathogens, and clinical outcomes of IFI-associated EOD among neonates in a center in China for the sake of providing references for prevention and treatment of fungal infections in neonates in future., Methods: The clinical data of IFI neonates who received treatment in a tertiary NICU of China from January 2009 to December 2022 were retrospectively analyzed, including causative pathogens and the incidence of EOD. The neonates were divided into EOD group and non-EOD (NEOD) group. The general characteristics, risk factors and clinical outcomes of the two groups were compared., Results: Included in this study were 223 IFI neonates (137 male and 86 female) with a median gestational age (GA) of 30.71 (29,35) weeks and a median birth weight (BW) of 1470 (1120,2150) g. Of them, 79.4% were preterm infants and 50.2% were born at a GA of ≥ 28, <32 weeks, and 37.7% with BW of 1000-1499 g. Candida albicans (C. albicans) was the most common Candida spp. in these neonates, accounting for 41.3% of all cases, followed by C. parapsilosis (30.5%) and C. glabrata (7.2%). EOD occurred in 40 (17.9%) of the 223 cases. Fungal meningitis was the most common EOD, accounting for 13.5% of the 40 EOD cases. There was no significant difference in the premature birth rate, delivery mode, GA and BW between EOD and NEOD groups, but the proportion of male infants with EOD was higher than that without. There was no significant difference in antenatal corticosteroid use, endotracheal intubation, invasive procedures, use of antibiotics, total parenteral nutrition, blood transfusion, postnatal corticosteroid use, fungal prophylaxis and the incidence of necrotizing enterocolitis between the two groups, but the proportion of C. albicans infection cases in EOD group was higher than that in NEOD group (57.5% vs. 37.7%). Compared with NEOD group, the proportion of cured or improved infants in EOD group was significantly lower (P < 0.05), and the number of infants who died or withdrew from treatment was larger (P < 0.05)., Conclusions: Our retrospective study showed that preterm infants were prone to fungal infection, especially very preterm infants. C. albicans was the most common Candida spp. for IFI, and was a high-risk factor for EOD. EOD can occur in both full-term and premature infants, so the possibility of EOD should be considered in all infants with IFI., (© 2024. The Author(s).)

Viruses have evolved intricate mechanisms to evade host antiviral responses and exploit cellular resources by manipulating the expression profile of host genes. During infection, viruses encode proteins with shutoff activity to globally inhibit host protein synthesis, which is an effective strategy for immune evasion. In this study, compelling evidence shows that infectious bursal disease virus (IBDV) infection triggers the suppression of host protein synthesis. Furthermore, using both in vitro and in vivo viral infection models, we have identified that IBDV specifically impedes the transcription of host genes via the shutoff activity of viral VP5, simultaneously conferring advantages to IBDV infection in these circumstances. The proposed mechanism suggests that VP5 competitively binds to RanBP1, disrupting the RanGDP/GTP gradient. This disruption interferes with cellular nucleocytoplasmic transport, impairing the nuclear import of proteins bearing nuclear localization signals. The nuclear transport of pivotal transcriptional regulatory factors, such as p65 and IFN regulatory factor 7, is also compromised, leading to the inhibition of pro-inflammatory cytokines and interferon expression. This newly discovered strategy employed by IBDV enables them to manipulate host gene expression, providing novel insights into how viruses evade host immune responses and establish infections.IMPORTANCEViruses manipulate host processes at various levels to regulate or evade both innate and adaptive immune responses, promoting self-survival and efficient transmission. The "host shutoff," a global suppression of host gene expression mediated by various viruses, is considered a critical mechanism for evading immunity. In this study, we have validated the presence of host shutoff during infectious bursal disease virus (IBDV) infection and additionally uncovered that the viral protein VP5 plays a pivotal role in inhibiting the overall synthesis of host proteins, including cytokines, through a transcription-dependent pathway. VP5 competitively binds with RanBP1, leading to disruption of the Ran protein cycle and consequently interfering with nucleocytoplasmic transport, which ultimately results in the suppression of host gene transcription. These findings unveil a novel strategy employed by IBDV to evade host innate immunity and rapidly establish infection. This study also suggests a novel supplement to understanding the pathway through which viruses inhibit host protein synthesis., Competing Interests: The authors declare no conflict of interest.

Recently, atypical infectious bursal disease (IBD) caused by a novel variant infectious bursal disease virus (varIBDV) suddenly appeared in immunized chicken flocks in East Asia and led to serious economic losses. The epizootic varIBDV can partly circumvent the immune protection of the existing vaccines against the persistently circulating very virulent IBDV (vvIBDV), but its mechanism is still unknown. This study proved that the neutralizing titer of vvIBDV antiserum to the epizootic varIBDV reduced by 7.0 log 2 , and the neutralizing titer of the epizootic varIBDV antiserum to vvIBDV reduced by 3.2 log 2 . In addition, one monoclonal antibody (MAb) 2-5C-6F had good neutralizing activity against vvIBDV but could not well recognize the epizootic varIBDV. The epitope of the MAb 2-5C-6F was identified, and two mutations of G318D and D323Q of capsid protein VP2 occurred in the epizootic varIBDV compared to vvIBDV. Subsequently, the indirect immunofluorescence assay based on serial mutants of VP2 protein verified that residue mutations 318 and 323 influenced the recognition of the epizootic varIBDV and vvIBDV by the MAb 2-5C-6F, which was further confirmed by the serial rescued mutated virus. The following cross-neutralizing assay directed by MAb showed residue mutations 318 and 323 also affected the neutralization of the virus. Further data also showed that the mutations of residues 318 and 323 of VP2 significantly affected the neutralization of the IBDV by antiserum, which might be deeply involved in the immune circumvention of the epizootic varIBDV in the vaccinated flock. This study is significant for the comprehensive prevention and control of the emerging varIBDV., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Fan, Wang, Jiang, Gao, Niu, Zhang, Huang, Bao, Liu, Wang, Gao, Li, Cui, Pan, Liu, Zhang, Wang and Qi.)

Infectious bursal disease (IBD) is an acute and highly contagious immunosuppressive disease caused by the infectious bursal disease virus (IBDV), which seriously threatens the healthy development of the poultry industry. Since its spread to China in the early 1990s, the very virulent IBDV (vvIBDV) characterized by high lethality, has been the focus of prevention and control. However, the novel variant IBDV (nVarIBDV), which has been widely prevalent in China since 2017, has brought a new threat to the poultry industry. In this study, the prevalence of IBDV in the important poultry-raising areas of China from 2019 to 2020 was detected. Of these, 45.1% (101/224) of the samples and 61.9% (26/42) of the chicken flocks were shown to be positive for IBDV. For 50 IBDVs, the sequences of the hypervariable region of the VP2 gene in segment A and of the B-marker of the VP1 gene in segment B were analyzed. The results revealed the coexistence of a number of different IBDV genotypes, including A2dB1 (nVar, 26/50, 52.0%), A3B3 (HLJ0504-like, 15/50, 30.0%), A1B1 (classical, 1/50, 2.0%), and A8B1 (attenuated, 1/50, 2.0%). This indicated that the newly emerging nVarIBDV of A2dB1 and the persistently circulating HLJ0504-like vvIBDV of A3B3 are the two important epidemic strains. Furthermore, we established that segment reassortment has occurred among these circulating strains. This study is the first to reveal the novel epidemic characteristics of IBDV since the report of the emerging nVarIBDV of A2dB1 in China., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Jiang, Wang, Zhang, Niu, Huang, Gao, Liu, Gao, Li, Pan, Liu, Zhang, Cui, Wang and Qi.)