Postnatal dysregulation of Notch signal disrupts dendrite development of adult-born neurons in the hippocampus and contributes to memory impairment

Deficits in the Notch pathway are involved in a number of neurologic diseases associated with mental retardation or/and dementia. The mechanisms by which Notch dysregulation are associated with mental retardation and dementia are poorly understood. We found that Notch1 is highly expressed in the adult-born immature neurons in the hippocampus of mice. Retrovirus mediated knockout of notch1 in single adult-born immature neurons decreases mTOR signaling and compromises their dendrite morphogenesis. In contrast, overexpression of Notch1 intracellular domain (NICD), to constitutively activate Notch signaling in single adult-born immature neurons, promotes mTOR signaling and increases their dendrite arborization. Using a unique genetic approach to conditionally and selectively knockout notch 1 in the postnatally born immature neurons in the hippocampus decreases mTOR signaling, compromises their dendrite morphogenesis, and impairs spatial learning and memory. Conditional overexpression of NICD in the postnatally born immature neurons in the hippocampus increases mTOR signaling and promotes dendrite arborization. These data indicate that Notch signaling plays a critical role in dendrite development of immature neurons in the postnatal brain, and dysregulation of Notch signaling in the postnatally born neurons disrupts their development and thus contributes to the cognitive deficits associated with neurological diseases

Establishment and Cross-Protection Efficacy of a Recombinant Avian Gammacoronavirus Infectious Bronchitis Virus Harboring a Chimeric S1 Subunit

Infectious bronchitis virus (IBV) is a gammacoronavirus that causes a highly contagious disease in chickens and seriously endangers the poultry industry. A diversity of serotypes and genotypes of IBV have been identified worldwide, and the currently available vaccines do not cross-protect. In the present study, an efficient reverse genetics technology based on Beaudette-p65 has been used to construct a recombinant IBV, rIBV-Beaudette-KC(S1), by replacing the nucleotides 21,704–22,411 with the corresponding sequence from an isolate of QX-like genotype KC strain. Continuous passage of this recombinant virus in chicken embryos resulted in the accumulation of two point mutations (G21556C and C22077T) in the S1 region. Further studies showed that the T248S (G21556C) substitution may be essential for the adaptation of the recombinant virus to cell culture. Immunization of chicks with the recombinant IBV elicited strong antibody responses and showed high cross-protection against challenges with virulent M41 and a QX-like genotype IBV. This study reveals the potential of developing rIBV-Beau-KC(S1) as a cell-based vaccine with a broad protective immunity against two different genotypes of IBV

Construction and evaluation of a novel triple cell epitopebased polypeptide vaccine against cow mastitis induced by Staphylococcus aureus, Escherichia coli and Streptococcus

Purpose: To construct a novel triple cell epitope-based polypeptide vaccine against cow mastitis induced by Staphylococcus aureus, Escherichia coli and  Streptococcus and to reduce the use of antibiotics.Methods: Based on bioinformatics approach, a novel triple epitope-based polypeptide (CM-TEP) was designed and subjected to Ni-NTA flow resin purification. Purified CM-TEP was immunized into mice to prepare a polyclonal antibody. Pull-down assays and enzyme-linked immunosorbent assay (ELISA) were used to detect the interaction between CM-TEP antibodies and S. aureus, E. coli and Streptococcus. Active immunity mice and challenge of bacterial pathogens were used to detect immune protection of CM-TEP. Additionally, the optimal expressing conditions of CM-TEP strain were analyzed using orthogonal test design.Results: A novel cow mastitis triple cell epitope-based polypeptide (CM-TEP) with a MW of 36 kDa was designed, purified and used to immunize mice to prepare a  polyclonal antibody. Pull-down assays and ELISA data showed that CM-TEP  antibodies directly interacted with S. aureus, E. coli and Streptococcus. CM-TEP displayed a significant immune protective effect against infection by S. aureus (50 %, p < 0.05) and E. coli (54.54 %, p < 0.05) and provided some immune protective effect (30.78 %, p > 0.05) against Streptococcus. The optimum expressing conditions of CM-TEP were as follows: IPTG concentration of 0.3 mmol/L, strain OD600 value of 1, inducing temperature of 37 oC, and inducing time of 8 h.Conclusion: The findings suggest that epitope-based vaccine of CM-TEP may be a useful strategy for treating cow mastitis induced by S. aureus, E. coli and Streptococcus.Keywords: Cow mastitis, Epitope vaccine, Immunogenicity, Immune protectiv

Corner-to-Center Long-range Context Model for Efficient Learned Image Compression

In the framework of learned image compression, the context model plays a pivotal role in capturing the dependencies among latent representations. To reduce the decoding time resulting from the serial autoregressive context model, the parallel context model has been proposed as an alternative that necessitates only two passes during the decoding phase, thus facilitating efficient image compression in real-world scenarios. However, performance degradation occurs due to its incomplete casual context. To tackle this issue, we conduct an in-depth analysis of the performance degradation observed in existing parallel context models, focusing on two aspects: the Quantity and Quality of information utilized for context prediction and decoding. Based on such analysis, we propose the \textbf{Corner-to-Center transformer-based Context Model (C$^3$M)} designed to enhance context and latent predictions and improve rate-distortion performance. Specifically, we leverage the logarithmic-based prediction order to predict more context features from corner to center progressively. In addition, to enlarge the receptive field in the analysis and synthesis transformation, we use the Long-range Crossing Attention Module (LCAM) in the encoder/decoder to capture the long-range semantic information by assigning the different window shapes in different channels. Extensive experimental evaluations show that the proposed method is effective and outperforms the state-of-the-art parallel methods. Finally, according to the subjective analysis, we suggest that improving the detailed representation in transformer-based image compression is a promising direction to be explored

Transferable Learned Image Compression-Resistant Adversarial Perturbations

Adversarial attacks can readily disrupt the image classification system, revealing the vulnerability of DNN-based recognition tasks. While existing adversarial perturbations are primarily applied to uncompressed images or compressed images by the traditional image compression method, i.e., JPEG, limited studies have investigated the robustness of models for image classification in the context of DNN-based image compression. With the rapid evolution of advanced image compression, DNN-based learned image compression has emerged as the promising approach for transmitting images in many security-critical applications, such as cloud-based face recognition and autonomous driving, due to its superior performance over traditional compression. Therefore, there is a pressing need to fully investigate the robustness of a classification system post-processed by learned image compression. To bridge this research gap, we explore the adversarial attack on a new pipeline that targets image classification models that utilize learned image compressors as pre-processing modules. Furthermore, to enhance the transferability of perturbations across various quality levels and architectures of learned image compression models, we introduce a saliency score-based sampling method to enable the fast generation of transferable perturbation. Extensive experiments with popular attack methods demonstrate the enhanced transferability of our proposed method when attacking images that have been post-processed with different learned image compression models.Comment: Accepted as poster at Data Compression Conference 2024 (DCC 2024