The role of a glycosyltransferase, ST6Gal I in regulating viral specific T and B cell responses

Glycosylation is one of the most abundant post-translational modifications of proteins. Glycoproteins participate in virtually all aspects of cellular functions. ST6Gal I is a glycosyltransferase highly expressed by B and T cells. Here, we interrogated the role of ST6Gal I in viral specific B and T cell immune responses, as well as examined how loss of this enzyme impacted viral pathogenesis. First, to understand how loss of ST6Gal I expression impacted viral specific humoral responses, we infected ST6Gal I-/- mice with influenza virus. We discovered that loss of ST6Gal I expression results in both reduced influenza specific antibodies levels and decreased viral-specific antibody secreting cells numbers. Following influenza infection, mice that received ST6Gal I-/- B cells showed reduced influenza-specific IgM responses compared to mice that received wild-type B cells. These experiments demonstrated that the expression of ST6Gal I by B cells is required for optimal viral-specific humoral response. We further examined how loss of ST6Gal I expression impacted the anti-influenza IgA response. We observed that immune ST6Gal I-/- mice displayed higher viral specific IgA levels and altered sialylation of IgG and IgA, which have been implicated in a human disease, IgA nephropathy. Moreover, ST6Gal I-/- mice exhibited increased immunoglobulin deposition in kidney glomeruli following influenza infection. These data suggest that ST6Gal I deficiency, together with influenza infection, may result in the initiation of a kidney disease. Finally, we examined how ST6Gal I expression regulated CD8 T cell responses. We discovered that ST6Gal I is differentially expressed during CD8 T cell activation. To understand its relevance, we infected ST6Gal I-/- mice and demonstrated that the early expansion of effector T cells was impaired in a cell intrinsic manner. Moreover, in the absence of ST6Gal I, the differentiation of CD8 T cells skewed towards memory precursor cells, whereas terminal effector cell expansion was impaired. Mechanistically, we identified delayed surface expression of IL-2Ralpha on ST6Gal I-/- CD8 T cells due to impaired IL-2/IL-2R signaling. These studies implicate that ST6Gal I expression enhances early proliferation of terminal effector CD8 T cells by promoting the rapid surface expression of IL2Ralpha during acute viral infection.

Towards Continual Learning Desiderata via HSIC-Bottleneck Orthogonalization and Equiangular Embedding

Deep neural networks are susceptible to catastrophic forgetting when trained on sequential tasks. Various continual learning (CL) methods often rely on exemplar buffers or/and network expansion for balancing model stability and plasticity, which, however, compromises their practical value due to privacy and memory concerns. Instead, this paper considers a strict yet realistic setting, where the training data from previous tasks is unavailable and the model size remains relatively constant during sequential training. To achieve such desiderata, we propose a conceptually simple yet effective method that attributes forgetting to layer-wise parameter overwriting and the resulting decision boundary distortion. This is achieved by the synergy between two key components: HSIC-Bottleneck Orthogonalization (HBO) implements non-overwritten parameter updates mediated by Hilbert-Schmidt independence criterion in an orthogonal space and EquiAngular Embedding (EAE) enhances decision boundary adaptation between old and new tasks with predefined basis vectors. Extensive experiments demonstrate that our method achieves competitive accuracy performance, even with absolute superiority of zero exemplar buffer and 1.02x the base model.Comment: Accepted to AAAI 202

Multi-View Class Incremental Learning

Multi-view learning (MVL) has gained great success in integrating information from multiple perspectives of a dataset to improve downstream task performance. To make MVL methods more practical in an open-ended environment, this paper investigates a novel paradigm called multi-view class incremental learning (MVCIL), where a single model incrementally classifies new classes from a continual stream of views, requiring no access to earlier views of data. However, MVCIL is challenged by the catastrophic forgetting of old information and the interference with learning new concepts. To address this, we first develop a randomization-based representation learning technique serving for feature extraction to guarantee their separate view-optimal working states, during which multiple views belonging to a class are presented sequentially; Then, we integrate them one by one in the orthogonality fusion subspace spanned by the extracted features; Finally, we introduce selective weight consolidation for learning-without-forgetting decision-making while encountering new classes. Extensive experiments on synthetic and real-world datasets validate the effectiveness of our approach.Comment: 34 pages,4 figures. Under revie

Towards Large-Scale Small Object Detection: Survey and Benchmarks

With the rise of deep convolutional neural networks, object detection has achieved prominent advances in past years. However, such prosperity could not camouflage the unsatisfactory situation of Small Object Detection (SOD), one of the notoriously challenging tasks in computer vision, owing to the poor visual appearance and noisy representation caused by the intrinsic structure of small targets. In addition, large-scale dataset for benchmarking small object detection methods remains a bottleneck. In this paper, we first conduct a thorough review of small object detection. Then, to catalyze the development of SOD, we construct two large-scale Small Object Detection dAtasets (SODA), SODA-D and SODA-A, which focus on the Driving and Aerial scenarios respectively. SODA-D includes 24828 high-quality traffic images and 278433 instances of nine categories. For SODA-A, we harvest 2513 high resolution aerial images and annotate 872069 instances over nine classes. The proposed datasets, as we know, are the first-ever attempt to large-scale benchmarks with a vast collection of exhaustively annotated instances tailored for multi-category SOD. Finally, we evaluate the performance of mainstream methods on SODA. We expect the released benchmarks could facilitate the development of SOD and spawn more breakthroughs in this field. Datasets and codes are available at: \url{https://shaunyuan22.github.io/SODA}

De Novo Transcriptome of the Hemimetabolous German Cockroach (\u3ci\u3eBlattella germanica\u3c/i\u3e)

Background: The German cockroach, Blattella germanica, is an important insect pest that transmits various pathogens mechanically and causes severe allergic diseases. This insect has long served as a model system for studies of insect biology, physiology and ecology. However, the lack of genome or transcriptome information heavily hinder our further understanding about the German cockroach in every aspect at a molecular level and on a genome-wide scale. To explore the transcriptome and identify unique sequences of interest, we subjected the B. germanica transcriptome to massively parallel pyrosequencing and generated the first reference transcriptome for B. germanica. Methodology/Principal Findings: A total of 1,365,609 raw reads with an average length of 529 bp were generated via pyrosequencing the mixed cDNA library from different life stages of German cockroach including maturing oothecae, nymphs, adult females and males. The raw reads were de novo assembled to 48,800 contigs and 3,961 singletons with highquality unique sequences. These sequences were annotated and classified functionally in terms of BLAST, GO and KEGG, and the genes putatively coding detoxification enzyme systems, insecticide targets, key components in systematic RNA interference, immunity and chemoreception pathways were identified. A total of 3,601 SSRs (Simple Sequence Repeats) loci were also predicted. Conclusions/Significance: The whole transcriptome pyrosequencing data from this study provides a usable genetic resource for future identification of potential functional genes involved in various biological processes

De Novo Transcriptome of the Hemimetabolous German Cockroach (\u3ci\u3eBlattella germanica\u3c/i\u3e)

Background: The German cockroach, Blattella germanica, is an important insect pest that transmits various pathogens mechanically and causes severe allergic diseases. This insect has long served as a model system for studies of insect biology, physiology and ecology. However, the lack of genome or transcriptome information heavily hinder our further understanding about the German cockroach in every aspect at a molecular level and on a genome-wide scale. To explore the transcriptome and identify unique sequences of interest, we subjected the B. germanica transcriptome to massively parallel pyrosequencing and generated the first reference transcriptome for B. germanica. Methodology/Principal Findings: A total of 1,365,609 raw reads with an average length of 529 bp were generated via pyrosequencing the mixed cDNA library from different life stages of German cockroach including maturing oothecae, nymphs, adult females and males. The raw reads were de novo assembled to 48,800 contigs and 3,961 singletons with highquality unique sequences. These sequences were annotated and classified functionally in terms of BLAST, GO and KEGG, and the genes putatively coding detoxification enzyme systems, insecticide targets, key components in systematic RNA interference, immunity and chemoreception pathways were identified. A total of 3,601 SSRs (Simple Sequence Repeats) loci were also predicted. Conclusions/Significance: The whole transcriptome pyrosequencing data from this study provides a usable genetic resource for future identification of potential functional genes involved in various biological processes

Systemic and mucosal infection program protective memory CD8 T cells in the vaginal mucosa.

Whether mucosal immunization is required for optimal protective CD8 T cell memory at mucosal surfaces is controversial. In this study, using an adoptive transfer system, we compare the efficacy of two routes of acute lymphocytic choriomeningitis viral infection on the generation, maintenance, and localization of Ag-specific CD8 T cells in tissues, including the vaginal mucosa. Surprisingly, at day 8, i.p. infection results in higher numbers of Ag-specific CD8 T cells in the vaginal mucosa and iliac lymph node, as well as 2-3x more Ag-specific CD8 T cells that coexpress both IFN-gamma and TNF-alpha in comparison to the intranasal route of infection. Expression of the integrin/activation marker CD103 (alphaEbeta7) is low on vaginal mucosal Ag-specific CD8 T cells in comparison to gut mucosal intraepithelial lymphocytes. At memory, no differences are evident in the number, cytokine production, or protective function of Ag-specific CD8 T cells in the vaginal mucosa comparing the two routes of infection. However, differences persist in the cytokine profile of genital tract vs peripheral Ag-specific CD8 T cells. So although the initial route of infection, as well as tissue microenvironment, appear to influence both the magnitude and quality of the effector CD8 T cell response, both systemic and mucosal infection are equally effective in the differentiation of protective memory CD8 T cell responses against vaginal pathogenic challenge

Numerical Simulation of Nonperiodic Rail Operation Diagram Characteristics

This paper succeeded in utilizing cellular automata (CA) model to simulate the process of the train operation under the four-aspect color light system and getting the nonperiodic diagram of the mixed passenger and freight tracks. Generally speaking, the concerned models could simulate well the situation of wagon in preventing trains from colliding when parking and restarting and of the real-time changes the situation of train speeds and displacement and get hold of the current train states in their departures and arrivals. Finally the model gets the train diagram that simulates the train operation in different ratios of the van and analyzes some parameter characters in the process of train running, such as time, speed, through capacity, interval departing time, and departing numbers

Impact of Road Bends on Traffic Flow in a Single-Lane Traffic System

Taking the characteristics of road bends as a research object, this work proposes the cellular model (CA) with road bends based on the NaSch model, with which the traffic flow is examined under different conditions, such as bend radius, bend arc length, and road friction coefficiency. The simulation results show that, with the increase of the bend radius, the peak flow will be continuously increased, and the fundamental diagram will become more similar to that of the classic NaSch model; the smaller the bend radius is, the easier it is for the occurrence of blockage; for different bend lengths, all the corresponding traffic flows show that the phenomenon of go-and-stop and the bends exert slight inhibitory effect on traffic flow; under the same bend radius, the inhibition effect of the bends on the traffic flow will be weakened with the increase of the friction coefficiency