Studies of intermediates and regulation in SNARE-mediated membrane fusion

At the synapse, neurotransmitters are released via Ca2+-triggered exocytotic fusion of synaptic vesicles with the presynaptic plasma membrane. The whole process is controlled by various proteins. SNAREs have been recognized as the key components that drive membrane fusion. In addition, many other proteins, such as Munc18/nSec1, Mun13, synaptotagmin, complexin, etc. are characterized to regulate synaptic transmission temporally and spatially.;The in vitro bulk fluorescence assay was applied to examine the kinetics of membrane fusion of liposomes mediated by recombinant neuronal SNAREs and led to the demonstration of hemifusion as an intermediate in the pathway. In order to monitor the fusion process in more sophisticated level, we developed the Single-liposome FRET assay and combined site-directed spin labeling (SDSL) and electron paramagnetic resonance (EPR) techniques to study the function of two SNARE regulators, synaptotagmin and complexin.;The EPR and fluorescence assay were also applied for the study of the SNAREs mediating the trafficking in yeast. It was found that supermolecular SNARE assembly precedes hemifusion, which was subsequently followed by distal leaflet mixing and formation of the cis-SNARE complex

Unraveling Feature Extraction Mechanisms in Neural Networks

The underlying mechanism of neural networks in capturing precise knowledge has been the subject of consistent research efforts. In this work, we propose a theoretical approach based on Neural Tangent Kernels (NTKs) to investigate such mechanisms. Specifically, considering the infinite network width, we hypothesize the learning dynamics of target models may intuitively unravel the features they acquire from training data, deepening our insights into their internal mechanisms. We apply our approach to several fundamental models and reveal how these models leverage statistical features during gradient descent and how they are integrated into final decisions. We also discovered that the choice of activation function can affect feature extraction. For instance, the use of the \textit{ReLU} activation function could potentially introduce a bias in features, providing a plausible explanation for its replacement with alternative functions in recent pre-trained language models. Additionally, we find that while self-attention and CNN models may exhibit limitations in learning n-grams, multiplication-based models seem to excel in this area. We verify these theoretical findings through experiments and find that they can be applied to analyze language modeling tasks, which can be regarded as a special variant of classification. Our contributions offer insights into the roles and capacities of fundamental components within large language models, thereby aiding the broader understanding of these complex systems.Comment: Accepted by EMNLP 202

Directed Self-Assembly of Block Copolymers Based on the Heterogeneous Nucleation Process

By introducing the heterogeneous nucleation concept to directed self-assembly of block copolymers, the ordering of dynamical process and defect pattern design in thin films of binary blend, AB diblock/C homopolymer (AB/C), are investigated by the time-dependent Ginzburg-Landau theory and simulated by the cell dynamics simulations. The detailed annealing process of a few isolated defects occurring in AB/C blend under triangular and hexagonal confinements is presented, and it indicates that angle-matched confinement of triangular and hexagonal potential well is favorable conditions for generating defect-free ordered structures. Meanwhile, we gave a model which composed of many double-spot potentials with controllable position and orientation to investigate the relationship between defect spacing and mismatched angle, and we found the relationship is similar to hard crystals. Additionally, as an example, the design of defect pattern of “NXU” for abbreviation of Ningxia University is proposed and tested. In this chapter, the feasibility of directed self-assembly of block copolymers based on the heterogeneous nucleation process is systematically confirmed

Domain Consistency Regularization for Unsupervised Multi-source Domain Adaptive Classification

Deep learning-based multi-source unsupervised domain adaptation (MUDA) has been actively studied in recent years. Compared with single-source unsupervised domain adaptation (SUDA), domain shift in MUDA exists not only between the source and target domains but also among multiple source domains. Most existing MUDA algorithms focus on extracting domain-invariant representations among all domains whereas the task-specific decision boundaries among classes are largely neglected. In this paper, we propose an end-to-end trainable network that exploits domain Consistency Regularization for unsupervised Multi-source domain Adaptive classification (CRMA). CRMA aligns not only the distributions of each pair of source and target domains but also that of all domains. For each pair of source and target domains, we employ an intra-domain consistency to regularize a pair of domain-specific classifiers to achieve intra-domain alignment. In addition, we design an inter-domain consistency that targets joint inter-domain alignment among all domains. To address different similarities between multiple source domains and the target domain, we design an authorization strategy that assigns different authorities to domain-specific classifiers adaptively for optimal pseudo label prediction and self-training. Extensive experiments show that CRMA tackles unsupervised domain adaptation effectively under a multi-source setup and achieves superior adaptation consistently across multiple MUDA datasets

Semi-automated Thermal Envelope Model Setup for Adaptive Model Predictive Control with Event-triggered System Identification

To reach carbon neutrality in the middle of this century, smart controls for building energy systems are urgently required. Model predictive control (MPC) demonstrates great potential in improving the performance of heating ventilation and air-conditioning (HVAC) systems, whereas its wide application in the building sector is impeded by the considerable manual efforts involved in setting up the control-oriented model. To facilitate the system identification (SI) of the building envelope as well as the configuration of the MPC algorithms with less human intervention, a semantic-assisted control framework is proposed in this paper. We first integrate different data sources required by the MPC algorithms such as the building topology, HVAC systems, sensor data stream and control settings in the form of a knowledge graph and then employ the data to set up the MPC algorithm automatically. Moreover, an event-triggered SI scheme is designed, to ensure the computational efficiency and accuracy of the MPC algorithm simultaneously. The proposed method is validated via simulations. The results demonstrate the practical relevance and effectiveness of the proposed semantics-assisted MPC framework with event-triggered learning of system dynamics

Combined PD-1 blockade and GITR triggering induce a potent antitumor immunity in murine cancer models and synergizes with chemotherapeutic drugs

BACKGROUND: The coinhibitory receptor Programmed Death-1 (PD-1) inhibits effector functions of activated T cells and prevents autoimmunity, however, cancer hijack this pathway to escape from immune attack. The costimulatory receptor glucocorticoid-induced TNFR related protein (GITR) is up-regulated on activated T cells and increases their proliferation, activation and cytokine production. We hypothesize that concomitant PD-1 blockade and GITR triggering would synergistically improve the effector functions of tumor-infiltrating T cells and increase the antitumor immunity. In present study, we evaluated the antitumor effects and mechanisms of combined PD-1 blockade and GITR triggering in a clinically highly relevant murine ID8 ovarian cancer model. METHODS: Mice with 7 days-established peritoneal ID8 ovarian cancer were treated intraperitoneally (i.p.) with either control, anti-PD-1, anti-GITR or anti-PD-1/GITR monoclonal antibody (mAb) and their survival was evaluated; the phenotype and function of tumor-associated immune cells in peritoneal cavity of treated mice was analyzed by flow cytometry, and systemic antigen-specific immune response was evaluated by ELISA and cytotoxicity assay. RESULTS: Combined anti-PD-1/GITR mAb treatment remarkably inhibited peritoneal ID8 tumor growth with 20% of mice tumor free 90 days after tumor challenge while treatment with either anti-PD-1 or anti-GITR mAb alone exhibited little antitumor effect. The durable antitumor effect was associated with a memory immune response and conferred by CD4(+) cells and CD8(+) T cells. The treatment of anti-PD-1/GITR mAb increased the frequencies of interferon-γ-producing effector T cells and decreased immunosuppressive regulatory T cells and myeloid-derived suppressor cells, shifting an immunosuppressive tumor milieu to an immunostimulatory state in peritoneal cavity. In addition, combined treatment of anti-PD-1/GITR mAb mounted an antigen-specific immune response as evidenced by antigen-specific IFN-γ production and cytolytic activity of spleen cells from treated mice. More importantly, combined treatment of anti-PD-1/GITR mAb and chemotherapeutic drugs (cisplatin or paclitaxel) further increased the antitumor efficacy with 80% of mice obtaining tumor-free long-term survival in murine ID8 ovarian cancer and 4 T1 breast cancer models. CONCLUSIONS: Combined anti-PD-1/GITR mAb treatment induces a potent antitumor immunity, which can be further promoted by chemotherapeutic drugs. A combined strategy of anti-PD-1/GITR mAb plus cisplatin or paclitaxel should be considered translation into clinic

Effect of intramuscular adipose tissue in the skeletal muscle of thigh on glucose metabolism in male patients with obesity

Objective·To investigate the correlation between intramuscular adipose tissue (IMAT) content and glucose metabolism in male patients with obesity.Methods·Eighty male patients with obesity were recruited from the Endocrinology Department of Renji Hospital, Shanghai Jiao Tong University School of Medicine from December 2019 to December 2020. According to the results of oral glucose tolerance test (OGTT), they were divided into normal glucose tolerance (NGT) group and impaired glucose regulation (IGR) group. General data and laboratory test indicators of the two groups were collected and compared. mDixon-Quant technique was used to measure the IMAT content in each skeletal muscle of the thigh in the two groups, and the proton density fat fractions (PDFF) of skeletal muscle in the two groups were compared. The multivariate Logistic regression model was used to analyze the independent influencing factors of IGR occurrence.Results·Compared with the NGT group, patients in the IGR group had a larger waist circumference (P=0.017), higher glutamic-pyruvic transaminase level, glutamic-oxaloacetic transaminase level, γ-glutamyl transferase (GGT) level, triacylglycerol (TAG) level, nonestesterified fatty acid (NEFA) level and sartorius PDFF (all P<0.05). After adjusting for confounding factors such as age, body mass index, GGT, TAG and NEFA, the results of multivariate Logistic regression analysis showed that PDFF of vastus lateralis, semitendinosus and sartorius were the risk factors for IGR (all P<0.05).Conclusion·Higher levels of IMAT content in vastus lateralis, semitendinosus and sartorius will increase the risk of IGR in male patients with obesity