Semantic-Aware Frame-Event Fusion based Pattern Recognition via Large Vision-Language Models

Pattern recognition through the fusion of RGB frames and Event streams has emerged as a novel research area in recent years. Current methods typically employ backbone networks to individually extract the features of RGB frames and event streams, and subsequently fuse these features for pattern recognition. However, we posit that these methods may suffer from key issues like sematic gaps and small-scale backbone networks. In this study, we introduce a novel pattern recognition framework that consolidates the semantic labels, RGB frames, and event streams, leveraging pre-trained large-scale vision-language models. Specifically, given the input RGB frames, event streams, and all the predefined semantic labels, we employ a pre-trained large-scale vision model (CLIP vision encoder) to extract the RGB and event features. To handle the semantic labels, we initially convert them into language descriptions through prompt engineering, and then obtain the semantic features using the pre-trained large-scale language model (CLIP text encoder). Subsequently, we integrate the RGB/Event features and semantic features using multimodal Transformer networks. The resulting frame and event tokens are further amplified using self-attention layers. Concurrently, we propose to enhance the interactions between text tokens and RGB/Event tokens via cross-attention. Finally, we consolidate all three modalities using self-attention and feed-forward layers for recognition. Comprehensive experiments on the HARDVS and PokerEvent datasets fully substantiate the efficacy of our proposed SAFE model. The source code will be made available at https://github.com/Event-AHU/SAFE_LargeVLM.Comment: In Peer Revie

Stable Isotope Metabolic Labeling with a Novel 15N-Enriched Bacteria Diet for Improved Proteomic Analyses of Mouse Models for Psychopathologies

The identification of differentially regulated proteins in animal models of psychiatric diseases is essential for a comprehensive analysis of associated psychopathological processes. Mass spectrometry is the most relevant method for analyzing differences in protein expression of tissue and body fluid proteomes. However, standardization of sample handling and sample-to-sample variability are problematic. Stable isotope metabolic labeling of a proteome represents the gold standard for quantitative mass spectrometry analysis. The simultaneous processing of a mixture of labeled and unlabeled samples allows a sensitive and accurate comparative analysis between the respective proteomes. Here, we describe a cost-effective feeding protocol based on a newly developed 15N bacteria diet based on Ralstonia eutropha protein, which was applied to a mouse model for trait anxiety. Tissue from 15N-labeled vs. 14N-unlabeled mice was examined by mass spectrometry and differences in the expression of glyoxalase-1 (GLO1) and histidine triad nucleotide binding protein 2 (Hint2) proteins were correlated with the animals' psychopathological behaviors for methodological validation and proof of concept, respectively. Additionally, phenotyping unraveled an antidepressant-like effect of the incorporation of the stable isotope 15N into the proteome of highly anxious mice. This novel phenomenon is of considerable relevance to the metabolic labeling method and could provide an opportunity for the discovery of candidate proteins involved in depression-like behavior. The newly developed 15N bacteria diet provides researchers a novel tool to discover disease-relevant protein expression differences in mouse models using quantitative mass spectrometry

Degradation of HK2 by chaperone-mediated autophagy promotes metabolic catastrophe and cell death

Hexokinase II (HK2), a key enzyme involved in glucose metabolism, is regulated by growth factor signaling and is required for initiation and maintenance of tumors. Here we show that metabolic stress triggered by perturbation of receptor tyrosine kinase FLT3 in non–acute myeloid leukemia cells sensitizes cancer cells to autophagy inhibition and leads to excessive activation of chaperone-mediated autophagy (CMA). Our data demonstrate that FLT3 is an important sensor of cellular nutritional state and elucidate the role and molecular mechanism of CMA in metabolic regulation and mediating cancer cell death. Importantly, our proteome analysis revealed that HK2 is a CMA substrate and that its degradation by CMA is regulated by glucose availability. We reveal a new mechanism by which excessive activation of CMA may be exploited pharmacologically to eliminate cancer cells by inhibiting both FLT3 and autophagy. Our study delineates a novel pharmacological strategy to promote the degradation of HK2 in cancer cells

HIMS-Net: Horizontal-vertical interaction and multiple side-outputs network for cyst segmentation in jaw images

Jaw cysts are mainly caused by abnormal tooth development, chronic oral inflammation, or jaw damage, which may lead to facial swelling, deformity, tooth loss, and other symptoms. Due to the diversity and complexity of cyst images, deep-learning algorithms still face many difficulties and challenges. In response to these problems, we present a horizontal-vertical interaction and multiple side-outputs network for cyst segmentation in jaw images. First, the horizontal-vertical interaction mechanism facilitates complex communication paths in the vertical and horizontal dimensions, and it has the ability to capture a wide range of context dependencies. Second, the feature-fused unit is introduced to adjust the network's receptive field, which enhances the ability of acquiring multi-scale context information. Third, the multiple side-outputs strategy intelligently combines feature maps to generate more accurate and detailed change maps. Finally, experiments were carried out on the self-established jaw cyst dataset and compared with different specialist physicians to evaluate its clinical usability. The research results indicate that the Matthews correlation coefficient (Mcc), Dice, and Jaccard of HIMS-Net were 93.61, 93.66 and 88.10% respectively, which may contribute to rapid and accurate diagnosis in clinical practice

Dynamic swarm class rebalancing for the process mining of rare events

Process mining is becoming an indispensable method in workflow model reconstructions, offering insights into mission critical systems. The efficacy of process mining depends on whether the underlying data mining algorithms can accurately classify or predict future events from process logs. However, exceptional events are scarce in most operational processes. Hence, the process logs generated from these processes are highly imbalanced. It is quite often that any model learned from imbalanced data tends to be overly generalized toward the normal classes but under-trained to recognize the rare classes. In this paper, we propose 3 methods to rectify this class imbalance problem. They are founded upon a meta-heuristic–swarm intelligence algorithm. The first method, and also the base of the remaining 2 methods, is Dynamic Multi-objective Rebalancing Algorithm, which considers both high accuracy and high confidence level of classification in its objective function, and it is draw upon the particle swarm optimization algorithm. The other two algorithms are hybrid methods by combining the first base method with over-sampling and under-sampling techniques. Experiments are conducted using the three above-mentioned methods to process rebalanced dataset, as well as using other classic resampling methods for comparison. According to the results, our proposed methods show satisfactory performance over other comparison methods, and we extracted meaningful decision rules from a rebalanced dataset in process mining

Discovery of Rhodanine Inhibitors Targeting <i>Of</i> ChtI Based on the π‑Stacking Effect and Aqueous Solubility

The application of some reported inhibitors against the chitinolytic enzyme Of ChtI was limited due to their unsatisfactory insecticidal activities. Hence, we first performed a synergetic design strategy combining the π-stacking effect with aqueous solubility to find novel rhodanine analogues with inhibitory activities against Of ChtI. Novel rhodanine compounds IAa–f and IBa–f have weak aqueous solubility, but they (IAd: Ki = 4.0 μM; IBd: Ki = 2.2 μM) showed better inhibitory activities against Of ChtI and comparable insecticidal efficiency toward Ostrinia furnacalis compared to the high aqueous solubility compounds IIAa–f and IIBa–f (IIAd: Ki = 21.6 μM; IIBd: Ki = 14.3 μM) without a large conjugate plane. Further optimized compounds IIIAa–j with a conjugate plane as well as a higher aqueous solubility exhibited similar good inhibitory activities against Of ChtI (IIIAe: Ki = 2.4 μM) and better insecticidal potency (IIIAe: mortality rate of 63.33%) compared to compounds IAa–f and IBa–f, respectively. Molecular docking studies indicated that the conjugate planarity with the π-stacking effect for rhodanine analogues is responsible for their enzyme inhibitory activity against Of ChtI. This study provides a new strategy for designing insect chitinolytic enzyme inhibitors as insect growth regulators for pest control

Proteomic and metabolomic profiling of a trait anxiety mouse model implicate affected pathways

BACKGROUND: Although anxiety disorders are the most prevalent psychiatric disorders, no molecular biomarkers exist for their premorbid diagnosis, accurate patient subcategorization, or treatment efficacy prediction. To unravel the neurobiological underpinnings and identify candidate biomarkers and affected pathways for anxiety disorders, we interrogated the mouse model of high anxiety-related behavior (HAB), normal anxiety-related behavior (NAB), and low anxiety-related behavior (LAB) employing a quantitative proteomics and metabolomics discovery approach. METHODS: We compared the cingulate cortex synaptosome proteomes of HAB and LAB mice by in vivo (15)N metabolic labeling and mass spectrometry and quantified the cingulate cortex metabolomes of HAB/NAB/LAB mice. The combined data sets were used to identify divergent protein and metabolite networks by in silico pathway analysis. Selected differentially expressed proteins and affected pathways were validated with immunochemical and enzymatic assays. RESULTS: Altered levels of up to 300 proteins and metabolites were found between HAB and LAB mice. Our data reveal alterations in energy metabolism, mitochondrial import and transport, oxidative stress, and neurotransmission, implicating a previously nonhighlighted role of mitochondria in modulating anxiety-related behavior. CONCLUSIONS: Our results offer insights toward a molecular network of anxiety pathophysiology with a focus on mitochondrial contribution and provide the basis for pinpointing affected pathways in anxiety-related behavior