Calcium channel α2δ1 proteins mediate trigeminal neuropathic pain states associated with aberrant excitatory synaptogenesis.

To investigate a potential mechanism underlying trigeminal nerve injury-induced orofacial hypersensitivity, we used a rat model of chronic constriction injury to the infraorbital nerve (CCI-ION) to study whether CCI-ION caused calcium channel α2δ1 (Cavα2δ1) protein dysregulation in trigeminal ganglia and associated spinal subnucleus caudalis and C1/C2 cervical dorsal spinal cord (Vc/C2). Furthermore, we studied whether this neuroplasticity contributed to spinal neuron sensitization and neuropathic pain states. CCI-ION caused orofacial hypersensitivity that correlated with Cavα2δ1 up-regulation in trigeminal ganglion neurons and Vc/C2. Blocking Cavα2δ1 with gabapentin, a ligand for the Cavα2δ1 proteins, or Cavα2δ1 antisense oligodeoxynucleotides led to a reversal of orofacial hypersensitivity, supporting an important role of Cavα2δ1 in orofacial pain processing. Importantly, increased Cavα2δ1 in Vc/C2 superficial dorsal horn was associated with increased excitatory synaptogenesis and increased frequency, but not the amplitude, of miniature excitatory postsynaptic currents in dorsal horn neurons that could be blocked by gabapentin. Thus, CCI-ION-induced Cavα2δ1 up-regulation may contribute to orofacial neuropathic pain states through abnormal excitatory synapse formation and enhanced presynaptic excitatory neurotransmitter release in Vc/C2

Why does dissolving salt in water decrease its dielectric permittivity

The dielectric permittivity of salt water decreases on dissolving more salt. For nearly a century, this phenomenon has been explained by invoking saturation in the dielectric response of the solvent water molecules. Herein, we employ an advanced deep neural network (DNN), built using data from density functional theory, to study the dielectric permittivity of sodium chloride solutions. Notably, the decrease in the dielectric permittivity as a function of concentration, computed using the DNN approach, agrees well with experiments. Detailed analysis of the computations reveals that the dominant effect, caused by the intrusion of ionic hydration shells into the solvent hydrogen-bond network, is the disruption of dipolar correlations among water molecules. Accordingly, the observed decrease in the dielectric permittivity is mostly due to increasing suppression of the collective response of solvent waters.Comment: has accepted by Physical Review Letter

Design, Synthesis, and Antifungal Activity of Novel 1,2,4-Triazolo[4,3-c]trifluoromethylpyrimidine Derivatives Bearing the Thioether Moiety

Crop disease caused by fungi seriously affected food security and economic development. Inspired by the utilization of fungicide containing 1,2,4-triazole and trifluoromethylpyrimidine, a novel series of 1,2,4-triazolo[4,3-c]trifluoromethylpyrimidine derivatives bearing the thioether moiety were synthesized. Meanwhile, the antifungal activities of the title compounds were evaluated and most compounds exhibited obvious antifungal activities against cucumber Botrytis cinerea, strawberry Botrytis cinerea, tobacco Botrytis cinerea, blueberry Botrytis cinerea, Phytophthora infestans, and Pyricularia oryzae Cav. Among the compounds, 4, 5h, 5o, and 5r showed significant antifungal activities against three of the four Botrytis cinerea, which indicated the potential to become the leading structures or candidates for resistance to Botrytis cinerea

GMS-3DQA: Projection-based Grid Mini-patch Sampling for 3D Model Quality Assessment

Nowadays, most 3D model quality assessment (3DQA) methods have been aimed at improving performance. However, little attention has been paid to the computational cost and inference time required for practical applications. Model-based 3DQA methods extract features directly from the 3D models, which are characterized by their high degree of complexity. As a result, many researchers are inclined towards utilizing projection-based 3DQA methods. Nevertheless, previous projection-based 3DQA methods directly extract features from multi-projections to ensure quality prediction accuracy, which calls for more resource consumption and inevitably leads to inefficiency. Thus in this paper, we address this challenge by proposing a no-reference (NR) projection-based \textit{\underline{G}rid \underline{M}ini-patch \underline{S}ampling \underline{3D} Model \underline{Q}uality \underline{A}ssessment (GMS-3DQA)} method. The projection images are rendered from six perpendicular viewpoints of the 3D model to cover sufficient quality information. To reduce redundancy and inference resources, we propose a multi-projection grid mini-patch sampling strategy (MP-GMS), which samples grid mini-patches from the multi-projections and forms the sampled grid mini-patches into one quality mini-patch map (QMM). The Swin-Transformer tiny backbone is then used to extract quality-aware features from the QMMs. The experimental results show that the proposed GMS-3DQA outperforms existing state-of-the-art NR-3DQA methods on the point cloud quality assessment databases. The efficiency analysis reveals that the proposed GMS-3DQA requires far less computational resources and inference time than other 3DQA competitors. The code will be available at https://github.com/zzc-1998/GMS-3DQA

PO-281 Vibration Training Restores Food Intake and Body Weight in a Rat Model of Depression

Objective Stress is well known to negatively affect body weight and food intake in animal models, but the underlying mechanisms have not yet been well elucidated and effective treatment is lacking. This project was initiated to study the potential beneficial effect of vibration training, a novel neuromuscular training method, in the treatment of depression. Methods Adult Sprague-Dawley male rats were randomly divided into the following three groups: 1) naïve control group, 2) depressive disorder group, and 3) depression with vibration training treatment group. To develop a depression phenotype, rats were individually and gently restricted in a modified, well-ventilated tube for 4 h every day for 21 days. Animals in vibration training treatment group were subjected to 30 min of vibration training (30 Hz, 5 days / week) for continuous 5 weeks. Body weight, physical and mental condition, and food intake were recorded daily and the data were statistically analyzed and compared between groups. Results 1. Daily body weight and food intake measurements revealed that both parameters decreased rapidly after the initiating daily restraint stress, compared with control group.  Intriguingly, both body weight and food intake of the depressive disorder group with 5-week vibration training were significantly improved. 2. The secretion of serotonin and dopamine in animals with chronic restraint stress were decreased compared with normal animals, and this attenuation was significantly prevented by vibration training. Conclusions The present study demonstrates that vibration training is capable of restoring food intake and body weight in a rat model of chronic restraint stress-induced depression

Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision

The rapid evolution of Multi-modality Large Language Models (MLLMs) has catalyzed a shift in computer vision from specialized models to general-purpose foundation models. Nevertheless, there is still an inadequacy in assessing the abilities of MLLMs on low-level visual perception and understanding. To address this gap, we present Q-Bench, a holistic benchmark crafted to systematically evaluate potential abilities of MLLMs on three realms: low-level visual perception, low-level visual description, and overall visual quality assessment. a) To evaluate the low-level perception ability, we construct the LLVisionQA dataset, consisting of 2,990 diverse-sourced images, each equipped with a human-asked question focusing on its low-level attributes. We then measure the correctness of MLLMs on answering these questions. b) To examine the description ability of MLLMs on low-level information, we propose the LLDescribe dataset consisting of long expert-labelled golden low-level text descriptions on 499 images, and a GPT-involved comparison pipeline between outputs of MLLMs and the golden descriptions. c) Besides these two tasks, we further measure their visual quality assessment ability to align with human opinion scores. Specifically, we design a softmax-based strategy that enables MLLMs to predict quantifiable quality scores, and evaluate them on various existing image quality assessment (IQA) datasets. Our evaluation across the three abilities confirms that MLLMs possess preliminary low-level visual skills. However, these skills are still unstable and relatively imprecise, indicating the need for specific enhancements on MLLMs towards these abilities. We hope that our benchmark can encourage the research community to delve deeper to discover and enhance these untapped potentials of MLLMs. Project Page: https://vqassessment.github.io/Q-Bench.Comment: 25 pages, 14 figures, 9 tables, preprint versio

Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models

Multi-modality foundation models, as represented by GPT-4V, have brought a new paradigm for low-level visual perception and understanding tasks, that can respond to a broad range of natural human instructions in a model. While existing foundation models have shown exciting potentials on low-level visual tasks, their related abilities are still preliminary and need to be improved. In order to enhance these models, we conduct a large-scale subjective experiment collecting a vast number of real human feedbacks on low-level vision. Each feedback follows a pathway that starts with a detailed description on the low-level visual appearance (*e.g. clarity, color, brightness* of an image, and ends with an overall conclusion, with an average length of 45 words. The constructed **Q-Pathway** dataset includes 58K detailed human feedbacks on 18,973 images with diverse low-level appearance. Moreover, to enable foundation models to robustly respond to diverse types of questions, we design a GPT-participated conversion to process these feedbacks into diverse-format 200K instruction-response pairs. Experimental results indicate that the **Q-Instruct** consistently elevates low-level perception and understanding abilities across several foundational models. We anticipate that our datasets can pave the way for a future that general intelligence can perceive, understand low-level visual appearance and evaluate visual quality like a human. Our dataset, model zoo, and demo is published at: https://q-future.github.io/Q-Instruct.Comment: 16 pages, 11 figures, page 12-16 as appendi

Trihydrophobin 1 Phosphorylation by c-Src Regulates MAPK/ERK Signaling and Cell Migration

c-Src activates Ras-MAPK/ERK signaling pathway and regulates cell migration, while trihydrophobin 1 (TH1) inhibits MAPK/ERK activation and cell migration through interaction with A-Raf and PAK1 and inhibiting their kinase activities. Here we show that c-Src interacts with TH1 by GST-pull down assay, coimmunoprecipitation and confocal microscopy assay. The interaction leads to phosphorylation of TH1 at Tyr-6 in vivo and in vitro. Phosphorylation of TH1 decreases its association with A-Raf and PAK1. Further study reveals that Tyr-6 phosphorylation of TH1 reduces its inhibition on MAPK/ERK signaling, enhances c-Src mediated cell migration. Moreover, induced tyrosine phosphorylation of TH1 has been found by EGF and estrogen treatments. Taken together, our findings demonstrate a novel mechanism for the comprehensive regulation of Ras/Raf/MEK/ERK signaling and cell migration involving tyrosine phosphorylation of TH1 by c-Src

Mitigation of Ice-Induced Vibration of Offshore Platform Based on Gated Recurrent Neural Network

Ice-induced vibration is one of the major risks that face the offshore platform located in cold regions. In this paper, the gated recurrent neural network (GRNN) is utilized to predict and suppress the response of offshore platforms subjected to ice load. First, a simplified model of the offshore platform is derived and validated based on the finite element model (FEM). The time history of the floating ice load is generated using the harmonic superposition method. Gated Recurrent Unit Network (GRU) and the Long-Short-Term Memory Network (LSTM) are composed in MATLAB to predict the behavior of the off-shore platform. Afterward, the linear quadratic regulator (LQR) control algorithm is used to calculate the controlling force for the training of the GRU/LSTM-based prediction controller. Numerical results show that the ice-induced vibration response prediction method based on GRU network design can predict the structural response with satisfying accuracy, and the ice-induced vibration response control method based on the LSTM network and GRU network design can learn the LQR method well and achieve good control effect. Time lag and other problems that the vibration control programs often encountered were solved well