Knocking Down Type 2 but Not Type 1 Calsequestrin Reduces Calcium Sequestration and Release in C 2 C 12 Skeletal Muscle Myotubes

We examined the roles of type 1 and type 2 calsequestrins (CSQ1 and CSQ2) in stored Ca2+ release of C2C12 skeletal muscle myotubes. Transduction of C2C12 myoblasts with CSQ1 or CSQ2 small interfering RNAs effectively reduced the expression of targeted CSQ protein to near undetectable levels. As compared with control infected or CSQ1 knockdown myotubes, CSQ2 and CSQ1/CSQ2 knockdown myotubes had significantly reduced stored Ca2+ release evoked by activators of intracellular Ca2+ release channel/ryanodine receptor (10 mM caffeine, 200 microM 4-chloro-m-cresol, or 10 mM KCl). Thus, CSQ1 is not essential for effective stored Ca2+ release in C2C12 myotubes despite our in vitro studies suggesting that CSQ1 may enhance ryanodine receptor channel activity. To determine the basis of the reduced stored Ca2+ release in CSQ2 knockdown myotubes, we performed immunoblot analyses and found a significant reduction in both sarco/endoplasmic reticulum Ca2+-ATPase and skeletal muscle ryanodine receptor proteins in CSQ2 and CSQ1/CSQ2 knockdown myotubes. Moreover, these knockdown myotubes exhibited reduced Ca2+ uptake and reduced stored Ca2+ release by UTP (400 microM) that activates a different family of intracellular Ca2+ release channels (inositol 1,4,5-trisphosphate receptors). Taken together, our data suggest that knocking down CSQ2, but not CSQ1, leads to reduced Ca2+ storage and release in C2C12 myotubes

Medial Habenula-Interpeduncular Nucleus Circuit Contributes to Anhedonia-Like Behavior in a Rat Model of Depression

The habenula is a nuclear complex composed of the lateral habenula (LHb) and medial habenula (MHb), two distinct structures. Much progress has been made to emphasize the role of the LHb in the pathogenesis of depression. In contrast, relatively less research has focused on the MHb. However, in recent years, the role of the MHb has begun to gain increasing attention. The MHb connects to the interpeduncular nucleus (IPN) both morphologically and functionally. The MHb-IPN pathway plays an important role in regulating higher brain functions, including cognition, reward, and decision making. It indicates a role of the MHb in the pathogenesis of depression. Thus, we investigated the role of the MHb-IPN pathway in depression. MHb metabolic activity was increased in the chronic unpredictable mild stress (CUMS)-exposed rat model of depression. MHb lesions in the CUMS-exposed rats reversed anhedonia-like behavior, as observed in the sucrose preference test, and significantly downregulated the elevated metabolic activity of the IPN. Substance P (SP)-containing neurons of the MHb were found to innervate the IPN and to be the main source of SP in the IPN. SP content of IPN tissue of the CUMS-exposed rats was increased and MHb lesions reversed this change. In the in vitro experiment, firing rate recordings showed that SP perfusion increased the activity of IPN neurons. Our results suggest that hyperactivity of the MHb-IPN circuit is involved in the anhedonia-like behavior of depression, and that SP mediates the effect of the MHb on IPN neurons

S-MAT: Semantic-Driven Masked Attention Transformer for Multi-Label Aerial Image Classification

Multi-label aerial scene image classification is a long-standing and challenging research problem in the remote sensing field. As land cover objects usually co-exist in an aerial scene image, modeling label dependencies is a compelling approach to improve the performance. Previous methods generally directly model the label dependencies among all the categories in the target dataset. However, most of the semantic features extracted from an image are relevant to the existing objects, making the dependencies among the nonexistant categories unable to be effectively evaluated. These redundant label dependencies may bring noise and further decrease the performance of classification. To solve this problem, we propose S-MAT, a Semantic-driven Masked Attention Transformer for multi-label aerial scene image classification. S-MAT adopts a Masked Attention Transformer (MAT) to capture the correlations among the label embeddings constructed by a Semantic Disentanglement Module (SDM). Moreover, the proposed masked attention in MAT can filter out the redundant dependencies and enhance the robustness of the model. As a result, the proposed method can explicitly and accurately capture the label dependencies. Therefore, our method achieves CF1s of 89.21%, 90.90%, and 88.31% on three multi-label aerial scene image classification benchmark datasets: UC-Merced Multi-label, AID Multi-label, and MLRSNet, respectively. In addition, extensive ablation studies and empirical analysis are provided to demonstrate the effectiveness of the essential components of our method under different factors

DFT-assisted low-dimensional carbon-based electrocatalysts design and mechanism study: a review

Low-dimensional carbon-based (LDC) materials have attracted extensive research attention in electrocatalysis because of their unique advantages such as structural diversity, low cost, and chemical tolerance. They have been widely used in a broad range of electrochemical reactions to relieve environmental pollution and energy crisis. Typical examples include hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen reduction reaction (ORR), carbon dioxide reduction reaction (CO2RR), and nitrogen reduction reaction (NRR). Traditional “trial and error” strategies greatly slowed down the rational design of electrocatalysts for these important applications. Recent studies show that the combination of density functional theory (DFT) calculations and experimental research is capable of accurately predicting the structures of electrocatalysts, thus revealing the catalytic mechanisms. Herein, current well-recognized collaboration methods of theory and practice are reviewed. The commonly used calculation methods and the basic functionals are briefly summarized. Special attention is paid to descriptors that are widely accepted as a bridge linking the structure and activity and the breakthroughs for high-volume accurate prediction of electrocatalysts. Importantly, correlated multiple descriptors are used to systematically describe the complicated interfacial electrocatalytic processes of LDC catalysts. Furthermore, machine learning and high-throughput simulations are crucial in assisting the discovery of new multiple descriptors and reaction mechanisms. This review will guide the further development of LDC electrocatalysts for extended applications from the aspect of DFT computations.</p

Predictive Analysis of Vehicular Lane Changes: An Integrated LSTM Approach

In the rapidly advancing domain of vehicular traffic management and autonomous driving, accurate lane change predictions are paramount for ensuring safety and optimizing traffic flow. This study introduces a comprehensive two-stage prediction model that harnesses the capabilities of long short-term memory (LSTM) for anticipating vehicular lane changes. Initially, we employed a variety of models, such as regression methods, SVMs, and a multilayer perceptron, to categorize lane change behaviors. The dataset was then segmented based on vehicle trajectories and lane change patterns. In the subsequent phase, we utilized the superior classification outcomes from LinearSVC to curate our training data. We developed two dedicated LSTM networks tailored to specific datasets: the lane-keeping LSTM (LK-LSTM) and the lane-changing LSTM (LC-LSTM). By integrating insights from both models, we achieved a comprehensive prediction of vehicular lane changes. Our results indicate that the unified prediction model markedly enhances prediction precision. Accurate lane change predictions offer valuable contributions to advanced driver-assistance systems (ADAS), with the potential to minimize traffic mishaps and enhance traffic fluidity. As we transition to a more autonomous automotive era, refining these predictions becomes essential in seamlessly merging human and automated driving experiences

Facial Landmark Detection Using Generative Adversarial Network Combined with Autoencoder for Occlusion

The performance of the facial landmark detection model will be in trouble when it is under occlusion condition. In this paper, we present an effective framework with the objective of addressing the occlusion problem for facial landmark detection, which includes a generative adversarial network with improved autoencoders (GAN-IAs) and deep regression networks. In this model, GAN-IA can restore the occluded face region by utilizing skip concatenation among feature maps to keep more details. Meanwhile, self-attention mechanism that is effective in modeling long-range dependencies is employed to recover harmonious images for occluded faces. Deep regression networks are used to learn a nonlinear mapping from facial appearance to facial shape. Benefited from the mutual cooperation of GAN-IA and deep regression networks, a robust facial landmark detection model is achieved for the occlusion problem and the performance of the model achieves obviously improvement on challenging datasets

A distributed electricity energy trading strategy under energy shortage environment

This paper presents a power dispatch strategy combining the main grid and distributed generators based on aggregative game theory and the Cournot price mechanism. Such a dispatch strategy aims to increase the electricity under the power shortage situation. Under the proposed strategy, this paper designs a discrete-time algorithm fusing the estimation technique and the Digging method to solve the power shortage problem in a distributed way. The distributed algorithm can provide privacy protection and information safety and improve the power grid's extendibility. Moreover, the simulation results show that the proposed algorithm has favorable performance and effectiveness in the numerical example