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The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a pandemic named coronavirus disease 2019 (COVID-19) that has become the greatest worldwide public health threat of this century. Recent studies have unraveled numerous mysteries of SARS-CoV-2 pathogenesis and thus largely improved the studies of COVID-19 vaccines and therapeutic strategies. However, important questions remain regarding its therapy. In this review, the recent research advances on COVID-19 mechanism are quickly summarized. We mainly discuss current therapy strategies for COVID-19, with an emphasis on antiviral agents, neutralizing antibody therapies, Janus kinase inhibitors, and steroids. When necessary, specific mechanisms and the history of therapy are present, and representative strategies are described in detail. Finally, we discuss key outstanding questions regarding future directions of the development of COVID-19 treatment.

Kidney disease is a significant health problem worldwide, affecting an estimated 10% of the global population. Kidney disease encompasses a diverse group of disorders that vary in their underlying pathophysiology, clinical presentation, and outcomes. These disorders include acute kidney injury (AKI), chronic kidney disease (CKD), glomerulonephritis, nephrotic syndrome, polycystic kidney disease, diabetic kidney disease, and many others. Despite their distinct etiologies, these disorders share a common feature of immune system dysregulation and metabolic disturbances. The immune system and metabolic pathways are intimately connected and interact to modulate the pathogenesis of kidney diseases. The dysregulation of immune responses in kidney diseases includes a complex interplay between various immune cell types, including resident and infiltrating immune cells, cytokines, chemokines, and complement factors. These immune factors can trigger and perpetuate kidney inflammation, causing renal tissue injury and progressive fibrosis. In addition, metabolic pathways play critical roles in the pathogenesis of kidney diseases, including glucose and lipid metabolism, oxidative stress, mitochondrial dysfunction, and altered nutrient sensing. Dysregulation of these metabolic pathways contributes to the progression of kidney disease by inducing renal tubular injury, apoptosis, and fibrosis. Recent studies have provided insights into the intricate interplay between immune and metabolic pathways in kidney diseases, revealing novel therapeutic targets for the prevention and treatment of kidney diseases. Potential therapeutic strategies include modulating immune responses through targeting key immune factors or inhibiting pro-inflammatory signaling pathways, improving mitochondrial function, and targeting nutrient-sensing pathways, such as mTOR, AMPK, and SIRT1. This review highlights the importance of the interplay between immune and metabolic pathways in kidney diseases and the potential therapeutic implications of targeting these pathways.

High-accuracy short-term electric load forecasting is essential for ensuring the security of power systems and enhancing energy efficiency. Power load sequences are characterized by strong randomness, non-stationarity, and nonlinearity over time. To improve the precision and efficiency of short-term load forecasting in microgrids, a hybrid predictive model combining Complementary Ensemble Empirical Mode Decomposition (CEEMD) and a multi-strategy enhanced Whale Optimization Algorithm (WOA) with Long Short-Term Memory (LSTM) neural networks has been proposed. Initially, this study employs CEEMD to decompose the short-term electric load time series. Subsequently, a multi-strategy enhanced WOA with chaotic initialization and reverse learning is introduced to enhance the search capability of model parameters and avoid entrapment in local optima. Finally, considering the distinct characteristics of each component, the multi-strategy improved WOA is utilized to optimize the LSTM model, establishing individual predictive models for each component, and the predictions are then aggregated. The proposed method’s forecasting accuracy has been validated through multiple case studies using the UC San Diego microgrid data, demonstrating its reliability and providing a solid foundation for microgrid system planning and stable operation.

In the context of rapid digital transformation, smart seaports have emerged as crucial entities for enhancing operational efficiency and promoting sustainable port governance. To achieve sustainable development, the integration of advanced technologies into seaport operations has become essential. However, the existing literature primarily highlights the construction achievements of smart seaports, with limited investigation into the configuration mechanisms that account for variations in efficiency. This study analyzes eight representative smart seaports in China from 2019 to 2024. Based on the Technology–Organizational–Environment (TOE) framework, six condition variables are identified. Comprehensive technical efficiency is measured using the three-stage super-efficiency Slack-Based Measure (SBM) model. Necessary Condition Analysis (NCA) and fuzzy-set Qualitative Comparative Analysis (fsQCA) are then employed to identify the configuration pathways leading to either high or non-high smart seaport operational efficiency. The findings indicate that no single factor is a necessary condition for high efficiency; instead, operational efficiency results from the synergistic interplay of multiple factors. Four distinct configuration pathways that lead to high efficiency are identified. Furthermore, a significant causal asymmetry exists between efficient and inefficient configurations, highlighting the contextual complexity inherent in smart seaport operational efficiency. This study provides a configurational perspective on the operational efficiency of smart seaports in order to offer policy and management insights for sustainable seaport operations.

Stomata play a critical role in the regulation of gas exchange and photosynthesis in plants. Stomatal closure participates in multiple stress responses, and is regulated by a complex network including abscisic acid (ABA) signaling and ion-flux-induced turgor changes. The slow-type anion channel SLAC1 has been identified to be a central controller of stomatal closure and phosphoactivated by several kinases. Here, we report the structure of SLAC1 in Arabidopsis thaliana ( At SLAC1) in an inactivated, closed state. The cytosolic amino (N)-terminus and carboxyl (C)-terminus of At SLAC1 are partially resolved and form a plug-like structure which packs against the transmembrane domain (TMD). Breaking the interactions between the cytosolic plug and transmembrane domain triggers channel activation. An inhibition-release model is proposed for SLAC1 activation by phosphorylation that the cytosolic plug dissociates from the transmembrane domain upon phosphorylation, and induces conformational changes to open the pore. These findings facilitate our understanding of the regulation of SLAC1 activity and stomatal aperture in plants. The anion channel SLAC1 controls stomatal closure upon phosphoactivation. Here via structural analysis and electrophysiology, the authors propose an inhibition-release model where phosphorylation causes dissociation of a cytosolic plug from the SLAC1 transmembrane domains to induce conformational change in the pore-forming helices.

A data driven method-based robot joint fault diagnosis method using deep residual neural network (DRNN) is proposed, where Resnet-based fault diagnosis method is introduced. The proposed method mainly deals with kinds of fault types, such as gain error, offset error and malfunction for both sensors and actuators, respectively. First, a deep residual network fault diagnosis model is derived by stacking small convolution cores and increasing the core size. meanwhile, the gaussian white noise is injected into the fault data set to verify the noise immunity for the proposed deep residual network. Furthermore, a simulation is conducted, where different fault diagnosis methods including support vector machine (SVM), artificial neural network (ANN), convolutional neural network (CNN), long-term memory network (LTMN) and deep residual neural network (DRNN) are compared, and the simulation results show the accuracy of fault diagnosis for robot system using DRNN is higher, meanwhile, DRNN needs less model training time. Visualization analysis proved the feasibility and effectiveness of the proposed method for robot joint sensor and actuator fault diagnosis using DRNN method.

Accurate prediction of shipping freight indices, represented by the Baltic Dry Index (BDI), is crucial for operational decision-making and risk management in the shipping industry. Existing models mainly rely on historical time-series data and often overlook the influence of unstructured information such as market sentiment. To address this limitation, this study proposes a dynamic freight rate prediction framework integrating a shipping text sentiment index. First, a shipping news sentiment index is constructed using a RoBERTa-based pre-trained model to quantify the impact of market sentiment on freight rate fluctuations. Second, the BDI series is decomposed and reconstructed through Variational Mode Decomposition (VMD) and Fuzzy C-Means (FCM) clustering to extract multiscale features. Finally, a deep learning based multi-step prediction model is developed by incorporating the sentiment index into the forecasting process. Empirical results show that the proposed model significantly outperforms benchmark models without sentiment information in terms of MAE, RMSE, and R2, and demonstrates greater robustness under extreme market conditions. These findings provide a novel methodological framework for improving freight rate forecasting accuracy and offer practical decision support for shipping enterprises.

This paper proposes a data-driven method-based fault diagnosis method using the deep convolutional neural network (DCNN). The DCNN is used to deal with sensor and actuator faults of robot joints, such as gain error, offset error, and malfunction for both sensors and actuators, and different fault types are diagnosed using the trained neural network. In order to achieve the above goal, the fused data of sensors and actuators are used, where both types of fault are described in one formulation. Then, the deep convolutional neural network is applied to learn characteristic features from the merged data to try to find discriminative information for each kind of fault. After that, the fully connected layer does prediction work based on learned features. In order to verify the effectiveness of the proposed deep convolutional neural network model, different fault diagnosis methods including support vector machine (SVM), artificial neural network (ANN), conventional neural network (CNN) using the LeNet-5 method, and long-term memory network (LTMN) are investigated and compared with DCNN method. The results show that the DCNN fault diagnosis method can realize high fault recognition accuracy while needing less model training time.

Lysosomes are critical for cellular metabolism and are heterogeneously involved in various cellular processes. The ability to measure lysosomal metabolic heterogeneity is essential for understanding their physiological roles. We therefore built a single-lysosome mass spectrometry (SLMS) platform integrating lysosomal patch-clamp recording and induced nano-electrospray ionization (nanoESI)/mass spectrometry (MS) that enables concurrent metabolic and electrophysiological profiling of individual enlarged lysosomes. The accuracy and reliability of this technique were validated by supporting previous findings, such as the transportability of lysosomal cationic amino acids transporters such as PQLC2 and the lysosomal trapping of lysosomotropic, hydrophobic weak base drugs such as lidocaine. We derived metabolites from single lysosomes in various cell types and classified lysosomes into five major subpopulations based on their chemical and biological divergence. Senescence and carcinoma altered metabolic profiles of lysosomes in a type-specific manner. Thus, SLMS can open more avenues for investigating heterogeneous lysosomal metabolic changes during physiological and pathological processes.Single-lysosome mass spectrometry (SLMS) integrates lysosomal patch-clamp recording and induced nanoESI/MS for concurrent metabolic and electrophysiological profiling of individual enlarged lysosomes.

To address the lag and limited sensitivity of conventional shipping freight indicators, this study develops a news-based sentiment measure for the shipping market and examines its association with BDI dynamics. Using shipping-related news headlines from 2019 to 2025, a RoBERTa classifier fine-tuned on manually annotated data is used to quantify headline sentiment, and a daily Cumulative Sentiment Index (CSI) is constructed using an event-smoothing window with exponential decay. A higher CSI indicates more positive market sentiment, while a lower CSI reflects more negative sentiment. Empirical evidence shows that CSI exhibits pronounced responses around major market events and is closely linked to BDI behavior in event windows. In addition, an EGARCH specification augmented with CSI indicates that sentiment is significantly associated with conditional volatility, suggesting that news-based sentiment contains incremental information relevant to BDI risk dynamics. Overall, the proposed CSI provides a quantitative approach to tracking shipping market sentiment using publicly available news headlines and offers a complementary perspective to transaction-based freight indices.