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The emergence of SARS-CoV-2 has resulted in the COVID-19 pandemic, leading to millions of infections and hundreds of thousands of human deaths. The efficient replication and population spread of SARS-CoV-2 indicates an effective evasion of human innate immune responses, although the viral proteins responsible for this immune evasion are not clear. In this study, we identified SARS-CoV-2 structural proteins, accessory proteins, and the main viral protease as potent inhibitors of host innate immune responses of distinct pathways. In particular, the main viral protease was a potent inhibitor of both the RLR and cGAS-STING pathways. Viral accessory protein ORF3a had the unique ability to inhibit STING, but not the RLR response. On the other hand, structural protein N was a unique RLR inhibitor. ORF3a bound STING in a unique fashion and blocked the nuclear accumulation of p65 to inhibit nuclear factor-κB signaling. 3CL of SARS-CoV-2 inhibited K63-ubiquitin modification of STING to disrupt the assembly of the STING functional complex and downstream signaling. Diverse vertebrate STINGs, including those from humans, mice, and chickens, could be inhibited by ORF3a and 3CL of SARS-CoV-2. The existence of more effective innate immune suppressors in pathogenic coronaviruses may allow them to replicate more efficiently in vivo. Since evasion of host innate immune responses is essential for the survival of all viruses, our study provides insights into the design of therapeutic agents against SARS-CoV-2.

Drug-drug interaction (DDI) event prediction is a challenging problem, and accurate prediction of DDI events is critical to patient health and new drug development. Recently, many machine learning-based techniques have been proposed for predicting DDI events. However, most of the existing methods do not effectively integrate the multidimensional features of drugs and provide poor mitigation of noise to get effective feature information. To address these limitations, we propose a DDI-Transform neural network framework for DDI event prediction. In DDI-Transform, we design a drug structure information feature extraction module and a drug bind-protein feature extraction module to obtain multidimensional feature information. A stack of DDI-Transform layers in the DDI-Transform network module are then used for adaptive learning, thus adaptively selecting the effective feature information for prediction. The results show that DDI-Transform can accurately predict DDI events and outperform the state-of-the-art models. Results on different scale datasets confirm the robustness of the method.

As the cornerstone of the modern information industry, designing a high-performance circuit is crucial. Due to the influence of external environmental and asymmetric arrangements, non-ideal factors in analog integrated circuits (ICs) cannot be ignored, which makes the design process heavily reliant on human experience, and the design efficiency is low. Recently, scholars have conducted extensive research on optimization design methods for analog ICs by combining artificial intelligence and optimization algorithms. In this article, the developments and perspectives on optimization design methods for analog ICs are reviewed. In traditional design methods, particle swarm optimization (PSO), the genetic algorithm (GA), and reinforcement learning (RL) have been applied with different computer-aided design (CAD) tools. A variety of circuit simulation software have been developed, such as Cadence, Ngspice, Pspice, etc. Due to its high precision, comprehensive functionality, and full-process simulation, Cadence has been widely used in the design of analog ICs. These methods can improve the design efficiency to a certain extent. In the iterative process, running the simulation software to obtain performance metrics can waste a lot of time. Thus, efficient optimization design methods have been proposed to improve the design efficiency by establishing a proxy model of the circuit, which can replace simulation software. Accordingly, three research directions in this field are proposed. In summary, this article can aid scholars in quickly understanding the current status of optimization design methods for analog ICs and provide guidance for future research.

To address the issues of inefficient resource allocation and severe ground congestion at hub airports during aircraft turnaround operations, a two-stage optimization model is constructed to coordinate the scheduling of stands and ground support vehicles. The model focuses on analyzing the scheduling rules, operational patterns, and collaborative mechanisms between stand allocation and ground support vehicles, taking into account the coupling relationship between airport operational and support resources. The pre-allocation of stands is conducted under the constraints of limited support resources, and the results are used as inputs for ground support vehicle scheduling. This combined optimization of stands and vehicle resources enhances the overall resource efficiency. The NSGA-II algorithm, combining local search strategies (LS-NSGA-II), is used to solve the model. Computational experiments conducted at Shenzhen Airport show some improvements: For the stand allocation model, the model incorporates ground support service constraints for tow tractors and driving distances for ferry buses, thereby avoiding potential service conflicts and resource wastage. Secondly, for the scheduling of vehicles, by analyzing the operational patterns and service characteristics of different vehicles, the model improved vehicle utilization efficiency by 37.5%, reduced travel distance by 20.4%, and decreased waiting times by 57.6%, compared to the first-come-first-served strategy currently employed at airports.

Substantial improvement in prognosis among metastatic renal cell carcinoma (mRCC) patients has been achieved, owing to the rapid development and utilization of immunotherapy. In particular, immune checkpoint inhibitors (ICIs) have been considered the backbone of systemic therapy for patients with mRCC alongside multi-targeted tyrosine kinase inhibitors (TKIs) in the latest clinical practice guidelines. However, controversies and challenges in optimal individualized treatment regarding immunotherapy remains still About 2/3 of the patients presented non-response or acquired resistance to ICIs. Besides, immune-related toxicities, namely immune-related adverse events, are still elusive and life-threatening. Thus, reliable biomarkers to predict immunotherapeutic outcomes for mRCC patients are needed urgently. Tumor microenvironment (TME), consisting of immune cells, vasculature, signaling molecules, and extracellular matrix and regulates tumor immune surveillance and immunological evasion through complex interplay, plays a critical role in tumor immune escape and consequently manipulates the efficacy of immunotherapy. Various studied have identified the different TME components are significantly associated with the outcome of mRCC patients receiving immunotherapy, making them potential valuable biomarkers in therapeutic guidance. The present review aims to summarize the latest evidence on the associations between the components of TME including immune cells, cytokines and extracellular matrix, and the therapeutic responses among mRCC patients with ICI-based treatment. We further discuss the feasibility and limitation of these components as biomarkers.

Coronaviruses employ various strategies for survival, among which the activation of endogenous or exogenous apoptosis stands out, with viral proteins playing a pivotal role. Notably, highly pathogenic coronaviruses such as SARS-CoV-2, SARS-CoV, and MERS-CoV exhibit a greater array of non-structural proteins compared to low-pathogenic strains, facilitating their ability to induce apoptosis via multiple pathways. Moreover, these viral proteins are adept at dampening host immune responses, thereby bolstering viral replication and persistence. This review delves into the intricate interplay between highly pathogenic coronaviruses and apoptosis, systematically elucidating the molecular mechanisms underpinning apoptosis induction by viral proteins. Furthermore, it explores the potential therapeutic avenues stemming from apoptosis inhibition as antiviral agents and the utilization of apoptosis-inducing viral proteins as therapeutic modalities. These insights not only shed light on viral pathogenesis but also offer novel perspectives for cancer therapy. Highlights • Apoptosis plays an important role in the pathogenesis of the highly pathogenic coronavirus • The structural and non-structural proteins of highly pathogenic coronaviruses exert significant influence over apoptosis regulation • Apoptosis inhibitors exhibits promising antiviral effects, thereby presenting a potential avenue for the development of novel therapeutics targeting COVID-19.

Background Carboxylesterase 4A (CES4A) belongs to the member of the carboxylesterase family, yet there has been limited research into its malignant biological behavior in malignant tumors. Here, we aim to investigate the expression, cellular biological functions, and the potential underlying mechanism of CES4A in nasopharyngeal carcinoma (NPC). Method A standardized mean difference (SMD) analysis was used to analyze the dysregulation of CES4A based on the gene expression omnibus (GEO) database. qRT-PCR and immunohistochemical staining (IHC) were used to identify the mRNA and protein levels of CES4A in NPC cell lines and tissues, respectively. CCK-8, colony formation, wound healing and transwell assays were utilized to estimate cellular growth and metastasis, respectively. Western blot was conducted to evaluate the activity of PI3K/AKT signaling pathway. Result Both mRNA and protein expression of CES4A was significantly diminished both in NPC cell lines and primary tumor tissues. Ectopic expression of CES4A restrains the proliferation, colony formation, migration and invasion of NPC. Additionally, KEGG analysis based on GEO data and high-throughput transcriptome sequencing of cell lines all strongly suggested that CES4A was involved in regulating phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) signaling pathway. It was observed that AKT and phosphorylated AKT were remarkably reduced in CES4A overexpressing NPC cells, indicating that PI3K/AKT signaling pathway is hindered by CES4A. Conclusion CES4A expression is silenced in NPC, functioning as a tumor suppressor by negatively modulating the PI3K/AKT signaling pathway.

The runway system is more likely to be a bottleneck area for airport operations because it serves as a link between the air routes and airport ground traffic. As a key problem of air traffic flow management, the aircraft runway scheduling problem (ARSP) is of great significance to improve the utilization of runways and reduce aircraft delays. This paper proposes a large neighborhood search algorithm combined with simulated annealing and the receding horizon control strategy (RHC-SALNS) which is used to solve the ARSP. In the framework of simulated annealing, the large neighborhood search process is embedded, including the breaking, reorganization and local search processes. The large neighborhood search process could expand the range of the neighborhood building in the solution space. A receding horizon control strategy is used to divide the original problem into several subproblems to further improve the solving efficiency. The proposed RHC-SALNS algorithm solves the ARSP instances taken from the actual operation data of Wuhan Tianhe Airport. The key parameters of the algorithm were determined by parametric sensitivity analysis. Moreover, the proposed RHC-SALNS is compared with existing algorithms with excellent performance in solving large-scale ARSP, showing that the proposed model and algorithm are correct and efficient. The algorithm achieves better optimization results in solving large-scale problems.

In this research, an efficient thermal-stress coupling design method for a Chiplet-based system with a coaxial through silicon via (CTSV) array is developed by combining the support vector machine (SVM) model and particle swarm optimization algorithm with linear decreasing inertia weight (PSO-LDIW). The complex and irregular relationship between the structural parameters and critical indexes is analyzed by finite element simulation. According to the simulation data, the SVM model is adopted to characterize the relationship between structural parameters and critical indexes of the CTSV array. Based on the desired critical indexes of the CTSV array, the multi-objective evaluation function is established. Afterwards, the structural parameters of the CTSV array are optimized through the PSO-LDIW algorithm. Finally, the effectiveness of the developed method is verified by the finite element simulation. The simulated peak temperature, peak stress of the Chiplet-based system, and peak stress of the copper column (306.16 K, 28.48 MPa, and 25.76 MPa) well agree with the desired targets (310 K, 30 MPa, and 25 MPa). Therefore, the developed thermal-stress coupling design method can effectively design CTSV arrays for manufacturing high-performance interconnect structures applied in Chiplet-based systems.

Limb numbness is a frequent symptom of post-stroke somatosensory dysfunction, which may be alleviated by non-invasive therapy such as acupuncture. However, the precise mechanism via acupuncture remains unknown. The goal of this study was to investigate how the amplitude of low-frequency fluctuations (ALFF) and functional connectivity (FC) changed between stroke patients with limb numbness and healthy people, as well as how acupuncture might work. 24 stroke sequelae patients with unilateral limb numbness and 14 matched healthy controls were enrolled in the study. The patients with limb numbness received acupuncture therapy three days a week for four weeks. We mainly assessed the clinical outcomes via the visual analogue scale (VAS). In addition, fMRI data from patients with unilateral limb numbness at baseline and after treatment (4th week) were collected, as well as data from healthy controls at baseline. Compared with the healthy subjects, the patient group demonstrated significantly decreased ALFF in several brain regions, mainly associated with the sensorimotor network (SMN) and default mode network (DMN), including left superior frontal gyrus (SFG), right temporal fusiform cortex (TFC), right middle frontal gyrus (MFG), bilateral middle temporal gyrus (MTG), right putamen (PUT), right precentral gyrus (preCG), right planum polare (PP), and left supplementary motor area (SMA). These regions were chosen as the seeds for investigating the FC alteration induced by acupuncture. Several sensorimotor-related brain regions were activated by acupuncture, and the FC of the left supramarginal gyrus (SMG) with right MTG, as well as brain-stem, cerebellum vermis 9 with right MFG showed enhancement following acupuncture in the patient group, which had a significant correlation with clinical outcomes. Acupuncture treatment may be used to stimulate brain areas associated with somatosensory processing and to strengthen the FC of sensorimotor and cognitive brain networks in order to achieve therapeutic effect.