A metagenome-wide association study of gut microbiota in hepatitis B virus-related cirrhosis in northwest China.

Background And Purpose: In recent years, research on the relationship between hepatitis B virus-related cirrhosis (HBV-LC) and gut microbiota has grown, but studies focusing on the Northwest Chinese population are scarce. This study characterized the gut microbiota composition and function in HBV-LC patients vs. healthy individuals in Northwest China, aiming to provide a scientific basis for region-specific precision therapies.

MRI-Derived Lymph Nodes Morphological and Topological Structure (LNs-MTS) Model for Evaluating Immune Status and Prognosis in Rectal Cancer.

Tumor-draining lymph nodes (LNs) immune status critically influences cancer progression and treatment response, yet reliable non-invasive assessment remains clinically unavailable. To address this critical gap, an MRI-derived LNs morphological and topological structure (LNs-MTS) model is developed to evaluate the immune status of LNs in rectal cancer (RC). Integrating multicenter imaging, transcriptomic, and immunohistochemical data from 1,156 stage I-II RC patients, enhanced immune activation in non-metastatic LNs sized ≥0.

Morphologic characteristics of distal intracranial arteries in relation to structural changes in the brain after chronic alcohol consumption.

This study focuses on the structural brain changes, alterations in Apparent Diffusion Coefficient (ADC) values, and the morphological characteristics of distal intracranial arteries in chronic alcohol consumption. A total of 50 chronic alcoholics and 43 non-alcoholics were recruited from neurology inpatient and outpatient services. Using the Brainnetome Atlas to segment the brain into 246 regions, ADC values were observed to be consistently higher in alcoholics, with statistically significant differences in 134 of these regions.

Safe Reinforcement Learning: Optimal Formation Control With Collision Avoidance of Multiple Satellite Systems.

This article addresses the collision avoidance and formation control problem for multisatellite systems. A novel safe reinforcement learning (RL) algorithm based on an adaptive dynamic programming framework is proposed. The highlights of the algorithm are the adaptive distance-varying learning method to integrate online data with historical data and the usage of the barrier function (BF) to achieve collision avoidance.

Targeting stromal cells in tumor microenvironment as a novel treatment strategy for glioma.

Glioma is the most common primary malignant tumor of the central nervous system in adults, characterized by high mortality, low cure rate and high recurrence rate. Among gliomas, glioblastoma multiforme (GBM) is the most malignant subtype. Currently, the standard treatment for patients with GBM is maximum surgical excision combined with radiotherapy and chemotherapy.

Prior Knowledge-Guided U-Net for Automatic Clinical Target Volume Segmentation in Postmastectomy Radiation Therapy of Breast Cancer.

Purpose: This study aimed to design and evaluate a prior-knowledge-guided U-Net (PK-UNet) for automatic clinical target volume (CTV) segmentation in postmastectomy radiation therapy for breast cancer.

Methods And Materials: A total of 102 computed tomography (CT) scans from breast cancer patients who underwent postmastectomy were retrospectively collected. Of these, 80 scans were used for training with 5-fold cross-validation, and 22 scans for independent testing.

Reinforcement Learning for Synchronization of Heterogeneous Multiagent Systems by Improved Q-Functions.

This article dedicates to investigating a methodology for enhancing adaptability to environmental changes of reinforcement learning (RL) techniques with data efficiency, by which a joint control protocol is learned using only data for multiagent systems (MASs). Thus, all followers are able to synchronize themselves with the leader and minimize their individual performance. To this end, an optimal synchronization problem of heterogeneous MASs is first formulated, and then an arbitration RL mechanism is developed for well addressing key challenges faced by the current RL techniques, that is, insufficient data and environmental changes.

[Correlations between serum BDNF, IL-18 and hs-CRP levels in patients with acute cerebral infarction and vascular cognitive impairment].

Objective: To explore the correlations between serum levels of brain-derived neurotrophic factor (BDNF), interleukin-18 (IL-18) and hypersensitivity C-reactive protein (hs-CRP) in patients with acute cerebral infarction and vascular cognitive impairment (VCI), and to provide some clinical bases for early prevention of VCI.

Methods: A total of 160 patients with acute cerebral infarction admitted in Department of Neurology of Jincheng People' s Hospital from May 2019 to April 2020 were enrolled in this study and were devided into three groups according to whether or not combined with cognitive impairment, including no cognitive impairment group (NCI, 57 cases), vascular cognitive impairment no dementia group (VCIND, 56 cases) and vascular dementia group (VaD, 47 cases). The cognitive function of all the patients were evaluated by Montreal cognitive assessment (MoCA).

Prediction of early neurologic deterioration in patients with perforating artery territory infarction using machine learning: a retrospective study.

Background: Early neurological deterioration (END) is a frequent complication in patients with perforating artery territory infarction (PAI), leading to poorer outcomes. Therefore, we aimed to apply machine learning (ML) algorithms to predict the occurrence of END in PAI and investigate related risk factors.

Methods: This retrospective study analyzed a cohort of PAI patients, excluding those with severe stenosis of the parent artery.

Construction and validation of an immune infiltration-related risk model for predicting prognosis and immunotherapy response in low grade glioma.

Background: Low grade glioma (LGG) is considered a heterogeneous tumor with highly variable survival and limited efficacy of immunotherapy. To identify high-risk subsets and apply immunotherapy effectively in LGG, the status and function of immune infiltration in the glioma microenvironment must be explored.

Methods: Four independent glioma cohorts comprising 1,853 patients were enrolled for bioinformatics analysis.

Learning-based near-optimal tracking control for industrial processes with slow and fast modes.

This paper devotes to solving the optimal tracking control (OTC) problem of singular perturbation systems in industrial processes under the framework of reinforcement learning (RL) technology. The encountered challenges include the different time scales in system operations and an unknown slow process. The immeasurability of slow process states especially increases the difficulty of finding the optimal tracking controller.

Association between morphologic features of intracranial distal arteries and brain atrophy indexes in cerebral small vessel disease: a voxel-based morphometry study.

Background: Brain atrophy represents a final common pathway for pathological processes in patients with cerebral small vessel disease (CSVD) and is now recognized as a strong independent predictor of clinical status and progression. The mechanism underlying brain atrophy in patients with CSVD is not yet fully comprehended. This study aims to investigate the association of morphologic features of intracranial distal arteries (A2, M2, P2 and more distal) with different brain structures [gray matter volume (GMV), white matter volume (WMV), and cerebrospinal fluid volume (CSFV)].

Effect of prophylactic antiviral intervention on T cell immunity in hepatitis B virus-infected pregnant women.

Background: Antiviral intervention in hepatitis B virus (HBV)-infected pregnant women can effectively reduce mother-to-child transmission. However, the immunological characteristics of pregnant women with chronic HBV infection and the effects of antiviral intervention during pregnancy on maternal immune response remain unknown. We aimed to investigate these effects by comparing mothers who received antiviral intervention during pregnancy with those who did not.

A novel inflammation-related signature for predicting prognosis and characterizing the tumor microenvironment in colorectal cancer.

Inflammation is a critical component of tumor progression, and it modifies the tumor microenvironment by various mechanisms. Here, we explore the effect of the inflammatory response on the tumor microenvironment in colorectal cancer (CRC). A prognostic signature consisting of inflammation-related genes (IRGs) was constructed and verified based on the inflammatory response by bioinformatics analysis.

Functional Characterization of Sugar Beet M14 Antioxidant Enzymes in Plant Salt Stress Tolerance.

Salt stress can cause cellular dehydration, which induces oxidative stress by increasing the production of reactive oxygen species (ROS) in plants. They may play signaling roles and cause structural damages to the cells. To overcome the negative impacts, the plant ROS scavenging system plays a vital role in maintaining the cellular redox homeostasis.

Comparative Ubiquitination Proteomics Revealed the Salt Tolerance Mechanism in Sugar Beet Monomeric Additional Line M14.

Post-translational modifications (PTMs) are important molecular processes that regulate organismal responses to different stresses. Ubiquitination modification is not only involved in human health but also plays crucial roles in plant growth, development, and responses to environmental stresses. In this study, we investigated the ubiquitination proteome changes in the salt-tolerant sugar beet monomeric additional line M14 under salt stress treatments.

Reinforcement learning based proportional-integral-derivative controllers design for consensus of multi-agent systems.

This paper develops a novel Proportional-Integral-Derivative (PID) tuning method for multi-agent systems with a reinforced self-learning capability for achieving the optimal consensus of all agents. Unlike the traditional model-based and data-driven PID tuning methods, the developed PID self-learning method updates the controller parameters by actively interacting with unknown environment, with the outcomes of guaranteed consensus and performance optimization of agents. Firstly, the PID control-based consensus problem of multi-agent systems is formulated.

Quantitative redox proteomics revealed molecular mechanisms of salt tolerance in the roots of sugar beet monomeric addition line M14.

Background: Salt stress is often associated with excessive production of reactive oxygen species (ROS). Oxidative stress caused by the accumulation of ROS is a major factor that negatively affects crop growth and yield. Root is the primary organ that senses and transmits the salt stress signal to the whole plant.

Neural network-based adaptive event-triggered sliding mode control for singular systems with an adaptive event-triggering communication scheme.

This paper studies the event-triggered sliding mode control problem for singular systems subject to the unknown nonlinear function and the exogenous disturbance. For saving the communication resources, a new adaptive event-triggering communication scheme (AETCS) is designed, which scheme uses the information on the nonlinear function part. Secondly, for the error system, we provide a novel integral sliding surface, which makes it beneficial to construct a new augmented delay system model by utilizing a delay system method.

Effect of curcumin on lung epithelial injury and ferroptosis induced by cigarette smoke.

Cigarette smoke (CS)-caused ferroptosis was involved in the pathogenesis of COPD, but the role of ferroptosis in lung epithelial injury and inflammation is not clear. Rats were treated with CS or CUR and BEAS-2B cells were exposed to CS extract (CSE), ferrostatin-1 (Fer-1), deferoxamine (DFO), or CUR to detect reactive oxygen species (ROS) accumulation, lipid peroxidation, iron overload, and ferroptosis-related protein, which were the characteristic changes of ferroptosis. Compared with the control group, CSE-treated BEAS-2B cells had more cell death, higher cytotoxicity, and lower cell viability.

Data-Driven H Optimal Output Feedback Control for Linear Discrete-Time Systems Based on Off-Policy Q-Learning.

This article develops two novel output feedback (OPFB) Q -learning algorithms, on-policy Q -learning and off-policy Q -learning, to solve H static OPFB control problem of linear discrete-time (DT) systems. The primary contribution of the proposed algorithms lies in a newly developed OPFB control algorithm form for completely unknown systems. Under the premise of satisfying disturbance attenuation conditions, the conditions for the existence of the optimal OPFB solution are given.

Cys-SH based quantitative redox proteomics of salt induced response in sugar beet monosomic addition line M14.

Background: Salt stress is a major abiotic stress that limits plant growth, development and productivity. Studying the molecular mechanisms of salt stress tolerance may help to enhance crop productivity. Sugar beet monosomic addition line M14 exhibits tolerance to salt stress.

Polymorphonuclear neutrophils promote endothelial apoptosis by enhancing adhesion upon stimulation by intermittent hypoxia.

Purpose: This study explored the interactive effects between polymorphonuclear neutrophils (PMNs) and vascular endothelial cells under intermittent hypoxia (IH) and investigated the mechanisms underlying these effects.

Methods: Endothelial cells were co-cultured with PMNs isolated from rats exposed to normoxia or IH. The PMN apoptotic rate was determined using flow cytometry.