Explore the vast range of titles available.

Inflammatory bowel diseases, including Crohn’s disease and ulcerative colitis, is a chronic relapsing gastrointestinal inflammatory disease mediated by dysregulated immune responses to resident intestinal microbiota. Current conventional approaches including aminosalicylates, corticosteroids, immunosuppressive agents, and biological therapies are focused on reducing intestinal inflammation besides inducing and maintaining disease remission, and managing complications. However, these therapies are not curative and are associated with various limitations, such as drug resistance, low responsiveness and adverse events. Recent accumulated evidence has revealed the involvement of mucin-degrading bacterium Akkermansia muciniphila ( A. muciniphila ) in the regulation of host barrier function and immune response, and how reduced intestinal colonisation of probiotic A. muciniphila can contribute to the process and development of inflammatory bowel diseases, suggesting that it may be a potential target and promising strategy for the therapy of inflammatory bowel disease. In this review, we summarise the current knowledge of the role of A. muciniphila in IBD, especially focusing on the related mechanisms, as well as the strategies based on supplementation with A. muciniphila , probiotics and prebiotics, natural diets, drugs, and herbs to promote its colonisation in the gut, and holds promise for A. muciniphila -targeted and -based therapies in the treatment of inflammatory bowel disease.

Urbanization transforms landscape structure and profoundly affects biodiversity and ecological processes. To understand and solve these ecological problems, at least three aspects of spatiotemporal patterns of urbanization need to be quantified: the speed, urban growth modes, and resultant changes in landscape pattern. In this study, we quantified these spatiotemporal patterns of urbanization in the central Yangtze River Delta region, China from 1979 to 2008, based on a hierarchical patch dynamics framework that guided the research design and the analysis with landscape metrics. Our results show that the urbanized area in the study region increased exponentially during the 30 years at the county, prefectural, and regional levels, with increasing speed down the urban hierarchy. Three growth modes—infilling, edge-expanding, and leapfrogging—operated concurrently and their relative dominance shifted over time. As urbanization progressed, patch density and edge density generally increased, and the connectivity of urban patches in terms of the average nearest neighbor distance also increased. While landscape-level structural complexity also tended to increase, the shape of individual patches became increasingly regular. Our results suggest that whether urban landscapes are becoming more homogenous or heterogeneous may be dependent on scale in time and space as well as landscape metrics used. The speed, growth modes, and landscape pattern are related to each other in complicated fashions. This complex relationship can be better understood by conceptualizing urbanization not simply as a dichotomous diffusion-coalescence switching process, but as a spiraling process of shifting dominance among multiple growth modes: the wax and wane of infilling, edge-expansion, and leapfrog across the landscape.

BackgroundGastroesophageal reflux disease (GERD) is a common disorder, and is typically treated with proton-pump inhibitors (PPIs) as the recommended first-line therapy. Recently, a new potassium-competitive acid blocker, vonoprazan, was launched in Japan. It is uncertain whether the standard dose of vonoprazan 20 mg is superior to that of PPIs for GERD, so a direct comparison of the therapeutic effects and adverse events between vonoprazan 20 mg and PPIs is needed.MethodsMEDLINE, the Cochrane Central Register of Controlled Trials, and Embase were chosen as the literature sources. Randomized controlled trials for vonoprazan 20 mg and PPIs published in English were searched. Data from studies meeting the eligibility criteria were extracted individually by two researchers and compared to maintain consistency.ResultsFifty-six articles were identified in the databases, and one study was manually searched and added to the analysis, ultimately yielding six eligible studies. For the main analysis, the risk ratios (RR) of efficacy and adverse events between vonoprazan and PPIs were 1.06 (0.99–1.13) and 1.08 (0.96–1.22), respectively. Subgroup analysis for patients with severe esophagitis at baseline showed significantly higher results for vonoprazan than lansoprazole, with an RR of 1.14 (1.06–1.22).ConclusionsOur findings suggest that vonoprazan is non-inferior to PPIs as therapy for patients with GERD. Subgroup analysis indicates that vonoprazan is more effective than PPIs for patients with severe erosive esophagitis. The safety outcomes for vonoprazan are similar to those for PPIs.

Bluetooth Low Energy Mesh (BLE Mesh) enables Bluetooth flexibility and coverage by introducing Low-Power Nodes (LPNs) and enhanced networking protocol. It is also a commonly used communication method in sensor networks. In BLE Mesh, LPNs are periodically woken to exchange messages in a stop-and-wait way, where the tradeoff between energy and efficiency is a hard problem. Related works have reduced the energy consumption of LPNs mainly in the direction of changing the bearer layer, improving time synchronization and broadcast channel utilization. These algorithms improve communication efficiency; however, they cause energy loss, especially for the LPNs. In this paper, we propose a constrained flooding algorithm based on time series prediction and lightweight GBN (Go-Back-N). On the one hand, the wake-up cycle of the LPNs is determined by the time series prediction of the surrounding load. On the other, LPNs exchange messages through lightweight GBN, which improves the window and ACK mechanisms. Simulation results validate the effectiveness of the Time series Prediction and LlightWeight GBN (TP-LW) algorithm in energy consumption and throughput. Compared with the original algorithm of BLE Mesh, when fewer packets are transmitted, the throughput is increased by 214.71%, and the energy consumption is reduced by 65.14%.

Mycoplasma pneumoniae (MP) is one of the pathogens that cause community-acquired pneumonia in children. Atopic diseases are also common in children. However, the impact of atopy on Mycoplasma pneumoniae pneumonia (MPP) in children is still unclear. The purpose of this study is to analyze the impact of atopy on the clinical characteristics of MPP in children, and provide a diagnosis and treatment plan. A total of 489 children hospitalized for MPP in our hospital from June 2023 to December 2023 were selected. They were divided into an atopic group ( n  = 172) and a non-atopic group ( n  = 317) based on whether they had atopy or not. Clinical data, treatment regimens, and laboratory indicators were compared between the two groups. Eosinophil count, lactate dehydrogenase and IgE levels were higher in the atopic group than in the non-atopic group. Additionally, neutrophil percentage, procalcitonin levels were lower in the atopic group than in the non-atopic group ( P  < 0.05). The proportion of bronchiolitis type on lung imaging was higher in the atopic group, and there was a higher incidence of severe pneumonia compared to the non-atopic group ( P  < 0.05). Atopy may lead to severe MPP and bronchiolitis-type MPP. Therefore, the treatment and prognosis of these children should be given more attention.

Road extraction from high-resolution remote sensing images (HRSIs) provides critical support for downstream tasks such as autonomous driving path planning and urban planning. Although deep learning-based pixel-level segmentation methods have achieved significant progress, they still face challenges in handling occlusions caused by vegetation and shadows, and often exhibit limited model robustness and generalization capability. To address these limitations, this paper proposes the SAM2MS model, which leverages the powerful generalization capabilities of the foundational vision model, segment anything model 2 (SAM2). Firstly, an adapter-based fine-tuning strategy is employed to effectively transfer the capabilities of SAM2 to the HRSI road extraction task. Secondly, we subsequently designed a subtraction block (Sub) to process adjacent feature maps, effectively eliminating redundancy during the decoding phase. Multiple Subs are cascaded to form the multi-scale subtraction module (MSSM), which progressively refines local feature representations, thereby enhancing segmentation accuracy. During training, a training-free lossnet module is introduced, establishing a multi-level supervision strategy that encompasses background suppression, contour refinement, and region-of-interest consistency. Extensive experiments on three large-scale and challenging HRSI road datasets, including DeepGlobe, SpaceNet, and Massachusetts, demonstrate that SAM2MS significantly outperforms baseline methods across nearly all evaluation metrics. Furthermore, cross-dataset transfer experiments (from DeepGlobe to SpaceNet and Massachusetts) conducted without any additional training validate the model’s exceptional generalization capability and robustness.

Preservatives are essential components in cosmetic products, but their safety issues have attracted widespread attention. There is an urgent need for safe and effective alternatives. Antimicrobial peptides (AMPs) are part of the innate immune system and have potent antimicrobial properties. Using machine learning-assisted rational design, we obtained a novel antibacterial peptide, IK-16-1, with significant antibacterial activity and maintaining safety based on β-defensins. IK-16-1 has broad-spectrum antimicrobial properties against Escherichia coli , Staphylococcus aureus , Pseudomonas aeruginosa , and Candida albicans , and has no haemolytic activity. The use of IK-16-1 holds promise in the cosmetics industry, since it can serve as a preservative synergist to reduce the amount of other preservatives in cosmetics. This study verified the feasibility of combining computational design with artificial intelligence prediction to design AMPs, achieving rapid screening and reducing development costs.

Persistent energy system inertia continues to hinder China’s carbon reduction progress despite global decarbonization trends. This study develops an explainable machine learning framework to dissect energy-related emission drivers through 14 secondary indicators spanning energy structure, industrial dynamics, social factors, and economic factors. Leveraging panel data from 260 Chinese cities (2000–2023), we conduct comparative analysis of six ML models and identify XGBoost as optimal for capturing nonlinear emission patterns. SHAP value decomposition and feature importance reveals that total energy consumption and energy consumption intensity remain the dominant contributors to carbon and pollution emissions, while the secondary industry still emerges as a critical driver. Our research establishes an actionable framework to identify drivers of carbon mitigation and pollution reduction, analyze their mechanisms, and support policymakers in optimizing policy implementation amid energy transition.

For unmanned ground vehicles in squad mission support systems (SMSS-UGVs), tracking the entire squad as a group, rather than focusing on individual members, can effectively mitigate issues such as target loss caused by occlusion and environmental interference. However, most existing group target tracking methods are designed for extended targets, which typically assume a rigid and unchanging shape. In contrast, pedestrian groups in SMSS-UGV scenarios exhibit inconsistent motions among members, resulting in continuous changes in the overall group shape. To address this challenge, this paper proposes a group target tracking method specifically tailored for SMSS-UGVs in pedestrian tracking scenarios. We introduce a tracking framework that incorporates a data selection mechanism based solely on positional information, enabling robust handling of dynamic group composition through adaptive shape modeling. Furthermore, a novel group target tracking method based on multi-ellipse shape modeling (ME-CGT-UGV) is presented, which effectively captures complex and evolving group formations. The experimental results show that the proposed method reduces orientation error by 86.13% compared to single-target tracking and by 54.79% compared to shapeless modeling methods. It also maintains strong performance under challenging conditions, including occlusions, environmental disturbances, sharp turns, and formation changes. These findings indicate that the proposed approach significantly enhances the effectiveness and operational reliability of SMSS-UGVs in real-world applications.

Rapid recombination of photogenerated carriers and weak driving forces to inject hot electrons are critical bottlenecks in solar-driven ammonia borane hydrolysis. Herein, aided by machine learning, plasmon polarization-induced multi-field coupling is developed to enhance ammonia borane hydrolytic activity. The reconstructed surface unsaturated Mo δ+ active sites exhibit well activity and high stability over 100 hours in AB hydrolysis, which deliver a turnover frequency up to 5806 min -1 , representing competitiveness compared to non-noble and noble-metal based catalysts ever reported. It is verified that the polarized electric field facilitates carrier separation through incorporating polarization components (O v and -OH), thereby promoting electron accumulation around Mo δ+ active sites. Meanwhile, the local electric field enables highly delocalized hot electrons through plasmon oscillation, thus lowering the reaction barrier between Mo δ+ and AB. In this work, the hot electrons are efficiently channeled via an enhanced feedback pathway, facilitating their transfer into B-H antibonding orbitals toward boosted AB hydrolysis. The authors find that reconstructing the Moδ+ active site via multi-field coupling effectively boosts ammonia borane hydrolysis. They provide key insights into the plasmon polarization-induced carrier kinetics and hydrolysis mechanism.