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In recent years, Multi-Armed Bandit (MAB) algorithms have gained substantial attention due to their effectiveness in real-world applications, such as recommendation systems, autonomous systems, and dynamic resource allocation. Traditional MAB algorithms, such as UCB and Thompson Sampling, often lack mechanisms to incorporate correlations between arms, limiting their adaptability and optimality in complex environments. This paper presents a novel MAB framework that integrates Active Inference through a dynamic Adaptive Influence Factor (AIF) mechanism. The AIF mechanism builds correlation matrices to capture inter-arm dependencies and dynamically adjusts exploration strategies through an influence factor, γ, which adapts over time based on pull counts. This adaptive exploration enhances decision-making in sparse and uncertain environments by leveraging correlations. The proposed framework is evaluated on movie recommendation data, with AIF-based algorithms, particularly AIF-TS, significantly outperforming traditional and correlated bandit approaches in settings with high data sparsity. These results demonstrate that dynamically adjusting exploration based on inter-arm relationships substantially improves performance in real-world applications, where data quality and relationships are often variable. The findings suggest that incorporating inter-arm correlations with active inference can lead to more efficient and effective decision-making in adaptive systems, highlighting the potential of AIF-based MAB algorithms in addressing real- world challenges.

The frequent occurrence and spread of wildfires pose a serious threat to the ecological environment and urban development. Therefore, assessing regional wildfire susceptibility is crucial for the early prevention of wildfires and formulation of disaster management decisions. However, current research on wildfire susceptibility primarily focuses on improving the accuracy of models, while lacking in-depth study of the causes and mechanisms of wildfires, as well as the impact and losses they cause to the ecological environment and urban development. This situation not only increases the uncertainty of model predictions but also greatly reduces the specificity and practical significance of the models. We propose a comprehensive evaluation framework to analyze the spatial distribution of wildfire susceptibility and the effects of influencing factors, while assessing the risks of wildfire damage to the local ecological environment and urban development. In this study, we used wildfire information from the period 2013–2022 and data from 17 susceptibility factors in the city of Guilin as the basis, and utilized eight machine learning algorithms, namely logistic regression (LR), artificial neural network (ANN), K-nearest neighbor (KNN), support vector regression (SVR), random forest (RF), gradient boosting decision tree (GBDT), light gradient boosting machine (LGBM), and eXtreme gradient boosting (XGBoost), to assess wildfire susceptibility. By evaluating multiple indicators, we obtained the optimal model and used the Shapley Additive Explanations (SHAP) method to explain the effects of the factors and the decision-making mechanism of the model. In addition, we collected and calculated corresponding indicators, with the Remote Sensing Ecological Index (RSEI) representing ecological vulnerability and the Night-Time Lights Index (NTLI) representing urban development vulnerability. The coupling results of the two represent the comprehensive vulnerability of the ecology and city. Finally, by integrating wildfire susceptibility and vulnerability information, we assessed the risk of wildfire disasters in Guilin to reveal the overall distribution characteristics of wildfire disaster risk in Guilin. The results show that the AUC values of the eight models range from 0.809 to 0.927, with accuracy values ranging from 0.735 to 0.863 and RMSE values ranging from 0.327 to 0.423. Taking into account all the performance indicators, the XGBoost model provides the best results, with AUC, accuracy, and RMSE values of 0.927, 0.863, and 0.327, respectively. This indicates that the XGBoost model has the best predictive performance. The high-susceptibility areas are located in the central, northeast, south, and southwest regions of the study area. The factors of temperature, soil type, land use, distance to roads, and slope have the most significant impact on wildfire susceptibility. Based on the results of the ecological vulnerability and urban development vulnerability assessments, potential wildfire risk areas can be identified and assessed comprehensively and reasonably. The research results of this article not only can improve the specificity and practical significance of wildfire prediction models but also provide important reference for the prevention and response of wildfires.

The molecular landscape in non-muscle-invasive bladder cancer (NMIBC) is characterized by large biological heterogeneity with variable clinical outcomes. Here, we perform an integrative multi-omics analysis of patients diagnosed with NMIBC ( n  = 834). Transcriptomic analysis identifies four classes (1, 2a, 2b and 3) reflecting tumor biology and disease aggressiveness. Both transcriptome-based subtyping and the level of chromosomal instability provide independent prognostic value beyond established prognostic clinicopathological parameters. High chromosomal instability, p53-pathway disruption and APOBEC-related mutations are significantly associated with transcriptomic class 2a and poor outcome. RNA-derived immune cell infiltration is associated with chromosomally unstable tumors and enriched in class 2b. Spatial proteomics analysis confirms the higher infiltration of class 2b tumors and demonstrates an association between higher immune cell infiltration and lower recurrence rates. Finally, the independent prognostic value of the transcriptomic classes is documented in 1228 validation samples using a single sample classification tool. The classifier provides a framework for biomarker discovery and for optimizing treatment and surveillance in next-generation clinical trials. Multiple molecular profiling methods are required to study urothelial non-muscle-invasive bladder cancer (NMIBC) due to its heterogeneity. Here the authors integrate multi-omics data of 834 NMIBC patients, identifying a molecular subgroup associated with multiple alterations and worse outcomes.

Since the 2019 novel coronavirus (2019-nCoV or officially named by the World Health Organization as COVID-19) outbreak in Wuhan, Hubei Province, China in 2019, there have been a few reports of its imaging findings. Here, we report two confirmed cases of 2019-nCoV pneumonia with chest computed tomography findings of multiple regions of patchy consolidation and ground-glass opacities in both lungs. These findings were characteristically located along the bronchial bundle or subpleural lungs.

Highlights This review highlights the gradient variations in the structural composition of musculoskeletal tissues and comprehensively examines recent progress in the fabrication and application of biomimetic gradient scaffolds for musculoskeletal repair. The challenges and prospects of gradient scaffolds for clinical application are discussed. The intricate hierarchical structure of musculoskeletal tissues, including bone and interface tissues, necessitates the use of complex scaffold designs and material structures to serve as tissue-engineered substitutes. This has led to growing interest in the development of gradient bone scaffolds with hierarchical structures mimicking the extracellular matrix of native tissues to achieve improved therapeutic outcomes. Building on the anatomical characteristics of bone and interfacial tissues, this review provides a summary of current strategies used to design and fabricate biomimetic gradient scaffolds for repairing musculoskeletal tissues, specifically focusing on methods used to construct compositional and structural gradients within the scaffolds. The latest applications of gradient scaffolds for the regeneration of bone, osteochondral, and tendon-to-bone interfaces are presented. Furthermore, the current progress of testing gradient scaffolds in physiologically relevant animal models of skeletal repair is discussed, as well as the challenges and prospects of moving these scaffolds into clinical application for treating musculoskeletal injuries.

IntroductionWith an increasing incidence and significant effects on patients, tinnitus has become a major disease burden. There is a dearth of therapies with established efficacy for tinnitus. Transcutaneous auricular vagus nerve stimulation (ta-VNS) is being investigated as a potential therapy for tinnitus, but the current body of evidence remains inconclusive due to conflicting results across different studies. As a result, this protocol aims to synthesise and update the evidence to clarify whether ta-VNS is effective and safe for alleviating tinnitus.Methods and analysisTo identify relevant randomised controlled trials (RCTs), seven representative bibliographical databases will be searched from their inception to December 2023: PubMed, Embase (via OVID), Cochrane Library, Chinese National Knowledge Infrastructure, Wangfang Database, Chinese BioMedical Literature Database, and Chongqing VIP Chinese Science and Technology Periodical Database. Publications in English or Chinese will be considered for inclusion. RCTs comparing ta-VNS with active treatments, no intervention, waitlist control or sham ta-VNS in adult patients with subjective tinnitus will be included. Studies on objective tinnitus will be excluded. Primary outcome is tinnitus symptom severity measured by validated scales. With all eligible trials included, when applicable, quantitative analysis via meta-analyses will be performed using RevMan V.5.4.1 software. Otherwise, a qualitative analysis will be conducted. The methodological quality of the included RCTs will be assessed using the Risk of Bias 2.0 tool. Sensitivity analyses, subgroup analysis and publication bias evaluation will also be performed. The Grading of Recommendations, Assessment, Development, and Evaluation approach will be used to grade the certainty of the evidence.Ethics and disseminationEthical approval is not required for this systematic review, as no primary data will be collected. The results will be reported and disseminated through publication in a peer-reviewed journal.PROSPERO registration numberCRD42022351917.

As the ability of liver regeneration is pivotal for liver disease patients, it will be of high significance and importance to identify the missing piece of the jigsaw influencing the liver regeneration. Here, we report that chronic stress impairs the liver regeneration capacity after partial hepatectomy with increased mortality in male mice. Anatomical tracing and functional mapping identified a neural circuit from noradrenergic neurons in the locus coeruleus (LC) to serotonergic neurons in the rostral medullary raphe region (rMR), which critically contributes to the inhibition of liver regeneration under chronic stress. In addition, hepatic sympathetic nerves were shown to be critical for the inhibitory effects on liver regeneration by releasing norepinephrine (NE), which acts on adrenergic receptor β2 (ADRB2) to block the proinflammatory macrophage activation. Collectively, we reveal a “brain-to-liver” neural connection that mediates chronic stress-evoked deficits in liver regeneration, thus shedding important insights into hepatic disease therapy. Whether and how chronic stress, often experienced by patients with chronic liver disease, affects liver regeneration remains mysterious. Here, authors show a “brain-to-liver” neural connection that mediates chronic stress-evoked deficits in liver regeneration.

Dominant negative pathogenic variants in KIF1A result in an allelically heterogeneous neurodegenerative condition that manifests as a variable clinical phenotype including seizures, cognitive deficits, optic nerve atrophy, spasticity, and peripheral neuropathy. One potential therapeutic strategy is allele-specific knockdown of pathogenic transcripts. However, targeting the over 100 known unique pathogenic variants is challenging. Alternatively, different pathogenic KIF1A variants in multiple patients can be knocked down by targeting shared common polymorphisms with antisense oligonucleotides, provided that the pathogenic variants are in cis with the targeted polymorphisms. Here, we use long-read sequencing data from fifty-six individuals to phase for polymorphisms. We identify four common polymorphisms that, if targetable, would make it possible for 54 of these individuals to receive antisense oligonucleotide therapy. Using patient-derived glutamatergic neurons, we characterize and quantify a cell-autonomous phenotype, dendrite neurite outgrowth length. In vitro we further demonstrate that antisense oligonucleotide-mediated knockdown of the pathogenic transcript rescues the dendrite neurite outgrowth phenotype in neurons from a patient with the P305L variant. Pathogenic variants in KIF1A give rise to KIF1A-associated neurodegenerative disorder (KAND). Here, the authors use antisense oligonucleotides targeting common polymorphisms to knock down pathogenic KIF1A expression in patient-derived stem cells.

In the current work, we studied the sensing process of the sensor (E)-2-((quinolin-8ylimino) methyl) phenol (QP) for fluoride anion (F–) with a “turn on” fluorescent response by density functional theory (DFT) and time-dependent density functional theory (TDDFT) calculations. The proton transfer process and the twisted intramolecular charge transfer (TICT) process of QP have been explored by using potential energy curves as functions of the distance of N-H and dihedral angle C-N=C-C both in the ground and the excited states. According to the calculated results, the fluorescence quenching mechanism of QP and the fluorescent response for F– have been fully explored. These results indicate that the current calculations completely reproduce the experimental results and provide compelling evidence for the sensing mechanism of QP for F–.

In this study, the engineering background of No. 2 complex connecting passage of Binhu Road Station/Jinhu Square Station of Nanning Metro Line 3 is investigated, where the artificial ground freezing technique is adopted. A three-dimensional finite element model is established to investigate the temperature development of the frozen soil curtain, with a simulation of the dynamic evolution of the frosted soil curtain. The finite element model is validated by comparing the overall trend of the measured temperature value and the resulting temperature value, which are roughly the same. According to the design scheme, the weakest part of the whole frozen soil curtain is the top of the bell mouth where the downhole tunnel intersects the connecting passage. It is recommended to make a row of smaller freezing holes to enhance the freezing effect in this area. The thickness of the frozen soil curtain reached 1.75 m or more, indicating that the whole frozen soil curtain meets the design requirements and shows the right features for excavation construction. After freezing for 40 days, the average thickness of the frozen soil curtain is 2.4 m, indicating that the freezing effect meets the design requirements. The project can be successfully carried out, which suggests that the underneath passage construction is feasible. As a result, the results of the numerical model are applicable for comparable projects using artificially freezing ground in metro station construction.