Search Results (743)

Pseudomonas aeruginosa elastase LasB accelerates refrigerated food spoilage through proteolytic degradation of muscle and milk proteins. While Marrubium vulgare essential oil terpenes exhibit antimicrobial activity, their weak potency and nonspecificity limit direct food preservation applications. This computational study aimed to rationally redesign terpene scaffolds into predicted selective LasB inhibitors. A virtual library of 635 terpene–peptide–phosphinic acid hybrids (expanded to 3940 conformers) was evaluated using consensus molecular docking (Glide/Flare) against LasB (PDB: 3DBK) and three human off-target proteases. Top candidates underwent duplicate 150 ns molecular dynamics simulations with MM/GBSA binding free-energy calculations. Computational screening identified thymol–Leu–Trp–phosphinic acid as the lead candidate with predicted binding affinity of −12.12 kcal/mol, comparable to reference inhibitor phosphoramidon (−11.87 kcal/mol), and predicted selectivity index of +0.12 kcal/mol representing a 2.3 kcal/mol advantage over human proteases. Molecular dynamics simulations indicated exceptional stability (98.7% stable frames, 0.12 Å inter-replica RMSD) with consistent zinc coordination. Structure–activity analysis revealed phosphinic zinc-binding groups (+1.57 kcal/mol), Leu–Trp linkers (+2.47 kcal/mol), and phenolic scaffolds (+1.35 kcal/mol) as predicted optimal structural features. This in silico study provides a computational framework and prioritized candidate set for developing natural product-derived food preservatives. All findings represent computational predictions requiring experimental validation through enzymatic assays, food model studies, and toxicological evaluation. Full article

Urban markets are key nodes for the persistence and adaptation of traditional edible plant knowledge, linking rural production with urban consumption. This study was based on monthly market surveys conducted throughout 2025 in an urban market in Baise City, Guangxi, China. A total of 54 edible plant taxa were recorded, including both native and introduced species, with herbaceous plants predominating alongside climbers, trees, and grasses. Ethnobotanical data were obtained through semi-structured interviews with 40 local informants (20 men and 20 women, aged 25–65 years) selected using purposive sampling, focusing on individuals actively involved in purchasing and preparing edible plants. High Cultural Food Significance Index (CFSI) values highlighted culturally central taxa, including Allium ascalonicum L., × Brassarda juncea (L.) Su Liu & Z.H. Feng, and Houttuynia cordata Thunb., reflecting frequent use and culinary–medicinal integration. Fidelity Level (FL) analyses identified species with strong consensus for specific therapeutic applications, such as × B. juncea, Alpinia galanga (L.) Willd., and Nelumbo nucifera Gaertn., while Informant Consensus Factor (FIC) values indicated moderate to high agreement across gastrointestinal, respiratory, inflammatory, and other health categories. These results underscore the persistence of the “food as medicine” concept, showing that edible plants function simultaneously as nutritional and preventive healthcare resources. The overlap of culinary and medicinal roles demonstrates dynamic food–medicine integration, with urban markets acting as cultural hubs that maintain dietary diversity, household food security, and ethnobotanical knowledge. Future studies should incorporate ethnozoological resources and longitudinal monitoring to capture the full scope of urban food–medicine systems. Full article

To address the challenge in multi-view learning within practical application scenarios—such as smart grid multi-source monitoring and complex environment perception—where view quality often exhibits significant dynamic time-varying characteristics due to environmental interference or sensor failures, rendering traditional static fusion methods inadequate for maintaining decision-making reliability, a general adaptive robust fusion method, termed the Consensus-Aware Residual Gating (CARG) mechanism, is proposed. This approach constructs a sample-level dynamic quality assessment framework. It computes three interpretable metrics—self-confidence, group consensus, and complementary uniqueness—for each feature view in real time, thereby accurately quantifying instantaneous data quality fluctuations. A multiplicative gating structure is employed to generate dynamic weights based on these metrics, embedding a structural inductive bias of group consensus priority. Specifically, when quality degradation triggers view conflicts, the mechanism prioritizes majority-consistent reliable signals to suppress noise; when high-value complementary information emerges, it cautiously incentivizes discriminative features to rectify group bias. This design achieves adaptive perception of quality variations and robust decision-making without relying on additional weight-prediction networks. Extensive experiments are conducted on general multi-view benchmarks. The results demonstrate that CARG surpasses mainstream algorithms in accuracy, robustness, and interpretability. It effectively shields decisions from anomalous feature interference and validates its efficacy as a universal fusion framework for dynamic environments. Full article

The coordination of switched fractional-order nonlinear heterogeneous multi-agent systems (FONHMASs) with cooperative and antagonistic interactions presents significant challenges due to the complex coupling of switched fractional-order dynamics. Crucially, existing control methods typically rely on integer-order assumptions and precise system modeling, which are inadequate for capturing the inherent non-local memory behaviors of fractional dynamics. Furthermore, they generally assume fixed agent dynamics, and cannot be applied to switched FONHMASs where the continuity of agents’ dynamics is violated at switching instants. Considering the constraints of precise modeling difficulties and limited task time for switched FONHMASs in practice, a distributed $D^{\alpha }$-type iterative learning control (ILC) protocol is proposed to achieve bipartite consensus in the presence of cooperative and antagonistic interactions. Also, without relying on repetitive initial conditions, based on a presented initial state learning mechanism and $D^{\alpha }$-type ILC protocol, the bipartite consensus error convergence property with each iteration is achieved. Additionally, in consideration of external disturbances, the robustness of the iterative bipartite consensus controller for the switched FONHMASs is analyzed. Simulation results confirm that the switched FONHMASs achieve the convergence and robustness of the bipartite consensus errors along the iteration direction. In addition, the proposed $D^{\alpha }$-type ILC protocol achieves a maximum root-mean-square-error (MRMSE) of 0.0168 in time domain, significantly outperforming the integer-order ILC (MRMSE = 0.3601) and fractional-order PID control (MRMSE = 0.7550), confirming its superiority. Full article

The generalization capability of the decision-making modules in unmanned ground vehicles (UGVs) is critical for their safe deployment in unseen environments. Prevailing evaluation methods, which rely on aggregated performance over static benchmark sets, lack the granularity to diagnose the root causes of model failure, as they often conflate the distinct influences of scenario similarity and intrinsic difficulty. To overcome this limitation, we introduce a fine-grained, dynamic evaluation framework that deconstructs generalization along the dual axes of multi-level difficulty and similarity. First, scenario similarity is quantified through a four-layer hierarchical decomposition, with results aggregated into a composite similarity score. Test scenarios are independently classified into ten discrete difficulty levels via a consensus mechanism integrating large language models and task-specific proxy models. By constructing a three-dimensional (3D) performance landscape across similarity, difficulty, and task performance, we enable detailed behavioral diagnosis. The framework assesses robustness by analyzing performance within the high-similarity band (90–100%), while the full 3D landscape characterizes generalization under distribution shift. Seven interpretable metrics are derived to quantify distinct facets of both generalization and robustness. This initial validation focuses on the path-planning layer under full state observability, establishing a foundational proof-of-concept for the framework. It not only ranks algorithms but also reveals non-trivial behavioral patterns, such as the decoupling between in-distribution robustness and out-of-distribution generalization. It provides a reliable and interpretable foundation for evaluating the readiness of UGVs for safe deployment in unseen environments. Full article

Photovoltaic-storage direct current (DC) flexible (PEDF) systems are susceptible to DC bus voltage disturbances, with the constant power load (CPL) characteristics further exacerbating the risk of system instability. To address these challenges, a collaborative control scheme integrating distributed consensus and demand-side response (DSR) based on a consensus protocol is proposed in this study. A fully distributed control architecture is constructed, wherein the upper layer achieves power coordination through voltage deviation of parallel DC/DC converters and neighborhood interaction, whilst the lower layer dynamically optimizes inter-unit power allocation via the DSR mechanism. Distributed state estimation (DSE) is incorporated to enhance voltage control accuracy. Simulations conducted in the MATLAB (R2022a)/Simulink environment demonstrate that the proposed strategy enables rapid stabilization of bus voltage under load step changes and photovoltaic fluctuation scenarios, with system disturbance rejection capability being effectively enhanced. The effectiveness of the approach in maintaining stable system operation and optimizing power distribution is validated. The results indicate that the voltage deviation of the PEDF system remains below 2% under compound disturbances, with the steady-state error being controlled within 2%. The proposed control strategy, through the integration of the power DSR mechanism, effectively improves the system’s anti-disturbance capability. Compared with conventional droop control methods, which typically result in voltage deviations of 3–5%, the proposed strategy achieves a reduction in voltage deviation of over 50%, demonstrating superior voltage regulation performance. Full article

Background/Objectives: The treatment of dual disorders (DDs) must be comprehensive and multidisciplinary. Evidence supports the effectiveness of psychotherapy in treating DDs. The second part of this consensus synthesizes the available evidence on psychological treatment for specific DDs. Methods: Two consensus methods were sequentially implemented: the nominal group technique and the Delphi method. Results: This consensus review encompassed a compilation of recommendations for the psychological treatment of neurodevelopmental, anxiety, post-traumatic stress, somatic symptom, eating, and personality disorders. Finally, recommendations for the future research agenda on the psychological treatment of DD were included. Conclusions: (1) Psychological treatment, particularly integrated treatment, is effective. (2) In the case of dual autism, interventions for substance use disorders should be adapted to this population’s characteristics. (3) More research is needed on dual social anxiety, panic, generalized anxiety, somatic symptom, and eating disorders, for which Cognitive Behavioral Therapy (CBT) is the most commonly used treatment. (4) For dual attention deficit hyperactivity disorder, multicomponent treatment is recommended (psychoeducation, CBT, and peer or family support). (5) For dual anxiety disorders, CBT is the first-line treatment. (6) For dual post-traumatic stress disorder, CBT (cognitive processing therapy and prolonged exposure therapy), acceptance and commitment therapy, stress inoculation training, and Eye Movement Desensitization and Reprocessing (EMDR) are effective. (7) For dual personality disorders, evidence is scarce. (8) For borderline personality disorder, dialectical behavior therapy, dynamic deconstructive psychotherapy, and dual-focus schema therapy show promise. (9) For antisocial personality disorder, CBT, contingency management, and counseling on impulsive lifestyles may be useful. (10) Much more evidence is needed from studies that overcome the methodological limitations of existing ones. Full article

Background: The year 2025 witnessed paradigm-shifting advances in gynecologic oncology, with pivotal clinical trial results redefining therapeutic standards across cervical, ovarian, endometrial, and vulvar cancers. Objectives: This systematic review aimed to comprehensively identify, synthesize, and critically evaluate pivotal phase II and III randomized controlled trials and major studies presented at the major annual meetings, alongside significant peer-reviewed publications from 2025 that introduce innovative therapeutic strategies across gynecologic malignancies. Methods: Conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, this review involved exhaustive searches of electronic databases (PubMed/MEDLINE, Embase), conference proceedings (ASCO 2025, ESMO 2025), and major oncology journals for records from January to December 2025. Inclusion criteria encompassed: (1) Phase II or III randomized controlled trials (RCTs) and (2) Non-randomized studies (including phase I and II trials), reporting on novel therapeutic approaches in gynecologic oncology. All studies were required to report primary survival endpoints (overall survival or progression-free survival) or key efficacy outcomes. Study selection, data extraction, and methodological quality assessment were performed independently by two reviewers, with disagreements resolved through consensus or third-party adjudication. Results: From 1842 records, 23 studies met inclusion criteria (17 phase-III RCTs and 6 non-phase III RCTs/early-phase studies), distributed as follows: cervical cancer (9 studies, 39%), ovarian cancer (9 studies, 39%), endometrial cancer (4 studies, 17.5%), and vulvar cancer (1 study, 4.5%). The major advances identified include: (1) In cervical cancer, the KEYNOTE-A18 trial established pembrolizumab combined with chemoradiotherapy as a new standard for high-risk locally advanced disease, while the PHENIX trial validated sentinel lymph node biopsy as a safe surgical de-escalation strategy. (2) In ovarian cancer, the ENGOT-ov65/KEYNOTE-B96 trial demonstrated the first statistically significant overall survival improvement with an immune checkpoint inhibitor in platinum-resistant recurrent disease, establishing pembrolizumab plus weekly paclitaxel as a new standard of care. Novel therapeutic mechanisms, including glucocorticoid receptor modulation (ROSELLA trial) and cadherin-6-targeted antibody-drug conjugates (REJOICE-Ovarian01), showed remarkable efficacy. (3) In endometrial cancer, updated analyses from NRG GY018 and RUBY trials solidified the role of first-line immuno-chemotherapy, with differential benefits according to mismatch repair status. (4) In vulvar cancer, a pivotal phase II study demonstrated meaningful clinical activity of anti-PD-1 therapy in advanced disease. (5) The extensive circulating tumor DNA analysis from the CALLA trial provided crucial insights into biomarker dynamics in cervical cancer. Conclusions: The convergence of high-impact data from 2025 established multiple new standards of care, emphasizing biomarker-driven approaches, immunotherapy integration across disease stages, and novel mechanisms to overcome resistance, while highlighting challenges in treatment sequencing and global access. Full article

This paper addresses the problem of cooperative coverage control for heterogeneous Autonomous Underwater Vehicle (AUV) swarms operating in complex underwater environments. The objective is to achieve optimal coverage of a target region while simultaneously ensuring collision avoidance—both among AUVs and with static obstacles—and satisfying the inherent dynamic constraints of the AUVs. To this end, we propose a hierarchical control framework that fuses Control Barrier Functions (CBFs) with consensus theory. First, addressing the heterogeneity and limited sensing ranges of the AUVs, a cooperative coverage model based on a modified Voronoi partition is constructed. A nominal controller based on consensus theory is designed to balance the ratio of task workload to individual capability for each AUV. By minimizing a Lyapunov-like function via gradient descent, the swarm achieves self-organized optimal coverage. Second, to guarantee system safety, multiple safety constraints are designed for the AUV double-integrator dynamics, utilizing Zeroing Control Barrier Functions (ZCBFs) and High-Order Control Barrier Functions (HOCBFs). This approach unifies the handling of collision avoidance and velocity limitations. Finally, the nominal coverage controller and safety constraints are integrated into a Quadratic Programming (QP) formulation. This constitutes a safety-critical layer that modifies the control commands in a minimally invasive manner. Theoretical analysis demonstrates the stability of the framework, the forward invariance of the safe set, and the convergence of the coverage task. Simulation experiments verify the effectiveness and robustness of the proposed method in navigating obstacles and efficiently completing heterogeneous cooperative coverage tasks in complex environments. Full article

For the first time, this paper introduces Nabla fractional calculus into the distributed Nash equilibrium (NE) seeking problem of aggregative games (AGs) with partial decision information in undirected communication networks, and proposes two novel fractional-order distributed algorithms. In the considered setting, each agent can access to only local information and collaboratively estimates the global aggregate through communication with its neighbors. Both algorithms adopt a backward-difference scheme followed by an implicit fractional-order gradient descent step. One updates local aggregate estimates via fractional-order dynamic tracking and the other uses fractional-order average dynamic consensus protocols. Under standard assumptions, convergence of both algorithms to the NE is rigorously proved using nabla fractional-order Lyapunov stability theory, achieving a Mittag-Leffler convergence rate. The feasibility of the developed schemes is verified via numerical experiments applied to a Nash-Cournot game and the coordination control of flexible robotic arms. Full article

Background/Objectives: Drug-induced sleep endoscopy (DISE) is used in obstructive sleep apnea (OSA) to visualize dynamic upper airway collapse, but sedation protocols vary widely with no consensus on the optimal agent or technique. This narrative review aims to clarify current sedation strategies for DISE in OSA and their clinical implications. Methods: We systematically searched PubMed, Scopus, Web of Science, and Cochrane Library for English-language publications on DISE sedation (2000–2025). Relevant clinical studies, guidelines, and reviews were included. Data were qualitatively synthesized due to heterogeneity among studies. Results: Sedation approaches in DISE varied considerably. Propofol, dexmedetomidine, and midazolam were the primary agents identified. Propofol provided rapid, titratable sedation but increased airway collapsibility at higher doses; dexmedetomidine produced a more natural sleep-like state with minimal respiratory depression; midazolam was less favored due to prolonged effects. Use of target-controlled infusion (TCI) and pharmacokinetic–pharmacodynamic (PK–PD) models improved control of propofol sedation. Co-sedative adjuncts (e.g., opioids) reduced the required sedative dose but added risk of respiratory depression. Careful titration to the lowest effective dose-often guided by bispectral index (BIS) monitoring—was emphasized to achieve adequate sedation without artifactual airway collapse. No universal DISE sedation protocol was identified. Conclusions: Optimal DISE sedation balances adequate depth with patient safety to ensure reliable findings. Using the minimum effective dose, guided by objective monitoring (e.g., BIS), is recommended. There is a need for standardized sedation protocols and further research (e.g., in obese patients) to resolve current controversies and improve DISE’s utility in OSA management. Full article

This article examines how the role of digital platforms is reshaping political communication and consensus-building in contemporary societies. It questions how algorithmic architectures are transforming the relationship between leadership, audiences, and power. Drawing on an empirical analysis of online interaction data, the study analyses Donald Trump’s political communication during the August 2025 summit with Putin in Alaska, presenting it as a paradigmatic example of networked leadership. The study focuses on the dynamics of mobilisation, polarisation, and identity construction within digital ecologies. The findings show that the leader’s centrality derives not only from traditional party structures, but also from the ability to coordinate heterogeneous communication flows as well as activate processes of affective and symbolic resonance. The article proposes a theoretical model that conceptualises Trump’s audience as a cognitive and emotional power device, highlighting the convergence of post-organisational populism, algorithmic mediatisation, and communicative governance. This leadership expresses forms of “algorithmic charisma” that redefine the modalities of political legitimacy. Methodologically, the study highlights the value of data-driven interpretive approaches, while also addressing their limitations related to algorithmic transparency and replicability. In conclusion, the article offers a critical reflection on emerging ecologies of consensus and the democratic implications of the ongoing “platformisation” of the public sphere. Full article

In fish gut microbiome studies, there are no standardized protocols regarding sampling region or post-feeding time, nor clear consensus on whether analyses should target resident (autochthonous) or transient (allochthonous) bacteria. This study examined the dynamics and interactions of both microbial communities in the anterior and posterior intestine of farmed gilthead sea bream and evaluated the resident microbiome at 24 and 48 h post-feeding. Microbial DNA was sequenced using the Oxford Nanopore Technology platform. Data were analyzed through statistical and discriminant approaches, as well as a Bayesian network framework to assess bacterial interactions. Transient communities showed higher richness and diversity, regardless of intestinal section, suggesting a more specialized and stable microbial environment in the mucus layer. The two communities differed markedly in structure and composition. Variations associated with intestinal region were less pronounced, particularly for autochthonous bacteria, and post-feeding fluctuations in the resident microbiome were minimal. Functionally, results indicated relevant synergies between communities. Protein metabolism pathways were enriched in autochthonous bacteria, whereas allochthonous microorganisms contributed mainly to bile acid and carbohydrate metabolism. Overall, resident and transient bacteria constitute distinct communities in the gut of gilthead sea bream, with numerous genera present in both but most being differentially represented and interconnected. Full article

The purpose of this article is to develop optimal control strategies for discrete-time multi-agent systems (DT-MASs) with unknown disturbances, with the goal of enhancing their consensus performance and disturbance rejection capabilities. Complex flight conditions, such as the scenario of multi-unmanned aerial vehicle (multi-UAV) maintaining consensus under strong wind gusts, pose significant challenges for MAS control. To address these challenges, this article develops an optimal controller for UAV-based MASs with unknown disturbances to reach consensus. First, a novel improved nonlinear extended state observer (INESO) is designed to estimate disturbances in real time, accompanied by a corresponding disturbance compensation scheme. Subsequently, the consensus error systems and cost functions are established based on the disturbance-free DT-MASs. Building on this, a policy iterative algorithm based on a momentum-accelerated Actor–Critic network is proposed for the disturbance-free DT-MASs to synthesize an optimal consensus controller, whose integration with the disturbance compensation scheme yields an optimal disturbance rejection controller for the disturbance-affected DT-MASs to achieve consensus control. Comparative quantitative analysis demonstrates significant performance improvements over a standard gradient Actor–Critic network: the proposed approach reduces convergence time by 12.8%, improves steady-state position accuracy by 22.7%, enhances orientation accuracy by 42.1%, and reduces overshoot by 22.7%. Finally, numerical simulations confirm the efficacy and superiority of the method. Full article

Cooperative task allocation is one of the critical enablers for multi-Autonomous Underwater Vehicle (AUV) missions, but existing approaches often assume reliable communication that rarely holds in real underwater acoustic environments. We study here the performance and robustness of the Consensus-Based Bundle Algorithm (CBBA) for multi-AUV task allocation under realistically degraded underwater communication conditions with dynamically appearing tasks. An integrated simulation framework that incorporates a Dubins-based kinematic model with minimum turning radius constraints, a configurable underwater acoustic communication model (range, delay, packet loss, and bandwidth), and a full implementation of improved CBBA with new features, complemented by 3D trajectory and network-topology visualization. We define five communication regimes, from ideal fully connected networks to severe conditions with short range and high packet loss. Within these regimes, we assess CBBA based on task allocation quality (total bundle value and task completion rate), convergence behavior (iterations and convergence rate), and communication efficiency (message delivery rate, average delay, and network connectivity), with additional metrics on the number of conflicts during dynamic task reallocation. Our simulation results indicate that CBBA maintains performance close to the optimum when the conditions are good and moderate but degrades significantly when connectivity becomes intermittent. We then introduce a local-communication-based conflict resolution strategy in the face of frequent task conflicts under very poor conditions: neighborhood-limited information exchange, negotiation within task areas, and decentralized local decisions. The proposed conflict resolution strategy significantly reduces the occurrence of conflicts and improves task completion under stringent communication constraints. This provides practical design insights for deploying multi-AUV systems under weak underwater acoustic networks. Full article