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Insect locomotion exhibits remarkable adaptability and flexibility despite the limited scale of its nervous system. However, the underlying principles that govern leg coordination remain difficult to extract and model computationally. Understanding how insects achieve stable and adaptive locomotion has long provided important inspiration for the development of control strategies in bio-inspired robotics. Nevertheless, many existing approaches rely on predefined coordination rules, manually tuned parameters, or hand-crafted reward functions, which limit the flexibility and transferability of the resulting control strategies. To address this limitation, this study proposes a data-driven framework based on adversarial inverse reinforcement learning (AIRL), which directly learns continuous locomotion control policies from stick insect walking data and infers latent reward structures and control strategies from biological behavioral demonstrations. Experimental results show that even when trained using only a short segment of flat-terrain demonstration data, the learned policy can still be extracted to learn adaptive and flexible leg coordination patterns under different environmental conditions. Furthermore, the learned reward network can be transferred across different dynamic systems to guide policy learning for robot models with different morphologies. Compared with methods relying solely on reward shaping, the proposed approach achieves faster convergence and produces more biologically consistent gait coordination. A preliminary deployment on a physical bio-inspired robot further demonstrates the potential of the learned policy for sim-to-real application. The proposed method provides a transferable data-driven framework for extracting and learning locomotion control strategies from biological behavior, with potential applications to bio-inspired robotic systems.

In order to better understand medical students' decision-making to pursue ophthalmology, and inform efforts to recruit more diverse and inspired ophthalmologists, we aim to characterize values and experiences of medical students pursuing ophthalmology. Retrospective cross-sectional study. U.S. medical students who responded to American Association of Medical Colleges (AAMC) Matriculating Student and Graduation Questionnaires (MSQ & GQ) between 2013 and 2022. We examined survey responses pertaining to personal values, medical school experiences, and use of resources. Univariate analyses were performed to compare the odds of pursuing ophthalmology versus other specialties. Association of values and experiences with odds of pursuing ophthalmology versus another specialty. Among 134,723 matriculating students who completed the MSQ, those interested in ophthalmology (2425 students) were more likely to value high income (OR 1.46, p<0.001) and work-life balance (OR 1.44, p<0.001), and less likely to value social change (OR 0.76, p<0.001), leadership (OR 0.94, p=0.011), or creativity (OR 0.94, p=0.025). Of 98,628 graduates who completed the GQ, those pursuing ophthalmology (2150 students) valued salary (OR 1.73, p<0.001), family considerations (OR 1.31, p<0.001), and work-life balance (OR 2.40, p<0.001) more than those pursuing other specialties. Students pursuing ophthalmology rated their clerkships in neurology (OR 1.22 [1.16-1.29], p<0.001), internal medicine (OR 1.19 [1.12-1.28], p<0.001), surgery (OR 1.18 [1.12-1.25], p<0.001), psychiatry (OR 1.16 [1.09-1.23], p<0.001), and emergency medicine (OR 1.08 [1.01-1.16], p=0.02) more highly compared to other core clerkships. Students pursuing ophthalmology were more likely to perform research (OR 4.75, p<0.001) than those pursuing other fields. These students also more frequently relied on web-based resources (OR 1.58, p<0.001), group panels (OR 1.39, p<0.001), and extra electives (OR 1.30, p<0.001) for specialty information compared to other students, but not mentorship, school workshops, or AAMC-provided information. Medical students pursuing ophthalmology valued lifestyle factors rather than leadership, creativity, or social change and were also more likely to perform research. They were more likely to rely on non-mentorship resources for specialty information. Efforts are needed to refine the perception of ophthalmology, increase focus on creativity and innovation, and improve the recruitment of diverse and inspired future ophthalmologists.

Direct ethanol fuel cells are hindered by the ethanol oxidation reaction (EOR) that favors the low-efficiency C2 pathway over the desirable C1 pathway. Here, we report a catalyst design integrating an ultrathin Turing-type nanonet with a Pd-based crystalline/amorphous (C/A) heterointerface, achieving a near-complete C1-pathway selectivity of 97.1% for alkaline EOR, which is the highest reported to date. Inspired by spatially decoupling C&#x2500;C cleavage and CO oxidation, we engineer two intimately integrated phases: strained interstitial-carbon-doped PdO (Cint-PdO) enriched with oxygen vacancies and defective amorphous PdCx (a-PdCx). This heterostructure is realized via a "carbon engineering" strategy combining salt-melt templating with secondary annealing. Atomic-resolution studies confirm atomically sharp C/A interfaces and the highly unsaturated a-PdCx phase. In situ Fourier-transform infrared spectroscopy (FTIR) directly visualizes CO2 emergence at ultralow overpotentials, while high-performance liquid chromatography (HPLC) verifies the near-complete C1 pathway. Density functional theory (DFT) reveals a dual-cooperative mechanism: Cint-PdO steers the EOR toward C1 pathway by facilitating CH3CO* dehydrogenation and subsequent C&#x2500;C cleavage via CH2CO*, thereby suppressing acetate formation; concurrently, a-PdCx dramatically accelerates CO oxidation and may also contribute to C&#x2500;C cleavage via an alternative direct CH3CO* pathway. This work establishes carbon-engineered C/A heterointerfaces as a powerful platform for overcoming the EOR selectivity bottleneck.

Hypoxaemia occurs in intermittent forms, such as obstructive sleep apnoea, and in continuous forms, such as at high altitude, and is increasingly recognized as a modulator of cardiometabolic risk. Although hypoxaemia alters postprandial glucose and lipid metabolism, its effects on ketone bodies remain unclear. Using a randomized crossover design, we examined whether 6&#xa0;h of normoxaemia or intermittent hypoxaemia (15 hypoxaemic cycles/h targeting &#x223c;85% peripheral oxyhaemoglobin saturation with 100% medical-grade nitrogen) alters plasma &#x3b2;-hydroxybutyrate (BHB) concentrations in 12 young adult females (mean [SD]: 21 [3] years) following a high-fat meal (33% of estimated daily energy requirements; 59% of energy from fat). In a follow-up session, a subset (n&#xa0;=&#xa0;8) completed 6&#xa0;h of continuous hypoxaemia (fraction of inspired oxygen &#x223c;12.0% in a normobaric chamber). Postprandial data were analysed using baseline-adjusted linear mixed-effects models, with Bonferroni post hoc tests. A time &#xd7; condition interaction (P&#xa0;=&#xa0;0.010) indicated that BHB concentrations at 360&#xa0;min were higher during continuous hypoxaemia (0.247&#xa0;mmol/L; 95% CI: 0.218-0.275) than normoxaemia (0.176&#xa0;mmol/L; 95% CI: 0.153-0.200; PBonferroni&#xa0;=&#xa0;0.029) and intermittent hypoxaemia (0.163&#xa0;mmol/L; 95% CI: 0.139-0.186; PBonferroni&#xa0;=&#xa0;0.002), representing increases of 13.0% and 14.2% in estimated marginal means, respectively. This response was accompanied by higher postprandial plasma glucose and triglyceride concentrations during continuous hypoxaemia than during normoxaemia and intermittent hypoxaemia (PBonferroni &#x2264; 0.002), despite similar plasma insulin and non-esterified fatty acid responses across conditions (P &#x2265; 0.081). These findings indicate that continuous hypoxaemia increases late postprandial plasma BHB concentrations in young adult females.

Inspired by the intrinsic organization observed in various biological and physical processes, which manifests through interacting subsystems connected by networks, where particles often compete for limited resources, we study a four-lane network system with a branching-merging geometry under resource-constrained conditions. The particle inflow in the network is regulated by the total number of particles considered in the system, quantified by a filling factor, while conflict between the particle flow at the merging point of the network is captured through a friction parameter. Utilizing mean-field approximations, we obtained the analytical expressions for the stationary-state attributes of the resource-constrained network, such as lane densities, flux, stationary phases, and phase boundaries. The analysis of systems' stationary-state behavior is facilitated by the construction of phase diagrams in the parameter space of the entry-exit rate for two distinct friction regimes. All theoretically obtained findings are validated by the extensive stochastic Monte Carlo simulations based on the Gillespie algorithm under the random sequential update. For a low friction regime, the system can exhibit up to five possible stationary phases, and its phase diagram features a metastable region corresponding to the passage lanes. In this region, the possible stationary phase exhibited by these lanes sensitively depends on the lanes' initial configuration or density, which is corroborated by spatiotemporal plots. In contrast, for a higher friction regime, the system stops exhibiting metastable behavior, and now the phase diagram becomes richer, supporting up to 11 possible stationary phases. In both regimes, the topology of the phase diagrams exhibits non-monotonic behavior in terms of complexity as well as the number of stationary phases as the reservoir feeds more particles to the network system. Lastly, the influence of the boundary rates and the friction parameter is investigated on the position and height of the shock, along with the examination of the finite-size effects, providing an additional insight into the underlying phase transitions of the system.

The human olfactory sensing system, based on ionic signal transmission, is featured with fastness, high efficiency and low energy consumption. However, bionic gas sensing materials exhibit performance limitations compared to materials based on electronic signal transmission. Herein, bionic olfactory fibres are prepared by electrospinning for rapid gas sensing at the ppb level, which consist of confined ionic liquids (ILs) within nano spacing in a polymer matrix. The fibres showed a high response (69.29%) to 500 ppb NH3, ultrafast response (4 s) and a low theoretical limit of detection (45 ppb). The excellent sensing performance is attributed to the sufficient gas transport pathways formed by gas convection within the fibrous pore structures. In addition, the rapid transport of solvated ions, caused by the encapsulation of target molecules around ILs in the confined nano spacing, also plays a role, as confirmed by experimental and simulation results. Moreover, bionic olfactory fibres demonstrate excellent gas cyclic stability, mechanical robustness and humidity resistance, which makes them highly suitable for disease diagnosis and seafood spoilage detection in humid environments. Using AI-driven data analysis on gas response from shrimp spoilage, 95% test accuracy was attained, enabling precise seafood freshness monitoring. This work provides a novel platform for intelligent gas perception through hardware-software codesign, showing promising potential to create bioinspired integrated sensing systems combining gas and solvated ion transport mediation with AI for decision-making analysis.

Electrochemical nitrogen reduction powered by renewable electricity plays a crucial role in sustainable and eco-friendly ammonia (NH3), but current techniques were still limited by the low efficiency and selectivity for practical application. In this study, we introduce an enzyme-inspired approach that utilizes a redox-reversible helical capsule H1 to interact with the well-modified N2-adducts for thorough electrochemical reduction of N2 into NH3. The strategy included the utilizing of hydroxyl groups that decorated on the capsule to encapsulate the N2-associated ionic liquid to form a substrate-involved intermediate, anodically shifting the redox potential of the N2 adduct. The pocket shielding approach enables the production of NH3 at a lower applied potential (-0.08&#xa0;V vs. reversible hydrogen electrode, RHE) with enzymatic kinetics, facilitating a rate of 182.5 &#xb5;g&#xb7;mg-1 cat&#xb7;h-1 and a Faradaic efficiency of 48.50%, which is comparable to that of iron-based native nitrogenase. A continuous working laboratory at 15 mA&#xb7;cm-2 yields a NH3 turnover number (TON) of 1640 over 100&#xa0;h. The new strategy of molecular electrocatalysts with an enzymatic activation manner and anodic shift of the applied potentials sheds light on the rational design of high-performance electro-catalytic technologies for nitrogen capture and conversion.

Obese patients are vulnerable to atelectasis, impaired oxygenation, and postoperative pulmonary complications (PPCs) during and after general anesthesia. Driving pressure-guided individualized positive end-expiratory pressure (PEEP) can improve intraoperative respiratory mechanics, but whether these physiological benefits translate into fewer PPCs in bariatric surgery remains uncertain. In this single-center, parallel-group randomized clinical trial, 116 adults with obesity scheduled for elective laparoscopic bariatric surgery were randomized 1:1 to a driving pressure-guided individualized PEEP strategy or a conventional fixed low-PEEP strategy. All patients received volume-controlled ventilation with a tidal volume of 7&#xa0;ml/kg predicted body weight and an inspired oxygen fraction of 0.50. After a standardized recruitment maneuver, the driving pressure group underwent stepwise PEEP titration to identify the PEEP level associated with the lowest driving pressure; the conventional group received fixed PEEP of 5&#xa0;cm&#xa0;H2O. The primary endpoint was the severity and incidence of PPCs within the first 5 postoperative days. Secondary endpoints included respiratory mechanics, oxygenation, hospital length of stay, postoperative nausea and vomiting, surgical site infection, and mortality. All 116 randomized patients completed the trial. Any PPC occurred in 41 of 58 patients (70.7%) in the driving pressure group and 44 of 58 patients (75.9%) in the conventional group (odds ratio, 0.77; 95% CI, 0.33 to 1.79; P&#x2009;=&#x2009;0.68). PPC severity scores were 1.0&#x2009;&#xb1;&#x2009;0.9 and 1.1&#x2009;&#xb1;&#x2009;0.8, respectively (mean difference, -0.10; 95% CI, -0.41 to 0.21; P&#x2009;=&#x2009;0.53). Severe PPCs (grade&#x2009;>&#x2009;&#x2009;=&#x2009;3) occurred in 3 patients (5.2%) in each group. Compared with conventional ventilation, driving pressure-guided ventilation produced lower intraoperative driving pressure, higher dynamic compliance, and a higher PaO2/FiO2 ratio 1 h after surgical start; these physiological improvements were not accompanied by shorter hospital stay or fewer postoperative adverse events. In obese patients undergoing laparoscopic bariatric surgery, a driving pressure-guided individualized PEEP strategy improved intraoperative respiratory mechanics and early oxygenation but did not reduce PPCs compared with conventional fixed low-PEEP ventilation. These findings suggest that optimizing respiratory-system driving pressure alone may be insufficient to improve short-term clinical pulmonary outcomes in this surgical population.

Programmable metamaterials that exhibit prescribed mechanical responses and adaptive deformation under external loading are highly desirable for multifunctional engineering applications. However, most existing designs rely on multi-material systems, which pose significant fabrication challenges with conventional additive manufacturing. Inspired by the unique soft-hard heterogeneous architecture of nacre, this study introduces a novel class of dual-phase (DP) metamaterials where spatially encoded soft and hard phases are realized through bending-dominated and stretching-dominated lattice architectures, respectively. By systematically varying the spatial coding patterns of soft-hard phases, representative DP metamaterials are shown to exhibit programmable nonlinear mechanical responses and tailored failure processes, achieved through geometry-based mechanical encoding governed by phase interactions and internal stress redistribution. Notably, the engineered sequenced failure processes and phase-coupling-induced strengthening effects lead to significantly enhanced energy absorption compared with the constituent architectures, while enabling customizable plateau stress. To efficiently explore the vast design space of DP metamaterials, a data-driven framework is then developed to model the relationship between spatial encodings and nonlinear mechanical responses. The trained model enables rapid and accurate inverse design of DP metamaterials matching the complex target responses for multifunctional applications. Overall, this work establishes a new geometry-based strategy for achieving highly programmable mechanical responses in single-material metamaterials.

Oncology dose optimization has moved beyond the maximum tolerated dose paradigm, yet many programs still implicitly target a threshold-style minimum effective dose (MED). In serious cancers, deliberately sub-therapeutic comparators are rarely ethical or approvable, so any sharp MED threshold is typically a fragile and only partially identifiable target. We propose a statistical and operational framework that reframes dose-finding as region-based optimization. First, we define a constraint-based Dose Optimization Region (DOR) where clinically meaningful benefit (potentially multi-endpoint and PD-informed) is credible, unacceptable toxicity is bounded, exposure targets are attainable within a prespecified window, and implementation is feasible. Second, within the DOR, we identify an Operational Optimal Dose (OOD) - a label-ready dosing strategy that integrates dose, schedule, exposure targets, and adjustmentrules - using comparative evidence across binary and time-to-eventendpoints, exposure - response (PK/PD) analyses, and time-to-event methods that account for delayed effects where follow-up allows. This approach turns regulatory evidence domains into region-definingconstraints and shifts the target from a single-point estimate to a strategy-level deliverable. It is intended to sit on top of conventional dose-finding designs and PK/PD modeling as a region-defining and reporting standard, rather than to replace existing methods. We illustrate its use with small-samplevisualizations based on pairwise superiority probabilities across in-region arms and with a real-world-inspired example to show how DOR&#x2192;OOD can summarize the totality of evidence in practice. The framework provides statisticians and clinicians with practical design patterns and reporting checklists, aligning with contemporary regulatory expectations and supporting dose optimization in oncology, with potential adaptation to other therapeutic areas. What this paper says in one page Threshold-style MED is often not directly identifiable from typical oncology data, and in serious cancers deliberately sub-therapeutic comparators are rarely ethical or approvable.Reframe dose optimization operationally: define a DOR via explicit constraints (clinically meaningful benefit, toxicity/tolerability, exposure attainment within a target window, implementability), then choose an OOD within that region.Optimize using the totality of evidence: within-region randomized or well-controlled dose schedule comparisons, exposure&#x2013;response (PK/PD) with attainment, binary and time-to-event summaries where follow-up allows, PROs, and real-world dosemodification/ adherence data.Deliver a label-ready dosage strategy: dose/range and schedule, exposure targets with expected attainment, prespecified adjustment/de-intensification rules, and subgroup algorithms.

The intuitionistic fuzzy twin support vector machine (IFTSVM) utilizes the intuitionistic fuzzy set (IFS) to enhance the robustness of twin support vector machine (TSVM). While IFTSVM has successfully mitigated the impact of noise and outliers, significant room for improvement remains: (1) IFS cannot reduce resampling noise deviation; (2) TSVM is sensitive to noise features. To address these problems, we propose a novel intuitionistic proximity-consensus fuzzy (IPCF) geometric TSVM with l0-norm-based best subset selection to reduce the influence of outliers, resampling noise and noise features, simultaneously. The proposed IPCF is induced by the distance to the hyperplane obtained from the least squares one-class support vector machine, rather than considering the distance to the class center as in IFS. This strategy can effectively alleviate the effects of both outliers and resampling noise. Moreover, by incorporating the l0-norm penalty, we can achieve significant feature selection and noise feature reduction at the same time. Due to the non-convexity, non-smoothness and non-continuity of l0-norm penalty, it is difficult to solve the optimization problem. Inspired by the recently proposed variable sorted active set algorithm, we design an intuitionistic proximity-consensus fuzzy-optimized variable sorted active set algorithm (IPCF-VSAS) for optimizing the problem. On both simulating datasets and UCI datasets, it is the experimental results that validate the advantages of the proposed method. Specifically, compared with other state-of-the-art support vector classifiers, ours typically performs optimally on the datasets with redundant features and noise.

In today's climate of increasing health and social care needs and a declining workforce, most organisations and leaders are not only looking for strategies to recruit new staff but also ways of retaining the staff already in service. Retention, with its multiple factors and no single solution, is a wicked problem. Of the multiple factors influencing staff intent to stay, leadership and culture are two major areas where a significant impact can be made. Front-line leaders are in an ideal position for cultivating workplaces where staff want to work. As part of a two-nation project exploring the impact of various interventions on staff retention, we developed and evaluated a leadership development programme for front-line leaders in health and social care. Two cohorts followed a 12 month programme inspired by the co-operative inquiry process and adult, person-centred and transformative learning. Participants were facilitated in moving through continuous cycles of: identifying individual or group action, executing and observing the impact of their actions, sharing their observations and reflecting on the meaning of the observations for leadership intended to encourage staff retention. They creatively expressed their development narrative at the end of the programme. In this paper we share the participant faction (one fictive narrative based on fourteen participant narratives). The narrative reveals how participants started with feelings of fumbling around in darkness but then discovering one's own (and others') colour as conscious attentiveness to the ways they were leading grew. Alongside finding the authentic self, participants came to the realization that you don't have to travel alone and that using critical thinking and creativity they dared surf the dynamic waves of current health and social care oceans. We conclude that leadership development programmes based on adult, transformative and person-centred learning theory creates a learning climate and culture that enables front-line leaders to start to consciously cultivate workplaces where staff want to stay.