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Supercapacitors exhibit considerable potential as energy storage devices due to their high power density, fast charging and discharging abilities, long cycle life, and eco‐friendliness. With the increasing environmental concerns associated with synthetic compounds, the use of environment friendly biopolymers to replace conventional petroleum‐based materials has been widely studied. Biomass‐based materials are biodegradable, renewable, environment friendly and non‐toxic. The unique hierarchical nanostructure, excellent mechanical properties and hydrophilicity allow them to be used to create functional conductive materials with precisely controlled structures and different properties. In this review, the latest development of biomass‐based supercapacitor materials is reviewed and discussed. This paper describes the physical and chemical properties of various biopolymers and their impact on supercapacitors, as well as the classification and basic principles of supercapacitors. Then, a comprehensive discussion is presented on the utilization of biomass‐based materials in supercapacitors and their recent applications across a range of supercapacitor devices. Finally, an overview of the future prospects and challenges pertaining to the utilization of biomass‐based materials in supercapacitors is provided. Starting from the physical and chemical properties of biopolymers, the classification and basic principles of supercapacitors, the impact of biomass‐based materials on supercapacitors is introduced. Then, the latest specific applications of biomass‐based materials are comprehensively discussed in terms of both electrode and electrolyte materials. Finally, the current challenges and future development directions are discussed and summarized.

The exploration of artificial luminogens with bright emission has been fully developed with the advancement of synthetic chemistry. However, many of them face problems like weakened emission in the aggregated state as well as poor renewability and sustainability. Therefore, the development of renewable and sustainable luminogens with anti-quenching function in the solid state, as well as to unveil the key factors that influence their luminescence behavior become highly significant. Herein, a new class of natural rosin-derived luminogens with aggregation-induced emission property (AIEgens) have been facilely obtained with good biocompatibility and targeted organelle imaging capability as well as photochromic behavior in the solid state. Mechanistic study indicates that the introduction of the alicyclic moiety helps suppress the excited-state molecular motion to enhance the solid-state emission. The current work fundamentally elucidates the role of alicyclic moiety in luminogen design and practically demonstrates a new source to large-scalely obtain biocompatible AIEgens. To date we have a myriad of luminogenes at our deposal but many of them face problems like weakened emission in the aggregated state as well as poor sustainability. Here, the authors develop a class of rosin-derived luminogens with aggregation induced emission properties providing good biocompatibility and demonstrate their application in organelle imaging.

Background Technological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in biological and similar complex systems. In the field of network biology and network medicine, there is a particular interest in predicting results from drug–drug, drug–disease, and protein–protein interactions to advance the speed of drug discovery. Existing data and modern computational methods allow to identify potentially beneficial and harmful interactions, and therefore, narrow drug trials ahead of actual clinical trials. Such automated data-driven investigation relies on machine learning techniques. However, traditional machine learning approaches require extensive preprocessing of the data that makes them impractical for large datasets. This study presents wide range of machine learning methods for predicting outcomes from biomedical interactions and evaluates the performance of the traditional methods with more recent network-based approaches. Results We applied a wide range of 32 different network-based machine learning models to five commonly available biomedical datasets, and evaluated their performance based on three important evaluations metrics namely AUROC, AUPR, and F1-score. We achieved this by converting link prediction problem as binary classification problem. In order to achieve this we have considered the existing links as positive example and randomly sampled negative examples from non-existant set. After experimental evaluation we found that Prone ,  ACT and L R W 5 are the top 3 best performers on all five datasets. Conclusions This work presents a comparative evaluation of network-based machine learning algorithms for predicting network links, with applications in the prediction of drug-target and drug–drug interactions, and applied well known network-based machine learning methods. Our work is helpful in guiding researchers in the appropriate selection of machine learning methods for pharmaceutical tasks.

Epidemic spreading processes in the real world depend on human behaviors and, consequently, are typically non-Markovian in that the key events underlying the spreading dynamics cannot be described as a Poisson random process and the corresponding event time is not exponentially distributed. In contrast to Markovian type of spreading dynamics for which mathematical theories have been well developed, we lack a comprehensive framework to analyze and fully understand non-Markovian spreading processes. Here we develop a mean-field theory to address this challenge, and demonstrate that the theory enables accurate prediction of both the transient phase and the steady states of non-Markovian susceptible-infected-susceptible spreading dynamics on synthetic and empirical networks. We further find that the existence of equivalence between non-Markovian and Markovian spreading depends on a specific edge activation mechanism. In particular, when temporal correlations are absent on active edges, the equivalence can be expected; otherwise, an exact equivalence no longer holds. When modelling epidemic spreading on complex networks, one useful simplification is to assume that the dynamics are Markovian, i.e. memoryless. Here the authors present a more general non-Markovian approach which is able to accurately reproduce the transient and stationary regime on different substrates.

Programmed cell death (PCD) induced by endoplasmic reticulum (ER) stress is implicated in variousplant physiological processes, yet its mechanism is still elusive. An activation of caspase-3-like enzymatic activity was clearly demonstrated but the role of the two known plant proteases with caspase-3-like activity, cathepsin B and proteasome subunit PBA1, remains to be established. Both genetic downregulation and chemical inhibition were used to investigate the function of cathepsin B and PBA1 in ER-stress-induced PCD (ERSID). Transcript level and activity labelling of cathepsin B were used to assess activation. To study tonoplast rupture, a plant PCD feature, both confocal and electronic microscopies were used. Cathepsin B downregulation reduced reactive oxygen species (ROS) accumulation and ERSID without affecting the induction of the unfolded protein response (UPR), but downregulation of PBA1 increased UPR and ERSID. Tonoplast rupture was not altered in the cathepsin B mutant and cathepsin B activation was independent of vacuolar processing enzyme (VPE). VPE activity was independent of cathepsin B. ERSID is regulated positively by cathepsin B and negatively by PBA1, revealing a complex picture behind caspase-3-like activity in plants. Cathepsin B may execute its function after tonoplast rupture and works in parallel with VPE.

Flexible tactile sensors with multifunctional sensing functions have attracted much attention due to their wide applications in artificial limbs, intelligent robots, human‐machine interfaces, and health monitoring devices. Here, a multifunctional flexible tactile sensor based on resistive effect for simultaneous sensing of pressure and temperature is reported. The sensor features a simple design with patterned metal film on a soft substrate with cavities and protrusions. The decoupling of pressure and temperature sensing is achieved by the reasonable arrangement of metal layers in the patterned metal film. Systematically experimental and numerical studies are carried out to reveal the multifunctional sensing mechanism and show that the proposed sensor exhibits good linearity, fast response, high stability, good mechanical flexibility, and good microfabrication compatibility. Demonstrations of the multifunctional flexible tactile sensor to monitor touch, breathing, pulse and objects grabbing/releasing in various application scenarios involving coupled temperature/pressure stimuli illustrate its excellent capability of measuring pressure and temperature simultaneously. These results offer an effective tool for multifunctional sensing of pressure and temperature and create engineering opportunities for applications of wearable health monitoring and human‐machine interfaces. A multifunctional flexible tactile sensor based on resistive effect for simultaneous sensing of pressure and temperature is developed with patterned metal film on a soft substrate with cavities and protrusions. The decoupling of pressure and temperature sensing can be achieved by the distinguishable electromechanical behaviors of patterned metal films at different locations.

A bstract With updated experimental data and improved theoretical calculations, several significant deviations are being observed between the Standard Model predictions and the experimental measurements of the branching ratios of B ¯ s 0 → D s ∗ + L − decays, where L is a light meson from the set { π , ρ , K (∗) }. Especially for the two channels B ¯ 0 → D + K − and B ¯ s 0 → D s + π − , both of which are free of the weak annihilation contribution, the deviations observed can even reach 4–5 σ . Here we exploit possible new-physics effects in these class-I non-leptonic B -meson decays within the framework of QCD factorization. Firstly, we perform a model-independent analysis of the effects from twenty linearly independent four-quark operators that can contribute, either directly or through operator mixing, to the quark-level b → c u ¯ d s transitions. It is found that, under the combined constraints from the current experimental data, the deviations observed could be well explained at the 1 σ level by the new-physics four-quark operators with γ μ (1 − γ 5 ) ⨂ γ μ (1 − γ 5 ) structure, and also at the 2 σ level by the operators with (1 + γ 5 ) ⨂ (1 − γ 5 ) and (1 + γ 5 ) ⨂ (1 + γ 5 ) structures. However, the new-physics four-quark operators with other Dirac structures fail to provide a consistent interpretation, even at the 2 σ level. Then, as two specific examples of model-dependent considerations, we discuss the case where the new-physics four-quark operators are generated by either a colorless charged gauge boson or a colorless charged scalar, with their masses fixed both at the 1 TeV. Constraints on the effective coefficients describing the couplings of these mediators to the relevant quarks are obtained by fitting to the current experimental data.

One-pot synthesis of heterocyclic aromatics with good optical properties from phenolic β-O-4 lignin segments is of high importance to meet high value added biorefinery demands. However, executing this process remains a huge challenge due to the incompatible reaction conditions of the depolymerization of lignin β-O-4 segments containing γ-OH functionalities and bioresource-based aggregation-induced emission luminogens (BioAIEgens) formation with the desired properties. In this work, benzannulation reactions starting from lignin β-O-4 moieties with 3-alkenylated indoles catalyzed by vanadium-based complexes have been successfully developed, affording a wide range of functionalized carbazoles with up to 92% yield. Experiments and density functional theory calculations suggest that the reaction pathway involves the selective cleavage of double C-O bonds/Diels-Alder cycloaddition/dehydrogenative aromatization. Photophysical investigations show that these carbazole products represent a class of BioAIEgens with twisted intramolecular charge transfer. Distinctions of emission behavior were revealed based on unique acceptor-donor-acceptor-type molecular conformations as well as molecular packings. This work features lignin β-O-4 motifs with γ-OH functionalities as renewable substrates, without the need to apply external oxidant/reductant systems. Here, we show a concise and sustainable route to functional carbazoles with AIE properties, building a bridge between lignin and BioAIE materials. Biorenewable feedstocks are important for developing more sustainable materials, but developing processes can be challenging due to incompatible reaction conditions. Here, the authors report the one-pot preparation of aggregation-induced emission luminogens from lignin moieties.

This study examines whether integrating sports and arts interventions enhances response joint attention (RJA) in children with mild autism and provides insights for diversifying intervention strategies for autism. 2024.6–2024.12,Twenty-four children with autism, aged 6–12 years, were recruited from an autism association in Anhui Province, China. Participants were randomized using a computer-generated sequence (allocation concealed from assessors) assigned to an experimental group (n = 12) or a control group (n = 12). Over 12 weeks, the experimental group participated in basketball and drawing lessons four times a week for 60 min per session, while the control group engaged only in routine activities and structured teaching provided by their school and the association. RJA performance was assessed pre- and post-intervention using eye-tracking technology, analyzing key metrics: time to first fixation (TFF), fixation count (FC), total fixation duration (TFD), total visit duration (TVD), visit count (VC), and the ratio of correct to incorrect for first responses. Post-intervention, the experimental group showed significantly greater improvements in RJA performance than the control group. Key metrics for the experimental group included TFF (0.52 ± 0.79), FC (36.35 ± 6.34), TFD (11.05 ± 1.33), TVD (17.05 ± 2.33), VC (24.25 ± 2.49), and correct-to-incorrect ratio (1.1 ± 0.1), all of which outperformed the control group: TFF (0.59 ± 0.11), FC (30.83 ± 2.14), TFD (9.47 ± 1.38), TVD (15.42 ± 1.51), VC (20.33 ± 1.87), and correct-to-incorrect ratio (0.97 ± 0.08),partialη 2 ranged from 0.25 to 0.78, with P  < 0.05. Integrating sports and arts interventions significantly improves RJA in children with autism, highlighting the potential of these methods in enhancing attention-related behaviors.

Objective This study aims to investigate the relationship between psychological resilience, thriving at work, and work performance among nurses, as well as analyse the mediating role of thriving at work in the relationship between psychological resilience and the work performance of nurses. The findings are intended to serve as a reference for nursing managers to design tailored work performance intervention programs. Method Using convenience sampling, 308 clinical nurses were selected from a tertiary hospital in Changsha City, Hunan Province, China, from February to April 2023. The Connor–Davidson Resilience Scale (CD-RISC), the Thriving at Work Scale, and the Work Performance Scale were employed for the questionnaire survey. Pearson correlation analysis was used to explore the relationship between psychological resilience, thriving at work and work performance. The SPSS 26.0 software’s ‘Process’ plugin was utilised for mediation effect analysis. Results Significantly positive correlations were found between psychological resilience and thriving at work ( r  = 0.806, P  < 0.01), thriving at work and work performance ( r  = 0.571, P  < 0.01) as well as psychological resilience and work performance ( r  = 0.572, P  < 0.01). Psychological resilience significantly predicted work performance positively ( β  = 0.558, t  = 11.165, P  < 0.01), and this prediction remained significant when thriving at work (the mediating variable), was introduced ( β  = 0.371, t  = 4.772, P  < 0.01). Psychological resilience significantly predicted thriving at work positively ( β  = 0.731, t  = 20.779, P  < 0.01), and thriving at work significantly predicted work performance positively ( β  = 0.256, t  = 3.105, P  < 0.05). The mediating effect size of thriving at work between psychological resilience and work performance was 33.49% ( P  < 0.05). Conclusion Thriving at work plays a partial mediating role between psychological resilience and work performance. The level of work performance among clinical nurses was relatively high. Nursing managers can enhance thriving at work by fostering psychological resilience among clinical nurses, thereby further improving their work performance to ensure high-quality and efficient nursing care.