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In the literature, organic materials with both aggregation-induced emission (AIE) and aggregation-caused quenching (ACQ) effects that can emit with multiple bands both in the solution and aggregated state are rarely reported. Herein we report a novel chiral dual-emissive bismacrocycle with tunable aggregation-induced emission colors. A facile four-step synthesis strategy is developed to construct this rigid bismacrocycle, (1,4)[8]cycloparaphenylenophane ( SCPP[8] ), which possesses a 1,2,4,5-tetraphenylbenzene core locked by two intersecting polyphenylene-based macrocycles. The luminescent behavior of SCPP[8] shows the unique characteristics of both ACQ effect and AIE effect, inducing remarkable redshift emission with near white-light emission. SCPP[8] is configurationally stable and possesses a novel shape-persistent bismacrocycle scaffold with a high strain energy. In addition, SCPP[8] displays enhanced circularly polarized luminescence properties due to AIE effect. Organic materials with both aggregation induced emission (AIE) and aggregation-caused quenching (ACQ) effects that can emit with multiple wavelengths in the solution and aggregated state are rarely reported. Here, the authors report a chiral dual-emissive bismacrocycle which shows the unique ACQ and AIE effects inducing redshift emission with near white-light emission.

The gut microbiota are mainly composed of Bacteroidetes and Firmicutes and are crucial for metabolism and immunity. Muribaculaceae are a family of bacteria within the order Bacteroidetes. Muribaculaceae produce short-chain fatty acids via endogenous (mucin glycans) and exogenous polysaccharides (dietary fibres). The family exhibits a cross-feeding relationship with probiotics, such as Bifidobacterium and Lactobacillus. The alleviating effects of a plant-based diet on inflammatory bowel disease, obesity, and type 2 diabetes are associated with an increased abundance of Muribaculaceae, a potential probiotic bacterial family. This study reviews the current findings related to Muribaculaceae and systematically introduces their diversity, metabolism, and function. Additionally, the mechanisms of Muribaculaceae in the alleviation of chronic diseases and the limitations in this field of research are introduced.

HighlightsPreparation strategies of cellulose nanopaper were elaborated.Functionalization of cellulose nanopaper and its advanced applications were summarized.Prospects and challenges of cellulose nanopaper were discussed.Cellulose nanopaper has shown great potential in diverse fields including optoelectronic devices, food packaging, biomedical application, and so forth, owing to their various advantages such as good flexibility, tunable light transmittance, high thermal stability, low thermal expansion coefficient, and superior mechanical properties. Herein, recent progress on the fabrication and applications of cellulose nanopaper is summarized and discussed based on the analyses of the latest studies. We begin with a brief introduction of the three types of nanocellulose: cellulose nanocrystals, cellulose nanofibrils and bacterial cellulose, recapitulating their differences in preparation and properties. Then, the main preparation methods of cellulose nanopaper including filtration method and casting method as well as the newly developed technology are systematically elaborated and compared. Furthermore, the advanced applications of cellulose nanopaper including energy storage, electronic devices, water treatment, and high-performance packaging materials were highlighted. Finally, the prospects and ongoing challenges of cellulose nanopaper were summarized.

Traditional elastomers are mostly dielectrics; existing conductive elastomers are conductive composites with electric conductors. Herein, we introduce a series of ionic conducting elastomers (ICE) by salt in polymer strategy. The ICEs possess good stretchability, transparency and ionic conductivity. Moreover, the ICEs exhibit very high stability in air, under high temperature and voltage, with excellent adhesion properties and no corrosive effects to metal electrodes. Touch sensors are fabricated using these ICEs—impedance spectra and impedance complex plane are tested and analyzed to clarify different stimulus of the touch sensors. These ICEs provide possibilities for flexible electronics and soft machines. Conductive elastomers are often made of composite materials and realization of high transparency and high elasticity at the same time is therefore hard to achieve. Here the authors use a salt in polymer strategy to fabricate ionic conducting elastomers (ICE), which show good elasticity and transparency and simultaneously high conductivity.

Sepsis is described as a life-threatening organ dysfunction and a heterogeneous syndrome that is a leading cause of morbidity and mortality in intensive care settings. Severe sepsis could incite an uncontrollable surge of inflammatory cytokines, and the host immune system's immunosuppression could respond to counter excessive inflammatory responses, characterized by the accumulated anti-inflammatory cytokines, impaired function of immune cells, over-proliferation of myeloid-derived suppressor cells and regulatory T cells, depletion of immune effector cells by different means of death, etc. In this review, we delve into the underlying pathological mechanisms of sepsis, emphasizing both the hyperinflammatory phase and the associated immunosuppression. We offer an in-depth exploration of the critical mechanisms underlying sepsis, spanning from individual immune cells to a holistic organ perspective, and further down to the epigenetic and metabolic reprogramming. Furthermore, we outline the strengths of artificial intelligence in analyzing extensive datasets pertaining to septic patients, showcasing how classifiers trained on various clinical data sources can identify distinct sepsis phenotypes and thus to guide personalized therapy strategies for the management of sepsis. Additionally, we provide a comprehensive summary of recent, reliable biomarkers for hyperinflammatory and immunosuppressive states, facilitating more precise and expedited diagnosis of sepsis.

This research focuses on the adaptive navigation of maritime autonomous surface ships (MASSs) in an uncertain environment. To achieve intelligent obstacle avoidance of MASSs in a port, an autonomous navigation decision-making model based on hierarchical deep reinforcement learning is proposed. The model is mainly composed of two layers: the scene division layer and an autonomous navigation decision-making layer. The scene division layer mainly quantifies the sub-scenarios according to the International Regulations for Preventing Collisions at Sea (COLREG). This research divides the navigational situation of a ship into entities and attributes based on the ontology model and Protégé language. In the decision-making layer, we designed a deep Q-learning algorithm utilizing the environmental model, ship motion space, reward function, and search strategy to learn the environmental state in a quantized sub-scenario to train the navigation strategy. Finally, two sets of verification experiments of the deep reinforcement learning (DRL) and improved DRL algorithms were designed with Rizhao port as a study case. Moreover, the experimental data were analyzed in terms of the convergence trend, iterative path, and collision avoidance effect. The results indicate that the improved DRL algorithm could effectively improve the navigation safety and collision avoidance.

ZnO, aside from TiO 2 , has been considered as a promising material for purification and disinfection of water and air and remediation of hazardous waste, owing to its high activity, environment-friendly feature and lower cost. However, their poor visible light utilization greatly limited their practical applications. Herein, we demonstrate the fabrication of different aspect ratios of the ZnO nanorods with surface defects by mechanical-assisted thermal decomposition method. The experiments revealed that ZnO nanorods with higher aspect ratio and surface defects show significantly higher photocatalytic performances.

In recent years, green extraction of bioactive compounds from herbal medicines has generated widespread interest. Deep eutectic solvents (DES) have widely replaced traditional organic solvents in the extraction process. In this study, the efficiencies of eight DESs in extracting flavonoids from Acanthopanax senticosus (AS) were compared. Response surface methodology (RSM) was employed to optimize the independent variable including ultrasonic power, water content, solid-liquid ratio, extraction temperature, and extraction time. DES composed of glycerol and levulinic acid (1:1) was chosen as the most suitable extraction medium. Optimal conditions were ultrasonic power of 500 W, water content of 28%, solid-liquid ratio of 1:18 g·mL−1, extraction temperature of 55 °C, and extraction time of 73 min. The extraction yield of total flavonoids reached 23.928 ± 0.071 mg·g−1, which was 40.7% higher compared with ultrasonic-assisted ethanol extraction. Macroporous resin (D-101, HPD-600, S-8 and AB-8) was used to recover flavonoids from extracts. The AB-8 resin showed higher adsorption/desorption performance, with a recovery rate of total flavonoids of up to 71.56 ± 0.256%. In addition, DES solvent could efficiently be reused twice. In summary, ultrasonic-assisted DES combined with the macroporous resin enrichment method is exceptionally effective in recovering flavonoids from AS, and provides a promising environmentally friendly and recyclable strategy for flavonoid extraction from natural plant sources.

The rapid proliferation of Internet of Things (IoT) devices and applications has led to an increasing demand for energy-efficient and secure communication in wireless sensor networks. In this article, we firstly propose an intelligent approach to maximize the energy efficiency of the UAV in a secure sensor network with wireless power transfer (WPT). All sensors harvest energy via downlink signal and use it to transmit uplink information to the UAV. To ensure secure data transmission, the UAV needs to optimize the transmission parameters to decode received information under malicious interference from an attacker. Code Division Multiple Access (CDMA) is adopted to improve uplink communication robustness. To maximize the UAV’s energy efficiency in data collection tasks, we formulate a constrained optimization problem that jointly optimizes charging power, charging duration, and data transmission duration. Applying Deep Deterministic Policy Gradient (DDPG) algorithm, we train an action policy to dynamically determine near-optimal transmission parameters in real time. Numerical results validate the superiority of proposed intelligent approach over exhaustive search and gradient ascent techniques. This work provides some important guidelines for the design of green secure wireless-powered sensor networks.

Programmed cell death (PCD) plays an important role in many aspects of individual development, maintenance of body homeostasis and pathological processes. Ferroptosis is a novel form of PCD characterized by the accumulation of iron-dependent lipid peroxides resulting in lethal cell damage. It contributes to tumor progression in an apoptosis-independent manner. In recent years, an increasing number of non-coding RNAs (ncRNAs) have been demonstrated to mediate the biological process of ferroptosis, hence impacting carcinogenesis, progression, drug resistance, and prognosis. However, the clear regulatory mechanism for this phenomenon remains poorly understood. Moreover, ferroptosis does not usually exist independently. Its interaction with PCD, like apoptosis, necroptosis, autophagy, pyroptosis, and cuproptosis, to destroy cells appears to exist. Furthermore, ncRNA seems to be involved. Here, we review the mechanisms by which ferroptosis occurs, dissect its relationship with other forms of death, summarize the key regulatory roles played by ncRNAs, raise relevant questions and predict possible barriers to its application in the clinic, offering new ideas for targeted tumour therapy.