An increasing number of evidences indicate microbes are implicated in human physiological mechanisms, including complicated disease pathology. Some microbes have been demonstrated to be associated with diverse important human diseases or disorders. Through investigating these disease-related microbes, we can obtain a better understanding of human disease mechanisms for advancing medical scientific progress in terms of disease diagnosis, treatment, prevention, prognosis and drug discovery. Based on the known microbe-disease association network, we developed a semi-supervised computational model of Laplacian Regularized Least Squares for Human Microbe–Disease Association (LRLSHMDA) by introducing Gaussian interaction profile kernel similarity calculation and Laplacian regularized least squares classifier. LRLSHMDA reached the reliable AUCs of 0.8909 and 0.7657 based on the global and local leave-one-out cross validations, respectively. In the framework of 5-fold cross validation, average AUC value of 0.8794 +/−0.0029 further demonstrated its promising prediction ability. In case studies, 9, 9 and 8 of top-10 predicted microbes have been manually certified to be associated with asthma, colorectal carcinoma and chronic obstructive pulmonary disease by published literature evidence. Our proposed model achieves better prediction performance relative to the previous model. We expect that LRLSHMDA could offer insights into identifying more promising human microbe-disease associations in the future.

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PSS layers were deposited on both sides of the CBC-IL membranes by a dip-coating technique to yield a sandwiched actuator system. Ionic conductivity and ionic exchange capacity of the CBC membrane can be increased up to 22.8 times and 1.5 times compared with pristine bacterial cellulose (BC), respectively, resulting in 8 times large bending deformation than the pure BC actuators with metallic electrodes in an open air environment. The developed CBC-IL actuators show significant progress in the development of biocompatible and soft actuating materials with quick response, low operating voltage and comparatively large bending deformation.

The development of ultralow voltage high‐performance bioartificial muscles with large bending strain, fast response time, and excellent actuation durability is highly desirable for promising applications such as soft robotics, active biomedical devices, flexible haptic displays, and wearable electronics. Herein, a novel high‐performance low‐priced bioartificial muscle based on functional carboxylated bacterial cellulose (FCBC) and polypyrrole (PPy) nanoparticles is reported, exhibiting a large bending strain of 0.93%, long actuated bending durability (96% retention for 5 h) under an ultralow harmonic input of 0.5 V, broad frequency bandwidth up to 10 Hz, fast response time (≈4 s) in DC responses, high energy density (6.81 KJ m−3), and high power density (5.11 KW m−3), all of which mainly stem from its high surface area and porosity, large specific capacitance, tuned mechanical properties, and strong ionic interactions of cations and anions in ionic liquid with FCBC and PPy nanoparticles. More importantly, bioinspired applications such as the grapple robot, bionic medical stent, bionic flower, and wings‐vibrating have been realized. These successful demonstrations offer a viable means for developing high‐performance bioartificial muscles for next‐generation soft bioelectronics including bioinspired robotics, biomedical microdevices, and wearable electronics.

New self-assembled hydrogels from poly-cyclodextrin and poly-adamantane are prepared via host–guest interactions, demonstrating potential applications as adsorption materials for wastewater treatment.

The increasing prevalence of fungal diseases, continual development of resistance, and stringent environmental regulations have revealed an urgent need to develop more selective, safer, resistance-breaking, and cost-effective fungicides. However, most new fungicidal lead compounds fail in their late stages of development as a result of poor solubility or permeability, meaning that they have suboptimal physicochemical properties. Hence, the exploration of advanced technologies for compound "fungicide-likeness" assessment might overcome these obstacles and bring more chemical entities to market. FungiPAD (http://chemyang.ccnu.edu.cn/ccb/database/FungiPAD/) is a free platform employed to predict physicochemical properties, bioavailability, and fungicide-likeness swiftly and powerfully using comprehensive approaches, such as physicochemical radars and qualitative and quantitative analyses. This platform contains data for over 16 000 physicochemical descriptors and the results of 2200 qualitative and 1100 quantitative analyses of marketed fungicides and provides comprehensive fungicide-likeness analysis for different compounds. The user-friendly interface facilitates interpretation and manipulation by non-computational scientists in support of fungicide discovery.

New two-component supramolecular hydrogels were prepared via a self-assembly process, demonstrating potential applications in adsorption and catalysis as well as sensor materials.

In human conversation an input post is open to multiple potential responses, which is typically regarded as a one-to-many problem. Promising approaches mainly incorporate multiple latent mechanisms to build the one-to-many relationship. However, without accurate selection of the latent mechanism corresponding to the target response during training, these methods suffer from a rough optimization of latent mechanisms. In this paper, we propose a multi-mapping mechanism to better capture the one-to-many relationship, where multiple mapping modules are employed as latent mechanisms to model the semantic mappings from an input post to its diverse responses. For accurate optimization of latent mechanisms, a posterior mapping selection module is designed to select the corresponding mapping module according to the target response for further optimization. We also introduce an auxiliary matching loss to facilitate the optimization of posterior mapping selection. Empirical results demonstrate the superiority of our model in generating multiple diverse and informative responses over the state-of-the-art methods.1 Multi-modal means the property with multiple modes.