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Aiming at active space debris capturing, this paper proposes a novel tensegrity spine robot, inspired by the structure and mechanism of vertebrates’ spine. It consists of a series of conical rigid elements, which are linked with springs and stabilized in a tension network. The continuous bending of the robot is achieved by the actuation of three sets of cables. A prototype is built and a closed-loop control method is proposed for proof-of-concept experiments, including bending motion control, capturing a non-cooperative target, and tests of load performance. The experimental results show that the proposed tensegrity spine robot has potential application prospects in space debris capturing.

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Candida albicans is an important human fungal pathogen. Our previous study disclosed that aryloxy‐phenylpiperazine skeleton was a promising molecule to suppress C. albicans virulence by inhibiting hypha formation and biofilm formation. In order to deeply understand the efficacy and mechanism of action of phenylpiperazine compounds, and obtain new derivatives with excellent activity against C. albicans, hence, we synthesized three series of (1‐heteroaryloxy‐2‐hydroxypropyl)‐phenylpiperazines and evaluated their inhibitory activity against C. albicans both in vitro and in vivo in this study. Compared with previously reported aryloxy‐phenylpiperazines, part of these heteroaryloxy derivatives improved their activities by strongly suppressing hypha formation and biofilm formation in C. albicans SC5314. Especially, (9H‐carbazol‐4‐yl)oxy derivatives 25, 26, 27 and 28 exhibited strong activity in reducing C. albicans virulence in both human cell lines in vitro and mouse infection models in vivo. The compound 27 attenuated the virulence of various clinical C. albicans strains, including clinical drug‐resistant C. albicans strains. Moreover, additive effects of the compound 27 with antifungal drugs against drug‐resistant C. albicans strains were also discussed. Furthermore, the compound 27 significantly improved the composition and richness of the faecal microbiota in mice infected by C. albicans. These findings indicate that these piperazine compounds have great potential to be developed as new therapeutic drugs against C. albicans infection.

Introduction: With the advancement of RNA-seq technology and machine learning, training large-scale RNA-seq data from databases with machine learning models can generally identify genes with important regulatory roles that were previously missed by standard linear analytic methodologies. Finding tissue-specific genes could improve our comprehension of the relationship between tissues and genes. However, few machine learning models for transcriptome data have been deployed and compared to identify tissue-specific genes, particularly for plants.Methods: In this study, an expression matrix was processed with linear models (Limma), machine learning models (LightGBM), and deep learning models (CNN) with information gain and the SHAP strategy based on 1,548 maize multi-tissue RNA-seq data obtained from a public database to identify tissue-specific genes. In terms of validation, V-measure values were computed based on k-means clustering of the gene sets to evaluate their technical complementarity. Furthermore, GO analysis and literature retrieval were used to validate the functions and research status of these genes.Results: Based on clustering validation, the convolutional neural network outperformed others with higher V-measure values as 0.647, indicating that its gene set could cover as many specific properties of various tissues as possible, whereas LightGBM discovered key transcription factors. The combination of three gene sets produced 78 core tissue-specific genes that had previously been shown in the literature to be biologically significant.Discussion: Different tissue-specific gene sets were identified due to the distinct interpretation strategy for machine learning models and researchers may use multiple methodologies and strategies for tissue-specific gene sets based on their goals, types of data, and computational resources. This study provided comparative insight for large-scale data mining of transcriptome datasets, shedding light on resolving high dimensions and bias difficulties in bioinformatics data processing.