4-Des­oxy-4β-(4-methoxy­carbonyl-1,2,3-triazol-1-yl)podophyllotoxin dichloro­methane solvate

In the title compound {systematic name: methyl 1-[12-oxo-10-(3,4,5-trimethoxy­phen­yl)-4,6,13-trioxa­tetra­cyclo­[7.7.0.03,7.011,15]hexa­deca-1,3(7),8-trien-16-yl]-1H-1,2,3-triazole-4-carboxyl­ate dichloro­methane solvate}, C26H25N3O9·CH2Cl2, the tetra­hydro­furan ring and the six-membered ring fused to it both display envelope conformations

Biotransformation of Oleanolic Acid by Penicillium melinii

Oleanolic acid (1) is a bioactive compound widely distributed in nature. Microbial transformation is an effective way to alter the structures of compounds. The result of biotransformation of oleanolic acid was carried out in this paper. Four transform products (2-5) from 1 by Penicillium melinii were isolated. Their structures were established by spectral data interpretation as 21β-hydroxyoleanolic acid (2), 21α-hydroxyoleanolic acid (3), canthic acid (4), and 7α, 21β-dihydroxyl oleanolic acid (5). Compound 3 had stronger antibacterial activities against MRSA and Staphylococcus aureus than the substrate. It was reported for the first time that the hydroxylation products of oleanic acid can be obtained by biotransformation using incubation with Penicillium melinii.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Trade-offs in carbon-degrading enzyme activities limit long-term soil carbon sequestration with biochar addition

We would like to thank all the authors whose data and work are included in this meta-analysis. This study was supported by the National Natural Science Foundation of China (32071595, 41830756 and 42177022). We also thank the Fundamental Research Funds for the Central Universities (Program no. 2662019QD055). We acknowledge Cunbin Gao, Qianqian Zhao and Qin Liu for their assistance in data collection. J.C. received funding from Aarhus Universitets Forskningsfond (AUFF-E-2019-7-1), EU H2020 Marie Skłodowska-Curie Actions (839806), Danish Independent Research Foundation (1127-00015B), and Nordic Committee of Agriculture and Food Research (https://nordicagriresearch.org/2020-5/). The authors declare no competing interests.Peer reviewedPublisher PD

Development and Application of a Tandem Force Sensor

In robot teaching for contact tasks, it is necessary to not only accurately perceive the traction force exerted by hands, but also to perceive the contact force at the robot end. This paper develops a tandem force sensor to detect traction and contact forces. As a component of the tandem force sensor, a cylindrical traction force sensor is developed to detect the traction force applied by hands. Its structure is designed to be suitable for humans to operate, and the mechanical model of its cylinder-shaped elastic structural body has been analyzed. After calibration, the cylindrical traction force sensor is proven to be able to detect forces/moments with small errors. Then, a tandem force sensor is developed based on the developed cylindrical traction force sensor and a wrist force sensor. The robot teaching experiment of drawer switches were made and the results confirm that the developed traction force sensor is simple to operate and the tandem force sensor can achieve the perception of the traction and contact forces

The Classification of Inertinite Macerals in Coal Based on the Multifractal Spectrum Method

Considering the heterogeneous nature and non-stationary property of inertinite components, we propose a texture description method with a set of multifractal descriptors to identify different macerals with few but effective features. This method is based on the multifractal spectrum calculated from the method of multifractal detrended fluctuation analysis (MF-DFA). Additionally, microscopic images of inertinite macerals were analyzed, which were verified to possess the property of multifractal. Simultaneously, we made an attempt to assess the influences of noise and blur on multifractal descriptors; the multifractal analysis was proven to be robust and immune to image quality. Finally, a classification model with a support vector machine (SVM) was built to distinguish different inertinite macerals from microscopic images of coal. The performance evaluation proves that the proposed descriptors based on multifractal spectrum can be successfully applied in the classification of inertinite macerals. The average classification precision can reach 95.33%, higher than that of description method with gray level co-occurrence matrix (GLCM; about 7.99%)

AdvMix: Adversarial Mixing Strategy for Unsupervised Domain Adaptive Object Detection

Recent object detection networks suffer from performance degradation when training data and test data are distinct in image styles and content distributions. In this paper, we propose a domain adaptive method, Adversarial Mixing (AdvMix), where the label-rich source domain and unlabeled target domain are jointly trained by the adversarial feature alignment and a self-training strategy. To diminish the style gap, we design the Adversarial Gradient Reversal Layer (AdvGRL), containing a global-level domain discriminator to align the domain features by gradient reversal, and an adversarial weight mapping function to enhance the stability of domain-invariant features by hard example mining. To eliminate the content gap, we introduce a region mixing self-supervised training strategy where a region of the target image with the highest confidence is selected to merge with the source image, and the synthesis image is self-supervised by the consistency loss. To improve the reliability of self-training, we propose a strict confidence metric combining both object and bounding box uncertainty. Extensive experiments conducted on three benchmarks demonstrate that AdvMix achieves prominent performance in terms of detection accuracy, surpassing existing domain adaptive methods by nearly 5% mAP