The Sorption of Sulfamethoxazole by Aliphatic and Aromatic Carbons from Lignocellulose Pyrolysis

Massive biomass waste with lignocellulose components can be used to produce biochar for environmental remediation. However, the impact of lignocellulose pyrolysis on biochar structure in relation to the sorption mechanism of ionizable antibiotics is still poorly understood. In this paper, diverse techniques including thermogravimetric analysis and 13C nuclear magnetic resonance were applied to investigate the properties of biochars as affected by the pyrolysis of cellulose and lignin in feedstock. Cellulose-derived biochars possessed more abundant groups than lignin-derived biochars, suggesting the greater preservation of group for cellulose during the carbonization. Higher sorption of sulfamethoxazole (SMX) was also observed by cellulose-derived biochars owing to hydrogen bond interaction. Sorption affinity gradually declined with the conversion aliphatic to aromatic carbon, whereas the enhanced specific surface area (SSA) subsequently promoted SMX sorption as evidenced by increased SSA-N2 and SSA-CO2 from 350 to 450 °C. The decreased Kd/SSA-N2 values with increasing pH values implied a distinct reduction in sorption per unit area, which could be attributed to enhanced electrostatic repulsion. This work elucidated the role of carbon phases from thermal conversion of lignocellulose on the sorption performance for sulfonamide antibiotics, which will be helpful to the structural design of carbonaceous adsorbents for the removal of ionizable antibiotics

Mesenchymal stromal cells pretreated with pro‐inflammatory cytokines promote skin wound healing through VEGFC ‐mediated angiogenesis

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Conformational and functional significance of residue proline 17 in chicken muscle adenylate kinase

AbstractThe effect of mutation proline 17 on the multiple conformations and catalytic function in chicken muscle adenylate kinase (AK) has been studied. The substitution of proline 17 with glycine or valine altered the distribution of multiple conformations. Compared with the wild-type enzyme, the P17G and P17V mutants contained decreased fraction of minor conformer from 18% to 9% and 11%, respectively. Due to the mutation, the enzyme showed lower secondary structural content, poorer affinity to substrates or substrate analogues, and reduced catalytic efficiency. The results revealed the significance of proline 17 in the conformation and function of AK

Identifying urban functional regions from high-resolution satellite images using a context-aware segmentation network

The automatic identification of urban functional regions (UFRs) is crucial for urban planning and management. A key issue involved in URF classification is to properly determine the basic functional units, for which popular practices are usually based upon existing land use boundaries or road networks. Such practices suffer from the unavailability of existing datasets, leading to difficulty in large-scale mapping. To deal with this problem, this paper presents a method to automatically obtain functional units for URF classification using high-resolution remote sensing images. We develop a context-aware segmentation network to simultaneously extract buildings and road networks from remote sensing images. The extracted road networks are used for partitioning functional units, upon which five main building types are distinguished considering building height, morphology, and geometry. Finally, the UFRs are classified according to the distribution of building types. We conducted experiments using a GaoFen-2 satellite image with a spatial resolution of 0.8 m acquired in Fuzhou, China. Experimental results showed that the proposed segmentation network performed better than other convolutional neural network segmentation methods (i.e., PSPNet, Deeplabv3+, DANet, and JointNet), with an increase of F1-score up to (Formula presented.) and (Formula presented.) for road and building extraction, respectively. Results also showed that the residential regions, accounting for most of the urban areas, identified by the proposed method had a user accuracy of (Formula presented.), implying the promise of the proposed method for deriving the spatial units and the types of urban functional regions

Empirical Study and Improvement on Deep Transfer Learning for Human Activity Recognition

Human activity recognition (HAR) based on sensor data is a significant problem in pervasive computing. In recent years, deep learning has become the dominating approach in this field, due to its high accuracy. However, it is difficult to make accurate identification for the activities of one individual using a model trained on data from other users. The decline on the accuracy of recognition restricts activity recognition in practice. At present, there is little research on the transferring of deep learning model in this field. This is the first time as we known, an empirical study was carried out on deep transfer learning between users with unlabeled data of target. We compared several widely-used algorithms and found that Maximum Mean Discrepancy (MMD) method is most suitable for HAR. We studied the distribution of features generated from sensor data. We improved the existing method from the aspect of features distribution with center loss and get better results. The observations and insights in this study have deepened the understanding of transfer learning in the activity recognition field and provided guidance for further research

Application of Optical Motion Capture Technology in Power Safety Entitative Simulation Training System

Abstract The safety production is critical to stable development of Chinese electric power industry. With the development of electric power enterprises, the requirements of its employees are also becoming higher and higher. In this paper, an optical motion capture system based on the virtual reality technology is proposed to meet the requirements of the power enterprise for the qualified business ability. Electric power equipment, power equipment model entitative operating environment and the human model are established by electric power simulation unit, ZigBee technology and OpenGL graphics library. The problem of missing feature points is solved by applying the human model driven algorithm and the Kalman filtering algorithm. The experimental results show that it is more accurate to use Kalman filtering algorithm to extract the feature point in tracking process of actual motion capture and real-time animation display. The average absolute error of 3D coordinates is 1.61 mm and the average relative error is 2.23%. The system can improve trainees' sense of experience and immersion

Supplemental Material - Characterizing the inherent activity of urinary bladder matrix for adhesion, migration, and activation of fibroblasts as compared with collagen-based synthetic scaffold

Supplemental Material for Characterizing the inherent activity of urinary bladder matrix for adhesion, migration, and activation of fibroblasts as compared with collagen-based synthetic scaffold by Xiaoyu Tang, Fengbo Yang, Guoping Chu, Xiaoxiao Li, Qiuyan Fu, Mingli Zou, Peng Zhao, and Guozhong Lu in Journal of Biomaterials Applications</p