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In this research, we have used a green approach to vanadium nanoparticle (NP) synthesis by the Salvia leriifolia watery extract. The NP characterization was performed by field emission scanning electron microscopy, fourier transform infrared, X-ray diffraction, and energy-dispersive X-ray spectroscopy. The NPs materialized in spherical morphology with an average size of 26.26 nm. The antioxidant activity was assessed using 2-2 diphenyl-1-picrylhydrazil (DPPH) assay, while the 3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide) assay was used to measure anti-colorectal cancer (against HCT-15‎, COLO 320, Caco-2, DLD-1‎, HT-29, and HCT-116 cell lines) and cytotoxicity (against HUVEC cell line) activity of vanadium NPs. Cancer cell line viability decreased dose dependently in the presence of V NPs. The half-maximal inhibitory concentration (IC50) values of V NPs were 149, 125, 173, 83, 131, and 105 µg/mL against Caco-2, COLO 320, DLD-1‎, HCT-15‎, HCT-116‎, and HT-29 cell lines, respectively. In radical scavenging activity, V NPs scavenged DPPH with the IC50 value of 33 µg/mL.

MXenes have shown great potential as an emerging two-dimensional (2D) material for micro-supercapacitors (MSCs) due to their high conductivity, rich surface chemistry, and high capacity. However, MXene sheets inherently tend to lay flat on the substrate during film formation to assemble into compact stacked structures, which hinders ion accessibility and prolongs ion transport paths, leading to highly dependent electrochemical properties on the thickness of the film. Here, we demonstrate a vertically aligned Ti3C2Tx MXene based micro-supercapacitor with an excellent electrochemical performance by a liquid nitrogen-assisted freeze-drying method. The vertical arrangement of the 2D MXene sheets allows for directional ion transport, enabling the vertical-MXene based MSCs to exhibit thickness-independent electrochemical properties even in thick films. In addition, the MSCs displayed a high areal capacitance of 87 mF cm−2 at 10 mV s−1 along with an excellent stability of ∼87.4% after 10 000 charge–discharge cycles. Furthermore, the vertical-MXene approach proposed here is scalable and can be extended to other systems involving directional transport. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the association between recurrence-free survival (RFS) and perirenal fat thickness (PFT) in a cohort of Chinese population with unilateral nephrolithiasis. We retrospectively reviewed the medical records of 81 patients with unilateral nephrolithiasis in our center from January 2019 to June 2019. PFT measured on computed tomography (CT) scans was evaluated. Kaplan-Meier curves and log-rank tests were used to assess significant differences in RSF between high-PFT and low-PFT groups within sexes. Univariable and multivariable Cox regression analyses were used to evaluate the potential risk factors for renal stone recurrence. High PFT was significantly associated with high BMI and hyperlipidemia (p =.003 and.047, respectively). The PFT of stone-bearing kidney was significantly greater than PFT of non-stone-bearing kidney (0.77 ± 0.60 cm vs. 0.67 ± 0.58 cm, p =.002). During the follow-up periods (median 31 months), 21 (25.9%) patients experienced ipsilateral renal stone recurrence. In addition, Kaplan–Meier survival curves showed that patients with low PFT had a significant better RFS than those with high PFT (p =.012). In the univariable Cox analyses, male sex and high PFT were significantly associated with a poor RFS (p =.042 and.018, respectively). Moreover, both male sex and high PFT retained significance in the multivariable analyses (p =.045 and.020, respectively). Our findings suggested that PFT is a noninvasive and feasible parameter, which may help in the risk stratification of renal stone recurrence in the follow-up periods. [ABSTRACT FROM AUTHOR]

Textile pattern design is a time‐consuming and tedious work. Mihui, our ongoing developing system, employs deep‐learning techniques to automatically generate huge volumes of patterns with the help of human guidance. However, trained as a black box, Mihui cannot provide customized service for each individual designer who shows unique aesthetics preferences. In this article, we introduce the recommendation module of Mihui. The module forwards all generated pattern images to a deep encoding network, where images are mapped into 128‐dimension vectors. For each user of Mihui, we create a profile by his/her purchased or downloaded history. A novel encoder network is proposed to learn a personal taste vector for each user, based on which, we recommend new patterns to him/her. Our records in Mihui show that the recommendation module effectively improve users' experience on Mihui. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) is an important means for machines to perceive human emotions and interact with human beings. Most of the existing facial expression recognition methods only use a single convolutional neural network to extract the global features of the face. Some insignificant details and features with low frequency are easy to be ignored, and part of the facial features are lost. This paper proposes a facial expression recognition method based on multi branch structure, which extracts the global and detailed features of the face from the global and local aspects respectively, so as to make a more detailed representation of the facial expression and further improve the accuracy of facial expression recognition. Specifically, we first design a multi branch network, which takes Resnet-50 as the backbone network. The network structure after Conv Block3 is divided into three branches. The first branch is used to extract the global features of the face, and the second and third branches are used to cut the face into two parts and three parts after Conv Block5 to extract the detailed features of the face. Finally, the global features and detail features are fused in the full connection layer and input into the classifier for classification. The experimental results show that the accuracy of this method is 73.7%, which is 4% higher than that of traditional Resnet-50, which fully verifies the effectiveness of this method.