Comparative Analysis of Platelet-Derived Extracellular Vesicles Using Flow Cytometry and Nanoparticle Tracking Analysis

Growing interest in extracellular vesicles (EVs) has prompted the advancements of protocols for improved EV characterization. As a high-throughput, multi-parameter, and single particle technique, flow cytometry is widely used for EV characterization. The comparison of data on EV concentration, however, is hindered by the lack of standardization between different protocols and instruments. Here, we quantified EV counts of platelet-derived EVs, using two flow cytometers (Gallios and CytoFLEX LX) and nanoparticle tracking analysis (NTA). Phosphatidylserine-exposing EVs were identified by labelling with lactadherin (LA). Calibration with silica-based fluorescent beads showed detection limits of 300 nm and 150 nm for Gallios and CytoFLEX LX, respectively. Accordingly, CytoFLEX LX yielded 40-fold higher EV counts and 13-fold higher counts of LA⁺CD41⁺ EVs compared to Gallios. NTA in fluorescence mode (F-NTA) demonstrated that only 9.5% of all vesicles detected in scatter mode exposed phosphatidylserine, resulting in good agreement of LA⁺ EVs for CytoFLEX LX and F-NTA. Since certain functional characteristics, such as the exposure of pro-coagulant phosphatidylserine, are not equally displayed across the entire EV size range, our study highlights the necessity of indicating the size range of EVs detected with a given approach along with the EV concentration to support the comparability between different studies.     

光伏电站箱变色谱普测现状与问题分析

介绍光伏电站的充油箱变色谱普测情况,通过统计分析氢气、总烃等指标超标率及故障类型,从设备、施工、运维等方面进行原因分析,提出了增加出厂前局部放电试验、加强工艺施工控制、有针对性的检修等整改措施和预防策略,对此类箱变的运行维护有一定的借鉴意义。     

Cytotoxic Potential of a-Azepano- and 3-Amino-3,4-SeCo-Triterpenoids

Semi-synthetic triterpenoids, holding an amino substituted seven-membered A-ring (azepano-ring), which could be synthesized from triterpenic oximes through a Beckmann type rearrangement followed by a reduction of lactame fragment, are considered to be novel promising agents exhibiting anti-microbial, alpha-glucosidase, and butyrylcholinesterase inhibitory activities. In this study, in an attempt to develop new antitumor candidates, a series of A-ring azepano- and 3-amino-3,4-seco-derivatives of betulin, oleanolic, ursolic, and glycyrrhetinic acids were evaluated for their cytotoxic activity against five human cancer cell lines and non-malignant mouse fibroblasts by means of a colorimetric sulforhodamine assay. Azepanoallobetulinic acid amide derivative 11 was the most cytotoxic compound of this series but showed little selectivity between the different human tumor cell lines. Flow cytometry experiments showed compound 11 to act mainly by apoptosis (44.3%) and late apoptosis (21.4%). The compounds were further screened at the National Cancer Institute towards a panel of 60 cancer cell lines. It was found that compounds 3, 4, 7, 8, 9, 11, 15, 16, 19, and 20 showed growth inhibitory (GI₅₀) against the most sensitive cell lines at submicromolar concentrations (0.20–0.94 μM), and their cytotoxic activity (LC₅₀) was also high (1–6 μM). Derivatives 3, 8, 11, 15, and 16 demonstrated a certain selectivity profile at GI₅₀ level from 5.16 to 9.56 towards K-562, CCRF-CEM, HL-60(TB), and RPMI-8226 (Leukemia), HT29 (Colon cancer), and OVCAR-4 (Ovarian cancer) cell lines. Selectivity indexes of azepanoerythrodiol 3 at TGI level ranged from 5.93 (CNS cancer cell lines SF-539, SNB-19 and SNB-75) to 14.89 for HCT-116 (colon cancer) with SI 9.56 at GI₅₀ level for the leukemia cell line K-562. The present study highlighted the importance of A-azepano-ring in the triterpenic core for the development of novel antitumor agents, and a future aim to increase the selectivity profile will thus lie in the area of modifications of azepano-triterpenic acids at their carboxyl group.     

Single-Cell Microgels for Diagnostics and Therapeutics

Cell encapsulation within hydrogel droplets is transforming what is feasible in multiple fields of biomedical science such as tissue engineering and regenerative medicine, in vitro modeling, and cell-based therapies. Recent advances have allowed researchers to miniaturize material encapsulation complexes down to single-cell scales, where each complex, termed a single-cell microgel, contains only one cell surrounded by a hydrogel matrix while remaining <100 μm in size. With this achievement, studies requiring single-cell resolution are now possible, similar to those done using liquid droplet encapsulation. Of particular note, applications involving long-term in vitro cultures, modular bioinks, high-throughput screenings, and formation of 3D cellular microenvironments can be tuned independently to suit the needs of individual cells and experimental goals. In this progress report, an overview of established materials and techniques used to fabricate single-cell microgels, as well as insight into potential alternatives is provided. This focused review is concluded by discussing applications that have already benefited from single-cell microgel technologies, as well as prospective applications on the cusp of achieving important new capabilities.     

虚实结合的可穿戴产品适合性检验研究

目的 为了更科学地研究和检验可穿戴产品的适合性,提出一种适合性检验方法,能够精确保留现实环境中的产品佩戴关系,并能将现实与虚拟的适合性检验研究相结合,得到合理的适合性检验结果.方法 以虚拟现实眼镜的适合性检验为例,通过高精度的三维测量技术将现实环境中的人、产品以及人—产品佩戴关系转化为三维虚拟信息,并以人—产品佩戴三维模型为参考基准对齐人和产品的虚拟模型,得到保留现实佩戴关系的人—产品佩戴模型组,再应用偏差分析法得出人—机佩戴区域的可视化适合性结果和统计数据,结合主观评价方法进一步分析产品的适合性.结论 虚实结合的产品适合性检验方法可在虚拟环境中高精度地保留现实环境中的人—产品佩戴关系,并能得到可视化的适合性检验结果,为检验和指导产品的适合性提供依据.     

keff uncertainty quantification and analysis due to nuclear data during the full lifetime burnup calculation for a small-sized prismatic high temperature gas-cooled reactor

To benefit from recent advances in modeling and computational algorithms,as well as the availability of new covariance data,sensitivity and uncertainty analyses are needed to quantify the impact of uncertain sources on the design parameters of small prismatic high-temperature gas-cooled reactors(HTGRs).In particular,the contribution of nuclear data to the keff uncertainty is an important part of the uncertainty analysis of small-sized HTGR physical calculations.In this study,a small-sized HTGR designed by China Nuclear Power Engineering Co.,Ltd.was selected for keff uncertainty analysis during full lifetime burnup calculations.Models of the cold zero power(CZP)condition and full lifetime burnup process were constructed using the Reactor Monte Carlo Code RMC for neutron transport calculation,depletion calculation,and sensitivity and uncertainty analysis.For the sensitivity analysis,the Contribution-Linked eigenvalue sensitivity/Uncertainty estimation via Track length importance Characterization(CLUTCH)method was applied to obtain sensitive infor-mation,and the"sandwich"method was used to quantify the keff uncertainty.We also compared the keff uncertainties to other typical reactors.Our results show that 235U is the largest contributor to keff uncertainty for both the CZP and depletion conditions,while the contribution of 239Pu is not very significant because of the design of low discharge burnup.It is worth noting that the radioactive capture reaction of 28Si significantly contributes to the keff uncer-tainty owing to its specific fuel design.However,the keff uncertainty during the full lifetime depletion process was relatively stable,only increasing by 1.12％owing to the low discharge burnup design of small-sized HTGRs.These numerical results are beneficial for neutronics design and core parameters optimization in further uncertainty prop-agation and quantification study for small-sized HTGR.     

Underwater image super-resolution using multi-stage information distillation networks

Recently, single image super-resolution (SISR) has been widely applied in the fields of underwater robot vision and obtained remarkable performance. However, most current methods generally suffered from the problem of a heavy burden on computational resources with large model sizes, which limited their real-world underwater robotic applications. In this paper, we introduce and tackle the super resolution (SR) problem for underwater robot vision and provide an efficient solution for near real-time applications. We present a novel lightweight multi-stage information distillation network, named MSIDN, for better balancing performance against applicability, which aggregates the local distilled features from different stages for more powerful feature representation. Moreover, a novel recursive residual feature distillation (RRFD) module is constructed to progressively extract useful features with a modest number of parameters in each stage. We also propose a channel interaction & distillation (CI&D) module that employs channel split operation on the preceding features to produce two-part features and utilizes the inter channel-wise interaction information between them to generate the distilled features, which can effectively extract the useful information of current stage without extra parameters. Besides, we present USR-2K dataset, a collection of over 1.6K samples for large-scale underwater image SR training, and a testset with an additional 400 samples for benchmark evaluation. Extensive experiments on several standard benchmark datasets show that the proposed MSIDN can provide state-of-the-art or even better performance in both quantitative and qualitative measurements.     

WavNet — Visual saliency detection using Discrete Wavelet Convolutional Neural Network

In the recent advancements in image and video analysis, the detection of salient regions in the image becomes the initial step. This plays a crucial role in deciding the performance of such algorithms. In this work, a Multi-Resolution Feature Extraction (MRFE) technique that makes use of Discrete Wavelet Convolutional Neural Network (DWCNN) for generating features is employed. An Enhanced Feature Extraction (EFE) module extracts additional features from the high level features of the DWCNN, which are used to frame both channel as well as spatial attention models for yielding contextual attention maps. A new hybrid loss function is also proposed, which is a combination of Balanced Cross Entropy (BCE) loss and Edge based Structural Similarity (ESSIM) loss that effectively identifies and segments the salient regions with clear boundaries. The method is tested exhaustively with five different benchmark datasets and is proved superior to the existing state-of-the-art methods with a minimum Mean Absolute error (MAE) of 0.03 and F-measure of 0.956.