Distinct Pharmacological Properties of Gaseous CO and CO-Releasing Molecule in Human Platelets

Carbon monoxide (CO)—gaseous or released by CO-RMs—both possess antiplatelet properties; however, it remains uncertain whether the mechanisms involved are the same. Here, we characterise the involvement of soluble guanylate cyclase (sGC) in the effects of CO—delivered by gaseous CO–saturated buffer (CO_G) and generated by CORM-A1—on platelet aggregation and energy metabolism, as well as on vasodilatation in aorta, using light transmission aggregometry, Seahorse XFe technique, and wire myography, respectively. ODQ completely prevented the inhibitory effect of CO_G on platelet aggregation, but did not modify antiplatelet effect of CORM-A1. In turn, CO_G did not affect, whereas CORM-A1 substantially inhibited energy metabolism in platelets. Even though activation of sGC by BAY 41-2272 or BAY 58-2667 inhibited significantly platelet aggregation, their effects on energy metabolism in platelets were absent or weak and could not contribute to antiplatelet effects of sGC activation. In contrast, vasodilatation of murine aortic rings, induced either by CO_G or CORM-A1, was dependent on sGC. We conclude that the source (CO_G vs. CORM-A1) and kinetics (rapid vs. slow) of CO delivery represent key determinants of the mechanism of antiplatelet action of CO, involving either impairment of energy metabolism or activation of sGG.     

Computational Investigation Identified Potential Chemical Scaffolds for Heparanase as Anticancer Therapeutics

Heparanase (Hpse) is an endo-β-D-glucuronidase capable of cleaving heparan sulfate side chains. Its upregulated expression is implicated in tumor growth, metastasis and angiogenesis, thus making it an attractive target in cancer therapeutics. Currently, a few small molecule inhibitors have been reported to inhibit Hpse, with promising oral administration and pharmacokinetic (PK) properties. In the present study, a ligand-based pharmacophore model was generated from a dataset of well-known active small molecule Hpse inhibitors which were observed to display favorable PK properties. The compounds from the InterBioScreen database of natural (69,034) and synthetic (195,469) molecules were first filtered for their drug-likeness and the pharmacophore model was used to screen the drug-like database. The compounds acquired from screening were subjected to molecular docking with Heparanase, where two molecules used in pharmacophore generation were used as reference. From the docking analysis, 33 compounds displayed higher docking scores than the reference and favorable interactions with the catalytic residues. Complex interactions were further evaluated by molecular dynamics simulations to assess their stability over a period of 50 ns. Furthermore, the binding free energies of the 33 compounds revealed 2 natural and 2 synthetic compounds, with better binding affinities than reference molecules, and were, therefore, deemed as hits. The hit compounds presented from this in silico investigation could act as potent Heparanase inhibitors and further serve as lead scaffolds to develop compounds targeting Heparanase upregulation in cancer.     

高稳高效海上风电安装平台关键制造技术研究

海上风电作为可再生清洁能源之一,受到世界各国的高度重视与大力发展。我国将海上风电提升至解决能源危机、减缓气候变化、调整能源结构的国家战略高度,到2030年我国单位国内生产总值二氧化碳排放将比2005年下降65%以上,非化石能源占一次能源消费比重将达到25%左右。安装平台不足将是我国海上风电场无法如期建成投产的主要障碍。对自升自航式海上风电安装平台系列高端装备及其设计制造的三大技术难题——腿站立作业易“失稳”、大平台大跨距大倾覆力矩自升易“失控”、高空吊装巨型叶片逾百螺栓精准定位易“失准”,以及焊缝缺陷修复和局部裂纹损伤的激光锻造修复再制造进行了介绍,研制的具有不同规格的系列装备在中国、英国、丹麦、德国等国家的著名海上风电场建设应用情况良好。     

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.     

Vulnerable objects detection for autonomous driving: A review

Object detection performed by Autonomous Vehicles (AV)s is a crucial operation that comes ahead of various autonomous driving tasks, such as object tracking, trajectories estimation, and collision avoidance. Dynamic road elements (pedestrians, cyclists, vehicles) impose a greater challenge due to their continuously changing location and behaviour. This paper presents a comprehensive review of the state-of-the-art object detection technologies focusing on both the sensory systems and algorithms used. It begins with a brief introduction on the autonomous driving operations and challenges. Then, different sensory systems employed on existing AVs are elaborated while illustrating their advantages, limitations and applications. Also, sensory systems employed by different research are reviewed. Moreover, due to the significant role Deep Neural Networks (DNN)s are playing in object detection tasks, different DNN-based networks are also highlighted. Afterwards, previous research on dynamic objects detection performed by AVs are reviewed in tabular forms. Finally, a conclusion summarizes the outcomes of the review and suggests future work towards the development of vehicles with higher automation levels.     

Achieving excellent energy storage density of Pb0.97La0.02(ZrxSn0.05Ti0.95-x)O3 ceramics by the B-site modification

The applications of antiferroelectric (AFE) materials in miniaturized and integrated electronic devices are limited by their low energy density. To address the above issue, the antiferroelectricity of the reinforced material was designed to improve its AFE-ferroelectric (FE) phase transition under electric fields. In this present study, the composition of Zr⁴⁺ (0.72 Å) and Ti⁴⁺ (0.605 Å) at B-site of Pb_0.97La_0.02(Zr_xSn_0.05Ti_0.95-x)O₃ ceramics with orthogonal reflections are synthesized via the tape-casting method. These ceramics are modified to enhance their antiferroelectricity by reducing their tolerance factor. A recoverable energy storage density W_rec 12.1 J/cm³ was obtained for x = 0.93 under 376 kV/cm, which is superior value than reported until now in lead-based energy storage systems. Moreover, the discharge energy density can reach 10.23 J/cm³, and 90 % of which can be released within 5.66 μs. This work provides a new window and potential materials for further industrialization of pulse power capacitors.     

食品安全国家标准中维生素的测定方法及进展

人体所必需的营养素除了蛋白质、脂类、糖类等能量物质外,维生素也是不可或缺的营养物质,是评价其营养质量的关键指标。检测食品中的维生素含量,是有效保证其质量与安全的必要条件。因此,对现有国家标准食品中维生素的检测方法进行综述,总结各方法的前处理及检出限等,为增强检测工作效率,以及食品检测方法的研究和发展提供参考。     

基于卷积神经网络的安全防御模式研究

云计算、大数据、5G通信等技术的快速发展，有力的促进了计算机网络在多个领域的普及和使用，人们已经进入到了“互联网+”时代，实现了工作、生活和学习的数字化、智能化和共享化。计算机网络在为人们提供各种便利服务的同时也面临着许多的安全威胁，比如木马、病毒等，不法分子攻击网络数据中心，破坏用户服务终端及服务器，为人们带来严重的威胁。网络安全学者或企业机构也提出了许多安全防御技术，比如入侵检测技术、深度包过滤技术、防火墙技术、数据加密技术等，一定程度上提高了网络安全防御的能力，但是随着互联网数据流量的增多，需要引入更加先进的人工智能技术，以便能够快速的采集、分析网络数据流，确定数据流中是否存在病毒或木马，以便及时的对其进行查杀，进一步提高网络安全防御的主动性、积极性     

Impact behavior of aminosilane functionalized nanosilica based shear thickening fluid impregnated Kevlar fabrics

In the present work, two types of shear thickening fluids have been synthesized by using neat and aminosilane functionalized silica nanoparticles and their viscosity curves have been obtained by the rheometer. Based on the values of peak viscosity of synthesized shear thickening fluids, the surface functionalized nanosilica based shear thickening fluid has been chosen as a best candidate due to the high viscosity for impregnation into the neat Kevlar of different layers viz. four (04) and eight (08) layers for velocity impact study. The experimental investigations reveal high energy absorption of shear thickening fluid impregnated Kevlar as compared to the neat Kevlar. The maximum energy absorption 62 J is achieved corresponding to the initial velocity 154 m∙s⁻¹ for 08 layers shear thickening fluid impregnated Kevlar specimen. The data have also been analytically determined and validated with the experimental data. The experimental data have good agreement with the analytical data within the accuracy of around 15 to 20%. The present findings can have significant inferences towards the fabrication of shear thickening fluids using nanomaterials for numerous applications such as soft armors, dampers, nanofinishing and so forth.     

Transmission line detection in aerial images: An instance segmentation approach based on multitask neural networks

Camera-based transmission line detection (TLD) is a fundamental and crucial task for automatically patrolling powerlines by aircraft. Motivated by instance segmentation, a TLD algorithm is proposed in this paper with a novel deep neural network, i.e., CableNet. The network structure is designed based on fully convolutional networks (FCNs) with two major improvements, considering the specific appearance characteristics of transmission lines. First, overlaying dilated convolutional layers and spatial convolutional layers are configured to better represent continuous long and thin cable shapes. Second, two branches of outputs are arranged to generate multidimensional feature maps for instance segmentation. Thus, cable pixels can be detected and assigned cable IDs simultaneously. Multiple experiments are conducted on aerial images, and the results show that the proposed algorithm obtains reliable detection performance and is superior to traditional TLD methods. Meanwhile, segmented pixels can be accurately identified as cable instances, contributing to line fitting for further applications.