Coronary Plaque Characterization From Optical Coherence Tomography Imaging With a Two-Pathway Cascade Convolutional Neural Network Architecture

Background: The morphological structure and tissue composition of a coronary atherosclerotic plaque determine its stability, which can be assessed by intravascular optical coherence tomography (OCT) imaging. However, plaque characterization relies on the interpretation of large datasets by well-trained observers. This study aims to develop a convolutional neural network (CNN) method to automatically extract tissue features from OCT images to characterize the main components of a coronary atherosclerotic plaque (fibrous, lipid, and calcification). The method is based on a novel CNN architecture called TwopathCNN, which is utilized in a cascaded structure. According to the evaluation, this proposed method is effective and robust in the characterization of coronary plaque composition from in vivo OCT imaging. On average, the method achieves 0.86 in F1-score and 0.88 in accuracy. The TwopathCNN architecture and cascaded structure show significant improvement in performance (p < 0.05). CNN with cascaded structure can greatly improve the performance of characterization compared to the conventional CNN methods and machine learning methods. This method has a higher efficiency, which may be proven to be a promising diagnostic tool in the detection of coronary plaques

Characterization of coronary atherosclerotic plaque composition based on Convolutional Neural Network (CNN)

The tissue composition and morphological structure of atherosclerotic plaques determine its stability or vulnerability. Intravascular optical coherence tomography ( IV OCT) has rapidly become the method of choice for assessing the pathology of the coronary arterial wall in vivo due to its superior resolution. However, in clinical practice, the analysis of plaque composition of OCT images mainly relies on the interpretation of images by well - trained experts, which is a time - consuming, labor - intensive procedure and it is also subjective . The purpose of this study is to use the Convolutional neural network ( CNN) method to automatically extract the best feature information from the OCT images to characterize the three basic components of atherosclerotic plaque (fibrous, lipid, and calcification). This study select ed the OCT images of 20 patients from Nanjing Drum Tower Hospital from 2015.12 to 2016.12. The OCT - reading expert first excluded the image s containing the brackets, and then divide d all the remaining images, resulting in 1500 plaque OCT images. The expert label ed plaque composition in each image, cut ting it into 11*11 image patches and obtained 87390 patches. 75000 of them were set as training examples and the others were set for testing. The classification accuracy of the test set serve d as the evaluation criterion. The experimental results show that the average classification accuracy of the fibrous, calcification, and lipid patches by the CNN classifier as over 75 %, especially to characterize the fibrous patches, whose accuracy could reach more than 80% . The proposed method is effective and robust in the analysis of atherosclerotic plaque composition in coronary OCT images, providing a base for further segmentation study

Critical Procedure Identification Method Considering the Key Quality Characteristics of the Product Manufacturing Process

The product&rsquo;s manufacturing process has an evident influence on product quality. In order to control the quality and identify the critical procedure of the product manufacturing process reasonably and effectively, a method combining genetic back-propagation (BP) neural network algorithm and grey relational analysis is proposed. Firstly, the genetic BP neural network algorithm is used to obtain the key quality characteristics (KQCs) in the product manufacturing process. At the same time, considering the three factors that have an essential impact on the quality of the procedures, the grey correlation analysis method is used to establish the correlation scoring matrix between the procedure and the KQCs to calculate the criticality of each procedure. Finally, taking the manufacturing process of the evaporator as a case, the application process of this method is introduced, and four critical procedures are identified. It provides a reference for the procedure quality control and improvement of enterprise in the future

Structure and electrochemical performance modulation of a LiNi0.8Co0.1Mn0.1O2 cathode material by anion and cation co-doping for lithium ion batteries

Ni-rich layered transition metal oxides show great energy density but suffer poor thermal stability and inferior cycling performance, which limit their practical application. In this work, a minor content of Co and B were co-doped into the crystal of a Ni-rich cathode (LiNi0.8Co0.1Mn0.1O2) using cobalt acetate and boric acid as dopants. The results analyzed by XRD, TEM, XPS and SEM reveal that the modified sample shows a reduced energy barrier for Li+ insertion/extraction and alleviated Li+/Ni2+ cation mixing. With the doping of B and Co, corresponding enhanced cycle stability was achieved with a high capacity retention of 86.1% at 1.0C after 300 cycles in the range of 2.7 and 4.3 V at 25 °C, which obviously outperformed the pristine cathode (52.9%). When cycled after 300 cycles at 5C, the material exhibits significantly enhanced cycle stability with a capacity retention of 81.9%. This strategy for the enhancement of the electrochemical performance may provide some guiding significance for the practical application of high nickel content cathodes

Highly Stabilized Ni-Rich Cathode Material with Mo Induced Epitaxially Grown Nanostructured Hybrid Surface for High-Performance Lithium-Ion Batteries

Capacity fading induced by unstable surface chemical properties and intrinsic structural degradation is a critical challenge for the commercial utilization of Ni-rich cathodes. Here, a highly stabilized Ni-rich cathode with enhanced rate capability and cycling life is constructed by coating the molybdenum compound on the surface of LiNi 0.815 Co 0.15 Al 0.035 O 2 secondary particles. The infused Mo ions in the boundaries not only induce the Li 2 MoO 4 layer in the outermost but also form an epitaxially grown outer surface region with a NiO-like phase and an enriched content of Mo 6+ on the bulk phase. The Li 2 MoO 4 layer is expected to reduce residential lithium species and promote the Li + transfer kinetics. The transition NiO-like phase, as a pillaring layer, could maintain the integrity of the crystal structure. With the suppressed electrolyte-cathode interfacial side reactions, structure degradation, and intergranular cracking, the modified cathode with 1% Mo exhibits a superior discharge capacity of 140 mAh g -1 at 10 C, a superior cycling performance with a capacity retention of 95.7% at 5 C after 250 cycles, and a high thermal stability

Constructing a Protective Pillaring Layer by Incorporating Gradient Mn4+to Stabilize the Surface/Interfacial Structure of LiNi0.815Co0.15Al0.035O2 Cathode

Nickel-rich layered oxides are regarded as very promising materials as cathodes for lithium-ion batteries because of their environmental benignancy, low cost, and high energy density. However, insufficient cycle performance and poor thermotic characteristics induced by structural degradation at high potentials and elevated temperatures pose challenging hurdles for nickel-rich cathodes. Here, a protective pillaring layer, in which partial Ni2+ ions occupy Li slabs induced by gradient Mn4+, is integrated into the primary particle of LiNi0.815Co0.15Al0.035O2 to stabilize the surface/ interfacial structure. With the stable outer surface provided by the enriched Mn4+ gradient concentration and the pillar effect of the NiO-like phase, Mn-incorporated quaternary cathodes show enhanced structural stability and improved Li+ diffusion as well as lithium-storage properties. Compared with the severe capacity fade of a pure layered structure, the cathode with gradient Mn4+ exhibits more stable cycling behavior with a capacity retention of 80.0% after 500 cycles at 5.0 C

Molecular characterization and function analysis of the rice OsDUF1664 family

The functions of a large number of genes, including gene families with domains of unknown functions (DUF), still remain unclear. In this study, we analyzed four members of OsDUF1664 (OsDUF1664.1-OsDUF1664.4) in rice Nipponbare. By phylogenetic analysis, DUF1664 members in rice and Arabidopsis were classified into three major groups (I, II, III). Under drought conditions, the expression level of OsDUF1664.3 in rice was significantly elevated. Overexpression of OsDUF1664.3 in Escherichia coli led to a significant enhancement of catalase (CAT) and peroxidase (POD) activities, and improved bacterial resistance to drought. The results of this study will provide important information for further study of the function of the OsDUF1664 family in rice

Molecular characterization and function analysis of the rice OsDUF617 family

With the advance of sequencing technology, the number of sequenced genomes has been rapidly increasing. However, the functions of a large number of genes, including gene families with domains of unknown functions (DUF), still remain unclear. In this study, we analysed eight members of OsDUF617 (OsDUF617.1-OsDUF617.8) in rice Nipponbare. By phylogenetic analysis, all these OsDUF617 proteins could be classified into three major groups (I, II, III). We used real-time polymerase chain reaction to examine the expression of all these OsDUF617 members in 15 distinct rice tissues. The expression of these members under various abiotic stress and abscisic acid (ABA) conditions was also examined. Under drought conditions, the expression level of OsDUF617.8 in rice was significantly elevated. Overexpression of OsDUF617.8 in Escherichia coli led to a significant enhancement of catalase (CAT) and peroxidase (POD) activities, and improved bacterial resistance to osmotic stress. The results of this study will provide important information for further study of the function of the OsDUF617 family in rice. Supplemental data for this article is available online at 10.1080/13102818.2021.1934541

Experimental and theoretical studies on the inhibition properties of three diphenyl disulfide derivatives on copper corrosion in acid medium

2,2'-Dithiosalicylic acid (DSA), 2-aminophenyl disulfide (APD) and 2,2-dibenzamidodiphenyl disulfide (DPD) were determined for corrosion inhibition of Cu in H2SO4 media by electrochemical tests, surface morphology analysis, quantum chemical calculations and molecular dynamics simulations. The results of polarization curves showed that DSA, APD and DPD reveal good anti-corrosion capacity. They can simultaneously inhibit the cathodic and anodic reactions of copper. Therefore, they belong to the mixed-type corrosion inhibitors. Impedance spectroscopy results showed that when DSA, APD and DPD adsorption on the surface of Cu, the charge transfer resistance increases significantly and typical capacitance behavior produced, which indicates that the formed inhibitor film is very dense and ordered. In addition, the adsorption of corrosion inhibitors on the Cu surface is conforming to Langmuir monolayer adsorption. The experimental results obtained by surface topography analysis are consistent with the results of electrochemical experiments. Their corrosion inhibition ability is DSA < APD < DPD. Theoretical calculations further explore the relationship between corrosion inhibition performance and their molecular configurations. (C) 2019 Elsevier B.V. All rights reserved