bluuebunny/crossref_metadata_2025 huggingface dataset:
Statistical Modeling and Optimization of Laser Cladding Stellite 6/WC Composite Coating
Authors: Wu Teng , Shi Wenqing , Xie Linyi , Gong Meimei , Huang Jiang , Xie Yuping , He Kuanfang
Published: May 2022

A response surface was employed to establish a statistical model for influencing factors and response targets to study the influence of laser cladding process parameters on the quality of Co-based WC composite coating and reduce defects in the cladding layer. Analysis showed that the cladding step and laser power were the most significant factors affecting the coating’s porosity area and surface flatness. Increasing the amount of WC and the laser power significantly enhanced the hardness of the coating. The validation experiments under optimized conditions showed that the predicted value of the model was in good agreement with the actual value, and the average error was less than 6%. This study presents the preparation of Co-based WC ceramic composite coating by laser cladding and the optimization of process parameters.
An Analysis of New Feature Extraction Methods Based on Machine Learning Methods for Classification Radiological Images
Authors: Zadeh Firoozeh Abolhasani , Ardalani Mohammadreza Vazifeh , Salehi Ali Rezaei , Jalali Farahani Roza , Hashemi Mandana , Mohammed Adil Hussein
Published: May 2022

The lungs are COVID-19’s most important focus, as it induces inflammatory changes in the lungs that can lead to respiratory insufficiency. Reducing the supply of oxygen to human cells negatively impacts humans, and multiorgan failure with a high mortality rate may, in certain circumstances, occur. Radiological pulmonary evaluation is a vital part of patient therapy for the critically ill patient with COVID-19. The evaluation of radiological imagery is a specialized activity that requires a radiologist. Artificial intelligence to display radiological images is one of the essential topics. Using a deep machine learning technique to identify morphological differences in the lungs of COVID-19-infected patients could yield promising results on digital images of chest X-rays. Minor differences in digital images that are not detectable or apparent to the human eye may be detected using computer vision algorithms. This paper uses machine learning methods to diagnose COVID-19 on chest X-rays, and the findings have been very promising. The dataset includes COVID-19-enhanced X-ray images for disease detection using chest X-ray images. The data were gathered from two publicly accessible datasets. The feature extractions are done using the gray level co-occurrence matrix methods. K-nearest neighbor, support vector machine, linear discrimination analysis, naïve Bayes, and convolutional neural network methods are used for the classification of patients. According to the findings, convolutional neural networks’ efficiency linked to imaging modalities with fewer human involvements outperforms other traditional machine learning approaches.
Classification Algorithm-Based fMRI Images for Evaluating the Effect of Yishen Tiaodu Acupuncture on the Recovery Period of Cerebral Infarction
Authors: Feng Zhuo , Hu Miaomiao , Yuan Wei , Zhao Xiaojun , Zeng Jiazhi , Zhou Kaibin
Published: May 2022

This study aimed to explore the application value of multifeature fusion classification algorithm based on deep learning and Yishen Tiaodu acupuncture in the diagnosis and treatment of patients with cerebral infarction in convalescence. Methods. 62 patients with cerebral infarction were randomly classified into the experimental group and the control group, with 31 patients in each group. All patients received the functional magnetic resonance imaging (fMRI) examination. The image processing method was the multifeature fusion classification algorithm based on deep learning. DICE coefficient, accuracy, and sensitivity were used to evaluate the image processing performance of traditional and new algorithms. Patients in the experimental group were treated with Yishen Tiaodu acupuncture, while patients in the control group were treated with ordinary acupuncture. The evaluation of the cyberchondria severity scale (CSS) and the activities of daily living (ADL) was performed at enrollment, 15 days after treatment, 28 days after treatment, and 1 month after treatment. The results showed that the quality of fMRI images processed by multifeature fusion classification algorithm based on deep learning was signally improved. The clinical efficacy of the traditional Chinese medicine (TCM) syndrome score (86.7% vs. 60.9%) and neurological impairment score (83.4% vs. 53.5%) in the experimental group were remarkably higher compared with the control group

P
<
0.05

. After treatment, the TCM syndrome score of the experimental group was markedly lower than that of the control group, while the ADL score was higher

P
<
0.05

. Conclusion. The performance of multifeature fusion classification algorithm based on deep learning in fMRI image processing of patients with cerebral infarction is better than that of traditional algorithms. Yishen Tiaodu acupuncture has a good therapeutic effect on the recovery of motor and neurological function in patients with cerebral infarction at convalescence.
Related-Key Multiple Impossible Differential Cryptanalysis on Full-Round LiCi-2 Designed for IoT
Authors: Zhang Kai , Lai Xuejia , Wang Lei , Guan Jie , Hu Bin , Wang Senpeng , Shi Tairong
Published: May 2022

LiCi-2 is an ultralightweight block cipher designed for constrained IoT devices. It is a successor of LiCi and has even better performance in both software and hardware implementation. In this paper, based on the idea of related-key multiple impossible differential cryptanalysis, a key recovery attack on full-round LiCi-2 is proposed. First, an interesting property is revealed that, with a single bit difference in the related key, a 10-round differential character with probability of 1 exists on LiCi-2. With an automatic approach, the boundaries of impossible differential distinguishers in terms of single-key setting and related-key setting are explored. Under our construction method, the longest length is 8 rounds for single-key setting and 18 rounds for related-key setting. Finally, based on these 18-round distinguishers, a 25-round key recovery attack is proposed with adding 3 rounds before and 4 rounds after the distinguisher. Our attack needs one related key. The time complexity for our attack is O(2123.44), the memory complexity is O(294), and the data complexity is O(260.68). As far as we know, no full-round attack has previously been reported on LiCi-2.
Magnetic Resonance Imaging Features on Deep Learning Algorithm for the Diagnosis of Nasopharyngeal Carcinoma
Authors: Huang Ruijie , Zhou Zhanmei , Wang Xintao , Cao Xiaohua
Published: May 2022

The objective of this research was to investigate the application values of magnetic resonance imaging (MRI) features of the deep learning‐based image super‐resolution reconstruction algorithm optimized convolutional neural network (OPCNN) algorithm in nasopharyngeal carcinoma (NPC) lesion diagnosis. A total of 54 patients with NPC were selected as research objects. Based on the traditional CNN structure, OPCNN was proposed. Besides, MRI processed by the traditional CNN model and the U‐net network model was introduced to be analyzed and compared with its algorithm. The used assessment parameters included volume transfer constant (Ktrans), rate constant (Kep), volume fraction (Ve), and apparent diffusion coefficient (ADC). The results showed that the values of Dice coefficient, peak signal‐to‐noise ratio (PSNR), and structural similarity (SSIM) of the OPCNN algorithm were significantly higher than those of the traditional CNN model and the U‐net network model. Meanwhile, the difference was statistically significant (P < 0.05). Ktrans, Kep, and Ve in tumor lesions were significantly higher than those in the healthy side, while the ADC was significantly lower than that in the healthy side (P < 0.05). The sensitivity, specificity, and accuracy of dynamic contrast‐enhancement magnetic resonance imaging (DCE‐MRI) in the diagnosis of nasopharyngeal carcinoma staging were slightly higher than those in T2‐weighted imaging (T2WI) and diffusion‐weighted imaging (DWI). The diagnostic sensitivity of DCE‐MRI was more than 85%, its diagnostic specificity was more than 75%, and its diagnostic accuracy was more than 90%. The AUC area of NPC diagnosed by combination of the three was significantly different from that diagnosed by single T2WI, DWI, and DCE‐MRI (P < 0.05). The diagnostic accuracy of MRI based on the OPCNN algorithm for nasopharyngeal carcinoma (93.2%) was significantly higher than that of single MRI (76.4%). In summary, the OPCNN algorithm proposed in this study could improve the quality of MRI images, and the effect was better than the traditional deep learning model, which had the value of clinical promotion. The application value of DCE‐MRI in the diagnosis of pathogenic lesions of nasopharyngeal carcinoma was better than conventional MRI. The combined application of T2WI, DWI, and DCE‐MRI in the screening of nasopharyngeal carcinoma lesions could greatly improve the diagnostic accuracy of nasopharyngeal carcinoma.
Detection of Peripheral Malarial Parasites in Blood Smears Using Deep Learning Models
Authors: Alharbi Amal H. , V Aravinda C. , Lin Meng , Ashwini B , Jabarulla Mohamed Yaseen , Shah Mohd Asif
Published: May 2022

Due to the plasmodium parasite, malaria is transmitted mostly through red blood cells. Manually counting blood cells is extremely time consuming and tedious. In a recommendation for the advanced technology stage and analysis of malarial disease, the performance of the XG-Boost, SVM, and neural networks is compared. In comparison to machine learning models, convolutional neural networks provide reliable results when analyzing and recognizing the same datasets. To reduce discrepancies and improve robustness and generalization, we developed a model that analyzes blood samples to determine whether the cells are parasitized or not. Experiments were conducted on 13,750 parasitized and 13,750 parasitic samples. Support vector machines achieved 94% accuracy, XG-Boost models achieved 90% accuracy, and neural networks achieved 80% accuracy. Among these three models, the support vector machine was the most accurate at distinguishing parasitized cells from uninfected ones. An accuracy rate of 97% was achieved by the convolution neural network in recognizing the samples. The deep learning model is useful for decision making because of its better accuracy.
Production, Characterization, and Antimicrobial Activity of Pigment from Streptomyces Species
Authors: Kazi Zareenkousar , Hungund Basavaraj S. , Yaradoddi Jayachandra S. , Banapurmath N. R. , Yusuf Abdulfatah Abdu , Kishore Karedla Lakshmi , Soudagar Manzoore Elahi M. , Khan T. M. Yunus , Elfasakhany Ashraf , Buyondo Kasumba Andrew
Published: May 2022

Production of potential pigments using the bacterial source can be an important area of research to disclose its possible value in diverse industrial applications. Soil samples from various terrestrial, rhizosphere, and forest habitats were collected from Karnataka, and pigment‐producing actinomycetes were recovered. In the present investigation, 25 strains were isolated using starch casein agar medium; further, phenotypic, biochemical, and morphological and the 16S rRNA gene sequence studies have suggested that the strain belonged to the Streptomyces species, strain BJZ10, an actinomycetes. With higher production of pigments, starch casein broth medium was used, and the extraction of actinobacterial pigments was done by exclusively using methanol solvent. The pigment was characterized by UV‐visible spectroscopy; absorption spectra range from 220 to 250 nm. The FTIR characterization was carried out, and the spectrum obtained for the strain BJZ10 indicated alkane, alkyls, alkynes, alcohols, esters, and sulfate functional groups. Antibacterial activity of the pigments was tested against Bacillus cereus (Gram‐positive) and Escherichia coli (Gram‐negative), and significant results were compared. The present study revealed the brown pigment synthesized by Streptomyces sp. strain BJZ10, known for its potential antimicrobial activity.
Meta-Analysis of the Effect of Honghua Injection in the Treatment of Coronary Heart Disease Angina Pectoris
Authors: Zhai Jiani , Ji Zhaochen , Jin Xinyao , Du Xuechen , Cao Lujia , Zheng Wenke
Published: May 2022

Objective. To evaluate the effectiveness of honghua injection combined with western medicine in the treatment of coronary heart disease angina pectoris. Methods. Computer extensively searched PubMed, Cochrane Library, Embase, China Biomedical Archives (SinoMed), China Knowledge Network (CNKI), Chinese Journal Full-text Database (VIP), Wanfang Data Knowledge Service Platform (Wanfang), and collected randomized controlled trials (RCTs) of honghua injection combined with western medicine in the treatment of coronary heart disease angina pectoris. Use Review Manager5.3 software for meta-analysis. Results. 21 RCTs were included, involving 1894 participants. Meta-analysis shows that honghua injection combined with western medicine can significantly improve the clinical efficacy (OR = 4.03, 95% CI[2.96,5.49]), electrocardiographic efficacy (OR = 3.39, 95%CI[2.44, 4.70]), can significantly reduce total cholesterol (TC) levels (MD = −0.39, 95% CI[−0.47, −0.31]), triacylglycerol (TG) levels (MD = −0.45, 95% CI[−0.51, −0.39]), increase high-density lipoprotein cholesterol (HDL-C) levels (MD = 0.29,95%CI[0.26,0.32]), reduce low-density lipoprotein Cholesterol (LDL-C) levels (MD = −0.59, 95%CI[−0.79, −0.38]). Five articles reported adverse reactions. Conclusion. Honghua injection combined with western medicine is more effective than western medicine alone in patients with coronary heart disease angina pectoris. More multicenter, large sample, high-quality RCTs are needed to provide evidence.
Burner: Recipe Automatic Generation for HPC Container Based on Domain Knowledge Graph
Authors: Zhong Shuaihao , Wang Duoqiang , Li Wei , Lu Feng , Jin Hai
Published: May 2022

As one of the emerging cloud computing technologies, containers are widely used in academia and industry. The cloud computing built by the container in the high performance computing (HPC) center can provide high-quality services to users at the edge. Singularity Definition File and Dockerfile (we refer to such files as recipes) have attracted wide attention due to their encapsulation of the application running environment in a container. However, creating a recipe requires extensive domain knowledge, which is error-prone and time-consuming. Accordingly, more than 34% of Dockerfiles in Github cannot successfully build container images. The crucial points about recipe creation include selecting the entities (base images and packages) and determining their relationships (correct installation order for transitive dependencies). Since the relationships between entities can be expressed accurately and efficiently by the knowledge graph, we introduce knowledge graph to generate high-quality recipes automatically. This paper proposes an automatic recipe generation system named Burner, enabling users with no professional computer background to generate the recipes. We first develop a toolset including a recipe parser and an entity-relationship miner. Our two-phase recipe parsing method can perform abstract syntax tree (AST) parsing more deeply on the recipe file to achieve entity extraction; the parsing success rate (PSR) of the two-phase parsing method is 10.1% higher than the one-phase parsing. Then, we build a knowledge base containing 2,832 entities and 62,614 entity relationships, meeting the needs of typical HPC applications. In the test of image build, the singularity image build success rate reaches 80%. Compared with the ItemCF recommendation method, our recommendation method TB-TFIDF achieves a performance improvement by up to 50.86%.
Experimental and Finite Element Study on 7A04 Aluminum Alloy Tube Confined High Strength Concrete Long Column
Authors: Zhang Zuo-Jin , Han Feng-Xia , Liu Qing
Published: May 2022

7A04 aluminum alloy has high strength and strong corrosion resistance, and its quality is only 1/3 of steel. Using 7A04 aluminum alloy to replace ordinary steel tube to confined concrete, it can effectively reduce the member section, reduce the weight of the structure, and more in line with the material requirements of large span materials and high-rise buildings. In this paper, nine groups of 7A04 aluminum alloy tube confined high strength concrete long column were tested under axial compression and eccentric compression with aspect ratio and eccentricity as variables. And the corresponding ABAQUS finite element model was established. The results show that the failure phenomena of the nine groups are basically the same, and the specimen deflection curve shape is similar to the sine half-wave curve. The ultimate bearing capacity of the specimen decreases with the increase of aspect ratio and eccentricity. When the axial compression or eccentricity of the specimen is small, the variation of the longitudinal and transverse strain at the midspan is similar in the elastic stage. When the eccentricity is large, the longitudinal and transverse strain at the midspan of the specimen is obviously tensile at one side and compressive at one side at the beginning. Finally, based on the test and simulation results, a formula for calculating the bearing capacity of 7A04 aluminum alloy tube high strength concrete long columns is proposed. This formula can better predict the stable bearing capacity of the specimen, which has certain reference significance for the structural design of 7A04 aluminum alloy tube high strength concrete long columns.
Structural Optimization and Thermal Management with PCM-Honeycomb Combination for Photovoltaic-Battery Integrated System
Authors: Li Xinxi , Zhang Jiangyun , Zhou Dequan , Zhang Guoqing , Wu Hongwei , Liu Rensheng
Published: May 2022

Power lithium–ion batteries retired from the electric vehicles (EVs) are confronting many problems such as environment pollution and energy dissipation. Traditional photovoltaic (PV) battery systems are exhibiting many issues such as being bulky and expensive, high working temperature, and short service span. In order to address these problems, in this study, a novel PV–battery device integrating PV controllers and battery module into an independent device is proposed. Phase change material (PCM) as the energy storage material has been utilized in battery module, and the aluminum honeycomb is combined with PCM to improve the heat conductivity under natural convection conditions. Three types of PV battery systems including the general PV–battery integrated system (G–PBIS), honeycomb PV–battery integrated system (H–PBIS), and honeycomb–paraffin PV–battery integrated system (HP–PBIS) have been investigated in detail. The results reveal that the maximum temperature of the HP–PBIS coupling with the double–layer10×165×75 mm3PCM was reduced to 53.72°C, exhibiting an optimum cooling effect among various PV battery systems. Thus, it can be concluded that the aluminum honeycomb provides the structural reliability and good thermal conductivity, and the PCM surrounding battery module can control the temperature rising and balance the temperature uniformly. Besides, the optimum PV–battery integrated system performs a promising future in energy storage fields.
Transcriptomic Analysis Uncovers Immunogenic Characteristics of Ferroptosis for Myocardial Infarction and Potential Therapeutic Prediction of Chinese Herbs
Authors: Jiang Feng , Zeng Zhicong , Zhou Xu , Tan Meiling , Zhang Weiwei , Li Mingyue , Zhang Xiaoduo , Song Yinzhi , Wei Shanyin , Lin Fengxia
Published: May 2022

Background. Inflammation and immune response play a key role in myocardial injury and repair after myocardial infarction (MI), while the relevant regulatory mechanisms of immune infiltration in MI have been fully explored. Ferroptosis is an iron-dependent form of regulated cell death characterized by an excessive accumulation of iron and lipid peroxides and involves in the pathogenesis of myocardial infarction. In the present study, by integrating intelligent data acquisition, data mining, network pharmacology, and computer-assisted target fishing, we developed a highly efficient system for screening immunity- and ferroptosis-related biomarkers and immunomodulatory ability of herbal ingredients. Results. Immune infiltration analysis of GSE97320 showed significant neutrophil infiltration in the myocardial infarction group compared to the healthy group, and 807 differentially expressed genes (DEGs) were obtained (526 up-regulated and 281 downregulated). Among these DEGs, 73 immune-related and 8 ferroptosis-related DEGs were obtained. Further protein-protein interaction network analysis revealed 30 hub genes. The DEGs were enriched in a total of 107 biological processes, of which neutrophil-related biological processes were the most significant, enriched in 31 cellular components such as bead-binding hemoglobin complex, hemoglobin complex, and enriched in 36 functions such as bead-binding hemoglobin complex and hemoglobin complex. The DEGs were also enriched in 21 KEGG pathways such as lipid-atherosclerosis and formation of neutrophil extracellular traps. Further analysis identified Toll-like receptor-4 (TLR4) as the key gene, and based on TLR4, 17 herbal ingredients and 6 herbal medicines were predicted by using HERB and Coremine databases. Further molecular docking analysis showed that TLR4 could bind to salvianolic acid b and stigmasterol. The molecular dynamics analysis revealed that TLR4 could bind to salvianolic acid b, stigmasterol, and resveratrol in the stable phase with the binding between TLR4 and salvianolic acid b being the most stable. Conclusions. TLR4 is a key gene that is related to ferroptosis and immune cell infiltration. Further analysis revealed that 17 herbal ingredients and 6 herbal medicines were predicted to have potential interactions with TLR4. These predicted herbal ingredients/medicines may act synergistically to protect against myocardial injury after MI through suppressing neutrophil extracellular traps. The protective effects may be associated with immune cell infiltration and ferroptosis.
Local Dimming Algorithm of Automotive LCD Instrument Based on Otsu and Maximum Entropy
Authors: Liu Tianfu , Tang Chunqiu
Published: May 2022

In order to reasonably reduce the energy consumption of automotive LCD instrument and improve the display quality, a new local dimming algorithm is proposed in this paper. Firstly, the gray image of the input image is obtained by using the maximum principle, and then, the LED backlight brightness value is obtained by using Otsu method and maximum entropy method. The BMA backlight smoothing algorithm is improved by combining bilinear interpolation algorithm, and the dimming image is obtained by using pixel compensation algorithm based on logarithm. Then, for low brightness images, high brightness images, low contrast images, and high contrast images, typical algorithms are used to simulate and compare, and the image processing effects of various local dimming algorithms are analyzed. Finally, the entropy weight method is used to objectively evaluate the local dimming algorithm. The results show that the new local dimming algorithm reduces the energy consumption and improves the display quality, which verifies the effectiveness of the proposed algorithm.
Optimization and Mechanical Properties of TiO2 Reinforced AA 7150 Composites Using Response Surface Methodology
Authors: Prasad S V G V A , Kamalakar V. , Madhusudhanan J. , Reddy Prasad Puthalapattu , Adilakshmi G. , Manoj Kumar Nellore , Gopalan Anitha , rajput Vinod Singh , Ashok Nagaraj
Published: May 2022

The goal of this research is to increase the performance of AA 7150 reinforced with TiO2 microparticles by optimizing the stir casting parameters. The response surface method's central composite design technique was used to optimize the three stir casting factors of stirring temperature (A), stirring speed (B), and stirring time (C). The ultimate tensile strength, hardness, impact strength, elastic modulus, and compressive strength were all tested. With the aid of analysis of variance, it was discovered that it had a substantial influence on the test samples' characteristics responses. 5 quadratic experiments were linked using factors' characteristics. At a level of 95% confidence, the models were found to be statistically important, and the variations were found to be less than 5%. The response surface was used to assess the parameter interaction profile. Each interaction's contour plots provided a range of stirring settings within which each property may be maximized.
An Information Entropy Embedding Feature Selection Based on Genetic Algorithm
Authors: Wu Yuzheng
Published: May 2022

Feature selection is of vital importance to reduce information redundancy and deal with the invalidation of basic classification approaches for massive dataset and too many features. In order to improve the classification accuracy and decrease time complexity, an algorithm with intelligent optimization genetic algorithm and weight distribution based on information entropy is proposed, called EEGA. Information entropy of features is defined as the population labels in GA rather than the direct iteration of individual fitness. Experiments have been performed by using several standard databases with four fitness algorithms. Experimental results have proved that EEGA performs better based on the measure of accuracy. Furthermore, it can significantly reduce the required time when figuring out the better results.
Observation on the Effect of MRI Image Scanning on Knee Pain in Football Injury
Authors: Yu Weidong
Published: May 2022

To study the effect of football injury on knee pain based on MRI image scanning, in this paper, a total of 31 knee injuries of 29 male professional football players from December 2012 to April 2015 were used as the experimental group. The players were

23.6
±
3.5

years old and received professional football training time

15.3
±
3.6

years; 31 outpatients of the same age group with acute knee joint acute injury were randomly selected as the control group; both groups were imaged with a 1.5 TMR scanner and knee joint standard array coil imaging, and 2 senior radiation surgeons evaluate knee cartilage, meniscus, ligaments, tendons, bone marrow, infrapatellar fat pad, and joint effusions. Pearson’s chi-squared test and nonparametric test for two independent samples were used for statistical testing of the evaluation results. The experimental results showed that there were significant differences in the incidence of articular cartilage, lateral collateral ligament, tendon or ligament injury, multiligament or tendon injury, and bone marrow edema between the two groups (

P
<
0.05

). There was no significant difference in the incidence of medial collateral ligament injury, infrapatellar fat pad edema, and joint effusion. MRI shows that knee injuries in male professional football players often involve ligaments or tendons, mostly multiligament or tendon injuries. The lesions of articular cartilage and meniscus are more common and serious, and bone marrow edema is also more common in football injuries. MRI has high diagnostic accuracy for various clinical knee injuries, and it belongs to a noninvasive examination method. It can not only reflect the pathological changes and changes of the knee joints of patients but also provide information for the formulation of clinical programs and the judgment of prognosis, for timely, accurate, and comprehensive imaging reference.
Pharmacokinetic Difference of Six Active Constituents of Huangqi Liuyi Decoction between Control and Diabetic Nephropathy Mouse Models
Authors: Wang Qun , Shi Ya , Liu Xingde , Liu Ting , Li Yongjun , Song Xinli , Chen Xiaolan , Jin Yang , Liu Wen , Wang Yonglin , Gong Zipeng
Published: May 2022

Huangqi Liuyi decoction is a famous traditional Chinese medicine (TCM) that has been widely used in China for the management of diabetes since the Song Dynasty. Today, it is commonly used for treating diabetic nephropathy (DN). Our previous experimental studies have suggested that the mixture HQD, containing astragalus saponin, astragalus flavone, astragalus polysaccharide, and glycyrrhetinic acid, could be used for the treatment of DN and to improve renal function. The objective of this study was to develop a sensitive and reliable high-performance liquid chromatography-tandem mass spectrometry method for simultaneous quantitation of astragaloside IV, calycosin-7-O-β-D-glucoside, calycosin-glucuronide, ononin, formononetin, and glycyrrhizic acid, which are the main active constituents in HQD, and to compare the pharmacokinetics of these active constituents in control and DN mice orally treated with HQD. The results indicated that the pharmacokinetic parameters of HQD were significantly different between the control and DN mouse groups. The absorption of HQD in the DN mice was greater than that in control mice.
A Systematic Literature Review and Bibliometric Analysis of Ophthalmology and COVID-19 Research
Authors: Forouhari Ali , Mansouri Vahid , Safi Sare , Ahmadieh Hamid , Ghaffari Jolfayi Amir
Published: May 2022

This review is proposed to summarize the updates on COVID-19 and ophthalmology along with the bibliometric features of articles that have been published since the beginning of the COVID-19 outbreak. The databases, including PubMed, Scopus, and Web of Science, were searched using “Coronavirus,” “COVID-19,” “SARS-CoV-2,” “pandemic,” “ophthalmology,” “ophthalmic,” and “eye” keywords. All published articles except commentaries, errata, and corrigenda up to April 2021 were included. Titles and abstracts were screened, and ophthalmology-focused articles were collected. The bibliographic information of the articles, such as the name and country of the first author, type of study, date of publication, language, and journal name, were extracted. Included studies were assessed using the Joanna Briggs Institute (JBI) critical appraisal checklist. After systematic searching, 2,669 distinct articles were screened by title/abstract, and 1,174 ophthalmology-focused articles were selected to be reviewed. Ophthalmology-focused publications accounted for less than 0.5 percent of the total COVID-19-related articles. Most of the articles were published in the Indian Journal of Ophthalmology, and the main publication type was “original article.” Almost 88% of the publications were in English. There was a decline in the publication rate during the initial months of 2021 compared with the middle and last months of 2020. Most of the publications were affiliated with the United States of America. However, Singapore and the United Kingdom were the countries with the highest number of publications after population adjustment. Furthermore, a comprehensive review on major topics including SARS-CoV-2 ocular tropism, ophthalmic manifestations, ocular complications due to COVID-19 treatment strategies, the pandemic effect on ophthalmology care and operations, myopia progression during the pandemic, and telemedicine was conducted.
Network Anomaly Traffic Detection Algorithm Based on RIC-SC-DeCN
Authors: Gong Xingyu , Cao Ke , Li Na , Jia Pengtao
Published: May 2022

In the research of network abnormal traffic detection, in view of the characteristics of high dimensionality and redundancy in traffic data and the loss of original information caused by the pooling operation in the convolutional neural network, which leads to the problem of unsatisfactory detection effect, this paper proposes a network abnormal traffic detection algorithm based on RIC-SC-DeCN to improve the above problems. Firstly, a recursive information correlation (RIC) feature selection mechanism is proposed, which reduces data redundancy through the maximum information correlation feature selection algorithm and recursive feature elimination method. Secondly, a skip-connected deconvolutional neural network model (SC-DeCN) is proposed to reduce the information loss by reconstructing the input signal. Finally, the RIC mechanism and the SC-DeCN model are merged to form a network abnormal traffic detection algorithm based on RIC-SC-DeCN. The experimental results on the CIC-IDS-2017 dataset show that the RIC feature selection mechanism proposed in this paper has the highest accuracy when using MSCNN as the detection model compared to the other three, which can reach 96.22%. Compared with the other five models, the SC-DeCN model has the highest detection accuracy, while the model training time is moderate and can reach 96.55%. Compared with the SC-DeCN model, the RIC-SC-DeCN model reduces the overall training time by 45.50%, while the accuracy rate is increased to 97.68%. It shows that the algorithm proposed in this paper has a good detection effect in the detection of network abnormal traffic.
Neonatal Oxidative Stress Impairs Cortical Synapse Formation and GABA Homeostasis in Parvalbumin-Expressing Interneurons
Authors: Scheuer Till , Endesfelder Stefanie , auf dem Brinke Elena , Bührer Christoph , Schmitz Thomas
Published: May 2022

Neonatal brain injury is often caused by preterm birth. Brain development is vulnerable to increased environmental stress, including oxidative stress challenges. Due to a premature change of the fetal living environment from low oxygen in utero into postnatal high-oxygen room air conditions ex utero, the immature preterm brain is exposed to a relative hyperoxia, which can induce oxidative stress and impair neuronal cell development. To simulate the drastic increase of oxygen exposure in the immature brain, 5-day-old C57BL/6 mice were exposed to hyperoxia (80% oxygen) for 48 hours or kept in room air (normoxia, 21% oxygen) and mice were analyzed for maturational alterations of cortical GABAergic interneurons. As a result, oxidative stress was indicated by elevated tyrosine nitration of proteins. We found perturbation of perineuronal net formation in line with decreased density of parvalbumin-expressing (PVALB) cortical interneurons in hyperoxic mice. Moreover, maturational deficits of cortical PVALB+ interneurons were obtained by decreased glutamate decarboxylase 67 (GAD67) protein expression in Western blot analysis and lower gamma-aminobutyric acid (GABA) fluorescence intensity in immunostaining. Hyperoxia-induced oxidative stress affected cortical synaptogenesis by decreasing synapsin 1, synapsin 2, and synaptophysin expression. Developmental delay of synaptic marker expression was demonstrated together with decreased PI3K-signaling as a pathway being involved in synaptogenesis. These results elucidate that neonatal oxidative stress caused by increased oxygen exposure can lead to GABAergic interneuron damage which may serve as an explanation for the high incidence of psychiatric and behavioral alterations found in preterm infants.
Detection and Antimicrobial Resistance of Staphylococcus Species from Chicken, Chicken Litter, and Humans in Addis Ababa, Ethiopia
Authors: Abunna Fufa , Adugna Biyansa , Tufa Takele Beyene , Ayana Dinka , Gutema Fanta D. , Waktole Hika , Regassa Fikru , Abdi Reta D.
Published: May 2022

Background. In veterinary medicine, three Staphylococcus species are of particular importance as primary causes of specific diseases; S. aureus (mastitis in ruminants, equine botryomycosis, and bumble foot in poultry), S. hycus (porcine exudative epidermitis), and S. intermedius (canine pyoderma). The disease conditions caused by Staphylococcus in poultry vary with site, route, and predisposing factors include wounds as a result of fighting/cannibalism, immunosuppression based on virus infection or parasite infestation, and bad husbandry conditions (overcrowding). The objectives of this study were to isolate and identify Staphylococcus spp from chicken and chicken litter and personnel at chicken farm and to determine the antimicrobial susceptibility profile of the isolates. Methods. A cross-sectional study was conducted on apparently healthy chickens, farm personnel, and chicken litter at poultry farms in Addis Ababa, Ethiopia. A total of 222 samples consisting of 101 cloacal swabs, 90 tracheal swabs, 17 pooled litter swabs, 7 nasal swabs, and 7 pooled hand and boot swabs were collected from six farms and examined for the presence of Staphylococcus species. Antimicrobial resistance against 10 antimicrobial agents was also conducted following recommended standard procedures. Results. Overall proportion of Staphylococcus was 64/222 (28.83%). Of the isolates, 40/64 (62.5%), 11/64 (17.2%), 3/64 (4.7%), and 10/64 (15.6%), were S. aureus, S. hycus, S. intermedius, and coagulase-negative staphylococci (CNS), respectively. Only one isolate of S. aureus was susceptible to all antimicrobials tested. Of the 10 antibiotics tested, the isolates demonstrated highest resistance against Penicillin G (96.9%) followed by Tetracycline (78.1%), and Amoxicillin and Erythromycin at the same level (65.6%). Conversely, the isolates were highly susceptible to Ciprofloxacin (95.3%) followed by Sulphamethoxazole-trimethoprim (85.9%). Out of 64 isolates, 61/64 (95.3%) were resistant to three or more antimicrobials tested. Of the isolates, 38/40 (95%) S. aureus, 10/11 (90.9%) S. hycus, 3/3 (100%) S. intermedius, and 10/10 (100%) CNS showed multidrug resistance. Conclusion. This study showed a considerable proportion of Staphylococcus spp in chicken litter and farm workers with a potential source of resistant Staphylococcus species, and more importantly multidrug resistance strains. Further studies on molecular characterization of the isolates will be essential to identify the resistant genes and establish epidemiological links in the transmission dynamics of resistant Staphylococcus species between poultry and humans.
Meta-Analysis of the Effect of Aerobic Training on Blood Pressure in Hypertensive Patients
Authors: Fu Yanping , Feng Qiongfang , Wei Yingna , Fan Liang , Pan Yandie , Ji Jingui , Lin Chengxia
Published: May 2022

Background. We aimed to evaluate the effect of different aerobic training methods and exercise duration on blood pressure in hypertensive patients, including systolic blood pressure (SBP) and diastolic blood pressure (DBP). Methods. Using the literature review method, the English database PubMed was retrieved to obtain relevant research literature, and the selected studies were analyzed and evaluated. Results. 14 clinical studies were included in this study, with a total of 1027 patients, including 681 in the aerobic training group and 409 in the control group. Based on heterogeneity test results, the differences of SBP and DBP before and after the physical intervention were combined using a random effect model. The results indicated that the aerobic training group could significantly reduce SBP compared with the control group,

WMD
=
−
9.91

, 95% CI (-14.21, -5.61),

P
<
0.0001

. The DBP was reduced significantly in the aerobic training group,

WMD
=
−
4.32

, 95% CI (- 7.02, -1.62),

P
<
0.001

. The results of subgroup analysis showed that both progressive training and nonprogressive training could reduce blood pressure in patients, and training time less than 12 weeks and more than 12 weeks could reduce blood pressure in patients. Conclusion. Implementing aerobic training can effectively reduce blood pressure in hypertensive patients. Progressive training, nonprogressive training, and different training cycles can all benefit hypertensive patients.
Optimal Inspection Scheduling Model for Monitoring Railway Track Grids
Authors: Li Qing , Liu Ling , Liu Rengkui , Bai Lei
Published: May 2022

The optimization of railway track inspection schedules is of great significance for the monitoring of railway track conditions and reasonable organization of track maintenance tasks. Here, an optimal inspection scheduling model for monitoring railway tracks is proposed. Railway track inspection activities are divided into equal‐length units in the time and spatial location dimensions. The various constraints and objectives on the time and location of different track inspection activities are defined in detail regarding subdivisions of individual management units. The objectives of the model development are to achieve a more optimized equilibrium and robustness of the track inspection schedule. Based on the maximum entropy criterion, entropy is used to measure the distribution equilibrium of the inspection schedule in the time dimension. The proposed model is verified using actual data from the Lanxin railway.
Ranking and Evaluation of Tight Sandstone Reservoirs and the Determination of the Lower Limit of Reservoir Physical Properties: A Case Study of Longfengshan Area in the Southern Songliao Basin, China
Authors: Wang Weiming , Liu Yingnan , Miao Changsheng , Liu Yuhu , Qu Xiyu , Zhang Yangchen , La Weihao , Lv Qixia
Published: May 2022

Ranking, evaluation, and the determination of the lower limit of physical properties (PPLL) are critical for selecting the sweet spots of tight reservoirs. This study investigated the tight reservoirs in the Longfengshan area in the southern Songliao Basin. Based on reservoir evaluation, this study determined the ranking criteria and the PPLL of tight sandstone reservoirs. The results are as follows. (1) Tight sandstone reservoirs can be divided into I, II, and III types based on the energy storage parameter and pore structure. Reservoirs with a porosity of > 6% are I-type reservoirs. The reservoirs of this type have high accumulation and seepage capacities, and their pore structures feature low displacement pressure and high structure coefficients. Reservoirs with a porosity of < 4% are III-type reservoirs. The reservoirs of this type have low accumulation and seepage capacities, and their pore structures feature high displacement pressure and low structure coefficients. The remaining reservoirs are II-type reservoirs. (2) The PPLL (denoted by porosity) of tight sandstone reservoirs was determined to be 2.50% using the water film thickness method and the minimum pore throat radius method. The water film thickness method, which comprehensively considers the geological factors including formation temperature, formation pressure, and the adsorption capacity of minerals, is innovative to a certain degree. As verified by the test data from the major exploration wells drilled in the Longfengshan area, the ranking criteria of tight reservoirs proposed in this study are effective and highly applicable and thus serve as effective guidance on the future exploration of the study area.
Targeting the Ezrin Adaptor Protein Sensitizes Metastatic Breast Cancer Cells to Chemotherapy and Reduces Neoadjuvant Therapy–induced Metastasis
Authors: Hoskin Victoria , Ghaffari Abdi , Laight Brian J. , SenGupta Sandip , Madarnas Yolanda , Nicol Christopher J.B. , Elliott Bruce E. , Varma Sonal , Greer Peter A.
Published: May 2022

The main cause of cancer-associated deaths is the spread of cancer cells to distant organs. Despite its success in the primary tumor setting, modern chemotherapeutic strategies are rendered ineffective at treating metastatic disease, largely due to the development of resistance. The adaptor protein ezrin has been shown to promote cancer metastasis in multiple preclinical models and is associated with poor prognosis in several cancer types, including breast cancer. Ezrin promotes pro-survival signaling, particularly in disseminated cancer cells, to facilitate metastatic outgrowth. However, the role of ezrin in breast cancer chemoresistance is not fully known. In this study, we show that upregulating or downregulating ezrin expression modifies the sensitivity of breast cancer cells to doxorubicin and docetaxel treatment in vitro and is associated with changes in PI3K/Akt and NFκB pathway activation. In addition, we tested the effects of systemic treatment with a small-molecule ezrin inhibitor, NSC668394, on lung metastatic burden in vivo as a monotherapy, or in combination with anthracycline- or taxane-based chemotherapy treatment. We show that anti-ezrin treatment alone reduces metastatic burden and markedly sensitizes metastases to doxorubicin or docetaxel in neoadjuvant as well as neoadjuvant plus adjuvant treatment models. Taken together, our findings demonstrate the impact of anti-ezrin treatment in modulating response to chemotherapy in breast cancer cells as well as the efficacy of anti-ezrin treatment in combination with chemotherapy at reducing metastatic burden.

Significance:

This work provides preclinical evidence for combining anti-ezrin treatment with chemotherapy as a novel strategy for effectively targeting metastasis, particularly in a neoadjuvant treatment setting.
miR-223 Exerts Translational Control of Proatherogenic Genes in Macrophages
Authors: Nguyen My-Anh , Hoang Huy-Dung , Rasheed Adil , Duchez Anne-Claire , Wyatt Hailey , Cottee Mary Lynn , Graber Tyson E. , Susser Leah , Robichaud Sabrina , Berber İbrahim , Geoffrion Michele , Ouimet Mireille , Kazan Hilal , Maegdefessel Lars , Mulvihill Erin E. , Alain Tommy , Rayner Katey J.
Published: May 2022

Background:

A significant burden of atherosclerotic disease is driven by inflammation. Recently, microRNAs (miRNAs) have emerged as important factors driving and protecting from atherosclerosis. miR-223 regulates cholesterol metabolism and inflammation via targeting both cholesterol biosynthesis pathway and NF
k
B signaling pathways; however, its role in atherosclerosis has not been investigated. We hypothesize that miR-223 globally regulates core inflammatory pathways in macrophages in response to inflammatory and atherogenic stimuli thus limiting the progression of atherosclerosis.

Methods and Results:

Loss of miR-223 in macrophages decreases
Abca1
gene and protein expression as well as cholesterol efflux to apoA1 (Apolipoprotein A1) and enhances proinflammatory gene expression. In contrast, overexpression of miR-223 promotes the efflux of cholesterol and macrophage polarization toward an anti-inflammatory phenotype. These beneficial effects of miR-223 are dependent on its target gene, the transcription factor
Sp3
. Consistent with the antiatherogenic effects of miR-223 in vitro, mice receiving

miR223
−/−

bone marrow exhibit increased plaque size, lipid content, and circulating inflammatory cytokines (ie, IL-1β). Deficiency of miR-223 in bone marrow–derived cells also results in an increase in circulating pro-atherogenic cells (total monocytes and neutrophils) compared with control mice. Furthermore, the expression of miR-223 target gene (
Sp3
) and pro-inflammatory marker (
Il-6
) are enhanced whereas the expression of
Abca1
and anti-inflammatory marker (
Retnla
) are reduced in aortic arches from mice lacking miR-223 in bone marrow–derived cells. In mice fed a high-cholesterol diet and in humans with unstable carotid atherosclerosis, the expression of miR-223 is increased. To further understand the molecular mechanisms underlying the effect of miR-223 on atherosclerosis in vivo, we characterized global RNA translation profile of macrophages isolated from mice receiving wild-type or

miR223
−/−

bone marrow. Using ribosome profiling, we reveal a notable upregulation of inflammatory signaling and lipid metabolism at the translation level but less significant at the transcription level. Analysis of upregulated genes at the translation level reveal an enrichment of miR-223-binding sites, confirming that miR-223 exerts significant changes in target genes in atherogenic macrophages via altering their translation.

Conclusions:

Our study demonstrates that miR-223 can protect against atherosclerosis by acting as a global regulator of RNA translation of cholesterol efflux and inflammation pathways.