Xu-Hui Li, Wenfeng Su, Feifei Wang, Ke Li, Jingjing Zhu, Si-Yu Zhu, Sishun Kang, Cong-Fen He, Jun-Xiang Li, Xiao-Ling Lin
Regenerative medicine and anti-aging research have made great strides at the molecular and cellular levels in dermatology and the medical aesthetic field, targeting potential treatments with skin therapeutic and intervention pathways, which make it possible to develop effective skin regeneration and repair ingredients. With the rapid development of computational biology, bioinformatics as well as artificial intelligence (A.I.), the development of new ingredients for regenerative medicine has been greatly accelerated, and the success rate has been improved. Some application cases have appeared in topical skin regeneration and repair scenarios. This review will briefly introduce the application of bioactive peptides in skin repair and anti-aging as emerging ingredients in cosmeceutics and emphasize how A.I. based computational biology technology may accelerate the development of innovative peptide molecules and ultimately translate them into potential skin regenerative and anti-aging scenarios. Typically, two research routines have been summarized and current limitations as well as directions were discussed for border applications in future research.
{"title":"Computational biology in topical bioactive peptide discovery for cosmeceutical application: a concise review","authors":"Xu-Hui Li, Wenfeng Su, Feifei Wang, Ke Li, Jingjing Zhu, Si-Yu Zhu, Sishun Kang, Cong-Fen He, Jun-Xiang Li, Xiao-Ling Lin","doi":"10.53388/bmec2023014","DOIUrl":"https://doi.org/10.53388/bmec2023014","url":null,"abstract":"Regenerative medicine and anti-aging research have made great strides at the molecular and cellular levels in dermatology and the medical aesthetic field, targeting potential treatments with skin therapeutic and intervention pathways, which make it possible to develop effective skin regeneration and repair ingredients. With the rapid development of computational biology, bioinformatics as well as artificial intelligence (A.I.), the development of new ingredients for regenerative medicine has been greatly accelerated, and the success rate has been improved. Some application cases have appeared in topical skin regeneration and repair scenarios. This review will briefly introduce the application of bioactive peptides in skin repair and anti-aging as emerging ingredients in cosmeceutics and emphasize how A.I. based computational biology technology may accelerate the development of innovative peptide molecules and ultimately translate them into potential skin regenerative and anti-aging scenarios. Typically, two research routines have been summarized and current limitations as well as directions were discussed for border applications in future research.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83657177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-10DOI: 10.4015/s1016237222500466
Qaysar Mohi ud Din, A. Jayanthy
Autism Spectrum Disorder (ASD), a neurodevelopmental disorder, impacts the subject’s social communication and interaction and the subjects exhibit restricted and repetitive behaviors. Subjects with ASD may need assistance throughout their life, depending on the severity. Early diagnosis of ASD is therefore critical for early intervention. ASD is diagnosed clinically based on behavioral assessments of the subjects, which results in delayed diagnosis, since the typical ASD traits due to aberrant brain development take time to develop. Neurological disorders associated with aberrant brain electrical activity have been detected by analyzing Electroencephalogram (EEG) signal patterns. In this study, we used features extracted from EEG brain waves to categorize ASD and normal subjects using Machine Learning (ML) classifiers. Autoregressive (AR) coefficients, Shannon entropy, Multifractal wavelet leader estimates, Multiscale wavelet variance and Discrete Fourier Transform (DFT) coefficients were extracted from EEG brain waves of ASD and normal subjects. Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), k-Nearest Neighbor (k-NN) and Feed-forward Neural Network (FNN) were utilized as classification algorithms to categorize the ASD subjects and the control subjects. An accuracy of 90% was achieved by k-NN algorithm using AR features, Shannon entropy, Multifractal wavelet leader estimates and Multiscale wavelet variance estimates in ASD categorization. An accuracy of 93% was achieved by k-NN using the DFT features. The findings of this study indicate that features extracted from EEG are sufficient enough for categorization of ASD subjects and the control subjects.
{"title":"DETECTION OF AUTISM SPECTRUM DISORDER BY FEATURE EXTRACTION OF EEG SIGNALS AND MACHINE LEARNING CLASSIFIERS","authors":"Qaysar Mohi ud Din, A. Jayanthy","doi":"10.4015/s1016237222500466","DOIUrl":"https://doi.org/10.4015/s1016237222500466","url":null,"abstract":"Autism Spectrum Disorder (ASD), a neurodevelopmental disorder, impacts the subject’s social communication and interaction and the subjects exhibit restricted and repetitive behaviors. Subjects with ASD may need assistance throughout their life, depending on the severity. Early diagnosis of ASD is therefore critical for early intervention. ASD is diagnosed clinically based on behavioral assessments of the subjects, which results in delayed diagnosis, since the typical ASD traits due to aberrant brain development take time to develop. Neurological disorders associated with aberrant brain electrical activity have been detected by analyzing Electroencephalogram (EEG) signal patterns. In this study, we used features extracted from EEG brain waves to categorize ASD and normal subjects using Machine Learning (ML) classifiers. Autoregressive (AR) coefficients, Shannon entropy, Multifractal wavelet leader estimates, Multiscale wavelet variance and Discrete Fourier Transform (DFT) coefficients were extracted from EEG brain waves of ASD and normal subjects. Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), k-Nearest Neighbor (k-NN) and Feed-forward Neural Network (FNN) were utilized as classification algorithms to categorize the ASD subjects and the control subjects. An accuracy of 90% was achieved by k-NN algorithm using AR features, Shannon entropy, Multifractal wavelet leader estimates and Multiscale wavelet variance estimates in ASD categorization. An accuracy of 93% was achieved by k-NN using the DFT features. The findings of this study indicate that features extracted from EEG are sufficient enough for categorization of ASD subjects and the control subjects.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"28 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81015555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-10DOI: 10.4015/s101623722250048x
K. Bhavani, Gopalakrishna M T
The cancer is an intimidating illness. Extra care is necessary while making a diagnosis. To aid the identification process, medical imaging plays a crucial role by producing images of the internal organs of the body for better diagnosis of cancer. Medical images are typically utilized by radiologists, engineers, and clinicians to spot the inner constitution of either individual patients or group of individuals. Most doctors prefer computed tomography (CT) images for initial screening of cancer — mainly lung cancer. To achieve deeper understanding and categorization of lung cancer, diverse machine learning techniques are employed in image classification. Many research works have been done on the classification of CT images with different algorithms, but they failed to reach 100% accuracy. By applying methods like Support Vector Machine, deep learning system like artificial neural network (ANN) and proposed convolution neural network (CNN), a computerized system can be built for truthful classification. The models are built as a classification system that can identify the nodule, if present in the lungs, as benign, malignant or normal or as benign or normal. Lung cancer datasets at Iraq National Center aimed at Cancer Diseases (IQ-OTHNCCD) and Iran Hospital-based CT images are used in this research. SVM, ANN, and proposed CNN classification techniques are applied to the datasets considered. This research work, proposes a model for classification of CT images with very promising accuracy on the datasets considered.
{"title":"COMPARATIVE ANALYSIS OF TRADITIONAL CLASSIFICATION AND DEEP LEARNING IN LUNG CANCER PREDICTION","authors":"K. Bhavani, Gopalakrishna M T","doi":"10.4015/s101623722250048x","DOIUrl":"https://doi.org/10.4015/s101623722250048x","url":null,"abstract":"The cancer is an intimidating illness. Extra care is necessary while making a diagnosis. To aid the identification process, medical imaging plays a crucial role by producing images of the internal organs of the body for better diagnosis of cancer. Medical images are typically utilized by radiologists, engineers, and clinicians to spot the inner constitution of either individual patients or group of individuals. Most doctors prefer computed tomography (CT) images for initial screening of cancer — mainly lung cancer. To achieve deeper understanding and categorization of lung cancer, diverse machine learning techniques are employed in image classification. Many research works have been done on the classification of CT images with different algorithms, but they failed to reach 100% accuracy. By applying methods like Support Vector Machine, deep learning system like artificial neural network (ANN) and proposed convolution neural network (CNN), a computerized system can be built for truthful classification. The models are built as a classification system that can identify the nodule, if present in the lungs, as benign, malignant or normal or as benign or normal. Lung cancer datasets at Iraq National Center aimed at Cancer Diseases (IQ-OTHNCCD) and Iran Hospital-based CT images are used in this research. SVM, ANN, and proposed CNN classification techniques are applied to the datasets considered. This research work, proposes a model for classification of CT images with very promising accuracy on the datasets considered.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"87 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76564229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study design is to evaluate the mid-term changes in bone mineral density (BMD) with combined calcium-restricted and ovariectomized miniature porcine models as a large animal model in osteoporosis. The combined old practice hangs on for almost 30 years. Four 6-month-old (T0) female miniature pigs were enrolled in this study. The pigs were fed a normal diet prior to the ovariectomy at the age of 1 year and 3 months (T1) but switched to a diet with restricted calcium content afterwards. Each of the pigs received dual-energy X-ray absorptiometry (DXA) once before ovariectomy, and once every three months (T2, T3, T4) after the ovariectomy to evaluate the changes in BMD. The body weight of all four subject pigs increased significantly during this study ([Formula: see text]). The initial changes in both the BMD levels (T1/T2) were found to be statistically insignificant ([Formula: see text] and [Formula: see text], respectively). However, upon comparison of later BMD changes (T3/T4, T1/T3 and T1/T4), statistically significant elevations were found ([Formula: see text] for all three comparisons). Ovariectomy and calcium-restricted diets are ineffective in achieving an osteoporotic porcine model based on BMD assessments. BMD levels of the subject pigs continued to rise until the point at which body growth had stopped because the ideal pigs for surgical experiments were far from maturity. This finding is not unexpected; after all, the subject pigs are not senile. Without violations of the physiology and Institutional Animal Care and Use Committee (IACUC) regulations, moreover, pigs could be fed by strictly calcium-restricted diets or deprived of soybean component feed. Furthermore, the alternative protocols in osteoporotic porcine model shall perform experiments as soon as possible after ovariectomy. We should take other studies about artificial osteoporotic pigs more into consideration whether it is based on a rational method.
{"title":"THE STUDY ON THE EFFECT OF ARTIFICIAL OSTEOPOROSIS CREATED BY COMBINED OVARIECTOMY AND CALCIUM-RESTRICTED DIETS IN A PORCINE MODEL","authors":"Jui-Yang Hsieh, Yao-Horng Wang, Jyh‐Horng Wang, Po‐Quang Chen, Yi-You Huang","doi":"10.4015/s1016237222500545","DOIUrl":"https://doi.org/10.4015/s1016237222500545","url":null,"abstract":"This study design is to evaluate the mid-term changes in bone mineral density (BMD) with combined calcium-restricted and ovariectomized miniature porcine models as a large animal model in osteoporosis. The combined old practice hangs on for almost 30 years. Four 6-month-old (T0) female miniature pigs were enrolled in this study. The pigs were fed a normal diet prior to the ovariectomy at the age of 1 year and 3 months (T1) but switched to a diet with restricted calcium content afterwards. Each of the pigs received dual-energy X-ray absorptiometry (DXA) once before ovariectomy, and once every three months (T2, T3, T4) after the ovariectomy to evaluate the changes in BMD. The body weight of all four subject pigs increased significantly during this study ([Formula: see text]). The initial changes in both the BMD levels (T1/T2) were found to be statistically insignificant ([Formula: see text] and [Formula: see text], respectively). However, upon comparison of later BMD changes (T3/T4, T1/T3 and T1/T4), statistically significant elevations were found ([Formula: see text] for all three comparisons). Ovariectomy and calcium-restricted diets are ineffective in achieving an osteoporotic porcine model based on BMD assessments. BMD levels of the subject pigs continued to rise until the point at which body growth had stopped because the ideal pigs for surgical experiments were far from maturity. This finding is not unexpected; after all, the subject pigs are not senile. Without violations of the physiology and Institutional Animal Care and Use Committee (IACUC) regulations, moreover, pigs could be fed by strictly calcium-restricted diets or deprived of soybean component feed. Furthermore, the alternative protocols in osteoporotic porcine model shall perform experiments as soon as possible after ovariectomy. We should take other studies about artificial osteoporotic pigs more into consideration whether it is based on a rational method.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"135 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76723508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-10DOI: 10.4015/s1016237222500454
Kirti Singh, I. Saini, Neetu Sood
Many physiological signals such as heart rate (HR), blood pressure (BP), and respiration (RESP) affect each other, and the inter-relation within and between these signals can be linear or nonlinear. Therefore, this paper’s main aim is to extract the relevant features using the information domain coupling technique based on conditional transfer entropy to detect the nonlinearity and coupling changes between the physiological signals and to classify the database using various machine learning classifiers to study the aging changes in the contribution of HR, BP, and RESP. In the proposed work, the physiological signals, i.e. HR, BP, and RESP, were pre-processed using various filtering methods, then features of physiological signals were extracted using linear and nonlinear techniques. After the pre-processing and extraction of features, the extracted features are classified using machine learning classifiers to classify the physiological signal database to study the aging changes in the contribution of HR, BP, and RESP. The data has been taken from the standard Fantasia database of healthy young and old subjects and self-recorded data of healthy young and old subjects for this study. Naive Bayes (NB), Support vector machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), and Artificial Neural Network (ANN) were trained using five-fold cross-validation on the physiological dataset. It is concluded from the results that by adding the coupling features, the efficiency of the final prediction of the classifier increased from [Formula: see text]% to [Formula: see text]% obtained by LR, [Formula: see text]% to [Formula: see text]% obtained by SVM, [Formula: see text]% to [Formula: see text]% obtained by KNN, [Formula: see text]% to [Formula: see text]% obtained by NB, and [Formula: see text]% to [Formula: see text]% obtained by ANN. The ANN performs well when provided with the coupling features, gives a maximum accuracy of [Formula: see text]% and very high sensitivity of [Formula: see text]% and specificity of [Formula: see text]%, and takes much less computational time, when compared to other machine learning algorithms on same length of database.
心率(HR)、血压(BP)和呼吸(RESP)等生理信号相互影响，这些信号内部和之间的相互关系可以是线性的，也可以是非线性的。因此，本文的主要目的是利用基于条件传递熵的信息域耦合技术提取相关特征，检测生理信号之间的非线性和耦合变化，并利用各种机器学习分类器对数据库进行分类，研究HR、BP和RESP贡献的老化变化。首先对生理信号HR、BP和RESP进行预处理，然后利用线性和非线性技术提取生理信号的特征。在对特征进行预处理和提取后，利用机器学习分类器对提取的特征进行分类，对生理信号数据库进行分类，研究HR、BP和RESP在衰老过程中的贡献变化。本研究数据取自健康青壮年受试者幻想曲标准数据库和健康青壮年受试者自录数据。在生理数据集上使用五重交叉验证对朴素贝叶斯(NB)、支持向量机(SVM)、k近邻(KNN)、逻辑回归(LR)和人工神经网络(ANN)进行训练。结果表明，通过加入耦合特征，分类器的最终预测效率由LR得到的[Formula: see text]%提高到[Formula: see text]%，由SVM得到的[Formula: see text]%提高到[Formula: see text]%，由KNN得到的[Formula: see text]%提高到[Formula: see text]%，由NB得到的[Formula: see text]%提高到[Formula: see text]%，由ANN得到的[Formula: see text]%提高到[Formula: see text]%。在具有耦合特征的情况下，与其他机器学习算法相比，在相同长度的数据库上，ANN的最大准确率为[Formula: see text]%，灵敏度为[Formula: see text]%，特异性为[Formula: see text]%，计算时间大大减少。
{"title":"ANALYSIS OF CARDIOVASCULAR, CARDIORESPIRATORY, AND VASCULO- RESPIRATORY SIGNALS USING DIFFERENT MACHINE LEARNING TECHNIQUES","authors":"Kirti Singh, I. Saini, Neetu Sood","doi":"10.4015/s1016237222500454","DOIUrl":"https://doi.org/10.4015/s1016237222500454","url":null,"abstract":"Many physiological signals such as heart rate (HR), blood pressure (BP), and respiration (RESP) affect each other, and the inter-relation within and between these signals can be linear or nonlinear. Therefore, this paper’s main aim is to extract the relevant features using the information domain coupling technique based on conditional transfer entropy to detect the nonlinearity and coupling changes between the physiological signals and to classify the database using various machine learning classifiers to study the aging changes in the contribution of HR, BP, and RESP. In the proposed work, the physiological signals, i.e. HR, BP, and RESP, were pre-processed using various filtering methods, then features of physiological signals were extracted using linear and nonlinear techniques. After the pre-processing and extraction of features, the extracted features are classified using machine learning classifiers to classify the physiological signal database to study the aging changes in the contribution of HR, BP, and RESP. The data has been taken from the standard Fantasia database of healthy young and old subjects and self-recorded data of healthy young and old subjects for this study. Naive Bayes (NB), Support vector machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), and Artificial Neural Network (ANN) were trained using five-fold cross-validation on the physiological dataset. It is concluded from the results that by adding the coupling features, the efficiency of the final prediction of the classifier increased from [Formula: see text]% to [Formula: see text]% obtained by LR, [Formula: see text]% to [Formula: see text]% obtained by SVM, [Formula: see text]% to [Formula: see text]% obtained by KNN, [Formula: see text]% to [Formula: see text]% obtained by NB, and [Formula: see text]% to [Formula: see text]% obtained by ANN. The ANN performs well when provided with the coupling features, gives a maximum accuracy of [Formula: see text]% and very high sensitivity of [Formula: see text]% and specificity of [Formula: see text]%, and takes much less computational time, when compared to other machine learning algorithms on same length of database.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"20 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87815970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-10DOI: 10.4015/s1016237222500521
B. Sathyamoorthy, U. Snehalatha, T. Rajalakshmi
The aim of the study is (i) to determine temperature distribution for various emotions from the facial thermal images; (ii) to extract statistical features from the facial region using GLCM feature extraction technique and to classify the emotions using machine learning classifiers such as SVM and Naïve Bayes; (iii) to develop the custom CNN model for the classification of various emotions and compare its performance with machine learning classifiers. Fifty normal subjects were considered for the study to analyze the facial emotions using thermal and digital images. The four different emotions, such as happy, angry, neutral and sad, were obtained with a total image of 200 thermal and 200 digital images. Ten statistical features were extracted using the GLCM method from both thermal and digital images and fed into the machine learning classifiers. After data augmentation, the images are fed into the custom CNN model for the classification of various emotions. The SVM classifier produced an accuracy of 80% in thermal images and 76.5% in digital images compared to the Naive Bayes classifier. The developed CNN model improved the classification accuracy to 94.3% and 90.3% for thermal and digital image, respectively, for the multi-class classification of facial emotions. The CNN model implemented using thermal images provided better classification accuracy than digital images in facial emotion recognition. Hence, it was proved that thermal imaging techniques resulted in better performance in predicting facial emotion than digital images.
{"title":"FACIAL EMOTION DETECTION OF THERMAL AND DIGITAL IMAGES BASED ON MACHINE LEARNING TECHNIQUES","authors":"B. Sathyamoorthy, U. Snehalatha, T. Rajalakshmi","doi":"10.4015/s1016237222500521","DOIUrl":"https://doi.org/10.4015/s1016237222500521","url":null,"abstract":"The aim of the study is (i) to determine temperature distribution for various emotions from the facial thermal images; (ii) to extract statistical features from the facial region using GLCM feature extraction technique and to classify the emotions using machine learning classifiers such as SVM and Naïve Bayes; (iii) to develop the custom CNN model for the classification of various emotions and compare its performance with machine learning classifiers. Fifty normal subjects were considered for the study to analyze the facial emotions using thermal and digital images. The four different emotions, such as happy, angry, neutral and sad, were obtained with a total image of 200 thermal and 200 digital images. Ten statistical features were extracted using the GLCM method from both thermal and digital images and fed into the machine learning classifiers. After data augmentation, the images are fed into the custom CNN model for the classification of various emotions. The SVM classifier produced an accuracy of 80% in thermal images and 76.5% in digital images compared to the Naive Bayes classifier. The developed CNN model improved the classification accuracy to 94.3% and 90.3% for thermal and digital image, respectively, for the multi-class classification of facial emotions. The CNN model implemented using thermal images provided better classification accuracy than digital images in facial emotion recognition. Hence, it was proved that thermal imaging techniques resulted in better performance in predicting facial emotion than digital images.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"102 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74980774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-28DOI: 10.4015/s1016237222500508
Nadia, Ekta Gandotra, Narendra Kumar
The nucleotide-binding domain leucine-rich repeat-containing (NLR) proteins plays significant role in the intestinal tissue repair and innate immunity. It recently added to the members of innate immunity effectors molecules. It also plays an essential role in intestinal microbiota and recently emerged as a crucial hit for developing ulcerative colitis (UC) and colitis-associated cancer (CAC). A machine learning-based approach for predicting NLR proteins has been developed. In this study, we present a comparison of three supervised machine learning algorithms. Using ProtR and POSSUM Packages, the features are extracted for the dataset used in this work. The models are trained with the input compositional features generated using dipeptide composition, amino acid composition, etc., as well as Position Specific Scoring Matrix (PSSM) based compositions. The dataset consists of 390 proteins for the negative and positive datasets. The five-fold cross-validation (CV) is used to optimize Sequential Minimal Optimization (SMO) library of Support Vector Machine (LIBSVM) and Random Forest (RF) parameters, and the best model was selected. The proposed work performs rationally well with an accuracy of 90.91% and 93.94% for RF as the best classifier for the Amino Acid Composition (AAC) and PSE_PSSM-based model. We believe that this method is a reliable, rapid and useful prediction method for NLR Protein.
核苷酸结合域富含亮氨酸重复序列(NLR)蛋白在肠道组织修复和先天免疫中起重要作用。它最近加入了先天免疫效应分子的成员。它在肠道微生物群中也起着至关重要的作用，最近被发现是溃疡性结肠炎(UC)和结肠炎相关癌症(CAC)的关键打击。一种基于机器学习的预测NLR蛋白的方法已经被开发出来。在这项研究中，我们提出了三种监督机器学习算法的比较。使用ProtR和POSSUM包，为本工作中使用的数据集提取特征。使用二肽组成、氨基酸组成等生成的输入组成特征以及基于位置特定评分矩阵(Position Specific Scoring Matrix, PSSM)的组合来训练模型。该数据集由390个蛋白质组成，分别用于阴性和阳性数据集。采用五重交叉验证(CV)对支持向量机(LIBSVM)和随机森林(RF)参数的序贯最小优化(SMO)库进行优化，选出最优模型。结果表明，RF作为氨基酸组成(AAC)和pse_pssm模型的最佳分类器，准确率分别为90.91%和93.94%。该方法是一种可靠、快速、实用的NLR蛋白预测方法。
{"title":"COMPARISON OF MACHINE LEARNING TECHNIQUES FOR PREDICTING NLR PROTEINS","authors":"Nadia, Ekta Gandotra, Narendra Kumar","doi":"10.4015/s1016237222500508","DOIUrl":"https://doi.org/10.4015/s1016237222500508","url":null,"abstract":"The nucleotide-binding domain leucine-rich repeat-containing (NLR) proteins plays significant role in the intestinal tissue repair and innate immunity. It recently added to the members of innate immunity effectors molecules. It also plays an essential role in intestinal microbiota and recently emerged as a crucial hit for developing ulcerative colitis (UC) and colitis-associated cancer (CAC). A machine learning-based approach for predicting NLR proteins has been developed. In this study, we present a comparison of three supervised machine learning algorithms. Using ProtR and POSSUM Packages, the features are extracted for the dataset used in this work. The models are trained with the input compositional features generated using dipeptide composition, amino acid composition, etc., as well as Position Specific Scoring Matrix (PSSM) based compositions. The dataset consists of 390 proteins for the negative and positive datasets. The five-fold cross-validation (CV) is used to optimize Sequential Minimal Optimization (SMO) library of Support Vector Machine (LIBSVM) and Random Forest (RF) parameters, and the best model was selected. The proposed work performs rationally well with an accuracy of 90.91% and 93.94% for RF as the best classifier for the Amino Acid Composition (AAC) and PSE_PSSM-based model. We believe that this method is a reliable, rapid and useful prediction method for NLR Protein.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"1 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82987582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-28DOI: 10.4015/s1016237222300024
R. Maity, Ruby Mishra, P. Pattnaik
Flying robots popularly known as drones or UAVs are emerging technologies of the current era. A significant amount of research work has been undertaken in this area in the last few years. Considering the current scenario where aerial vehicles are taking a major part of the market it is important to have an effective and robust design of flying robots. This paper aims to examine the categories of flying robots based on the features that include a range from petite to large and its body structure, wing designs, tail design, propulsion system, and gripper mechanisms along with the associated materials and manufacturing techniques. Again the work is intended to review the respective challenges faced by each category. Mostly the challenges faced by flying robots are design challenges, material selection, and fabrication challenges which are discussed in the paper. In this paper, we have summarized various designs of flying robots developed to date as well as we have focused on major features to be taken care of while designing flying robots. This paper has tried to focus on different design aspects and challenges faced by flying robots so that further research can be carried out to develop effective flying robots in the future.
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Pub Date : 2022-10-26DOI: 10.4015/s1016237222500090
M. Mehrabani, M. Farahvash, Reza Samanipour, S. Tabatabaee, Adel Marzban, J. Rahmati, A. Tavakoli
The influence of facial appearance on people’s mental confidence is well-known in the modern life. The aim of this study was to evaluate the clinical efficacy of human-derived collagen gel injection on nasolabial folds and reveal a safe and cost-reasonable candidate for aesthetic and therapeutic cases. This assessment was a quasi-experimental interventional study on patients referred to the plastic surgery clinic of Imam Hospital in 2016–2017 who intended to treat nasolabial folds with outpatient methods and rapid recovery time regardless of age and gender restrictions. Allogenic collagen was injected at the site of nasolabial folds and the durability of the fillers was evaluated by the researcher, the neutral examiner and the participants based on the wrinkle severity rating scale (WSRS) and the global aesthetic improvement scale (GAIS). In terms of severity of nasolabial folds before intervention, the mean and severe state comprised 37 (69.81%) and 16 (30.19%) patients, respectively. The majority of subjects (more than 80%) in both assessments (the examiner and the researcher) demonstrated the improvement of the folds. The agreement between the two evaluators was relatively approximate ([Formula: see text] and [Formula: see text]). Regardless of the evaluation group, the trend of changes was statistically significant ([Formula: see text]). Eventually, the duration of the filler efficacy was estimated to be 4–6 months. The allogenic collagen filler is recommended as an almost safe and cost-effective agent for nasolabial fold treatment in short to medium periods in case of low risk of the transmission of contamination and no need for allergic testing.
{"title":"THE USE OF ALLOGENIC COLLAGEN GEL IN NASOLABIAL FOLD TREATMENT: AN EXPERIMENTAL ASSESSMENT","authors":"M. Mehrabani, M. Farahvash, Reza Samanipour, S. Tabatabaee, Adel Marzban, J. Rahmati, A. Tavakoli","doi":"10.4015/s1016237222500090","DOIUrl":"https://doi.org/10.4015/s1016237222500090","url":null,"abstract":"The influence of facial appearance on people’s mental confidence is well-known in the modern life. The aim of this study was to evaluate the clinical efficacy of human-derived collagen gel injection on nasolabial folds and reveal a safe and cost-reasonable candidate for aesthetic and therapeutic cases. This assessment was a quasi-experimental interventional study on patients referred to the plastic surgery clinic of Imam Hospital in 2016–2017 who intended to treat nasolabial folds with outpatient methods and rapid recovery time regardless of age and gender restrictions. Allogenic collagen was injected at the site of nasolabial folds and the durability of the fillers was evaluated by the researcher, the neutral examiner and the participants based on the wrinkle severity rating scale (WSRS) and the global aesthetic improvement scale (GAIS). In terms of severity of nasolabial folds before intervention, the mean and severe state comprised 37 (69.81%) and 16 (30.19%) patients, respectively. The majority of subjects (more than 80%) in both assessments (the examiner and the researcher) demonstrated the improvement of the folds. The agreement between the two evaluators was relatively approximate ([Formula: see text] and [Formula: see text]). Regardless of the evaluation group, the trend of changes was statistically significant ([Formula: see text]). Eventually, the duration of the filler efficacy was estimated to be 4–6 months. The allogenic collagen filler is recommended as an almost safe and cost-effective agent for nasolabial fold treatment in short to medium periods in case of low risk of the transmission of contamination and no need for allergic testing.","PeriodicalId":8862,"journal":{"name":"Biomedical Engineering: Applications, Basis and Communications","volume":"43 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90696768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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