Pub Date : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635351
Theofilos Zacheilas, K. Moirogiorgou, N. Papandroulakis, E. Sotiriades, M. Zervakis, A. Dollas
Aquaculture faces the issue of net integrity on cage farming. Holes on the net need to be detected but as yet the process is not fully automated. This work is a second-generation embedded system to detect in real time holes in aquaculture nets from a video input. It extends previous results by processing video rather than still images, under lighting variation, haze, and different size of holes along each frame. The modeling and simulation of the new algorithm has been done in MATLAB; the system has been designed and implemented on a Field Programmable Gate Array (FPGA) - based platform. The proposed system has substantially better performance vs. software at a much lower energy consumption.
{"title":"An FPGA-Based System for Video Processing to Detect Holes in Aquaculture Nets","authors":"Theofilos Zacheilas, K. Moirogiorgou, N. Papandroulakis, E. Sotiriades, M. Zervakis, A. Dollas","doi":"10.1109/BIBE52308.2021.9635351","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635351","url":null,"abstract":"Aquaculture faces the issue of net integrity on cage farming. Holes on the net need to be detected but as yet the process is not fully automated. This work is a second-generation embedded system to detect in real time holes in aquaculture nets from a video input. It extends previous results by processing video rather than still images, under lighting variation, haze, and different size of holes along each frame. The modeling and simulation of the new algorithm has been done in MATLAB; the system has been designed and implemented on a Field Programmable Gate Array (FPGA) - based platform. The proposed system has substantially better performance vs. software at a much lower energy consumption.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":" 45","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120828903","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635506
A. Vukicevic, A. Zabotti, V. Milic, A. Hočevar, O. Lucia, G. Filippou, A. Tzioufas, S. Vita, Nenad Filipović
Salivary gland ultrasonography (SGUS) represents a promising tool for diagnosing Primary Sjögren's syndrome (pSS), which is manifest with abnormalities in salivary glands (SG). In this study, we propose a fully automatic method for scoring SGs in SGUS images, which is the most important step towards SG the pSS diagnosis. A two-centric cohort included 600 images (150 patients) annotated by experienced clinicians. The aim of the study was to assess various deep learning classifiers (MobileNetV2, VGG19, Dense-Net, Squeeze-Net, Inception_v3, and ResNet) for the purpose of the pSS scoring in SGUS. The training was performed using the ADAM optimizer and cross entropy loss function. Top performing algorithms were MobileNetV2, ResNet, and Dense-Net. The assessment showed that deep learning algorithms reached clinicians-level performances in the almost real-time. Considering that, the further work should be regarded towards evaluation on larger and international data sets with the goal to establish SGUS as an effective noninvasive pSS diagnostic tool.
{"title":"Scoring Primary Sjögren's syndrome affected salivary glands ultrasonography images by using deep learning algorithms","authors":"A. Vukicevic, A. Zabotti, V. Milic, A. Hočevar, O. Lucia, G. Filippou, A. Tzioufas, S. Vita, Nenad Filipović","doi":"10.1109/BIBE52308.2021.9635506","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635506","url":null,"abstract":"Salivary gland ultrasonography (SGUS) represents a promising tool for diagnosing Primary Sjögren's syndrome (pSS), which is manifest with abnormalities in salivary glands (SG). In this study, we propose a fully automatic method for scoring SGs in SGUS images, which is the most important step towards SG the pSS diagnosis. A two-centric cohort included 600 images (150 patients) annotated by experienced clinicians. The aim of the study was to assess various deep learning classifiers (MobileNetV2, VGG19, Dense-Net, Squeeze-Net, Inception_v3, and ResNet) for the purpose of the pSS scoring in SGUS. The training was performed using the ADAM optimizer and cross entropy loss function. Top performing algorithms were MobileNetV2, ResNet, and Dense-Net. The assessment showed that deep learning algorithms reached clinicians-level performances in the almost real-time. Considering that, the further work should be regarded towards evaluation on larger and international data sets with the goal to establish SGUS as an effective noninvasive pSS diagnostic tool.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130371675","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635374
L. S. Becirovic, Amar Deumic, L. G. Pokvic, A. Badnjević
Machine learning algorithms have been drawing attention in lung disease research. However, due to their algorithmic learning complexity and the variability of their architecture, there is an ongoing need to analyze their performance. This study reviews the input parameters and the performance of machine learning applied to diagnosis of chronic obstructive pulmonary disease (COPD). One research focus of this study was on clearly identifying problems and issues related to the implementation of machine learning in clinical studies. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, 179, 1032, and 36,500 titles were identified from the PubMed, Scopus, and Google Scholar databases respectively. Studies that used machine learning to detect COPD and provided performance measures were included in our analysis. In the final analysis, 24 studies were included. The analysis of machine learning methods to detect COPD reveals the limited usage of the methods and the lack of standards that hinder the implementation of machine learning in clinical applications. The performance of machine learning for diagnosis of COPD was considered satisfactory for several studies; however, given the limitations indicated in our study, further studies are warranted to extend the potential use of machine learning to clinical settings.
{"title":"Aritificial Inteligence Challenges in COPD management: a review","authors":"L. S. Becirovic, Amar Deumic, L. G. Pokvic, A. Badnjević","doi":"10.1109/BIBE52308.2021.9635374","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635374","url":null,"abstract":"Machine learning algorithms have been drawing attention in lung disease research. However, due to their algorithmic learning complexity and the variability of their architecture, there is an ongoing need to analyze their performance. This study reviews the input parameters and the performance of machine learning applied to diagnosis of chronic obstructive pulmonary disease (COPD). One research focus of this study was on clearly identifying problems and issues related to the implementation of machine learning in clinical studies. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, 179, 1032, and 36,500 titles were identified from the PubMed, Scopus, and Google Scholar databases respectively. Studies that used machine learning to detect COPD and provided performance measures were included in our analysis. In the final analysis, 24 studies were included. The analysis of machine learning methods to detect COPD reveals the limited usage of the methods and the lack of standards that hinder the implementation of machine learning in clinical applications. The performance of machine learning for diagnosis of COPD was considered satisfactory for several studies; however, given the limitations indicated in our study, further studies are warranted to extend the potential use of machine learning to clinical settings.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128412919","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635481
G. Apostolopoulos, A. Koutras, D. Anyfantis, Ioanna Christoyianni
Computer-aided Diagnosis (CAD) systems have become a significant assistance tool, that are used to help identify abnormal/normal regions of interest in mammograms faster and more effectively than human readers. In this work, we propose a new approach for breast cancer identification of all type of lesions in digital mammograms by combining low-and high-level mammogram descriptors in a compact form. The proposed method consists of two major stages: Initially, a feature extraction process that utilizes two dimensional discrete transforms based on ART, Shapelets and textural representations based on Gabor filter banks, is used to extract low-level visual descriptors. To further improve our method's performance, the semantic information of each mammogram given by radiologists is encoded in a 16-bit length word high-level feature vector. All features are stored in a quaternion and fused using the L2 norm prior to their presentation to the classification module. For the classification task, each ROS is recognized using two different classification models, Ada Boost and Random Forest. The proposed method is evaluated on regions taken from the DDSM database. The results show that Ada Boost outperforms Random Forest in terms of accuracy (99.2%$(pm 0.527)$ against 93.78% $(pm 1.659))$, precision, recall and F-measure. Both classifiers achieve a mean accuracy of 33% and 38% higher than using only visual descriptors, showing that semantic information can indeed improve the diagnosis when it is combined with standard visual features.
{"title":"A Comparative Analysis of Breast Cancer Diagnosis by Fusing Visual and Semantic Feature Descriptors","authors":"G. Apostolopoulos, A. Koutras, D. Anyfantis, Ioanna Christoyianni","doi":"10.1109/BIBE52308.2021.9635481","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635481","url":null,"abstract":"Computer-aided Diagnosis (CAD) systems have become a significant assistance tool, that are used to help identify abnormal/normal regions of interest in mammograms faster and more effectively than human readers. In this work, we propose a new approach for breast cancer identification of all type of lesions in digital mammograms by combining low-and high-level mammogram descriptors in a compact form. The proposed method consists of two major stages: Initially, a feature extraction process that utilizes two dimensional discrete transforms based on ART, Shapelets and textural representations based on Gabor filter banks, is used to extract low-level visual descriptors. To further improve our method's performance, the semantic information of each mammogram given by radiologists is encoded in a 16-bit length word high-level feature vector. All features are stored in a quaternion and fused using the L2 norm prior to their presentation to the classification module. For the classification task, each ROS is recognized using two different classification models, Ada Boost and Random Forest. The proposed method is evaluated on regions taken from the DDSM database. The results show that Ada Boost outperforms Random Forest in terms of accuracy (99.2%$(pm 0.527)$ against 93.78% $(pm 1.659))$, precision, recall and F-measure. Both classifiers achieve a mean accuracy of 33% and 38% higher than using only visual descriptors, showing that semantic information can indeed improve the diagnosis when it is combined with standard visual features.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134315547","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635304
Xiaozhi Yuan, Daniel Bibl, Kahlil Khan, Lei Sun
In silico approach can make vaccine designs more efficient and cost-effective. It complements the traditional process and becomes extremely valuable in coping with pandemics such as COVID-19. A recent study proposed an artificial intelligence-based framework to predict and design multi-epitope vaccines for the SARS-CoV-2 virus. However, we found several issues in its dataset design as well as its neural network design. To achieve more reliable predictions of the potential vaccine subunits, we create a more reliable and larger dataset for machine learning experiments. We apply natural language processing techniques and build neural networks composed of convolutional layer and recurrent layer to identify peptide sequences as vaccine candidates. We also train a classifier using embeddings from a pre-trained Transformer protein language model, which provides a baseline for comparison. Experimental results demonstrate that our models achieve high performance in classification accuracy and the area under the receiver operating characteristic curve.
{"title":"Predicting Multi-Epitope Vaccine Candidates Using Natural Language Processing and Deep Learning","authors":"Xiaozhi Yuan, Daniel Bibl, Kahlil Khan, Lei Sun","doi":"10.1109/BIBE52308.2021.9635304","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635304","url":null,"abstract":"In silico approach can make vaccine designs more efficient and cost-effective. It complements the traditional process and becomes extremely valuable in coping with pandemics such as COVID-19. A recent study proposed an artificial intelligence-based framework to predict and design multi-epitope vaccines for the SARS-CoV-2 virus. However, we found several issues in its dataset design as well as its neural network design. To achieve more reliable predictions of the potential vaccine subunits, we create a more reliable and larger dataset for machine learning experiments. We apply natural language processing techniques and build neural networks composed of convolutional layer and recurrent layer to identify peptide sequences as vaccine candidates. We also train a classifier using embeddings from a pre-trained Transformer protein language model, which provides a baseline for comparison. Experimental results demonstrate that our models achieve high performance in classification accuracy and the area under the receiver operating characteristic curve.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133263479","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635230
Filippos Bitsas, Irini Georgia Dimitriou, G. Manis
Now days, new methods, ideas and applications are reinforcing safety in our home environment. Children's safety is a major concern for all parents, especially the new ones. Potential dangers are hidden everywhere, even in the children's room. Motivated by the necessity for additional safety, we employed smart technology to develop a sensor based system for reducing hazards from electricity, such as electric shocks. A smart system for additional protection was designed, targeting the periods in which parents are absent and the children alone in their room. The proposed system adds value in existing safety measures, since it works complementary to them. The main idea is based on the detection of the presence of adults in the room. Depending on parents' presence, the smart system decides which sockets are allowed to be active and which are not. Android software forwards observations on the activity to the parent's mobile phone and allows easier management. A prototype of the system has been developed and tested, without the participation of children in the experiments.
{"title":"Smart Protection from Electricity Hazards in Children's Room","authors":"Filippos Bitsas, Irini Georgia Dimitriou, G. Manis","doi":"10.1109/BIBE52308.2021.9635230","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635230","url":null,"abstract":"Now days, new methods, ideas and applications are reinforcing safety in our home environment. Children's safety is a major concern for all parents, especially the new ones. Potential dangers are hidden everywhere, even in the children's room. Motivated by the necessity for additional safety, we employed smart technology to develop a sensor based system for reducing hazards from electricity, such as electric shocks. A smart system for additional protection was designed, targeting the periods in which parents are absent and the children alone in their room. The proposed system adds value in existing safety measures, since it works complementary to them. The main idea is based on the detection of the presence of adults in the room. Depending on parents' presence, the smart system decides which sockets are allowed to be active and which are not. Android software forwards observations on the activity to the parent's mobile phone and allows easier management. A prototype of the system has been developed and tested, without the participation of children in the experiments.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133424800","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635428
Momcilo Prodanovic, B. Stojanovic, Danica Prodanovic, N. Filipovic, S. Mijailovich
Hypertrophic and Dilated Cardiomyopathies are caused by inherited mutations in sarcomeric proteins: Myosin (M), Troponin (Tn), Tropomyosin (Tm) and Myosin Binding Protein-C (MyBP-C). A quantitative understanding of how mutations change protein behaviour, and hence cardiac muscle contraction, and how adaptations to these changes result in disease, could accelerate the design of novel personalized treatments and therapeutics. Newly developed multiscale computational tools, tightly interlaced with multiple experiments, can enhance efforts to correct the problems associated with cardiomyopathies and prevent or more effectively manage the disease. Using these computational tools, we examined the effects of mutations in myosin and troponin on cardiac muscle contractility and overall heart functional behaviour. We also examined the effects of potential therapeutics that modulate protein interactions and cardiac muscle contractility.
{"title":"Computational Modeling of Sarcomere Protein Mutations and Drug Effects on Cardiac Muscle Behavior","authors":"Momcilo Prodanovic, B. Stojanovic, Danica Prodanovic, N. Filipovic, S. Mijailovich","doi":"10.1109/BIBE52308.2021.9635428","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635428","url":null,"abstract":"Hypertrophic and Dilated Cardiomyopathies are caused by inherited mutations in sarcomeric proteins: Myosin (M), Troponin (Tn), Tropomyosin (Tm) and Myosin Binding Protein-C (MyBP-C). A quantitative understanding of how mutations change protein behaviour, and hence cardiac muscle contraction, and how adaptations to these changes result in disease, could accelerate the design of novel personalized treatments and therapeutics. Newly developed multiscale computational tools, tightly interlaced with multiple experiments, can enhance efforts to correct the problems associated with cardiomyopathies and prevent or more effectively manage the disease. Using these computational tools, we examined the effects of mutations in myosin and troponin on cardiac muscle contractility and overall heart functional behaviour. We also examined the effects of potential therapeutics that modulate protein interactions and cardiac muscle contractility.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"303 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124698509","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635241
R. W. Oei, Jiewen Zhang, Jin Zhong, Guanqun Hou, Nuntawat Chanajarunvit, N. Xu
Actin cytoskeleton has been identified as a potential therapeutic target for cancer. Therefore, to identify cell responses to such chemical agents has been an essential part in the past studies, which is often measured visually. This kind of visual recognition task currently is performed by human experts, which poses a great challenge since the features can hardly be detected using only human eyes. This article presents the application of convolutional neural networks (CNNs) in classifying human breast epithelial cells based on different dosages of drug exposure. MCF-10A cell line was chosen for the experiments and was treated with 90 nM and 400 nM cytochalasin D. The CNNs were evaluated on a large immunofluorescence images of intracellular actin filament networks captured after the exposure of different drug concentrations. During the image pre-processing, we implemented image enhancement and data augmentation approaches. Two well-known CNNs, VGG-16 and ResNet-50, were trained with or without transfer learning. The study revealed that the CNN performed better in the classification task compared to human experts. In conclusion, ResN et-50 with transfer learning achieved the best performance.
{"title":"Convolutional Neural Networks for Cellular Drug Response Prediction Using Immunofluorescence Images of Intracellular Actin Filament Networks","authors":"R. W. Oei, Jiewen Zhang, Jin Zhong, Guanqun Hou, Nuntawat Chanajarunvit, N. Xu","doi":"10.1109/BIBE52308.2021.9635241","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635241","url":null,"abstract":"Actin cytoskeleton has been identified as a potential therapeutic target for cancer. Therefore, to identify cell responses to such chemical agents has been an essential part in the past studies, which is often measured visually. This kind of visual recognition task currently is performed by human experts, which poses a great challenge since the features can hardly be detected using only human eyes. This article presents the application of convolutional neural networks (CNNs) in classifying human breast epithelial cells based on different dosages of drug exposure. MCF-10A cell line was chosen for the experiments and was treated with 90 nM and 400 nM cytochalasin D. The CNNs were evaluated on a large immunofluorescence images of intracellular actin filament networks captured after the exposure of different drug concentrations. During the image pre-processing, we implemented image enhancement and data augmentation approaches. Two well-known CNNs, VGG-16 and ResNet-50, were trained with or without transfer learning. The study revealed that the CNN performed better in the classification task compared to human experts. In conclusion, ResN et-50 with transfer learning achieved the best performance.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123596548","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635257
Žiko B. Milanović, Edina H. Avdović, Dušica M Simijonović, Z. Marković
Different phenolic coumarin derivatives represent a widespread class of compounds that have shown remarkable activity in removing reactive oxygen species. For this reason, within this study, the antiradical activity of previously synthesized phenolic derivatives of 4,7 -dihydroxycoumarin: (E)-3-(1-((2-hydroxyphenyl)amino) ethylidene) -2,4-dioxochroman-7-yl (A-20H), $(E)$ -3-(1((3-hydroxyphenyl)amino)ethylidene)-2,4-dioxochroman-7-yl acetate (A-30H), $(E)$. -3-(1((4-hydroxyphenyl)amino) ethylidene) -2,4-dioxochroman-7-yl (A-40H) acetate against the 2,2-diphenyl-1-picrylhydrazyl (DPPH·) radical was investigated. All research is supported by Density Functional Theory $(mathbf{DFT}/mathbf{M06}-mathbf{2X/6-311++}mathbf{G}(mathbf{d, p})$ level of theory and CPCM solvation model-methanol) in combination with global chemical reactivity parameters. The results of experimental scavenging activity towards DPPH· indicate that A-20H shows the best activity. The most probable scavenging route was determined based on the thermodynamic parameters. A good correlation between experiment and theory showed that Hydrogen Atom Transfer (HAT, $Deltatext{rGHAT}$) was the dominant pathway of the reduction of DPPH·. In general, the results of global chemical reactivity parameters show that the A-40H compound shows the best electron-donating properties, which is correlated with thermodynamic parameters obtained for the Single Electron Transfer (SET, $Delta{text{rGSET}}$) mechanism.
{"title":"Estimation of antiradical properties of series of 4, 7 - dihydroxycoumarin derivatives towards DPPH radical-experimental and DFT study","authors":"Žiko B. Milanović, Edina H. Avdović, Dušica M Simijonović, Z. Marković","doi":"10.1109/BIBE52308.2021.9635257","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635257","url":null,"abstract":"Different phenolic coumarin derivatives represent a widespread class of compounds that have shown remarkable activity in removing reactive oxygen species. For this reason, within this study, the antiradical activity of previously synthesized phenolic derivatives of 4,7 -dihydroxycoumarin: (E)-3-(1-((2-hydroxyphenyl)amino) ethylidene) -2,4-dioxochroman-7-yl (A-20H), $(E)$ -3-(1((3-hydroxyphenyl)amino)ethylidene)-2,4-dioxochroman-7-yl acetate (A-30H), $(E)$. -3-(1((4-hydroxyphenyl)amino) ethylidene) -2,4-dioxochroman-7-yl (A-40H) acetate against the 2,2-diphenyl-1-picrylhydrazyl (DPPH·) radical was investigated. All research is supported by Density Functional Theory $(mathbf{DFT}/mathbf{M06}-mathbf{2X/6-311++}mathbf{G}(mathbf{d, p})$ level of theory and CPCM solvation model-methanol) in combination with global chemical reactivity parameters. The results of experimental scavenging activity towards DPPH· indicate that A-20H shows the best activity. The most probable scavenging route was determined based on the thermodynamic parameters. A good correlation between experiment and theory showed that Hydrogen Atom Transfer (HAT, $Deltatext{rGHAT}$) was the dominant pathway of the reduction of DPPH·. In general, the results of global chemical reactivity parameters show that the A-40H compound shows the best electron-donating properties, which is correlated with thermodynamic parameters obtained for the Single Electron Transfer (SET, $Delta{text{rGSET}}$) mechanism.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123699361","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 : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635369
Styliani P. Zelilidou, E. Tripoliti, Kostas I. Vlachos, S. Konitsiotis, D. Fotiadis
This paper presents a clustering-based method for the detection of Multiple Sclerosis (MS) lesions, by including anatomical information, brain geometry and lesion features, while volume quantification is performed. The proposed method utilizes Fluid Attenuated Inversion Recovery (FLAIR) images for the delineation of the plaques and brain atrophy estimation. The methodology includes five steps: (i) image preprocessing, (ii) image segmentation utilizing the K-means clustering algorithm, (iii) post processing for elimination of false positives, (iv) delineation and visualization of the MS lesions, and (v) brain atrophy estimation. It is implemented in two different datasets; (a) a dataset of 3D FLAIR MR Images, acquired in 30 MS patients, and (b) a dataset of 15 FLAIR MR Images, provided by the MICCAI Challenge 2016. A sensitivity 73.80%, and 71.52% was achieved for the two datasets, respectively. Brain atrophy was determined only on the first dataset, since follow up scans are available.
{"title":"Clustering based Segmentation of MR Images for the Delineation and Monitoring of Multiple Sclerosis Progression","authors":"Styliani P. Zelilidou, E. Tripoliti, Kostas I. Vlachos, S. Konitsiotis, D. Fotiadis","doi":"10.1109/BIBE52308.2021.9635369","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635369","url":null,"abstract":"This paper presents a clustering-based method for the detection of Multiple Sclerosis (MS) lesions, by including anatomical information, brain geometry and lesion features, while volume quantification is performed. The proposed method utilizes Fluid Attenuated Inversion Recovery (FLAIR) images for the delineation of the plaques and brain atrophy estimation. The methodology includes five steps: (i) image preprocessing, (ii) image segmentation utilizing the K-means clustering algorithm, (iii) post processing for elimination of false positives, (iv) delineation and visualization of the MS lesions, and (v) brain atrophy estimation. It is implemented in two different datasets; (a) a dataset of 3D FLAIR MR Images, acquired in 30 MS patients, and (b) a dataset of 15 FLAIR MR Images, provided by the MICCAI Challenge 2016. A sensitivity 73.80%, and 71.52% was achieved for the two datasets, respectively. Brain atrophy was determined only on the first dataset, since follow up scans are available.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"380 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122774745","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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