Pub Date : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635273
A. Ding, Ying Li, Qilei Chen, Yu Cao, Benyuan Liu, Shu Han Chen, Xiaowei Liu
Esophagogastroduodenoscopy (EGD) is a common procedure that visualizes the esophagus, stomach, and the duodenum by inserting a camera, attached to a long flexible tube, into the patient's mouth and down the stomach. A comprehensive EGD needs to examine all gastric locations, but since the camera is controlled manually, it is easy to miss some surface area and create diagnostic blind spots, which often result in life-costing oversights of early gastric cancer and other serious illnesses. In order to address this problem, we train a convolutional neural network to classify gastric locations based on the camera feed during an EGD, and based on the classifier and a triggering algorithm we propose, we build a video processing system that checks off each location as visited, allowing human operators to keep track of which locations they have visited and which they have not. Based on collected clinical patient reports, we consider six gastric locations, and we add a background class to our classifier to accomodate for the frames in EGD videos that do not resemble the six defined classes (including when the camera is outside of the patient body). Our best classifier achieves 98 % accuracy within the six gastric locations and 88 % accuracy including the background class, and our video processing system clearly checks off gastric locations in an expected order when being tested on recorded EGD videos. Lastly, we use class activation mapping to provide human-readable insight into how our trained classifier works.
{"title":"Gastric Location Classification During Esophagogastroduodenoscopy Using Deep Neural Networks","authors":"A. Ding, Ying Li, Qilei Chen, Yu Cao, Benyuan Liu, Shu Han Chen, Xiaowei Liu","doi":"10.1109/BIBE52308.2021.9635273","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635273","url":null,"abstract":"Esophagogastroduodenoscopy (EGD) is a common procedure that visualizes the esophagus, stomach, and the duodenum by inserting a camera, attached to a long flexible tube, into the patient's mouth and down the stomach. A comprehensive EGD needs to examine all gastric locations, but since the camera is controlled manually, it is easy to miss some surface area and create diagnostic blind spots, which often result in life-costing oversights of early gastric cancer and other serious illnesses. In order to address this problem, we train a convolutional neural network to classify gastric locations based on the camera feed during an EGD, and based on the classifier and a triggering algorithm we propose, we build a video processing system that checks off each location as visited, allowing human operators to keep track of which locations they have visited and which they have not. Based on collected clinical patient reports, we consider six gastric locations, and we add a background class to our classifier to accomodate for the frames in EGD videos that do not resemble the six defined classes (including when the camera is outside of the patient body). Our best classifier achieves 98 % accuracy within the six gastric locations and 88 % accuracy including the background class, and our video processing system clearly checks off gastric locations in an expected order when being tested on recorded EGD videos. Lastly, we use class activation mapping to provide human-readable insight into how our trained classifier works.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"57 50","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120839570","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/BIBE52308.2021.9635427
V. Oikonomou, S. Nikolopoulos, Y. Kompatsiaris
The recognition of Steady State Visual Evoked Potentials (SSVEP) constitutes a challenging problem in Brain Computer Interfaces (BCI), especially when the number of EEG sensors is limited. In this work, we propose a new sparse representation classification scheme that extends current schemes by exploiting the graph properties of relevant features. Based on this scheme each test signal is represented as a linear combination of train signals. Our expectation is that this constrained linear combination, exploiting the graph's structure of the training data, will lead to representations that are more robust. Moreover, in order to avoid overfitting and provide a model with good generalization abilities we adopt the bayesian framework and, in particular, the Variational Bayesian Framework since we use a specific prior distribution to exploit the graph structure of the data. The proposed algorithm has been evaluated on two SSVEP datasets achieving state-of- the-art performance against well known classification methods in SSVEP literature.
{"title":"Sparse Graph-based Representations of SSVEP Responses Under the Variational Bayesian Framework","authors":"V. Oikonomou, S. Nikolopoulos, Y. Kompatsiaris","doi":"10.1109/BIBE52308.2021.9635427","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635427","url":null,"abstract":"The recognition of Steady State Visual Evoked Potentials (SSVEP) constitutes a challenging problem in Brain Computer Interfaces (BCI), especially when the number of EEG sensors is limited. In this work, we propose a new sparse representation classification scheme that extends current schemes by exploiting the graph properties of relevant features. Based on this scheme each test signal is represented as a linear combination of train signals. Our expectation is that this constrained linear combination, exploiting the graph's structure of the training data, will lead to representations that are more robust. Moreover, in order to avoid overfitting and provide a model with good generalization abilities we adopt the bayesian framework and, in particular, the Variational Bayesian Framework since we use a specific prior distribution to exploit the graph structure of the data. The proposed algorithm has been evaluated on two SSVEP datasets achieving state-of- the-art performance against well known classification methods in SSVEP literature.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"512 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":"123429462","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/BIBE52308.2021.9635462
M. Papadogiorgaki, M. Venianaki, Paulos Charonyktakis, M. Antonakakis, I. Tsamardinos, M. Zervakis, V. Sakkalis
Electrocardiogram (ECG) signal constitutes a valuable technique that provides considerable information towards the early diagnosis of several cardiovascular diseases, especially regarding the detection of abnormal heart rate, namely arrhythmias. In this paper, innovative methodologies that allow for the efficient classification of cardiac rhythm are presented. The proposed methods are based on ECG signal analysis, extraction of significant features, as well as classification algorithms. Several clinical, time- and frequency-domain features are either calculated, or automatically extracted by means of a Convolutional Neural Network, while traditional machine learning algorithms, such as k-Nearest Neighbors and Random Forests are employed in order to classify the ECG signals among 7 different cases of abnormal and normal heart rate. The learning methods are carried out within the JADBio software tool, that also performs feature selection prior to classification. The experimental results demonstrate high performance of the deployed methods in terms of relevant statistical metrics, while they yielded an average validation Area Under the Curve (AUC) of 99.9%.
{"title":"Heart Rate Classification Using ECG Signal Processing and Machine Learning Methods","authors":"M. Papadogiorgaki, M. Venianaki, Paulos Charonyktakis, M. Antonakakis, I. Tsamardinos, M. Zervakis, V. Sakkalis","doi":"10.1109/BIBE52308.2021.9635462","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635462","url":null,"abstract":"Electrocardiogram (ECG) signal constitutes a valuable technique that provides considerable information towards the early diagnosis of several cardiovascular diseases, especially regarding the detection of abnormal heart rate, namely arrhythmias. In this paper, innovative methodologies that allow for the efficient classification of cardiac rhythm are presented. The proposed methods are based on ECG signal analysis, extraction of significant features, as well as classification algorithms. Several clinical, time- and frequency-domain features are either calculated, or automatically extracted by means of a Convolutional Neural Network, while traditional machine learning algorithms, such as k-Nearest Neighbors and Random Forests are employed in order to classify the ECG signals among 7 different cases of abnormal and normal heart rate. The learning methods are carried out within the JADBio software tool, that also performs feature selection prior to classification. The experimental results demonstrate high performance of the deployed methods in terms of relevant statistical metrics, while they yielded an average validation Area Under the Curve (AUC) of 99.9%.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"22 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":"115018126","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/BIBE52308.2021.9635433
F. K. H. D. Barros, André L. Jeller Selleti, Vinicius Queiroz, R. M. Pereira, C. Silla
Researchers dealing with real-world data - such as in the healthcare domain - tend to face class imbalance issues. More specifically, publicly available datasets containing Chest X-Ray (CXR) of Pneumonia diseases (including COVID-19) usually have an imbalanced class distribution. This dataset imbalance causes automatic diagnosis systems to classify majority classes with much more accuracy than the minority ones. Several resampling algorithms were proposed in the past to deal with the class imbalance issue. Hierarchical classifiers have also been proposed to increase the predictive performance of classifiers, but there is little research in the literature verifying if using existing resampling algorithms with hierarchical classifiers are a good alternative to improve classification performance. This work proposes an experimental classification schema to investigate the effectiveness of using resampling algorithms in the identification of COVID-19 and other types of Pneumonia through CXR images. The proposed schema uses resampling algorithms to rebalance the class distribution, in a Local Hierarchical Classification scenario. The experimental evaluation, which is supported by inferential statistical analysis, showed that using specific resampling algorithms with Local Hierarchical Classifiers brings a statistically significant increase to the macro-averaged Fl-Score, and improves the predictive performance for the minority classes.
{"title":"Analyzing the Impact of Resampling Approaches on Chest X-Ray Images for COVID-19 Identification in a Local Hierarchical Classification Scenario","authors":"F. K. H. D. Barros, André L. Jeller Selleti, Vinicius Queiroz, R. M. Pereira, C. Silla","doi":"10.1109/BIBE52308.2021.9635433","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635433","url":null,"abstract":"Researchers dealing with real-world data - such as in the healthcare domain - tend to face class imbalance issues. More specifically, publicly available datasets containing Chest X-Ray (CXR) of Pneumonia diseases (including COVID-19) usually have an imbalanced class distribution. This dataset imbalance causes automatic diagnosis systems to classify majority classes with much more accuracy than the minority ones. Several resampling algorithms were proposed in the past to deal with the class imbalance issue. Hierarchical classifiers have also been proposed to increase the predictive performance of classifiers, but there is little research in the literature verifying if using existing resampling algorithms with hierarchical classifiers are a good alternative to improve classification performance. This work proposes an experimental classification schema to investigate the effectiveness of using resampling algorithms in the identification of COVID-19 and other types of Pneumonia through CXR images. The proposed schema uses resampling algorithms to rebalance the class distribution, in a Local Hierarchical Classification scenario. The experimental evaluation, which is supported by inferential statistical analysis, showed that using specific resampling algorithms with Local Hierarchical Classifiers brings a statistically significant increase to the macro-averaged Fl-Score, and improves the predictive performance for the minority classes.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"243 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":"114155860","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/BIBE52308.2021.9635536
H. Nieto-Chaupis
From data of USA, Japan, Germany, UK, Italy and Russian, it is claimed that the Global pandemic dictated by the dynamics of Corona virus exhibits distributions that would correspond to a morphology of Bessel-like type. Under the assumption that the pandemic contains phases of infection denoted by the velocity and acceleration of propagation of virus, then a model of polynomials given by the integer-order Bessel functions is proposed. These polynomials enter in a statistical approach to define the law of infections as function of time for the ongoing global pandemic. From this, the data evolution and their different behaviors are interpreted in terms of the different phases including the Delta variant for the recent months until August 2021.
{"title":"Theory and Parameterization of Infections and Waves by Covid-19: A 6-Countries Data Analysis","authors":"H. Nieto-Chaupis","doi":"10.1109/BIBE52308.2021.9635536","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635536","url":null,"abstract":"From data of USA, Japan, Germany, UK, Italy and Russian, it is claimed that the Global pandemic dictated by the dynamics of Corona virus exhibits distributions that would correspond to a morphology of Bessel-like type. Under the assumption that the pandemic contains phases of infection denoted by the velocity and acceleration of propagation of virus, then a model of polynomials given by the integer-order Bessel functions is proposed. These polynomials enter in a statistical approach to define the law of infections as function of time for the ongoing global pandemic. From this, the data evolution and their different behaviors are interpreted in terms of the different phases including the Delta variant for the recent months until August 2021.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"22 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":"121856040","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/BIBE52308.2021.9635445
H. Kondylakis, Dimitrios G. Katehakis, A. Kouroubali, K. Marias, G. Flouris, Theodoros Patkos, I. Fundulaki, D. Plexousakis
Informal care is fundamental in the wellbeing and resilience of elderly and people with chronic conditions. However, solutions for the effective collaboration of healthcare professionals, patients and informal carers are not yet widely available. CareKeeper builds on a state-of-the-art personal health system, augmenting it with Artificial Intelligence and Big Data technologies, to boost informal care coordination. In this paper we report on the design of the platform with the aim of providing a light-weighted communication solution to support practical challenges about sharing the responsibility of caring, such as the frequency of visits, support to routinely activities and timely intervention in case of emergency and need.
{"title":"CareKeeper: A Platform for Intelligent Care Coordination","authors":"H. Kondylakis, Dimitrios G. Katehakis, A. Kouroubali, K. Marias, G. Flouris, Theodoros Patkos, I. Fundulaki, D. Plexousakis","doi":"10.1109/BIBE52308.2021.9635445","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635445","url":null,"abstract":"Informal care is fundamental in the wellbeing and resilience of elderly and people with chronic conditions. However, solutions for the effective collaboration of healthcare professionals, patients and informal carers are not yet widely available. CareKeeper builds on a state-of-the-art personal health system, augmenting it with Artificial Intelligence and Big Data technologies, to boost informal care coordination. In this paper we report on the design of the platform with the aim of providing a light-weighted communication solution to support practical challenges about sharing the responsibility of caring, such as the frequency of visits, support to routinely activities and timely intervention in case of emergency and need.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"10 15 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":"130623332","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/BIBE52308.2021.9635289
A. Blagojević, T. Šušteršič, Nenad Filipović
Since the novel SARS-CoV-2 virus appeared, interest in developing epidemiological mechanisms that would help in prevention of its spread has increased. Epidemiological models are the most important mechanisms for examining the spread of the virus. For that purpose, we propose deep learning approach, LSTM neural network model. LSTM is a special kind of neural network structure capable of learning long-term dependencies in sequence prediction problems. The model was fed with official statistical data available online for Belgium in the period of March 15th, 2020 to March 15th, 2021. Results show that LSTM is capable of predicting in long-term manner with the low values of RMSE and MAE. Higher values of RMSE and MAE are observed in the infected cases (RMSE was 397.23 and MAE was 315.35) which is expected due to thousands of infected people per day in Belgium. In future studies, we will include more phenomena, especially medical intervention and asymptomatic infection, in order to better describe the COVID-19 spread and development.
{"title":"Epidemiological forecasting of COVID-19 infection using deep learning approach","authors":"A. Blagojević, T. Šušteršič, Nenad Filipović","doi":"10.1109/BIBE52308.2021.9635289","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635289","url":null,"abstract":"Since the novel SARS-CoV-2 virus appeared, interest in developing epidemiological mechanisms that would help in prevention of its spread has increased. Epidemiological models are the most important mechanisms for examining the spread of the virus. For that purpose, we propose deep learning approach, LSTM neural network model. LSTM is a special kind of neural network structure capable of learning long-term dependencies in sequence prediction problems. The model was fed with official statistical data available online for Belgium in the period of March 15th, 2020 to March 15th, 2021. Results show that LSTM is capable of predicting in long-term manner with the low values of RMSE and MAE. Higher values of RMSE and MAE are observed in the infected cases (RMSE was 397.23 and MAE was 315.35) which is expected due to thousands of infected people per day in Belgium. In future studies, we will include more phenomena, especially medical intervention and asymptomatic infection, in order to better describe the COVID-19 spread and development.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"48 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":"124181924","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/BIBE52308.2021.9635451
Seweryn Kalisz, M. Marczyk
The application of machine learning methods in biomedical image analysis has recently become of particular interest to researchers. One of the most common diagnostic methods with low cost and high availability is X-ray imaging. It allows the acquisition of frontal images of the chest, which can be used in the medical diagnosis of various diseases and prognosis. Due to the presence of ribs on the image, some pathologic changes may go unnoticed. The goal of this work is to develop a method, using deep learning techniques, to remove ribs from chest X-ray images. The Bone Suppression dataset, consisting of 35 pairs of standard X-ray and soft-tissue only images, was used to develop the model. COVIDx was used as an external test set. Due to the small number of images in the training cohort, a data augmentation technique was used to generate new, noisy image pairs. A deep learning model using convolutional denoising autoencoder architecture was developed to remove the ribs from the X-ray image. The effects of two image down-sampling methods and learning rate changes were evaluated. The resulting images are characterized by partial or complete suppression of the ribs. It should be noted that the problem was not posed by images of patients suffering from COVID-19, which are characterized by much more complex structures.
{"title":"Autoencoder-based bone removal algorithm from x-ray images of the lung","authors":"Seweryn Kalisz, M. Marczyk","doi":"10.1109/BIBE52308.2021.9635451","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635451","url":null,"abstract":"The application of machine learning methods in biomedical image analysis has recently become of particular interest to researchers. One of the most common diagnostic methods with low cost and high availability is X-ray imaging. It allows the acquisition of frontal images of the chest, which can be used in the medical diagnosis of various diseases and prognosis. Due to the presence of ribs on the image, some pathologic changes may go unnoticed. The goal of this work is to develop a method, using deep learning techniques, to remove ribs from chest X-ray images. The Bone Suppression dataset, consisting of 35 pairs of standard X-ray and soft-tissue only images, was used to develop the model. COVIDx was used as an external test set. Due to the small number of images in the training cohort, a data augmentation technique was used to generate new, noisy image pairs. A deep learning model using convolutional denoising autoencoder architecture was developed to remove the ribs from the X-ray image. The effects of two image down-sampling methods and learning rate changes were evaluated. The resulting images are characterized by partial or complete suppression of the ribs. It should be noted that the problem was not posed by images of patients suffering from COVID-19, which are characterized by much more complex structures.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"41 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":"128138688","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/BIBE52308.2021.9635360
A. Nikolic, M. Topalovic, V. Simić, N. Filipovic
Calculation of turbulent fluid flow in this paper is performed using two-equation turbulent finite element model that can calculate values in the viscous sublayer. Implicit integration of the equations is used for determining the fluid velocity, pressure, turbulence, kinetic energy, and dissipation of turbulent kinetic energy. These values are calculated in the finite element nodes for each step of incremental-iterative procedure. Developed turbulent finite element model with the customized generation of finite element meshes is used for solving complex blood flow problems. FEM Analysis results for the artery geometry of the selected anonymous patient provides us with data about important hemodynamics parameters such are blood velocity field and wall shear stress. Cardiologists could use proposed tools and methods to supplement clinical investigation of the hemodynamic conditions inside bifurcation of arteries.
{"title":"Calculation of blood flow in carotid artery bifurcation by turbulent finite element method","authors":"A. Nikolic, M. Topalovic, V. Simić, N. Filipovic","doi":"10.1109/BIBE52308.2021.9635360","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635360","url":null,"abstract":"Calculation of turbulent fluid flow in this paper is performed using two-equation turbulent finite element model that can calculate values in the viscous sublayer. Implicit integration of the equations is used for determining the fluid velocity, pressure, turbulence, kinetic energy, and dissipation of turbulent kinetic energy. These values are calculated in the finite element nodes for each step of incremental-iterative procedure. Developed turbulent finite element model with the customized generation of finite element meshes is used for solving complex blood flow problems. FEM Analysis results for the artery geometry of the selected anonymous patient provides us with data about important hemodynamics parameters such are blood velocity field and wall shear stress. Cardiologists could use proposed tools and methods to supplement clinical investigation of the hemodynamic conditions inside bifurcation of arteries.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"289 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":"128919971","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/BIBE52308.2021.9635259
S. Jeremic, M. S. Pirkovic, Jelena Đorović Jovanović, Z. Marković
In this contribution are estimated and compared free radical scavenger capacity of 1,2,5- trihydroxyanthraquinone (AN) and 1,2,5-trihydroxythioxanthone (TX). For this purpose, $mathbf{M06}-mathbf{2X/6}-311++mathbf{G}(mathbf{d, p})$ method is used. Scavenger capacities of both molecules are determined in benzene and water. It is found that both antioxidants generate stable radicals in water following SPLET mechanism. On the other hand, the most plausible mechanism for that purpose in benzene is HAT. In the presence of three selected free radicals $(mathbf{HO}^{bullet},mathbf{HOO}^{bullet}mathbf{and} mathbf{CH_{3}OO}^{bullet})$ these molecules manifest their scavenger capacity following HAT and SPLET competitively in both estimated environment conditions. The reactivity of observed molecules toward free radicals decreases following the order: $mathbf{HO}^{bullet}ggmathbf{HOO}^{bullet}>mathbf{CH_{3}OO}^{bullet}$. Comparing thermodynamic parameters that describe homolytic O-H cleavage for estimated antioxidants, it is concluded that TX shows somewhat higher scavenger capacity.
{"title":"Free radical scavenger capacity of 1,2,5-trihydroxyanthraquinone and 1,2,5-trihydroxythioxanthone: a theoretical comparative study","authors":"S. Jeremic, M. S. Pirkovic, Jelena Đorović Jovanović, Z. Marković","doi":"10.1109/BIBE52308.2021.9635259","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635259","url":null,"abstract":"In this contribution are estimated and compared free radical scavenger capacity of 1,2,5- trihydroxyanthraquinone (AN) and 1,2,5-trihydroxythioxanthone (TX). For this purpose, $mathbf{M06}-mathbf{2X/6}-311++mathbf{G}(mathbf{d, p})$ method is used. Scavenger capacities of both molecules are determined in benzene and water. It is found that both antioxidants generate stable radicals in water following SPLET mechanism. On the other hand, the most plausible mechanism for that purpose in benzene is HAT. In the presence of three selected free radicals $(mathbf{HO}^{bullet},mathbf{HOO}^{bullet}mathbf{and} mathbf{CH_{3}OO}^{bullet})$ these molecules manifest their scavenger capacity following HAT and SPLET competitively in both estimated environment conditions. The reactivity of observed molecules toward free radicals decreases following the order: $mathbf{HO}^{bullet}ggmathbf{HOO}^{bullet}>mathbf{CH_{3}OO}^{bullet}$. Comparing thermodynamic parameters that describe homolytic O-H cleavage for estimated antioxidants, it is concluded that TX shows somewhat higher scavenger capacity.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"19 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":"115457300","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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