Dimitrios Pleouras, A. Sakellarios, G. Karanasiou, S. Kyriakidis, Panagiota I. Tsompou, Vassiliki I. Kigka, D. Fotiadis
Atherosclerosis is the one of the major causes of mortality worldwide, urging the need for its treatment. This study is aiming to investigate the role of diabetes in the atherosclerotic plaque growth mechanisms through the utilization of a multi-level numerical model. To accomplish this, we developed a proof-of-concept mathematical model of the diabetes effect to plaque growth, that has been coupled to a stateof-the-art multi-level numerical model of plaque growth. Diabetes main effect is the increase of the average blood glucose concentration, which causes the decrease of the endothelial nitric oxide production rate by affecting several biologic pathways. Nitric oxide is a signaling molecule that regulates the endothelial flow rates, and any abnormal alteration leads to endothelial dysfunction, the major culprit of atherosclerosis. The derived model considers the modeling of blood flow in lumen and of species transport and reactions in the arterial wall. The considered factors include: (i) LDL, (ii) HDL, (iii) oxidized LDL, (iv) monocytes, (v) macrophages, (vi) cytokines, (vii) smooth muscle cells (contractile & synthetic), and (viii) collagen. The model is validated using 10 patients' reconstructed arterial data in two time-points. More specifically, baseline geometries are used as an input to our model, while follow-up geometries are used as benchmark for our model's output. The results presented a high coefficient of determination between the simulated with diabetes effect and the real follow-up geometries of 0.634.
动脉粥样硬化是世界范围内死亡的主要原因之一，迫切需要对其进行治疗。本研究旨在通过多层次数值模型探讨糖尿病在动脉粥样硬化斑块生长机制中的作用。为了实现这一目标，我们开发了糖尿病对斑块生长影响的概念验证数学模型，该模型已与最先进的斑块生长多层次数值模型相结合。糖尿病的主要作用是平均血糖浓度升高，通过影响几种生物途径引起内皮细胞一氧化氮生成速率降低。一氧化氮是调节内皮血流速率的信号分子，任何异常改变都会导致内皮功能障碍，这是动脉粥样硬化的罪魁祸首。导出的模型考虑了管腔内血流和动脉壁内物质运输和反应的建模。考虑的因素包括:(i) LDL， (ii) HDL， (iii)氧化LDL， (iv)单核细胞，(v)巨噬细胞，(vi)细胞因子，(vii)平滑肌细胞(收缩和合成)，(viii)胶原蛋白。利用10例患者在两个时间点的重建动脉数据对模型进行了验证。更具体地说，基线几何被用作模型的输入，而后续几何被用作模型输出的基准。结果表明，模拟的糖尿病效应与实际随访几何值之间的决定系数为0.634。
{"title":"Atherosclerotic Plaque Growth Prediction in Coronary Arteries using a Computational Multi-level Model: The Effect of Diabetes","authors":"Dimitrios Pleouras, A. Sakellarios, G. Karanasiou, S. Kyriakidis, Panagiota I. Tsompou, Vassiliki I. Kigka, D. Fotiadis","doi":"10.1109/BIBE.2019.00132","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00132","url":null,"abstract":"Atherosclerosis is the one of the major causes of mortality worldwide, urging the need for its treatment. This study is aiming to investigate the role of diabetes in the atherosclerotic plaque growth mechanisms through the utilization of a multi-level numerical model. To accomplish this, we developed a proof-of-concept mathematical model of the diabetes effect to plaque growth, that has been coupled to a stateof-the-art multi-level numerical model of plaque growth. Diabetes main effect is the increase of the average blood glucose concentration, which causes the decrease of the endothelial nitric oxide production rate by affecting several biologic pathways. Nitric oxide is a signaling molecule that regulates the endothelial flow rates, and any abnormal alteration leads to endothelial dysfunction, the major culprit of atherosclerosis. The derived model considers the modeling of blood flow in lumen and of species transport and reactions in the arterial wall. The considered factors include: (i) LDL, (ii) HDL, (iii) oxidized LDL, (iv) monocytes, (v) macrophages, (vi) cytokines, (vii) smooth muscle cells (contractile & synthetic), and (viii) collagen. The model is validated using 10 patients' reconstructed arterial data in two time-points. More specifically, baseline geometries are used as an input to our model, while follow-up geometries are used as benchmark for our model's output. The results presented a high coefficient of determination between the simulated with diabetes effect and the real follow-up geometries of 0.634.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116164565","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}
Nolwenn Jegou, Franck Desaize, Gobert N. Lee, M. Bajger, O. Acosta, J. Leseur, R. Crevoisier, Martin Caon
In radiotherapy, computed tomography (CT) images are typically used for radiation treatment planning. Accurate segmentation of radiation sensitive healthy tissues, organs-atrisk (OARs), is important for radiation treatment planning for brain tumor. 3D Slicer has been applied in many medical applications including tumor segmentation on head MR images. However, to the best of our knowledge, there have been no studies using 3D Slicer for segmenting OARs on head CT images. This preliminary study evaluates the segmentation of seven OARs on head CTs using 3D Slicer. Results are comparable to state-ofthe- art approaches but a larger dataset is required to verify the results.
{"title":"Organs-at-Risk Contouring on Head CT for RT Planning Using 3D Slicer– A Preliminary Study","authors":"Nolwenn Jegou, Franck Desaize, Gobert N. Lee, M. Bajger, O. Acosta, J. Leseur, R. Crevoisier, Martin Caon","doi":"10.1109/BIBE.2019.00097","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00097","url":null,"abstract":"In radiotherapy, computed tomography (CT) images are typically used for radiation treatment planning. Accurate segmentation of radiation sensitive healthy tissues, organs-atrisk (OARs), is important for radiation treatment planning for brain tumor. 3D Slicer has been applied in many medical applications including tumor segmentation on head MR images. However, to the best of our knowledge, there have been no studies using 3D Slicer for segmenting OARs on head CT images. This preliminary study evaluates the segmentation of seven OARs on head CTs using 3D Slicer. Results are comparable to state-ofthe- art approaches but a larger dataset is required to verify the results.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115706416","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}
M. Strauch, Karl Krüger, L. Mukunda, Alja Lüdke, C. Galizia, D. Merhof
The odorant receptor neurons on the fruit fly antenna are highly sensitive to a broad range of chemicals. A compound signal of receptor activity on the antenna can be read out in real time with functional neuroimaging, and individual receptor responses to hundreds of odorants are available in a database. Utilizing the fruit fly antenna as chemosensor enables applications ranging from biomarker detection to identification of unknown chemicals in samples. Here, we propose to connect neural response spaces, mapping odorant responses from one fly to another and to database space. A map is defined exactly for reference odorants common to both subject and target space, while the map for the remaining odorants is estimated based on radial basis function interpolation. On a data set with chemically diverse odorants, mapping to another antenna allows identifying unlabelled subject space odorants by the proximity of their mapped position to labelled odorants in target space. Furthermore, mapping from antenna to database space predicts the individual receptor responses significantly better than a random baseline model, suggesting that receptor responses can be inferred from the compound antenna signal given a sufficiently dense net of reference odorants to support the map.
{"title":"Interpolating Maps between Neural Response Spaces for Chemosensing with Fruit Fly Antenna Sensors","authors":"M. Strauch, Karl Krüger, L. Mukunda, Alja Lüdke, C. Galizia, D. Merhof","doi":"10.1109/BIBE.2019.00135","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00135","url":null,"abstract":"The odorant receptor neurons on the fruit fly antenna are highly sensitive to a broad range of chemicals. A compound signal of receptor activity on the antenna can be read out in real time with functional neuroimaging, and individual receptor responses to hundreds of odorants are available in a database. Utilizing the fruit fly antenna as chemosensor enables applications ranging from biomarker detection to identification of unknown chemicals in samples. Here, we propose to connect neural response spaces, mapping odorant responses from one fly to another and to database space. A map is defined exactly for reference odorants common to both subject and target space, while the map for the remaining odorants is estimated based on radial basis function interpolation. On a data set with chemically diverse odorants, mapping to another antenna allows identifying unlabelled subject space odorants by the proximity of their mapped position to labelled odorants in target space. Furthermore, mapping from antenna to database space predicts the individual receptor responses significantly better than a random baseline model, suggesting that receptor responses can be inferred from the compound antenna signal given a sufficiently dense net of reference odorants to support the map.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125181346","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}
The therapeutic effects of acupoint-treatment for some diseases have been confirmed in many scientific experiments. Compared to the use of drugs, acupoint-based treatment can effectively alleviate some diseases without the side effect. Therefore, understanding the relationship between acupoints and diseases is important. In our work, we compile a database about diseases and their corresponding acupoints from a large number of books and research papers. We analyze the disease-acupoint correlation using these data and visualize their connections in an interactive way.
{"title":"Visualizing the Associations between Acupoints Based on Diseases They Treat","authors":"Kun-Chan Lan, Jun-Xiang Zhang, Ying-Hsiu Lin","doi":"10.1109/BIBE.2019.00174","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00174","url":null,"abstract":"The therapeutic effects of acupoint-treatment for some diseases have been confirmed in many scientific experiments. Compared to the use of drugs, acupoint-based treatment can effectively alleviate some diseases without the side effect. Therefore, understanding the relationship between acupoints and diseases is important. In our work, we compile a database about diseases and their corresponding acupoints from a large number of books and research papers. We analyze the disease-acupoint correlation using these data and visualize their connections in an interactive way.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126879741","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}
B. Mathur, A. Topiwala, Saul Schaffer, M. Kam, H. Saeidi, T. Fleiter, A. Krieger
Trauma is among the leading causes of death in the United States with up to 29% of pre-hospital trauma deaths attributed to uncontrolled hemorrhages. This paper reports a semi-autonomous robotic system capable of assessing trauma using 2D and 3D image analysis and enabling remote focused assessment with sonography for trauma (FAST) en route to the hospital for earlier trauma diagnosis and faster initialization of life saving care. The system was able to accurately calculate FAST scan positions of patient specific phantoms using the measured phantom sizes and positions of the umbilicus. The system was capable of accurately classifying and localizing wounds, so they can be avoided during the ultrasound scan. These objects were localized with an accuracy of 0.94 ± 0.179cm and FAST exam locations were estimated with an accuracy of 2.2 ± 1.88cm. A radiologist successfully completed a remote FAST scan of the phantom using the system with improved image quality over manual scans, demonstrating feasibility of the system.
{"title":"A Semi-Autonomous Robotic System for Remote Trauma Assessment","authors":"B. Mathur, A. Topiwala, Saul Schaffer, M. Kam, H. Saeidi, T. Fleiter, A. Krieger","doi":"10.1109/BIBE.2019.00122","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00122","url":null,"abstract":"Trauma is among the leading causes of death in the United States with up to 29% of pre-hospital trauma deaths attributed to uncontrolled hemorrhages. This paper reports a semi-autonomous robotic system capable of assessing trauma using 2D and 3D image analysis and enabling remote focused assessment with sonography for trauma (FAST) en route to the hospital for earlier trauma diagnosis and faster initialization of life saving care. The system was able to accurately calculate FAST scan positions of patient specific phantoms using the measured phantom sizes and positions of the umbilicus. The system was capable of accurately classifying and localizing wounds, so they can be avoided during the ultrasound scan. These objects were localized with an accuracy of 0.94 ± 0.179cm and FAST exam locations were estimated with an accuracy of 2.2 ± 1.88cm. A radiologist successfully completed a remote FAST scan of the phantom using the system with improved image quality over manual scans, demonstrating feasibility of the system.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126951648","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}
Thodoris Koutsandreas, Ajdini Bajram, C. Mastrokalou, E. Pilalis, A. Chatziioannou, Ilias Maglogiannis
The revolution of single-cell technologies established a novel framework to investigate gene expression profiles in the level of individual cells. Scientists are able to investigate the biological variability of the same tissue, producing isolated transcriptomic data for each single cell. As a result, each transcriptomic experiment could extract a unique expression profile for each cell, posing new challenges in the translation analysis of all these profiles. Pathway analysis tools need to be adapted, not only to analyze simultaneously numerous gene expression profiles, but also to compare them, detecting functional differences and commonalities among the cells of the same issue, separating them to functional subclusters. In this study, we used the output of a single-cell experiment in the hematopoietic system, in order to determine a novel framework for the functional comparison of single cells, based on their pathway analysis with Gene Ontology annotation. Thousands of expression profiles of single cells, congregated in 15 different hematopoietic classes, were translated into networks of significant biological mechanisms, through the use of BioInfoMiner platform. We propose a novel framework to exploit these results and construct appropriate feature spaces of functional omponents, with a view to perform supervised learning to different hematopoietic cell types and separate their respective single cells, according to their functional profile. The constructed classification model performed interestingly high precision and sensitivity scores for some cell types, while the overall performance needs to be improved with further conceptual and technical refinements.
{"title":"Combining Pathway Analysis and Supervised Machine Learning for the Functional Classification of Single-Cell Transcriptomic Data","authors":"Thodoris Koutsandreas, Ajdini Bajram, C. Mastrokalou, E. Pilalis, A. Chatziioannou, Ilias Maglogiannis","doi":"10.1109/BIBE.2019.00160","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00160","url":null,"abstract":"The revolution of single-cell technologies established a novel framework to investigate gene expression profiles in the level of individual cells. Scientists are able to investigate the biological variability of the same tissue, producing isolated transcriptomic data for each single cell. As a result, each transcriptomic experiment could extract a unique expression profile for each cell, posing new challenges in the translation analysis of all these profiles. Pathway analysis tools need to be adapted, not only to analyze simultaneously numerous gene expression profiles, but also to compare them, detecting functional differences and commonalities among the cells of the same issue, separating them to functional subclusters. In this study, we used the output of a single-cell experiment in the hematopoietic system, in order to determine a novel framework for the functional comparison of single cells, based on their pathway analysis with Gene Ontology annotation. Thousands of expression profiles of single cells, congregated in 15 different hematopoietic classes, were translated into networks of significant biological mechanisms, through the use of BioInfoMiner platform. We propose a novel framework to exploit these results and construct appropriate feature spaces of functional omponents, with a view to perform supervised learning to different hematopoietic cell types and separate their respective single cells, according to their functional profile. The constructed classification model performed interestingly high precision and sensitivity scores for some cell types, while the overall performance needs to be improved with further conceptual and technical refinements.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127203386","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}
M. Astrinaki, A. Kanterakis, H. Latsoudis, G. Potamias, D. Kafetzopoulos
Over the last 10 years, Next-Generation Sequencing (NGS) has become a powerful tool in clinical genetics and precision medicine. Techniques like Whole Genome Sequencing (WGS), Whole Exome Sequencing (WES) and Target Sequencing are commonly used for the elucidation of common and rare variants in mendelian and complex diseases. One of the most vital parts of NGS pipelines is the prioritization of annotated variants according to their clinical significance. During this process, a clinical geneticist is presented with annotation information from external databases for each of the thousands of potential variants. The vast amounts of data and the vague nature of existing guidelines for variant reporting, like ACMG (American College of Medical Genetics) can make this procedure very cumbersome and time consuming. Here we present the main computational challenges and existing solutions for this task. We also present Zazz, an online environment for variant annotation, query and exploration. Zazz can efficiently support the submission of complex and dynamically generated queries to hundreds of millions of variants each having hundreds of annotation fields. Zazz also leverages the capabilities of modern browsers to dynamically filter, explore and visualize multidimensional data.
{"title":"Zazz: Variant Annotation and Exploration of Next Generation Sequencing Variants","authors":"M. Astrinaki, A. Kanterakis, H. Latsoudis, G. Potamias, D. Kafetzopoulos","doi":"10.1109/BIBE.2019.00159","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00159","url":null,"abstract":"Over the last 10 years, Next-Generation Sequencing (NGS) has become a powerful tool in clinical genetics and precision medicine. Techniques like Whole Genome Sequencing (WGS), Whole Exome Sequencing (WES) and Target Sequencing are commonly used for the elucidation of common and rare variants in mendelian and complex diseases. One of the most vital parts of NGS pipelines is the prioritization of annotated variants according to their clinical significance. During this process, a clinical geneticist is presented with annotation information from external databases for each of the thousands of potential variants. The vast amounts of data and the vague nature of existing guidelines for variant reporting, like ACMG (American College of Medical Genetics) can make this procedure very cumbersome and time consuming. Here we present the main computational challenges and existing solutions for this task. We also present Zazz, an online environment for variant annotation, query and exploration. Zazz can efficiently support the submission of complex and dynamically generated queries to hundreds of millions of variants each having hundreds of annotation fields. Zazz also leverages the capabilities of modern browsers to dynamically filter, explore and visualize multidimensional data.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126114005","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}
Sleep stage classification is one of the most critical steps in the effective diagnosis and treatment of sleeprelated disorders. Classic approaches involve trained human sleep scorers, utilizing a manual scoring technique, according to certain standards. This paper examines the implementation of an algorithm for the automation of the sleep scoring process. EEG recordings data are acquired from three different groups comprising of healthy subjects and people with minor sleep disorders. A mixture of time domain and frequency domain features are extracted. Temporal feature changes are utilized in order to capture contextual information of the EEG signal. Multiple classifiers are tested, culminating in a voting classifier, achieving a maximum accuracy of 90.8% for the healthy subjects' group. The main novelty introduced by the proposed solution is the algorithm's high accuracy when tested on a mixed dataset of healthy and patient subjects. The promising capabilities that derive from the successful implementation of this solution are discussed in the conclusions.
{"title":"Classification of Sleep Stages for Healthy Subjects and Patients with Minor Sleep Disorders","authors":"C. Timplalexis, K. Diamantaras, I. Chouvarda","doi":"10.1109/BIBE.2019.00068","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00068","url":null,"abstract":"Sleep stage classification is one of the most critical steps in the effective diagnosis and treatment of sleeprelated disorders. Classic approaches involve trained human sleep scorers, utilizing a manual scoring technique, according to certain standards. This paper examines the implementation of an algorithm for the automation of the sleep scoring process. EEG recordings data are acquired from three different groups comprising of healthy subjects and people with minor sleep disorders. A mixture of time domain and frequency domain features are extracted. Temporal feature changes are utilized in order to capture contextual information of the EEG signal. Multiple classifiers are tested, culminating in a voting classifier, achieving a maximum accuracy of 90.8% for the healthy subjects' group. The main novelty introduced by the proposed solution is the algorithm's high accuracy when tested on a mixed dataset of healthy and patient subjects. The promising capabilities that derive from the successful implementation of this solution are discussed in the conclusions.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123046834","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}
Gonul Gunal Degirmendereli, Sharlene D. Newman, F. Yarman-Vural
In this paper, we aim to measure the information content of brain anatomic regions using the functional magnetic resonance images (fMRI) recorded during a complex problem solving (CPS) task. We, also, analyze the brain regions, activated in different phases of the problem solving process. Previous studies have widely used machine learning approaches to examine the active anatomic regions for cognitive states of human subjects based on their fMRI data. This study proposes an information theoretic method for analyzing the activity in anatomic regions. Briefly, we define and estimate two types of Shannon entropy, namely, static and dynamic entropy, to understand how complex problem solving processes lead to changes in information content of anatomic regions. We investigate the relationship between the problem-solving task phases and the Shannon entropy measures suggested in this study, for the underlying brain activity during a Tower of London (TOL) problem solving process. We observe that the dynamic entropy fluctuations in brain regions during the CPS task provides a measure for the information content of the main phases of complex problem solving, namely planning and execution. We, also, observe that static entropy measures of anatomic regions are consistent with the experimental findings of neuroscience. The preliminary results show strong promise in using the suggested static and dynamic entropy as a measure for characterizing the brain states related to the problem solving process. This capability would be useful in revealing the hidden cognitive states of subjects performing a specific cognitive task.
{"title":"On the Entropy of Brain Anatomic Regions for Complex Problem Solving","authors":"Gonul Gunal Degirmendereli, Sharlene D. Newman, F. Yarman-Vural","doi":"10.1109/BIBE.2019.00115","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00115","url":null,"abstract":"In this paper, we aim to measure the information content of brain anatomic regions using the functional magnetic resonance images (fMRI) recorded during a complex problem solving (CPS) task. We, also, analyze the brain regions, activated in different phases of the problem solving process. Previous studies have widely used machine learning approaches to examine the active anatomic regions for cognitive states of human subjects based on their fMRI data. This study proposes an information theoretic method for analyzing the activity in anatomic regions. Briefly, we define and estimate two types of Shannon entropy, namely, static and dynamic entropy, to understand how complex problem solving processes lead to changes in information content of anatomic regions. We investigate the relationship between the problem-solving task phases and the Shannon entropy measures suggested in this study, for the underlying brain activity during a Tower of London (TOL) problem solving process. We observe that the dynamic entropy fluctuations in brain regions during the CPS task provides a measure for the information content of the main phases of complex problem solving, namely planning and execution. We, also, observe that static entropy measures of anatomic regions are consistent with the experimental findings of neuroscience. The preliminary results show strong promise in using the suggested static and dynamic entropy as a measure for characterizing the brain states related to the problem solving process. This capability would be useful in revealing the hidden cognitive states of subjects performing a specific cognitive task.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131306315","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}
Usage of different bio materials for dental implants have come a long way since its introduction. Progressive researches made over past few decades evolved new bio materials enabling optimal utilization of implants by exploiting its material characteristics to its fullest. The aim of identifying new bio materials is to obviate the chances of biological rejection and enhance its utility. This article is aimed to present a consolidated review on various dental bio materials explored since 2011 till date.
{"title":"Evolution of BioMaterials for Dental Implants and Futuristic Developments","authors":"T. Sengupta, P. Muthu","doi":"10.1109/BIBE.2019.00118","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00118","url":null,"abstract":"Usage of different bio materials for dental implants have come a long way since its introduction. Progressive researches made over past few decades evolved new bio materials enabling optimal utilization of implants by exploiting its material characteristics to its fullest. The aim of identifying new bio materials is to obviate the chances of biological rejection and enhance its utility. This article is aimed to present a consolidated review on various dental bio materials explored since 2011 till date.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123880373","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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