Pub Date : 2021-10-25DOI: 10.1109/BIBE52308.2021.9635306
D. Cvetković, A. Cvetkovic, Danijela D. Nikodijević, Jovana V. Jovankić, Milena G. Milutinović, V. Stojić, N. Zdravković, Slobodanka Mltrović
Despite the obvious progress in the field of diagnosis and therapy, further measures are needed to increase the effectiveness of treatment and reduce morbidity and mortality from breast cancer. An immunofluorescence method was used to determine the protein expression of VEGF and CXCR-4 in tumor and peritumoral tissue. Peritumoral tissue is not only a passive factor, but actively participates in the process of tumor growth and development, as well as in the processes of recurrence and metastasis. Markers of neoangiogenesis in tumor and peritumoral tissue such as protein expression of VEGF and CXCR-4 receptors may serve as reliable predictors of disease outcome in breast cancer patients, which may provide useful suggestions in treatment choices.
{"title":"Determination of VEGF and CXCR4 in Tumor and Peritumoral Tissue of Patients with Breast Cancer as a Predictive Factor","authors":"D. Cvetković, A. Cvetkovic, Danijela D. Nikodijević, Jovana V. Jovankić, Milena G. Milutinović, V. Stojić, N. Zdravković, Slobodanka Mltrović","doi":"10.1109/BIBE52308.2021.9635306","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635306","url":null,"abstract":"Despite the obvious progress in the field of diagnosis and therapy, further measures are needed to increase the effectiveness of treatment and reduce morbidity and mortality from breast cancer. An immunofluorescence method was used to determine the protein expression of VEGF and CXCR-4 in tumor and peritumoral tissue. Peritumoral tissue is not only a passive factor, but actively participates in the process of tumor growth and development, as well as in the processes of recurrence and metastasis. Markers of neoangiogenesis in tumor and peritumoral tissue such as protein expression of VEGF and CXCR-4 receptors may serve as reliable predictors of disease outcome in breast cancer patients, which may provide useful suggestions in treatment choices.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"52 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":"130331047","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/BIBE52308.2021.9635485
Harsh Bhasin, R. Agrawal
The classification of Mild Cognitive Impairment can be done using 2-D CNN, which take a single slice at a time as input and do not consider pixel information from adjacent slices or spatial correlation amongst the slices of the brain volume or 3-D CNN, which requires huge computation time and memory as a significantly large number of parameters involved in 3D-CNN in comparison to 2D-CNN. To reduce the spatial correlation, computational complexity, and memory requirement, we use t-Distributed Stochastic Neighbor Embedding (t-SNE) on MRI volume to reduce its dimensions. Also, we use parallel CNN instead of sequential to analyze MRI volumes and a combination of RELU, sigmoid, and SIREN activation functions to learn better features for the classification of MCI. To check the efficacy of the proposed t-SNE Multiple-Activation Parallel Convolution Network, experiments are performed on publicly available Alzheimer's Disease Neuroimaging Initiative dataset, and performance is compared with existing methods. We obtain classification accuracy of 94.15 and 94.89 on MCI-C Vs. MCI-NC data and MCI Vs. Controls data respectively.
{"title":"Multiple-Activation Parallel Convolution Network in Combination with t-SNE for the Classification of Mild Cognitive Impairment","authors":"Harsh Bhasin, R. Agrawal","doi":"10.1109/BIBE52308.2021.9635485","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635485","url":null,"abstract":"The classification of Mild Cognitive Impairment can be done using 2-D CNN, which take a single slice at a time as input and do not consider pixel information from adjacent slices or spatial correlation amongst the slices of the brain volume or 3-D CNN, which requires huge computation time and memory as a significantly large number of parameters involved in 3D-CNN in comparison to 2D-CNN. To reduce the spatial correlation, computational complexity, and memory requirement, we use t-Distributed Stochastic Neighbor Embedding (t-SNE) on MRI volume to reduce its dimensions. Also, we use parallel CNN instead of sequential to analyze MRI volumes and a combination of RELU, sigmoid, and SIREN activation functions to learn better features for the classification of MCI. To check the efficacy of the proposed t-SNE Multiple-Activation Parallel Convolution Network, experiments are performed on publicly available Alzheimer's Disease Neuroimaging Initiative dataset, and performance is compared with existing methods. We obtain classification accuracy of 94.15 and 94.89 on MCI-C Vs. MCI-NC data and MCI Vs. Controls data respectively.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"24 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114085782","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/BIBE52308.2021.9635460
Charles A. Ellis, Rongen Zhang, V. Calhoun, Darwin A. Carbajal, Robyn Miller, May D. Wang
In recent years, more biomedical studies have begun to use multimodal data to improve model performance. Many studies have used ablation for explainability, which requires the modification of input data. This can create out-of-distribution samples and lead to incorrect explanations. To avoid this problem, we propose using a gradient-based feature attribution approach, called layer-wise relevance propagation (LRP), to explain the importance of modalities both locally and globally for the first time. We demonstrate the feasibility of the approach with sleep stage classification as our use-case and train a 1-D convolutional neural network with electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) data. We also analyze the relationship of our local explainability results with clinical and demographic variables to determine whether they affect our classifier. Across all samples, EEG is the most important modality, followed by EOG and EMG. For individual sleep stages, EEG and EOG have higher relevance for awake and non-rapid eye movement 1 (NREM1). EOG is most important for REM, and EEG is most relevant for NREM2-NREM3. Also, LRP gives consistent levels of importance to each modality for the correctly classified samples across folds but inconsistent levels of importance for incorrectly classified samples. Our statistical analyses suggest that medication has a significant effect upon patterns learned for EEG and EOG NREM2 and that subject sex and age significantly affects the EEG and EOG patterns learned, respectively. Our results demonstrate the viability of gradient-based approaches for explaining multimodal electrophysiology classifiers and suggest their generalizability for other multimodal classification domains.
{"title":"A Gradient-based Approach for Explaining Multimodal Deep Learning Classifiers","authors":"Charles A. Ellis, Rongen Zhang, V. Calhoun, Darwin A. Carbajal, Robyn Miller, May D. Wang","doi":"10.1109/BIBE52308.2021.9635460","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635460","url":null,"abstract":"In recent years, more biomedical studies have begun to use multimodal data to improve model performance. Many studies have used ablation for explainability, which requires the modification of input data. This can create out-of-distribution samples and lead to incorrect explanations. To avoid this problem, we propose using a gradient-based feature attribution approach, called layer-wise relevance propagation (LRP), to explain the importance of modalities both locally and globally for the first time. We demonstrate the feasibility of the approach with sleep stage classification as our use-case and train a 1-D convolutional neural network with electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) data. We also analyze the relationship of our local explainability results with clinical and demographic variables to determine whether they affect our classifier. Across all samples, EEG is the most important modality, followed by EOG and EMG. For individual sleep stages, EEG and EOG have higher relevance for awake and non-rapid eye movement 1 (NREM1). EOG is most important for REM, and EEG is most relevant for NREM2-NREM3. Also, LRP gives consistent levels of importance to each modality for the correctly classified samples across folds but inconsistent levels of importance for incorrectly classified samples. Our statistical analyses suggest that medication has a significant effect upon patterns learned for EEG and EOG NREM2 and that subject sex and age significantly affects the EEG and EOG patterns learned, respectively. Our results demonstrate the viability of gradient-based approaches for explaining multimodal electrophysiology classifiers and suggest their generalizability for other multimodal classification domains.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"222 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":"122884896","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/BIBE52308.2021.9635216
Milica G. Nikolić, T. Šušteršič, Nenad Filipović
The paper describes mathematical model and numerical simulation of Mason-Weaver equation using finite difference method, FDM, for simulation of sedimentation process. Different FDM schemes have been developed and tested for several different initial conditions. Possible issues with numerical convergence and conservation of concentration are explained. Performed analysis can be important for any numerical simulation that captures sedimentation process. The results of this research can be further used in modelling epithelial cell behavior and lung-on-a-chip systems.
{"title":"Numerical Simulation of Sedimentation Process using Mason-Weaver Equation","authors":"Milica G. Nikolić, T. Šušteršič, Nenad Filipović","doi":"10.1109/BIBE52308.2021.9635216","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635216","url":null,"abstract":"The paper describes mathematical model and numerical simulation of Mason-Weaver equation using finite difference method, FDM, for simulation of sedimentation process. Different FDM schemes have been developed and tested for several different initial conditions. Possible issues with numerical convergence and conservation of concentration are explained. Performed analysis can be important for any numerical simulation that captures sedimentation process. The results of this research can be further used in modelling epithelial cell behavior and lung-on-a-chip systems.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"118 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":"130138681","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/BIBE52308.2021.9635163
Aishwarya Purohit, S. Acharya, James Green
Computational prediction of protein-protein interaction (PPI) from protein sequence is important as many cellular functions are made possible through PPI. The Protein Interaction Prediction Engine (PIPE) software suite was developed for such predictions. The specific location of interaction is predicted by the PIPE-Sites predictor, which depends on PIPE engine. This PIPE-Sites predictor is here updated through the use of a large high-quality dataset of known PPI sites. Additionally, a similarity-weighted score had been recently developed in PIPE4 and has been proven to be more accurate for the likelihood of PPI prediction. However, PIPE-Sites are shown to be ineffective when applied to similarity-weighted score data. Thus, we here propose and evaluate a new sequence-based PPI site prediction method, named Panorama. This new method leverages similarity-weighted score data to further increase performance over two different performance metrics when evaluated on both $boldsymbol{H}$. sapiens and $boldsymbol{S}$, cerevisiae PPI site data.
{"title":"A novel Greedy approach for Sequence based Computational prediction of Binding-Sites in Protein-Protein Interaction","authors":"Aishwarya Purohit, S. Acharya, James Green","doi":"10.1109/BIBE52308.2021.9635163","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635163","url":null,"abstract":"Computational prediction of protein-protein interaction (PPI) from protein sequence is important as many cellular functions are made possible through PPI. The Protein Interaction Prediction Engine (PIPE) software suite was developed for such predictions. The specific location of interaction is predicted by the PIPE-Sites predictor, which depends on PIPE engine. This PIPE-Sites predictor is here updated through the use of a large high-quality dataset of known PPI sites. Additionally, a similarity-weighted score had been recently developed in PIPE4 and has been proven to be more accurate for the likelihood of PPI prediction. However, PIPE-Sites are shown to be ineffective when applied to similarity-weighted score data. Thus, we here propose and evaluate a new sequence-based PPI site prediction method, named Panorama. This new method leverages similarity-weighted score data to further increase performance over two different performance metrics when evaluated on both $boldsymbol{H}$. sapiens and $boldsymbol{S}$, cerevisiae PPI site data.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"20 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":"124546686","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/BIBE52308.2021.9635424
M. Neghina, R. Petruse, S. Ćuković, Caliri Schiau, Nenad Filipović
Assessment of the spinal disorders is a notoriously difficult problem, even in controlled environments where the patients are instructed to stand upright. The method presented here considers the analysis of the mathematical curvature of the scaled and interpolated spinal line, in both the sagittal and frontal planes. Although the number of assumptions for spine normality is kept to a (reasonable) minimum, we demonstrate good detection of sharp or otherwise unnatural local bending in adolescent spinal alignments.
{"title":"Automatic Curvature Analysis for Finely Interpolated Spinal Curves","authors":"M. Neghina, R. Petruse, S. Ćuković, Caliri Schiau, Nenad Filipović","doi":"10.1109/BIBE52308.2021.9635424","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635424","url":null,"abstract":"Assessment of the spinal disorders is a notoriously difficult problem, even in controlled environments where the patients are instructed to stand upright. The method presented here considers the analysis of the mathematical curvature of the scaled and interpolated spinal line, in both the sagittal and frontal planes. Although the number of assumptions for spine normality is kept to a (reasonable) minimum, we demonstrate good detection of sharp or otherwise unnatural local bending in adolescent spinal alignments.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"94 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":"122468297","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/BIBE52308.2021.9635320
Sandi Baressi Segota, N. Anđelić, I. Lorencin, J. Musulin, D. Štifanić, Z. Car
Simplified Molecular Input Line Entry System (SMILES) is a type of chemical notation. The SMILES format allows the representation of chemical structures in a shape easily readable by computer programs. This allows many techniques, such as Artificial Neural Networks (ANNs) to be applied on the SMILES formatted data. One of the highest-performing ANN types is the Convolutional Neural Networks (CNNs), designed to work on images or matrix-shaped data. In this paper, the authors will present the preparation of the SMILES dataset for use by CNNs. The paper will start with a brief description of the SMILES format, followed by the explanation of the dataset transformation into an NPY matrix-based format, with an example of utilization via the application of popular CNN architectures on a transformed dataset. The proposed architecture achieves satisfactory results (AUC=0.92), with the transformation algorithm speed also proving satisfactory (0.08 seconds per data point)
{"title":"Preparation of Simplified Molecular Input Line Entry System Notation Datasets for use in Convolutional Neural Networks","authors":"Sandi Baressi Segota, N. Anđelić, I. Lorencin, J. Musulin, D. Štifanić, Z. Car","doi":"10.1109/BIBE52308.2021.9635320","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635320","url":null,"abstract":"Simplified Molecular Input Line Entry System (SMILES) is a type of chemical notation. The SMILES format allows the representation of chemical structures in a shape easily readable by computer programs. This allows many techniques, such as Artificial Neural Networks (ANNs) to be applied on the SMILES formatted data. One of the highest-performing ANN types is the Convolutional Neural Networks (CNNs), designed to work on images or matrix-shaped data. In this paper, the authors will present the preparation of the SMILES dataset for use by CNNs. The paper will start with a brief description of the SMILES format, followed by the explanation of the dataset transformation into an NPY matrix-based format, with an example of utilization via the application of popular CNN architectures on a transformed dataset. The proposed architecture achieves satisfactory results (AUC=0.92), with the transformation algorithm speed also proving satisfactory (0.08 seconds per data point)","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"8 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":"122705732","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/BIBE52308.2021.9635269
Smiljana Tomasevic, I. Šaveljić, L. Velicki, N. Filipovic
The aim of this work was to evaluate the impact of Bicuspid Aortic Valve (BAV), on displacements, Von Mises stress, shear stress and pressure distribution within the aortic root by using computational Finite Element (FE) method. The three-dimensional (3D) patient-specific geometry of dilated aortic root with BAV was reconstructed based on Computed Tomography (CT) scan images, in order to obtain the 3D finite element mesh. Two types of analyses: i) structural analysis and ii) computational fluid dynamics (CFD) were performed, with applied equivalent material characteristics of BAV and boundary conditions. The initial results for this single case, displacements and Von Mises stress distribution (for structural analysis), as well as shear stress and pressure distribution (for CFD analysis) were quantified concerning anatomical patient's structures. The regions of abnormal stresses on the aortic leaflets and annulus, with asymmetrically open bicuspid valve, were related to the increased pressures and shear stresses and analyzed for this patient-specific case. Due to the difficulties in obtaining such characteristics in vitro or in vivo, the performed computational analysis gave better insight into the biomechanics of the aortic root with BAV that is needed to achieve improvements in surgical repair techniques and presurgical planning.
{"title":"Computational Finite Element Analysis of Aortic Root with Bicuspid Valve","authors":"Smiljana Tomasevic, I. Šaveljić, L. Velicki, N. Filipovic","doi":"10.1109/BIBE52308.2021.9635269","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635269","url":null,"abstract":"The aim of this work was to evaluate the impact of Bicuspid Aortic Valve (BAV), on displacements, Von Mises stress, shear stress and pressure distribution within the aortic root by using computational Finite Element (FE) method. The three-dimensional (3D) patient-specific geometry of dilated aortic root with BAV was reconstructed based on Computed Tomography (CT) scan images, in order to obtain the 3D finite element mesh. Two types of analyses: i) structural analysis and ii) computational fluid dynamics (CFD) were performed, with applied equivalent material characteristics of BAV and boundary conditions. The initial results for this single case, displacements and Von Mises stress distribution (for structural analysis), as well as shear stress and pressure distribution (for CFD analysis) were quantified concerning anatomical patient's structures. The regions of abnormal stresses on the aortic leaflets and annulus, with asymmetrically open bicuspid valve, were related to the increased pressures and shear stresses and analyzed for this patient-specific case. Due to the difficulties in obtaining such characteristics in vitro or in vivo, the performed computational analysis gave better insight into the biomechanics of the aortic root with BAV that is needed to achieve improvements in surgical repair techniques and presurgical planning.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"95 3 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":"116495222","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/BIBE52308.2021.9635538
Biozid Bostami, V. Calhoun, H. V. D. Horn, V. Vergara
Neuroscience studies have begun to benefit from combining large numbers of data from different sites to increase statistical power. Pooling data from various sites into a single analysis introduces additional variability from site-effects due to differences in scanner protocols, imaging protocol, and acquisition methods, among others. These site-effects can reduce statistical power or lead to erroneous conclusions. Harmonization is the process of combining data aiming at reducing site variability. One recent approach for harmonizing data called ComBat has been shown to be helpful in the context of functional MRI and static functional connectivity. However, ComBat has not been applied to the analysis of dynamic functional network connectivity (dFNC). Here we explore the impact of ComBat harmonization on dFNC data collected from two different mild traumatic brain injury (mTBI) studies. Results show that ComBat harmonization of dFNC can reduce site effects producing a more robust analysis of patient effects across sites.
{"title":"Harmonization of Multi-site Dynamic Functional Connectivity Network Data","authors":"Biozid Bostami, V. Calhoun, H. V. D. Horn, V. Vergara","doi":"10.1109/BIBE52308.2021.9635538","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635538","url":null,"abstract":"Neuroscience studies have begun to benefit from combining large numbers of data from different sites to increase statistical power. Pooling data from various sites into a single analysis introduces additional variability from site-effects due to differences in scanner protocols, imaging protocol, and acquisition methods, among others. These site-effects can reduce statistical power or lead to erroneous conclusions. Harmonization is the process of combining data aiming at reducing site variability. One recent approach for harmonizing data called ComBat has been shown to be helpful in the context of functional MRI and static functional connectivity. However, ComBat has not been applied to the analysis of dynamic functional network connectivity (dFNC). Here we explore the impact of ComBat harmonization on dFNC data collected from two different mild traumatic brain injury (mTBI) studies. Results show that ComBat harmonization of dFNC can reduce site effects producing a more robust analysis of patient effects across sites.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"27 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":"127166394","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/BIBE52308.2021.9635169
Miloš Anić, Momcilo Prodanovic, S. Milenkovic, Nenad D Filipović, N. Grujovic, F. Živić
This paper presents a short review of the piezoelectric materials in energy harvesting. Energy harvesting principle, as the method for obtaining energy from environment has been described. Materials and material combinations for creating an energy harvesting composites are discussed, such as ceramic- and polymer-based composites and their mechanical properties. The list of the mostly used piezoelectric materials is presented and elaborated. Possible applications of the energy harvesting materials are discussed, including nanogenerators, biosensors and biomedical applications.
{"title":"The Review of Materials for Energy Harvesting","authors":"Miloš Anić, Momcilo Prodanovic, S. Milenkovic, Nenad D Filipović, N. Grujovic, F. Živić","doi":"10.1109/BIBE52308.2021.9635169","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635169","url":null,"abstract":"This paper presents a short review of the piezoelectric materials in energy harvesting. Energy harvesting principle, as the method for obtaining energy from environment has been described. Materials and material combinations for creating an energy harvesting composites are discussed, such as ceramic- and polymer-based composites and their mechanical properties. The list of the mostly used piezoelectric materials is presented and elaborated. Possible applications of the energy harvesting materials are discussed, including nanogenerators, biosensors and biomedical applications.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"1 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":"125813567","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：