Emmi Turppa, I. Poļaka, Edgars Vasiljevs, J. Kortelainen, Gidi Shani, M. Leja, H. Haick
The SNIFFPHONE device is a portable multichannel gas sensor, aiming to detect gastric cancer (GC) from breath samples. It employs gold nanoparticle (GNP) sensors reacting to volatile organic compounds (VOCs) in the exhaled breath, a non-invasive technique to support early diagnosis. This study evaluates the repeatability of the SNIFFPHONE classification result for measurements conducted on healthy subjects over a short period of time of less than 10 minutes. Due to the portable nature of the device, repeatability is studied with respect to varying measurement location. We find the classification results repeatable with a statistically significant 81 % Pearson correlation coefficient, even though the raw sensor responses are not concluded repeatable.
{"title":"Repeatability Study on a Classifier for Gastric Cancer Detection from Breath Sensor Data","authors":"Emmi Turppa, I. Poļaka, Edgars Vasiljevs, J. Kortelainen, Gidi Shani, M. Leja, H. Haick","doi":"10.1109/BIBE.2019.00087","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00087","url":null,"abstract":"The SNIFFPHONE device is a portable multichannel gas sensor, aiming to detect gastric cancer (GC) from breath samples. It employs gold nanoparticle (GNP) sensors reacting to volatile organic compounds (VOCs) in the exhaled breath, a non-invasive technique to support early diagnosis. This study evaluates the repeatability of the SNIFFPHONE classification result for measurements conducted on healthy subjects over a short period of time of less than 10 minutes. Due to the portable nature of the device, repeatability is studied with respect to varying measurement location. We find the classification results repeatable with a statistically significant 81 % Pearson correlation coefficient, even though the raw sensor responses are not concluded repeatable.","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":"130222942","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}
Nowadays, genome-wide expression differences between various experimental conditions are mainly monitored using RNA-sequencing. Albeit in active use for over a decade and great progress in RNA-Seq analytics, experts have not been yet able to eliminate its technical and systematic biases, inherent to every high-throughput experimental technique. The vast majority of the attempts made towards confronting RNA-sequencing data analysis challenges are primarily focusing on the development of new analysis methods. However, less effort has been devoted in combined statistical analysis approaches. Here, we present the latest developments in PANDORA, a p-value combination algorithm, implemented in the metaseqR Bioconductor package. PANDORA was proved to successfully combine results of differential expression analysis algorithms. Its power is further enhanced by more recent and powerful algorithms in order enhance clarity of the reported differentially expressed gene lists.
{"title":"Combined Statistics for Differential Expression Analysis of RNA-Sequencing Data","authors":"Dionysios Fanidis, P. Moulos","doi":"10.1109/BIBE.2019.00038","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00038","url":null,"abstract":"Nowadays, genome-wide expression differences between various experimental conditions are mainly monitored using RNA-sequencing. Albeit in active use for over a decade and great progress in RNA-Seq analytics, experts have not been yet able to eliminate its technical and systematic biases, inherent to every high-throughput experimental technique. The vast majority of the attempts made towards confronting RNA-sequencing data analysis challenges are primarily focusing on the development of new analysis methods. However, less effort has been devoted in combined statistical analysis approaches. Here, we present the latest developments in PANDORA, a p-value combination algorithm, implemented in the metaseqR Bioconductor package. PANDORA was proved to successfully combine results of differential expression analysis algorithms. Its power is further enhanced by more recent and powerful algorithms in order enhance clarity of the reported differentially expressed gene lists.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"64 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":"130131659","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}
Ming-Hung Chen, Mao-Jan Lin, Yu-Cheng Li, Yi-Chang Lu
In this paper, we propose a new pair hidden Markov model (Pair-HMM) algorithm, namely Banded Pair-HMM, which is a heuristic approach for variant calling applications. When compared to the conventional Pair-HMM, our Banded Pair-HMM can reduce the execution time at a minor cost in accuracy. In addition, a hardware accelerator is implemented using TSMC 40nm technology based on the proposed algorithm. As demonstrated later in the paper, the proposed hardware accelerator runs 4× faster than the conventional Pair-HMM hardware, and over 17,000× faster than the original Pair-HMM software.
{"title":"Banded Pair-HMM Algorithm for DNA Variant Calling and Its Hardware Accelerator Design","authors":"Ming-Hung Chen, Mao-Jan Lin, Yu-Cheng Li, Yi-Chang Lu","doi":"10.1109/BIBE.2019.00107","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00107","url":null,"abstract":"In this paper, we propose a new pair hidden Markov model (Pair-HMM) algorithm, namely Banded Pair-HMM, which is a heuristic approach for variant calling applications. When compared to the conventional Pair-HMM, our Banded Pair-HMM can reduce the execution time at a minor cost in accuracy. In addition, a hardware accelerator is implemented using TSMC 40nm technology based on the proposed algorithm. As demonstrated later in the paper, the proposed hardware accelerator runs 4× faster than the conventional Pair-HMM hardware, and over 17,000× faster than the original Pair-HMM software.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"201 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":"122514954","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. Koutli, Natalia Theologou, Athanasios Tryferidis, D. Tzovaras
E-health home based solutions reduce healthcare costs and allow aging population to continue their daily life independently. Our objective, is to combine simple IoT sensors and machine learning techniques, in order to provide a home based solution that is able to detect behavioral changes of elderly people who live alone. For this purpose, we introduce a non-intrusive, spatio-temporal abnormal behavior detection approach. In this approach, motion and door sensor signals are elaborated to produce contextual metrics, which are filtered from any deviant observations, after performing a silhouette analysis on five outlier detection algorithms. Next, the combination of a classification and a regression based approach is proposed for detecting abnormalities in the metrics, both in the contexts of space and time. IoT sensor data from ten elderly people houses have been collected and seven different machine learning algorithms have been analyzed in order to evaluate the performance of the individual as well as the combined approach.
{"title":"Abnormal Behavior Detection for Elderly People Living Alone Leveraging IoT Sensors","authors":"M. Koutli, Natalia Theologou, Athanasios Tryferidis, D. Tzovaras","doi":"10.1109/BIBE.2019.00173","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00173","url":null,"abstract":"E-health home based solutions reduce healthcare costs and allow aging population to continue their daily life independently. Our objective, is to combine simple IoT sensors and machine learning techniques, in order to provide a home based solution that is able to detect behavioral changes of elderly people who live alone. For this purpose, we introduce a non-intrusive, spatio-temporal abnormal behavior detection approach. In this approach, motion and door sensor signals are elaborated to produce contextual metrics, which are filtered from any deviant observations, after performing a silhouette analysis on five outlier detection algorithms. Next, the combination of a classification and a regression based approach is proposed for detecting abnormalities in the metrics, both in the contexts of space and time. IoT sensor data from ten elderly people houses have been collected and seven different machine learning algorithms have been analyzed in order to evaluate the performance of the individual as well as the combined approach.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"26 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131805407","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}
We demonstrate 3D-view imaging in strongly scattering media, with embedded absorbing objects, illuminated with ultra-fast photon pulse. The time-resolved photon propagation process was simulated using the Dynamic Radiative Transfer System (DRTS). Three-dimensional views were obtained at various camera positions using scattered photons. By judiciously selecting time-frame imaging and single-angle directional detection, we extract clean images of the embedded objects. Our modeling method, optical media parameters and image extraction techniques can be useful in biological imaging applications in tissues, where scattering is the dominant optical propagation process. These results have the potential of accomplishing a full 3D reconstruction of the volume of interest.
{"title":"Imaging with Ultra Fast Light Pulse in Scattering Media using the DRTS Method","authors":"A. Georgakopoulos, K. Politopoulos, E. Georgiou","doi":"10.1109/BIBE.2019.00130","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00130","url":null,"abstract":"We demonstrate 3D-view imaging in strongly scattering media, with embedded absorbing objects, illuminated with ultra-fast photon pulse. The time-resolved photon propagation process was simulated using the Dynamic Radiative Transfer System (DRTS). Three-dimensional views were obtained at various camera positions using scattered photons. By judiciously selecting time-frame imaging and single-angle directional detection, we extract clean images of the embedded objects. Our modeling method, optical media parameters and image extraction techniques can be useful in biological imaging applications in tissues, where scattering is the dominant optical propagation process. These results have the potential of accomplishing a full 3D reconstruction of the volume of interest.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"29 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":"131934963","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}
A. Illanes, T. Sühn, N. Esmaeili, I. Maldonado, Anna Schaufler, Chien-Hsi Chen, Axel Boese, M. Friebe
In this work we summarize applications of a novel approach for providing complementary information for guiding medical interventional devices (MID) and that have been recently published by our research team. This approach consist of using an audio sensor located in the proximal end of the MID in order to extract meaningful information concerning the interaction between the tip of the instrument and the tissue. The approach was successfully evaluated with different setups and MIDs.
{"title":"Surgical Audio Guidance SurAG: Extracting Non-Invasively Meaningful Guidance Information During Minimally Invasive Procedures","authors":"A. Illanes, T. Sühn, N. Esmaeili, I. Maldonado, Anna Schaufler, Chien-Hsi Chen, Axel Boese, M. Friebe","doi":"10.1109/BIBE.2019.00108","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00108","url":null,"abstract":"In this work we summarize applications of a novel approach for providing complementary information for guiding medical interventional devices (MID) and that have been recently published by our research team. This approach consist of using an audio sensor located in the proximal end of the MID in order to extract meaningful information concerning the interaction between the tip of the instrument and the tissue. The approach was successfully evaluated with different setups and MIDs.","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":"130772858","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}
David Israel Medina, M. Méndez, J. S. Murguía, I. Chouvarda
Sleep is an essential process in our life, which covers 1/3 of our lifetime. But this process can be affected by disorders producing serious consequences at physiological and behavioral level. One of the major indexes connected to the sleep disorders is the dynamic of the sleep macrostructure that is used for the assessment of sleep quality. Beyond sleep macrostructure, recently attention is also given to a finer structure of sleep called Cyclic Alternating Pattern (CAP). CAP is composed by short cortical events (A-phases), where some transition processes can be observed. With the aim to unveil properties of this transition phenomenon, in this work, we present a wavelet singularity analysis of the EEG signal during the onset and offset of A-phases. The results showed that EEG signal presents significant differences between A-phases and activity of background when the average singularity is considered. This finding can help both in better delineating the A-phases of CAP sleep and in understanding the mechanisms behind the CAP dynamics.
{"title":"Wavelet Singularity Analysis for CAP Sleep Delineation","authors":"David Israel Medina, M. Méndez, J. S. Murguía, I. Chouvarda","doi":"10.1109/BIBE.2019.00143","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00143","url":null,"abstract":"Sleep is an essential process in our life, which covers 1/3 of our lifetime. But this process can be affected by disorders producing serious consequences at physiological and behavioral level. One of the major indexes connected to the sleep disorders is the dynamic of the sleep macrostructure that is used for the assessment of sleep quality. Beyond sleep macrostructure, recently attention is also given to a finer structure of sleep called Cyclic Alternating Pattern (CAP). CAP is composed by short cortical events (A-phases), where some transition processes can be observed. With the aim to unveil properties of this transition phenomenon, in this work, we present a wavelet singularity analysis of the EEG signal during the onset and offset of A-phases. The results showed that EEG signal presents significant differences between A-phases and activity of background when the average singularity is considered. This finding can help both in better delineating the A-phases of CAP sleep and in understanding the mechanisms behind the CAP dynamics.","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":"130976437","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}
Panagiotis Rizogiannis, P. Ktonas, H. Tsekou, T. Paparrigopoulos, D. Dikeos, E. Ventouras
Sleep spindles are rhythmic transient waveforms present in the electroencephalogram (EEG) of non-rapid eye movement (NREM) sleep. In the present study a period-amplitude analysis method was applied for the automated detection of sleep spindles in all-night sleep EEG recordings of young healthy subjects. The method relies on the characterization of individual half-waves of the EEG data, by estimating electrographic parameters such as amplitude and duration and by assigning a grade to each half-wave depending on where it lies in the amplitude-frequency plane. The grading is followed by the detection system, checking consecutive half-wave characteristics and implementing a set of rules for determining the start and the end of spindle bursts and for retaining or rejecting sleep spindle indications provided during the various stages of the detection system. The sensitivity and false positive rate across subjects was 78.9% and 10.9%, respectively, providing indication that the method could be successfully applied to larger sets of healthy subjects of various age groups, as well as to patient populations.
{"title":"Automated Sleep Spindle Detection System using Period-Amplitude Analysis","authors":"Panagiotis Rizogiannis, P. Ktonas, H. Tsekou, T. Paparrigopoulos, D. Dikeos, E. Ventouras","doi":"10.1109/BIBE.2019.00142","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00142","url":null,"abstract":"Sleep spindles are rhythmic transient waveforms present in the electroencephalogram (EEG) of non-rapid eye movement (NREM) sleep. In the present study a period-amplitude analysis method was applied for the automated detection of sleep spindles in all-night sleep EEG recordings of young healthy subjects. The method relies on the characterization of individual half-waves of the EEG data, by estimating electrographic parameters such as amplitude and duration and by assigning a grade to each half-wave depending on where it lies in the amplitude-frequency plane. The grading is followed by the detection system, checking consecutive half-wave characteristics and implementing a set of rules for determining the start and the end of spindle bursts and for retaining or rejecting sleep spindle indications provided during the various stages of the detection system. The sensitivity and false positive rate across subjects was 78.9% and 10.9%, respectively, providing indication that the method could be successfully applied to larger sets of healthy subjects of various age groups, as well as to patient populations.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"4 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":"131301120","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 reduction of dopamine generating neurons in the brain regions known as substantia nigra (SN) is the reason for Parkinson's Disease (PD). To detect such symptom, for each subject, our algorithm only needs to analyze 3 slices around the center of a MRI DICOM volume, i.e., mid-brain area. In each slice, a window covering the SN becomes the region of interest (ROI) for further analysis. The ROIs are pre-processed by denoising and removing intensity non-uniformity. Local Binary Pattern (LBP) and Histogram Oriented Gradient (HOG) are used for feature extraction. Random Forest (RF) and Support Vector Machine (SVM) are used as classifiers with Principle Component Analysis (PCA) as feature reduction method. For evaluation, we use MRI T2 scans from the Parkinson's Progression Markers Initiative (PPMI) data set. We conducted experiments to illustrate the different classification capabilities of LBP, HOG and the fusion of these features for PD prognosis. Analysis shows that the SVM classifier with fusion feature descriptors has the most accurate classification outcome for PD assessment.
{"title":"Parkinson's Disease Mid-Brain Assessment using MR T2 Images","authors":"S. Soltaninejad, Pengda Xu, I. Cheng","doi":"10.1109/BIBE.2019.00045","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00045","url":null,"abstract":"The reduction of dopamine generating neurons in the brain regions known as substantia nigra (SN) is the reason for Parkinson's Disease (PD). To detect such symptom, for each subject, our algorithm only needs to analyze 3 slices around the center of a MRI DICOM volume, i.e., mid-brain area. In each slice, a window covering the SN becomes the region of interest (ROI) for further analysis. The ROIs are pre-processed by denoising and removing intensity non-uniformity. Local Binary Pattern (LBP) and Histogram Oriented Gradient (HOG) are used for feature extraction. Random Forest (RF) and Support Vector Machine (SVM) are used as classifiers with Principle Component Analysis (PCA) as feature reduction method. For evaluation, we use MRI T2 scans from the Parkinson's Progression Markers Initiative (PPMI) data set. We conducted experiments to illustrate the different classification capabilities of LBP, HOG and the fusion of these features for PD prognosis. Analysis shows that the SVM classifier with fusion feature descriptors has the most accurate classification outcome for PD assessment.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"11 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":"121656224","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}
Georgios C. Manikis, R. Pat-Horenczyk, D. Fotiadis, M. Tsiknakis, P. Simos, Konstantina D. Kourou, P. Poikonen-Saksela, H. Kondylakis, E. Karademas, K. Marias, Dimitrios G. Katehakis, L. Koumakis, A. Kouroubali
Coping with breast cancer and its consequences has now become a major socioeconomic challenge. The BOUNCE EU H2020 project aims at building a quantitative mathematical model of factors associated with optimal adjustment capacity to cancer. This paper gives an overview of the project targets and on the algorithmic methods focusing on modeling the psychological resilience trajectories during breast cancer treatment.
应对乳腺癌及其后果现已成为一项重大的社会经济挑战。BOUNCE EU H2020项目旨在建立与癌症最佳调节能力相关因素的定量数学模型。本文概述了项目目标和算法方法，重点是建模乳腺癌治疗期间的心理弹性轨迹。
{"title":"Computational Modeling of Psychological Resilience Trajectories During Breast Cancer Treatment","authors":"Georgios C. Manikis, R. Pat-Horenczyk, D. Fotiadis, M. Tsiknakis, P. Simos, Konstantina D. Kourou, P. Poikonen-Saksela, H. Kondylakis, E. Karademas, K. Marias, Dimitrios G. Katehakis, L. Koumakis, A. Kouroubali","doi":"10.1109/BIBE.2019.00082","DOIUrl":"https://doi.org/10.1109/BIBE.2019.00082","url":null,"abstract":"Coping with breast cancer and its consequences has now become a major socioeconomic challenge. The BOUNCE EU H2020 project aims at building a quantitative mathematical model of factors associated with optimal adjustment capacity to cancer. This paper gives an overview of the project targets and on the algorithmic methods focusing on modeling the psychological resilience trajectories during breast cancer treatment.","PeriodicalId":318819,"journal":{"name":"2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"6 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":"116979075","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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