2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)最新文献

{"title":"A Low Power Digitally Assisted Analog Front End for Neural Interface with Optical Reception","authors":"Kanishka De, R. Ahmed, Cheng Hao, C. Hutchens","doi":"10.1109/BIBE.2017.00-72","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-72","url":null,"abstract":"This paper presents the design and implementation of a low power, low noise, digitally assisted analog front end for Micro Neural Interface(MNI) in 180nm CMOS technology used for Neural Spike Detection. The inductively coupled RF power harvested system uses a low power two stage neural amplifier with a mid-Band gain of 59.1 dB with a 3-dB band width(BW) of 0.45 to 8KHz, input referred noise of 8.7µVrms and power consumption of 3.19uW on less than 1V [1]. The central controller operates at 200KHz at 500mV consuming a 33.4nW of power while transmitting 8-bit neural data at 200kbps data rate to the final stage [1]. The simulation results and silicon correlation shows the suitability of the Neural Spike Detection system. Fabrication was carried out in UMC 180nm CMOS.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130807829","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}

{"title":"GPU-Accelerated GATK HaplotypeCaller with Load-Balanced Multi-Process Optimization","authors":"Shanshan Ren, K. Bertels, Z. Al-Ars","doi":"10.1109/BIBE.2017.000-5","DOIUrl":"https://doi.org/10.1109/BIBE.2017.000-5","url":null,"abstract":"Due to its high-throughput and low cost, Next Generation Sequencing (NGS) technology is becoming increasingly popular in many genomics research labs. However, handling the massive raw data generated by the NGS platforms poses a significant computational challenge to genomics analysis tools. This paper presents a GPU acceleration of the GATK HaplotypeCaller (GATK HC), a widely used DNA variant caller in the clinic. Moreover, this paper proposes a load-balanced multi-process optimization of GATK HaplotypeCaller to address its implementation limitation which forces the sequential execution of the program and prevents effective utilization of hardware acceleration. In single-threaded mode, the GPU-based GATK HC is 1.71x and 1.21x faster than the baseline HC implementation and the vectorized GATK HC implementation, respectively. Moreover, the GPU-based implementation achieves up to 2.04x and 1.40x speedup in load-balanced multi-process mode over the baseline implementation and the vectorized GATK HC implementation in non-load-balanced multi-process mode, respectively.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133681340","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}

{"title":"Implementation and Performance Comparison of Some Heuristic Algorithms for Block Sorting","authors":"A. Asaithambi, Swapnoneel Roy, Sandhya Turlapaty","doi":"10.1109/BIBE.2017.00-80","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-80","url":null,"abstract":"Block Sorting is an APX-hard combinatorial optimization problem motivated by applications in genome rearrangements. While previous works on block sorting have mainly focused on theoretical analyses of approximation algorithms, in this work we develop an implementation framework for block sorting in the Java programming language. Our framework uses a simple data structure for a block and includes methods to: manipulate blocks; extract useful information about blocks at various stages in a block sorting algorithm; and perform block moves. We use this framework to implement algorithms for block sorting, based on three different heuristics: 1) each block move guarantees a reduction of at least one block; 2) block moves are performed only on those blocks which are not members of the longest increasing subsequence (of blocks) in the original permutation; and 3) each block move either reduces the number of blocks by two or three blocks, or reduces the number of reversals or inversions in the original permutation. We analyze and compare these algorithms for their performance as the order of permutations is varied, using the measures of number of block moves and approximation ratios when sorting kernelized permutations of a given order. To the best of our knowledge this is the first work that is focused on implementation and experimental performance analysis of any algorithm for block sorting. We believe our results will be useful for researchers and practitioners working in this area.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"345 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113986599","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}

{"title":"Effects of Open Versus Closed Eyes on Physiological Conditions During a Working Memory Task","authors":"K. Onishi, H. Hagiwara","doi":"10.1109/BIBE.2017.00-60","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-60","url":null,"abstract":"The purpose of this study was to investigate the effect of open versus closed eyes during a working memory task on physiological indices of workload. We used the NASA-TLX as a subjective evaluation, and electroencephalograms and oxygenated hemoglobin (oxyHb) concentrations measured via near-infrared spectroscopy as physiological indices of workload. Subjects performed n-back tasks while simultaneously performing an alpha attenuation test (eyes closed [30 s], eyes open [30 s] x 3) for 3 min. Increasing the task load increased the weighted workload and oxyHb. Since Fz / αPz in the open-eyes state shows the same trend as oxyHb, we were able to confirm that Fz / αPz in the open-eyes state is an index of the degree of the workload. However, Fz / αPz varied little between tasks or over time in the closed-eyes state. These results suggest that Fz / αPz is suitable as an index for indicating the degree of a workload only when the eyes are open.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124449119","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}

{"title":"Analysis of Macrostructure of Sleep Apnea with Respect to age and Gender Factors on CAP Database","authors":"Amirabbas Rezaee, B. Aliahmad, P. Peidaee","doi":"10.1109/BIBE.2017.00-10","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-10","url":null,"abstract":"The word “apnea”? means “without breath.”? Obstructive Sleep Apnea (OSA) is the most common form of sleep apnea. If OSA left untreated, it can cause high blood pressure and other cardiovascular disease, weight gain, memory problems, impotency and headaches. There are more than 40 million American who suffer from OSA and sadly 38000 cardiovascular deaths are cause by sleep apnea every year. In this study, we investigate the possibility of identifying any form of sleep apnea based on statistical analysis of polysomnography data for seven types of sleep disorder. The analysis explores the probability that gender, age or combination of these factors provide any conclusive evidence of an existing pattern that can be utilized for effective diagnosis. This novel approach of distinguishing sleep apnea in patients based on association of polysomnography data with their age and gender conducted in 4 different broad categories. The functionality of Macro-indices has been examined in order to find the best way to distinguish non-pathologic to pathologic groups. Gender and age are categorized into different groups so that we could test whether data analysis of macro index for any subcategory indicate significant difference from the control sample. The statistical analysis of macro indices for two gender and 6 age subcategories demonstrates that significant statistical differences exist for some indices in a particular subcategory.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130904199","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}

{"title":"The Influence of the Analysis Technique on Myocardial T2 Estimation Using Cardiac Magnetic Resonance Imaging (CMR)","authors":"M. Hammad, A. Khalifa, W. Al-Atabany, El-Sayed H. Ibrahim","doi":"10.1109/INTELCIS.2017.8260083","DOIUrl":"https://doi.org/10.1109/INTELCIS.2017.8260083","url":null,"abstract":"Cardiovascular disease is the main cause of death worldwide. Magnetic resonance imaging (MRI) has been considered as a noninvasive technique for characterizing myocardial tissues. Specifically, T2 mapping technique has been demonstrated to be an excellent tool to detect myocardial edema by estimation of the transverse relaxation time constant (T2) of myocardial tissue. However, there are several factors that could influence the analysis technique and affect the estimating T2 value. These factors include the type of exponential fitting model, which either be single exponential or exponential plus constant fitting model, the signal intensity estimation method, which either be Average, Median or Mapping method and signal-to-noise ratio (SNR) level of the T2-weighted images. In this paper, we discuss the effect of these factors on T2 estimation using a numerical phantom, T2 calibrated phantoms with different T2 values, and human subjects. The results of numerical phantom showed that the average percentage error for T2 measurement when using the single exponential fitting model reached 0.6 %, 0.4 %, and 5.9 % for the Average, Median, and Mapping methods, respectively. However, the exponential plus constant fitting model resulted in high average percentage error among the three signal intensity estimation methods (above 26 %). The experiments of calibrated phantom resulted in high correlation (R2 > 0.99) between the estimated and reference T2 values when using the single exponential fitting model compared to low correlation (R2 < 0.73) when using the exponential plus constant fitting model at high SNR levels. Finally, the results of human subjects were in agreement with both numerical and calibrated phantoms. Based on the results of this paper, the single exponential fitting model with the median estimation method is the preferable analysis technique for T2 measurement as it results in a lower error for T2 measurements at low SNR levels and higher correlation values at high SNR levels compared to the other analysis techniques.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132778792","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}

{"title":"Parkinsonian Axial Movement Capture using Wearable Sensors during the Pull Test","authors":"Dung Phan, M. Horne, P. Pathirana, Parisa Farzanehfar, Sajeewani Karunarathne","doi":"10.1109/BIBE.2017.000-4","DOIUrl":"https://doi.org/10.1109/BIBE.2017.000-4","url":null,"abstract":"The aim of this research was to analyse the characteristic movements of patients with Parkinsons disease (PD) during the pull test. In this experiment, flexibility of participants were measured from two wearable sensors attached on their upper and lower back. In particular, as Bradykinesia and axial Bradykinesia are vital characteristics which are challenging to measure, we designed a test system engaging a minimal number of wearable sensors to capture the characteristic movements of the back. We utilised a time delay between two sensors to analyse rigidity of human back. In order to measure the characteristics of patient and control groups, the principal component analysis (PCA) was applied to extract the significant features to distinguish the two groups. Consequently, their differences were shown in PCA with a satisfactory separation of controls and patients.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130613038","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}

{"title":"An Extended Type Cell Detection and Counting Method based on FCN","authors":"Runkai Zhu, D. Sui, Hong Qin, A. Hao","doi":"10.1109/BIBE.2017.00-79","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-79","url":null,"abstract":"Cell detection and counting are critical and essential tasks for many biological and clinical studies. Traditionally, these tasks are usually performed by visual inspection, which is time consuming and prone to induce subjective bias. These make automatic cell counting and detection essential for large- scale and objective studies. Unfortunately, the hard examples such as cell blur, clutter, bleed-through and imaging noise make these tasks extremely challenging. Over the last few years, automatic cell detection and counting have evolved from earlier methods that are often based on filters to the current state-of- the-art deep learning methods. In this paper, we propose a novel efficient method for robust counting and detection task based on fully convolution networks (FCN). Our method is able to handle most of detection and counting problems from different kinds of cell datasets, and can cover most senior microscopy images, such as bright field, pathology stained material and electron. Extensive experiments on the public and private datasets demonstrate the effectiveness and reliability of our approach.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132585032","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}

{"title":"A Comparative Survey on Simultaneous EEG-fMRI Methodologies","authors":"Spyridon Manganas, N. Bourbakis","doi":"10.1109/BIBE.2017.00-87","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-87","url":null,"abstract":"The simultaneous recording and analysis of EEG and fMRI data has received considerable attention and nowadays is considered to be the state-of-the-art multimodal imaging method. Due to the complementary nature of these modalities several new perspectives and insights within the field of human brain mapping have been created. In addition, many fusion methods have been proposed in order to combine the aforementioned modalities. In this paper a brief report of the current knowledge of simultaneous EEG-fMRI data fusion along with a literature review of the methods that combines them are presented. The reviewed methods are then evaluated using a number of related features and a maturity score based on these feature is calculated for each method. The results from this survey lead us to use an EEG-fMRI approach to study the space mapping on visually impaired brain during navigation.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"225 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115663144","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}

{"title":"Near-Infrared Transillumination Guides Administration of Dental 2D Radiography and CBCT Imaging","authors":"Keith Angelino, David Edlund, G. Bhatia, Sharon Wu, Pratik Shah","doi":"10.1109/BIBE.2017.00-33","DOIUrl":"https://doi.org/10.1109/BIBE.2017.00-33","url":null,"abstract":"Cone beam computed tomography (CBCT) can provide enhanced information for diagnosing dental diseases when compared to 2D radiographs. However, other nonionizing imaging modes such as near-infrared transillumination may have clinical potential to supplement radiographic methods. In a study of 13 extracted human teeth, we compared clinical features between 2D radiograph, CBCT, and near-infrared transillumination imaging. We found that near-infrared imaging independently, and in some cases exclusively, was successful in identifying early demineralization and shallow enamel features, while radiography was optimal for capturing deeper decay and developed caries. We report that near-infrared transillumination imaging is well-suited for rapid screening of patients for detection of early problem sites and as a preliminary assessment tool to guide administration of radiographs.","PeriodicalId":262603,"journal":{"name":"2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129134076","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}