Charles C. N. Wang, Pei-Chun Chang, P. Sheu, J. Tsai
Maize is a well-studied crop. It has been used as a model plant for C4 studies of photosynthesis, as its leaves possess the Kranz Structure (KS). Unfortunately, only few studies addressed the use of computational models to describe dry maize. In particular, the mechanism of KS formation remains unclear during leaf development. This study aims to develop a computational model to answer the following two questions for leaf development in dry maze: (1) How Auxin inhibits BDL, and (2) How the MP transcription activates BDL in the seed of dry maize in early stages of embryonic leaves. We first analyze dry maize based on the S-systems model and compare it with two different regulatory networks: (1) Auxin inhibits BODENLOS (BDL), and (2) MONOPTEROS (MP) activates BODENLOS (BDL). Our hypotheses are: (1) Auxin does not inhibit BDL, and (2) MP does not activate BDL. In the second stage, we compare the S-systems parameter estimation method (SPEM) and the engineering method to analyze the two regulatory networks. Our result suggests a general mechanism for studying how the transient accumulation of Auxin activates self-sustaining and how, similar to other genetic switches, it results in unequivocal developmental responses of leaves in dry maize. The MP activates BDL are very important to the Auxin signaling mediated by MP and BDL proteins which are essential for cell-fate specification events in early embryogenesis of maize.
{"title":"Computational Modeling of the Early Development of Embryonic Leaves in Maize","authors":"Charles C. N. Wang, Pei-Chun Chang, P. Sheu, J. Tsai","doi":"10.1109/BIBE.2018.00076","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00076","url":null,"abstract":"Maize is a well-studied crop. It has been used as a model plant for C4 studies of photosynthesis, as its leaves possess the Kranz Structure (KS). Unfortunately, only few studies addressed the use of computational models to describe dry maize. In particular, the mechanism of KS formation remains unclear during leaf development. This study aims to develop a computational model to answer the following two questions for leaf development in dry maze: (1) How Auxin inhibits BDL, and (2) How the MP transcription activates BDL in the seed of dry maize in early stages of embryonic leaves. We first analyze dry maize based on the S-systems model and compare it with two different regulatory networks: (1) Auxin inhibits BODENLOS (BDL), and (2) MONOPTEROS (MP) activates BODENLOS (BDL). Our hypotheses are: (1) Auxin does not inhibit BDL, and (2) MP does not activate BDL. In the second stage, we compare the S-systems parameter estimation method (SPEM) and the engineering method to analyze the two regulatory networks. Our result suggests a general mechanism for studying how the transient accumulation of Auxin activates self-sustaining and how, similar to other genetic switches, it results in unequivocal developmental responses of leaves in dry maize. The MP activates BDL are very important to the Auxin signaling mediated by MP and BDL proteins which are essential for cell-fate specification events in early embryogenesis of maize.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131421395","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}
Protein Secondary Structural Class (PSSC) prediction is an important step to find its further folds, tertiary structure and functions, which in turn have potential applications in drug discovery. Various computational methods have been developed to predict the PSSC, however, predicting PSSC on the basis of protein sequences is still a challenging task. In this study, we propose an effective approach to extract features using two techniques (i) SkipXGram bi-gram: in which skipped bi-gram features are extracted and (ii) Character embedded features: in which features are extracted using word embedding approach. The combined feature sets from the proposed feature modeling approach are explored using various machine learning classifiers. The best performing classifier (i.e. Random Forest) is benchmarked against state-of-the-art PSSC prediction models. The proposed model was assessed on two low sequence similarity benchmark datasets i.e. 25PDB and FC699. The performance analysis demonstrates that the proposed model consistently outperformed state-of-the-art models by a factor of 3% to 23% and 4% to 6% for 25PDB and FC699 datasets respectively.
{"title":"Protein Secondary Structural Class Prediction Using Effective Feature Modeling and Machine Learning Techniques","authors":"Sanjay S. Bankapur, Nagamma Patil","doi":"10.1109/BIBE.2018.00012","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00012","url":null,"abstract":"Protein Secondary Structural Class (PSSC) prediction is an important step to find its further folds, tertiary structure and functions, which in turn have potential applications in drug discovery. Various computational methods have been developed to predict the PSSC, however, predicting PSSC on the basis of protein sequences is still a challenging task. In this study, we propose an effective approach to extract features using two techniques (i) SkipXGram bi-gram: in which skipped bi-gram features are extracted and (ii) Character embedded features: in which features are extracted using word embedding approach. The combined feature sets from the proposed feature modeling approach are explored using various machine learning classifiers. The best performing classifier (i.e. Random Forest) is benchmarked against state-of-the-art PSSC prediction models. The proposed model was assessed on two low sequence similarity benchmark datasets i.e. 25PDB and FC699. The performance analysis demonstrates that the proposed model consistently outperformed state-of-the-art models by a factor of 3% to 23% and 4% to 6% for 25PDB and FC699 datasets respectively.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129649763","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}
Rajkumar Theagarajan, Bhanu Bir, D. Angeles, Federico Pala
Premature neonates are subjected to clinically required but painful procedures throughout their hospitalization. Since neonates are non-verbal, pain scoring tools are used to measure their pain responses. Although a number of pain instruments have been developed to assist health professionals, these tools are subjective and may underestimate the pain response of neonates. This could lead to the pain being misread resulting in mis-diagnosis and under/over treatment. In this paper, a deep learning based approach is used to detect pain in videos of premature neonates during painful clinical procedures. A Conditional Generative Adversarial Network (CGAN) is used to continuously learn the representation and classify painful facial expressions in neonates from real and synthetic data. A Long Short-Term Memory (LSTM) is used for modeling the temporal changes in facial expression to further improve the classification. Furthermore, the proposed approach is able to implicitly learn the intensity of pain as a probability score directly from the facial expressions without any manual annotation. Experimental results show that this approach achieves an accuracy of 95.34% on the iCOPE Classification Of Pain Expressions (video) dataset, 88.27% on the Loma Linda Infant Pain Expressions (video) dataset and 94.12% on the Infant Classification Of Pain Expressions (static images) dataset outperforming state-of-the-art approaches.
{"title":"[Regular Paper] KnowPain: Automated System for Detecting Pain in Neonates from Videos","authors":"Rajkumar Theagarajan, Bhanu Bir, D. Angeles, Federico Pala","doi":"10.1109/BIBE.2018.00032","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00032","url":null,"abstract":"Premature neonates are subjected to clinically required but painful procedures throughout their hospitalization. Since neonates are non-verbal, pain scoring tools are used to measure their pain responses. Although a number of pain instruments have been developed to assist health professionals, these tools are subjective and may underestimate the pain response of neonates. This could lead to the pain being misread resulting in mis-diagnosis and under/over treatment. In this paper, a deep learning based approach is used to detect pain in videos of premature neonates during painful clinical procedures. A Conditional Generative Adversarial Network (CGAN) is used to continuously learn the representation and classify painful facial expressions in neonates from real and synthetic data. A Long Short-Term Memory (LSTM) is used for modeling the temporal changes in facial expression to further improve the classification. Furthermore, the proposed approach is able to implicitly learn the intensity of pain as a probability score directly from the facial expressions without any manual annotation. Experimental results show that this approach achieves an accuracy of 95.34% on the iCOPE Classification Of Pain Expressions (video) dataset, 88.27% on the Loma Linda Infant Pain Expressions (video) dataset and 94.12% on the Infant Classification Of Pain Expressions (static images) dataset outperforming state-of-the-art approaches.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133787678","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}
Whole-genome multiple alignments are widely used in genomics and evolution, and yet their accuracy is imperfect, due in part to the computational complexity of the task at hand. Identifying portions of these alignments that are likely to be incorrect would allow researchers to either work on improving them or flagging them for exclusion from downstream analyses. We introduce MSA-ED, a machine learning tool for the detection of errors in whole-genome multiple alignments. MSA-ED uses random forests or artificial neural networks to identify and classify several types of alignment errors. It is trained on labeled data obtained by using an evolution simulator to generate fake orthologous sequences and their correct alignment, and comparing it to the alignment produced by Multiz, a popular whole-genome aligner. Key to the success of MSA-ED is the engineering of several types of evolutionarily-inspired features that boost prediction accuracy. MSA-ED is shown to be able to detect certain types of errors with good accuracy. It is then applied to actual genomic alignments to identify putative alignment errors. Availability: https://github.com/jaspal1329/MSA-ED
{"title":"[Regular Paper] Detection of Errors in Multi-genome Alignments Using Machine Learning Approaches","authors":"Jaspal Singh, R. Ramakrishnan, M. Blanchette","doi":"10.1109/BIBE.2018.00017","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00017","url":null,"abstract":"Whole-genome multiple alignments are widely used in genomics and evolution, and yet their accuracy is imperfect, due in part to the computational complexity of the task at hand. Identifying portions of these alignments that are likely to be incorrect would allow researchers to either work on improving them or flagging them for exclusion from downstream analyses. We introduce MSA-ED, a machine learning tool for the detection of errors in whole-genome multiple alignments. MSA-ED uses random forests or artificial neural networks to identify and classify several types of alignment errors. It is trained on labeled data obtained by using an evolution simulator to generate fake orthologous sequences and their correct alignment, and comparing it to the alignment produced by Multiz, a popular whole-genome aligner. Key to the success of MSA-ED is the engineering of several types of evolutionarily-inspired features that boost prediction accuracy. MSA-ED is shown to be able to detect certain types of errors with good accuracy. It is then applied to actual genomic alignments to identify putative alignment errors. Availability: https://github.com/jaspal1329/MSA-ED","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122003260","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}
This paper presents a technique to remotely assess gait deterioration due to memory impairment using micro-Doppler radar for elderly adults, aged 75 years and over. We introduce a micro-Doppler radar system to extract gait velocity parameters and investigate the relationship between the extracted parameters and the scores of a memory test using a scenery picture. The experimental results show significant differences between the low-and high-memory ability groups in the extracted parameters, and verifies the effectiveness of not only the gait speed but also the leg speed parameters for the screening of memory impairment.
{"title":"Remote Assessment of Gait Deterioration Due to Memory Impairment in Elderly Adults Using Micro-Doppler Radar","authors":"K. Saho, K. Uemura, M. Matsumoto","doi":"10.1109/BIBE.2018.00042","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00042","url":null,"abstract":"This paper presents a technique to remotely assess gait deterioration due to memory impairment using micro-Doppler radar for elderly adults, aged 75 years and over. We introduce a micro-Doppler radar system to extract gait velocity parameters and investigate the relationship between the extracted parameters and the scores of a memory test using a scenery picture. The experimental results show significant differences between the low-and high-memory ability groups in the extracted parameters, and verifies the effectiveness of not only the gait speed but also the leg speed parameters for the screening of memory impairment.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122388440","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 Corticospinal Tract (CST) is a part of pyramidal tract (PT) and it can innervate the voluntary movement of skeletal muscle through spinal interneurons (the 4th layer of the Rexed gray board layers), and anterior horn motorneurons (which control trunk and proximal limb muscles). Spinal cord injury (SCI) is a highly disabling disease often caused by traffic accidents. The recovery of CST and the functional reconstruction of spinal anterior horn motor neurons play an essential role in the treatment of SCI. However, the localization and reconstruction of CST are still challenging issues, the accuracy of the geometric reconstruction can directly affect the results of the surgery. The main contribution of this paper is the reconstruction of the CST based on the fiber orientation distributions (FODs) tractography. Differing from tensor-based tractography in which the primary direction is a determined orientation, the direction of FODs tractography is determined by the probability. The spherical harmonics (SPHARM) can be used to approximate the efficiency of FODs tractography. We manually delineate the three ROIs (the posterior limb of the internal capsule, the cerebral peduncle, and the anterior pontine area) by the ITK-SNAP software, and use the pipeline software to reconstruct both the left and right sides of the CST fibers. Our results demonstrate that FOD-based tractography can show more and correct anatomical CST fiber bundles.
{"title":"[Regular Paper] Corticospinal Tract (CST) Reconstruction Based on Fiber Orientation Distributions (FODs) Tractography","authors":"Youshan Zhang","doi":"10.1109/BIBE.2018.00066","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00066","url":null,"abstract":"The Corticospinal Tract (CST) is a part of pyramidal tract (PT) and it can innervate the voluntary movement of skeletal muscle through spinal interneurons (the 4th layer of the Rexed gray board layers), and anterior horn motorneurons (which control trunk and proximal limb muscles). Spinal cord injury (SCI) is a highly disabling disease often caused by traffic accidents. The recovery of CST and the functional reconstruction of spinal anterior horn motor neurons play an essential role in the treatment of SCI. However, the localization and reconstruction of CST are still challenging issues, the accuracy of the geometric reconstruction can directly affect the results of the surgery. The main contribution of this paper is the reconstruction of the CST based on the fiber orientation distributions (FODs) tractography. Differing from tensor-based tractography in which the primary direction is a determined orientation, the direction of FODs tractography is determined by the probability. The spherical harmonics (SPHARM) can be used to approximate the efficiency of FODs tractography. We manually delineate the three ROIs (the posterior limb of the internal capsule, the cerebral peduncle, and the anterior pontine area) by the ITK-SNAP software, and use the pipeline software to reconstruct both the left and right sides of the CST fibers. Our results demonstrate that FOD-based tractography can show more and correct anatomical CST fiber bundles.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127569074","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}
Fully convolutional neural networks have shown remarkable success in performing semantic segmentation. The use of convolutional layers for the entire architecture and skip connections to combine different resolution features or predictions have been adopted in successful networks, such as U-Net and DenseNet. However, these models employ several max-pooling layers that cause the network to lose spatial information and require them to mimic an autoencoder architecture to perform semantic segmentation at the original input resolution. In this paper, we propose a network that extracts features automatically with convolutional layers, like the fully convolutional neural network, but retains the spatial information of each of the extracted features. It then utilises the extracted features to make predictions with an efficient upsampling method. We evaluate the network performance on a liver segmentation task where it performs with comparable accuracy to other state-of-the-art networks while being much smaller in terms of the number of parameters as well as faster in computation time.
{"title":"[Regular Paper] Adjacent Network for Semantic Segmentation of Liver CT Scans","authors":"I. Astono, J. Welsh, S. Chalup","doi":"10.1109/BIBE.2018.00015","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00015","url":null,"abstract":"Fully convolutional neural networks have shown remarkable success in performing semantic segmentation. The use of convolutional layers for the entire architecture and skip connections to combine different resolution features or predictions have been adopted in successful networks, such as U-Net and DenseNet. However, these models employ several max-pooling layers that cause the network to lose spatial information and require them to mimic an autoencoder architecture to perform semantic segmentation at the original input resolution. In this paper, we propose a network that extracts features automatically with convolutional layers, like the fully convolutional neural network, but retains the spatial information of each of the extracted features. It then utilises the extracted features to make predictions with an efficient upsampling method. We evaluate the network performance on a liver segmentation task where it performs with comparable accuracy to other state-of-the-art networks while being much smaller in terms of the number of parameters as well as faster in computation time.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126787818","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}
Word embedding is the state-of-the-art representation to capture semantic information of terms. It benefits a wide range of natural language processing and related applications, not only in general fields of artificial intelligence but also in bioinformatics. Although recent efforts of using word embedding to represent medical concepts have provided remarkable analyses, many essential problems remain unsolved. Examples include representation of complex concepts (i.e., formed by multiple tokens), leveraging of a large corpus to maximize the trainable concepts, and downstream analyses on a biomedical-related dataset. Our study focused on training effective representations for biomedical concepts including complex ones. We used an efficient technique to index all possible concepts of UMLS thesaurus (Unified Medical Language System) in a huge corpus of 15,4 billion tokens. By this way, we can obtain the vector representations for more than 650,000 concepts, the largest ever reported resource to date. Furthermore, evaluations of trained vectors on retrieval task show superior performance compared to recent studies.
{"title":"Learning Effective Distributed Representation of Complex Biomedical Concepts","authors":"Khai Nguyen, R. Ichise","doi":"10.1109/BIBE.2018.00073","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00073","url":null,"abstract":"Word embedding is the state-of-the-art representation to capture semantic information of terms. It benefits a wide range of natural language processing and related applications, not only in general fields of artificial intelligence but also in bioinformatics. Although recent efforts of using word embedding to represent medical concepts have provided remarkable analyses, many essential problems remain unsolved. Examples include representation of complex concepts (i.e., formed by multiple tokens), leveraging of a large corpus to maximize the trainable concepts, and downstream analyses on a biomedical-related dataset. Our study focused on training effective representations for biomedical concepts including complex ones. We used an efficient technique to index all possible concepts of UMLS thesaurus (Unified Medical Language System) in a huge corpus of 15,4 billion tokens. By this way, we can obtain the vector representations for more than 650,000 concepts, the largest ever reported resource to date. Furthermore, evaluations of trained vectors on retrieval task show superior performance compared to recent studies.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124402677","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}
Recently, research on exoskeleton robots is actively being carried out. The exoskeleton system has the purpose of assisting or amplifying human muscle strength. Such an exoskeleton system is classified into a system composed of a rigid material and a system composed of a flexible material. In the case of electrons, the degree of freedom of the human body is limited and the weight of the system is heavy. On the other hand, when soft actuators are used, the activity is maximized without constraining the human joint degrees of freedom. Typically, there is a soft exosuit at Harvard and can be divided into two cases: pneumatic actuators and wire motors. In the soft suit, the system using pneumatic actuator has a drawback that it must be used near the compressor. In order to overcome this disadvantage, this research developed a compact mobile compressor. The air consumption of the artificial muscles was calculated before the design and the air supply of the compressor to be designed was determined based on this calculation. The developed compressor has several small pistons arranged in a circle so that the performance of a conventional large piston can be outputted without increasing the required torque of the motor. The overall shape was designed through 3D modeling and confirmed its operation. The design of compressor performance was simulated based on energy equation, ideal gas equation, orifice equation, and kinematic equation. The performance of the compressor was verified by comparing the flow rate and pressure test results with simulation results
{"title":"Design of a Portable Radial Piston Pneumatic Compressor for Wearable Robot System","authors":"Ryeo-Won Kang, Ho Seon Choi, Y. Baek","doi":"10.1109/BIBE.2018.00023","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00023","url":null,"abstract":"Recently, research on exoskeleton robots is actively being carried out. The exoskeleton system has the purpose of assisting or amplifying human muscle strength. Such an exoskeleton system is classified into a system composed of a rigid material and a system composed of a flexible material. In the case of electrons, the degree of freedom of the human body is limited and the weight of the system is heavy. On the other hand, when soft actuators are used, the activity is maximized without constraining the human joint degrees of freedom. Typically, there is a soft exosuit at Harvard and can be divided into two cases: pneumatic actuators and wire motors. In the soft suit, the system using pneumatic actuator has a drawback that it must be used near the compressor. In order to overcome this disadvantage, this research developed a compact mobile compressor. The air consumption of the artificial muscles was calculated before the design and the air supply of the compressor to be designed was determined based on this calculation. The developed compressor has several small pistons arranged in a circle so that the performance of a conventional large piston can be outputted without increasing the required torque of the motor. The overall shape was designed through 3D modeling and confirmed its operation. The design of compressor performance was simulated based on energy equation, ideal gas equation, orifice equation, and kinematic equation. The performance of the compressor was verified by comparing the flow rate and pressure test results with simulation results","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120871733","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}
O. Mukhmetov, Dastan Igali, Yong Zhao, S. Fok, S. L. Teh, A. Mashekova, Ng Yin Kwee
Breast cancer has become one of the main causes of death among women in the many countries. The chances of patient survival can be considerably enhanced if breast tumors are identified at the earliest stage. In this study, we propose a cost effective and non-invasive detection technique, which is based on precision breast geometry, infrared breast temperature and inverse thermal modeling in order to prove that the inverse FEM is applicable for detection of the location and size of the tumor inside the breast. As a first step in this comprehensive study, we develop a novel and cost-effective experiment to obtain repeatable data for FEM validation, in which an artificial breast was 3D printed based on realistic breast geometry, tumors were simulated with heaters and thermograms of the breast were taken. Then forward thermal modeling was performed and validated with the experimental data. Furthermore, the effect of tumors on the thermal profile of the breast was examined by using both experimental and numerical approaches.
{"title":"Finite Element Modelling for the Detection of Breast Tumor","authors":"O. Mukhmetov, Dastan Igali, Yong Zhao, S. Fok, S. L. Teh, A. Mashekova, Ng Yin Kwee","doi":"10.1109/BIBE.2018.00078","DOIUrl":"https://doi.org/10.1109/BIBE.2018.00078","url":null,"abstract":"Breast cancer has become one of the main causes of death among women in the many countries. The chances of patient survival can be considerably enhanced if breast tumors are identified at the earliest stage. In this study, we propose a cost effective and non-invasive detection technique, which is based on precision breast geometry, infrared breast temperature and inverse thermal modeling in order to prove that the inverse FEM is applicable for detection of the location and size of the tumor inside the breast. As a first step in this comprehensive study, we develop a novel and cost-effective experiment to obtain repeatable data for FEM validation, in which an artificial breast was 3D printed based on realistic breast geometry, tumors were simulated with heaters and thermograms of the breast were taken. Then forward thermal modeling was performed and validated with the experimental data. Furthermore, the effect of tumors on the thermal profile of the breast was examined by using both experimental and numerical approaches.","PeriodicalId":127507,"journal":{"name":"2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115819024","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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