Pub Date : 2019-03-01DOI: 10.1109/ICBCB.2019.8854665
Weijun Zhu, Xiaokai Liu, Zhenfei Wang, Yongwen Fan, Jianwei Wang
RNA hybridization is one of the most important operations in popular RNA simulation software in bioinformatics. However, it is a challenging task to decide whether a specific RNA hybridization is effective within an acceptable time, since this mission has the exponentially computational complexity caused by the combinatorial problem. We hereby introduce a machine learning (ML)-based technique to address this problem. And the Random Forest (RF) algorithm is employed, and many groups of RNA molecular coding and their classification in terms of the results of hybridization are inputted to RF for ML training. The trained ML models are applied to predict the classification of RNA hybridization results. The experiment results show that the average computation efficiency of the RF-based approach is 190690 times higher than that of the existing approach, while the predictive accuracy of the former method is 97.7%, compared with the latter one.
{"title":"Predicting RNA Molecular Specific Hybridization via Random Forest","authors":"Weijun Zhu, Xiaokai Liu, Zhenfei Wang, Yongwen Fan, Jianwei Wang","doi":"10.1109/ICBCB.2019.8854665","DOIUrl":"https://doi.org/10.1109/ICBCB.2019.8854665","url":null,"abstract":"RNA hybridization is one of the most important operations in popular RNA simulation software in bioinformatics. However, it is a challenging task to decide whether a specific RNA hybridization is effective within an acceptable time, since this mission has the exponentially computational complexity caused by the combinatorial problem. We hereby introduce a machine learning (ML)-based technique to address this problem. And the Random Forest (RF) algorithm is employed, and many groups of RNA molecular coding and their classification in terms of the results of hybridization are inputted to RF for ML training. The trained ML models are applied to predict the classification of RNA hybridization results. The experiment results show that the average computation efficiency of the RF-based approach is 190690 times higher than that of the existing approach, while the predictive accuracy of the former method is 97.7%, compared with the latter one.","PeriodicalId":136995,"journal":{"name":"2019 IEEE 7th International Conference on Bioinformatics and Computational Biology ( ICBCB)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121745226","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 : 2019-03-01DOI: 10.1109/ICBCB.2019.8854648
R. Zheng, Yun Wang, Zhaoying Lyu, A. Armaou
Diabetic nephropathy (DN) is a diabetic complication that seriously endangers human health. Its pathogenesis involves a variety of factors. The purpose of this paper is to determine key genes in the disease progression that will be potential therapeutic targets of DN. Based on gene expression profiles and the databases of interactions of proteins-proteins, transcription factors-genes, transcription factors-miRNAs and miRNAs-genes, the differentially expressed genes of DN were screened. The regulatory network of DN differential genes was established and key genes of DN were identified using the entity grammar system. According to the regulatory interaction between genes, key genes were defined as the ones that could regulate the state of other genes from abnormal towards normal expression. Identified key genes include BMP2 (bone morphogenetic protein 2), VEGFA (vascular endothelial growth factor A), F3 (coagulation factor III/tissue factor), EGR2 (early growth response protein 2), CDS1 (CDP- diacylglycerol synthase 1) and PLCE1 (phospholipase C epsilon 1). These findings provide clues for the successful drug development of DN.
{"title":"Gene Regulatory Network Construction and Key Gene Recognition of Diabetic Nephropathy","authors":"R. Zheng, Yun Wang, Zhaoying Lyu, A. Armaou","doi":"10.1109/ICBCB.2019.8854648","DOIUrl":"https://doi.org/10.1109/ICBCB.2019.8854648","url":null,"abstract":"Diabetic nephropathy (DN) is a diabetic complication that seriously endangers human health. Its pathogenesis involves a variety of factors. The purpose of this paper is to determine key genes in the disease progression that will be potential therapeutic targets of DN. Based on gene expression profiles and the databases of interactions of proteins-proteins, transcription factors-genes, transcription factors-miRNAs and miRNAs-genes, the differentially expressed genes of DN were screened. The regulatory network of DN differential genes was established and key genes of DN were identified using the entity grammar system. According to the regulatory interaction between genes, key genes were defined as the ones that could regulate the state of other genes from abnormal towards normal expression. Identified key genes include BMP2 (bone morphogenetic protein 2), VEGFA (vascular endothelial growth factor A), F3 (coagulation factor III/tissue factor), EGR2 (early growth response protein 2), CDS1 (CDP- diacylglycerol synthase 1) and PLCE1 (phospholipase C epsilon 1). These findings provide clues for the successful drug development of DN.","PeriodicalId":136995,"journal":{"name":"2019 IEEE 7th International Conference on Bioinformatics and Computational Biology ( ICBCB)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130437241","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 : 2019-03-01DOI: 10.1109/ICBCB.2019.8854647
Jie Li, Jiating Qian, Xuanxuan Mao, C. Xue, Yongchang Cao
Severe diarrhea in suckling piglets occurred in southern China in 2017 and a new porcine coronavirus, named as porcine enteric alphacoronavirus (PEAV), was identified. Here we reported a detailed process to sequence, assemble and analyze the whole genome of PEAV. GDS04 has 87% amino acid identity comparing to the S proteins of the HKU2 strains without recombination, and their receptor-binding domains in S protein differ at 20 amino acids, suggesting that GDS04 could be a new mutant virus from bat-like CoVs and responsible for the most recent diarrhea endemic in pig herds in southern China.
{"title":"Whole-Genome Sequencing to Determine Origin of Diarrhea in Suckling Piglets in Southern China","authors":"Jie Li, Jiating Qian, Xuanxuan Mao, C. Xue, Yongchang Cao","doi":"10.1109/ICBCB.2019.8854647","DOIUrl":"https://doi.org/10.1109/ICBCB.2019.8854647","url":null,"abstract":"Severe diarrhea in suckling piglets occurred in southern China in 2017 and a new porcine coronavirus, named as porcine enteric alphacoronavirus (PEAV), was identified. Here we reported a detailed process to sequence, assemble and analyze the whole genome of PEAV. GDS04 has 87% amino acid identity comparing to the S proteins of the HKU2 strains without recombination, and their receptor-binding domains in S protein differ at 20 amino acids, suggesting that GDS04 could be a new mutant virus from bat-like CoVs and responsible for the most recent diarrhea endemic in pig herds in southern China.","PeriodicalId":136995,"journal":{"name":"2019 IEEE 7th International Conference on Bioinformatics and Computational Biology ( ICBCB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134543941","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 : 2019-03-01DOI: 10.1109/icbcb.2019.8854640
Icbcb
{"title":"Proceedings of 2019 IEEE 7th International Conference on Bioinformatics and Computational Biology ICBCB 2019","authors":"Icbcb","doi":"10.1109/icbcb.2019.8854640","DOIUrl":"https://doi.org/10.1109/icbcb.2019.8854640","url":null,"abstract":"","PeriodicalId":136995,"journal":{"name":"2019 IEEE 7th International Conference on Bioinformatics and Computational Biology ( ICBCB)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124247148","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 : 2019-03-01DOI: 10.1109/ICBCB.2019.8854672
Zhiqiang Zhao, Qiaoli Zheng, Yan Zhang, Xin Shi
Since head pose estimation is influenced by illumination variation, expression, noise disturbance and other factors, which results in low rate of recognition, a method of head pose estimation based on multi-feature fusion is proposed in this paper. At first, a pose feature combining the second-order histogram of oriented gradient (HOG) and the uniform pattern of local binary pattern (UP-LBP) is proposed, which is used for head pose estimation in single image. Then, an improved random forest algorithm is adopted for classification of head pose and solving the instability problem of the algorithm. Finally, the improved random forest classifier is used for head pose estimation of the novel pose feature. The experimental results show that, the method proposed in this paper is more capable of classification and with better robustness to illumination variation.
{"title":"A Head Pose Estimation Method Based on Multi-feature Fusion","authors":"Zhiqiang Zhao, Qiaoli Zheng, Yan Zhang, Xin Shi","doi":"10.1109/ICBCB.2019.8854672","DOIUrl":"https://doi.org/10.1109/ICBCB.2019.8854672","url":null,"abstract":"Since head pose estimation is influenced by illumination variation, expression, noise disturbance and other factors, which results in low rate of recognition, a method of head pose estimation based on multi-feature fusion is proposed in this paper. At first, a pose feature combining the second-order histogram of oriented gradient (HOG) and the uniform pattern of local binary pattern (UP-LBP) is proposed, which is used for head pose estimation in single image. Then, an improved random forest algorithm is adopted for classification of head pose and solving the instability problem of the algorithm. Finally, the improved random forest classifier is used for head pose estimation of the novel pose feature. The experimental results show that, the method proposed in this paper is more capable of classification and with better robustness to illumination variation.","PeriodicalId":136995,"journal":{"name":"2019 IEEE 7th International Conference on Bioinformatics and Computational Biology ( ICBCB)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129815070","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 : 2019-03-01DOI: 10.1109/ICBCB.2019.8854657
Yuhan Dong, Rui Wen, Kai Zhang, Lin Zhang
As a common endocrine disease, diabetes has been plagued the lives of patients. An accurate blood glucose (BG) prediction approach can not only be used in daily BG management to reduce the occurrence of hypoglycemia or hyperglycemia, but also regulate the amount and time of insulin injection combined with insulin pump. Data driven methods have become an effective way for predicting BG. While time series analysis methods only deal with one patient at a time and most machine learning approaches simply use multiple patients' data to capture the population characteristics of BG but ignore the individual characteristics. To overcome these shortcomings, we propose a novel neural network approach based on GRU in which both population and individual characteristics of BG fluctuation are well integrated by pre-training and fine-tune processes. The proposed approach not only overcomes the problem of insufficient data for individual patient, but also makes full use of the population and individual differences of BG fluctuation. Compared with other machine learning or neural network approaches, the numerical results suggest that the proposed approach gains significant improvements on prediction accuracy.
{"title":"A Novel RNN-Based Blood Glucose Prediction Approach Using Population and Individual Characteristics","authors":"Yuhan Dong, Rui Wen, Kai Zhang, Lin Zhang","doi":"10.1109/ICBCB.2019.8854657","DOIUrl":"https://doi.org/10.1109/ICBCB.2019.8854657","url":null,"abstract":"As a common endocrine disease, diabetes has been plagued the lives of patients. An accurate blood glucose (BG) prediction approach can not only be used in daily BG management to reduce the occurrence of hypoglycemia or hyperglycemia, but also regulate the amount and time of insulin injection combined with insulin pump. Data driven methods have become an effective way for predicting BG. While time series analysis methods only deal with one patient at a time and most machine learning approaches simply use multiple patients' data to capture the population characteristics of BG but ignore the individual characteristics. To overcome these shortcomings, we propose a novel neural network approach based on GRU in which both population and individual characteristics of BG fluctuation are well integrated by pre-training and fine-tune processes. The proposed approach not only overcomes the problem of insufficient data for individual patient, but also makes full use of the population and individual differences of BG fluctuation. Compared with other machine learning or neural network approaches, the numerical results suggest that the proposed approach gains significant improvements on prediction accuracy.","PeriodicalId":136995,"journal":{"name":"2019 IEEE 7th International Conference on Bioinformatics and Computational Biology ( ICBCB)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131071550","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 : 2019-03-01DOI: 10.1109/ICBCB.2019.8854629
Dechun Zhao, Li Wang, Shanshan Cheng
In order to monitor the development of Parkinson's disease for a long time and to study the mechanism of deep brain stimulation for Parkinson's disease, an adaptive deep brain stimulation system based on ADS1292 was designed for Parkinson's rats. The system consists of a main control unit, an acquisition module, a stimulation module and a wireless communication module, the system uses high-performance, low-power MSP430 as the main control chip. The high resolution, low noise analog to digital conversion chip ADS1292 acquires local field potential (LFP) signals. The stimulus module uses the DAC8532 to output a square wave with adjustable amplitude, frequency, and pulse width. At the same time, the wireless communication module uses the Bluetooth to transmit the LFP signal to the upper computer for processing and receive the control command from the upper computer for achieving adaptive adjustment of the stimulus parameters. Through verification, the system completes the generation of stimulation signals and the acquisition of weak bioelectric signals. The system is light in weight, small in size and low in power consumption, and is very suitable for small animal experiments such as rats.
{"title":"Adaptive Deep Brain Stimulation System Based on ADS1292","authors":"Dechun Zhao, Li Wang, Shanshan Cheng","doi":"10.1109/ICBCB.2019.8854629","DOIUrl":"https://doi.org/10.1109/ICBCB.2019.8854629","url":null,"abstract":"In order to monitor the development of Parkinson's disease for a long time and to study the mechanism of deep brain stimulation for Parkinson's disease, an adaptive deep brain stimulation system based on ADS1292 was designed for Parkinson's rats. The system consists of a main control unit, an acquisition module, a stimulation module and a wireless communication module, the system uses high-performance, low-power MSP430 as the main control chip. The high resolution, low noise analog to digital conversion chip ADS1292 acquires local field potential (LFP) signals. The stimulus module uses the DAC8532 to output a square wave with adjustable amplitude, frequency, and pulse width. At the same time, the wireless communication module uses the Bluetooth to transmit the LFP signal to the upper computer for processing and receive the control command from the upper computer for achieving adaptive adjustment of the stimulus parameters. Through verification, the system completes the generation of stimulation signals and the acquisition of weak bioelectric signals. The system is light in weight, small in size and low in power consumption, and is very suitable for small animal experiments such as rats.","PeriodicalId":136995,"journal":{"name":"2019 IEEE 7th International Conference on Bioinformatics and Computational Biology ( ICBCB)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114487457","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 : 2019-03-01DOI: 10.1109/ICBCB.2019.8854642
Xiaohan Duan, Ziya Yu, Li Tong, Linyuan Wang
Visual attention is an important issue in the field of neuroscience and computer vision. According to recent research of visual cognitive computation, receptive fields are thought to be shifted with the influence of spatial attention. In the traditional method, researchers decoded various positions of attention based on constant population receptive field (pRF) parameters. Comparing with previous attention decoding researches, recent discovery may help improve the decoding accuracy. In this research, to get a better accuracy, a new decoding method is proposed with introducing the shift of pRF parameters. Firstly, we adopted two-dimensional Gaussian receptive field model to characterize the population receptive field(pRF) of each voxel in seven visual areas [V1-V4, inferior occipital gyrus (IOG), posterior fusiform gyrus (pFus), and mid-fusiform gyrus (mFus)]. Then, we introduced a parameter to measure the shift of pRF. With the shifted pRF parameters, the attention position could be decoded by maximum likelihood estimation. With published fMRI dataset, a better decoding accuracy could be obtained in most regions, especially in higher regions. The result also indicated that with the modulation of spatial attention, pRF parameters of voxels in high regions were shifted much more than those in early regions.
{"title":"Decoding Attention Position Based on Shifted Receptive Field in Visual Cortex","authors":"Xiaohan Duan, Ziya Yu, Li Tong, Linyuan Wang","doi":"10.1109/ICBCB.2019.8854642","DOIUrl":"https://doi.org/10.1109/ICBCB.2019.8854642","url":null,"abstract":"Visual attention is an important issue in the field of neuroscience and computer vision. According to recent research of visual cognitive computation, receptive fields are thought to be shifted with the influence of spatial attention. In the traditional method, researchers decoded various positions of attention based on constant population receptive field (pRF) parameters. Comparing with previous attention decoding researches, recent discovery may help improve the decoding accuracy. In this research, to get a better accuracy, a new decoding method is proposed with introducing the shift of pRF parameters. Firstly, we adopted two-dimensional Gaussian receptive field model to characterize the population receptive field(pRF) of each voxel in seven visual areas [V1-V4, inferior occipital gyrus (IOG), posterior fusiform gyrus (pFus), and mid-fusiform gyrus (mFus)]. Then, we introduced a parameter to measure the shift of pRF. With the shifted pRF parameters, the attention position could be decoded by maximum likelihood estimation. With published fMRI dataset, a better decoding accuracy could be obtained in most regions, especially in higher regions. The result also indicated that with the modulation of spatial attention, pRF parameters of voxels in high regions were shifted much more than those in early regions.","PeriodicalId":136995,"journal":{"name":"2019 IEEE 7th International Conference on Bioinformatics and Computational Biology ( ICBCB)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130504765","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 : 2019-03-01DOI: 10.1109/ICBCB.2019.8854651
Wei Zhang, Qinqin Yao, Jianming Zhang, Guangye Li
As a common phenomenon in biological systems, synchronization plays a vital role in construction of genetic oscillator networks with specific cellular functions. Considering the complexity of connections, optimal synchronous design of oscillator networks become feasible with optimization approaches. In order to improve the efficiency of optimal synchronous design, a novel VonPSO algorithm that applies Von-Neumann topology is proposed to solve the combinational optimization problem involved in optimizing directed interactions within coupled oscillator networks. This VonPSO algorithm applies mutation and crossover operations to generate new candidates that represent the network adjacent matrices. Using order parameter to evaluate the degree of synchronization, this paper applies a twostages optimization framework that adjusts network topologies and coupling parameters at two independent stages. Simulation outcomes indicate that the proposed framework is effective to improve the synchronous indexes between coupled genetic oscillators using the VonPSO algorithm. Experimental outcomes indicate that synchronization of coupled oscillator networks can be significantly enhanced by the two-stages optimization using VonPSO algorithm.
{"title":"Synchronous Optimal Design of Genetic Oscillator Networks Using a Novel VonPSO Algorithm","authors":"Wei Zhang, Qinqin Yao, Jianming Zhang, Guangye Li","doi":"10.1109/ICBCB.2019.8854651","DOIUrl":"https://doi.org/10.1109/ICBCB.2019.8854651","url":null,"abstract":"As a common phenomenon in biological systems, synchronization plays a vital role in construction of genetic oscillator networks with specific cellular functions. Considering the complexity of connections, optimal synchronous design of oscillator networks become feasible with optimization approaches. In order to improve the efficiency of optimal synchronous design, a novel VonPSO algorithm that applies Von-Neumann topology is proposed to solve the combinational optimization problem involved in optimizing directed interactions within coupled oscillator networks. This VonPSO algorithm applies mutation and crossover operations to generate new candidates that represent the network adjacent matrices. Using order parameter to evaluate the degree of synchronization, this paper applies a twostages optimization framework that adjusts network topologies and coupling parameters at two independent stages. Simulation outcomes indicate that the proposed framework is effective to improve the synchronous indexes between coupled genetic oscillators using the VonPSO algorithm. Experimental outcomes indicate that synchronization of coupled oscillator networks can be significantly enhanced by the two-stages optimization using VonPSO algorithm.","PeriodicalId":136995,"journal":{"name":"2019 IEEE 7th International Conference on Bioinformatics and Computational Biology ( ICBCB)","volume":"245 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114370017","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 : 2019-03-01DOI: 10.1109/ICBCB.2019.8854652
Qing Li, Rongshuai Liu, Weipeng Zhang, Yuxi Feng
Cell microcapsules are prepared and characterized by digital injection of coaxial micro-fluidics to better control cells number and reduce the redundancy space in microcapsules. The results show that the diameter of the inner and outer nozzles of the micro coaxial nozzle have a significant effect on the particle size of the cell microcapsules, particle size distribution and the number of encapsulated cells. The particle size of the lymphocyte microcapsules increases linearly with the diameter of the outer nozzle, not significantly influenced by the change of the diameter of the inner nozzle. The number of lymphocytes in the microcapsules increases exponentially with the inner nozzle diameter, and is not significantly affected by the diameter of the outer nozzle. So, we can flexibly control the particle size of the microcapsules and the number of encapsulated cell, reduce the redundant space by the ratio between the inner and outer nozzle diameters.
{"title":"Characterization of Cell Microcapsules and Preparation via Digital Coaxial Injection","authors":"Qing Li, Rongshuai Liu, Weipeng Zhang, Yuxi Feng","doi":"10.1109/ICBCB.2019.8854652","DOIUrl":"https://doi.org/10.1109/ICBCB.2019.8854652","url":null,"abstract":"Cell microcapsules are prepared and characterized by digital injection of coaxial micro-fluidics to better control cells number and reduce the redundancy space in microcapsules. The results show that the diameter of the inner and outer nozzles of the micro coaxial nozzle have a significant effect on the particle size of the cell microcapsules, particle size distribution and the number of encapsulated cells. The particle size of the lymphocyte microcapsules increases linearly with the diameter of the outer nozzle, not significantly influenced by the change of the diameter of the inner nozzle. The number of lymphocytes in the microcapsules increases exponentially with the inner nozzle diameter, and is not significantly affected by the diameter of the outer nozzle. So, we can flexibly control the particle size of the microcapsules and the number of encapsulated cell, reduce the redundant space by the ratio between the inner and outer nozzle diameters.","PeriodicalId":136995,"journal":{"name":"2019 IEEE 7th International Conference on Bioinformatics and Computational Biology ( ICBCB)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114491965","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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