Based on our previous research, Auricularia delicata has been detected as unique and important local mushroom in economic and ecological values which were newly recorded in Turgo tropical forest ecosystem, with exactly restricted distribution only in Bingungan forest. This research aimed to know habitat preferences of Auricularia delicata by using ecological informatics approach regarding to management forest-fungi efforts. To yield communicative interpretation, we used some analyses from Pearson correlation among physical and chemical characteristics of substrate as Auricularia delicata habitat, Bray-Curtis similarity and distance indices, NMDS (Non-Metric Multidimensional Scaling) ordination, hierarchical clustering with UPGMA (Unweighted Pair Group Method with Arithmetic Mean) and non-hierarchical clustering with K-means, till we could categorize habitat preferences from the very good, good, poor, and very poor categories.
{"title":"Ecological Informatics Approach to Analyze Habitat Preferences of Auricularia delicata (Italic) in Bingungan Forest, Turgo Natural Forest Conservation Area","authors":"D. Prasetiya, T. Aminatun","doi":"10.1145/3314367.3314382","DOIUrl":"https://doi.org/10.1145/3314367.3314382","url":null,"abstract":"Based on our previous research, Auricularia delicata has been detected as unique and important local mushroom in economic and ecological values which were newly recorded in Turgo tropical forest ecosystem, with exactly restricted distribution only in Bingungan forest. This research aimed to know habitat preferences of Auricularia delicata by using ecological informatics approach regarding to management forest-fungi efforts. To yield communicative interpretation, we used some analyses from Pearson correlation among physical and chemical characteristics of substrate as Auricularia delicata habitat, Bray-Curtis similarity and distance indices, NMDS (Non-Metric Multidimensional Scaling) ordination, hierarchical clustering with UPGMA (Unweighted Pair Group Method with Arithmetic Mean) and non-hierarchical clustering with K-means, till we could categorize habitat preferences from the very good, good, poor, and very poor categories.","PeriodicalId":20485,"journal":{"name":"Proceedings of the 2019 9th International Conference on Bioscience, Biochemistry and Bioinformatics - ICBBB '19","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83414892","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}
Khurram Ejaz, M. Rahim, U. I. Bajwa, N. Rana, A. Rehman
Segmentation methods are so much efficient to segment complex tumor from challenging datasets. MACCAI BRATS 2013-2017 brain tumor dataset (FLAIR, T2) had been taken for high grade glioma (HGG). This data set is challenging to segment tumor due to homogenous intensity and difficult to separate tumor boundary from other normal tissues, so our goal is to segment tumor from mixed intensities. It can be accomplished step by step. Therefore image maximum and minimum intensities has been adjusted because need to highlight the tumor portion then thresholding perform to localize the tumor region, has applied statistical features(kurtosis, skewness, mean and variance) so tumor portion become more visualize but cann't separate tumor from boundary and then apply unsupervised clusters like kmean but it gives hard crisp membership and many tumor membership missed so texture features(Correlation, energy, homogeneity and contrast) with combination of Gabor filter has been applied but dimension of data increase and intensities became disturb due high dimension operation over MRI. Tumor boundary become more visualize if combine FLAIR over T2 sequence image then we apply FCM and result is: tumor boundaries become more visualized then applied one statistical feature (Kurtosis) and one texture feature(Energy) so tumor portion separate from other tissue and better segmentation accuracy have been checked with comparison parameters like dice overlap and Jaccard index.
{"title":"An Unsupervised Learning with Feature Approach for Brain Tumor Segmentation Using Magnetic Resonance Imaging","authors":"Khurram Ejaz, M. Rahim, U. I. Bajwa, N. Rana, A. Rehman","doi":"10.1145/3314367.3314384","DOIUrl":"https://doi.org/10.1145/3314367.3314384","url":null,"abstract":"Segmentation methods are so much efficient to segment complex tumor from challenging datasets. MACCAI BRATS 2013-2017 brain tumor dataset (FLAIR, T2) had been taken for high grade glioma (HGG). This data set is challenging to segment tumor due to homogenous intensity and difficult to separate tumor boundary from other normal tissues, so our goal is to segment tumor from mixed intensities. It can be accomplished step by step. Therefore image maximum and minimum intensities has been adjusted because need to highlight the tumor portion then thresholding perform to localize the tumor region, has applied statistical features(kurtosis, skewness, mean and variance) so tumor portion become more visualize but cann't separate tumor from boundary and then apply unsupervised clusters like kmean but it gives hard crisp membership and many tumor membership missed so texture features(Correlation, energy, homogeneity and contrast) with combination of Gabor filter has been applied but dimension of data increase and intensities became disturb due high dimension operation over MRI. Tumor boundary become more visualize if combine FLAIR over T2 sequence image then we apply FCM and result is: tumor boundaries become more visualized then applied one statistical feature (Kurtosis) and one texture feature(Energy) so tumor portion separate from other tissue and better segmentation accuracy have been checked with comparison parameters like dice overlap and Jaccard index.","PeriodicalId":20485,"journal":{"name":"Proceedings of the 2019 9th International Conference on Bioscience, Biochemistry and Bioinformatics - ICBBB '19","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84168251","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}
R. Isemin, A. Mikhalev, O. Milovanov, D. Klimov, N. Muratova, K. Krysanova, Yu. A. Teplitskii, A. Greben′kov, V. Kogh-Tatarenko
The results of comparative experiments on the production of biofuel from poultry litter (PL) by the method of low-temperature pyrolysis (Torrefaction) and hydrothermal carbonization are presented. Comparison of the obtained results shows that as a result of hydrothermal carbonation of PL (processing temperature 180-210 °C, treatment time 240 minutes), the carbon content in the manure can be increased by 1.35 times, and the oxygen content is reduced in 2.2 times, the lower heat of combustion of the fuel obtained, which is similar in its characteristics to lignites, can be increased by 1.25 times to 19.7 MJ/kg. As a result of low-temperature pyrolysis in the fluidized bed at a temperature of 300 °C in a nitrogen medium and superheated water vapor, the carbon content can be increased by 1.16 times, and the oxygen content is reduced in 2.8 times. The lowest heat of combustion of the fuel produced can be increase by 1.13 times to 18.8 MJ/kg. Considering that the technology of PL treatment by low-temperature pyrolysis in the fluidized bed requires significantly less processing time (360-480 seconds), this technology can be fully considered as an alternative to hydrothermal carbonization.
{"title":"Comparative Studies Between Hydrothermal Carbonation and Torrefaction for Biofuel Production from Poultry Litter","authors":"R. Isemin, A. Mikhalev, O. Milovanov, D. Klimov, N. Muratova, K. Krysanova, Yu. A. Teplitskii, A. Greben′kov, V. Kogh-Tatarenko","doi":"10.1145/3314367.3314372","DOIUrl":"https://doi.org/10.1145/3314367.3314372","url":null,"abstract":"The results of comparative experiments on the production of biofuel from poultry litter (PL) by the method of low-temperature pyrolysis (Torrefaction) and hydrothermal carbonization are presented. Comparison of the obtained results shows that as a result of hydrothermal carbonation of PL (processing temperature 180-210 °C, treatment time 240 minutes), the carbon content in the manure can be increased by 1.35 times, and the oxygen content is reduced in 2.2 times, the lower heat of combustion of the fuel obtained, which is similar in its characteristics to lignites, can be increased by 1.25 times to 19.7 MJ/kg. As a result of low-temperature pyrolysis in the fluidized bed at a temperature of 300 °C in a nitrogen medium and superheated water vapor, the carbon content can be increased by 1.16 times, and the oxygen content is reduced in 2.8 times. The lowest heat of combustion of the fuel produced can be increase by 1.13 times to 18.8 MJ/kg. Considering that the technology of PL treatment by low-temperature pyrolysis in the fluidized bed requires significantly less processing time (360-480 seconds), this technology can be fully considered as an alternative to hydrothermal carbonization.","PeriodicalId":20485,"journal":{"name":"Proceedings of the 2019 9th International Conference on Bioscience, Biochemistry and Bioinformatics - ICBBB '19","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74241431","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}
Anjana Umapathy, A. Sreenivasan, D. S. Nairy, S. Natarajan, B. Rao
Diabetic Retinopathy (DR) is one of the most common causes of blindness in adults. The need for automating the detection of DR arises from the deficiency of ophthalmologists in certain regions where screening is done, and this paper is aimed at mitigating this bottleneck. Images from publicly available datasets STARE, HRF, and MESSIDOR along with a novel dataset of images obtained from the Retina Institute of Karnataka are used for training the models. This paper proposes two methods to automate the detection. The first approach involves extracting features using retinal image processing and textural feature extraction, and uses a Decision Tree classifier to predict the presence of DR. The second approach applies transfer learning to detect DR in fundus images. The accuracies obtained by the two approaches are 94.4% and 88.8% respectively, which are competent to current automation methods. A comparison between these models is made. On consultation with Retina Institute of Karnataka, a web application which predicts the presence of DR that can be integrated into screening centres is made.
{"title":"Image Processing, Textural Feature Extraction and Transfer Learning based detection of Diabetic Retinopathy","authors":"Anjana Umapathy, A. Sreenivasan, D. S. Nairy, S. Natarajan, B. Rao","doi":"10.1145/3314367.3314376","DOIUrl":"https://doi.org/10.1145/3314367.3314376","url":null,"abstract":"Diabetic Retinopathy (DR) is one of the most common causes of blindness in adults. The need for automating the detection of DR arises from the deficiency of ophthalmologists in certain regions where screening is done, and this paper is aimed at mitigating this bottleneck. Images from publicly available datasets STARE, HRF, and MESSIDOR along with a novel dataset of images obtained from the Retina Institute of Karnataka are used for training the models. This paper proposes two methods to automate the detection. The first approach involves extracting features using retinal image processing and textural feature extraction, and uses a Decision Tree classifier to predict the presence of DR. The second approach applies transfer learning to detect DR in fundus images. The accuracies obtained by the two approaches are 94.4% and 88.8% respectively, which are competent to current automation methods. A comparison between these models is made. On consultation with Retina Institute of Karnataka, a web application which predicts the presence of DR that can be integrated into screening centres is made.","PeriodicalId":20485,"journal":{"name":"Proceedings of the 2019 9th International Conference on Bioscience, Biochemistry and Bioinformatics - ICBBB '19","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76983840","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 new approach using the Hilbert vibration decomposition (HVD) for extracting the respiration from the photoplethysmographic (PPG) signal is proposed. It is suggested that the largest energy component of the PPG signal acquired using the HVD is analogous to the respiratory signal. The proposed PPG-derived respiration (PDR) technique is examined over the Capnobase and MIMIC datasets by evaluating the correlation and respiratory rate errors calculated between the derived and reference respiratory rates (RRs). Upon comparing the performance of the proposed approach with the existing techniques, the proposed approach is seen to be yielding better correlation and smaller errors in the RRs computed from the PDR and recorded respiration signals on both the datasets. The experimental analysis suggests that the proposed technique can be employed for efficacious computation of the respiration from the PPG signal. Efficient and reliable extraction of the respiratory signal from PPG will help in the improvement of low-cost and less discomfort mobile-based healthcare systems.
{"title":"Extraction of Respiration from PPG Signals Using Hilbert Vibration Decomposition","authors":"H. Sharma","doi":"10.1145/3314367.3314369","DOIUrl":"https://doi.org/10.1145/3314367.3314369","url":null,"abstract":"A new approach using the Hilbert vibration decomposition (HVD) for extracting the respiration from the photoplethysmographic (PPG) signal is proposed. It is suggested that the largest energy component of the PPG signal acquired using the HVD is analogous to the respiratory signal. The proposed PPG-derived respiration (PDR) technique is examined over the Capnobase and MIMIC datasets by evaluating the correlation and respiratory rate errors calculated between the derived and reference respiratory rates (RRs). Upon comparing the performance of the proposed approach with the existing techniques, the proposed approach is seen to be yielding better correlation and smaller errors in the RRs computed from the PDR and recorded respiration signals on both the datasets. The experimental analysis suggests that the proposed technique can be employed for efficacious computation of the respiration from the PPG signal. Efficient and reliable extraction of the respiratory signal from PPG will help in the improvement of low-cost and less discomfort mobile-based healthcare systems.","PeriodicalId":20485,"journal":{"name":"Proceedings of the 2019 9th International Conference on Bioscience, Biochemistry and Bioinformatics - ICBBB '19","volume":"76 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79298149","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}
Comparative genomics is a field of research that compares genomes of different organisms to identify common patterns. It is a powerful method used to identify the genetic diseases that cause mutations. Multiple Sequence Alignment (MSA) is an intermediate step in comparative genomics analysis that aligns three or more biological sequences of similar length. MSA is an NP-hard problem for which no efficient algorithm exists to perform this in a reasonable amount of time. However, humans across evolution have developed special intuition to identify visual patterns in short periods of time. Hence, a citizen science approach can be devised to solve the MSA problem by transforming it into a human computing game on creating visually similar patterns. In this paper, we introduce the mobile game "Genenigma", which harnesses the human computing capability to align multiple sequences of genomes and use the results to help geneticists to understand the genetic code. The usability and performance scores of "Genenigma" predicts a larger user base than existing mobile games built for this purpose.
{"title":"An Interactive Gameplay to Crowdsource Multiple Sequence Alignment of Genome Sequences: Genenigma","authors":"D. Meedeniya, S. A. P. A. Rukshan, A. Welivita","doi":"10.1145/3314367.3314374","DOIUrl":"https://doi.org/10.1145/3314367.3314374","url":null,"abstract":"Comparative genomics is a field of research that compares genomes of different organisms to identify common patterns. It is a powerful method used to identify the genetic diseases that cause mutations. Multiple Sequence Alignment (MSA) is an intermediate step in comparative genomics analysis that aligns three or more biological sequences of similar length. MSA is an NP-hard problem for which no efficient algorithm exists to perform this in a reasonable amount of time. However, humans across evolution have developed special intuition to identify visual patterns in short periods of time. Hence, a citizen science approach can be devised to solve the MSA problem by transforming it into a human computing game on creating visually similar patterns. In this paper, we introduce the mobile game \"Genenigma\", which harnesses the human computing capability to align multiple sequences of genomes and use the results to help geneticists to understand the genetic code. The usability and performance scores of \"Genenigma\" predicts a larger user base than existing mobile games built for this purpose.","PeriodicalId":20485,"journal":{"name":"Proceedings of the 2019 9th International Conference on Bioscience, Biochemistry and Bioinformatics - ICBBB '19","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81580813","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}
C. Sandeep, S. Sreelatha, M. Baskaran, X. Hong, T. Aung, V. M. Murukeshan
Glaucoma is one of the leading cause of blindness characterized by increased intra ocular pressure (IOP), visual field defects and irreversible loss of vision. Remedial intervention of glaucoma primarily aims at the reduction of IOP and subsequent examination concerning the related anomalies in the aqueous outflow system (AOS) especially with newer angle procedures. Thus, high resolution imaging of the iridocorneal angle (ICA) region comprising trabecular meshwork (TM) is extremely valuable to clinicians and vision analysts in comprehending the disease state for the efficacious analysis and treatment of glaucoma. Imaging of the AOS inside the eye using the digitally scanned Bessel-Gauss beam light sheet microscopy has been used in this study to obtain high resolution optical sections with minimal phototoxicity and photobleaching. This paper investigates the effect of long working distance objectives in obtaining high resolution TM images while offering non-contact and non-invasive approach in imaging. A series of experiments were conducted to optimize various imaging parameters using porcine eyes as test samples. Investigations carried out by illuminating both the anterior segment region and limbal region resulted in promising results. A delineated network of collagen fibers in a meshwork fashion can be clearly seen in the obtained images of the TM. The optical sectioning capability of this technique is demonstrated and the structural features match well with previous literature reports.
{"title":"Bessel-Gauss Beam Light Sheet Assisted Fluorescence Imaging of Trabecular Meshwork in the Iridocorneal Region Using Long Working Distance Objectives","authors":"C. Sandeep, S. Sreelatha, M. Baskaran, X. Hong, T. Aung, V. M. Murukeshan","doi":"10.1145/3314367.3314380","DOIUrl":"https://doi.org/10.1145/3314367.3314380","url":null,"abstract":"Glaucoma is one of the leading cause of blindness characterized by increased intra ocular pressure (IOP), visual field defects and irreversible loss of vision. Remedial intervention of glaucoma primarily aims at the reduction of IOP and subsequent examination concerning the related anomalies in the aqueous outflow system (AOS) especially with newer angle procedures. Thus, high resolution imaging of the iridocorneal angle (ICA) region comprising trabecular meshwork (TM) is extremely valuable to clinicians and vision analysts in comprehending the disease state for the efficacious analysis and treatment of glaucoma. Imaging of the AOS inside the eye using the digitally scanned Bessel-Gauss beam light sheet microscopy has been used in this study to obtain high resolution optical sections with minimal phototoxicity and photobleaching. This paper investigates the effect of long working distance objectives in obtaining high resolution TM images while offering non-contact and non-invasive approach in imaging. A series of experiments were conducted to optimize various imaging parameters using porcine eyes as test samples. Investigations carried out by illuminating both the anterior segment region and limbal region resulted in promising results. A delineated network of collagen fibers in a meshwork fashion can be clearly seen in the obtained images of the TM. The optical sectioning capability of this technique is demonstrated and the structural features match well with previous literature reports.","PeriodicalId":20485,"journal":{"name":"Proceedings of the 2019 9th International Conference on Bioscience, Biochemistry and Bioinformatics - ICBBB '19","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82342332","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}
Semantic segmentation of colon gland is notoriously challenging due to their complex texture, huge variation, and the scarcity of training data with accurate annotations. It is even hard for experts, let alone computer-aided diagnosis systems. Recently, some deep convolutional neural networks (DCNN) based methods have been introduced to tackle this problem, achieving much impressive performance. However, these methods always tend to miss segmented results for the important regions of colon gland or make a wrong segmenting decision.In this paper, we address the challenging problem by proposed a novel framework through conditional generative adversarial network. First, the generator in the framework is trained to learn a mapping from gland colon image to a confidence map indicating the probabilities of being a pixel of gland object. The discriminator is responsible to penalize the mismatch between colon gland image and the confidence map. This additional adversarial learning facilitates the generator to produce higher quality confidence map. Then we transform the confidence map into a binary image using a fixed threshold to fulfill the segmentation task. We implement extensive experiments on the public benchmark MICCAI gland 2015 dataset to verify the effectiveness of the proposed method. Results demonstrate that our method achieve a better segmentation result in terms of visual perception and two quantitative metrics, compared with other methods.
{"title":"Semantic Segmentation of Colon Gland with Conditional Generative Adversarial Network","authors":"Liye Mei, Xiaopeng Guo, Chaowei Cheng","doi":"10.1145/3314367.3314370","DOIUrl":"https://doi.org/10.1145/3314367.3314370","url":null,"abstract":"Semantic segmentation of colon gland is notoriously challenging due to their complex texture, huge variation, and the scarcity of training data with accurate annotations. It is even hard for experts, let alone computer-aided diagnosis systems. Recently, some deep convolutional neural networks (DCNN) based methods have been introduced to tackle this problem, achieving much impressive performance. However, these methods always tend to miss segmented results for the important regions of colon gland or make a wrong segmenting decision.In this paper, we address the challenging problem by proposed a novel framework through conditional generative adversarial network. First, the generator in the framework is trained to learn a mapping from gland colon image to a confidence map indicating the probabilities of being a pixel of gland object. The discriminator is responsible to penalize the mismatch between colon gland image and the confidence map. This additional adversarial learning facilitates the generator to produce higher quality confidence map. Then we transform the confidence map into a binary image using a fixed threshold to fulfill the segmentation task. We implement extensive experiments on the public benchmark MICCAI gland 2015 dataset to verify the effectiveness of the proposed method. Results demonstrate that our method achieve a better segmentation result in terms of visual perception and two quantitative metrics, compared with other methods.","PeriodicalId":20485,"journal":{"name":"Proceedings of the 2019 9th International Conference on Bioscience, Biochemistry and Bioinformatics - ICBBB '19","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76373091","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}
Before last two decades, astrocytes were treated as supporting cells of neuron. Now it is regarded as important and strong participant in central nervous system. Astrocytes also play important role in many neuronal disorders like Alzheimer's, Parkinson's etc. Astrocytes release gliotransmitters like glutamate. Astrocytes take part in synapse in calcium dependent manner. However, it is not clear about the effect of astrocyte geometry on calcium distribution. In this study we present a geometry based mathematical model of an astrocytes. Mathematical model is developed in the form of reaction diffusion equation by considering the effect of endogenous and exogenous buffers on cytosolic calcium concentration. In present study it is concluded that the effect of geometry also visible and found significant on cytosolic calcium distribution in astrocytes.
{"title":"Computational Modelling of Calcium Buffering in a Star Shaped Astrocyte","authors":"A. Jha, B. Jha","doi":"10.1145/3314367.3314379","DOIUrl":"https://doi.org/10.1145/3314367.3314379","url":null,"abstract":"Before last two decades, astrocytes were treated as supporting cells of neuron. Now it is regarded as important and strong participant in central nervous system. Astrocytes also play important role in many neuronal disorders like Alzheimer's, Parkinson's etc. Astrocytes release gliotransmitters like glutamate. Astrocytes take part in synapse in calcium dependent manner. However, it is not clear about the effect of astrocyte geometry on calcium distribution. In this study we present a geometry based mathematical model of an astrocytes. Mathematical model is developed in the form of reaction diffusion equation by considering the effect of endogenous and exogenous buffers on cytosolic calcium concentration. In present study it is concluded that the effect of geometry also visible and found significant on cytosolic calcium distribution in astrocytes.","PeriodicalId":20485,"journal":{"name":"Proceedings of the 2019 9th International Conference on Bioscience, Biochemistry and Bioinformatics - ICBBB '19","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84061383","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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