Pub Date : 2018-07-01DOI: 10.1109/IWOBI.2018.8464203
Tobias Steinmetzer, Ingrid Bönninger, Barbara Priwitzer, F. Reinhardt, Markus Reckhardt, Dorela Erk, C. Travieso-González
We present a new method for detecting gait disorders according to their stadium using cluster methods for sensor data. 21 healthy and 18 Parkinson subjects performed the Time Up and Go test. The time series were segmented into separate steps. For the analysis the horizontal acceleration measured by a mobile sensor system was considered. We used Dynamic Time Warping and Hierarchical Custering to distinguish the stadiums. A specificity of 92% was achieved.
我们提出了一种新的方法来检测步态障碍根据他们的体育场使用聚类方法的传感器数据。21名健康受试者和18名帕金森受试者进行了Time Up and Go测试。时间序列被分割成不同的步骤。为了进行分析，考虑了移动传感器系统测量的水平加速度。我们使用动态时间扭曲和分层集群来区分体育场。特异性达到92%。
{"title":"Clustering of Human Gait with Parkinson's Disease by Using Dynamic Time Warping","authors":"Tobias Steinmetzer, Ingrid Bönninger, Barbara Priwitzer, F. Reinhardt, Markus Reckhardt, Dorela Erk, C. Travieso-González","doi":"10.1109/IWOBI.2018.8464203","DOIUrl":"https://doi.org/10.1109/IWOBI.2018.8464203","url":null,"abstract":"We present a new method for detecting gait disorders according to their stadium using cluster methods for sensor data. 21 healthy and 18 Parkinson subjects performed the Time Up and Go test. The time series were segmented into separate steps. For the analysis the horizontal acceleration measured by a mobile sensor system was considered. We used Dynamic Time Warping and Hierarchical Custering to distinguish the stadiums. A specificity of 92% was achieved.","PeriodicalId":127078,"journal":{"name":"2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117122372","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 : 2018-07-01DOI: 10.1109/IWOBI.2018.8464205
Cristian Vargas-Mora, M. Acon, R. Mora-Rodríguez, S. Quirós
The genetic instability caused by the disruption of the mechanism of the DNA-damage response (DDR) has been linked to cancer development. One of the most important and studied mechanism of the DDR is the p53 pathway. This protein acts as a tumor suppressor. MDM2, MDM4 and PLK1 inhibit its proapoptotic activity by binding to its sequence-specific DNA-binding site. To model the interactions between the species with the purpose of finding key points in the regulation of proliferation in cancer cell lines, we propose a transcriptional regulatory network conformed by miRNAs, mARNs and transcription factors involved in the modulation of p53 tumor suppressor protein using Ordinary Differential Equations. Our results suggest miR-34a has a strong control in the regulation of MDM4 and its overexpression results in the decrease of the expression of this protein without significantly affecting the expression of p53. We propose that the combination of miR-34a and small molecule inhibitors of MDM2 may be a therapeutic alternative for treating cancer progression and relapse prevention.
{"title":"A Transcriptional Regulatory Network Model Reveals miR-34a as a Potential Regulator of Proliferation in Cancer Cell Lines","authors":"Cristian Vargas-Mora, M. Acon, R. Mora-Rodríguez, S. Quirós","doi":"10.1109/IWOBI.2018.8464205","DOIUrl":"https://doi.org/10.1109/IWOBI.2018.8464205","url":null,"abstract":"The genetic instability caused by the disruption of the mechanism of the DNA-damage response (DDR) has been linked to cancer development. One of the most important and studied mechanism of the DDR is the p53 pathway. This protein acts as a tumor suppressor. MDM2, MDM4 and PLK1 inhibit its proapoptotic activity by binding to its sequence-specific DNA-binding site. To model the interactions between the species with the purpose of finding key points in the regulation of proliferation in cancer cell lines, we propose a transcriptional regulatory network conformed by miRNAs, mARNs and transcription factors involved in the modulation of p53 tumor suppressor protein using Ordinary Differential Equations. Our results suggest miR-34a has a strong control in the regulation of MDM4 and its overexpression results in the decrease of the expression of this protein without significantly affecting the expression of p53. We propose that the combination of miR-34a and small molecule inhibitors of MDM2 may be a therapeutic alternative for treating cancer progression and relapse prevention.","PeriodicalId":127078,"journal":{"name":"2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124850660","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 : 2018-07-01DOI: 10.1109/IWOBI.2018.8464209
Mauricio Rodriguez Calvo, Federico Ruiz-Ugalde
When a robot is designed, usually the weight of the same motors limits the maximum payload of the robot. While the designer tries to increase the power of the motors to give the robot better payload capacity, by doing so, it also makes the robot itself heavier and then the improvement in payload capacity is lost in lifting the robot itself. Many approaches to improve payload capacity consist in using an existing commercial electric motor and modifying it by adding external accessories to avoid non-safe motor internal heat. They usually use a mechanic reduction to increase payload capacity, but they lose maximum speed at the same time. Currently, there are no proper solutions for a motor in a humanoid robot application that manages to lift very heavy objects at high speed and that at the same time the motors are small and lightweight. In order to obtained more power from the robot joint actuator, in this paper we propose and compared a new thermal channel and thermal sealed jacket design for a permanent magnet electric motor to solve this problem. The design takes advantage of the fractional-slot concentrated-winding motor configuration and its fill factor to force liquid and air directly to the winding separately, in order to examine their head extradition to increase its performance in a specify scenario. The propose design shows an improvement in relation to the head extraction of the sealed jacket design with respect to the thermal channels when forcing liquid through them. Both designs are simulated assuming an extreme scenario where the winding reaches 100 °C for validation purposes. The experiments were carried out in the Ansys CFD Software to evaluate their heat transfer behavior.
{"title":"Comparative Efficiency Study of Two Proposed Designs Tested in Water and Air Cooling Conditions for a High Power Humanoid Robot Hollow Joint","authors":"Mauricio Rodriguez Calvo, Federico Ruiz-Ugalde","doi":"10.1109/IWOBI.2018.8464209","DOIUrl":"https://doi.org/10.1109/IWOBI.2018.8464209","url":null,"abstract":"When a robot is designed, usually the weight of the same motors limits the maximum payload of the robot. While the designer tries to increase the power of the motors to give the robot better payload capacity, by doing so, it also makes the robot itself heavier and then the improvement in payload capacity is lost in lifting the robot itself. Many approaches to improve payload capacity consist in using an existing commercial electric motor and modifying it by adding external accessories to avoid non-safe motor internal heat. They usually use a mechanic reduction to increase payload capacity, but they lose maximum speed at the same time. Currently, there are no proper solutions for a motor in a humanoid robot application that manages to lift very heavy objects at high speed and that at the same time the motors are small and lightweight. In order to obtained more power from the robot joint actuator, in this paper we propose and compared a new thermal channel and thermal sealed jacket design for a permanent magnet electric motor to solve this problem. The design takes advantage of the fractional-slot concentrated-winding motor configuration and its fill factor to force liquid and air directly to the winding separately, in order to examine their head extradition to increase its performance in a specify scenario. The propose design shows an improvement in relation to the head extraction of the sealed jacket design with respect to the thermal channels when forcing liquid through them. Both designs are simulated assuming an extreme scenario where the winding reaches 100 °C for validation purposes. The experiments were carried out in the Ansys CFD Software to evaluate their heat transfer behavior.","PeriodicalId":127078,"journal":{"name":"2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128701552","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 : 2018-07-01DOI: 10.1109/IWOBI.2018.8464206
Geovanni Figueroa-Mata, Erick Mata-Montero, Juan Carlos Valverde-Otarola, Dagoberto Arias-Aguilar
The fast and accurate identification of forest species is fundamental to support their conservation, sustainable management, and, more specifically, the fight against illegal logging. Traditionally, identifications are done by using dichotomous or polytomous keys based on physical characteristics of trees. However, these techniques are of little use when the trees have been cut, removed from their natural environment, and consequently there is only a partial subset of information on all those traits. In these cases, it may be possible to resort to the anatomical characteristics of the wood, which are less affected by environmental factors and therefore have a high diagnostic value in the identification. For some years now, computers have been used to support the identification processes through interactive keys and access to global repositories of digital images, among others. However, techniques based on machine learning have recently been developed and applied successfully to the identification of both plant and animal species. Consequently, automatic or semiautomatic techniques have been proposed to support botanists, taxonomists and non-experts in the species identification process. This article presents an overview of the use of these techniques as well as the current challenges and opportunities for the identification of forest species based on xylotheque samples.
{"title":"Automated Image-based Identification of Forest Species: Challenges and Opportunities for 21st Century Xylotheques","authors":"Geovanni Figueroa-Mata, Erick Mata-Montero, Juan Carlos Valverde-Otarola, Dagoberto Arias-Aguilar","doi":"10.1109/IWOBI.2018.8464206","DOIUrl":"https://doi.org/10.1109/IWOBI.2018.8464206","url":null,"abstract":"The fast and accurate identification of forest species is fundamental to support their conservation, sustainable management, and, more specifically, the fight against illegal logging. Traditionally, identifications are done by using dichotomous or polytomous keys based on physical characteristics of trees. However, these techniques are of little use when the trees have been cut, removed from their natural environment, and consequently there is only a partial subset of information on all those traits. In these cases, it may be possible to resort to the anatomical characteristics of the wood, which are less affected by environmental factors and therefore have a high diagnostic value in the identification. For some years now, computers have been used to support the identification processes through interactive keys and access to global repositories of digital images, among others. However, techniques based on machine learning have recently been developed and applied successfully to the identification of both plant and animal species. Consequently, automatic or semiautomatic techniques have been proposed to support botanists, taxonomists and non-experts in the species identification process. This article presents an overview of the use of these techniques as well as the current challenges and opportunities for the identification of forest species based on xylotheque samples.","PeriodicalId":127078,"journal":{"name":"2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124302179","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 : 2018-07-01DOI: 10.1109/IWOBI.2018.8464132
Marvin Coto-Jiménez, John Goddard Close, L. D. Persia, H. Rufiner
Over the past several decades, numerous speech enhancement techniques have been proposed to improve the performance of modern communication devices in noisy environments. Among them, there is a large range of classical algorithms (e.g. spectral subtraction, Wiener filtering and Bayesian-based enhancement), and more recently several deep neural network-based. In this paper, we propose a hybrid approach to speech enhancement which combines two stages: In the first stage, the well-known Wiener filter performs the task of enhancing noisy speech. In the second stage, a refinement is performed using a new multi-stream approach, which involves a collection of denoising autoencoders and auto-associative memories based on Long Short-term Memory (LSTM) networks. We carry out a comparative performance analysis using two objective measures, using artificial noise added at different signal-to-noise levels. Results show that this hybrid system improves the signal's enhancement significantly in comparison to the Wiener filtering and the LSTM networks separately.
{"title":"Hybrid Speech Enhancement with Wiener filters and Deep LSTM Denoising Autoencoders","authors":"Marvin Coto-Jiménez, John Goddard Close, L. D. Persia, H. Rufiner","doi":"10.1109/IWOBI.2018.8464132","DOIUrl":"https://doi.org/10.1109/IWOBI.2018.8464132","url":null,"abstract":"Over the past several decades, numerous speech enhancement techniques have been proposed to improve the performance of modern communication devices in noisy environments. Among them, there is a large range of classical algorithms (e.g. spectral subtraction, Wiener filtering and Bayesian-based enhancement), and more recently several deep neural network-based. In this paper, we propose a hybrid approach to speech enhancement which combines two stages: In the first stage, the well-known Wiener filter performs the task of enhancing noisy speech. In the second stage, a refinement is performed using a new multi-stream approach, which involves a collection of denoising autoencoders and auto-associative memories based on Long Short-term Memory (LSTM) networks. We carry out a comparative performance analysis using two objective measures, using artificial noise added at different signal-to-noise levels. Results show that this hybrid system improves the signal's enhancement significantly in comparison to the Wiener filtering and the LSTM networks separately.","PeriodicalId":127078,"journal":{"name":"2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117078621","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 : 2018-07-01DOI: 10.1109/IWOBI.2018.8464191
Martín Solís, T. Moreira, R. Gonzalez, Tatiana Fernandez, M. Hernandez
This study analyzes the performance of four machine learning algorithms with different perspectives for defining data files, in the prediction of university student desertion. The algorithms used were: Random Forest, Neural Networks, Support Vector Machines and Logistic Regression. It was found that the Random Forest algorithm with 10 variables randomly sampled as candidates in each division, was the best for predicting dropouts and that the ideal perspective for training the algorithm is to use information on all semesters that students take within a given period of time, using a classification variable that defines the non-dropout as the graduated student. In a first validation sample, this approach correctly predicted 91% of dropouts, with a sensitivity of 87%.
{"title":"Perspectives to Predict Dropout in University Students with Machine Learning","authors":"Martín Solís, T. Moreira, R. Gonzalez, Tatiana Fernandez, M. Hernandez","doi":"10.1109/IWOBI.2018.8464191","DOIUrl":"https://doi.org/10.1109/IWOBI.2018.8464191","url":null,"abstract":"This study analyzes the performance of four machine learning algorithms with different perspectives for defining data files, in the prediction of university student desertion. The algorithms used were: Random Forest, Neural Networks, Support Vector Machines and Logistic Regression. It was found that the Random Forest algorithm with 10 variables randomly sampled as candidates in each division, was the best for predicting dropouts and that the ideal perspective for training the algorithm is to use information on all semesters that students take within a given period of time, using a classification variable that defines the non-dropout as the graduated student. In a first validation sample, this approach correctly predicted 91% of dropouts, with a sensitivity of 87%.","PeriodicalId":127078,"journal":{"name":"2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117234193","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 : 2018-07-01DOI: 10.1109/IWOBI.2018.8464213
Jus Lozej, Blaž Meden, V. Štruc, P. Peer
Iris segmentation is an important research topic that received significant attention from the research community over the years. Traditional iris segmentation techniques have typically been focused on hand-crafted procedures that, nonetheless, achieved remarkable segmentation performance even with images captured in difficult settings. With the success of deep-learning models, researchers are increasingly looking towards convolutional neural networks (CNNs) to further improve on the accuracy of existing iris segmentation techniques and several CNN-based techniques have already been presented recently in the literature. In this paper we also consider deep-learning models for iris segmentation and present an iris segmentation approach based on the popular U-Net architecture. Our model is trainable end-to-end and, hence, avoids the need for hand designing the segmentation procedure. We evaluate the model on the CASIA dataset and report encouraging results in comparison to existing techniques used in this area.
{"title":"End-to-End Iris Segmentation Using U-Net","authors":"Jus Lozej, Blaž Meden, V. Štruc, P. Peer","doi":"10.1109/IWOBI.2018.8464213","DOIUrl":"https://doi.org/10.1109/IWOBI.2018.8464213","url":null,"abstract":"Iris segmentation is an important research topic that received significant attention from the research community over the years. Traditional iris segmentation techniques have typically been focused on hand-crafted procedures that, nonetheless, achieved remarkable segmentation performance even with images captured in difficult settings. With the success of deep-learning models, researchers are increasingly looking towards convolutional neural networks (CNNs) to further improve on the accuracy of existing iris segmentation techniques and several CNN-based techniques have already been presented recently in the literature. In this paper we also consider deep-learning models for iris segmentation and present an iris segmentation approach based on the popular U-Net architecture. Our model is trainable end-to-end and, hence, avoids the need for hand designing the segmentation procedure. We evaluate the model on the CASIA dataset and report encouraging results in comparison to existing techniques used in this area.","PeriodicalId":127078,"journal":{"name":"2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130542568","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 : 2018-07-01DOI: 10.1109/IWOBI.2018.8464211
O. Solís-Villalta, Federico Ruiz-Ugalde
One of the main tasks in robotics today, is to bring robots closer to humans in everyday situations. This requires the robot to understand how its environment (objects, people, conditions) behaves. One method that tries to connect the environment to the robot is called object model. This proposed system (object model) is able to give the robot an understanding of the physics of the environment. Object models have been used to give robots the ability to understand and control object behavior. This information helps robots to be more capable for skilled manipulation tasks, by predicting how the object will react to external stimulus. The object model used as case of study in this paper, uses an analytical representation for describing object behavior. This analytical representation has the advantage of using meaningful object properties and quickly allowing the robot to manipulate the object without doing a lot of trial and error repetitions. A challenge of this approach is that it can be very difficult to derive a mathematical/mechanical model of the object behavior. Therefore, this model, in most cases, will not describe all the peculiarities and details of object behavior. As a result, predictions are good but not perfect. This paper proposes a method to improve the prediction performance of such system, by learning the error of the analytical model and using this to correct the original prediction. Our results show that such a system is able to improve the prediction performance of the system. A quantitative evaluation using cross validation is provided to demonstrate the ability of our system to reduce the error exhibited by the prediction system (object model).
{"title":"Learning the Prediction Error for Improving an Analytical-Based Prediction (Object-Model) System for Manipulation Tasks","authors":"O. Solís-Villalta, Federico Ruiz-Ugalde","doi":"10.1109/IWOBI.2018.8464211","DOIUrl":"https://doi.org/10.1109/IWOBI.2018.8464211","url":null,"abstract":"One of the main tasks in robotics today, is to bring robots closer to humans in everyday situations. This requires the robot to understand how its environment (objects, people, conditions) behaves. One method that tries to connect the environment to the robot is called object model. This proposed system (object model) is able to give the robot an understanding of the physics of the environment. Object models have been used to give robots the ability to understand and control object behavior. This information helps robots to be more capable for skilled manipulation tasks, by predicting how the object will react to external stimulus. The object model used as case of study in this paper, uses an analytical representation for describing object behavior. This analytical representation has the advantage of using meaningful object properties and quickly allowing the robot to manipulate the object without doing a lot of trial and error repetitions. A challenge of this approach is that it can be very difficult to derive a mathematical/mechanical model of the object behavior. Therefore, this model, in most cases, will not describe all the peculiarities and details of object behavior. As a result, predictions are good but not perfect. This paper proposes a method to improve the prediction performance of such system, by learning the error of the analytical model and using this to correct the original prediction. Our results show that such a system is able to improve the prediction performance of the system. A quantitative evaluation using cross validation is provided to demonstrate the ability of our system to reduce the error exhibited by the prediction system (object model).","PeriodicalId":127078,"journal":{"name":"2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122204630","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 : 2018-07-01DOI: 10.1109/IWOBI.2018.8464190
Dennis Jiménez-Vargas, M. Acon, Ö. Sahin, Erol Eyupoglu, Y. Riazalhosseini, J. Molina-Mora, J. Guevara-Coto, R. Mora-Rodríguez
Chemotherapeutic drugs have been used as important strategies in cancer treatment. However, chemotherapy-resistant tumors arise especially in relapsing and progressive disease. Understanding of mechanisms underlaying Cisplatin-CDDP chemotherapy resistance may help find new therapeutic targets to revert this phenotype. The aim of this work, through an integrative Systems Biology approach, is to optimize an in silico model of TFs-miRNAs gene expression regulatory network of CDDP-chemoresistant cancer cell lines. By identifying modules of co-expressed genes in this regulatory network we expect further understanding of CDDP-chemoresistant phenotype. A set of deregulated genes was determined for two CDDP-chemoresistant cancer cell lines by considering gene copy number and transcriptomics. These genes were used as input targets for the construction and fitting of a large scale ordinary differential equations (ODE) model using our biocomputational platform previously reported. Model optimization was performed using COPASI and modules of correlated genes were determined using WGCNA. A model of 108 deregulated target genes, 44 transcription factors and 21 miRNAs was successfully constructed and optimized. Eleven modules of correlated genes were determined along with their gene product annotation. This report contributes to the understanding of the complex regulatory networks of CDDP-resistance and the future design of therapeutic strategies to overcome drug resistance.
{"title":"Modules of Correlated Genes in a Gene Expression Regulatory Network of CDDP-Resistant Cancer Cells","authors":"Dennis Jiménez-Vargas, M. Acon, Ö. Sahin, Erol Eyupoglu, Y. Riazalhosseini, J. Molina-Mora, J. Guevara-Coto, R. Mora-Rodríguez","doi":"10.1109/IWOBI.2018.8464190","DOIUrl":"https://doi.org/10.1109/IWOBI.2018.8464190","url":null,"abstract":"Chemotherapeutic drugs have been used as important strategies in cancer treatment. However, chemotherapy-resistant tumors arise especially in relapsing and progressive disease. Understanding of mechanisms underlaying Cisplatin-CDDP chemotherapy resistance may help find new therapeutic targets to revert this phenotype. The aim of this work, through an integrative Systems Biology approach, is to optimize an in silico model of TFs-miRNAs gene expression regulatory network of CDDP-chemoresistant cancer cell lines. By identifying modules of co-expressed genes in this regulatory network we expect further understanding of CDDP-chemoresistant phenotype. A set of deregulated genes was determined for two CDDP-chemoresistant cancer cell lines by considering gene copy number and transcriptomics. These genes were used as input targets for the construction and fitting of a large scale ordinary differential equations (ODE) model using our biocomputational platform previously reported. Model optimization was performed using COPASI and modules of correlated genes were determined using WGCNA. A model of 108 deregulated target genes, 44 transcription factors and 21 miRNAs was successfully constructed and optimized. Eleven modules of correlated genes were determined along with their gene product annotation. This report contributes to the understanding of the complex regulatory networks of CDDP-resistance and the future design of therapeutic strategies to overcome drug resistance.","PeriodicalId":127078,"journal":{"name":"2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114122203","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 : 2018-07-01DOI: 10.1109/IWOBI.2018.8464192
Israel Chaves-Arbaiza, Daniel García-Vaglio, Federico Ruiz-Ugalde
A robot that will execute everyday object manipulation tasks needs a competent body that can handle as many different objects as possible in as many ways as possible. To accomplish this, we must design the robot body in such a way that will allow it to achieve many different ways to manipulate the objects. In this work, we present a design method to mount a two-arm assembly in a torso and a mobile base (to complete a humanoid robot) taking into account a voxelized structure of points to reach and a variable orientation and relative position of both arms. We make use of the concept of capability maps to calculate a score for different possible two-arm assembly placements. An object to manipulate is selected. In particular, we are using the object size as the most important manipulation feature. We test many possible arm mounting configurations with many different object positions and we run an object reaching simulation for each of these combinations. The successfulness of each of these trails is used to derive a capability map. A score for each map (that corresponds to a different arm mounting configuration) is derived and used to select the final placement of the robot. With this work, we present an effective process to determine the best arms bases placement accord to the collaborative reaching results for a two-arm humanoid robot.
{"title":"Smart Placement of a Two-Arm Assembly for An Everyday Object Manipulation Humanoid Robot Based on Capability Maps","authors":"Israel Chaves-Arbaiza, Daniel García-Vaglio, Federico Ruiz-Ugalde","doi":"10.1109/IWOBI.2018.8464192","DOIUrl":"https://doi.org/10.1109/IWOBI.2018.8464192","url":null,"abstract":"A robot that will execute everyday object manipulation tasks needs a competent body that can handle as many different objects as possible in as many ways as possible. To accomplish this, we must design the robot body in such a way that will allow it to achieve many different ways to manipulate the objects. In this work, we present a design method to mount a two-arm assembly in a torso and a mobile base (to complete a humanoid robot) taking into account a voxelized structure of points to reach and a variable orientation and relative position of both arms. We make use of the concept of capability maps to calculate a score for different possible two-arm assembly placements. An object to manipulate is selected. In particular, we are using the object size as the most important manipulation feature. We test many possible arm mounting configurations with many different object positions and we run an object reaching simulation for each of these combinations. The successfulness of each of these trails is used to derive a capability map. A score for each map (that corresponds to a different arm mounting configuration) is derived and used to select the final placement of the robot. With this work, we present an effective process to determine the best arms bases placement accord to the collaborative reaching results for a two-arm humanoid robot.","PeriodicalId":127078,"journal":{"name":"2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114120315","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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