Modelado de un sistema celular artificial para generación de formas y procesado de información

[Resumen] En el ámbito de la informática se han modelado distintos procesos naturales para adaptar sus fundamentos en la resolución de problemas. En los últimos años algunos investigadores han centrado su atención en el comportamiento de las células no nerviosas. El motivo de este interés se debe a las características que presentan dichas células en cuanto a autoorganización y procesado de señales. Las células naturales de un organismo son capaces de autoorganizarse usando unas pocas señales y la información contenida en el ADN de las mismas. Además, si se piensa en una célula, esta recibe múltiples señales de distintas fuentes y referidas a varios problemas, y, la célula, es capaz de dar una respuesta coordinada a todos, procesando la información en paralelo con otras células. Adaptar este comportamiento en un modelo artificial supondría una nueva herramienta que facilitaría afrontar problemas como los multiobjetivo. El objetivo de esta tesis es realizar un nuevo paso para la consecución de ese objetivo. Así se busca estudiar e identificar los mecanismos más útiles del modelo biológico y crear un modelo artificial que los incluya. Para comprobar el comportamiento de ese nuevo modelo, se plantea realizar algunas pruebas clásicas que se basan en la generación y autoorganización de distintas formas geométricas. Además, también se hace una primera incursión en el estudio de la aplicación de este tipo de modelos a la resolución de problemas de clasificación de entradas, que no se había hecho anteriormente con ningún otro modelo dentro de la Embriogénesis Artificial.[Abstract] Fundamentals of different natural processes in the field of Computer Science have been modelled in order to apply them in problem-solving situations. In recent years, the behaviour of non-nervous cells has been the focus of attention of some researchers. The main reason of this attention consists in the features shown by these cells in terms of self-organisation and signal processing capacities. Natural cells of an organism are able to self-organise themselves by using a few signals and the information contained in their DNA. Moreover, cells receive many signals from different sources which are associated with several problems and they are able to process all those signals and coordinate their response at the same time as their neighbours, processing the signals and giving a coordinate response. The Artificial Models, which can adapt that behaviour, are the new tools facing challenges such as multi-objective problems. This thesis is aimed at making another step towards this objective. Thus, the main focus of this work is to study and identify the most relevant mechanisms of the biological model and develop an artificial model by adapting these mechanisms. In order to check the behavior of the development model, some standard assays based in the generation and self-organization of different geometrical forms were performed. Furthermore, the model presented herein is the first one of this kind of models applied to a new area such as the resolution of pattern classification problems, where no other Artificial Embryogeny model was applied before

DoME: A Deterministic Technique for Equation Development and Symbolic Regression

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Based on a solid mathematical background, this paper proposes a method for Symbolic Regression that enables the extraction of mathematical expressions from a dataset. Contrary to other approaches, such as Genetic Programming, the proposed method is deterministic and, consequently, does not require the creation of a population of initial solutions. Instead, a simple expression is grown until it fits the data. This method has been compared with four well-known Symbolic Regression techniques with a large number of datasets. As a result, on average, the proposed method returns better performance than the other techniques, with the advantage of returning mathematical expressions that can be easily used by different systems. Additionally, this method makes it possible to establish a threshold at the complexity of the expressions generated, i.e., the system can return mathematical expressions that are easily analyzed by the user, as opposed to other techniques that return very large expressions.This study is partially supported by Instituto de Salud Carlos III, grant number PI17/01826 (Collaborative Project in Genomic Data Integration (CICLOGEN) funded by the Instituto de Salud Carlos III from the Spanish National Plan for Scientific and Technical Research and Innovation 2013–2016 and the European Regional Development Funds (FEDER)—“A way to build Europe”. It was also partially supported by different grants and projects from the Xunta de Galicia [ED431D 2017/23; ED431D 2017/16; ED431G/01; ED431C 2018/49; IN845D-2020/03]. The authors thank the CyTED, Spain and each National Organism for Science and Technology for funding the IBEROBDIA project (P918PTE0409). In this regard, Spain specifically thanks the Ministry of Economy and Competitiveness for the financial support for this project through the State Program of I+D+I Oriented to the Challenges of Society 2017–2020 (International Joint Programming 2018), project (PCI2018-093284). Funding for open access charge: Universidade da Coruña/CISUGXunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2018/49Xunta de Galicia; IN845D-2020/0

Detection of Chocolate Properties Using Near-Infrared Spectrophotometry †

Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021.[Abstract] Knowing the chemical composition of a substance provides valuable information about it. That is why numerous techniques have been developed to try to obtain it. One of them is the Near Infrared Spectrometry technique, a non-destructive technique that analyzes the electromagnetic spectrum in search of waves of a certain length. The aim of this project is to combine this technology with machine learning techniques to try to detect the presence of milk, as well as the level of cocoa present in an ounce of chocolate. This has given satisfactory results in both cases, so it is considered that the combination of these techniques offers great possibilities.The authors would like to thank the support from RNASA-IMEDIR group

Convolutional Neural Networks for Sleep Stage Scoring on a Two-Channel EEG Signal

This is a pre-print of an article published in Soft Computing. The final authenticated version is available online at: https://doi.org/10.1007/s00500-019-04174-1[Abstract] Sleeping problems have become one of the major diseases all over the world. To tackle this issue, the basic tool used by specialists is the Polysomnogram, which is a collection of different signals recorded during sleep. After its recording, the specialists have to score the different signals according to one of the standard guidelines. This process is carried out manually, which can be highly time consuming and very prone to annotation errors. Therefore, over the years, many approaches have been explored in an attempt to support the specialists in this task. In this paper, an approach based on convolutional neural networks is presented, where an in-depth comparison is performed in order to determine the convenience of using more than one signal simultaneously as input. Additionally, the models were also used as parts of an ensemble model to check whether any useful information can be extracted from signal processing a single signal at a time which the dual-signal model cannot identify. Tests have been performed by using a well-known dataset called expanded sleep-EDF, which is the most commonly used dataset as benchmark for this problem. The tests were carried out with a leave-one-out cross-validation over the patients, which ensures that there is no possible contamination between training and testing. The resulting proposal is a network smaller than previously published ones, but which overcomes the results of any previous models on the same dataset. The best result shows an accuracy of 92.67% and a Cohen’s Kappa value over 0.84 compared to human experts.Instituto de Salud Carlos III; PI17/01826Xunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431G/0

Estimation of the Alcoholic Degree in Beers through Near Infrared Spectrometry Using Machine Learning

[Abstract] It is a fact that, non-destructive measurement technologies have gain a lot of attention over the years. Among those technologies, NIR technology is the one which allows the analysis of electromagnetic spectrum looking for carbon-link interactions. This technology analyzes the electromagnetic spectrum in the band between 700 nm and 2500 nm, a band very close to the visible spectrum. Traditionally, the devices used to measure are utterly expensive and enormously bulky. That is why this project was focused on a portable spectrophotometer to make measures. This device is smaller and cheaper than the common spectrophotometer, although at the cost of a lower resolution. In this work, that device in combination with the use of machine learning was used to detect if a beer contains alcohol or it can be labeled as non-alcoholic drink.Xunta de Galicia; ED431G/0

Identification and Functional Analysis of Healing Regulators in Drosophila

© 2015 Álvarez-Fernández et al. Wound healing is an essential homeostatic mechanism that maintains the epithelial barrier integrity after tissue damage. Although we know the overall steps in wound healing, many of the underlying molecular mechanisms remain unclear. Genetically amenable systems, such as wound healing in Drosophila imaginal discs, do not model all aspects of the repair process. However, they do allow the less understood aspects of the healing response to be explored, e.g., which signal(s) are responsible for initiating tissue remodeling? How is sealing of the epithelia achieved? Or, what inhibitory cues cancel the healing machinery upon completion? Answering these and other questions first requires the identification and functional analysis of wound specific genes. A variety of different microarray analyses of murine and humans have identified characteristic profiles of gene expression at the wound site, however, very few functional studies in healing regulation have been carried out. We developed an experimentally controlled method that is healing-permissive and that allows live imaging and biochemical analysis of cultured imaginal discs. We performed comparative genome-wide profiling between Drosophila imaginal cells actively involved in healing versus their non-engaged siblings. Sets of potential wound-specific genes were subsequently identified. Importantly, besides identifying and categorizing new genes, we functionally tested many of their gene products by genetic interference and overexpression in healing assays. This non-saturated analysis defines a relevant set of genes whose changes in expression level are functionally significant for proper tissue repair. Amongst these we identified the TCP1 chaperonin complex as a key regulator of the actin cytoskeleton essential for the wound healing response. There is promise that our newly identified wound-healing genes will guide future work in the more complex mammalian wound healing response.CAF and FP were supported by the EU FP6 STREP project WOUND and ST held a Spanish FPU PhD studentship. Research in the EMB laboratory is funded by grants of the EU (FP6 STREP project WOUND), the Spanish Ministry of Economy and Competitivity (DGI and CONSOLIDER grants) and the Generalitat de Catalunya (SGR)Peer Reviewe

Population Subset Selection for the Use of a Validation Dataset for Overfitting Control in Genetic Programming

[Abstract] Genetic Programming (GP) is a technique which is able to solve different problems through the evolution of mathematical expressions. However, in order to be applied, its tendency to overfit the data is one of its main issues. The use of a validation dataset is a common alternative to prevent overfitting in many Machine Learning (ML) techniques, including GP. But, there is one key point which differentiates GP and other ML techniques: instead of training a single model, GP evolves a population of models. Therefore, the use of the validation dataset has several possibilities because any of those evolved models could be evaluated. This work explores the possibility of using the validation dataset not only on the training-best individual but also in a subset with the training-best individuals of the population. The study has been conducted with 5 well-known databases performing regression or classification tasks. In most of the cases, the results of the study point out to an improvement when the validation dataset is used on a subset of the population instead of only on the training-best individual, which also induces a reduction on the number of nodes and, consequently, a lower complexity on the expressions.Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431C 2018/49Xunta de Galicia; ED431D 2017/23Instituto de Salud Carlos III; PI17/0182

Application of Artificial Neural Networks for the Monitoring of Episodes of High Toxicity by DSP in Mussel Production Areas in Galicia

[Abstract] This study seeks to support, through the use of Artificial Neural Networks (ANN), the decision to perform closings after days without sampling in the Vigo estuary. The opening and closing of the mussel production areas are based on the toxicity analysis of this bivalve’s meat. Sometimes it is not possible to obtain the necessary data for effective closing. If there is evidence of an increase in toxicity levels, “Precautionary Closings” on mussel extraction is done. A small error in the forecast of the state of the areas could mean serious losses for the mussel industry and a huge risk for public health. Unlike in previous studies, this study aims to manage the state of the mussel production areas, whilst the others focused on predicting the harmful algae blooms. Having achieved test sensitivity values of 67.40% and test accuracy of 83.00%, these results may lead to new research that involves obtaining more accurate models that can be integrated into a support system.Xunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2018/4

Classification of Signals by Means of Genetic Programming

[Abstract] This paper describes a new technique for signal classification by means of Genetic Programming (GP). The novelty of this technique is that no prior knowledge of the signals is needed to extract the features. Instead of it, GP is able to extract the most relevant features needed for classification. This technique has been applied for the solution of a well-known problem: the classification of EEG signals in epileptic and healthy patients. In this problem, signals obtained from EEG recordings must be correctly classified into their corresponding class. The aim is to show that the technique described here, with the automatic extraction of features, can return better results than the classical techniques based on manual extraction of features. For this purpose, a final comparison between the results obtained with this technique and other results found in the literature with the same database can be found. This comparison shows how this technique can improve the ones found.Instituto de Salud Carlos III; RD07/0067/0005Xunta de Galicia; 10SIN105004P

System for Automatic Assessment of Alzheimer’s Disease Diagnosis Based on Deep Learning Techniques

[Abstract] Automatic detection of Alzheimer’s disease is a very active area of research. This is due to its usefulness in starting the protocol to stop the inevitable progression of this neurodegenerative disease. This paper proposes a system for the detection of the disease by means of Deep Learning techniques in magnetic resonance imaging (MRI). As a solution, a model of neuronal networks (ANN) and two sets of reference data for training are proposed. Finally, the goodness of this system is verified within the domain of the application