Disseny, control i implementació d'un inversor trifàsic per un vehicle elèctric

Premi al millor Projecte de Fi de Carrera presentat durant l'any 2014 en l'àmbit d'Automoció que atorga la CÀTEDRA SEAT-UPCEl projecte consisteix en una breu descripció dels diferents processos que es duen a terme desde la concepció d'un convertidor, orientat al control d'un motor síncron d'imants permanents, fins a la seva aplicació definitiva. Concretament, el convertidor desenvolupat té com a objectiu final la seva integració en un vehicle elèctric de competició. Es fa una petita introducció a la Formula Student, competició en la que el vehicle elèctric CAT06e va competir la temporada 2012-2013 i el CAT07e competirà la temporada d'enguany. Ambdós traccionats per dos convertidors com el desenvolupat en aquest projecte. Es tracten diversos aspectes teòrics fonamentals pel correcte desenvolupament del convertidor. Al tractar-se d'un inversor trifàsic que alimenta un motor síncron d'imants permanents, es descriuen les equacions usades en el disseny i es presenten les transformacions matricials necessàries en el control. Una vegada establertes les equacions, s'exposa el procediment seguit per mesurar determinats paràmetres que posteriorment s'usaran en el control. Es presenten els diversos càlculs duts a terme per el dimensionament de les principals parts del convertidor, com el bus de contínua o els semiconductors. També es fa referència al disseny mecànic del conjunt així com la refrigeració de l'electrònica de potència. Aquests dissenys es duen a terme en base les condicions de contorn imposades per l'aplicació final, és a dir, del vehicle de competició. Seguidament es descriuen les parts del codi implementat en el convertidor més importants, com el càlcul de posició i velocitat o el control de parell. Finalment es presenten els resultats experimentals extrets de les proves en la bancada.Award-winnin

HiMMe, A Next-Generation Sequencing Quality Assessment and Correction Tool Based on Hidden Markov Models

Both deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) play a crucial role in the existence and proper development of all living organisms. In addition, it is through these molecules that the genetic information is passed from parent to offspring. It is no surprise that, over the last decades, a lot of efforts have been put into developing technology that help us better understand their underlying mechanisms. Similarly to how computers work using only ones and zeros, DNA and RNA only need four different characters to encrypt all the genetic information. Thanks to the sequencing technology development over the past decades, it is possible nowadays to sequence these molecules in a relatively fast and inexpensive way. However, as in any measurement, there is noise involved and this needs to be addressed if one is to reach conclusions based on these kind of data. The hidden Markov model (HMM) is a perfect fit for this case. Through a Markov chain, the model can capture genetic patterns, while, by introducing the emission probabilities, the noise involved in the process can be taken into account. In addition, previous knowledge can be used by training the model to fit, for instance, a given organism or sequencing technology. In this thesis, the HMM theory is applied for two purposes, (1) to assess the reliability of sequencing data, and (2) to correct potential errors in the sequences observed. The results show that the HMM model is capable of identifying genetic patterns in the sequence and to repair potential errors, thus improving the reliability of the data before any downstream analysis is performed. For these purposes, HiMMe has been developed and is publicly available on https://github.com/jordiabante/HiMMe

STATISTICAL SIGNAL PROCESSING METHODS FOR EPIGENETIC LANDSCAPE ANALYSIS

Since the DNA structure was discovered in 1953, a great deal of effort has been put into studying this molecule in detail. We now know DNA comprises an organism’s genetic makeup and constitutes a blueprint for life. The study of DNA has dramatically increased our knowledge about cell function and evolution and has led to remarkable discoveries in biology and medicine. Just as DNA is replicated during cell division, several chemical marks are also passed onto progeny during this process. Epigenetics studies these marks and represents a fascinating research area given their crucial role. Among all known epigenetic marks, 5mc DNA methylation is probably one of the most important ones given its well-established association with various biological processes, such as development and aging, and disease, such as cancer. The work in this dissertation focuses primarily on this epigenetic mark, although it has the potential to be applied to other heritable marks. In the 1940s, Waddington introduced the term epigenetic landscape to conceptually describe cell pluripotency and differentiation. This concept lived in the abstract plane until Jenkinson et al. 2017 & 2018 estimated actual epigenetic landscapes from WGBS data, and the work led to startling results with biological implications in development and disease. Here, we introduce an array of novel computational methods that draw from that work. First, we present CPEL, a method that uses a variant of the original landscape proposed by Jenkinson et al., which, together with a new hypothesis testing framework, allows for the detection of DNA methylation imbalances between homologous chromosomes. Then, we present CpelTdm, a method that builds upon CPEL to perform differential methylation analysis between groups of samples using targeted bisulfite sequencing data. Finally, we extend the original probabilistic model proposed by Jenkinson et al. to estimate methylation landscapes and perform differential analysis from nanopore data. Overall, this work addresses immediate needs in the study of DNA methylation. The methods presented here can lead to a better characterization of this critical epigenetic mark and enable biological discoveries with implications for diagnosing and treating complex human diseases

Dataset from "Employing hidden Markov models to assess the genetic content of genome assemblies"

<p>Dataset used to reach the conclusions in "Employing hidden Markov models to assess the genetic content of genome assemblies".</p

Disseny, control i implementació d'un inversor trifàsic per un vehicle elèctric

Premi al millor Projecte de Fi de Carrera presentat durant l'any 2014 en l'àmbit d'Automoció que atorga la CÀTEDRA SEAT-UPCEl projecte consisteix en una breu descripció dels diferents processos que es duen a terme desde la concepció d'un convertidor, orientat al control d'un motor síncron d'imants permanents, fins a la seva aplicació definitiva. Concretament, el convertidor desenvolupat té com a objectiu final la seva integració en un vehicle elèctric de competició. Es fa una petita introducció a la Formula Student, competició en la que el vehicle elèctric CAT06e va competir la temporada 2012-2013 i el CAT07e competirà la temporada d'enguany. Ambdós traccionats per dos convertidors com el desenvolupat en aquest projecte. Es tracten diversos aspectes teòrics fonamentals pel correcte desenvolupament del convertidor. Al tractar-se d'un inversor trifàsic que alimenta un motor síncron d'imants permanents, es descriuen les equacions usades en el disseny i es presenten les transformacions matricials necessàries en el control. Una vegada establertes les equacions, s'exposa el procediment seguit per mesurar determinats paràmetres que posteriorment s'usaran en el control. Es presenten els diversos càlculs duts a terme per el dimensionament de les principals parts del convertidor, com el bus de contínua o els semiconductors. També es fa referència al disseny mecànic del conjunt així com la refrigeració de l'electrònica de potència. Aquests dissenys es duen a terme en base les condicions de contorn imposades per l'aplicació final, és a dir, del vehicle de competició. Seguidament es descriuen les parts del codi implementat en el convertidor més importants, com el càlcul de posició i velocitat o el control de parell. Finalment es presenten els resultats experimentals extrets de les proves en la bancada.Award-winnin

Disseny, control i implementació d'un inversor trifàsic per un vehicle elèctric

Premi al millor Projecte de Fi de Carrera presentat durant l'any 2014 en l'àmbit d'Automoció que atorga la CÀTEDRA SEAT-UPCEl projecte consisteix en una breu descripció dels diferents processos que es duen a terme desde la concepció d'un convertidor, orientat al control d'un motor síncron d'imants permanents, fins a la seva aplicació definitiva. Concretament, el convertidor desenvolupat té com a objectiu final la seva integració en un vehicle elèctric de competició. Es fa una petita introducció a la Formula Student, competició en la que el vehicle elèctric CAT06e va competir la temporada 2012-2013 i el CAT07e competirà la temporada d'enguany. Ambdós traccionats per dos convertidors com el desenvolupat en aquest projecte. Es tracten diversos aspectes teòrics fonamentals pel correcte desenvolupament del convertidor. Al tractar-se d'un inversor trifàsic que alimenta un motor síncron d'imants permanents, es descriuen les equacions usades en el disseny i es presenten les transformacions matricials necessàries en el control. Una vegada establertes les equacions, s'exposa el procediment seguit per mesurar determinats paràmetres que posteriorment s'usaran en el control. Es presenten els diversos càlculs duts a terme per el dimensionament de les principals parts del convertidor, com el bus de contínua o els semiconductors. També es fa referència al disseny mecànic del conjunt així com la refrigeració de l'electrònica de potència. Aquests dissenys es duen a terme en base les condicions de contorn imposades per l'aplicació final, és a dir, del vehicle de competició. Seguidament es descriuen les parts del codi implementat en el convertidor més importants, com el càlcul de posició i velocitat o el control de parell. Finalment es presenten els resultats experimentals extrets de les proves en la bancada.Award-winnin

DIVE: a reference-free statistical approach to diversity-generating and mobile genetic element discovery

Abstract Diversity-generating and mobile genetic elements are key to microbial and viral evolution and can result in evolutionary leaps. State-of-the-art algorithms to detect these elements have limitations. Here, we introduce DIVE, a new reference-free approach to overcome these limitations using information contained in sequencing reads alone. We show that DIVE has improved detection power compared to existing reference-based methods using simulations and real data. We use DIVE to rediscover and characterize the activity of known and novel elements and generate new biological hypotheses about the mobilome. Building on DIVE, we develop a reference-free framework capable of de novo discovery of mobile genetic elements

An information-theoretic approach to the modeling and analysis of whole-genome bisulfite sequencing data

Abstract Background DNA methylation is a stable form of epigenetic memory used by cells to control gene expression. Whole genome bisulfite sequencing (WGBS) has emerged as a gold-standard experimental technique for studying DNA methylation by producing high resolution genome-wide methylation profiles. Statistical modeling and analysis is employed to computationally extract and quantify information from these profiles in an effort to identify regions of the genome that demonstrate crucial or aberrant epigenetic behavior. However, the performance of most currently available methods for methylation analysis is hampered by their inability to directly account for statistical dependencies between neighboring methylation sites, thus ignoring significant information available in WGBS reads. Results We present a powerful information-theoretic approach for genome-wide modeling and analysis of WGBS data based on the 1D Ising model of statistical physics. This approach takes into account correlations in methylation by utilizing a joint probability model that encapsulates all information available in WGBS methylation reads and produces accurate results even when applied on single WGBS samples with low coverage. Using the Shannon entropy, our approach provides a rigorous quantification of methylation stochasticity in individual WGBS samples genome-wide. Furthermore, it utilizes the Jensen-Shannon distance to evaluate differences in methylation distributions between a test and a reference sample. Differential performance assessment using simulated and real human lung normal/cancer data demonstrate a clear superiority of our approach over DSS, a recently proposed method for WGBS data analysis. Critically, these results demonstrate that marginal methods become statistically invalid when correlations are present in the data. Conclusions This contribution demonstrates clear benefits and the necessity of modeling joint probability distributions of methylation using the 1D Ising model of statistical physics and of quantifying methylation stochasticity using concepts from information theory. By employing this methodology, substantial improvement of DNA methylation analysis can be achieved by effectively taking into account the massive amount of statistical information available in WGBS data, which is largely ignored by existing methods

HiMMe: using genetic patterns as a proxy for genome assembly reliability assessment

Abstract Background The information content of genomes plays a crucial role in the existence and proper development of living organisms. Thus, tremendous effort has been dedicated to developing DNA sequencing technologies that provide a better understanding of the underlying mechanisms of cellular processes. Advances in the development of sequencing technology have made it possible to sequence genomes in a relatively fast and inexpensive way. However, as with any measurement technology, there is noise involved and this needs to be addressed to reach conclusions based on the resulting data. In addition, there are multiple intermediate steps and degrees of freedom when constructing genome assemblies that lead to ambiguous and inconsistent results among assemblers. Methods Here we introduce HiMMe, an HMM-based tool that relies on genetic patterns to score genome assemblies. Through a Markov chain, the model is able to detect characteristic genetic patterns, while, by introducing emission probabilities, the noise involved in the process is taken into account. Prior knowledge can be used by training the model to fit a given organism or sequencing technology. Results Our results show that the method presented is able to recognize patterns even with relatively small k-mer size choices and limited computational resources. Conclusions Our methodology provides an individual quality metric per contig in addition to an overall genome assembly score, with a time complexity well below that of an aligner. Ultimately, HiMMe provides meaningful statistical insights that can be leveraged by researchers to better select contigs and genome assemblies for downstream analysis

Ranking genomic features using an information-theoretic measure of epigenetic discordance

Abstract Background Establishment and maintenance of DNA methylation throughout the genome is an important epigenetic mechanism that regulates gene expression whose disruption has been implicated in human diseases like cancer. It is therefore crucial to know which genes, or other genomic features of interest, exhibit significant discordance in DNA methylation between two phenotypes. We have previously proposed an approach for ranking genes based on methylation discordance within their promoter regions, determined by centering a window of fixed size at their transcription start sites. However, we cannot use this method to identify statistically significant genomic features and handle features of variable length and with missing data. Results We present a new approach for computing the statistical significance of methylation discordance within genomic features of interest in single and multiple test/reference studies. We base the proposed method on a well-articulated hypothesis testing problem that produces p- and q-values for each genomic feature, which we then use to identify and rank features based on the statistical significance of their epigenetic dysregulation. We employ the information-theoretic concept of mutual information to derive a novel test statistic, which we can evaluate by computing Jensen-Shannon distances between the probability distributions of methylation in a test and a reference sample. We design the proposed methodology to simultaneously handle biological, statistical, and technical variability in the data, as well as variable feature lengths and missing data, thus enabling its wide-spread use on any list of genomic features. This is accomplished by estimating, from reference data, the null distribution of the test statistic as a function of feature length using generalized additive regression models. Differential assessment, using normal/cancer data from healthy fetal tissue and pediatric high-grade glioma patients, illustrates the potential of our approach to greatly facilitate the exploratory phases of clinically and biologically relevant methylation studies. Conclusions The proposed approach provides the first computational tool for statistically testing and ranking genomic features of interest based on observed DNA methylation discordance in comparative studies that accounts, in a rigorous manner, for biological, statistical, and technical variability in methylation data, as well as for variability in feature length and for missing data