Regulon-Specific Control of Transcription Elongation across the Yeast Genome

Transcription elongation by RNA polymerase II was often considered an invariant non-regulated process. However, genome-wide studies have shown that transcriptional pausing during elongation is a frequent phenomenon in tightly-regulated metazoan genes. Using a combination of ChIP-on-chip and genomic run-on approaches, we found that the proportion of transcriptionally active RNA polymerase II (active versus total) present throughout the yeast genome is characteristic of some functional gene classes, like those related to ribosomes and mitochondria. This proportion also responds to regulatory stimuli mediated by protein kinase A and, in relation to cytosolic ribosomal-protein genes, it is mediated by the silencing domain of Rap1. We found that this inactive form of RNA polymerase II, which accumulates along the full length of ribosomal protein genes, is phosphorylated in the Ser5 residue of the CTD, but is hypophosphorylated in Ser2. Using the same experimental approach, we show that the in vivo–depletion of FACT, a chromatin-related elongation factor, also produces a regulon-specific effect on the expression of the yeast genome. This work demonstrates that the regulation of transcription elongation is a widespread, gene class–dependent phenomenon that also affects housekeeping genes

Approach to predictability via anticipated ynchronization

8 pages.-- PACS nrs.: 05.45.Xt, 95.10.Fh, 87.18.Sn.Predictability of chaotic systems is limited, besides the precision of the knowledge of the initial conditions, by the error of the models used to extract the nonlinear dynamics from the time series. In this paper we analyze the predictions obtained from the anticipated synchronization scheme using a chain of slave neural network approximate replicas of the master system. We compare the maximum prediction horizons obtained with those attainable using standard prediction techniques.This work was partially supported by MCyT (Spain) and FEDER (EU) Project Nos. REN2000-1572, FIS2004-5073-C04-03, FIS2004-953, TIC2002-04255-C04-01 and the ANPCyT (Argentina) Project No. PICT03-000000-00988.Peer reviewe

A Unified Framework for Reservoir Computing and Extreme Learning Machines based on a Single Time-delayed Neuron

In this paper we present a unified framework for extreme learning machines and reservoir computing (echo state networks), which can be physically implemented using a single nonlinear neuron subject to delayed feedback. The reservoir is built within the delay-line, employing a number of “virtual” neurons. These virtual neurons receive random projections from the input layer containing the information to be processed. One key advantage of this approach is that it can be implemented efficiently in hardware. We show that the reservoir computing implementation, in this case optoelectronic, is also capable to realize extreme learning machines, demonstrating the unified framework for both schemes in software as well as in hardware

Reconstrucción de la dinámica no lineal de sistemas caóticos con retraso mediante redes neuronales

ABSTRACT: This thesis is motivated by the intense research in chaos-based communications in the last years. This thesis focuses on the reconstruction of the nonlinear dynamic of time-delay systems by using an embedding-like approach. This method works with a special embedding space that includes both short time and feedback time delayed values of the system variable. We use a new type of modular neural network based on the structure of time-delay systems. We also carefully investigate the time delay identification from the time series, a crucial parameter to construct the special embedding vector. Finally we use the reconstructed models to show the vulnerability of the chaos-based communication system based on time-delay systems and to study the predictability of these systems. Although we mainly study electro-optical feedback systems, the techniques investigated in this thesis have a general applicability to scalar time-delay systems.RESUMEN: El objetivo de esta tesis es estudiar la confidencialidad de los sistemas de comunicaciones caóticas basados en sistemas con retraso. Para ello, la tesis se centra en la reconstrucción de la dinámica no lineal de sistemas con retraso usando un embedding especial. Este embedding especial incluye no solo los tiempos cercanos sino también los tiempos centrados alrededor del retraso del sistema. Dado que conocer el tiempo de retraso es esencial para la construcción del este embedding, en la tesis también se analiza la identificación del tiempo de retraso a partir de la serie temporal. Usando un nuevo tipo de red neuronal modular y este embedding hemos construido modelos que reconstruyen la dinámica no lineal de los sistemas escalares con retraso a partir de la serie temporal. A continuación estos modelos se usan para mostrar la vulnerabilidad de los sistemas de comunicación basados en sistemas con retraso y para estudiar la predictibilidad de estos sistemas. Aunque básicamente nos hemos centrado en el estudio de sistemas opto-electrónicos con retraso, las técnicas presentadas en esta tesis se pueden aplicar a cualquier sistema escalar con retraso

Unmasking Optical Chaotic Cryptosystems Based on Delayed Optoelectronic Feedback

29 páginas, 22 figuras, 3 tablas.The authors analyze the security of optical chaotic communication systems. The chaotic carrier is generated by a laser diode subject to delayed optoelectronic feedback. Transmitters with one and two fixed delay times are considered. A new type of neural networks, modular neural networks, is used to reconstruct the nonlinear dynamics of the transmitter from experimental time series in the single-delay case, and from numerical simulations in single and two-delay cases. The authors show that the complexity of the model does not increase when the delay time is increased, in spite of the very high dimension of the chaotic attractor. However, it is found that nonlinear dynamics reconstruction is more difficult when the feedback strength is increased. The extracted model is used as an unauthorized receiver to recover the message. Therefore, the authors conclude that optical chaotic cryptosystems based on optoelectronic feedback systems with several fixed time delays are vulnerable.This work was supported by CICYT (Spain) under Project TEC2009-14581-C02-02.Peer reviewe

Delay-based reservoir computing: tackling performance degradation due to system response time

We analyze the degradation of the computational capacity of delay-based reservoir computers due to system response time. We demonstrate that this degradation is reduced when the delay time is greater than the data injection time. Performance improvement is demonstrated on several benchmarking tasks.Peer reviewe

Reservoir computing with an ensemble of time-delay reservoirs

Reservoir computing (RC) has attracted a lot of attention in the field of machine learning because of its promising performance in a broad range of applications. However, it is difficult to implement standard RC in hardware. Reservoir computers with a single nonlinear neuron subject to delayed feedback (delay-based RC) allow efficient hardware implementation with similar performance to standard RC. We propose and study two different ways to build ensembles of delay-based RC with several delayed neurons (time-delay reservoirs): one using decoupled neurons and the other using coupled neurons through the feedback lines. In both cases, the outputs of the different neurons are linearly combined to solve some benchmark tasks. Simulation results show that these schemes achieve better performance than the single-neuron case. Moreover, the proposed architectures boost the RC processing speed with respect to the single-neuron case. Both schemes are found to be robust against small mismatches between delayed neuron parameters.This work has been funding by the Ministerio de Economía y Competitividad (MINECO/FEDER, UE), Spain under project TEC2015-65212-C3-1-P. Silvia Ortín was supported by the Conselleria d’Innovacio, Recerca i Turisme del Govern de les Illes Balears and the European Social Fund.Peer Reviewe

A Fast Machine Learning Model for ECG-Based Heartbeat Classification and Arrhythmia Detection

We present a fully automatic and fast ECG arrhythmia classifier based on a simple brain-inspired machine learning approach known as Echo State Networks. Our classifier has a low-demanding feature processing that only requires a single ECG lead. Its training and validation follows an inter-patient procedure. Our approach is compatible with an online classification that aligns well with recent advances in health-monitoring wireless devices and wearables. The use of a combination of ensembles allows us to exploit parallelism to train the classifier with remarkable speeds. The heartbeat classifier is evaluated over two ECG databases, the MIT-BIH AR and the AHA. In the MIT-BIH AR database, our classification approach provides a sensitivity of 92.7% and positive predictive value of 86.1% for the ventricular ectopic beats, using the single lead II, and a sensitivity of 95.7% and positive predictive value of 75.1% when using the lead V1'. These results are comparable with the state of the art in fully automatic ECG classifiers and even outperform other ECG classifiers that follow more complex feature-selection approaches.This work was partially funded by the Spanish Ministerio de Economía y Competitividad (MINECO) and Fondo Europeo de Desarrollo Regional (FEDER) and the European Social Fund through project TEC2016-80063-C3-3-R (MINECO/AEI/FEDER/UE). MA was supported by the Beca de colaboración 012/2016 UIB fellowship on Information processing in neural and photonic systems. MS was supported by the Spanish Ministerio de Economía, Industria y Competitividad through a Ramón y Cajal Fellowship (RYC-2015-18140). SO was supported by the Conselleria d'Innovació, Recerca i Turisme del Govern de les Illes Balears and the European Social Fund.Peer reviewe

Improving prediction of childhood obesity using microbiome data

Trabajo presentado en el IFISC Poster Party (online).-- The IFISC Poster Party is an annual activity where PhD students and postdoctoral researchers of IFISC present their research in a poster format.-- Biocomplexity.Peer reviewe

Automated real-time method for ventricular heartbeat classification

[Background and objective] In this work, we develop a fully automatic and real-time ventricular heartbeat classifier based on a single ECG lead. Single ECG lead classifiers can be especially useful for wearable technologies that provide continuous and long-term monitoring of the electrocardiogram. These wearables usually have a few non-standard leads and the quality of the signals depends on the user physical activity.[Methods] The proposed method uses an Echo State Network (ESN) to classify ECG signals following the Association for the Advancement of Medical Instrumentation (AAMI) recommendations with an inter-patient scheme. To achieve real-time classification, the classifier itself and the feature extraction approach are fast and computationally efficient. In addition, our approach allows transferring the knowledge from one database to another without additional training.[Results] The classification performance of the proposed model is validated on the MIT-BIH arrhythmia and INCART databases. The sensitivity and precision of the proposed method for MIT-BIH arrhythmia database are 95.3 and 88.8 for the modified lead II and 90.9 and 89.2 for the V1 lead. The results reported are further compared to the existing methodologies in literature. Our methodology is a competitive single lead ventricular heartbeat classifier, that is comparable to state-of-the-art algorithms using multiple leads.[Conclusions] The proposed fully automated, single-lead and real-time heartbeat classifier of ventricular heartbeats reports an improved classification accuracy in different leads of the evaluated databases in comparison with other single lead heartbeat classifiers. These results open the possibility of applying our methodology to wearable long-term monitoring devices with an unconventional placement of the electrodes.This work is partially supported by the Spanish Ministerio de Economa y Competitividad (MINECO) and Fondo Europeo de Desarrollo Regional (FEDER) through project TEC2016-80063-C3-3-R. Silvia Ortín was supported by the Conselleria d’Innovació, Recerca i Turisme del Govern de les Illes Balears and the European Social Fund. Miguel Cornelles Soriano was supported by the Spanish Ministerio de Economa, Industria y Competitividad through a Ramon y Cajal Fellowship (RYC-2015-18140).Peer reviewe