A black-box model for neurons

We explore the identification of neuronal voltage traces by artificial neural networks based on wavelets (Wavenet). More precisely, we apply a modification in the representation of dynamical systems by Wavenet which decreases the number of used functions; this approach combines localized and global scope functions (unlike Wavenet, which uses localized functions only). As a proof-of-concept, we focus on the identification of voltage traces obtained by simulation of a paradigmatic neuron model, the Morris-Lecar model. We show that, after training our artificial network with biologically plausible input currents, the network is able to identify the neuron's behaviour with high accuracy, thus obtaining a black box that can be then used for predictive goals. Interestingly, the interval of input currents used for training, ranging from stimuli for which the neuron is quiescent to stimuli that elicit spikes, shows the ability of our network to identify abrupt changes in the bifurcation diagram, from almost linear input-output relationships to highly nonlinear ones. These findings open new avenues to investigate the identification of other neuron models and to provide heuristic models for real neurons by stimulating them in closed-loop experiments, that is, using the dynamic-clamp, a well-known electrophysiology technique.Peer ReviewedPostprint (author's final draft

Real-time crowd density mapping using a novel sensory fusion model of infrared and visual systems

Crowd dynamic management research has seen significant attention in recent years in research and industry in an attempt to improve safety level and management of large scale events and in large public places such as stadiums, theatres, railway stations, subways and other places where high flow of people at high densities is expected. Failure to detect the crowd behaviour at the right time could lead to unnecessary injuries and fatalities. Over the past decades there have been many incidents of crowd which caused major injuries and fatalities and lead to physical damages. Examples of crowd disasters occurred in past decades include the tragedy of Hillsborough football stadium at Sheffield where at least 93 football supporters have been killed and 400 injured in 1989 in Britain's worst-ever sporting disaster (BBC, 1989). Recently in Cambodia a pedestrians stampede during the Water Festival celebration resulted in 345 deaths and 400 injuries (BBC, 2010) and in 2011 at least 16 people were killed and 50 others were injured in a stampede in the northern Indian town of Haridwar (BBC, 2011). Such disasters could be avoided or losses reduced by using different technologies. Crowd simulation models have been found effective in the prediction of potential crowd hazards in critical situations and thus help in reducing fatalities. However, there is a need to combine the advancement in simulation with real time crowd characterisation such as the estimation of real time density in order to provide accurate prognosis in crowd behaviour and enhance crowd management and safety, particularly in mega event such as the Hajj. This paper addresses the use of novel sensory technology in order to estimate people’s dynamic density du ring one of the Hajj activities. The ultimate goal is that real time accurate estimation of density in different areas within the crowd could help to improve the decision making process and provide more accurate prediction of the crowd dynamics. This paper investigates the use of infrared and visual cameras supported by auxiliary sensors and artificial intelligence to evaluate the accuracy in estimating crowd density in an open space during Muslims Pilgrimage to Makkah (Mecca)

In silico phenotyping via co-training for improved phenotype prediction from genotype

Motivation: Predicting disease phenotypes from genotypes is a key challenge in medical applications in the postgenomic era. Large training datasets of patients that have been both genotyped and phenotyped are the key requisite when aiming for high prediction accuracy. With current genotyping projects producing genetic data for hundreds of thousands of patients, large-scale phenotyping has become the bottleneck in disease phenotype prediction. Results: Here we present an approach for imputing missing disease phenotypes given the genotype of a patient. Our approach is based on co-training, which predicts the phenotype of unlabeled patients based on a second class of information, e.g. clinical health record information. Augmenting training datasets by this type of in silico phenotyping can lead to significant improvements in prediction accuracy. We demonstrate this on a dataset of patients with two diagnostic types of migraine, termed migraine with aura and migraine without aura, from the International Headache Genetics Consortium. Conclusions: Imputing missing disease phenotypes for patients via co-training leads to larger training datasets and improved prediction accuracy in phenotype prediction. Availability and implementation: The code can be obtained at: http://www.bsse.ethz.ch/mlcb/research/bioinformatics-and-computational-biology/co-training.html Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Balanced model order reduction method for systems depending on a parameter

We provide an analytical framework for balanced realization model order reduction of linear control systems which depend on an unknown parameter. Besides recovering known results for the first order corrections, we obtain explicit novel expressions for the form of second order corrections for singular values and singular vectors. The final result of our procedure is an order reduced model which incorporates the uncertain parameter. We apply our algorithm to a system of masses and springs with parameter dependent coefficients.Postprint (author's final draft

Identifying transcription factors and microRNAs as key regulators of pathways using Bayesian inference on known pathway structures

Background: Transcription factors and microRNAs act in concert to regulate gene expression in eukaryotes. Numerous computational methods based on sequence information are available for the prediction of target genes of transcription factors and microRNAs. Although these methods provide a static snapshot of how genes may be regulated, they are not effective for the identification of condition-specific regulators. Results: We propose a new method that combines: a) transcription factors and microRNAs that are predicted to target genes in pathways, with b) microarray expression profiles of microRNAs and mRNAs, in conjunction with c) the known structure of molecular pathways. These elements are integrated into a Bayesian network derived from each pathway that, through probability inference, allows for the prediction of the key regulators in the pathway. We demonstrate 1) the steps to discretize the expression data for the computation of conditional probabilities in a Bayesian network, 2) the procedure to construct a Bayesian network using the structure of a known pathway and the transcription factors and microRNAs predicted to target genes in that pathway, and 3) the inference results as potential regulators of three signaling pathways using microarray expression profiles of microRNA and mRNA in estrogen receptor positive and estrogen receptor negative tumors. Conclusions: We displayed the ability of our framework to integrate multiple sets of microRNA and mRNA expression data, from two phenotypes, with curated molecular pathway structures by creating Bayesian networks. Moreover, by performing inference on the network using known evidence, e.g., status of differentially expressed genes, or by entering hypotheses to be tested, we obtain a list of potential regulators of the pathways. This, in turn, will help increase our understanding about the regulatory mechanisms relevant to the two phenotypes

La aversión al riesgo en el mercado español de renta variable

Artículo de revistaLa aversión al riesgo de los inversores incide directamente en los mercados financieros, y los distintos niveles en la misma entre individuos explican la existencia de ciertos valores y contratos que permiten transmitir dicho riesgo para lograr su distribución óptima. Aunque el análisis habitual sobre la misma se centra en la estimación de parámetros de la función de utilidad, en este documento se analiza únicamente su evolución temporal a través de dos indicadores calculados a partir de sus efectos en el mercado de derivados sobre el índice Ibex-35. Estos indicadores se construyen con la metodología descrita por Breeden y Litzenberger para el cómputo del precio de los activos contingentes de Arrow, calculando una función de probabilidad ponderada por preferencias del índice bursátil y comparándola con la función de probabilidad obtenida de un modelo estadístico. La evolución de los indicadores calculados se relaciona no solo con variables financieras, sino también con otras variables que indican la situación económica del inversor representativo, como se muestra en este documento. A pesar de que la aversión al riesgo afecta a la rentabilidad de los activos, los indicadores construidos no contienen información sobre la evolución futura del Ibex-35

Empirical modeling and prediction of neuronal dynamics

Mathematical modeling of neuronal dynamics has experienced a fast growth in the last decades thanks to the biophysical formalism introduced by Hodgkin and Huxley in the 1950s. Other types of models (for instance, integrate and fire models), although less realistic, have also contributed to understand neuronal dynamics. However, there is still a vast volume of data that have not been associated with a mathematical model, mainly because data are acquired more rapidly than they can be analyzed or because it is difficult to analyze (for instance, if the number of ionic channels involved is huge). Therefore, developing new methodologies to obtain mathematical or computational models associated with data (even without previous knowledge of the source) can be helpful to make future predictions. Here, we explore the capability of a wavelet neural network to identify neuronal (single-cell) dynamics. We present an optimized computational scheme that trains the ANN with biologically plausible input currents. We obtain successful identification for data generated from four different neuron models when using all variables as inputs of the network. We also show that the empiric model obtained is able to generalize and predict the neuronal dynamics generated by variable input currents different from those used to train the artificial network. In the more realistic situation of using only the voltage and the injected current as input data to train the network, we lose predictive ability but, for low-dimensional models, the results are still satisfactory. We understand our contribution as a first step toward obtaining empiric models from experimental voltage traces.DA has been funded by the Collaboration in University Departments AGAUR Grant (COLAB 2020). AG and EF have been partially funded by the MINECO-FEDER-UE-MTM Grant RTI2018-093860-B-C21, the Generalitat de Catalunya-AGAUR projects 2021SGR01039 (AG) and 2021SGR00376 (EF), the Grants PID-2021-122954NB-I00 and PID2022-137708NB-I00 funded by MCIN/ AEI/ 10.13039/ 501100011033 and by ERDF ‘A way of making Europe’ (AG) and the Severo Ochoa and María de Maeztu Program for Centers and Units of Excellence in R &D (CEX2020-001084-M) (AG). We are grateful to the Institut d’Organització i Control (IOC-UPC) for the access to its high-performance computing facilities to perform all the computations of this work.Postprint (published version

A Local Genetic Algorithm for the Identification of Condition-Specific MicroRNA-Gene Modules

Transcription factor and microRNA are two types of key regulators of gene expression. Their regulatory mechanisms are highly complex. In this study, we propose a computational method to predict condition-specific regulatory modules that consist of microRNAs, transcription factors, and their commonly regulated genes. We used matched global expression profiles of mRNAs and microRNAs together with the predicted targets of transcription factors and microRNAs to construct an underlying regulatory network. Our method searches for highly scored modules from the network based on a two-step heuristic method that combines genetic and local search algorithms. Using two matched expression datasets, we demonstrate that our method can identify highly scored modules with statistical significance and biological relevance. The identified regulatory modules may provide useful insights on the mechanisms of transcription factors and microRNAs

The ZFHX1A gene is differentially autoregulated by its isoforms

The Zfhx1a gene expresses two different isoforms; the full length Zfhx1a-1 and a truncated isoform termed Zfhx1a-2 lacking the first exon. Deletion analysis of the Zfhx1a-1 promoter localized cell-specific repressors, and a proximal G-string that is critically required for transactivation. Transfection of Zfhx1a-1 cDNA, but not Zfhx1a-2, downregulates Zfhx1a-1 promoter activity. Mutation of an E2-box disrupted the binding of both Zfhx1a isoforms. Consistent with this, transfected Zfhx1a-1 does not regulate the transcriptional activity of the E-box mutated Zfhx1a-1 promoter. Competitive EMSAs and transfection assays show that Zfhx1a-2 can function as a dominant negative isoform since it is able to compete and displace Zfhx1a-1 from its binding site and overcome Zfhx1a-1 induced repression of the Zfhx1a-1 promoter in cells. Hence, the Zfhx1a-1 gene is autoregulated in part by negative feedback on its own promoter which is, in turn, modified by the availability of the negative dominant isoform Zfhx1a-2.Fil: Manavella, Pablo Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Centro de Investigaciones en Bioquímica Clínica e Inmunología; ArgentinaFil: Roqueiro, Gonzalo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Centro de Investigaciones en Bioquímica Clínica e Inmunología; ArgentinaFil: Darling, Douglas S.. University of Louisville; Estados UnidosFil: Cabanillas, Ana Maria de Los A.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Centro de Investigaciones en Bioquímica Clínica e Inmunología; Argentin

Cultivo de quinua bajo dos sistemas y densidades de siembra

En el presente trabajo, se muestran los resultados obtenidos de un Servicio Tecnico Especializado entre la empresa YOMEL S.A. y la Estación Experimental Agropecuaria San Juan, INTA, para el estudio del uso de una sembradora Spin 200 eléctrica neumática con sistema Airdrill (YOMEL) en la siembra de quinua para dos densidades de siembra. Dicha sembradora presenta un sistema de cobertura total, lo que garantiza distribución de semillas uniforme sobre toda la cama de siembra a diferencia de la siembra convencional en línea. Se utilizó también una sembradora hortícola marca BISIG para la comparación de resultados en el comportamiento del cultivo, datos de crecimiento y rendimiento. La información que se produjo con esta experiencia servirá para brindar información sobre alternativas a la mecanización de la siembra de quinua, brindando perspectivas diferentes en los modelos de siembra actuales.EEA San JuanFil: Bárcena, Nadia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Juan; Argentina.Fil: Roqueiro, Gonzalo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Juan; Argentina.Fil: Guillén, Lucas. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Juan; Argentina