Biology subjects

Rueda, C.

Publications and source records attributed to Rueda, C..

5 recordsLinked to original sources

bioRxiv2025

Big Data, Sound Science, Lasting Impact: a framework for passive acoustic monitoring

Marine passive acoustic monitoring (PAM) has produced petabytes of data that are used by researchers, resource managers, industry, and regulators to understand how marine animals use sound and the impacts of anthropogenic noise on species and ecosystems throughout the global ocean. These big data provide unprecedented opportunities to study underwater soundscapes and marine ecology but also enormous challenges to efficiently extract information. To address these challenges, a U.S. federally funded and led Sound Cooperative (SoundCoop) project built community-focused cyberinfrastructure to promote improved, scalable and sustainable processing and access of marine PAM data for management, science, industry and military applications. Driven by cross-institutional participation representing a diversity of data collection methods and conditions, the SoundCoop project established guidance for standardized processing of ocean sound level metrics using freeware software toolkits and developed core tools and processes that support open science. Four examples of comparative analyses that connect disparate PAM monitoring efforts, and integrate non-acoustic data illustrate how comparable, interoperable sound level metrics support a more coherent and synoptic perspective on global ocean soundscapes using methods that current and future PAM projects can leverage. Such a framework around PAM big data offers the opportunity to revolutionize large-scale marine ecology and oceanography in similar ways to other transformative approaches for understanding environmental or ecological patterns and processes at global scales.

ecology↗

bioRxiv2022

CIRCUST: a novel methodology for reconstruction of temporal order of molecular rhythms; validation and application towards a human circadian gene expression atlas

AO_SCPLOWBSTRACTC_SCPLOWThe circadian system drives near-24-h oscillations in behaviors and biological processes. The underlying core molecular clock regulates the expression of other genes, and it has been shown that the expression of more than 50 percent of genes in mammals displays 24-h rhythmic patterns, with the specific genes that cycle varying from one tissue to another. Determining rhythmic gene expression patterns in human tissues sampled as single timepoints has several challenges, including the reconstruction of temporal order of highly noisy data. Previous methodologies have attempted to address these challenges in one or a small number of tissues for which clock gene evolutionary conservation is assumed to be preserved. Here we introduce CIRCUST, a novel CIRCular-robUST methodology for analyzing molecular rhythms, that relies on circular statistics, is robust against noise, and requires fewer assumptions than existing methodologies. Next, we validated the method against two controlled experiments in which sampling times were known, and finally, CIRCUST was applied to 34 tissues from the Genotype-Tissue Expression (GTEx) dataset with the aim towards building a comprehensive daily rhythm gene expression atlas in humans. The validation and application shown here indicate that CIRCUST provides a flexible framework to formulate and solve the issues related to the analysis of molecular rhythms in human tissues. CIRCUST methodology is publicly available at https://github.com/yolandalago/CIRCUST/.

bioinformatics↗

bioRxiv2021

Electrophysiological and Transcriptomic Features Reveal a Circular Taxonomy of Cortical Neurons

The complete understanding of the mammalian brain requires exact knowledge of the function of each of the neurons composing its parts. To achieve this goal, an exhaustive, precise, reproducible, and robust neuronal taxonomy should be defined. In this paper, a new circular taxonomy based on transcriptomic features and novel electrophysiological features is proposed. The approach is validated by analysing more than 1850 electrophysiological signals of different mouse visual cortex neurons proceeding from the Allen Cell Types Database. The study is conducted on two different levels: neurons and their cell-type aggregation into Cre Lines. At the neuronal level, electrophysiological features have been extracted with a promising model that has already proved its worth in neuronal dynamics. At the Cre Line level, electrophysiological and transcriptomic features are joined on cell types with available genetic information. A taxonomy with a circular order is revealed by a simple transformation of the first two principal components that allow the characterization of the different Cre Lines. Moreover, the proposed methodology locates other Cre Lines in the taxonomy that do not have transcriptomic features available. Finally, the taxonomy is validated by Machine Learning methods which are able to discriminate the different neuron types with the proposed electrophysiological features.

neuroscience↗

bioRxiv2021

A Simple Parametric Representation of the Hodgkin-Huxley Model

The Hodgkin-Huxley model, decades after its first presentation, is still a reference model in neuroscience as it has successfully reproduced the electrophysiological activity of many organisms. The primary signal in the model represents the membrane potential of a neuron. A parametric and simple representation of this signal is presented in this paper. The new proposal is an adapted Frequency Modulated Mobius multicomponent model defined as a flexible decomposition in waves that describe the signal morphology. A specific feature of the new model is that the parameters are subject to interpretable restrictions. A broad simulation experiment is conducted to show the new model accurately represents the simulated Hodgkin-Huxley signal. Moreover, the model potential to predict the neurons relevant characteristics, described with parameters of the Hodgkin Huxley model, is shown using different Machine Learning methods. The proposed model is also validated with real data from Squid Giant Axons. The comparison of the parameter configuration between the simulated and real data demonstrated the flexibility of the model as well as interesting differences. Author summaryAlejandro Rodriguez-Collado. I received the double degree in Statistics and Computer Engineering and the Masters degree in Business Intelligence and Big Data from the Universidad de Valladolid in 2019 and 2020, respectively. I work as researcher and Professor for the Department of Statistics and Operational Research at the Universidad de Valladolid. My main research interests include oscillatory signal processing, neuroscience, multivariate data analysis and supervised learning. Cristina Rueda. I received the BS degree in mathematics from the Universidad de Valladolid in 1987 and the PhD degree in statistical science from the Universidad de Valladolid in 1989. I am currently Professor in the Department of Statistics and Operational Research at the Universidad de Valladolid. My main research interests include statistical inference methods under restrictions, circular data, computational biology, and statistical methods for signal analysis.

neuroscience↗

bioRxiv2020

Spontaneous recurrent seizures in an intra-amygdala kainate microinjection model of temporal lobe epilepsy are differentially sensitive to antiseizure drugs

The discovery and development of novel antiseizure drugs (ASDs) that are effective in controlling pharmacoresistant spontaneous recurrent seizures (SRSs) continues to represent a significant unmet clinical need. The Epilepsy Therapy Screening Program (ETSP) has undertaken efforts to address this need by adopting animal models that represent the salient features of human pharmacoresistant epilepsy and employing these models for preclinical testing of investigational ASDs. One such model that has garnered increased interest in recent years is the mouse variant of the Intra-Amygdala Kainate (IAK) microinjection model of mesial temporal lobe epilepsy (MTLE). In establishing a version of this model, several methodological variables were evaluated for their effect(s) on pertinent quantitative endpoints. Although administration of a benzodiazepine 40 minutes after kainate (KA) induced status epilepticus (SE) is commonly used to improve survival, data presented here demonstrates similar outcomes (mortality, hippocampal damage, latency periods, and 90-day SRS natural history) between mice given midazolam and those that were not. Using a version of this model that did not interrupt SE with a benzodiazepine, a 90-day natural history study was performed and survival, latency periods, SRS frequencies and durations, and SRS clustering data were quantified. Finally, an important step towards model adoption is to assess the sensitivities or resistances of SRSs to a panel of approved and clinically used ASDs. Accordingly, the following ASDs were evaluated for their effects on SRSs in these mice: phenytoin (20 mg/kg, b.i.d.), carbamazepine (30 mg/kg, t.i.d.), valproate (240 mg/kg, t.i.d.), diazepam (4 mg/kg, b.i.d.), and phenobarbital (25 and 50 mg/kg, b.i.d.). Valproate, diazepam, and phenobarbital significantly attenuated SRS frequency relative to vehicle controls at doses devoid of observable adverse behavioral effects. Only diazepam significantly increased seizure freedom. Neither phenytoin nor carbamazepine significantly altered SRS frequency or freedom under these experimental conditions. These data demonstrate that SRSs in this IAK model of MTLE are pharmacoresistant to two representative sodium channel-inhibiting ASDs (phenytoin and carbamazepine) but not to GABA receptor modulating ASDs (diazepam and phenobarbital) or a mixed-mechanism ASD (valproate). Accordingly, this model is being incorporated into the NINDS-funded ETSP testing platform for treatment resistant epilepsy. HighlightsO_LIAn intra-amygdala kainate model of TLE was evaluated for pharmacoresistant seizures C_LIO_LIAdministration of midazolam during status epilepticus did not affect mortality C_LIO_LIModel characteristics were evaluated over a 90-day natural history study C_LIO_LISpontaneous seizures were resistant to phenytoin and carbamazepine C_LIO_LISpontaneous seizures were sensitive to valproic acid, diazepam, and phenobarbital C_LI

neuroscience↗