Biology subjects

Ojeda, A.

Publications and source records attributed to Ojeda, A..

9 recordsLinked to original sources

bioRxiv2025

Major antigenic differences in Aeromonas salmonicida isolates correlate with the emergence of a new strain causing furunculosis in Chilean salmon farms

Aeromonas salmonicida is the etiological agent of furunculosis, a septicemic disease with high mortality rates affecting salmonids and other teleost species worldwide. Reviewing molecular diagnostic protocols for routine diagnostics, we realized that the amplification of the vapA target gene failed in some cases of furunculosis. Therefore, we hypothesized that the emergence of a new strain may be involved in recent outbreaks. In this work, we demonstrate that the vapA locus is absent in the new strain, which explains why it lacks the major membrane component VapA protein, a critical virulence factor. In addition, we found that the vapA-absent strain differs from its counterparts in outer membrane protein and lipopolysaccharide profiles, suggesting profound changes at the membrane structure level and in antigenic properties. These features along with sequence analysis information allowed us to infer that a complex genomic rearrangement, probably an indel encompassing the entire vapA locus, gave rise to this membrane phenotype. Although the causes for pathogen evolution and emergence were not fully elucidated, our results strongly suggest that the vapA-absent strain is responsible for a raising proportion of recent furunculosis cases, and that it may be related to a less virulent disease and a low serological response upon vaccination with the A. salmonicida antigen formulation currently used in Chile.

microbiology↗

bioRxiv2024

Establishment of bovine extraembryonic endoderm cells

Understanding the mechanisms of hypoblast development and its role in the implantation is critical for improving farm animal reproduction, but it is hampered by the lack of research models. Here we report that a chemical cocktail (FGF4, BMP4, IL-6, XAV939, and A83-01) enables de novo derivation and long-term culture of bovine extraembryonic endoderm cells (bXENs). Transcriptomic and epigenomic analyses confirmed the identity of bXENs and revealed that they are resemble hypoblast lineages of early bovine peri-implantation embryos. We showed that bXENs help maintain the stemness of bovine ESCs and prevent them from differentiation. In the presence of a signaling cocktail sustaining bXENs, the growth and progression of epiblasts are also facilitated in the developing pre-implantation embryo. Furthermore, through 3D assembly of bXENs with bovine ESCs and TSCs, we developed an improved bovine blastocyst like structure (bovine blastoid) that resembles blastocyst. The bovine XENs and blastoids established in this study represent accessible in vitro models for understanding hypoblast development and improving reproductive efficiency in livestock species.

developmental biology↗

bioRxiv2021

Uncertainty avoidance versus conditioned reinforcement: exploring paradoxical choice in rats

Paying a cost to reduce uncertainty can be adaptive, because better informed decision-makers can align their preferences to opportunities. However, birds and mammals display an appetite for information that they cannot use to functionally alter behaviour or its outcomes. We explore two putative motivational mechanisms for this paradoxical behaviour. The information hypothesis, proposes that reducing uncertainty is reinforcing per se, consistent with the concept of curiosity: a motivation to know, in the absence of instrumental benefits. In contrast, the conditioned reinforcement hypothesis sees information-seeking as a consequence of asymmetries in secondarily acquired reinforcement: responding increments caused by post-choice stimuli announcing positive outcomes (S+) exceed decrements caused by stimuli signalling absence of reward (S-). We contrast these hypotheses experimentally. Rats chose between two equally profitable options delivering food probabilistically after a fixed delay. In the informative option (Info), the outcome (food/no food) was signalled immediately after choice, whereas in the non-informative option (NoInfo) outcomes were uncertain until the delay lapsed. Subjects preferred Info when (1) outcomes were signalled by salient auditory cues, (2) only the absence of reward was signalled, and (3) only reward was signalled, though acquisition was slower when rewards were not explicitly signalled. Our results show that a salient good news signal is not required as a conditioned reinforcer to generate paradoxical preferences. Terminal preferences support the information hypothesis but the slower acquisition of Info preference when S+ is not present is consistent with the conditioning account. We conclude that both uncertainty reduction and conditioned reinforcement influence choice.

animal behavior and cognition↗

bioRxiv2020

Mapping Cognitive Brain Functions at Scale

A fundamental set of cognitive abilities enable humans to efficiently process goal-relevant information, suppress irrelevant distractions, maintain information in working memory, and act flexibly in different behavioral contexts. Yet, studies of human cognition and their underlying neural mechanisms usually evaluate these cognitive constructs in silos, instead of comprehensively in-tandem within the same individual. Here, we developed a scalable, mobile platform, "BrainE" (short for Brain Engagement), to rapidly assay several essential aspects of cognition simultaneous with wireless electroencephalography (EEG) recordings. Using BrainE, we rapidly assessed five aspects of cognition including (1) selective attention, (2) response inhibition, (3) working memory, (4) flanker interference and (5) emotion interference processing, in 102 healthy young adults. We evaluated stimulus encoding in all tasks using the EEG neural recordings, and isolated the cortical sources of the spectrotemporal EEG dynamics. Additionally, we used BrainE in a two-visit study in 24 young adults to investigate the reliability of the neuro-cognitive data as well as its plasticity to transcranial magnetic stimulation (TMS). We found that stimulus encoding on multiple cognitive tasks could be rapidly assessed, identifying common as well as distinct task processes in both sensory and cognitive control brain regions. Event related synchronization (ERS) in the theta (3-7 Hz) and alpha (8-12 Hz) frequencies as well as event related desynchronization (ERD) in the beta frequencies (13-30 Hz) were distinctly observed in each task. The observed ERS/ERD effects were overall anticorrelated. The two-visit study confirmed high test-retest reliability for both cognitive and neural data, and neural responses showed specific TMS protocol driven modulation. We also show that the global cognitive neural responses are sensitive to mental health symptom self-reports. This first study with the BrainE platform showcases its utility in studying neuro-cognitive dynamics in a rapid and scalable fashion. HighlightsO_LIRapid and scalable EEG recordings reveal common and distinct cortical activations across five core cognitive tasks. C_LIO_LIData acquired across visits one-week-apart show high test-retest reliability for both cognitive and neural measurements. C_LIO_LIEvoked neural responses during emotion interference processing demonstrate specific short-term plasticity driven by type of neurostimulation. C_LIO_LICognitively evoked neural responses are sensitive to variations in mental health symptoms. C_LI

neuroscience↗

bioRxiv2019

SimBSI: An open-source Simulink library for developingclosed-loop brain signal interfaces in animals and humans

ObjectiveA promising application of BCI technology is in the development of personalized therapies that can target neural circuits linked to mental or physical disabilities. Typical BCIs, however, offer limited value due to simplistic designs and poor understanding of the conditions being treated. Building BCIs on more solid grounds may require the characterization of the brain dynamics supporting cognition and behavior at multiple scales, from single-cell and local field potential (LFP) recordings in animals to non-invasive electroencephalography (EEG) in humans. Despite recent efforts, a unifying software framework to support closed-loop studies in both animals and humans, is still lacking. The objective of this paper is to develop such a neurotechnology software framework.\n\nApproachHere we develop the Simulink for Brain Signal Interfaces library (SimBSI). Simulink is a mature graphical programming environment within MATLAB that has gained traction for processing electrophysiological data. SimBSI adds to this ecosystem: 1) advanced human EEG source imaging, 2) cross-species multimodal data acquisition based on the Lab Streaming Layer library, and 3) a graphical experimental design platform.\n\nMain resultsWe used several examples to demonstrate the capabilities of the library, ranging from simple signal processing, to online EEG source imaging, cognitive task design, and closed-loop neuromodulation. We further demonstrate the simplicity of developing a sophisticated experimental environment for rodents within this environment.\n\nSignificanceWith the SimBSI library we hope to aid BCI practitioners of dissimilar backgrounds in the development of, much needed, single and cross-species closed-loop neuroscientific experiments. These experiments may provide the necessary mechanistic data for BCIs to become effective therapeutic tools.

bioengineering↗

bioRxiv2019

A Bayesian framework for unifying data cleaning, source separation and imaging of electroencephalographic signals

Electroencephalographic (EEG) source imaging depends upon sophisticated signal processing algorithms for data cleaning, source separation, and localization. Typically, these problems are addressed separately using a variety of heuristics, making it difficult to systematize a methodology for extracting robust EEG source estimates on a wide range of experimental paradigms. In this paper, we propose a unifying Bayesian framework in which these apparently dissimilar problems can be understood and solved in a principled manner using a single algorithm. We explicitly model the effect of non-brain sources by augmenting the lead field matrix with a dictionary of stereotypical artifact scalp projections. We propose to populate the artifact dictionary with non-brain scalp projections obtained by running Independent Component Analysis (ICA) on an EEG database. Within a parametric empirical Bayes (PEB) framework, we use an anatomical brain atlas to parameterize a source prior distribution that encourages sparsity in the number of cortical regions. We show that, in our inversion algorithm, PEB+ (PEB with the addition of artifact modeling), the sparsity prior has the property of inducing the segregation of the cortical activity into a few maximally independent components with known anatomical support. Artifacts produced by electrooculographic and electromyographic activity as well as single-channel spikes are also segregated into their respective components. Of theoretical relevance, we use our framework to point out the connections between Infomax ICA and distributed source imaging. We use real data to demonstrate that PEB+ outperforms Infomax for source separation on short segments of data and, unlike the popular Artifact Subspace Removal algorithm, it can reduce artifacts without significantly distorting clean epochs. Finally, we analyze mobile brain/body imaging data to characterize the brain dynamics supporting heading computation during full-body rotations. In this example, we run PEB+ followed by the spectral analysis of the activity in the retrosplenial cortex, largely replicating the findings of previous experimental literature.

neuroscience↗

bioRxiv2018

Automated EEG mega-analysis II: Cognitive aspects of event related features

In this paper, we present the results of a large-scale analysis of event-related responses based on raw EEG data from 17 studies performed at six experimental sites associated with four different institutions. The analysis corpus represents 1,155 recordings containing approximately 7.8 million event instances acquired under several different experimental paradigms. Such large-scale analysis is predicated on consistent data organization and event annotation as well as an effective automated pre-processing pipeline to transform raw EEG into a form suitable for comparative analysis. A key component of this analysis is the annotation of study-specific event codes using a common vocabulary to describe relevant event features. We demonstrate that Hierarchical Event Descriptors (HED tags) capture statistically significant cognitive aspects of EEG events common across multiple recordings, subjects, studies, paradigms, headset configurations, and experimental sites. We use representational similarity analysis (RSA) to show that EEG responses annotated with the same cognitive aspect are significantly more similar than those that do not share that cognitive aspect. These RSA similarity results are supported by visualizations that exploit the non-linear similarities of these associations. We apply temporal overlap regression to reduce confounds caused by adjacent events instances and extract time and time-frequency EEG features (regressed ERPs and ERSPs) that are comparable across studies and replicate findings from prior, individual studies. Likewise, we use second-level linear regression to separate effects of different cognitive aspects on these features, across all studies. This work demonstrates that EEG mega-analysis (pooling of raw data across studies) can enable investigations of brain dynamics in a more generalized fashion than single studies afford. A companion paper complements this event-based analysis by addressing commonality of the time and frequency statistical properties of EEG across studies at the channel and dipole level.

neuroscience↗

bioRxiv2018

Automated EEG mega-analysis I: Spectral and amplitude characteristics across studies

Significant achievements have been made in the fMRI field by pooling statistical results from multiple studies (meta-analysis). More recently, fMRI standardization efforts have focused on enabling the joint analysis of raw fMRI data across studies (mega-analysis), with the hope of achieving more detailed insights. However, it has not been clear if such analyses in the EEG field are possible or equally fruitful. Here we present the results of a large-scale EEG mega-analysis using 18 studies from six sites representing several different experimental paradigms. We demonstrate that when meta-data are consistent across studies, both channel-level and source-level EEG mega-analysis are possible and can provide insights unavailable in single studies. The analysis uses a fully-automated processing pipeline to reduce line noise, interpolate noisy channels, perform robust referencing, remove eye-activity, and further identify outlier signals. We define several robust measures based on channel amplitude and dispersion to assess the comparability of data across studies and observe the effect of various processing steps on these measures. Using ICA-based dipolar sources, we also observe consistent differences in overall frequency baseline amplitudes across brain areas. For example, we observe higher alpha in posterior vs anterior regions and higher beta in temporal regions. We also detect consistent differences in the slope of the aperiodic portion of the EEG spectrum across brain areas. In a companion paper, we apply mega-analysis to assess commonalities in event-related EEG features across studies. The continuous raw and preprocessed data used in this analysis are available through the DataCatalog at https://cancta.net.

neuroscience↗

bioRxiv2016

A theory of working memory without consciousness or sustained activity

Working memory and conscious perception are thought to share similar brain mechanisms, yet recent reports of non-conscious working memory challenge this view. Combining visual masking with magnetoencephalography, we demonstrate the reality of non-conscious working memory and dissect its neural mechanisms. In a spatial delayed-response task, participants reported the location of a subjectively unseen target above chance-level after a long delay. Conscious perception and conscious working memory were characterized by similar signatures: a sustained desynchronization in the alpha/beta band over frontal cortex, and a decodable representation of target location in posterior sensors. During non-conscious working memory, such activity vanished. Our findings contradict models that identify working memory with sustained neural firing, but are compatible with recent proposals of activity-silent working memory. We present a theoretical framework and simulations showing how slowly decaying synaptic changes allow cell assemblies to go dormant during the delay, yet be retrieved above chance-level after several seconds.

neuroscience↗