Biology subjects

Sanchez-Jimenez, A.

Publications and source records attributed to Sanchez-Jimenez, A..

6 recordsLinked to original sources

bioRxiv2026

Event-Centered Prediction: How Future Interaction Points Shape Human Anticipation of Motion

Prediction in dynamic situations, in which relevant elements evolve over time, is a fundamental cognitive function. The brain relies on specialized predictive mechanisms, including time compaction, a process that supports dynamic processing by embedding temporal information into space and transforming future interactions into salient spatial representations. Here we investigated how future interactions are salient during dynamic events and how this salience shapes behavior. Participants performed a visuomotor prediction task in which they estimated the future trajectory of a moving object after observing only the initial portion of its motion, while another object was simultaneously present and could generate either interactive (collision) or non-interactive (crossing) dynamics. Although accurate performance required extrapolating motion solely from kinematic information, participants predictions were systematically biased toward locations associated with future interactions. Prediction accuracy was reduced in situations involving potential future interactions compared to non-interactive dynamics. Importantly, participants consistently responded closer to predicted interaction points, even when this strategy did not improve accuracy or trajectory extrapolation. Substantial inter-individual variability was observed, revealing conservative and risk-taking predictive strategies with systematic group differences. When participants were explicitly instructed to improve performance, overall accuracy improved only marginally, while predictive behavior shifted toward greater reliance on interaction-related locations, particularly among those who had not already adopted this strategy. We propose that this interaction-driven bias reflects a core property of time compaction, supporting the idea that predictive cognition relies on future interactions as stable reference points under dynamic uncertainty.

neuroscience↗

bioRxiv2025

Efficient memorization of dynamic stimuli with future interactions

In nature, survival requires coping with complex time-changing situations in real time. In this process, memory plays a major role since the retrieval of critical information is key to rapid and reliable decision making. This work explores modulation of human memory under the hypothesis that in dynamic scenarios such critical information is encoded as a static map of future interactions. Specifically, the reported results show that dynamic visual stimuli that contain future interactions are better recalled than equivalent stimuli that do not. This is in line with the proposed hypothesis since the former type of stimulus would be encoded in a more simplified way than the latter. Moreover, dynamic stimuli with future interactions are better recalled than simpler dynamic stimuli, which reinforces that the former are processed by a static representation - their map of interactions. This cognitive strategy seems to be modulated by the complexity of the stimulus, since in simple situations differences in recall appear only in men, whereas when complexity increases, such differences do not show gender bias. Therefore, this work proposes an answer to how memory can help us reliably cope with dynamic situations, demonstrating that those critical for survival (such as fighting, chasing, fleeing, etc., which involve interactions) are better remembered, allowing more efficient learning and decision making, essential to deal with our complex and changing world.

animal behavior and cognition↗

bioRxiv2023

Internal representation of future interactions in rats

Animals and humans receive the most critical information from parts of the environment that are immediately inaccessible, possibly only visually explored, and highly dynamic. The brain must effectively process potential interactions between elements in such an environment to make appropriate decisions in critical situations. We trained male Long-Evans rats to discriminate static and dynamic visuospatial stimuli displayed on an inaccessible computer screen. We demonstrated that the rats learned to discriminate dynamic visuospatial stimuli faster, with greater accuracy and shorter reaction time than complementary static stimuli. Furthermore, we provide behavioral evidence indicating that rats internally represent dynamic environments as static maps that capture meaningful future interactions. These observations highlight the ecological importance of dynamic stimuli in the outside world and support previous findings in humans that internal static representations can encapsulate relevant spatiotemporal information of dynamic environments. Such a mechanism would allow animals and humans to process complex time-changing situations neatly.

animal behavior and cognition↗

bioRxiv2023

The carboxyl-terminal processing proteases Prc and CtpA modulate cell-surface signalling activity and Pseudomonas aeruginosa virulence

Cell-surface signalling (CSS) is a signal transfer system of Gram-negative bacteria used to detect extracellular signals and modulate gene transcription in response. These three-protein systems are formed by an outer membrane receptor, a cytoplasmic membrane-embedded anti-{sigma} factor and a cytosolic extracytoplasmic function {sigma} factor ({sigma}ECF). In absence of an inducing signal, the anti-{sigma} factor binds to and keeps the {sigma}ECF factor sequestered, thus preventing its interaction with the RNA polymerase and the transcription of signal response genes. Presence of the signal triggers a signalling cascade that extends from the outer membrane to the cytosol and results in {sigma}ECF factor activation. Recently, we and others have reported that CSS {sigma}ECF factor activation requires the regulated and sequential proteolysis of the cognate anti-{sigma} factor, and the function of the Prc and RseP proteases. However, many features of this proteolytic cascade are still unclear. In this work, we have identified another protease that modulates CSS activity, namely the periplasmic carboxyl-terminal processing protease CtpA. We show that both CtpA and the previously identified protease Prc control CSS activation by modulating the levels of the anti-{sigma} factor. CtpA functions upstream of Prc in the proteolytic cascade and seems to prevent the Prc-mediated proteolysis of the CSS anti-{sigma} factor. Importantly, using zebrafish embryos and the A549 cell line as hosts, we show that mutants in the rseP and ctpA proteases of the human pathogen Pseudomonas aeruginosa are considerably attenuated in virulence while the prc mutation increases virulence likely by enhancing the production of outer membrane vesicles. Because proteases are druggable proteins, the identification of regulatory proteases involved in P. aeruginosa virulence holds promise for the development of novel strategies to fight this clinically relevant pathogen.

microbiology↗

bioRxiv2021

Benchmarking of tools for axon length measurement in individually-labeled projection neurons

Projection neurons are the commonest neuronal type in the mammalian forebrain and their individual characterization is a crucial step to understand how neural circuitry operates. These cells have an axon whose arborizations extend over long distances, branching in complex patterns and/or in multiple brain regions. Axon length is a principal estimate of the functional impact of the neuron, as it directly correlates with the number of synapses formed by the axon in its target regions; however, its measurement by direct 3D axonal tracing is a slow and labor-intensive method. On the contrary, axon length estimations have been recently proposed as an effective and accessible alternative, allowing a fast approach to the functional significance of the single neuron. Here, we analyze the accuracy and efficiency of the most used length estimation tools - design-based stereology by virtual planes or spheres, and mathematical correction of the 2D projected-axon length - in contrast with direct measurement, to quantify individual axon length. To this end, we computationally simulated each tool, applied them over a dataset of 951 3D-reconstructed axons (from NeuroMorpho.org), and compared the generated length values with their 3D reconstruction counterparts. Additionally, the computational results were compared with estimated and direct measurements of individual axon lengths performed on actual brain tissue sections, to analyze the practical difficulties and biases arising in real cases. The evaluated reliability of each axon length estimation method is then balanced with the required human effort, experience and know-how, and economic affordability. This work, therefore, aims to provide a constructive benchmark to help guide the selection of the most efficient method for measuring specific axonal morphologies according to the particular circumstances of the conducted research. AUTHOR SUMMARYCharacterization of single neurons is a crucial step to understand how neural circuitry operates. Visualization of individual neurons is feasible thanks to labelling techniques that allows precise measurements at cellular resolution. This milestone gave access to powerful estimators of the functional impact of a neuron, such as axon length. Although techniques relying on direct 3D reconstruction of individual axons are the gold standard, handiness and accessibility are still an issue. Indirect estimations of axon length have been proposed as agile and effective alternatives, each offering different solutions to the accuracy-cost tradeoff. In this work we report a computational benchmarking between three experimental tools used for axon length estimation on brain tissue sections. Performance of each tool was simulated and tested for 951 3D-reconstructed axons, by comparing estimated axon lengths against direct measurements. Assessment of suitability to different research and funding circumstances is also provided, taking into consideration factors such as training expertise, economic cost and required equipment, alongside methodological results. These findings could be an important reference for research on neuronal wiring, as well as for broader studies involving neuroanatomical and neural circuit modelling.

neuroscience↗

bioRxiv2020

Logical modeling of dendritic cells in vitro differentiation from human monocytes unravels novel transcriptional regulatory interactions

Dendritic cells are the major specialized antigen-presenting cells, thereby connecting innate and adaptive immunity. Because of their role in establishing adaptive immunity, they have been used as targets for immunotherapy. Monocytes can differentiate into dendritic cells in vitro in the presence of colony-stimulating factor 2 (CSF2) and interleukin 4 (IL4), activating four signalling pathways (MAPK, JAK/STAT, NFKB, and PI3K). However, the transcriptional regulation responsible for dendritic cell differentiation from monocytes (moDCs) remains unknown. By curating scientific literature on moDCs differentiation, we established a preliminary logical model that helped us identify missing information for the activation of genes responsible for this differentiation, including missing targets for key transcription factors (TFs). Using ChIP-seq and RNA-seq data from the Blueprint consortium, we defined active and inactive promoters, together with differentially expressed genes in monocytes, moDCs, and macrophages (which correspond to an alternative cell fate). We then used this functional genomic information to predict novel targets for the identified TFs. We established a second logical model integrating this information, which enabled us to recapitulate the main established facts regarding moDCs differentiation. Prospectively, the resulting model should be useful to develop novel immunotherapies based on moDCs regulatory network.

immunology↗