Biology subjects

Duarte, R.

Publications and source records attributed to Duarte, R..

16 recordsLinked to original sources

bioRxiv2025

Life on the road: fish communities composition in roadside ditches of the Atlantic Forest

Temporary aquatic habitats are essential for maintaining regional fish diversity, especially in tropical ecosystems. However, artificial ephemeral environments such as roadside ditches remain largely overlooked in ecological research, despite their potential to support native, threatened and even invasive species. In this study, we first assess the fish community composition in roadside ditches of the Atlantic Forest, southeastern Brazil. These habitats exhibited extreme environmental conditions, including hypoxic waters, elevated temperature fluctuations and very acidic waters. Throughout the year, we recorded 17 fish species, including endangered and exotic taxa, across 36 ditches sampled in the Preto River microbasin. Species richness was higher during the Wet Period, likely due to increased hydrological connectivity with nearby streams. Although species richness was higher during the Wet Period, species composition did not differ between periods. Redundancy analysis revealed low explanation of environmental variables on community structure, while a significant positive correlation between spatial distance and community dissimilarity suggested dispersal limitation as a major structuring force. These findings highlight both the ecological relevance of artificial temporary habitats in fragmented landscapes and the requirement to integrate such environments into regional species inventories, especially as natural wetlands continue to decline.

ecology↗

bioRxiv2025

The ATO gene family governs Candida albicans colonisation in the dysbiotic gastrointestinal tract

The fungal pathogen Candida albicans colonises the human gut where short-chain fatty acids (SCFAs) offer sources of carbon. This fungus harbours one of the largest microbial families of ATO (Acetate Transport Ortholog) genes, which encode putative SCFA transport proteins. Here, we generate C. albicans null mutants lacking individual or all known putative SCFA transporter genes and compare their phenotypes in vitro and in vivo. We show that blocking ATO function in C. albicans impairs SCFA uptake and growth, particularly on acetate. The uptake of acetate is largely dependent on a functional Ato1 (also known as Frp3/Ato3) and it is effectively abolished upon deletion of all ATO genes. We further demonstrate that deletion of the entire ATO gene family, but not inactivation of ATO1 alone, compromises the stable colonisation of C. albicans in the murine gastrointestinal tract following bacterial disruption by broad-spectrum antibiotics. Our data suggest that the ATO gene family has expanded and diversified during the evolution of C. albicans to promote the fitness of this fungal commensal during gut colonisation, in part through SCFA utilisation. IMPORTANCEThe human gut is rich in microbial fermentation products such as SCFAs, which serve as key nutrients for both bacteria and fungi. C. albicans, a common fungal resident of the gut and a cause of opportunistic infections, carries an unusually large family of ATO genes. This study reveals that this ATO gene family is required for the efficient uptake of acetate, the most abundant SCFA in the gut, and for stable colonisation of the gut. These findings uncover a new layer of metabolic adaptation in fungal commensals of humans and suggest that transporter gene expansion can shape microbial fitness in response to environmental nutrient signals.

microbiology↗

bioRxiv2024

Nonlinear dendritic integration supports Up-Down states in single neurons

Changes in the activity profile of cortical neurons are due to effects at the scale of local and long-range networks. Accordingly, abrupt transitions in the state of cortical neurons--a phenomenon known as Up-Down states--have been attributed to variation in the activity of afferent neurons. However, cellular physiology and morphology may also play a role in causing Up-Down states. This study examines the impact of dendritic nonlinearities, particularly those mediated by voltage-dependent NMDA receptors, on the response of cortical neurons to balanced excitatory/inhibitory synaptic inputs. Using a neuron model with two segregated dendritic compartments, we compared cells with and without dendritic nonlinearities. NMDA receptors boosted somatic firing in the balanced condition and increased the correlation between membrane potentials across the compartments of the neuron model. Dendritic nonlinearities elicited strong bimodality in the distribution of the somatic potential when the cell was driven with cortical-like input. Moreover, dendritic nonlinearities could detect small input fluctuations and lead to Up-Down states whose statistics and dynamics closely resemble electrophysiological data. Up-Down states also occurred in recurrent networks with oscillatory firing activity, as in anaesthetized animal models, when dendritic NMDA receptors were partially disabled. These findings suggest that there is a dissociation between cellular and network-level features that could both contribute to the emergence of Up-Down states. Our study highlights the complex interplay between dendritic integration and activity-driven dynamics in the origin of cortical bistability. Significance statementIn several physiological states, such as sleep or quiet wakefulness, the membrane of cortical cells shows characteristic bistability. Cells are either fully depolarized and ready to spike, or in a silent, hyperpolarized state. This dynamics, known as Up-Down states, has often been attributed to changes in network activity. However, whether cell-specific properties, such as dendritic nonlinearities, play a role in neuronal bistability remains unclear. This study uses a dendritic model of a pyramidal cell and shows that the presence of NMDA receptors drives the Up-Down states in response to small fluctuations in network activity. Thus, single cells can enter the Up-Down state dynamics independently of the ongoing network activity.

neuroscience↗

bioRxiv2023

A Layered Microcircuit Model of Somatosensory Cortex with Three Interneuron Types and Cell-Type-Specific Short-Term Plasticity

Three major types of GABAergic interneurons, parvalbumin- (PV), somatostatin- (SOM) and vasoactive intestinal peptide-expressing (VIP) cells, play critical but distinct roles in the cortical microcircuitry. Their specific electrophysiology and connectivity shape their inhibitory functions. To study the network dynamics and signal processing specific to these cell types in the cerebral cortex, we developed a multi-layer model incorporating biologically realistic interneuron parameters from rodent somatosensory cortex. The model is fitted to in vivo data on cell-type-specific population firing rates. With a protocol of cell-type-specific stimulation, network responses when activating different neuron types are examined. The model reproduces the experimentally observed inhibitory effects of PV and SOM cells and disinhibitory effect of VIP cells on excitatory cells. We further create a version of the model incorporating cell-type-specific short-term synaptic plasticity (STP). While the ongoing activity with and without STP is similar, STP modulates the responses of Exc, SOM, and VIP cells to cell-type-specific stimulation, presumably by changing the dominant inhibitory pathways. With slight adjustments, the model also reproduces sensory responses of specific interneuron types recorded in vivo. Our model provides predictions on network dynamics involving cell-type-specific short-term plasticity and can serve to explore the computational roles of inhibitory interneurons in sensory functions.

neuroscience↗

bioRxiv2023

Dendrites support formation and reactivation of sequential memories through Hebbian plasticity

Storage and retrieval of sequences require memory that is sensitive to the temporal order of features. For example, in human language, words that are stored in long-term memory are retrieved based on the order of phonemes. It is currently unknown whether Hebbian learning supports the formation of memories that are structured in time. We investigated whether word-like memories can emerge in a network of neurons with dendritic structures. Dendrites provide neuronal processing memory on the order of 100 ms and have been implicated in structured memory formation. We compared a network of neurons with dendrites and two networks of point neurons that have previously been shown to acquire stable long-term memories and process sequential information. The networks were equipped with voltage-based, spike-timing dependent plasticity (STDP) and were homeostatically balanced with inhibitory STDP. In the learning phase, networks were exposed to phoneme sequences and word labels, which led to the formation of overlapping cell assemblies. In the retrieval phase, networks only received phoneme sequences as input, and we measured the firing activity of the corresponding word populations. The dendritic network correctly reactivated the word populations with a success rate of 80%, including words composed of the same phonemes in a different order. The networks of point neurons reactivated only words that contained phonemes that were unique to these words and confused words with shared phonemes (success rate below 20%). These results suggest that the slow timescale and non-linearity of dendritic depolarization allowed neurons to establish connections between neural groups that were sensitive to serial order. Inhibitory STDP prevented the potentiation of connections between unrelated neural populations during learning. During retrieval, it maintained the dendrites hyperpolarized and limited the reactivation of incorrect cell assemblies. Thus, the addition of dendrites enables the encoding of temporal relations into associative memories.

neuroscience↗

bioRxiv2023

Towards reproducible models of sequence learning: replication and analysis of a modular spiking network with reward-based learning

To acquire statistical regularities from the world, the brain must reliably process, and learn from, spatiotemporally structured information. Although an increasing number of computational models have attempted to explain how such sequence learning may be implemented in the neural hardware, many remain limited in functionality or lack biophysical plausibility. If we are to harvest the knowledge within these models and arrive at a deeper mechanistic understanding of sequential processing in cortical circuits, it is critical that the models and their findings are accessible, reproducible, and quantitatively comparable. Here we illustrate the importance of these aspects by providing a thorough investigation of a recent model proposed by Cone and Shouval (2021). We re-implement the modular columnar architecture and reward-based learning rule in the open-source NEST simulator, and successfully replicate the main findings of the original study. Building on these, we perform an in-depth analysis of the models robustness to parameter settings and underlying assumptions, highlighting its strengths and weaknesses. We demonstrate a limitation of the model consisting in the hard-wiring of the sequence order in the connectivity patterns, and suggest possible solutions. Finally, we show that the core functionality of the model is retained under more biologically-plausible constraints.

neuroscience↗

bioRxiv2022

The Tripod neuron: a minimal structural reduction of the dendritic tree

Neuron models with explicit dendritic dynamics have shed light on mechanisms for coincidence detection, pathway selection, and temporal filtering. However, it is still unclear which morphological and physiological features are required to capture these phenomena. In this work, we introduce the Tripod neuron model and propose a minimal structural reduction of the dendritic tree that is able to reproduce these dendritic computations. The Tripod is a three-compartment model consisting of two segregated passive dendrites and a somatic compartment modeled as an adaptive, exponential integrate-and-fire neuron. It incorporates dendritic geometry, membrane physiology, and receptor dynamics as measured in human pyramidal cells. We characterize the response of the Tripod to glutamatergic and GABAergic inputs and identify parameters that support supra-linear integration, coincidence-detection, and pathway-specific gating through shunting inhibition. Following NMDA spikes, the Tripod neuron generates plateau potentials whose duration depends on the dendritic length and the strength of synaptic input. When fitted with distal compartments, the Tripod neuron encodes previous activity into a dendritic depolarized state. This dendritic memory allows the neuron to perform temporal binding and we show that the neuron solves transition and sequence detection tasks on which a single-compartment model fails. Thus, the Tripod neuron can account for dendritic computations previously explained only with more detailed neuron models or neural networks. Due to its simplicity, the Tripod model can be used efficiently in simulations of larger cortical circuits.

neuroscience↗

bioRxiv2022

Acute IL-6 exposure triggers canonical IL-6R signalling in hiPSC microglia, but not neural progenitor cells.

BackgroundExposure to elevated interleukin (IL)-6 levels in utero is consistently associated with increased risk for psychiatric disorders with a putative neurodevelopmental origin, such as schizophrenia (SZ) and autism spectrum condition (ASC). Although rodent models provide causal evidence for this association, we lack a detailed understanding of the cellular and molecular mechanisms in human model systems. To close this gap, we characterised the response of hiPSC-derived microglia-like cells (MGL) and neural progenitor cells (NPCs) to IL-6 in monoculture. ResultsWe observed that human forebrain NPCs did not respond to acute IL-6 exposure in monoculture at both a protein and transcript level due to the absence of IL-6Ra expression and sIL-6Ra secretion. By contrast, acute IL-6 exposure resulted in STAT3 phosphorylation and increased IL-6, JMJD3 and IL-10 expression in MGL, confirming activation of canonical IL-6R signalling. Bulk RNAseq identified 156 upregulated genes (FDR <0.05) in MGL following acute IL-6 exposure, including IRF8, REL, HSPA1A/B and OXTR, which significantly overlapped with an upregulated gene set from post-mortem brain tissue from individuals with schizophrenia. Acute IL-6 stimulation significantly increased MGL motility suggestive of a gain of surveillance function, consistent with gene ontology pathways highlighted from the RNAseq data. Finally, MGLs displayed elevated CCL1, CXCL1, MIP-1A/B, IL-8, IL-13, IL-16, IL-18, MIF and Serpin-E1 secretion post 3h and 24h IL-6 exposure. ConclusionOur data provide evidence for cell specific effects of acute IL-6 exposure in a human model system and strongly suggest microglia-NPC co-culture models are required to study how IL-6 influences human cortical neural progenitor cell development in vitro.

neuroscience↗

bioRxiv2022

Signal denoising through topographic modularity of neural circuits

Information from the sensory periphery is conveyed to the cortex via structured projection pathways that spatially segregate stimulus features, providing a robust and efficient encoding strategy. Beyond sensory encoding, this prominent anatomical feature extends throughout the neocortex. However, the extent to which it influences cortical processing is unclear. In this study, we combine cortical circuit modeling with network theory to demonstrate that the sharpness of topographic projections acts as a bifurcation parameter, controlling the macroscopic dynamics and representational precision across a modular network. By shifting the balance of excitation and inhibition, topographic modularity gradually increases task performance and improves the signal-to-noise ratio across the system. We demonstrate that in biologically constrained networks, such a denoising behavior is contingent on recurrent inhibition. We show that this is a robust and generic structural feature that enables a broad range of behaviorally-relevant operating regimes, and provide an in-depth theoretical analysis unravelling the dynamical principles underlying the mechanism.

neuroscience↗

bioRxiv2020

Unsupervised learning and clustered connectivity enhance reinforcement learning in spiking neural networks

Reinforcement learning is a learning paradigm that can account for how organisms learn to adapt their behavior in complex environments with sparse rewards. However, implementations in spiking neuronal networks typically rely on input architectures involving place cells or receptive fields. This is problematic, as such approaches either scale badly as the environment grows in size or complexity, or presuppose knowledge on how the environment should be partitioned. Here, we propose a learning architecture that combines unsupervised learning on the input projections with clustered connectivity within the representation layer. This combination allows input features to be mapped to clusters; thus the network self-organizes to produce task-relevant activity patterns that can serve as the basis for reinforcement learning on the output projections. On the basis of the MNIST and Mountain Car tasks, we show that our proposed model performs better than either a comparable unclustered network or a clustered network with static input projections. We conclude that the combination of unsupervised learning and clustered connectivity provides a generic representational substrate suitable for further computation.

neuroscience↗

bioRxiv2019

Passing the message: representation transfer in modular balanced networks

Neurobiological systems rely on hierarchical and modular architectures to carry out intricate computations using minimal resources. A prerequisite for such systems to operate adequately is the capability to reliably and efficiently transfer information across multiple modules. Here, we study the features enabling a robust transfer of stimulus representations in modular networks of spiking neurons, tuned to operate in a balanced regime. To capitalize on the complex, transient dynamics that such networks exhibit during active processing, we apply reservoir computing principles and probe the systems computational efficacy with specific tasks. Focusing on the comparison of random feed-forward connectivity and biologically inspired topographic maps, we find that, in a sequential set-up, structured projections between the modules are strictly necessary for information to propagate accurately to deeper modules. Such mappings not only improve computational performance and efficiency, they also reduce response variability, increase robustness against interference effects, and boost memory capacity. We further investigate how information from two separate input streams is integrated and demonstrate that it is more advantageous to perform non-linear computations on the input locally, within a given module, and subsequently transfer the result downstream, rather than transferring intermediate information and performing the computation downstream. Depending on how information is integrated early on in the system, the networks achieve similar task-performance using different strategies, indicating that the dimensionality of the neural responses does not necessarily correlate with nonlinear integration, as predicted by previous studies. These findings highlight a key role of topographic maps in supporting fast, robust and accurate neural communication over longer distances. Given the prevalence of such structural feature, particularly in the sensory systems, elucidating their functional purpose remains an important challenge towards which this work provides relevant, new insights. At the same time, these results shed new light on important requirements for designing functional hierarchical spiking networks.\n\nAuthor summaryTo interact with the external environment in real-time, cortical microcircuits must employ efficient and reliable mechanisms for passing information between different modules and for integrating input from multiple sources. In this study we investigate, from a functional perspective, how structural features influence these mechanisms in the context of stimulus representation, integration and transfer in modular spiking networks. We demonstrate that biologically plausible patterned connectivity, inspired by cortical topographic maps, is necessary for information to propagate across multiple modules in a useful manner. Compared to purely random projections, topographic maps improve computational performance and efficiency considerably, leading to more stable responses and increased robustness against interference effects. In addition, architectural specificities also play an important role in how networks combine information from different sources. Our results suggest that early and local integration of input streams enables more accurate non-linear computations in deeper modules than first transferring intermediate representations and only then performing the computations downstream. These findings demonstrate that the wiring architecture can profoundly impact information transmission and integration in neuronal circuits, with structured connectivity in form spatially segregated topographic projections playing a potentially key role in sustaining accurate cortical communication over multiple networks.

neuroscience↗

bioRxiv2019

Predictive factors for unfavourable treatment in MDR-TB and XDR-TB patients in Rio de Janeiro State, Brazil, 2000-2016

SettingThe State of Rio de Janeiro stands out as having the second highest incidence and the highest mortality rate due to TB in Brazil. This study aims at identifying the factors associated with the unfavourable treatment of MDR/XDR-TB patients in that State.\n\nMethodData on 2269 MDR-TB cases reported in 2000-2016 in Rio de Janeiro State were collected from the Tuberculosis Surveillance System. Bivariate and multivariate logistic regressions were run to estimate the factors associated with unfavourable outcomes (failure, default, and death) and, specifically, default and death.\n\nResultsThe proportion of unfavourable outcomes was 41.9% among MDR-TB and 81.5% among XDR-TB. Having less than 8 years of schooling, and being an Afro-Brazilian, under 40 years old and drug user were associated with unfavourable outcome and default. Bilateral disease, HIV positive, and comorbidities were associated with death. XDR-TB cases had a 4.7-fold higher odds of an unfavourable outcome, with 29.3% of such cases being in the first treatment for multidrug resistance.\n\nConclusionAbout 30% of XDR-TB cases may have occurred by primary transmission. The high rates of failure and death in this category reflect the limitation of treatment options. This highlights the urgency to incorporate new drugs in the treatment.

epidemiology↗

bioRxiv2019

Neuronal memory for language processing

In language processing, an interpretation is computed incrementally within memory while utterances unfold in time. Here, we investigate the nature of this processing memory in a spiking network model of sentence comprehension. We show that the history dependence of neuronal responses endows circuits of biological neurons with adequate memory to assign semantic roles and resolve binding relations between words in a stream of language input. A neurobiological read-write memory is proposed where short-lived spiking activity encodes information into coupled dynamic variables that move at slower timescales. This state-dependent network does not rely on persistent activity, excitatory feedback, or synaptic plasticity for storage. Instead, information is maintained in adaptive neuronal conductances and can be accessed directly during comprehension without cued retrieval of previous input words. This work provides a step towards a computational neurobiology of language.

neuroscience↗

bioRxiv2018

Introducing risk inequality metrics in tuberculosis policy development

Global stakeholders including the World Health Organization rely on predictive models for developing strategies and setting targets for tuberculosis care and control programs. Failure to account for variation in individual risk leads to substantial biases that impair data interpretation and policy decisions1,2. Anticipated impediments to estimating heterogeneity for each parameter are discouraging despite considerable technical progress in recent years. Here we identify acquisition of infection as the single process where heterogeneity most fundamentally impacts model outputs, due to cohort selection imposed by dynamic forces of infection. Individuals with higher risk of acquiring infection are predominantly affected by the pathogen, leaving the unaffected pool with those whose intrinsic risk is lower. This causes susceptibility pools to attain average risks which are lower under higher forces of infection. Interventions that modify the force of infection change the strength of selection, and therefore alter average risks in the pools which feed further incidence. Inability to account for these dynamics is what makes homogenous models unsuitable. We introduce concrete metrics to approximate risk inequality in tuberculosis, demonstrate their utility in mathematical models, and pack the information into a risk inequality coefficient which can be calculated and reported by national tuberculosis programs for use in policy development and modeling.

epidemiology↗

bioRxiv2018

Using High Throughput DNA Sequencing to Evaluate the Accuracy of Serial Dilution Based Tests of Microbial Activities in Oil Pipelines

Microbial activities have detrimental effects on industrial infrastructure. If not controlled, microbial presence can result in corrosion, biofilm formation, and product degradation. Serial dilution tests are routinely used for evaluating presence and abundance of microorganisms by diluting samples and culturing microbes in specific media designed to support microorganisms with particular properties, such as sulfate reduction.\n\nA high-throughput sequencing approach was used to evaluate changes in microbial composition during four standard serial dilution tests. Analysis of 159 isolates revealed significant differences in the microbial compositions of sequential serial dilution titers and identified several cases where: (a) bacteria known to have a detrimental metabolic function (such as acid production) were lost in the serial dilution medium designed to test for this function; (b) bacteria virtually absent in the original sample became dominant in the serial dilution medium. These observations raise concerns regarding the accuracy and overall usefulness of serial dilution tests.

microbiology↗

bioRxiv2017

Leveraging heterogeneity for neural computation with fading memory in layer 2/3 cortical microcircuits

Complexity and heterogeneity are intrinsic to neurobiological systems, manifest in every process, at every scale, and are inextricably linked to the systems emergent collective behaviours and function. However, the majority of studies addressing the dynamics and computational properties of biologically inspired cortical microcircuits tend to assume (often for the sake of analytical tractability) a great degree of homogeneity in both neuronal and synaptic/connectivity parameters. While simplification and reductionism are necessary to understand the brains functional principles, disregarding the existence of the multiple heterogeneities in the cortical composition, which may be at the core of its computational proficiency, will inevitably fail to account for important phenomena and limit the scope and generalizability of cortical models. We address these issues by studying the individual and composite functional roles of heterogeneities in neuronal, synaptic and structural properties in a biophysically plausible layer 2/3 microcircuit model, built and constrained by multiple sources of empirical data. This approach was made possible by the emergence of large-scale, well curated databases, as well as the substantial improvements in experimental methodologies achieved over the last few years. Our results show that variability in single neuron parameters is the dominant source of functional specialization, leading to highly proficient microcircuits with much higher computational power than their homogeneous counterparts. We further show that fully heterogeneous circuits, which are closest to the biophysical reality, owe their response properties to the differential contribution of different sources of heterogeneity.

neuroscience↗