Biology subjects

Allard, A.

Publications and source records attributed to Allard, A..

10 recordsLinked to original sources

bioRxiv2026

Graph-based characterization of in vitro neuronal network maturation using machine learning and digital holographic microscopy

SignificanceDigital Holographic Microscopy (DHM) provides label-free quantitative phase images (QPIs) of living cells and has become a powerful tool for studying cellular morphology and dynamics. While most DHM studies have focused on cell-level analysis, the quantitative characterization of neuronal network organization and maturation from DHM images remains largely unexplored, highlighting the need for dedicated computational approaches. AimWe aimed to develop an automated framework combining deep-learning-based image analysis and graph theory to quantitatively characterize the organization, connectivity, and maturation of neuronal networks in primary rat cortical cultures imaged by DHM. ApproachTwo U-Net convolutional neural networks were trained on manually annotated DHM phase images to segment neuronal cell bodies and neurites. The resulting segmentation maps were used to infer putative morphological connections between neurons and generate graph representations of neuronal networks, referred to as graph fingerprints. A panel of 18 connectomics-inspired graph features was then computed to characterize local and global properties of network organization across four stages of culture maturation. ResultsThe mean area under the receiver operating characteristic curves was 0.98 for cell-body and 0.91 for neurite segmentation, indicating near-perfect identification. Graph-theoretical analysis revealed reproducible topological changes during network maturation in vitro, including increased density, reduced modularity, and progressive network integration. Correlation analysis showed that the 18 graph features grouped into two highly correlated families. A Random Forest classifier identified density and modularity as the most informative descriptors, achieving an accuracy of 87% in classifying maturation stages of neuronal cultures. ConclusionsOur results demonstrate that combining DHM, deep-learning-based segmentation, and graphtheoretical analysis enables quantitative characterization of neuronal network organization and maturation from label-free phase images. This framework provides a foundation for future studies of pharmacological experiments, neuronal network phenotyping, and human induced pluripotent stem cell (hiPSC)-derived neuronal cultures, where quantitative assessment of network organization remains a major challenge.

neuroscience↗

bioRxiv2026

Inferring biodiversity from indicator species using co-occurrence network structure

Biodiversity loss is accelerating worldwide, yet comprehensive assessment of species assemblages across environmental gradients remains impractical for ecological forecasting and conservation. Indicator species are therefore widely used as proxies for community composition, but existing approaches struggle to reconstruct full assemblages, particularly for rare or poorly sampled species. Here we present an integrated framework combining indicator species analysis, ecological network theory, and machine learning to infer the occurrence of non-indicator species from assemblage data. By leveraging structural properties of species co-occurrence networks, the approach captures latent community structure without explicit environmental modeling, enabling inference under sparse sampling. Across species abundance gradients, we identify three distinct regimes of predictability: high predictability for abundant species, reduced performance for species of intermediate prevalence, and unexpectedly strong predictability for rare species driven by strong co-occurrence structure with indicator species. By exploiting complementary information captured by multiple network metrics, the framework recovers species with diverse connectivity profiles and consistently outperforms richness-based or random indicator selection in both accuracy and coverage. Overall, this data-efficient approach provides a transferable pathway for biodiversity monitoring and forecasting, while offering new insights into the network organization of ecological communities.

ecology↗

bioRxiv2025

Function aligns with geometry in locally connected neuronal networks

The geometry of the brain imposes fundamental constraints on neuronal network organization and dynamics, yet how these constraints give rise to observed patterns of brain activity remains unclear. Here, we investigate how geometric eigenmodes relate to functional connectivity gradients in three-dimensional neural systems using a combination of generative network simulations and cellular-resolution calcium imaging in larval zebrafish. We show that functional connectivity gradients emerging from network activity naturally align with the geometric eigenmodes of the underlying spatial embedding when connectivity is predominantly local. By systematically increasing the prevalence of long-range connections, we reveal a robust geometry-function correspondence that progressively deteriorates as local connectivity is disrupted. We then show that spatial filtering can artificially imprint geometric patterns on functional gradients, highlighting an important methodological confound. To validate our computational results, we conduct volumetric calcium imaging experiments at cellular resolution in the optic tectum of zebrafish larvae, uncovering functional gradients that closely align with geometric eigenmodes. As predicted from simulations, the eigenmode-gradient mapping exhibits a cutoff point that quantitatively reflects the spatial extent of the regions connectivity kernel, inferred from single-neuron morphologies. This geometry-functional alignment disappears at brain-wide scale, where long-range connections are more prevalent. Our findings demonstrate how short-range anatomical connectivity anchors functional connectivity gradients to the brains geometry.

neuroscience↗

bioRxiv2023

A probabilistic methodology to reconstruct biodiversity from implicit interactions with indicator species along latitudinal co-occurrence networks

The relationship between species presence, biodiversity reconstruction, and latitudinal gradients is a complex and multifaceted topic that has been the subject of extensive research in ecology. Recent studies have provided valuable insights into the patterns and drivers of these phenomena. Also, with the ongoing decline in biodiversity, there is a need for efficient field monitoring techniques. Indicator species (IS) emerged as a promising tool to monitor diversity because their presence indicates a maximum number of conditionally co-occurring species. We aim to assess the effectiveness of IS for biodiversity reconstruction implicitly based on their co-occurrence with other species through a network-based methodology. The IS are identified based on various network metrics and the likelihood of species occurrences is computed based on (1) their conditional occurrence probability with IS and (2) the occurrence probability of IS. We test the approach with field observations of birds in the Cote-Nord region of Quebec. From our methodology, the climate latitudinal gradient plays a significant role on the alternation in composition of IS with an almost complete turnover between northern and southern networks. The latitudinal gradient impacts also the nature of the inter-specific interactions with more avoidance relationship toward the Tropics and more cooperation liaisons toward the north. Regarding the effectiveness in the reconstruction of assemblages occurrence, we observe a strong negative correlation (r [≤] -0.95) between the percentage of sites occupied and the dissimilarity between the original and the estimated occurrences. More precisely, species must be present in more than 29% and 33% of northern and southern sites to recover well from its co-occurrence with IS. Therefore, it is more challenging to reconstruct biodiversity in communities closet to Tropics due to higher complex interactions and interspecific competition in these areas, which make it more difficult to infer community composition. In conclusion, our method demonstrates that it is possible to predict local species assemblages based on their implicit interactions with local IS. Nevertheless, the relatively low success of less present species illustrates the need for further theoretical development to reconstruct biodiversity, mainly to recover the occurrence of rare species.

ecology↗

bioRxiv2022

NBS-SNI, an extension of the Network-based statistic: Investigating differences in functional connections between important structural actors in case-control studies

Elucidating the coupling between the structure and the function of the brain and its development across maturation has attracted a lot of interest in the field of network neuroscience in the last fifteen years. Mounting evidence support the hypothesis that the onset of certain brain disorders is linked with the interplay between the structural architecture of the brain and its functional processes, often accompanied with unusual connectivity features. This paper introduces a method called the Network-based statistic-simultaneous node investigation (NBS-SNI) that integrates both representations into a single framework, and identifies connectivity abnormalities in case-control studies. With this method, significance is given to the properties of the nodes, as well as to their connections. This approach builds on the well-established Network-based statistic (NBS) proposed in 2010. We uncover and identify the regimes in which NBS-SNI offers a gain in statistical resolution to identify a contrast of interest using synthetic data. We also apply our method on two real case-control studies, one consisting of individuals diagnosed with autism and the other consisting of individuals diagnosed with early-psychosis. Using NBS-SNI and node properties such as the closeness centrality and local information dimension, we found hypo and hyperconnected subnetworks and show that our method can offer a 9 percentage points gain in prediction power over the standard NBS. AUTHOR SUMMARYWe propose an extension to the well-known Network-based statistic (NBS) dubbed NBS-SNI, where the extension SNI stands for simultaneous node investigation. The goal of this approach is to integrate nodal properties such as centrality measures into the statistical network-based framework of NBS to probe for abnormal connectivity between important nodes in case-control studies. We expose the regimes where NBS-SNI offers greater statistical resolution for identifying a contrast of interest using synthetic data and test the approach with a real autism-healthy dataset which contains both the structural (DTI) and functional (fMRI) brain networks of each individual. We also tested our approach on a second dataset of individuals diagnosed with early-psychosis. In the second case, our framework is supplemented by incorporating the anatomically derived measures of intrinsic curvature index and gray matter volume directly as a node property, rather than the structural networks, thereby illustrating the versatility of our approach.

neuroscience↗

bioRxiv2022

Long-term effect of forest harvesting on boreal species assemblages under climate change

Logging is the main human disturbance impacting biodiversity in forest ecosystems. However, the impact of forest harvesting on biodiversity is modulated by abiotic conditions through complex relationships that remain poorly documented. Therefore, the interplay between forest management and climate change can no longer be ignored. Our aim was to study the expected long-term variations in the assemblage of bird and beetle communities following modifications in forest management under different climate change scenarios. We developed species distribution models to predict the occurrence of 87 species of birds and beetles in eastern Canadian boreal forests over the next century. We simulated three climate scenarios (baseline, RCP4.5 and RCP8.5) under which we varied the level of harvesting. We also analyzed the regional assemblage dissimilarity by decomposing it into balanced variations in species occupancy and occupancy gradient. We predict that forest harvesting will alter the diversity by increasing assemblage dissimilarity under all the studied climate scenarios, mainly due to species turnover. Species turnover intensity was greater for ground-dwelling beetles, probably because they have lower dispersal capacity than flying beetles or birds. A good dispersal capacity allows species to travel more easily between ecosystems across the landscape when they search for suitable habitats after a disturbance. Regionally, an overall increase in the probability of occupancy is projected for bird species, whereas a decrease is predicted for beetles, a variation that could reflect differences in ecological traits between taxa. Our results further predict a decrease in the number of species that increase their occupancy after harvest under the most severe climatic scenario for both taxa. We anticipate that under severe climate change, increasing forest disturbance will be detrimental to beetles associated with old forests but also with young forests after disturbances.

ecology↗

bioRxiv2022

Effects of global change on animal biodiversity in boreal forest landscape: an assemblage dissimilarity analysis

Despite an increasing number of studies highlighting the impacts of climate change on boreal species, the main factors that will drive changes in species assemblages remain ambiguous. We quantify two climate-induced pathways based on direct and indirect effects on species occupancy and assemblage dis-similarity under different harvest management scenarios. The direct climate effects illustrate the impact of climate variables while the indirect effects are reflected through the changes in land cover composition. To understand the main causes in assemblage dissimilarity, we analyze the regional and the latitudinal species assemblage dissimilarity by decomposing it into balanced variation in species occupancy and occurrence and occupancy and occurrence gradient. We develop empirical models to predict the distribution of more than 100 bird and beetle species in the Cote-Nord region of Quebec over the next century. Our results show that the two pathways are complementary and alter biodiversity, mainly caused by balanced variation in species occupancy and occurrence. At the regional scale, both effects have an impact on decreasing the number of winning species. Yet, responses are much larger in magnitude under mixed climate effects (a mixture of direct and indirect effects). Regional assemblage dissimilarity reached 0.77 and 0.69 under mixed effects versus 0.09 and 0.10 under indirect effects for beetles and birds, respectively, between RCP8.5 and baseline climate scenarios when considering harvesting. Therefore, inclusion of climatic variables considers aspects other than just those related to forest landscapes, such as life cycles of animal species. Latitudinally, assemblage dissimilarity increased following the climate conditions pattern. Our analysis contributes to the understanding of how climate change alters biodiversity by reshaping community composition and highlights the importance of climate variables in biodiversity prediction.

ecology↗

bioRxiv2019

Actin modulates shape and mechanics of tubular membranes

The actin cytoskeleton shapes cells and also organizes internal membranous compartments. In particular, it interacts with membranes in intracellular transport of material in mammalian cells, yeast or plant cells. Tubular membrane intermediates, pulled along microtubule tracks, are involved during these processes, and destabilize into vesicles. While the role of actin in this destabilization process is still debated, literature also provide examples of membranous structures stabilization by actin. To directly address this apparent contradiction, we mimic the geometry of tubular intermediates with preformed membrane tubes. The growth of an actin sleeve at the tube surface is monitored spatio-temporally. Depending on network cohesiveness, actin is able to stabilize, or maintain membrane tubes under pulling. Indeed, on a single tube, thicker portions correlate with the presence of actin. Such structures relax over several minutes, and may provide enough time and curvature geometries for other proteins to act on tube stability.

biophysics↗

bioRxiv2018

A direct proof that sole actin dynamics drive membrane deformations

Cell membrane deformations are crucial for proper cell function. Specialized protein assemblies initiate inward or outward membrane deformations that turn into, for example, filopodia or endocytic intermediates. Actin dynamics and actin-binding proteins are involved in this process, although their detailed role remains controversial. We show here that a dynamic, branched actin network is sufficient, in absence of any membrane-associated proteins, to initiate both inward and outward membrane deformation. With actin polymerization triggered at the membrane of liposomes, we produce inward filopodia-like structures at low tension, while outward endocytosis-like structures are robustly generated regardless of tension. Our results are reminiscent of endocytosis in mammalian cells, where actin polymerization forces are required when membrane tension is increased, and in yeast, where they are always required to overcome the opposing turgor pressure. By combining experimental observations with physical modeling, we propose a mechanism for actin-driven endocytosis under high tension or high pressure conditions.

biophysics↗

bioRxiv2016

The risk of sustained sexual transmission of Zika is underestimated

Pathogens can follow more than one transmission route during outbreaks - from needle sharing plus sexual transmission of HIV to small droplet aerosol plus fomite transmission of influenza. Thus, controlling an infectious disease outbreak often requires characterizing the risk associated with multiple mechanisms of transmission. For example, during the Ebola virus outbreak in West Africa, weighing the relative importance of funeral versus health care worker transmission was essential to stopping disease spread [1]. Strategic policy decisions regarding interventions must rely on accurately characterizing risks associated with multiple transmission routes. The ongoing Zika virus outbreak challenges our conventional methodologies for translating case-counts into route-specific transmission risk. Critically, most approaches will fail to accurately estimate the risk of seeing sustained sexual transmission of a pathogen that is primarily vectored by a mosquito - ...

epidemiology↗