Biology subjects

Ibanez, R.

Publications and source records attributed to Ibanez, R..

8 recordsLinked to original sources

bioRxiv2026

Wildlife damage to tropical agroforests: predicting primate-induced fruit loss using species' energy requirements

1. Quantitatively assessing wildlife damage to food crops in tropical agroforestry systems is notoriously problematic. At the same time, the animal species likely to destroy these crops, such as primates, are often endangered and a source of conflict. Resolving these conflicts may require the implementation of financial compensation where non-monetary solutions are unavailable. Currently, however, there is no suitable method for assessing fruit losses due to the complexities of agroforestry plots used by most small households practising polyculture-based agriculture. 2. We constructed an energy balance model to predict the quantity of cultivated fruit and other locally sourced food required by crop-dependent primates to meet their metabolic needs. The proposed method is based on the principle that, under conditions of energy balance, an animal's daily energy intake corresponds to its total energy expenditure. The ecophysiological model incorporated the daily food and energy intake of wild primates, measured using the bite-size method and the nutritional composition of food, as well as their total energy expenditure and body composition, measured using doubly labelled water. We tested the model's predictions on lemurs foraging in several agroforests in Mayotte, and on two frugivorous primate species in Madagascar during different seasons. Additionally, we evaluated whether more conventional dietary analysis methods (feeding-time sampling), or published energetics data could reliably inform the model, and reduce the costs and constraints of field observations. 3. The predictions were generally consistent with direct estimates of food intake (91% +- 25%, n = 14 across different seasons), though this was influenced by the lemurs evolutionary-driven seasonal fattening strategy. Time-derived estimates using correction factors or published measurements of total energy expenditure were also consistent, but further studies are required to confirm these findings. 4. Policy implications. The study provides proof of concept for a promising approach to quantifying wildlife-related agricultural losses in small-scale agroforestry. This innovative method provides policymakers, farmers' associations, and non-governmental organisations with sufficiently accurate quantitative estimates to support the development of evidence-based financial compensation schemes.

ecology↗

bioRxiv2026

Gray matter Volume Modulates the Effect of Acute Physical Activity on Reading Comprehension and Cognitive Load in Adolescents. The Cogni-Action Project

BackgroundPhysical activity has been associated with better reading comprehension and reduces cognitive load (CL), but the role of brain volume in modulating this relationship remains unclear. Therefore, this study aims to determine whether the gray matter volume in key regions modulates the effects of different physical activity modalities on reading comprehension and associated CL. MethodsThirteen male adolescents (12-13 years). Adolescents with MRI data participated in a randomized cross-over trial comparing three conditions: 1) sedentary behavior (SC, emulating a school class), 2) moderate-intensity continuous training (MICT), and 3) cooperative high-intensity interval training (C-HIIT), with physical activity conditions duration adjusted to match SC energy expenditure. Gray matter volumes were measured in the bilateral hippocampus, left pars opercularis, and the brainstem. CL was assessed via pupil dilation during reading using eye-tracking. Reading comprehension was measured through seven-question multiple-choice tests with expert-validated items. ResultsC-HIIT demonstrated superior effects on both CL and reading comprehension compared to MICT and SC, with significant brain volume modulation effects across all examined regions. Brain volume interactions with physical activity modalities systematically modified the pattern of cognitive responses, with C-HIIT consistently benefiting from these modulations, whereas the effects of MICT were generally attenuated. ConclusionThis study suggests that selecting the appropriate physical activity modality may be relevant for cognitive outcomes during reading in adolescents. C-HIIT yielded lower CL and better reading comprehension, and these effects were not explained by brain volume alone but by its interaction with exercise modality.

neuroscience↗

bioRxiv2022

Functional traits trade-offs define plant population stability worldwide

O_LIEcological theory posits that temporal stability patterns in plant populations are associated with differences in species ecological strategies. However, empirical evidence is lacking about which traits, or trade-offs, underlie species stability, specially across different ecosystems. C_LIO_LITo address this, we compiled a global collection of long-term permanent vegetation records (>7000 plots from 78 datasets) from a wide range of habitats and combined this with existing trait databases. We tested whether the observed inter-annual variability in species abundance (coefficient of variation) was related to multiple individual traits and multivariate axes of trait variations (PCoA axes). C_LIO_LIWe found that species with greater leaf dry matter content and seed mass were consistently more stable over time (lower variability in species abundance) although other leaf traits played a significant role as well, albeit weaker. Using multivariate axes did not improve predictions by specific traits. C_LIO_LIOur results confirm existing theory, providing compelling empirical evidence on the importance of specific traits, which point at ecological trade-offs in different resource use and dispersal strategies, on the stability of plant populations worldwide. C_LI

ecology↗

bioRxiv2021

LOTVS: a global collection of permanent vegetation plots

Analysing temporal patterns in plant communities is extremely important to quantify the extent and the consequences of ecological changes, especially considering the current biodiversity crisis. Long-term data collected through the regular sampling of permanent plots represent the most accurate resource to study ecological succession, analyse the stability of a community over time and understand the mechanisms driving vegetation change. We hereby present the LOng-Term Vegetation Sampling (LOTVS) initiative, a global collection of vegetation time-series derived from the regular monitoring of vascular plants in permanent plots. With 79 datasets from five continents and 7789 vegetation time-series monitored for at least six years and mostly on an annual basis, LOTVS possibly represents the largest collection of temporally fine-grained vegetation time-series derived from permanent plots and made accessible to the research community. As such, it has an outstanding potential to support innovative research in the fields of vegetation science, plant ecology and temporal ecology.

ecology↗

bioRxiv2021

Graph convolutional network for protein sequence sampling and prediction using structural data

The increasing integration between protein engineering and machine learning has led to many interesting results. A problem still to solve is to evaluate the likelihood that a sequence will fold into a target structure. This problem can be also viewed as sequence prediction from a known structure. In the current work, we propose improvements in the recent architecture of Geometric Vector Perceptrons [1] in order to optimize the sampling of sequences from a known backbone structure. The proposed model differs from the original in that there is: (i) no updating in the vectorial embedding, only in the scalar one, (ii) only one layer of decoding. The first aspect improves the accuracy of the model and reduces the use of memory, the second allows for training of the model with several tasks without incurring data leakage. We treat the trained classifier as an Energy-Based Model and sample sequences by sampling amino acids in a non-autoreggresive manner in the empty positions of the sequence using energy-guided criteria and followed by random mutation optimization. We improve the median identity of samples from 40.2% to 44.7%. An additional question worth investigating is whether sampled and original sequences fold into similar structures independent of their identity. We chose proteins in our test set whose sampled sequences show low identity (under 30%) but for which our model predicted favorable energies. We used AlphaFold [2, 3] and observed that the predicted structures for sampled sequences highly resemble the predicted structures for original sequences, with an average TM-score of 0.84.

bioinformatics↗

bioRxiv2021

Deep learning model for the prediction and classification of protein toxins across all domains of life

Toxins are widely produced by different organisms to disrupt the physiology of other organisms, and support their own existence. Their study is useful to understand protein evolution, environmental adaptation and survival competition. In-silico predictions of toxic proteins can support empirical frameworks, and help in the safety measurements needed for various industrial related processes. Some in-silico methods are slow, hard to implement or lack taxa representation in their training datasets. Here we present a deep learning model to classify protein toxins, through the use of Convolutional Neural Networks (ConvTOX). ConvTOX is able to accurately identify toxic proteins across the domains of life, with accuracies over 80% for animal and plant toxins, and over 50% for bacterial toxins. Moreover, ConvTOX is able to generalize the identification of differences among toxin types, such as neurotoxins and myotoxins, and to accurately identify structural similarities between different protein toxins. ConvTOX overcomes limitations from previous models by being able to predict toxin proteins from across all domains of life, and by not being limited to only short toxin peptides. Limitations are still clear in terms of lower accuracies for specific phylogenetic groups (such as bacterial toxins), but still this works presents itself as a one step forward for the universal use, classification and study of toxic proteins.

synthetic biology↗

bioRxiv2020

Deep learning enables the design of functional de novo antimicrobial proteins

Protein sequences are highly dimensional and present one of the main problems for the optimization and study of sequence-structure relations. The intrinsic degeneration of protein sequences is hard to follow, but the continued discovery of new protein structures has shown that there is convergence in terms of the possible folds that proteins can adopt, such that proteins with sequence identities lower than 30% may still fold into similar structures. Given that proteins share a set of conserved structural motifs, machine-learning algorithms can play an essential role in the study of sequence-structure relations. Deep-learning neural networks are becoming an important tool in the development of new techniques, such as protein modeling and design, and they continue to gain power as new algorithms are developed and as increasing amounts of data are released every day. Here, we trained a deep-learning model based on previous recurrent neural networks to design analog protein structures using representations learning based on the evolutionary and structural information of proteins. We test the capabilities of this model by creating de novo variants of an antifungal peptide, with sequence identities of 50% or lower relative to the wild-type (WT) peptide. We show by in silico approximations, such as molecular dynamics, that the new variants and the WT peptide can successfully bind to a chitin surface with comparable relative binding energies. These results are supported by in vitro assays, where the de novo designed peptides showed antifungal activity that equaled or exceeded the WT peptide.

bioengineering↗

bioRxiv2019

Lens Transmittance Shapes UV Sensitivity In The Eyes Of Frogs From Diverse Ecological And Phylogenetic Backgrounds

The amount of short wavelength (UV, violet and blue) light that reaches the retina depends on the transmittance properties of the ocular media, especially the lens, and varies greatly across species in all vertebrate groups studied previously. We measured the lens transmittance in 32 anuran amphibians with different habits, geographic distributions, and phylogenetic positions and used them together with eye size and pupil shape to evaluate the relationship with diel activity pattern, elevation and latitude. We found an unusually high lens UV transmittance in the most basal species, and a range that extends into the visible spectrum for the rest of the sample, with lenses even absorbing violet light in some diurnal species. However, other diurnal frogs had lenses that transmit UV light like the nocturnal species. This unclear pattern in the segregation of ocular media transmittance and diel activity is shared with other vertebrates and is consistent with the absence of significant correlations in our statistical analyses. Although we did not detect a significant phylogenetic effect, closely related species tend to have similar transmittances, irrespective of whether they share the same diel pattern or not, suggesting that ocular media transmittance properties might be related to phylogeny.

neuroscience↗