Biology subjects

Nascimento, M.

Publications and source records attributed to Nascimento, M..

10 recordsLinked to original sources

bioRxiv2026

Challenges of Dry Sanitization to Control Salmonella Dry Surface Biofilms

Salmonella is a pathogen linked to foodborne outbreaks, including low-moisture foods. Its ability to resist desiccation can contribute to the formation of dry surface biofilms (DSB). This study evaluated the impact of 3 DSB formation protocols (P1=48-h hydrated phase/48-h dry phase, P2=24-h/120-h and P3=8-h/48-h) on the resistance of Salmonella DSB to 70% alcohol, a commercial product (based on 0.015% quaternary ammonium and 25% isopropyl alcohol), gaseous ozone (45 ppm), hot air (90 {degrees}C) and UV-C light (254 nm). The type of DSB protocol impacted the efficacy of the sanitizers (p < 0.05). The biofilm with the shortest hydration phase showed the greatest susceptibility; three out of the five sanitizers evaluated (70% alcohol, commercial product, and UV-C) promoted significant reductions in P3, with counts below the detection limit (0.8 log CFU/cm{superscript 2}) after 5 to 15 min exposure. Regarding protocols P1 and P2, in general, the best performance was from UV-C, especially against DSB on polypropylene, where it achieved reductions of 1.3 log CFU/cm{superscript 2} for P1 and 2.9 log CFU/cm{superscript 2} for P2 after 15 to 30 min of exposure. In contrast, hot air and ozone showed less effectiveness, with reductions [≤]1.2 log CFU/cm{superscript 2}. In most scenarios, confocal microscopy images corroborated the plate count results (log CFU/cm{superscript 2}). In summary, our data indicates limited action of dry sanitizers on Salmonella DSB, requiring validation and optimization of sanitization processes to ensure the microbiological safety of low-moisture products.

microbiology↗

bioRxiv2026

Optimizing resource allocation in Miscanthus breeding with sparse testing designs for genomic prediction

Phenotyping high-biomass perennial crops is laborious and the rate of genetic gain in perennial crop breeding programs is typically low. So, it is especially important to identify methods that produce efficiency gains in the breeding process. Miscanthus is a C4 perennial grass with favorable characteristics for producing biomass as a feedstock for biofuels and diverse biobased products. Increasing biomass yield will increase profitability and environmental benefits, so is a key target for Miscanthus breeding. In addition, the identification of well-adapted genotypes across a wide range of environmental conditions requires the establishment of multi-environment trials (METs). Sparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations. A Miscanthus sacchariflorus (MSA) population comprising 336 genotypes observed across three environments was analyzed. Three prediction models considering main effects (environments, genotypes, genomic) and interaction effects (genotype-by-environment; GxE interaction) were implemented for forecasting dry biomass yield (YDY), total culm (TCM), average internode length (AIL), and culm node number (CNN). Multiple calibration sets based on different compositions and sizes were considered to evaluate performance in terms of the predictive ability (PA) and the mean square error (MSE) for a fixed testing set size. The training set size ranged from 52 to 112 to predict a fixed set of 224 unobserved genotypes across all three environments. The results showed that the model accounting for GxE interaction presented the highest PA and the lowest MSE for CNN (PA: [~]0.77, MSE: [~]0.5) and YDY (PA: [~]0.70, MSE: [~]1.3) while for TCM and AIL these ranged from [~]0.28 to 0.41 and [~]1.3 to 4.3, respectively. Overall, varying training sets and allocation strategies did not affect PA and MSE, with 52 non-overlapping and 0 overlapping genotypes per environment as the optimal cost-effective allocation framework. This suggests that implementing sparse testing designs could significantly reduce phenotyping costs by fivefold, without compromising PA in breeding programs for perennial crops such as Miscanthus.

genomics↗

bioRxiv2026

Multi-trait Multi-environment Genomic Prediction Strategies for Miscanthus sacchariflorus Populations

Genomic selection holds the potential to serve as a strategic tool to enhance the genetic gain of complex traits in Miscanthus breeding programs. The development of improved cultivars requires their assessment for various traits across diverse environments to ensure suitable overall performance. Hence, the multi-trait multi-environment (MTME) genomic prediction (GP) models offer an opportunity to improve selection accuracy. This study aims to evaluate the potential of five GP models: (1) three MTME models including genotype-by-trait-by-environment interaction (GxExT) and (2) two single-trait multi-environment (STME) models (with and without GxE interaction). A Miscanthus sacchariflorus population comprising 336 genotypes evaluated in three environments and scored for four traits (biomass yield YDY, total culm number TCM, average internode length AIL, and culm node number CNN) was analyzed. The predictive ability of the models was evaluated considering three cross-validation schemes resembling realistic scenarios (CV1: predicting new genotypes, CVP: predicting missing traits in a given environment, and CV2: predicting partially observed genotypes). On average, in all cross-validation schemes compared to the STME the predictive ability of the MTME models was 10% to 70% higher for TCM and AIL. On the other hand, for YDY and CNN, both STME models performed similarly or slightly better (between 5 to 64%) than the MTME models in most environments. While the MTME models were not successful for all traits when compared to their STME counterparts, MTME models improved the prediction of the performance of genotypes that were untested across environments or lacked trait information in a specific environment. Overall, our study suggests that MTME GP models can be implemented in Miscanthus breeding programs to improve the predictive ability of the complex traits, shorten breeding cycles, and accelerate selection decisions.

genomics↗

bioRxiv2026

Weather Characterization for Optimizing Genomic Prediction in Miscanthus sacchariflorus

Environmental factors affect crop growth and development thus their consideration across sites and years become essential for genotypic evaluation. Genomic selection (GS) has been broadly implemented to accelerate breeding cycles by skipping field evaluations thus allowing early identification of outperforming genotypes. In this study, 7,740 phenotypic records corresponding to 516 Miscanthus sacchariflorus genotypes evaluated in five locations across three years were considered for analysis. Additionally, environmental data on six weather covariates was implemented to characterize similarities between locations. Different sets of locations of variable sizes were used for model calibration based on two cross-validations (CV00 and CV0) schemes leaving out one location at a time. Predictive ability across locations of the best model varied between 0.45 and 0.90 for both schemes. These results were compared to associate predictive ability in function of weather patterns between training and testing sets to allow models calibration optimization. We found it is feasible to optimize resource allocation by considering environmentally correlated sets. In most cases, the information from only one and, at most, two locations were enough to deliver better results than using all four locations, reducing training sets by up to 75%. The results obtained shed light on helping breeders make informed decisions considering weather data when designing evaluations.

genomics↗

bioRxiv2026

ENHANCING GENOMIC PREDICTION MODELS IN MISCANTHUS POPULATIONS BY INCORPORATING THE GENOTYPE-BY-ENVIRONMENT INTERACTION

Giant Miscanthus giganteus (Mxg) is one of the most promising perennial crops to generate biomass feedstock for bioenergy and biobased products. It is derived from the natural inter-species hybridization of Miscanthus sacchariflorus (Msa) and Miscanthus sinensis (Msi) species, thus population improvement within these species is crucial. Genomic selection (GS) is an attractive option to accelerate breeding of perennial grasses, such as Miscanthus, which requires up to three years of evaluation to produce reliable phenotypic data. Hence, genotypes are observed in multiple years and locations causing inconsistent response patterns from one year to the next, between location, and/or location-by-year combinations. These inconsistencies are known as the genotype-by-environment interaction effect (GxE). Although GS has been successfully implemented in multiple annual crops where straightforward cross-validation schemes exist to assess the levels of predictive ability that can be reached, for perennial crops new cross-validation schemes will help avoid data contamination. Here, we propose a series of cross-validation schemes to evaluate model performance for perennial crops. We perform a case study by analyzing one panel of each species (516 genotypes of Msa, 280 genotypes of Msi) scored for biomass yield at different locations around the world over several years. The results of the different cross-validation schemes provide insights about the usefulness of GS to accelerate the breeding process of Miscanthus species. In addition, leveraging the GxE effects of different types significantly increases predictive ability (up to 10% in Msa and 30% for Msi) compared to the conventional approaches based on main effects only.

genomics↗

bioRxiv2025

Neuronal expression of S100B triggered by oligomeric Aβ peptide protects against cytoskeletal damage and synaptic loss

Alzheimers disease (AD) is a complex neurodegenerative disorder characterized by the intracellular deposition of Tau protein and extracellular deposition of amyloid-{beta} peptide (A{beta}). AD is also characterized by neuroinflammation and synapse loss, among others. The S100 family is a group of calcium-binding proteins with intra- and extracellular functions, that are important modulators of inflammatory responses. S100B, which is upregulated in AD patients and the most abundant member of this family, was shown to inhibit in vitro the aggregation and toxicity of A{beta}42, acting as a neuroprotective holdase-type chaperone. Although S100B is primarily produced by astrocytes, it is also expressed by various cells, including neurons. In this work, we investigated if S100B neuronal expression is triggered as a response to A{beta} toxic species, to provide protection during disease progression. We used the AD mouse model A{beta}PPswe/PS1A246E to show that neuronal S100B levels are significantly higher in 10-month-old animals, and cellular assays to demonstrate that A{beta} oligomers significantly increase S100B expression in SH-SY5Y cells, but not monomeric or fibrillar A{beta}. Using primary cultures of rat hippocampal neurons, we showed that S100B partially reverts A{beta}-induced cofilin-actin rods (synapse disruptors), and rescues the decrease in active synapses and post- (PSD-95) synaptic marker, imposed by A{beta} peptide. Altogether, these findings establish the neuroprotective activity of S100B in response to proteotoxic stress in cells, highlighting its chaperone function as a crucial factor in understanding proteostasis regulation in the diseased brain and identifying potential therapeutic targets.

neuroscience↗

bioRxiv2023

Using visual scores and categorical data for genomic prediction of complex traits in breeding programs

Most genomic prediction methods are based on assumptions of normality due to their simplicity, robustness, and ease of implementation. However, in plant and animal breeding, target traits are often collected as categorical data, thus violating the normality assumption, which could affect the prediction of breeding values and the estimation of crucial genetic parameters. In this study, we examined the main challenges of categorical phenotypes in genomic prediction and genetic parameter estimation using mixed models, Bayesian approaches, and machine learning techniques. We evaluated these approaches using simulated and real breeding data sets. Our contribution in this study is a five-fold demonstration: (i) collecting data using an intermediate number of categories (1 to 3 and 1 to 5 scores) is the best strategy, even considering errors and subjectivity associated with visual scores; (ii) in the context of genomic prediction, Linear Mixed Models and Bayesian Linear Regression Models are robust to the normality violation, but marginal gains can be achieved when using Bayesian Ordinal Regression Models (BORM) and Random Forest Classification technique; (iii) genetic parameters are better estimated using BORM; (iv) our conclusions using simulated data are also applicable to real data in autotetraploid blueberry, which can guide breeders decisions; and (v) a comparison of continuous and categorical phenotype testing for complex traits with low heritability, found that investing in the evaluation of 600-1000 categorical data points with low error, when it is not feasible to collect continuous phenotypes, is a strategy for improving predictive abilities. Our findings suggest the best approaches for effectively using categorical traits to explore genetic information in breeding programs, and highlight the importance of investing in the training of evaluator teams and in high-quality phenotyping. Key messageAn approach for handling categorical data with potential errors and subjectivity in scores was evaluated in simulated and blueberry recurrent selection breeding schemes to assist breeders in their decision-making.

genetics↗

bioRxiv2022

Single and Multi-trait Genomic Prediction for agronomic traits in Euterpe edulis

Popularly known as jucaizeiro, Euterpe edulis has been gaining prominence in the fruit growing sector and has demanded the development of superior genetic materials. As it is a native species and still little studied, the application of more sophisticated techniques can result in higher gains with less time. Until now, there are no studies that apply genomic prediction for this crop, especially in multi-trait analysis. In this sense, this study aimed to apply new methods and breeding techniques for the jucaizeiro, to optimize this breeding program through the application of genomic selection. This data consisted of 275 jucaizeiro genotypes from a population of Rio Novo do Sul-ES, Brazil. The genomic prediction was performed using the multi-trait (G-BLUP MT) and single-trait (G-BLUP ST) models and the selection of superior matrices was based on the selection index of Mulamba and Mock. Similar results for predictive ability were observed for both models. However, the G-BLUP ST model provided greater selection gains when compared to the G-BLUP MT. For this reason, the genomic estimated breeding values (GEBVs) from the G-BLUP ST were used to select the six superior genotypes (UFES.A.RN.390, UFES.A.RN.386, UFES.A.RN.080, UFES.A.RN.383, UFES.S.RN.098, and UFES.S.RN.093), to provide superior genetic materials for the development of seedlings and implantation of productive orchards, which will meet the demands of the productive, industrial and consumer market. Key messageIn the first genomic selection study for Euterpe edulis, substantial gains for multiple traits of fruit production was reported. This is a key factor for the sustainable use of the species in the Atlantic Forest.

genetics↗

bioRxiv2021

Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice

The biggest challenge for the reproduction of flood-irrigated rice is to identify superior genotypes that present development of high-yielding varieties with specific grain qualities, resistance to abiotic and biotic stresses in addition to superior adaptation to the target environment.Thus, the objectives of this study were to propose a multi-trait and multi-environment Bayesian model to estimate genetic parameters for the flood-irrigated rice crop. To this end, twenty-five rice genotypes belonging to the flood-irrigated rice breeding program were evaluated. Grain yield and flowering were evaluated in the agricultural year 2017/2018. The experimental design used in all experiments was a randomized block design with three replications. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. The flowering is highly heritable by the Bayesian credibility interval: h2 = 0.039-0.80, and 0.02-0.91, environment 1 and 2, respectively. The genetic correlation between traits was significantly different from zero in the two environments (environment 1: -0.80 to 0.74; environment 2: -0.82 to 0.86. The relationship of CVe and CVg higher for flowering in the reduced model (CVg/CVe= 5.83 and 13.98, environments 1 and 2, respectively). For the complete model, this trait presented an estimate of the relative variation index of: CVe= 4.28 and 4.21, environments 1 and 2, respectively. In summary, the multi-trait and multi-environment Bayesian model allowed a reliable estimate of the genetic parameter of flood-irrigated rice. Bayesian analyzes provide robust inference of genetic parameters. Therefore, we recommend this model for genetic evaluation of flood-irrigated rice genotypes, and their generalization, in other crops. Precise estimates of genetic parameters bring new perspectives on the application of Bayesian methods to solve modeling problems in the genetic improvement of flood-irrigated rice.

plant biology↗

bioRxiv2018

Subset selection of markers for genome-enabled prediction of genetic val-ues using radial basis function neural networks

This paper aimed to evaluate the efficiency of subset selection of markers for genome-enabled prediction of genetic values using radial basis function neural networks (RBFNN). For this purpose, an F1 population from hybridization of divergent parents with 500 individuals genotyped with 1,000 SNP-type markers was simulated. Phenotypic traits were determined by adopting three different gene action models - additive, additive-dominant, and epistasic, complying with two dominance situations: partial and complete with quantitative traits admitting heritability (h2) equal to 30 and 60%, each one controlled by 50 loci, considering two alleles per locus, totaling 12 different scenarios. To evaluate the predictive ability of RR_BLUP and the neural networks, a cross-validation procedure with five replicates were trained using 80% of the individuals of the population. Two methods were used: dimensionality reduction and stepwise regression. The square of the correlation between the predicted genomic estimated breeding value (GEBV) and the phenotype value was used to measure predictive reliability. For h2 = 0.3 in the additive scenario, the R2 values were 59% for neural network (RBFNN) and 57% for RR-BLUP, and in the epistatic scenario, R2 values were 50% and 41%, respectively. Additionally, when analyzing the mean-squared error root, the difference in performance between the techniques is even greater. For the additive scenario, the estimates were 91 for RR-BLUP and 5 for neural networks and, in the most critical scenario, they were 427 for RR-BLUP and 20 for neural network. The results showed that the use of neural networks and variable selection techniques allows capturing epistasis interactions, leading to an improvement in the accuracy of prediction of the genetic value and, mainly, to a large reduction of the mean square error, which indicates greater genomic value.

genetics↗