Explore the vast range of titles available.

BackgroundThe root system plays a major role in plant growth and development and root system architecture is reported to be the main trait related to plant adaptation to drought. However, phenotyping root systems in situ is not suited to high-throughput methods, leading to the development of non-destructive methods for evaluations in more or less controlled root environments. This study used a root phenotyping platform with a panel of 20 japonica rice accessions in order to: (i) assess their genetic diversity for a set of structural and morphological root traits and classify the different types; (ii) analyze the plastic response of their root system to a water deficit at reproductive phase and (iii) explore the ability of the platform for high-throughput phenotyping of root structure and morphology.ResultsHigh variability for the studied root traits was found in the reduced set of accessions. Using eight selected traits under irrigated conditions, five root clusters were found that differed in root thickness, branching index and the pattern of fine and thick root distribution along the profile. When water deficit occurred at reproductive phase, some accessions significantly reduced root growth compared to the irrigated treatment, while others stimulated it. It was found that root cluster, as defined under irrigated conditions, could not predict the plastic response of roots under drought.ConclusionsThis study revealed the possibility of reconstructing the structure of root systems from scanned images. It was thus possible to significantly class root systems according to simple structural traits, opening up the way for using such a platform for medium to high-throughput phenotyping. The study also highlighted the uncoupling between root structures under non-limiting water conditions and their response to drought.

Cassava (Manihot esculenta Crantz) is a staple food and a key industrial crop across tropical regions, but traditional phenotyping for critical quality traits like dry matter content (DMC) and starch content (StC) is a laborious and low-throughput process. This study investigates the efficacy of a handheld near-infrared spectrometer device (NIRS) for the non-destructive, rapid prediction of these traits. The research methodology involved collecting spectral data from 2,236 cassava clones from 19 field trials in Brazil, using two sample types: fresh roots and mashed roots. Six spectral pre-processing methods and three machine learning algorithms-Partial Least Squares (PLS), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGB)-were evaluated to optimize predictive models. Model performance was assessed using the coefficient of determination in calibration ([Formula: see text]), the root mean squared error of calibration ([Formula: see text]), and the Kappa index to quantify the consistency of clone selection. Results show that mashed samples consistently yielded superior predictive performance across all models. Specific preprocessing methods, such as Savitzky-Golay filtering combined with Standard Normal Variate (SG + SNV) and first-derivative transformations, significantly enhanced model accuracy. Among the algorithms, PLS demonstrated the best overall performance, with high predictive accuracy ([Formula: see text] >0.96) and low prediction errors ([Formula: see text]<1.3 for DMCo), especially with mashed samples. High Kappa index values, consistently approaching 1.0, confirmed a good alignment between NIRS-based selection and traditional phenotypic methods. This study validates a portable spectrometer as a reliable and efficient tool for high-throughput phenotyping in cassava breeding programs. The findings confirm that portable NIRS devices, when used with optimal sample preparation (mashed roots) and robust modeling (PLS), can effectively yield good predictions for plant selection. This approach can significantly accelerate breeding cycles by enabling rapid, early-stage selection decisions, thereby overcoming a major bottleneck and contributing to a more efficient and sustainable genetic improvement of cassava.

Abstract Fuzzy logic enables automated decision-making and classifies genotype suitability across environments. This study assessed the adaptability and stability of upland rice genotypes using fuzzy logic. To do so, eight lines from the Federal University of Goiás, 10 from the Federal University of Lavras, and two commercial cultivars were evaluated for grain yield, plant height, and flowering days in 13 environments in Goiás. The trials used a randomized complete block design with three replications. Adaptability and stability were analyzed using fuzzy controllers, which classified genotypes into four groups: general adaptability, poorly adapted, favorable, and unfavorable environments. The CSD 08004 line exhibited broad adaptability and stability for yield and plant height and was close to general adaptability for flowering days, making it suitable for cultivation in Goiás.

Selecting appropriate donors and acquiring information about the genetic basis of inheritance is essential for breeding programs. In this study, a diallel cross was produced by crossing 15 progenies with five commercial lines of wide diversity for different rice traits (grain yield, plant height, days to flowering, panicle blast, brown spots, leaf scald, and grain discoloration) in an incomplete crossing design. The 20 parents and the 25 F2 crosses constituting the diallel cross were evaluated in a triple lattice design for different traits in a field test. The analysis of variance revealed significant differences between parents and their crosses for all traits, showing high variability. The general combining ability (GCA) and the specific combining ability (SCA) were significant, with a greater contribution of the SCA compared to GCA for the variation among crosses, indicating that non-additive effects were more prevalent for the traits evaluated. The results suggested that commercial lines such as IRGA 424 and BRS Catiana can be used to improve CNA 12T population.

To attain success with selection process, the breeders need to understand the genetic variability into each population developed by breeding program. In this purpose, breeders need to estimate the variance components and genetic and phenotypic parameters. Besides, they need to have the ability to select simultaneously genetically top progenies for multiple traits. This study used a field trial, with a panel of 198 S0:2 lowland rice progenies, evaluated in three environments during two years in order to: (i) estimate genetic parameters for different traits in a rice breeding population, (ii) evaluate the impact of direct selection and selection performed through three selection indices in a rice population developed by recurrent selection, and (iii) compare three selection indices using different economic weights with direct and indirect selection. Significant genetic variation was found among progenies for: grain yield, plant height, days to flowering, panicle blast, leaf scald and grain discoloration. The direct selection provided high gains for single trait selection, but this method may results in undesirable changes in related traits. Thus, between the evaluated indices and in comparison, to indirect selection the Mulamba and Mock index provided simultaneously greater gains for all the evaluated traits. Furthermore, we found that when the random weights and b-variation index were used as economic weight, it was found lower gains or no gains, showing that the gains attained with Smith and Hazel and Tai indices were linked to economic weights used.

In the above mentioned publication, the affiliations of the last 4 authors were interchanged.

Temperature is a determining factor for common bean production in several regions of Brazil. This study formed a thematic collection for high temperatures of mulatto common bean from the Active Germplasm Bank of Embrapa Rice and Beans. The experiments were conducted under field conditions in suitable temperature environment (STE), and in a high temperature environment (HTE) where 94 accessions were evaluated. Joint analysis of the agronomic data obtained in these experiments, together with the database of ecogeographical and morphological characterization of mulatto common bean accessions constituted the study to elaborate the thematic collection for high temperature conditions, totaling 30 accessions, of which 28 had their root growth characterized in a rhizotron system in a greenhouse. Data analysis identified superior genotypes in relation to the studied characteristics, but there were no significant correlations. It was observed that the grain yield averages per accession in the field experiments in STE and in HTE of the 96 accessions were 2068.9 and 1227.2 kg ha −1 , respectively, altered to the value of 2400.8 and 1271.5 kg ha −1 when selecting the 30 accessions which compose the thematic collection. The root architecture varied for the different evaluated accessions. The resulting thematic core collection potentially reflects the maximum diversity of mulatto common beans in a smaller sample size for high temperatures.

Selecting appropriate donors and acquiring information about the genetic basis of inheritance is essential for breeding programs. In this study, a diallel cross was produced by crossing 15 progenies with five commercial lines of wide diversity for different rice traits (grain yield, plant height, days to flowering, panicle blast, brown spots, leaf scald, and grain discoloration) in an incomplete crossing design. The 20 parents and the 25 F2 crosses constituting the diallel cross were evaluated in a triple lattice design for different traits in a field test. The analysis of variance revealed significant differences between parents and their crosses for all traits, showing high variability. The general combining ability (GCA) and the specific combining ability (SCA) were significant, with a greater contribution of the SCA compared to GCA for the variation among crosses, indicating that non-additive effects were more prevalent for the traits evaluated. The results suggested that commercial lines such as IRGA 424 and BRS Catiana can be used to improve CNA 12T population.

Transcriptome studies have reported the dysregulation of cell cycle-related genes and the global inhibition of host mRNA translation in COVID-19 cases. However, the key genes and cellular mechanisms that are most affected by the severe outcome of this disease remain unclear. For this work, the RNA-seq approach was used to study the differential expression in buffy coat cells of two groups of people infected with SARS-CoV-2: (a) Mild, with mild symptoms; and (b) SARS (Severe Acute Respiratory Syndrome), who were admitted to the intensive care unit with the severe COVID-19 outcome. Transcriptomic analysis revealed 1009 up-regulated and 501 down-regulated genes in the SARS group, with 10% of both being composed of long non-coding RNA. Ribosome and cell cycle pathways were enriched among down-regulated genes. The most connected proteins among the differentially expressed genes involved transport dysregulation, proteasome degradation, interferon response, cytokinesis failure, and host translation inhibition. Furthermore, interactome analysis showed Fibrillarin to be one of the key genes affected by SARS-CoV-2. This protein interacts directly with the N protein and long non-coding RNAs affecting transcription, translation, and ribosomal processes. This work reveals a group of dysregulated processes, including translation and cell cycle, as key pathways altered in severe COVID-19 outcomes.

Xenarthrans—anteaters, sloths, and armadillos—have essential functions for ecosystem maintenance, such as insect control and nutrient cycling, playing key roles as ecosystem engineers. Because of habitat loss and fragmentation, hunting pressure, and conflicts with domestic dogs, these species have been threatened locally, regionally, or even across their full distribution ranges. The Neotropics harbor 21 species of armadillos, 10 anteaters, and 6 sloths. Our data set includes the families Chlamyphoridae (13), Dasypodidae (7), Myrmecophagidae (3), Bradypodidae (4), and Megalonychidae (2). We have no occurrence data on Dasypus pilosus (Dasypodidae). Regarding Cyclopedidae, until recently, only one species was recognized, but new genetic studies have revealed that the group is represented by seven species. In this data paper, we compiled a total of 42,528 records of 31 species, represented by occurrence and quantitative data, totaling 24,847 unique georeferenced records. The geographic range is from the southern United States, Mexico, and Caribbean countries at the northern portion of the Neotropics, to the austral distribution in Argentina, Paraguay, Chile, and Uruguay. Regarding anteaters, Myrmecophaga tridactyla has the most records (n = 5,941), and Cyclopes sp. have the fewest (n = 240). The armadillo species with the most data is Dasypus novemcinctus (n = 11,588), and the fewest data are recorded for Calyptophractus retusus (n = 33). With regard to sloth species, Bradypus variegatus has the most records (n = 962), and Bradypus pygmaeus has the fewest (n = 12). Our main objective with Neotropical Xenarthrans is to make occurrence and quantitative data available to facilitate more ecological research, particularly if we integrate the xenarthran data with other data sets of Neotropical Series that will become available very soon (i.e., Neotropical Carnivores, Neotropical Invasive Mammals, and Neotropical Hunters and Dogs). Therefore, studies on trophic cascades, hunting pressure, habitat loss, fragmentation effects, species invasion, and climate change effects will be possible with the Neotropical Xenarthrans data set. Please cite this data paper when using its data in publications. We also request that researchers and teachers inform us of how they are using these data.