15 research outputs found
Caracterização estrutural e status de conservação do estrato herbáceo de dunas da Praia de São Marcos, Maranhão, Brasil
Avaliação do uso de prática de ecodesign nas indústrias do Rio Grande do Sul: um estudo introdutório
O momento histórico atual demanda mudanças de comportamento quanto as questões ambientais, fazendo com que dois aspectos fundamentais repercutam diretamente no desenvolvimento de produto das empresas: questões internas
de reciclagem e meios de processamento de resíduos e questões externas, como pressão do governo e do mercado. Normas governamentais de preservação ao meio ambiente e a necessidade de manter vantagem competitiva, uma vez que o mercado, principalmente europeu, tem uma grande demanda por produtos verdes. No mercado brasileiro, a questão ambiental ainda não é uma das prioridades, no entanto, para atender ao mercado globalizado, torna-se necessário incluir características “verdes” ao produto, isto é desenvolver produtos considerando questões ambientais nas fases de projeto. Projetos que incluam essas questões são chamados de ecodesign. Poucas pesquisas têm sido desenvolvidas no sentido de verificar o nível de conscientização e o grau de desenvolvimento de produtos com esse enfoque. Esse artigo pretende analisar critérios que podem avaliar a adequação ambiental de empresas e desenvolver um instrumento de coleta de dados capaz de levantar questões relativas ao desenvolvimento de produtos segundo os princípios de ecodesign. No final, são tecidos alguns comentários pertinentes a uma pesquisa realizada com empresas
do estado do Rio Grande do Sul e é apresentado o instrumento de coleta de dados
Maize Yield Prediction at an Early Developmental Stage Using Multispectral Images and Genotype Data for Preliminary Hybrid Selection
Assessing crop production in the field often requires breeders to wait until the end of the season to collect yield-related measurements, limiting the pace of the breeding cycle. Early prediction of crop performance can reduce this constraint by allowing breeders more time to focus on the highest-performing varieties. Here, we present a multimodal deep learning model for predicting the performance of maize (Zea mays) at an early developmental stage, offering the potential to accelerate crop breeding. We employed multispectral images and eight vegetation indices, collected by an uncrewed aerial vehicle approximately 60 days after sowing, over three consecutive growing cycles (2017, 2018 and 2019). The multimodal deep learning approach was used to integrate field management and genotype information with the multispectral data, providing context to the conditions that the plants experienced during the trial. Model performance was assessed using holdout data, in which the model accurately predicted the yield (RMSE 1.07 t/ha, a relative RMSE of 7.60% of 16 t/ha, and R2 score 0.73) and identified the majority of high-yielding varieties, outperforming previously published models for early yield prediction. The inclusion of vegetation indices was important for model performance, with a normalized difference vegetation index and green with normalized difference vegetation index contributing the most to model performance. The model provides a decision support tool, identifying promising lines early in the field trial
Segmentation of Sandplain Lupin Weeds from Morphologically Similar Narrow-Leafed Lupins in the Field
Narrow-leafed lupin (Lupinus angustifolius) is an important dryland crop, providing a protein source in global grain markets. While agronomic practices have successfully controlled many dicot weeds among narrow-leafed lupins, the closely related sandplain lupin (Lupinus cosentinii) has proven difficult to control, reducing yield and harvest quality. Here, we successfully trained a segmentation model to detect sandplain lupins and differentiate them from narrow-leafed lupins under field conditions. The deep learning model was trained using 9171 images collected from a field site in the Western Australian grain belt. Images were collected using an unoccupied aerial vehicle at heights of 4, 10, and 20 m. The dataset was supplemented with images sourced from the WeedAI database, which were collected at 1.5 m. The resultant model had an average precision of 0.86, intersection over union of 0.60, and F1 score of 0.70 for segmenting the narrow-leafed and sandplain lupins across the multiple datasets. Images collected at a closer range and showing plants at an early developmental stage had significantly higher precision and recall scores (p-value < 0.05), indicating image collection methods and plant developmental stages play a substantial role in the model performance. Nonetheless, the model identified 80.3% of the sandplain lupins on average, with a low variation (±6.13%) in performance across the 5 datasets. The results presented in this study contribute to the development of precision weed management systems within morphologically similar crops, particularly for sandplain lupin detection, supporting future narrow-leafed lupin grain yield and quality
Genetic and Genomic Resources for Soybean Breeding Research
Soybean (Glycine max) is a legume species of significant economic and nutritional value. The yield of soybean continues to increase with the breeding of improved varieties, and this is likely to continue with the application of advanced genetic and genomic approaches for breeding. Genome technologies continue to advance rapidly, with an increasing number of high-quality genome assemblies becoming available. With accumulating data from marker arrays and whole-genome resequencing, studying variations between individuals and populations is becoming increasingly accessible. Furthermore, the recent development of soybean pangenomes has highlighted the significant structural variation between individuals, together with knowledge of what has been selected for or lost during domestication and breeding, information that can be applied for the breeding of improved cultivars. Because of this, resources such as genome assemblies, SNP datasets, pangenomes and associated databases are becoming increasingly important for research underlying soybean crop improvement
Expanding Gene-Editing Potential in Crop Improvement with Pangenomes
Pangenomes aim to represent the complete repertoire of the genome diversity present within a species or cohort of species, capturing the genomic structural variance between individuals. This genomic information coupled with phenotypic data can be applied to identify genes and alleles involved with abiotic stress tolerance, disease resistance, and other desirable traits. The characterisation of novel structural variants from pangenomes can support genome editing approaches such as Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR associated protein Cas (CRISPR-Cas), providing functional information on gene sequences and new target sites in variant-specific genes with increased efficiency. This review discusses the application of pangenomes in genome editing and crop improvement, focusing on the potential of pangenomes to accurately identify target genes for CRISPR-Cas editing of plant genomes while avoiding adverse off-target effects. We consider the limitations of applying CRISPR-Cas editing with pangenome references and potential solutions to overcome these limitations
DNABERT-based explainable lncRNA identification in plant genome assemblies
Long non-coding ribonucleic acids (lncRNAs) have been shown to play an important role in plant gene regulation, involving both epigenetic and transcript regulation. LncRNAs are transcripts longer than 200 nucleotides that are not translated into functional proteins but can be translated into small peptides. Machine learning models have predominantly used transcriptome data with manually defined features to detect lncRNAs, however, they often underrepresent the abundance of lncRNAs and can be biased in their detection. Here we present a study using Natural Language Processing (NLP) models to identify plant lncRNAs from genomic sequences rather than transcriptomic data. The NLP models were trained to predict lncRNAs for seven model and crop species (Zea mays, Arabidopsis thaliana, Brassica napus, Brassica oleracea, Brassica rapa, Glycine max and Oryza sativa) using publicly available genomic references. We demonstrated that lncRNAs can be accurately predicted from genomic sequences with the highest accuracy of 83.4% for Z. mays and the lowest accuracy of 57.9% for B. rapa, revealing that genome assembly quality might affect the accuracy of lncRNA identification. Furthermore, we demonstrated the potential of using NLP models for cross-species prediction with an average of 63.1% accuracy using target species not previously seen by the model. As more species are incorporated into the training datasets, we expect the accuracy to increase, becoming a more reliable tool for uncovering novel lncRNAs. Finally, we show that the models can be interpreted using explainable artificial intelligence to identify motifs important to lncRNA prediction and that these motifs frequently flanked the lncRNA sequence
Distribution of saltmarsh plant communities associated with environmental factors along a latitudinal gradient on the south-west Atlantic coast
Aim To produce an inventory of south-west Atlantic saltmarshes (from latitude 31 48¢ S to 43 20¢ S) using remotely sensed images and field sampling; to quantify their total area; to describe the biogeographical variation of the main habitats characterized by dominant vascular plants, in relation to major environmental factors; to test the hypothesis of predominance of the reversal pattern in plant distribution (sedges and grasses dominate the lower, regularly inundated zones, while the upper zones are occupied by more halophytic species) previously described; and to compare these south-west Atlantic saltmarshes with others world-wide
Cell proliferation markers at the invasive tumor front of oral squamous cell carcinoma: comparative analysis in relation to clinicopathological parameters of patients
Floristica da restinga de Camburi, Vitória, ES The flora of Camburi restinga, Vitória, Espírito Santo State, Brazil
Este trabalho consistiu no levantamento florístico e classificação da vegetação de restinga em Camburi, Vitória, ES. Foram realizadas coletas mensais na área de estudo, que totalizaram 211 espécies distribuídas em 70 famílias, sendo Fabaceae (19 espécies), Myrtaceae (14), Euphorbiaceae (10), Rubiaceae (10), Cyperaceae (9), Sapindaceae (7) e Lauraceae (7) as mais importantes quanto ao número de espécies. A região apresenta remanescentes das comunidades mata seca, aberta de Ericaceae e brejo herbáceo, além de áreas degradadas com espécies pioneiras. A maioria das espécies possui ampla distribuição pela costa brasileira, no entanto, outros padrões foram encontrados. Erythroxylum tênue Plowman, Ocotea nutans (Nees) Mez, Miconia brevipes Benth., Prescottia plantaginea Lindl., Pseudolaelia vellozicola (Hoehne) Porto & Brade e Coccocypselum hirsutum Bartl. ex DC. são citadas pela primeira vez para as restingas do Espírito Santo.<br>The flora of the Camburi restinga in the municipality of Vitoria, Espírito Santo State, Brazil, was surveyed and vegetation types were classified. Monthly visits to the area resulted in a list of 211 species from 70 plant families of which the most important, according to species richness, were Fabaceae (19), Myrtaceae (14), Euphorbiaceae (10), Rubiaceae (10), Cyperaceae (9), Sapindaceae (7) and Lauraceae (7). This coastal plain still supports remnant patches of dry restinga forest, open Ericaceae scrub and sedge marsh, as well as disturbed areas dominated by pioneer species. Most of the species are widely distributed along the Brazilian coast other patterns, however, being found. Erythmxylum tenue Plowman, Ocotea nutans (Nees) Mez, Miconia brevipes Benth., Prescottia pilntaginea Lindl., Pseudolaelia vellozicola (Hoehne) Porto & Brade and Coccocypselum hirsutum Bartl. ex DC. are reported for the first time in the restingas of Espírito Santo