Evidence map›Paper›PMID 38467669›Full record

ArticleScientific reports2024

Artificial neural networks optimize the establishment of a Brazilian germplasm core collection of winter squash (Cucurbita moschata D.).

Ronaldo Silva Gomes, Ronaldo Machado Júnior, Cleverson Freitas de Almeida, Rebeca Lourenço de Oliveira, Moysés Nascimento, Maicon Nardino, Wellington Ferreira do Nascimento, Derly José Henriques da Silva

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Ronaldo Silva GomesAgronomy Department, Federal University of Viçosa-UFV, PH Rolfs Avenue, Viçosa, MG, 36570-000, Brazil. ronaldo.s.gomes@ufv.br.
Ronaldo Machado JúniorAgronomy Department, Federal University of Viçosa-UFV, PH Rolfs Avenue, Viçosa, MG, 36570-000, Brazil.
Cleverson Freitas de AlmeidaAgronomy Department, Federal University of Viçosa-UFV, PH Rolfs Avenue, Viçosa, MG, 36570-000, Brazil.
Rebeca Lourenço de OliveiraAgronomy Department, Federal University of Viçosa-UFV, PH Rolfs Avenue, Viçosa, MG, 36570-000, Brazil.
Moysés NascimentoStatistics Departament, Federal University of Viçosa-UFV, PH Rolfs Avenue, Viçosa, MG, 36570-000, Brazil.
Maicon NardinoAgronomy Department, Federal University of Viçosa-UFV, PH Rolfs Avenue, Viçosa, MG, 36570-000, Brazil.
Wellington Ferreira do NascimentoScience Center of Chapadinha, Federal University of Maranhão- UFMA, 222 Rode, km 04, Chapadinha, MA, 65500-00, Brazil.
Derly José Henriques da SilvaAgronomy Department, Federal University of Viçosa-UFV, PH Rolfs Avenue, Viçosa, MG, 36570-000, Brazil.

Funding

Coordenação de Aperfeiçoamento de Pessoal e Nível Superior (CAPES) 001Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) Granted to the author Derly José Henriques da SilvaNational Council of Technological and Scientific Development (CNPq) Post doctorate scholarship grante, granted to the author Ronaldo Silva Gomes
6 · The paper itself

Abstract

With widespread cultivation, Cucurbita moschata stands out for the carotenoid content of its fruits such as β and α-carotene, components with pronounced provitamin A function and antioxidant activity. C. moschata seed oil has a high monounsaturated fatty acid content and vitamin E, constituting a lipid source of high chemical-nutritional quality. The present study evaluates the agronomic and chemical-nutritional aspects of 91 accessions of C. moschata kept at the BGH-UFV and propose the establishment of a core collection based on multivariate approaches and on the implementation of Artificial Neural Networks (ANNs). ANNs was more efficient in identifying similarity patterns and in organizing the distance between the genotypes in the groups. The averages and variances of traits in the CC formed using a 15% sampling of accessions, were closer to those of the complete collection, particularly for accumulated degree days for flowering, the mass of seeds per fruit, and seed and oil productivity. Establishing the 15% CC, based on the broad characterization of this germplasm, will be crucial to optimize the evaluation and use of promising accessions from this collection in C. moschata breeding programs, especially for traits of high chemical-nutritional importance such as the carotenoid content and the fatty acid profile.

Indexed as

CucurbitaBrazilCarotenoidsFruitPlant BreedingCarotenoidsComputational intelligenceCucurbitaFatty acid profileGermplasm collectionKohonen maps

Identifiers

PMID38467669
PMCPMC10928206

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.