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Record W2302155521 · doi:10.1021/acs.jafc.5b05585

Comparative Proteome Analysis of the Tuberous Roots of Six Cassava (<i>Manihot esculenta</i>) Varieties Reveals Proteins Related to Phenotypic Traits

2016· article· en· W2302155521 on OpenAlexaff
G. Schmitz, Jonathan Andrade, Teresa Losada Valle, Carlos Alberto Labate, João Roberto Oliveira do Nascimento

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCassava research and cyanide
Canadian institutionsUniversity of Guelph
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsProteomeBiologyManihotManihot esculentaCultivarCropPhenotypeCarotenoidStarchAbiotic componentBotanyBiotechnologyFood scienceAgronomyGeneGenetics

Abstract

fetched live from OpenAlex

Cassava (Manihot esculenta Crantz) is a staple food and an important source of starch, and the attributes of its tuberous root largely depend on the variety. The proteome of cassava has been investigated; however, to date, no study has focused on varieties that reveal the molecular basis of phenotypical characteristics. Therefore, we aimed to compare the proteome of the tuberous roots of six cassava varieties that differed in carbohydrates, carotenoids, and resistance to diseases, among other attributes. Two-dimensional gels showed 146 differential spots between the varieties, and the functional roles of some differential proteins were correlated to phenotypic characteristics of the varieties, such as the amount of carbohydrates or carotenoids and the resistance to biotic or abiotic stresses. The results obtained here highlight elements that might help to direct the improvement of new cultivars of cassava, which is an economically and socially relevant crop worldwide.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score0.165

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.227
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2016
Admission routes1
Has abstractyes

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