Validação de marcadores moleculares e seleção assistida do gene FGR para presença de aroma em arroz.

A principal dificuldade no melhoramento de arroz aromático é a seleção dessa característica recessiva dentro de populações segregantes. Para isso, foram desenvolvidos diversos métodos sensoriais e químicos. Nos casos mais simples, estes envolvem cheirar e mastigar os grãos. Porém, a avaliação do aroma através desses métodos é onerosa, pouco confiável e necessita de um painel de analistas. Além disso, a capacidade de distinguir amostras aromáticas de não aromáticas é variável entre os analistas, diminuindo com as sucessivas avaliações, pela saturação dos sentidos ou por danos físicos na língua, causados pela abrasão ao mastigar o grão. Assim, a necessidade de se obter um método preciso e confiável para a determinação do aroma em arroz resultou em muitas pesquisas para o desenvolvimento de marcadores moleculares

BRS A701 CL: nova cultivar de arroz irrigado para o sistema Clearfield no Rio Grande do Sul.

A Embrapa apresenta seu novo lançamento comercial, a BRS A701 CL, fonte de tolerância aos herbicidas do grupo das imidazolinonas, sendo considerada de segunda geração. A BRS A701CL foi desenvolvida utilizando gene que confere resistência a herbicidas da classe das imidazolinonas, identificado na própria espécie Oryza sativa L., não havendo necessidade de ?importação? de genes de outras espécies para a composição do genoma da nova cultivar. Os testes de campo foram iniciados em 2010/11 e, em 27/10/2015 o Ministério da Agricultura, Pecuária e Abastecimento (MAPA) concedeu o registro nº 34460 para cultivo no Rio Grande do Sul

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Relatório de estágio em farmácia comunitária

Relatório de estágio realizado no âmbito do Mestrado Integrado em Ciências Farmacêuticas, apresentado à Faculdade de Farmácia da Universidade de Coimbr