Effect of arbuscular mycorrhizal fungi on growth of two genotypes of Malpighia emarginata D.C.

O objetivo deste trabalho foi avaliar o efeito da inoculação de fungos micorrízicos arbusculares (FMA) no crescimento da aceroleira (Malpighia emarginata D.C.). Estacas semi-lenhosas de dois genótipos de aceroleira (Barbados e Miró), com dois pares de folhas, foram plantadas para enraizamento das mudas. Após dois meses, montou-se um experimento em telado, inoculando dois isolados de FMA (Gigaspora margarita Becker & Hall e Glomus etunicatum Becker & Gerdemann) nessas mudas, em solo com 3 mg/dm3 de fósforo. Ao término do experimento (110 dias), observou-se que a inoculação de FMA proporcionou maior altura, aumentou a biomassa seca da parte aérea e a área foliar, e evidenciou correlações positivas entre algumas características de crescimento da planta e o número de esporos de FMA, em comparação com as plantas não colonizadas. Melhores respostas de crescimento foram obtidas nos dois genótipos com a inoculação de G. margarita. A concentração de P na parte aérea das plantas não variou significativamente entre os tratamentos com inoculação. A associação com FMA reduziu em pelo menos dois meses o tempo de produção de mudas dos dois genótipos de aceroleira.The objective of this research was to evaluate the effect of inoculation with arbuscular mycorrhizal fungi (AMF) on growth of seedlings of Malpighia emarginata D.C. Stakes of these two genotypes (Barbados and Miró), with two pairs of leaves, were planted for rooting of the seedlings. After two months, a greenhouse experiment was performed, inoculating two AMF species (Gigaspora margarita Becker & Hall and Glomus etunicatum Backer & Gerdemann) under the seedlings, in soils with 3 mg/dm3 phosphorus. At the end of the experiment (110 days), it was observed that the inoculation with AMF enhanced height, shoot dry mass, leaf area, and presented positive correlations among some plant growth characters and among those and number of AMF spores, in comparison with the uninoculated controls. The inoculation with G. margarita produced higher growth responses for both genotypes. The P concentration on the shoots did not vary among treatments. The symbiosis with AMF reduced at least in two months the time for seedlings production on both genotypes of Malpighia emarginata

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

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 science. © The Author(s) 2019. Published by Oxford University Press