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

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Multiplicação in vitro de gemas axilares de acácia-negra (Acacia mearnsii De Wild.) In vitro multiplication of black watlle (Acacia mearnsii De Wild.) axillary buds

A acácia-negra (Acacia mearnsii De Wild.) é uma espécie de difícil micropropagação devido à pequena capacidade de multiplicação e desenvolvimento de gemas. O presente estudo visou determinar a influência de diferentes citocininas na proliferação de gemas axilares em segmentos nodais de A. mearnsii. Plântulas germinadas in vitro forneceram explantes que foram inoculadas em meio básico B5 (GAMBORG et al., 1968). Testaram-se as citocininas: BAP (6-benzilaminopurina), BA (6-benziladenosina), 2iP (gama,gama-isopenteniladenina) e cinetina (6-furfuralamino-purina). Diferentes concentrações desses reguladores de crescimento foram empregadas: 1 mgL-1, 2 mgL-1 e 3 mgL-1. Utilizou-se o delineamento de blocos casualizados, em arranjo fatorial, com seis repetições e cinco plantas por parcela. As avaliações foram feitas aos 30 dias, através da contagem de gemas alongadas e da presença de calos. A utilização de BAP a 2 mgL-1 promoveu a maior taxa de multiplicação de gemas (3,5 brotos/explante).<br>Black wattle (Acacia mearnsii de Wild.) is difficult to micropropagate due to the low ability of multiplication and development of shoots. Thus, the present study aimed at determining the influence of various cytokinins on axillary bud proliferation in nodal segments of A. mearnsii. Explants from in vitro germinated seedlings were inoculated on B5 (Gamborg et al., 1968) basal medium. BAP (6-benzylaminopurine), BA (6-benzyladenine), 2iP (gamma,gamma-dimethylallylamino-purine) and Kinetin (6-furfurylaminopurine) were tested at the concentrations 1 mgL-1, 2 mgL-1, and 3 mgL-1. A randomized block design, in factorial arrangement with 6 replications, and 5 plants per plot was used. The assessments were made after 30 days, by counting the elongated shoots and the presence of callus. The use of BAP at 2 mgL-1 promoted the highest rate of bud multiplication (3,5 shoots/explant)