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

Fungos endofíticos em Annona spp.: isolamento, caracterização enzimática e promoção do crescimento em mudas de pinha (Annona squamosa L.) Endophytic fungi of Annona spp.: isolation, enzymatic characterization of isolates and plant growth promotion in Annona squamosa L. seedlings

A partir de folhas, caules e raízes de plantas de pinha e graviola coletadas em Pernambuco foram obtidos 110 e 90 isolados fúngicos endofíticos, respectivamente. Vinte e nove isolados foram selecionados e avaliados quanto à produção de enzimas extracelulares, através do método qualitativo em placas com meios sólidos específicos, e à capacidade de estimular o crescimento de mudas de pinha. Esses isolados foram identificados como pertencentes aos gêneros Acremonium (10,34%), Aspergillus (3,45%), Chaetomium (3,45%), Colletotrichum (10,34%), Cylindrocladium (13,8%), Fusarium (31,03%), Glomerella (3,45%), Nigrospora (6,9%), Penicillium (6,9%) e Phomopsis (10,34%). Dezenove isolados apresentaram atividade lipolítica, cinco atividade proteolítica e nenhum deles atividades celulolítica ou amilolítica. Onze isolados dos gêneros Acremonium (GFR6 e GRR1), Colletotrichum (GFR4 e PFR4), Phomopsis (PFR3 e GCR4), Cylindrocladium (GRR4), Chaetomium (GRR7) e Fusarium (GRR5, PRR1 e PRR6) promoveram eficientemente o crescimento vegetal. Os índices de aumento da biomassa seca da parte aérea de mudas de pinha variou de 23,2 a 32,7%, sendo que nenhum isolado promoveu a biomassa seca da raiz. Destaca-se também que 20 isolados apresentaram efeito deletério significativo (P = 0,05) na biomassa seca da raiz das mudas de pinha. Em tecidos aparentemente sadios de plantas de pinha e graviola são encontrados alguns fungos que podem promover o crescimento da parte aérea, como também reduzir o crescimento da raiz e outros sem efeito no crescimento de mudas de pinha.<br>Endophytic isolates of fungi were obtained from leaves, stems and roots of 110 sweetsop and 90 soursop plants from Pernambuco. Twenty-nine isolates were analyzed for production of extracellular enzymes by qualitative assay in Petri dishes containing specific solid media, and for the capacity to promote growth of sweetsop seedlings. These isolates were identified as Acremonium (10.34%), Aspergillus (3.45%), Chaetomium (3.45%), Colletotrichum (10.34%), Cylindrocladium (13.8%), Fusarium (31.03%), Glomerella (3.45%), Nigrospora (6.9%), Penicillium (6.9%) and Phomopsis (10.34%). Nineteen isolates showed lypolytic activity while five showed proteolytic activity; cellulolytic and amylolytic activity were not detected. Eleven isolates of the genera Acremonium (GFR6 and GRR1), Colletotrichum (GFR4 and PFR4), Phomopsis (PFR3 and GCR4), Cylindrocladium (GRR4), Chaetomium (GRR7) and Fusarium (GRR5, PRR1 and PRR6) efficiently improved plant growth. Increase in shoot dry matter of sweetsop seedlings ranged from 23.2 to 32.7%; there was no increase in root dry matter. It is worthy of note that 20 isolates caused significant (P = 0.05) reduction in root dry matter of sweetsop seedlings. In apparently healthy tissues of sweetsop and soursop plants, some fungi promote shoot growth or reduce root growth, while others have no effect on growth of sweetsop seedlings

Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology

Gould E, Fraser H, Parker T, et al. Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology. 2023.Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different (mostly social science) fields, and has found substantial variability among results, despite analysts having the same data and research question. We implemented an analogous study in ecology and evolutionary biology, fields in which there have been no empirical exploration of the variation in effect sizes or model predictions generated by the analytical decisions of different researchers. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment), and the project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future