Using Biotic Interaction Networks for Prediction in Biodiversity and Emerging Diseases

Networks offer a powerful tool for understanding and visualizing inter-species interactions within an ecology. Previously considered examples, such as trophic networks, are just representations of experimentally observed direct interactions. However, species interactions are so rich and complex it is not feasible to directly observe more than a small fraction. In this paper, using data mining techniques, we show how potential interactions can be inferred from geographic data, rather than by direct observation. An important application area for such a methodology is that of emerging diseases, where, often, little is known about inter-species interactions, such as between vectors and reservoirs. Here, we show how using geographic data, biotic interaction networks that model statistical dependencies between species distributions can be used to infer and understand inter-species interactions. Furthermore, we show how such networks can be used to build prediction models. For example, for predicting the most important reservoirs of a disease, or the degree of disease risk associated with a geographical area. We illustrate the general methodology by considering an important emerging disease - Leishmaniasis. This data mining approach allows for the use of geographic data to construct inferential biotic interaction networks which can then be used to build prediction models with a wide range of applications in ecology, biodiversity and emerging diseases

Influence of Azadirachta indica and Cnidoscolus angustidens aqueous extract on cattle ruminal gas production and degradability in vitro

Mitigation of ruminant greenhouse gas (GHG) emissions is crucial for more appropriate livestock production. Thus, there is a need of further research evaluating feed supplementation strategies to mitigate enteric GHG emissions and other gases produced within the rumen.This study was conducted as a completely randomized experimental design to determine the effectiveness of liquid extracts from A. indica (AZI), C. angustidens (CNA), or their combination (Mix. 1:1) at dosages of 0, 36, 72, and 108 mg of liquid extract/g DM substrate incubated in reducing GHG production in vitro, particularly methane (CH4), from the diet of steers during anaerobic incubation in rumen fluid. Total gas production, CH4, CO, H2S, and fermentative characteristics were all measured in vitro.Treatment AZI at a dose of 108 mg of liquid extract/g DM substrate produced the highest (P< 0.05) gas volume at 6 h, whereas CNA at a dose of 72 mg of liquid extract/ g DM substrate produced the least (P< 0.05) at 6 and 24 h, and Mix. at a dose of 72 mg of liquid extract/g DM substrate produced the least (P < 0.05) at 48 h. In addition, CH4 levels at 6 and 24 h of incubation (36 mg/g DM substrate) were highest (P< 0.05) for CNA, and lowest (P< 0.05) for AZI, whereas this variable was lowest (P< 0.05) at 72 mg of liquid extract for CNA at 24 and 48 h. At 6 and 24 h, CO volume was highest (P< 0.05) for AZI at 108 mg of liquid extract and lowest (P< 0.05) for Mix. at 72 mg of liquid extract. Treatment Mix. had a high (P< 0.05) concentration of short chain fatty acids at 72 mg of liquid extract/g DM of substrate.In general, herbaceous perennial plants, such as AZI and CNA, could be considered suitable for mitigating enteric GHG emissions from animals. Specifically, the treatment Mix. achieved a greater sustainable reduction of 67.6% in CH4 and 47.5% in H2S production when compared to either AZI. This reduction in CH4 might suggest the potential of the combination of both plant extracts for mitigating the production of GHG from ruminants

Quality of the diet during the COVID-19 pandemic in 11 Latin-American countries

Abstract Background and objectives The confinement by COVID-19 has affected the food chain and environments, which added to factors such as anxiety, frustration, fear and stress have modified the quality of the diet in the population around the world. The purpose of this study was to explore diet quality during the COVID-19 pandemic in 11 Latin American countries. Methodology Multicentric, cross-sectional study. An online survey was applied to residents of 11 Latin-American countries, during April and May 2020, when confinement was mandatory. Diet quality was evaluated using a validated questionnaire. Result 10,573 people participated in the study. The quality of the food by country shows that Colombia presented the best quality, while Chile and Paraguay presented the lowest. When comparing the overall results of diet quality by gender, schooling and age, women, people with more schooling and people under 30 years of age, presented better diet quality. The regression model showed that the variables associated with diet quality were: age (df = 3, F = 4. 57, p &lt; 0.001), sex (df = 1, F = 131.01, p &lt; 0.001), level of education (df = 1, F = 38.29, p &lt; 0.001), perception of weight change (df = 2, F = 135.31, p &lt; 0.001), basis services (df = 1, F = 8.63, p = 0.003), and quarantine (df = 1, F = 12.14, p = 0.001). Conclusion It is necessary for governments to intervene to reverse these indicators, considering that inadequate feeding favors the appearance of no communicable diseases, which favor a higher risk of infection and worse prognosis with COVID-19

VizieR Online Data Catalog: Stellar kinematics in CALIFA survey (Falcon-Barroso+, 2017)

This study is based on observations of 300 galaxies drawn from the CALIFA mother and extended samples1, which are part of the photometric catalog of the seventh data release (Abazajian et al., 2009ApJS..182..543A) of the Sloan Digital Sky Survey (SDSS). (1 data file)

VizieR Online Data Catalog: Stellar kinematics in CALIFA survey (Falcon-Barroso+, 2017)

This study is based on observations of 300 galaxies drawn from the CALIFA mother and extended samples1, which are part of the photometric catalog of the seventh data release (Abazajian et al., 2009ApJS..182..543A) of the Sloan Digital Sky Survey (SDSS). (1 data file)

Association between convalescent plasma treatment and mortality in COVID-19: a collaborative systematic review and meta-analysis of randomized clinical trials

Abstract Background Convalescent plasma has been widely used to treat COVID-19 and is under investigation in numerous randomized clinical trials, but results are publicly available only for a small number of trials. The objective of this study was to assess the benefits of convalescent plasma treatment compared to placebo or no treatment and all-cause mortality in patients with COVID-19, using data from all available randomized clinical trials, including unpublished and ongoing trials (Open Science Framework, https://doi.org/10.17605/OSF.IO/GEHFX ). Methods In this collaborative systematic review and meta-analysis, clinical trial registries (ClinicalTrials.gov, WHO International Clinical Trials Registry Platform), the Cochrane COVID-19 register, the LOVE database, and PubMed were searched until April 8, 2021. Investigators of trials registered by March 1, 2021, without published results were contacted via email. Eligible were ongoing, discontinued and completed randomized clinical trials that compared convalescent plasma with placebo or no treatment in COVID-19 patients, regardless of setting or treatment schedule. Aggregated mortality data were extracted from publications or provided by investigators of unpublished trials and combined using the Hartung–Knapp–Sidik–Jonkman random effects model. We investigated the contribution of unpublished trials to the overall evidence. Results A total of 16,477 patients were included in 33 trials (20 unpublished with 3190 patients, 13 published with 13,287 patients). 32 trials enrolled only hospitalized patients (including 3 with only intensive care unit patients). Risk of bias was low for 29/33 trials. Of 8495 patients who received convalescent plasma, 1997 died (23%), and of 7982 control patients, 1952 died (24%). The combined risk ratio for all-cause mortality was 0.97 (95% confidence interval: 0.92; 1.02) with between-study heterogeneity not beyond chance (I2 = 0%). The RECOVERY trial had 69.8% and the unpublished evidence 25.3% of the weight in the meta-analysis. Conclusions Convalescent plasma treatment of patients with COVID-19 did not reduce all-cause mortality. These results provide strong evidence that convalescent plasma treatment for patients with COVID-19 should not be used outside of randomized trials. Evidence synthesis from collaborations among trial investigators can inform both evidence generation and evidence application in patient care

Association between convalescent plasma treatment and mortality in COVID-19: a collaborative systematic review and meta-analysis of randomized clinical trials

Abstract Background Convalescent plasma has been widely used to treat COVID-19 and is under investigation in numerous randomized clinical trials, but results are publicly available only for a small number of trials. The objective of this study was to assess the benefits of convalescent plasma treatment compared to placebo or no treatment and all-cause mortality in patients with COVID-19, using data from all available randomized clinical trials, including unpublished and ongoing trials (Open Science Framework, https://doi.org/10.17605/OSF.IO/GEHFX ). Methods In this collaborative systematic review and meta-analysis, clinical trial registries (ClinicalTrials.gov, WHO International Clinical Trials Registry Platform), the Cochrane COVID-19 register, the LOVE database, and PubMed were searched until April 8, 2021. Investigators of trials registered by March 1, 2021, without published results were contacted via email. Eligible were ongoing, discontinued and completed randomized clinical trials that compared convalescent plasma with placebo or no treatment in COVID-19 patients, regardless of setting or treatment schedule. Aggregated mortality data were extracted from publications or provided by investigators of unpublished trials and combined using the Hartung–Knapp–Sidik–Jonkman random effects model. We investigated the contribution of unpublished trials to the overall evidence. Results A total of 16,477 patients were included in 33 trials (20 unpublished with 3190 patients, 13 published with 13,287 patients). 32 trials enrolled only hospitalized patients (including 3 with only intensive care unit patients). Risk of bias was low for 29/33 trials. Of 8495 patients who received convalescent plasma, 1997 died (23%), and of 7982 control patients, 1952 died (24%). The combined risk ratio for all-cause mortality was 0.97 (95% confidence interval: 0.92; 1.02) with between-study heterogeneity not beyond chance (I2 = 0%). The RECOVERY trial had 69.8% and the unpublished evidence 25.3% of the weight in the meta-analysis. Conclusions Convalescent plasma treatment of patients with COVID-19 did not reduce all-cause mortality. These results provide strong evidence that convalescent plasma treatment for patients with COVID-19 should not be used outside of randomized trials. Evidence synthesis from collaborations among trial investigators can inform both evidence generation and evidence application in patient care