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Animal‐mediated pollination is a key ecosystem service required to some extent by almost three‐quarters of the leading human food crops in global food production. Anthropogenic pressures such as habitat loss and land‐use intensification are causing shifts in ecological community composition, potentially resulting in declines in pollination services and impacting crop production. Previous research has often overlooked interspecific differences in pollination contribution, yet such differences mean that biodiversity declines will not necessarily negatively impact pollination. Here, we use a novel species‐level ecosystem service contribution matrix along with mixed‐effects models to explore how groups of terrestrial species who contribute differently to crop pollination respond globally to land‐use type, land‐use intensity, and availability of natural habitats in the surrounding landscape. We find that the species whose contribution to crop pollination is higher generally respond less negatively (and in some cases positively) to human disturbance of land, compared to species that contribute less or not at all to pollination. This result may be due to these high‐contribution species being less sensitive to anthropogenic land conversions, which has led humans to being more reliant on them for crop pollination. However, it also suggests that there is potential for crop pollination to be resilient in the face of anthropogenic land conversions. With such a high proportion of food crops requiring animal‐mediated pollination to some extent, understanding how anthropogenic landscapes impact ecological communities and the consequences for pollination is critical for ensuring food security. [ABSTRACT FROM AUTHOR]

Background: The present research aims to investigate the clinical diagnostic value of LncRNA HOXA distal transcript antisense RNA (HOTTIP) in acute respiratory distress syndrome (ARDS) of sepsis and its predictive significance for mortality. Methods: One hundred eighteenth patients with sepsis and 96 healthy individuals were enrolled. RT-qPCR to examine HOTTIP levels. The incidence of ARDS and death was recorded. The diagnostic significance of HOTTIP in sepsis ARDS was examined using ROC and logistic regression analysis. The correlation between HOTTIP and disease severity was evaluated using Pearson's coefficients. Kaplan-Meier analysis and COX regression were employed to examine the predictive significance of mortality. Validation of HOTTIP target miRNA by dual-luciferase assay. Results: HOTTIP was persistently up-regulated in patients with ARDS sepsis than in patients without ARDS patients (P < 0.05). HOTTIP was a risk factor for the development of ARDS, which could be diagnosed in ARDS patients from non-ARDS patients (AUC = 0.847). Both the SOFA score (r = 0.6793) and the APACHE II score (r = 0.6384) were positively correlated with the HOTTIP levels. Furthermore, serum HOTTIP was an independent predictor of short-term mortality (HR = 4.813. 95%CI: 1.471–15.750, P = 0.009) and noticeably predicted the occurrence of short-term death (log rank = 0.020). miR-574-5p, a target miRNA for HOTTIP, was reduced in patients with sepsis ARDS and negatively correlated with HOTTIP. Conclusions: The presence of HOTTIP serves as a diagnostic biomarker for the occurrence of ARDS, exhibits correlation with disease severity, and provides predictive value of short-term mortality in sepsis patients. HOTTIP may be involved in ARDS progression by targeting miR-574-5p. [ABSTRACT FROM AUTHOR]

Although species are being lost at alarming rates, previous research has provided conflicting results on the extent and even direction of global biodiversity change at the local scale. Here, we assessed the ability to detect global biodiversity trends using local species richness and how it is affected by the number of monitoring sites, sampling interval (i.e. time between original survey and re‐survey of the site), measurement error (error of the measurement of the local species richness), spatial grain of monitoring (a proxy for the taxa mobility) and spatial sampling biases (i.e. site‐selection biases). We use PREDICTS model‐based estimates as a proxy for the real‐world distribution of biodiversity and randomly selected monitoring sites to calculate local species richness trends. We found that while a monitoring network with hundreds of sites could detect global change in species richness within a 30‐year period, the number of sites for detecting trends doubled for a decade, increased 10‐fold within three years and yearly trends were undetectable. Measurement errors had a non‐linear effect on statistical power, with a 1% error reducing statistical power by a slight margin and a 5% error drastically reducing the power to reliably detect any trend. The ability to detect global change in local species richness was also related to spatial grain, making it harder to detect trends for sites sampled at smaller plot sizes. Spatial sampling biases not only reduced the ability to detect negative global biodiversity trends but sometimes yielded positive trends. We conclude that detecting accurate global biodiversity trends using local richness may simply be unfeasible with current approaches. We suggest that monitoring a representative network of sites implemented at the national level, combined with models accounting for errors and biases, can help improve our understanding of global biodiversity change. [ABSTRACT FROM AUTHOR]

Most land on Earth has been changed by humans and past changes of land can have lasting influences on current species assemblages. Yet few globally representative studies explicitly consider such influences even though auxiliary data, such as from remote sensing, are readily available. Time series of satellite‐derived data have been commonly used to quantify differences in land‐surface attributes such as vegetation cover, which will among other things be influenced by anthropogenic land conversions and modifications. Here we quantify differences in current and past (up to five years before sampling) vegetation cover, and assess whether such differences differentially influence taxonomic and functional groups of species assemblages between spatial pairs of sites. Specifically, we correlated between‐site dissimilarity in photosynthetic activity of vegetation (the enhanced vegetation index) with the corresponding dissimilarity in local species assemblage composition from a global database using a common metric for both, the Bray–Curtis index. We found that dissimilarity in species assemblage composition was on average more influenced by dissimilarity in past than current photosynthetic activity, and that the influence of past dissimilarity increased when longer time periods were considered. Responses to past dissimilarity in photosynthetic activity also differed among taxonomic groups (plants, invertebrates, amphibians, reptiles, birds and mammals), with reptiles being among the most influenced by more dissimilar past photosynthetic activity. Furthermore, we found that assemblages dominated by smaller and more vegetation‐dependent species tended to be more influenced by dissimilarity in past photosynthetic activity than prey‐dependent species. Overall, our results have implications for studies that investigate species responses to current environmental changes and highlight the importance of past changes continuing to influence local species assemblage composition. We demonstrate how local species assemblages and satellite‐derived data can be linked and provide suggestions for future studies on how to assess the influence of past environmental changes on biodiversity. [ABSTRACT FROM AUTHOR]

Land-use change is the single biggest driver of biodiversity loss in the tropics. Biodiversity models can be useful tools to inform policymakers and conservationists of the likely response of species to anthropogenic pressures, including land-use change. However, such models generalize biodiversity responses across wide areas and many taxa, potentially missing important characteristics of particular sites or clades. Comparisons of biodiversity models with independently collected field data can help us understand the local factors that mediate broad-scale responses. We collected independent bird occurrence and abundance data along two elevational transects in Mount Kilimanjaro, Tanzania and the Taita Hills, Kenya. We estimated the local response to land use and compared our estimates with modelled local responses based on a large database of many different taxa across Africa. To identify the local factors mediating responses to land use, we compared environmental and species assemblage information between sites in the independent and African-wide datasets. Bird species richness and abundance responses to land use in the independent data followed similar trends as suggested by the African-wide biodiversity model, however the land-use classification was too coarse to capture fully the variability introduced by local agricultural management practices. A comparison of assemblage characteristics showed that the sites on Kilimanjaro and the Taita Hills had higher proportions of forest specialists in croplands compared to the Africa-wide average. Local human population density, forest cover and vegetation greenness also differed significantly between the independent and Africa-wide datasets. Biodiversity models including those variables performed better, particularly in croplands, but still could not accurately predict the magnitude of local species responses to most land uses, probably because local features of the land management are still missed. Overall, our study demonstrates that local factors mediate biodiversity responses to land use and cautions against applying biodiversity models to local contexts without prior knowledge of which factors are locally relevant. [ABSTRACT FROM AUTHOR]

Remotely sensed data - available at medium to high resolution across global spatial and temporal scales - are a valuable resource for ecologists. In particular, products from NASA's MODerate-resolution Imaging Spectroradiometer ( MODIS), providing twice-daily global coverage, have been widely used for ecological applications. We present MODISTools, an R package designed to improve the accessing, downloading, and processing of remotely sensed MODIS data. MODISTools automates the process of data downloading and processing from any number of locations, time periods, and MODIS products. This automation reduces the risk of human error, and the researcher effort required compared to manual per-location downloads. The package will be particularly useful for ecological studies that include multiple sites, such as meta-analyses, observation networks, and globally distributed experiments. We give examples of the simple, reproducible workflow that MODISTools provides and of the checks that are carried out in the process. The end product is in a format that is amenable to statistical modeling. We analyzed the relationship between species richness across multiple higher taxa observed at 526 sites in temperate forests and vegetation indices, measures of aboveground net primary productivity. We downloaded MODIS derived vegetation index time series for each location where the species richness had been sampled, and summarized the data into three measures: maximum time-series value, temporal mean, and temporal variability. On average, species richness covaried positively with our vegetation index measures. Different higher taxa show different positive relationships with vegetation indices. Models had high R2 values, suggesting higher taxon identity and a gradient of vegetation index together explain most of the variation in species richness in our data. MODISTools can be used on Windows, Mac, and Linux platforms, and is available from CRAN and GitHub (). [ABSTRACT FROM AUTHOR]

INTRODUCTION Use of imaging to predict pre-operative likelihood of histopathology in pediatric tumors involving the cerebellum and/or fourth ventricle is potentially beneficial to the neurosurgical and neuro-oncological teams. The objective of the current report was to assess the correlation between radiologic evidence of tumor exit from the outlet foramina of the fourth ventricle with final histopathologic diagnosis. METHODS In 50 consecutive pediatric patients who underwent craniotomy for primary resection of a cerebellar neoplasm, pre-operative MRI was reviewed for evidence of tumor exit from the outlet foramina of fourth ventricle. RESULTS Evidence of tumor exit from the outlet foramina of fourth ventricle on MRI was found to be especially predictive of ependymoma. Specifically, this radiologic finding was 90% specific (37/41) and 100% sensitive (9/9) for histopathologic diagnosis of ependymoma. Of note, 2 of 18 medulloblastomas and 2 of 2 grade IV gliomas also demonstrated evidence of tumor exit. CONCLUSION Evidence of tumor exit from the outlet foramina of fourth ventricle on pre-operative MRI predicts histopathologic diagnosis of ependymoma with high accuracy. [ABSTRACT FROM AUTHOR]