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ABSTRACT 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 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

Abstract Background MicroRNA (miRNA) plays a critical function in the progression of acute coronary syndrome (ACS) and is associated with major adverse cardiovascular events (MACEs) after undergoing percutaneous coronary intervention (PCI). This research was designed to probe the diagnostic accuracy of miR-483-5p in patients with ACS and its predictive value of MACEs. Methods 118 patients with ACS (40 with unstable angina pectoris [UAP] and 78 with acute myocardial infarction [AMI]) and 75 healthy controls were enrolled. Serum miR-483-5p was detected in the subjects by reverse transcription-quantitative real-time PCR (RT-qPCR). ROC curve and logistic regression models were employed to estimate the diagnosis. Patients were monitored for 6 months after PCI to document the occurrence of MACEs. Kaplan-Meier survival was conducted to explore the predictive significance of miR-483-5p for the MACEs. Results Serum miR-483-5p levels were higher in ACS patients and associated with SYNTAX score and Gensini score. miR-483-5p was effective in identifying ACS patients from healthy individuals (AUC = 0.919) and AMI patients from ACS patients (AUC = 0.867), demonstrating a high diagnostic value, proven by logistic regression (OR = 9.664, 95%CI = 4.462–20.928, P

Background Biodiversity varies in space and time, and often in response to environmental heterogeneity. Indicators in the form of local biodiversity measures–such as species richness or abundance–are common tools to capture this variation. The rise of readily available remote sensing data has enabled the characterization of environmental heterogeneity in a globally robust and replicable manner. Based on the assumption that differences in biodiversity measures are generally related to differences in environmental heterogeneity, these data have enabled projections and extrapolations of biodiversity in space and time. However so far little work has been done on quantitatively evaluating if and how accurately local biodiversity measures can be predicted. Methods Here I combine estimates of biodiversity measures from terrestrial local biodiversity surveys with remotely-sensed data on environmental heterogeneity globally. I then determine through a cross-validation framework how accurately local biodiversity measures can be predicted within (“predictability”) and across similar (“transferability”) biodiversity surveys. Results I found that prediction errors can be substantial, with error magnitudes varying between different biodiversity measures, taxonomic groups, sampling techniques and types of environmental heterogeneity characterizations. And although errors associated with model predictability were in many cases relatively low, these results question–particular for transferability–our capability to accurately predict and project local biodiversity measures based on environmental heterogeneity. I make the case that future predictions should be evaluated based on their accuracy and inherent uncertainty, and ecological theories be tested against whether we are able to make accurate predictions from local biodiversity data.

BackgroundThere is no effective prognostic signature that could predict the prognosis of nasopharyngeal carcinoma (NPC).MethodsWe constructed a prognostic signature based on five microRNAs using random forest and Least Absolute Shrinkage And Selection Operator (LASSO) algorithm on the GSE32960 cohort (N = 213). We verified its prognostic value using three independent external validation cohorts (GSE36682, N = 62; GSE70970, N = 246; and TCGA-HNSC, N = 523). Through principal component analysis, receiver operating characteristic curve analysis, and C-index calculation, we confirmed the predictive accuracy of this prognostic signature.ResultsWe calculated the risk score based on the LASSO algorithm and divided the patients into high- and low-risk groups according to the calculated optimal cutoff value. The patients in the high-risk group tended to have a worse prognosis outcome and chemotherapy response. The time-dependent receiver operating characteristic curve showed that the 1-year overall survival rate of the five-microRNA signature had an area under the curve of more than 0.83. A functional annotation analysis of the five-microRNA signature showed that the patients in the high-risk group were usually accompanied by activation of DNA repair and MYC-target pathways, while the patients in the low-risk group had higher immune-related pathway signals.ConclusionsWe constructed a five-microRNA prognostic signature, which could accurately predict the prognosis of nasopharyngeal carcinoma, and constructed a nomogram that could conveniently predict the overall survival of patients.

ObjectivesTo investigate the utility of spectral computed tomography (CT) parameters for the prediction of the preoperative Masaoka-Koga stage of thymic epithelial tumors (TETs).Materials and MethodsFifty-four patients with TETs, aged from 37 to 73 years old, an average age of 55.56 ± 9.79 years, were included in the study.According to the Masaoka-Koga staging method, there were 19 cases of stage I, 15 cases of stage II, 8 cases of stage III, and 12 cases of stage IV disease. All patients underwent dual-phase enhanced energy spectral CT scans. Regions of interest (ROIs) were defined in sections of the lesion with homogeneous density, the thoracic aorta at the same level as the lesion, the outer fat layer of the lesion, and the anterior chest wall fat layer. The single-energy CT value at 40-140 keV, iodine concentration, and energy spectrum curve of all lesion and thoracic aorta were obtained. The energy spectrum CT parameters of the lesions, extracapsular fat of the lesions, and anterior chest wall fat in stage I and stage II were obtained. The energy spectrum CT parameters of the lesions, enlarged lymph nodes and intravascular emboli in the 3 groups were obtained. The slope of the energy spectrum curve and the normalized iodine concentration were calculated.ResultsIn stage I lesions, there was a statistically significant difference between the slope of the energy spectrum curve for the lesion and those of the fat outside the lesion and the anterior chest wall in the arteriovenous phase (P0.05). The energy spectrum curve of the tumor parenchyma was consistent with that of the enlarged lymph nodes and intravascular emboli. The two radiologists have strong consistency in evaluating TETs Masaoka-Koga staging, The Kappa coefficient is 0.873,(95%CI:0.768-0.978).ConclusionSpectral CT parameters, especially the energy spectrum curve and slope, are valuable for preoperative TET and can be used in preoperative staging prediction.

Abstract 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 (https://github.com/seantuck12/MODISTools).