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MicroRNAs (miRNA) are small non-coding RNAs that regulate translation of mRNA and protein. Loss or enhanced expression of miRNAs is associated with several diseases, including cancer. However, the identification of circulating miRNA in healthy donors is not well characterized. Microvesicles, also known as exosomes or microparticles, circulate in the peripheral blood and can stimulate cellular signaling. In this study, we hypothesized that under normal healthy conditions, microvesicles contain miRNAs, contributing to biological homeostasis. Microvesicles were isolated from the plasma of normal healthy individuals. RNA was isolated from both the microvesicles and matched mononuclear cells and profiled for 420 known mature miRNAs by real-time PCR. Hierarchical clustering of the data sets indicated significant differences in miRNA expression between peripheral blood mononuclear cells (PBMC) and plasma microvesicles. We observed 71 miRNAs co-expressed between microvesicles and PBMC. Notably, we found 33 and 4 significantly differentially expressed miRNAs in the plasma microvesicles and mononuclear cells, respectively. Prediction of the gene targets and associated biological pathways regulated by the detected miRNAs was performed. The majority of the miRNAs expressed in the microvesicles from the blood were predicted to regulate cellular differentiation of blood cells and metabolic pathways. Interestingly, a select few miRNAs were also predicted to be important modulators of immune function. This study is the first to identify and define miRNA expression in circulating plasma microvesicles of normal subjects. The data generated from this study provides a basis for future studies to determine the predictive role of peripheral blood miRNA signatures in human disease and will enable the definition of the biological processes regulated by these miRNA.

MicroRNAs (miRNAs) have emerged as important regulators in the post‐transcriptional control of gene expression. The discovery of their presence not only in tissues but also in extratissular fluids, including blood, urine and cerebro‐spinal fluid, together with their changes in expression in various pathological conditions, has implicated these extracellular miRNAs as informative biomarkers of disease. However, exploiting miRNAs in this capacity requires methodological rigour. Here, we report several key procedural aspects of miRNA isolation from plasma and serum, as exemplified by research in cardiovascular and pulmonary diseases. We also highlight the advantages and disadvantages of various profiling methods to determine the expression levels of plasma‐ and serum‐derived miRNAs. Attention to such methodological details is critical, as circulating miRNAs become diagnostic tools for various human diseases.

Abstract Background Quantitative PCR (qPCR) aims to measure the DNA or RNA concentration in diagnostic and biological samples based on the quantification cycle (Cq) value observed in the amplification curves. Results of qPCR experiments are regularly calculated as if all assays are 100% efficient or reported as just Cq, ΔCq, or ΔΔCq values. Contents When the reaction shows specific amplification, it should be deemed to be positive, regardless of the observed Cq. Because the Cq is highly dependent on amplification efficiency that can vary among targets and samples, accurate calculation of the target quantity and relative gene expression requires that the actual amplification efficiency be taken into account in the analysis and reports. PCR efficiency is frequently derived from standard curves, but this approach is affected by dilution errors and hampered by properties of the standard and the diluent. These factors affect accurate quantification of clinical and biological samples used in diagnostic applications and collected in challenging conditions. PCR efficiencies determined from individual amplification curves avoid these confounders. To obtain unbiased efficiency-corrected results, we recommend absolute quantification with a single undiluted calibrator with a known target concentration and efficiency values derived from the amplification curves of the calibrator and the unknown samples. Summary For meaningful diagnostics or biological interpretation, the reported results of qPCR experiments should be efficiency corrected. To avoid ambiguity, the Minimal Information for Publications on Quantitative Real-Time PCR Experiments (MIQE) guidelines checklist should be extended to require the methods that were used (1) to determine the PCR efficiency and (2) to calculate the reported target quantity and relative gene expression value.

Space-based, global-extent digital elevation models (DEMs) are key inputs to many Earth sciences applications. However, many of these applications require the use of a ‘bare-Earth’ DEM versus a digital surface model (DSM), the latter of which may include systematic positive biases due to tree canopies in forested areas. Critical topographic features may be obscured by these biases. Vegetation-free datasets have been created by using statistical relationships and machine learning to train on local-scale datasets (e.g., lidar) to de-bias the global-extent datasets. Recent advances in satellite platforms coupled with increased availability of computational resources and lidar reference products has allowed for a new generation of vegetation- and urban-canopy removals. One of these is the Forest And Buildings removed Copernicus DEM (FABDEM), based on the most recent and most accurate global DSM Copernicus-30. Among the more challenging landscapes to quantify surface elevations are densely forested mountain catchments, where even airborne lidar applications struggle to capture surface returns. The increasing affordability and availability of UAV-based lidar platforms have resulted in new capacity to fly modest spatial extents with unrivalled point densities. These data allow an unprecedented ability to validate global sub-canopy DEMs against representative UAV-based lidar data. In this work, the FABDEM is validated against up-scaled lidar data in a steep and forested mountain catchment considering elevation, slope, and Terrain Position Index (TPI) metrics. Comparisons of FABDEM with SRTM, MERIT, and the Copernicus-30 dataset are made. It was found that the FABDEM had a 24% reduction in elevation RMSE and a 135% reduction in bias compared to the Copernicus-30 dataset. Overall, the FABDEM provides a clear improvement over existing deforested DEM products in complex mountain topography such as the MERIT DEM. This study supports the use of FABDEM in forested mountain catchments as the current best-in-class data product.

The interaction of mountain terrain with meteorological processes causes substantial temporal and spatial variability in snow accumulation and ablation. Processes impacted by complex terrain include large-scale orographic enhancement of snowfall, small-scale processes such as gravitational and wind-induced transport of snow, and variability in the radiative balance such as through terrain shadowing. In this study, a multi-scale modelling approach is proposed to simulate the temporal and spatial evolution of high-mountain snowpacks. The multi-scale approach combines atmospheric data from a numerical weather prediction system at the kilometre scale with process-based downscaling techniques to drive the Canadian Hydrological Model (CHM) at spatial resolutions allowing for explicit snow redistribution modelling. CHM permits a variable spatial resolution by using the efficient terrain representation by unstructured triangular meshes. The model simulates processes such as radiation shadowing and irradiance to slopes, blowing-snow transport (saltation and suspension) and sublimation, avalanching, forest canopy interception and sublimation, and snowpack melt. Short-term, kilometre-scale atmospheric forecasts from Environment and Climate Change Canada's Global Environmental Multiscale Model through its High Resolution Deterministic Prediction System (HRDPS) drive CHM and are downscaled to the unstructured mesh scale. In particular, a new wind-downscaling strategy uses pre-computed wind fields from a mass-conserving wind model at 50 m resolution to perturb the mesoscale HRDPS wind and to account for the influence of topographic features on wind direction and speed. HRDPS-CHM was applied to simulate snow conditions down to 50 m resolution during winter 2017/2018 in a domain around the Kananaskis Valley (∼1000 km2) in the Canadian Rockies. Simulations were evaluated using high-resolution airborne light detection and ranging (lidar) snow depth data and snow persistence indexes derived from remotely sensed imagery. Results included model falsifications and showed that both wind-induced and gravitational snow redistribution need to be simulated to capture the snowpack variability and the evolution of snow depth and persistence with elevation across the region. Accumulation of windblown snow on leeward slopes and associated snow cover persistence were underestimated in a CHM simulation driven by wind fields that did not capture lee-side flow recirculation and associated wind speed decreases. A terrain-based metric helped to identify these lee-side areas and improved the wind field and the associated snow redistribution. An overestimation of snow redistribution from windward to leeward slopes and subsequent avalanching was still found. The results of this study highlight the need for further improvements of snowdrift-permitting models for large-scale applications, in particular the representation of subgrid topographic effects on snow transport.

Altered inflammatory cytokine profiles are often observed in individuals suffering from major depression. Recent clinical work reports on elevated IL-6 and decreased IL-10 in depression. Elevated IL-6 has served as a consistent biomarker of depression and IL-10 is proposed to influence depressive behavior through its ability to counterbalance pro-inflammatory cytokine expression. Clinical and animal studies suggest a role for IL-10 in modifying depressive behavior. Murine restraint stress (RST) is regularly employed in the study of behavioral and biological symptoms associated with depressive disorders. While responses to acute RST exposure have been widely characterized, few studies have examined the ongoing and longitudinal effects of extended RST and fewer still have examined the lasting impact during the post-stress period. Consistent with clinical data, we report that a protocol of prolonged murine RST produced altered cytokine profiles similar to those observed in major depressive disorder. Parallel to these changes in circulating cytokines, IL-10 mRNA expression was diminished in the cortex and hippocampus throughout the stress period and following cessation of RST. Moreover, chronic RST promoted depressive-like behavior throughout the 28-day stress period and these depressive-like complications were maintained weeks after cessation of RST. Because of the correlation between IL-10 suppression and depressive behavior and because many successful antidepressant therapies yield increases in IL-10, we examined the effects of IL-10 treatment on RST-induced behavioral changes. Behavioral deficits induced by RST were reversed by exogenous administration of recombinant IL-10. This work provides one of the first reports describing the biological and behavioral impact following prolonged RST and, taken together, this study provides details on the correlation between responses to chronic RST and those seen in depressive disorders.

Despite debate in the rainfall–runoff hydrology literature about the merits of physics-based and spatially distributed models, substantial work in cold-region hydrology has shown improved predictive capacity by including physics-based process representations, relatively high-resolution semi-distributed and fully distributed discretizations, and the use of physically identifiable parameters that require limited calibration. While there is increasing motivation for modelling at hyper-resolution (< 1 km) and snowdrift-resolving scales (≈ 1 to 100 m), the capabilities of existing cold-region hydrological models are computationally limited at these scales.Here, a new distributed model, the Canadian Hydrological Model (CHM), is presented. Although designed to be applied generally, it has a focus for application where cold-region processes play a role in hydrology. Key features include the ability to do the following: capture spatial heterogeneity in the surface discretization in an efficient manner via variable-resolution unstructured meshes; include multiple process representations; change, remove, and decouple hydrological process algorithms; work at both a point and spatially distributed scale; scale to multiple spatial extents and scales; and utilize a variety of forcing fields (boundary and initial conditions). This paper focuses on the overall model philosophy and design, and it provides a number of cold-region-specific features and examples.

The global coral bleaching event of 2014–2017 resulted from the latest in a series of heat stress events that have increased in intensity. We assessed global- and basin-scale variations in sea surface temperature-based heat stress products for 1985–2017 to provide the context for how heat stress during 2014–2017 compared with the past 3 decades. Previously, undefined “Heat Stress Year” periods (used to describe interannual variation in heat stress) were identified for the Northern and Southern Hemispheres, in which heat stress peaks during or shortly after the boreal and austral summers, respectively. The proportion of reef pixels experiencing bleaching-level heat stress increased through the record, accelerating during the last decade. This increase in accumulated heat stress at a bleaching level is a result of longer stress events rather than an increase in the peak stress intensity. Thresholds of heat stress extent for the three tropical ocean basins were established to designate “global” events, and a Global Bleaching Index was defined that relates heat stress extent to that observed in 1998. Notably, during the 2014–2017 global bleaching event, more than three times as many reefs were exposed to bleaching-level heat stress as in the 1998 global bleaching.

The melt of seasonal snowpack in mountain regions provides downstream river basins with a critical supply of freshwater. Snowdrift‐permitting models have been proposed as a way to accurately simulate snowpack heterogeneity that stems from differences in energy inputs, over winter redistribution, sublimation, melt, and variations in precipitation. However, these spatial scales can be computationally intractable for large extents. In this work, the multiscale Canadian Hydrological Model (CHM) was applied to simulate snowpacks at snowdrift‐permitting scales (≈50 m) across the Canadian Cordillera and adjacent regions (1.37 million km2) forced by downscaled atmospheric data. The use of a multiscale land surface representation resulted in a reduction of computational elements of 98% while preserving land‐surface heterogeneity. CHM includes complex terrain windflow and radiative transfer calculations, lapses temperature, humidity, and precipitation with elevation, redistributes snow by avalanching, wind transport and forest canopy interception and calculates the energetics of canopy and surface snowpacks. Model outputs were compared to a set of multiscale observations including snow‐covered area (SCA) from Sentinel and Landsat imagery, snow depth from uncrewed aerial system lidar, and point surface observations of depth and density. Including snow redistribution and sublimation processes improved the summer SCA r2 from 0.7 to 0.9. At larger scales, inclusion of snow redistribution processes delayed full snowpack ablation by an average of 33 days, demonstrating process emergence with scale. These simulations show how multiscale modeling can improve snowpack predictions to support prediction of water supply, droughts, and floods. Plain Language Summary The spring melting of snowpacks in mountainous regions is crucial for providing freshwater to downstream river basins. Accurate simulation of mountain snowpacks requires accounting for factors like energy input, redistribution of snow, and forest canopies. However, including all these factors can be computationally challenging for large areas. In this study, the Canadian Hydrological Model (CHM) was used to simulate snowpacks at fine scales (about 50 m) across the Canadian Cordillera and nearby regions. By using a multiscale approach, the computational requirements were reduced substantially while maintaining the range of landscape features. The CHM accounts for various factors such as windflow, mountain shadowing, temperature, humidity, and precipitation changes with elevation, as well as snow redistribution through avalanching and wind transport. The model was validated against multiscale observations including satellite imagery, lidar data, and point observations. By incorporating snow redistribution and sublimation processes in the model, the accuracy of snow cover predictions improved over spring and summer. At larger scales, considering snow redistribution delayed the complete melting of snowpacks by an average of 33 days, showcasing the importance of scale‐dependent redistribution and ablation processes. These simulations demonstrate how multiscale modeling enhances snowpack predictions, aiding in forecasts of water supply, droughts, and floods. Key Points A novel, large extent, snowdrift permitting scale simulation of ≈1.4 M km2 was performed The inclusion of snow redistribution was scale emergent and delayed full snowcover ablation by 33 days on average The inclusion of snow redistribution processes improved the summer simulated versus observed snow‐covered area r2 from 0.7 to 0.9

The transcription factors E2F and Myc participate in the control of cell proliferation and apoptosis, and can act as oncogenes or tumor suppressors depending on their levels of expression. Positive feedback loops in the regulation of these factors are predicted--and recently shown experimentally--to lead to bistability, which is a phenomenon characterized by the existence of low and high protein levels (\"off\" and \"on\" levels, respectively), with sharp transitions between levels being inducible by, for example, changes in growth factor concentrations. E2F and Myc are inhibited at the posttranscriptional step by members of a cluster of microRNAs (miRs) called miR-17-92. In return, E2F and Myc induce the transcription of miR-17-92, thus forming a negative feedback loop in the interaction network. The consequences of the coupling between the E2F/Myc positive feedback loops and the E2F/Myc/miR-17-92 negative feedback loop are analyzed using a mathematical model. The model predicts that miR-17-92 plays a critical role in regulating the position of the off-on switch in E2F/Myc protein levels, and in determining the on levels of these proteins. The model also predicts large-amplitude protein oscillations that coexist with the off steady state levels. Using the concept and model prediction of a \"cancer zone,\" the oncogenic and tumor suppressor properties of miR-17-92 is demonstrated to parallel the same properties of E2F and Myc.