Multinational Validation of \u3cem\u3eAnxiety\u3c/em\u3e, \u3cem\u3eHopelessness\u3c/em\u3e, and \u3cem\u3eIneffective Airway Clearance\u3c/em\u3e

The effective use of nursing diagnosis internationally depends in part on incorporating language and cultural difference into the common language of nursing. International validation studies can provide a basis for this effort. This study tested three diagnoses—anxiety, hopelessness, and ineffective airway clearance—through multinational validation. The Diagnostic Content Validity (DCV) model was used to collect data from critical care nurses in six countries. Defining characteristics rated as critical (\u3e .80) by the total sample were dyspnea for ineffective airway clearance and panic and nervousness for anxiety. No critical defining characteristics for hopelessness were identified. DCV ratios for all defining characteristics are compared by country

Impact of measured and simulated tundra snowpack properties on heat transfer

Snowpack microstructure controls the transfer of heat to, as well as the temperature of, the underlying soils. In situ measurements of snow and soil properties from four field campaigns during two winters (March and November 2018, January and March 2019) were compared to an ensemble of CLM5.0 (Community Land Model) simulations, at Trail Valley Creek, Northwest Territories, Canada. Snow micropenetrometer profiles allowed for snowpack density and thermal conductivity to be derived at higher vertical resolution (1.25 mm) and a larger sample size (n=1050) compared to traditional snowpit observations (3 cm vertical resolution; n=115). Comparing measurements with simulations shows CLM overestimated snow thermal conductivity by a factor of 3, leading to a cold bias in wintertime soil temperatures (RMSE=5.8 ∘C). Two different approaches were taken to reduce this bias: alternative parameterisations of snow thermal conductivity and the application of a correction factor. All the evaluated parameterisations of snow thermal conductivity improved simulations of wintertime soil temperatures, with that of Sturm et al. (1997) having the greatest impact (RMSE=2.5 ∘C). The required correction factor is strongly related to snow depth () and thus differs between the two snow seasons, limiting the applicability of such an approach. Improving simulated snow properties and the corresponding heat flux is important, as wintertime soil temperatures are an important control on subnivean soil respiration and hence impact Arctic winter carbon fluxes and budgets

Simulating net ecosystem exchange under seasonal snow cover at an Arctic tundra site

Estimates of winter (snow-covered non-growing season) CO2 fluxes across the Arctic region vary by a factor of 3.5, with considerable variation between measured and simulated fluxes. Measurements of snow properties, soil temperatures, and net ecosystem exchange (NEE) at Trail Valley Creek, NWT, Canada, allowed for the evaluation of simulated winter NEE in a tundra environment with the Community Land Model (CLM5.0). Default CLM5.0 parameterisations did not adequately simulate winter NEE in this tundra environment, with near-zero NEE (< 0.01 gCm^-2d^-1) simulated between November and mid-May. In contrast, measured NEE was broadly positive (indicating net CO2 release) from snow-cover onset until late April. Changes to the parameterisation of snow thermal conductivity, required to correct for a cold soil temperature bias, reduced the duration for which no NEE was simulated. Parameter sensitivity analysis revealed the critical role of the minimum soil moisture threshold of decomposition (Ψmin) in regulating winter soil respiration. The default value of this parameter (Ψmin) was too high, preventing simulation of soil respiration for the vast majority of the snow-covered season. In addition, the default rate of change of soil respiration with temperature (Q10) was too low, further contributing to poor model performance during winter. As Ψmin and Q10 had opposing effects on the magnitude of simulated winter soil respiration, larger negative values of Ψmin and larger positive values of Q10 are required to simulate wintertime NEE more adequately.Natural Environment Research CouncilPeer Reviewe

Luminosity Function Constraints on the Evolution of Massive Red Galaxies Since z~0.9

We measure the evolution of the luminous red galaxy (LRG) luminosity function in the redshift range 0.1<z<0.9 using samples of galaxies from the Sloan Digital Sky Survey as well as new spectroscopy of high-redshift massive red galaxies. Our high-redshift sample of galaxies is largest spectroscopic sample of massive red galaxies at z~0.9 collected to date and covers 7 square deg, minimizing the impact of large scale structure on our results. We find that the LRG population has evolved little beyond the passive fading of its stellar populations since z~0.9. Based on our luminosity function measurements and assuming a non-evolving Salpeter stellar initial mass function, we find that the most massive (L>3L*) red galaxies have grown by less than 50% (at 99% confidence), since z=0.9, in stark contrast to the factor of 2-4 growth observed in the L* red galaxy population over the same epoch. We also investigate the evolution of the average LRG spectrum since z~0.9 and find the high-redshift composite to be well-described as a passively evolving example of the composite galaxy observed at low-redshift. From spectral fits to the composite spectra, we find at most 5% of the stellar mass in massive red galaxies may have formed within 1Gyr of z=0.9. While L* red galaxies are clearly assembled at z<1, 3L* galaxies appear to be largely in place and evolve little beyond the passive evolution of their stellar populations over the last half of cosmic history.Comment: 19 pages, 19 figures, 7 tables; accepted for publication in Ap

Predictors of Outcome in Aneurysmal Subarachnoid Hemorrhage Patients:Observations From a Multicenter Data Set

A table containing information on the qRT-PCR performed with seven novel miRNAs and two known miRNAs. Per miRNA, this information includes mean CT, range of CT, cDNA dilution, the number of samples (of 12) with CT < 40, the average read depth, and primer used. (XLSX 8 kb

Socioeconomic status in childhood and C reactive protein in adulthood: a systematic review and meta-analysis

Inflammation plays a central role in cardio-metabolic disease and may represent a mechanism linking low socioeconomic status (SES) in early life and adverse cardio-metabolic health outcomes in later life. Accumulating evidence suggests an association between childhood SES and adult inflammation, but findings have been inconsistent

Machine Learning SNP Based Prediction for Precision Medicine

In the past decade, precision genomics based medicine has emerged to provide tailored and effective healthcare for patients depending upon their genetic features. Genome Wide Association Studies have also identified population based risk genetic variants for common and complex diseases. In order to meet the full promise of precision medicine, research is attempting to leverage our increasing genomic understanding and further develop personalized medical healthcare through ever more accurate disease risk prediction models. Polygenic risk scoring and machine learning are two primary approaches for disease risk prediction. Despite recent improvements, the results of polygenic risk scoring remain limited due to the approaches that are currently used. By contrast, machine learning algorithms have increased predictive abilities for complex disease risk. This increase in predictive abilities results from the ability of machine learning algorithms to handle multi-dimensional data. Here, we provide an overview of polygenic risk scoring and machine learning in complex disease risk prediction. We highlight recent machine learning application developments and describe how machine learning approaches can lead to improved complex disease prediction, which will help to incorporate genetic features into future personalized healthcare. Finally, we discuss how the future application of machine learning prediction models might help manage complex disease by providing tissue-specific targets for customized, preventive interventions