Ferroelectric Sm-doped BiMnO3 thin films with ferromagnetic transition temperature enhanced to 140 K.

A combined chemical pressure and substrate biaxial pressure crystal engineering approach was demonstrated for producing highly epitaxial Sm-doped BiMnO(3) (BSMO) films on SrTiO(3) single crystal substrates, with enhanced magnetic transition temperatures, TC up to as high as 140 K, 40 K higher than that for standard BiMnO(3) (BMO) films. Strong room temperature ferroelectricity with piezoresponse amplitude, d(33) = 10 pm/V, and long-term retention of polarization were also observed. Furthermore, the BSMO films were much easier to grow than pure BMO films, with excellent phase purity over a wide growth window. The work represents a very effective way to independently control strain in-plane and out-of-plane, which is important not just for BMO but for controlling the properties of many other strongly correlated oxides.This research was funded by the Engineering and Physical Sciences Research Council, (EP/P50385X/1), the European Research Council (ERC-2009-AdG 247276 NOVOX). The TEM work at Texas A&M University was funded by the U.S. National Science Foundation (NSF-1007969).This is the final published manuscript. It is available online through ACS in Applied Materials and Interfaces here: http://pubs.acs.org/doi/abs/10.1021/am501351c

Element release and reaction-induced porosity alteration during shale-hydraulic fracturing fluid interactions

The use of hydraulic fracturing techniques to extract oil and gas from low permeability shale reservoirs has increased significantly in recent years. During hydraulic fracturing, large volumes of water, often acidic and oxic, are injected into shale formations. This drives fluid-rock interaction that can release metal contaminants (e.g., U, Pb) and alter the permeability of the rock, impacting the transport and recovery of water, hydrocarbons, and contaminants. To identify the key geochemical processes that occur upon exposure of shales to hydraulic fracturing fluid, we investigated the chemical interaction of hydraulic fracturing fluids with a variety of shales of different mineralogical texture and composition. Batch reactor experiments revealed that the dissolution of both pyrite and carbonate minerals occurred rapidly, releasing metal contaminants and generating porosity. Oxidation of pyrite and aqueous Fe drove precipitation of Fe(III)-(oxy)hydroxides that attenuated the release of these contaminants via co-precipitation and/or adsorption. The precipitation of these (oxy)hydroxides appeared to limit the extent of pyrite reaction. Enhanced removal of metals and contaminants in reactors with higher fluid pH was inferred to reflect increased Fe-(oxy)hydroxide precipitation associated with more rapid aqueous Fe(II) oxidation. The precipitation of both Al- and Fe-bearing phases revealed the potential for the occlusion of pores and fracture apertures, whereas the selective dissolution of calcite generated porosity. These pore-scale alterations of shale texture and the cycling of contaminants indicate that chemical interactions between shales and hydraulic fracturing fluids may exert an important control on the efficiency of hydraulic fracturing operations and the quality of water recovered at the surface

The C-Band All-Sky Survey (C-BASS): Constraining diffuse Galactic radio emission in the North Celestial Pole region

The C-Band All-Sky Survey C-BASS is a high-sensitivity all-sky radio survey at an angular resolution of 45 arcmin and a frequency of 4.7 GHz. We present a total intensity 4.7 GHz map of the North Celestial Pole (NCP) region of sky, above declination +80 deg, which is limited by source confusion at a level of ~0.6 mK rms. We apply the template-fitting (cross-correlation) technique to WMAP and Planck data, using the C-BASS map as the synchrotron template, to investigate the contribution of diffuse foreground emission at frequencies ~20-40 GHz. We quantify the anomalous microwave emission (AME) that is correlated with far-infrared dust emission. The AME amplitude does not change significantly (<10%) when using the higher frequency C-BASS 4.7 GHz template instead of the traditional Haslam 408 MHz map as a tracer of synchrotron radiation. We measure template coefficients of $9.93\pm0.35$ and $9.52\pm0.34$ K per unit $\tau_{353}$ when using the Haslam and C-BASS synchrotron templates, respectively. The AME contributes $55\pm2\,\mu$K rms at 22.8 GHz and accounts for ~60% of the total foreground emission. Our results suggest that a harder (flatter spectrum) component of synchrotron emission is not dominant at frequencies >5 GHz; the best-fitting synchrotron temperature spectral index is $\beta=-2.91\pm0.04$ from 4.7 to 22.8 GHz and $\beta=-2.85\pm0.14$ from 22.8 to 44.1 GHz. Free-free emission is weak, contributing ~$7\,\mu$K rms (~7%) at 22.8 GHz. The best explanation for the AME is still electric dipole emission from small spinning dust grains.Comment: 18 pages, 6 figures, version matches version accepted by MNRA

The potential for REDD+ to reduce forest degradation in Vietnam

Natural forests in Vietnam have experienced rapid declines in the last 70 years, as a result of degradation from logging and conversion of natural forests to timber and rubber plantations. Degradation of natural forests leads to loss of biodiversity and ecosystem services, impacting the livelihoods of surrounding communities. Efforts to address ongoing loss of natural forests, through mechanisms such as Reduced Emissions from Deforestation and Degradation (REDD+), require an understanding of the links between forest degradation and the livelihoods of local communities, which have rarely been studied in Vietnam. We combined information from livelihood surveys, remote sensing and forest inventories around a protected natural forest area in North Central Vietnam. For forest-adjacent communities, we found natural forests contributed an average of 28% of total household income with plantation forests contributing an additional 15%. Although officially prohibited, logging contributed more than half of the total income derived from natural forests. Analysis of Landsat images over the period 1990 to 2014 combined with forest inventory data, demonstrates selective logging was leading to ongoing degradation of natural forests resulting in loss of 3.3±0.8 Mg biomass ha-1 yr-1 across the protected area. This is equivalent to 1.5% yr-1 of total forest biomass, with rates as high as 3% yr-1 in degraded and easily accessible parts of the protected area. We estimate that preventing illegal logging would incur local opportunity costs of USD $4.10±0.90 per Mg CO2, similar to previous estimates for tropical forest protected areas and substantially less than the opportunity costs in timber or agricultural concessions. Our analysis suggests activities to reduce forest degradation in protected areas are likely to be financially viable through Vietnam's REDD+ program

An Automated Machine Learning-based Model Predicts Postoperative Mortality Using Readily-Extractable Preoperative Electronic Health Record Data

Background Rapid, preoperative identification of patients with the highest risk for medical complications is necessary to ensure that limited infrastructure and human resources are directed towards those most likely to benefit. Existing risk scores either lack specificity at the patient level or utilise the American Society of Anesthesiologists (ASA) physical status classification, which requires a clinician to review the chart. Methods We report on the use of machine learning algorithms, specifically random forests, to create a fully automated score that predicts postoperative in-hospital mortality based solely on structured data available at the time of surgery. Electronic health record data from 53 097 surgical patients (2.01% mortality rate) who underwent general anaesthesia between April 1, 2013 and December 10, 2018 in a large US academic medical centre were used to extract 58 preoperative features. Results Using a random forest classifier we found that automatically obtained preoperative features (area under the curve [AUC] of 0.932, 95% confidence interval [CI] 0.910–0.951) outperforms Preoperative Score to Predict Postoperative Mortality (POSPOM) scores (AUC of 0.660, 95% CI 0.598–0.722), Charlson comorbidity scores (AUC of 0.742, 95% CI 0.658–0.812), and ASA physical status (AUC of 0.866, 95% CI 0.829–0.897). Including the ASA physical status with the preoperative features achieves an AUC of 0.936 (95% CI 0.917–0.955). Conclusions This automated score outperforms the ASA physical status score, the Charlson comorbidity score, and the POSPOM score for predicting in-hospital mortality. Additionally, we integrate this score with a previously published postoperative score to demonstrate the extent to which patient risk changes during the perioperative period

The causal effect of obesity on prediabetes and insulin resistance reveals the important role of adipose tissue in insulin resistance

Reverse causality has made it difficult to establish the causal directions between obesity and prediabetes and obesity and insulin resistance. To disentangle whether obesity causally drives prediabetes and insulin resistance already in non-diabetic individuals, we utilized the UK Biobank and METSIM cohort to perform a Mendelian randomization (MR) analyses in the non-diabetic individuals. Our results suggest that both prediabetes and systemic insulin resistance are caused by obesity (p = 1.2x10(-3)and p = 3.1x10(-24)). As obesity reflects the amount of body fat, we next studied how adipose tissue affects insulin resistance. We performed both bulk RNA-sequencing and single nucleus RNA sequencing on frozen human subcutaneous adipose biopsies to assess adipose cell-type heterogeneity and mitochondrial (MT) gene expression in insulin resistance. We discovered that the adipose MT gene expression and body fat percent are both independently associated with insulin resistance (p Author summary Obesity is a global health epidemic predisposing to type 2 diabetes (T2D) and other cardiometabolic disorders. Previous studies have shown that obesity has a causal effect on T2D; however, it remains unknown whether obesity causes prediabetes and insulin resistance already in non-diabetic individuals. By utilizing almost half a million individuals from the UK Biobank and the Finnish METSIM cohort, we identified a significant causal effect of obesity on prediabetes and insulin resistance among the non-diabetic individuals. Next, we investigated the role of subcutaneous adipose tissue in these obesogenic effects. We discovered that the adipose mitochondrial gene expression and body fat percent are independently associated with insulin resistance after adjusting for the tissue heterogeneity. For the latter, we estimated the adipose cell type proportions by utilizing single-nucleus RNA sequencing of frozen adipose tissue biopsies. Moreover, we established a prediction model to estimate insulin resistance using body fat percent and adipose RNA-sequencing data, which enlightens the importance of adipose tissue in insulin resistance and provides a helpful tool to impute the insulin resistance for existing adipose RNA-sequencing cohorts. Overall, we discover the potential causal effect of obesity on prediabetes and insulin resistance and the key role of adipose tissue in insulin resistance.Peer reviewe

C-Band All-Sky Survey (C-BASS): Simulated parametric fitting in single pixels in total intensity and polarization

The cosmic microwave background (CMB) B-mode signal is potentially weaker than the diffuse Galactic foregrounds over most of the sky at any frequency. A common method of separating the CMB from these foregrounds is via pixel-based parametric-model fitting. There are not currently enough all-sky maps to fit anything more than the most simple models of the sky. By simulating the emission in seven representative pixels, we demonstrate that the inclusion of a 5 GHz data point allows for more complex models of low-frequency foregrounds to be fitted than at present. It is shown that the inclusion of the C-BASS data will significantly reduce the uncertainties in a number of key parameters in the modelling of both the galactic foregrounds and the CMB. The extra data allow estimates of the synchrotron spectral index to be constrained much more strongly than is presently possible, with corresponding improvements in the accuracy of the recovery of the CMB amplitude. However, we show that to place good limits on models of the synchrotron spectral curvature will require additional low-frequency data

The C-Band All-Sky Survey (C-BASS): Simulated parametric fitting in single pixels in total intensity and polarization

The cosmic microwave background (CMB) B-mode signal is potentially weaker than the diffuse Galactic foregrounds over most of the sky at any frequency. A common method of separating the CMB from these foregrounds is via pixel-based parametric-model fitting. There are not currently enough all-sky maps to fit anything more than the most simple models of the sky. By simulating the emission in seven representative pixels, we demonstrate that the inclusion of a 5 GHz data point allows for more complex models of low-frequency foregrounds to be fitted than at present. It is shown that the inclusion of the C-BASS data will significantly reduce the uncertainties in a number of key parameters in the modelling of both the galactic foregrounds and the CMB. The extra data allow estimates of the synchrotron spectral index to be constrained much more strongly than is presently possible, with corresponding improvements in the accuracy of the recovery of the CMB amplitude. However, we show that to place good limits on models of the synchrotron spectral curvature will require additional low-frequency data