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\"The house next door (with Susan DiLallo): Married mother of three Laura Sherman was thrilled when her new neighbor invited her on some errands. But a few quick tasks became a long lunch--and now things could go too far with a man who isn't what he seems ; The killer's wife (with Max DiLallo): Four girls have gone missing. Detective McGrath knows the only way to find them is to get close to the suspect's wife--maybe too close ; We. Are. Not. Alone (with Tim Arnold): The first message from space. It will change the world. It's first contact. Undeniable proof of alien life. Disgraced Air Force scientist Robert Barnett found it. Now he's the target of a desperate nationwide manhunt--and Earth's future hangs in the balance\"-- Provided by publisher.

As an effort to reduce hospital readmissions, early follow-up visits were recommended by the Society of Hospital Medicine. However, published literature on the effect of follow-up visits is limited with mixed conclusions. Our goal here is to fully explore the relationship between follow-up visits and the all-cause non-elective 30-day readmission rate (RR) after adjusting for confounders. To conduct this retrospective observational study, we extracted data for 55,378 adult inpatients from Advocate Health Care, a large, multi-hospital system serving a diverse population in a major metropolitan area. These patients were discharged to Home or Home with Home Health services between June 1, 2013 and April 30, 2015. Our findings from time-dependent Cox proportional hazard models showed that follow-up visits were significantly associated with a reduced RR (adjusted hazard ratio: 0.86; 95% CI: 0.82-0.91), but in a complicated way because the interaction between follow-up visits and a readmission risk score was significant with p-value < 0.001. Our analysis using logistic models on an adjusted data set confirmed the above findings with the following additional results. First, time matter. Follow-up visits within 2 days were associated with the greatest reduction in RR (adjusted odds ratio: 0.72; 95% CI: 0.63-0.83). Visits beyond 2 days were also associated with a reduction in RR, but the strength of the effect decreased as the time between discharge and follow-up visit increased. Second, the strength of such association varied for patients with different readmission risk scores. Patients with a risk score of 0.113, high but not extremely high risk, had the greatest reduction in RR from follow-up visits. Patients with an extremely high risk score (> 0.334) saw no RR reduction from follow-up visits. Third, a patient was much more likely to have a 2-day follow-up visit if that visit was scheduled before the patient was discharged from the hospital (30% versus < 5%). Follow-up visits are associated with a reduction in readmission risk. The timing of follow-up visits can be important: beyond two days, the earlier, the better. The effect of follow-up visits is more significant for patients with a high but not extremely high risk of readmission.

We use the GEOS-Chem global 3-D model and two inverse methods (the maximum a posteriori and ensemble Kalman filter) to infer regional methane (CH4) emissions and the corresponding stable-carbon-isotope source signatures from 2004–2020 across the globe using in situ and satellite remote sensing data. We use the Siegel estimator to determine linear trends from the in situ data. Over our 17-year study period, we estimate a linear increase of 3.6 Tg yr−1 yr−1 in CH4 emissions from tropical continental regions, including North Africa, southern Africa, tropical South America, and tropical Asia. The second-largest increase in CH4 emissions over this period (1.6 Tg yr−1 yr−1) is from China. For boreal regions we estimate a negative emissions trend of −0.2 Tg yr−1 yr−1, and for northern and southern temperate regions we estimate trends of 0.03 Tg yr−1 yr−1 and 0.2 Tg yr−1 yr−1, respectively. These increases in CH4 emissions are accompanied by a progressively isotopically lighter atmospheric δ13C signature over the tropics, particularly since 2012, which is consistent with an increased biogenic emissions source and/or a decrease in a thermogenic/pyrogenic emissions source with a heavier isotopic signature. Previous studies have linked increased tropical biogenic emissions to increased rainfall. Over China, we find a weaker trend towards isotopically lighter δ13C sources, suggesting that heavier isotopic source signatures make a larger contribution to this region. Satellite remote sensing data provide additional evidence of emissions hotspots of CH4 that are consistent with the location and seasonal timing of wetland emissions. The collective evidence suggests that increases in tropical CH4 emissions are from biogenic sources, with a significant fraction from wetlands. To understand the influence of our results on changes in the hydroxyl radical (OH), we also report regional CH4 emissions estimates using an alternative scenario of a 0.5 % yr−1 decrease in OH since 2004, followed by a larger 1.5 % drop in 2020 during the first COVID-19 lockdown. We find that our main findings are broadly insensitive to those idealised year-to-year changes in OH, although the corresponding change in atmospheric CH4 in 2020 is inconsistent with independent global-scale constraints for the estimated annual-mean atmospheric growth rate.

Background The main goal of this study is to explore the use of features representing patient-level electronic health record (EHR) data, generated by the unsupervised deep learning algorithm autoencoder, in predictive modeling. Since autoencoder features are unsupervised, this paper focuses on their general lower-dimensional representation of EHR information in a wide variety of predictive tasks. Methods We compare the model with autoencoder features to traditional models: logistic model with least absolute shrinkage and selection operator (LASSO) and Random Forest algorithm. In addition, we include a predictive model using a small subset of response-specific variables (Simple Reg) and a model combining these variables with features from autoencoder (Enhanced Reg). We performed the study first on simulated data that mimics real world EHR data and then on actual EHR data from eight Advocate hospitals. Results On simulated data with incorrect categories and missing data, the precision for autoencoder is 24.16% when fixing recall at 0.7, which is higher than Random Forest (23.61%) and lower than LASSO (25.32%). The precision is 20.92% in Simple Reg and improves to 24.89% in Enhanced Reg. When using real EHR data to predict the 30-day readmission rate, the precision of autoencoder is 19.04%, which again is higher than Random Forest (18.48%) and lower than LASSO (19.70%). The precisions for Simple Reg and Enhanced Reg are 18.70 and 19.69% respectively. That is, Enhanced Reg can have competitive prediction performance compared to LASSO. In addition, results show that Enhanced Reg usually relies on fewer features under the setting of simulations of this paper. Conclusions We conclude that autoencoder can create useful features representing the entire space of EHR data and which are applicable to a wide array of predictive tasks. Together with important response-specific predictors, we can derive efficient and robust predictive models with less labor in data extraction and model training.

Mobile, near-source measurements are broadly used for determining δ13CH4 of individual methane (CH4) emissions sources. To answer the need for robust and comparable measurement methods, we aim to define the best practices to determine isotopic signatures of CH4 sources from atmospheric measurements, considering instrument accuracy and precision. Using the Keeling and Miller-Tans methods, we verify the impact of linear fitting methods, averaging approaches, and for the Miller-Tans method, different background composition. Measurement techniques include Isotope Ratio Mass Spectrometry (IRMS) and Cavity Ring Down Spectroscopy (CRDS). The use of the active AirCore system for sampling, coupled to CRDS for measurement, is examined. Due to their higher precision and accuracy, the chosen data processing strategy does not significantly influence IRMS results. Comparatively lower-precision CRDS measurements are more sensitive to methodological choices. Fitting methods with forced symmetry like Major Axis or Bivariate Correlated Errors and Intrinsic Scatter (BCES) with orthogonal sub-method introduce significant bias in the determined δ13CH4 signatures using measurements from the lower-precision CRDS. The most reliable results are obtained for non-averaged data using fitting methods, which include uncertainties of x- and y-axis values, like York fitting or BCES (Y|X) sub-method, where x is treated as an independent variable. The Ordinary Least Squares method provides sufficiently robust results and can be used to determine δ13CH4 in near-source conditions. The present recommendations are aimed at laboratories measuring δ13CH4 source signatures to encourage consistency in the required methods for data analysis.

Analysis of naturally occurring isotopic variations is a promising tool for investigating Zn transport and cycling in geological and biological settings. Here, we present the recently installed double-spike (DS) technique at the MAGIC laboratories at Imperial College London. The procedure improves on previous published DS methods in terms of ease of measurement and precisions obtained. The analytical method involves addition of a ⁶⁴Zn-⁶⁷Zn double-spike to the samples prior to digestion, separation of Zn from the sample matrix by ion exchange chromatography, and isotopic analysis by multiple-collector inductively coupled plasma mass spectrometry. The accuracy and reproducibility of the method were validated by analyses of several in-house and international elemental reference materials. Multiple analyses of pure Zn standard solutions consistently yielded a reproducibility of about ±0.05‰ (2 SD) for δ⁶⁶Zn, and comparable precisions were obtained for analyses of geological and biological materials. Highly fractionated Zn standards analyzed by DS and standard sample bracketing yield slightly varying results, which probably originate from repetitive fractionation events during manufacture of the standards. However, the δ⁶⁶Zn values (all reported relative to JMC Lyon Zn) for two less fractionated in-house Zn standard solutions, Imperial Zn (0.10 ± 0.08‰: 2 SD) and London Zn (0.08 ± 0.04‰), are within uncertainties to data reported with different mass spectrometric techniques and instruments. Two standard reference materials, blend ore BCR 027 and ryegrass BCR 281, were also measured, and the δ⁶⁶Zn were found to be 0.25 ± 0.06‰ (2 SD) and 0.40 ± 0.09‰, respectively. Taken together, these standard measurements ascertain that the double-spike methodology is suitable for accurate and precise Zn isotope analyses of a wide range of natural samples. The newly installed technique was consequently applied to soil samples and soil leachates to investigate the isotopic signature of plant available Zn. We find that the isotopic composition is heavier than the residual, indicating the presence of loosely bound Zn deposited by atmospheric pollution, which is readily available to plants. [graphic removed]

Nitrogen trifluoride (NF ₃) has potential to make a growing contribution to the Earth’s radiative budget; however, our understanding of its atmospheric burden and emission rates has been limited. Based on a revision of our previous calibration and using an expanded set of atmospheric measurements together with an atmospheric model and inverse method, we estimate that the global emissions of NF ₃ in 2011 were 1.18 ± 0.21 Gg⋅y ⁻¹, or ∼20 Tg CO ₂-eq⋅y ⁻¹ (carbon dioxide equivalent emissions based on a 100-y global warming potential of 16,600 for NF ₃). The 2011 global mean tropospheric dry air mole fraction was 0.86 ± 0.04 parts per trillion, resulting from an average emissions growth rate of 0.09 Gg⋅y ⁻² over the prior decade. In terms of CO ₂ equivalents, current NF ₃ emissions represent between 17% and 36% of the emissions of other long-lived fluorinated compounds from electronics manufacture. We also estimate that the emissions benefit of using NF ₃ over hexafluoroethane (C ₂F ₆) in electronics manufacture is significant—emissions of between 53 and 220 Tg CO ₂-eq⋅y ⁻¹ were avoided during 2011. Despite these savings, total NF ₃ emissions, currently ∼10% of production, are still significantly larger than expected assuming global implementation of ideal industrial practices. As such, there is a continuing need for improvements in NF ₃ emissions reduction strategies to keep pace with its increasing use and to slow its rising contribution to anthropogenic climate forcing.

Coming weeks after the signing of the Munich Agreement, W. Macmahon Ball's day trip to the Sachsenhausen Concentration Camp outside Berlin provides a remarkable episode in Australian public broadcasting. It reveals much about Macmahon Ball and much about the Third Reich at the time of Ball's visit. Accessible in the form of draft scripts Ball submitted to the ABC during his 1938 European trip, Ball's account of his experiences of October 20 offers a detailed portrayal of the camp and an insightful analysis of the psychology of both its inmates and its guards. Adapted from the source document.

We report a 40-year history of SF6 atmospheric mole fractions measured at the Advanced Global Atmospheric Gases Experiment (AGAGE) monitoring sites, combined with archived air samples, to determine emission estimates from 1978 to 2018. Previously we reported a global emission rate of 7.3±0.6 Gg yr−1 in 2008 and over the past decade emissions have continued to increase by about 24 % to 9.04±0.35 Gg yr−1 in 2018. We show that changing patterns in SF6 consumption from developed (Kyoto Protocol Annex-1) to developing countries (non-Annex-1) and the rapid global expansion of the electric power industry, mainly in Asia, have increased the demand for SF6-insulated switchgear, circuit breakers, and transformers. The large bank of SF6 sequestered in this electrical equipment provides a substantial source of emissions from maintenance, replacement, and continuous leakage. Other emissive sources of SF6 occur from the magnesium, aluminium, and electronics industries as well as more minor industrial applications. More recently, reported emissions, including those from electrical equipment and metal industries, primarily in the Annex-1 countries, have declined steadily through substitution of alternative blanketing gases and technological improvements in less emissive equipment and more efficient industrial practices. Nevertheless, there are still demands for SF6 in Annex-1 countries due to economic growth, as well as continuing emissions from older equipment and additional emissions from newly installed SF6-insulated electrical equipment, although at low emission rates. In addition, in the non-Annex-1 countries, SF6 emissions have increased due to an expansion in the growth of the electrical power, metal, and electronics industries to support their continuing development. There is an annual difference of 2.5–5 Gg yr−1 (1990–2018) between our modelled top-down emissions and the UNFCCC-reported bottom-up emissions (United Nations Framework Convention on Climate Change), which we attempt to reconcile through analysis of the potential contribution of emissions from the various industrial applications which use SF6. We also investigate regional emissions in East Asia (China, S. Korea) and western Europe and their respective contributions to the global atmospheric SF6 inventory. On an average annual basis, our estimated emissions from the whole of China are approximately 10 times greater than emissions from western Europe. In 2018, our modelled Chinese and western European emissions accounted for ∼36 % and 3.1 %, respectively, of our global SF6 emissions estimate.

We present a novel high-resolution inverse modelling system (\"FLEXVAR\") based on FLEXPARTCOSMO back trajectories driven by COSMO meteorological fields at 7 km×7 km resolution over the European COSMO-7 domain and the four-dimensional variational (4DVAR) data assimilation technique. FLEXVAR is coupled offline with the global inverse modelling system TM5-4DVAR to provide background mole fractions (\"baselines\") consistent with the global observations assimilated in TM5-4DVAR. We have applied the FLEXVAR system for the inverse modelling of European CH4 emissions in 2018 using 24 stations with in situ measurements, complemented with data from five stations with discrete air sampling (and additional stations outside the European COSMO-7 domain used for the global TM5-4DVAR inversions). The sensitivity of the FLEXVAR inversions to different approaches to calculate the baselines, different parameterizations of the model representation error, different settings of the prior error covariance parameters, different prior inventories, and different observation data sets are investigated in detail. Furthermore, the FLEXVAR inversions are compared to inversions with the FLEXPART extended Kalman filter (\"FLExKF\") system and with TM5-4DVAR inversions at 1° × 1° resolution over Europe. The three inverse modelling systems show overall good consistency of the major spatial patterns of the derived inversion increments and in general only relatively small differences in the derived annual total emissions of larger country regions. At the same time, the FLEXVAR inversions at 7 km × 7 km resolution allow the observations to be better reproduced than the TM5-4DVAR simulations at 1° × 1°. The three inverse models derive higher annual total CH4 emissions in 2018 for Germany, France, and BENELUX compared to the sum of anthropogenic emissions reported to UNFCCC and natural emissions estimated from the Global Carbon Project CH4 inventory, but the uncertainty ranges of top-down and bottom-up total emission estimates overlap for all three country regions. In contrast, the top-down estimates for the sum of emissions from the UK and Ireland agree relatively well with the total of anthropogenic and natural bottom-up inventories.