A consolidated environmental monitoring plan for Aberdeen Proving Ground, Maryland

The US Army operates facilities in Edgewood and Aberdeen under several licenses from the Nuclear Regulatory Commission (NRC). Compliance with each license is time consuming and could potentially result in duplicated efforts to demonstrate compliance with existing environmental regulations. The goal of the ERM plan is to provide the sampling necessary to ensure that operations at Edgewood and Aberdeen are within applicable regulatory guidelines and to provide a means of ensuring that adverse effects to the environment are minimized. Existing sampling plans and environmental data generated from those plans are briefly reviewed as part of the development of the present ERM plan. The new ERM plan was designed to provide data that can be used for assessing risks to the environment and to humans using Aberdeen and Edgewood areas. Existing sampling is modified and new sampling is proposed based on the results of the long-term DU fate study. In that study, different environmental pathways were identified that would show transport of DU at Aberdeen. Those pathways would also be impacted by other radioactive constituents from Aberdeen and Edgewood areas. The ERM plan presented in this document includes sampling from Edgewood and Aberdeen facilities. The main radioactive constituents of concern at Edgewood are C, P, N, S, H, I, Co, Cs, Ca, Sr and U that are used in radiolabeling different compounds and tracers for different reactions and syntheses. Air and water sampling are the thrust of efforts at the Edgewood area

Effects of transportation, handling and environment on slaughter cattle. I, Weight loss and carcass yield

Includes bibliographical references (page 11)

Mapping Brucellosis Increases Relative to Elk Density using Hierarchical Bayesian Models

The relationship between host density and parasite transmission is central to the effectiveness of many management strategies. We applied hierarchical Bayesian methods to an 18-yr dataset on elk (Cervus elaphus) brucellosis in the Greater Yellowstone Ecosystem (GYE) and found that increases in brucellosis seroprevalence were strongly correlated with elk densities. Elk that were densely aggregated on supplemental feeding grounds had higher seroprevalence in 1991, but by 2008 many areas distant from the feeding grounds were of comparable seroprevalence. Thus, brucellosis appears to be expanding its range into areas of higher elk density, which is likely to further complicate the United States brucellosis eradication program. The data could not differentiate among linear and non-linear effects of host density, which is a critical area where research can inform management actions. This study is an example of how the dynamics of host populations can affect their ability to serve as disease reservoirs

Point of care coagulometry in prehospital emergency care: an observational study

Background: Haemostatic impairment can have a crucial impact on the outcome of emergency patients, especially in cases of concomitant antithrombotic drug treatment. In this prospective observational study we used a point of care (POC) coagulometer in a prehospital physician-based emergency medical system in order to test its validity and potential value in the treatment of emergency patients. Methods: During a study period of 12 months, patients could be included if venous access was mandatory for further treatment. The POC device CoaguChek® was used to assess international normalized ratio (INR) after ambulance arrival at the scene. Results were compared with in-hospital central laboratory assessment of INR. The gain of time was analysed as well as the potential value of POC testing through a questionnaire completed by the responsible prehospital emergency physician. Results: A total of 103 patients were included in this study. POC INR results were highly correlated with results of conventional assessment of INR (Bland-Altman-bias: 0.014). Using a cutoff value of INR >1.3, the device’s sensitivity to detect coagulopathy was 100 % with a specificity of 98.7 %. The median gain of time was 69 min. Treating emergency physicians considered the value of prehospital POC INR testing ‘high’ in 9 % and ‘medium’ in 21 % of all patients. In patients with tracer diagnosis ‘neurology’, the value of prehospital INR assessment was considered ‘high’ or ‘medium’ (63 %) significantly more often than in patients with non-neurological tracer diagnoses (24 %). Conclusions: Assessment of INR through a POC coagulometer is feasible in prehospital emergency care and provides valuable information on haemostatic parameters in patients. Questionnaire results suggest that POC INR testing may present a valuable technique in selected patients. Whether this information translates into an improved management of respective patients has to be evaluated in further studies

High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy with Cardiovascular Deep Learning

Left ventricular hypertrophy (LVH) results from chronic remodeling caused by a broad range of systemic and cardiovascular disease including hypertension, aortic stenosis, hypertrophic cardiomyopathy, and cardiac amyloidosis. Early detection and characterization of LVH can significantly impact patient care but is limited by under-recognition of hypertrophy, measurement error and variability, and difficulty differentiating etiologies of LVH. To overcome this challenge, we present EchoNet-LVH - a deep learning workflow that automatically quantifies ventricular hypertrophy with precision equal to human experts and predicts etiology of LVH. Trained on 28,201 echocardiogram videos, our model accurately measures intraventricular wall thickness (mean absolute error [MAE] 1.4mm, 95% CI 1.2-1.5mm), left ventricular diameter (MAE 2.4mm, 95% CI 2.2-2.6mm), and posterior wall thickness (MAE 1.2mm, 95% CI 1.1-1.3mm) and classifies cardiac amyloidosis (area under the curve of 0.83) and hypertrophic cardiomyopathy (AUC 0.98) from other etiologies of LVH. In external datasets from independent domestic and international healthcare systems, EchoNet-LVH accurately quantified ventricular parameters (R2 of 0.96 and 0.90 respectively) and detected cardiac amyloidosis (AUC 0.79) and hypertrophic cardiomyopathy (AUC 0.89) on the domestic external validation site. Leveraging measurements across multiple heart beats, our model can more accurately identify subtle changes in LV geometry and its causal etiologies. Compared to human experts, EchoNet-LVH is fully automated, allowing for reproducible, precise measurements, and lays the foundation for precision diagnosis of cardiac hypertrophy. As a resource to promote further innovation, we also make publicly available a large dataset of 23,212 annotated echocardiogram videos

Diffuse Glioneuronal tumour with Oligodendroglioma‐like features and Nuclear Clusters (DGONC) – a molecularly‐defined glioneuronal CNS tumour class displaying recurrent monosomy 14

Aims: DNA methylation-based central nervous system (CNS) tumour classification has identified numerous molecularly distinct tumour types, and clinically relevant subgroups among known CNS tumour entities that were previously thought to represent homogeneous diseases. Our study aimed at characterizing a novel, molecularly defined variant of glioneuronal CNS tumour. Patients and methods: DNA methylation profiling was performed using the Infinium MethylationEPIC or 450 k BeadChip arrays (Illumina) and analysed using the 'conumee' package in R computing environment. Additional gene panel sequencing was also performed. Tumour samples were collected at the German Cancer Research Centre (DKFZ) and provided by multinational collaborators. Histological sections were also collected and independently reviewed. Results: Genome-wide DNA methylation data from >25 000 CNS tumours were screened for clusters separated from established DNA methylation classes, revealing a novel group comprising 31 tumours, mainly found in paediatric patients. This DNA methylation-defined variant of low-grade CNS tumours with glioneuronal differentiation displays recurrent monosomy 14, nuclear clusters within a morphology that is otherwise reminiscent of oligodendroglioma and other established entities with clear cell histology, and a lack of genetic alterations commonly observed in other (paediatric) glioneuronal entities. Conclusions: DNA methylation-based tumour classification is an objective method of assessing tumour origins, which may aid in diagnosis, especially for atypical cases. With increasing sample size, methylation analysis allows for the identification of rare, putative new tumour entities, which are currently not recognized by the WHO classification. Our study revealed the existence of a DNA methylation-defined class of low-grade glioneuronal tumours with recurrent monosomy 14, oligodendroglioma-like features and nuclear clusters

Large-scale mass wasting in the western Indian Ocean constrains onset of East African rifting

Faulting and earthquakes occur extensively along the flanks of the East African Rift System, including an offshore branch in the western Indian Ocean, resulting in remobilization of sediment in the form of landslides. To date, constraints on the occurrence of submarine landslides at margin scale are lacking, leaving unanswered a link between rifting and slope instability. Here, we show the first overview of landslide deposits in the post-Eocene stratigraphy of the Tanzania margin and we present the discovery of one of the biggest landslides on Earth: the Mafia mega-slide. The emplacement of multiple landslides, including the Mafia mega-slide, during the early-mid Miocene is coeval with cratonic rifting in Tanzania, indicating that plateau uplift and rifting in East Africa triggered large and potentially tsunamigenic landslides likely through earthquake activity and enhanced sediment supply. This study is a first step to evaluate the risk associated with submarine landslides in the region

Electrocardiographic Deep Learning for Predicting Post-Procedural Mortality

Background. Pre-operative risk assessments used in clinical practice are limited in their ability to identify risk for post-operative mortality. We hypothesize that electrocardiograms contain hidden risk markers that can help prognosticate post-operative mortality. Methods. In a derivation cohort of 45,969 pre-operative patients (age 59+- 19 years, 55 percent women), a deep learning algorithm was developed to leverage waveform signals from pre-operative ECGs to discriminate post-operative mortality. Model performance was assessed in a holdout internal test dataset and in two external hospital cohorts and compared with the Revised Cardiac Risk Index (RCRI) score. Results. In the derivation cohort, there were 1,452 deaths. The algorithm discriminates mortality with an AUC of 0.83 (95% CI 0.79-0.87) surpassing the discrimination of the RCRI score with an AUC of 0.67 (CI 0.61-0.72) in the held out test cohort. Patients determined to be high risk by the deep learning model's risk prediction had an unadjusted odds ratio (OR) of 8.83 (5.57-13.20) for post-operative mortality as compared to an unadjusted OR of 2.08 (CI 0.77-3.50) for post-operative mortality for RCRI greater than 2. The deep learning algorithm performed similarly for patients undergoing cardiac surgery with an AUC of 0.85 (CI 0.77-0.92), non-cardiac surgery with an AUC of 0.83 (0.79-0.88), and catherization or endoscopy suite procedures with an AUC of 0.76 (0.72-0.81). The algorithm similarly discriminated risk for mortality in two separate external validation cohorts from independent healthcare systems with AUCs of 0.79 (0.75-0.83) and 0.75 (0.74-0.76) respectively. Conclusion. The findings demonstrate how a novel deep learning algorithm, applied to pre-operative ECGs, can improve discrimination of post-operative mortality