National Petroleum Reserve – Alaska (NPR-A) Watershed Hydrology

During a five-year period, which represents the entire project span, the research team performed discharge measurements on seven gaging stations distributed on the National Petroleum Reserve- Alaska (NPR-A), an area of approximately 23 million acres that extends from the north side of the Brooks Range to the Arctic Ocean. Specifically, 225 discharge measurements were taken during that period. In addition, records of air temperature and rainfall, as well as wind speed and wind direction from stations that collected such data were analyzed. The air temperature data indicate that the entire region followed a pronounced warming trend, ending with the 2010/2011 winter, which was the warmest winter recorded at the stations. Rainfall data suggest a trend in increasing precipitation during the summer months from the coastal plain to the foothill area. Unusually dry conditions were experienced over the entire area in 2007 and in 2011. The overall highest mean wind speed was recorded in June at the two stations where wind data were available; the lowest mean wind speed was recorded in December at one station and in March at the other station. Wind roses indicate two main wind directions—roughly from the northeast and southwest—with winds from the northeast predominant.List of Figures ................................................................................................................................ iii List of Tables ................................................................................................................................. iv Acknowledgments and Disclaimer ................................................................................................. v Abstract .......................................................................................................................................... vi CHAPTER 1 Introduction ........................................................................................................... 1 CHAPTER 2 Discharge Measurements ...................................................................................... 3 2.1 Fieldwork ......................................................................................................................... 3 2.2 Data Analysis ................................................................................................................... 7 CHAPTER 3 Meteorological Data ............................................................................................ 10 3.1 Methodology .................................................................................................................. 11 3.2 Results ............................................................................................................................ 11 3.2.1 Rainfall .................................................................................................................... 11 3.2.2 Air Temperature ...................................................................................................... 15 3.2.3 Wind ........................................................................................................................ 20 CHAPTER 4 Information Technology ...................................................................................... 28 4.1 Aquatic Informatics Aquarius Software ......................................................................... 28 4.2 Telemetry Data Retrieval ............................................................................................... 28 4.3 Near-Real-Time Data Delivery Online .......................................................................... 28 4.4 Information Technology Infrastructure .......................................................................... 30 References ..................................................................................................................................... 31 Appendices .................................................................................................................................... 3

Methods for low bitrate coding enhancement. Part I: Spectral restoration

Perceptual audio coders are widely used when storage space or streaming bandwidth for audio content is limited. If the used bitrate is low, various coding artifacts can be introduced that degrade the perceived audio quality. A suite of algorithms has been developed to conceal these coding artifacts and to improve the perceived sound quality in automotive environments. This paper is a continuation of a previous paper and introduces two post-processing algorithms for restoring the spatial signal quality of decoded compressed audio signals. Both algorithms work single-ended, i.e. without access to the bitrate or other side information. The merit of the algorithms is demonstrated by listening tests. A second part of the paper describes algorithms that enhance the spatial image

Methods for low bitrate coding enhancement. Part II: Spatial enhancement

Perceptual audio coders are widely used when storage space or streaming bandwidth for audio content is limited. If the used bitrate is low, various coding artifacts can be introduced that degrade the perceived audio quality. A suite of algorithms has been developed to conceal these coding artifacts and to improve the perceived sound quality in automotive environments. This paper is a continuation of a previous paper and introduces two post-processing algorithms for restoring the spatial signal quality of decoded compressed audio signals. Both algorithms work single-ended, i.e. without access to the bitrate or other side information. The merit of the algorithms is demonstrated by listening tests. A previous paper presents algorithms that enhance the timbral sound characteristics

Entwicklung eines neuen medikationsbasierten Chronic Disease Scores (med-CDS; BMBF-FZ: 01ET1004B)

AbstractThe adult small intestine contains more than half of the body's lymphocytes in order to maintain homeostasis with the commensal microbiota. Birth marks a transition of the intestine from a sterile to an increasingly colonized environment. The data described in this article are incremented into the work published by Torow et al. titled “Active suppression of intestinal CD4+TCRαβ+ T lymphocyte maturation during the postnatal period” [1]. While most of the CD4 T cells found in the adult small intestine have an activated phenotype marked by expression of helper lineage specific genes neonatal lymphocytes exhibit a naïve phenotype. Further, direct comparison of neonatal CD4 T cells from the small intestine and the gut draining mesenteric lymph node (mLN) reveals a global transcriptional ‘inactivity’ of the small intestinal CD4 T cells. Here, we describe in more detail the experimental design, sample preparation and analysis that were performed to obtain and interpret the microarray data. The data set is publicly available through the Gene Expression Omnibus (GEO) database with accession number GSE60515, and the analysis and interpretation of these data are included in Torow et al. [1

A novel superior medication-based chronic disease score predicted all-cause mortality in independent geriatric cohorts.

Objectives: On the basis of current treatment guidelines, we developed and validated a medication-based chronic disease score (medCDS) and tested its association with all-cause mortality of older outpatients.Study Design and Setting: Considering the most prevalent chronic diseases in the elderly German population, we compiled a list of evidence-based medicines used to treat these disorders. Based on this list, a score (medCDS) was developed to predict mortality using data of a large longitudinal cohort of older outpatients (training sample; MultiCare Cohort Study). By assessing receiver-operating characteristics (ROC) curves, the performance of medCDS was then confirmed in independent cohorts (ESTHER, KORA-Age) of community-dwelling older patients and compared with already existing medication-based scores and a score using selected anatomical-therapeutic-chemical (ATC) codes.Results: The final medCDS score had an ROC area under the curve (AUC) of 0.73 (95% CI 0.70-0.76). In the validation cohorts, its ROC AUCs were 0.79 (0.76-0.82, KORA-Age) and 0.74 (0.71-0.78, ESTHER), which were superior to already existing medication-based scores (RxRisk, CDS) and scores based on pharmacological ATC code subgroups (ATC3) or age and sex alone (Age&Sex).Conclusions: A new medCDS, which is based on actual treatment standards, predicts mortality of older outpatients significantly better than already existing scores. (C) 2018 Elsevier Inc. All rights reserved