Program on application of communications satellites to educational development: Design of a 12 channel FM microwave receiver

The design, fabrication, and performance of elements of a low cost FM microwave satellite ground station receiver is described. It is capable of accepting 12 contiguous color television equivalent bandwidth channels in the 11.72 to 12.2 GHz band. Each channel is 40 MHz wide and incorporates a 4 MHz guard band. The modulation format is wideband FM and the channels are frequency division multiplexed. Twelve independent CATV compatible baseband outputs are provided. The overall system specifications are first discussed, then consideration is given to the receiver subsystems and the signal branching network

Design of a 12 channel fm microwave receiver

The design, fabrication, and performance of elements of a low cost FM microwave satellite ground station receiver is described. It is capable of accepting 12 contiguous color television equivalent bandwidth channels in the 11.72 to 12.2 GHz band. Each channel is 40 MHz wide and incorporates a 4 MHz guard band. The modulation format is wideband FM and the channels are frequency division multiplexed. Twelve independent CATV compatible baseband outputs are provided. The overall system specifications are first discussed, then consideration is given to the receiver subsystems and the signal branching network

Exploratory topic modeling with distributional semantics

As we continue to collect and store textual data in a multitude of domains, we are regularly confronted with material whose largely unknown thematic structure we want to uncover. With unsupervised, exploratory analysis, no prior knowledge about the content is required and highly open-ended tasks can be supported. In the past few years, probabilistic topic modeling has emerged as a popular approach to this problem. Nevertheless, the representation of the latent topics as aggregations of semi-coherent terms limits their interpretability and level of detail. This paper presents an alternative approach to topic modeling that maps topics as a network for exploration, based on distributional semantics using learned word vectors. From the granular level of terms and their semantic similarity relations global topic structures emerge as clustered regions and gradients of concepts. Moreover, the paper discusses the visual interactive representation of the topic map, which plays an important role in supporting its exploration.Comment: Conference: The Fourteenth International Symposium on Intelligent Data Analysis (IDA 2015

Lessons Learned from Two Case Studies in Higher Education

As places where future citizens are educated, knowledge is (co-)produced and societal developments are critically reflected, higher education institutions (HEIs) can play a key role in addressing sustainability challenges. In order to accelerate mutual learning, shared problem understanding, and joint development of sustainable solutions, interinstitutional exchange and collaboration between HEIs is crucial. However, little research to date has focused on institutional HEI networks in the field of sustainability. More specifically, we still understand little about the concrete development, implementation, and adaptation of such networks. This article explores early-stage HEI networks for sustainability from a conceptual and empirical stance in order to develop a framework that facilitates structured descriptions of these networks, as well as to foster cross-HEI learning on their effective performance. It therefore combines insights from an explorative literature review, two case studies and an interactive workshop at the ISCN Conference 2018. As results, we first suggest an analytical framework to facilitate a systematic characterization of HEI networks. Second, by applying the framework to the two case studies, we present and discuss lessons learned on how a single HEI can contribute to establishing a network and how it can utilize its network membership effectively to strengthen its efforts for sustainability

The impact of adjusting for baseline in pharmacogenomic genome-wide association studies of quantitative change.

In pharmacogenomic studies of quantitative change, any association between genetic variants and the pretreatment (baseline) measurement can bias the estimate of effect between those variants and drug response. A putative solution is to adjust for baseline. We conducted a series of genome-wide association studies (GWASs) for low-density lipoprotein cholesterol (LDL-C) response to statin therapy in 34,874 participants of the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort as a case study to investigate the impact of baseline adjustment on results generated from pharmacogenomic studies of quantitative change. Across phenotypes of statin-induced LDL-C change, baseline adjustment identified variants from six loci meeting genome-wide significance (SORT/CELSR2/PSRC1, LPA, SLCO1B1, APOE, APOB, and SMARCA4/LDLR). In contrast, baseline-unadjusted analyses yielded variants from three loci meeting the criteria for genome-wide significance (LPA, APOE, and SLCO1B1). A genome-wide heterogeneity test of baseline versus statin on-treatment LDL-C levels was performed as the definitive test for the true effect of genetic variants on statin-induced LDL-C change. These findings were generally consistent with the models not adjusting for baseline signifying that genome-wide significant hits generated only from baseline-adjusted analyses (SORT/CELSR2/PSRC1, APOB, SMARCA4/LDLR) were likely biased. We then comprehensively reviewed published GWASs of drug-induced quantitative change and discovered that more than half (59%) inappropriately adjusted for baseline. Altogether, we demonstrate that (1) baseline adjustment introduces bias in pharmacogenomic studies of quantitative change and (2) this erroneous methodology is highly prevalent. We conclude that it is critical to avoid this common statistical approach in future pharmacogenomic studies of quantitative change

Perch-type Characteristics of Overwintering Red-tailed Hawks (Buteo jamaicensis) and American Kestrels (Falco sparverius)

Red-tailed Hawks (Buteo jamaicensis) and American Kestrels (Falco sparverius) are primarily sitand-wait predators that rely on perches to forage most efficiently. Overwintering Red-tailed Hawks and American Kestrels use available perches (e.g., utility poles and wires, trees, fences, gates, etc.) to hunt for prey items in agricultural fields in northeast Arkansas. Observations were made from December 2011-March 2012 and November 2012-March 2013 in three representative cover-types (short rice stubble, soybean stubble, and fallow areas including roadsides) to determine which perch-types were used by Red-tailed Hawks and American Kestrels. Utility pole crossbeams at an average height of 6.3 m were the main perchtypes used by Red-tailed Hawks, demonstrating the use of man-made structures’. These perches were generally in or near fallow areas or short rice stubble fields. Conversely, American Kestrels usually perched on wires at an average height of 4.9 m, over fallow roadsides’. Fallow areas had high prey density and vegetation cover. Niche separation via differential use of perches may be one factor that allows these raptors to avoid inter-specific competition

Do changes in health reveal the possibility of undiagnosed pancreatic cancer? Development of a risk-prediction model based on healthcare claims data.

Background and objectiveEarly detection methods for pancreatic cancer are lacking. We aimed to develop a prediction model for pancreatic cancer based on changes in health captured by healthcare claims data.MethodsWe conducted a case-control study on 29,646 Medicare-enrolled patients aged 68 years and above with pancreatic ductal adenocarcinoma (PDAC) reported to the Surveillance Epidemiology an End Results (SEER) tumor registries program in 2004-2011 and 88,938 age and sex-matched controls. We developed a prediction model using multivariable logistic regression on Medicare claims for 16 risk factors and pre-diagnostic symptoms of PDAC present within 15 months prior to PDAC diagnosis. Claims within 3 months of PDAC diagnosis were excluded in sensitivity analyses. We evaluated the discriminatory power of the model with the area under the receiver operating curve (AUC) and performed cross-validation by bootstrapping.ResultsThe prediction model on all cases and controls reached AUC of 0.68. Excluding the final 3 months of claims lowered the AUC to 0.58. Among new-onset diabetes patients, the prediction model reached AUC of 0.73, which decreased to 0.63 when claims from the final 3 months were excluded. Performance measures of the prediction models was confirmed by internal validation using the bootstrap method.ConclusionModels based on healthcare claims for clinical risk factors, symptoms and signs of pancreatic cancer are limited in classifying those who go on to diagnosis of pancreatic cancer and those who do not, especially when excluding claims that immediately precede the diagnosis of PDAC