71 research outputs found

    CTS attenuation and cross-polarization measurements at 11.7 GHz

    Get PDF
    The results of data obtained 80 days preceding the solar eclipse shutdown of the CTS 11.7 GHz righthand circularly polarized beacon transmitter are presented. Attenuation and cross polarization isolation were measured. It was determined that depolarization presents a serious limitation to satellite system reliability when frequency reuse by polarization diversity is employed. A 27 db isolation margin would reduce reliability below 99.95%. For the same percentage the required fade margin was below 3 db

    ATS-6 attenuation diversity measurements at 20 and 30 GHz

    Get PDF
    The results of data obtained at The University of Texas at Austin in conjunction with the ATS-6 millimeter wave experiment are presented. Attenuation measurements at 30 GHz and sky noise data at 20 GHz were obtained simultaneously at each of two sites separated by 11 km. Space diversity reduces outage time for a system in Austin, Texas with a 10 dB fade margin at 30 GHz from 15 hours to 16 minutes per year. The maximum cloud height shows a good correlation to the maximum attenuations measured

    Swine embryo culture and transfer for export to England

    Get PDF
    A major threat to swine enterprises is the possible introduction of disease when new breeding animals are purchased and introduced. So, methods of introducing new genetic material while minimizing the potential for introducing disease are needed. Transfer of embryos from a donor sow in another herd or country would minimize disease risks. Already used to introduce new breeding stock into Specific Pathogen Free herds and other closed herds, embryos now are placed in the recipient gilt’s or sow1s uterus within a few hours after their recovery from the donor. That method prevents export and limits application of swine embryo transfer in this country, so we evaluated the feasibility of using an in vitro culture system to store embryos between donor sows and recipient females.; Swine Day, Manhattan, KS, November 8, 197

    Advancing data science in drug development through an innovative computational framework for data sharing and statistical analysis

    Get PDF
    Background Novartis and the University of Oxford’s Big Data Institute (BDI) have established a research alliance with the aim to improve health care and drug development by making it more efficient and targeted. Using a combination of the latest statistical machine learning technology with an innovative IT platform developed to manage large volumes of anonymised data from numerous data sources and types we plan to identify novel patterns with clinical relevance which cannot be detected by humans alone to identify phenotypes and early predictors of patient disease activity and progression. Method The collaboration focuses on highly complex autoimmune diseases and develops a computational framework to assemble a research-ready dataset across numerous modalities. For the Multiple Sclerosis (MS) project, the collaboration has anonymised and integrated phase II to phase IV clinical and imaging trial data from ≈35,000 patients across all clinical phenotypes and collected in more than 2200 centres worldwide. For the “IL-17” project, the collaboration has anonymised and integrated clinical and imaging data from over 30 phase II and III Cosentyx clinical trials including more than 15,000 patients, suffering from four autoimmune disorders (Psoriasis, Axial Spondyloarthritis, Psoriatic arthritis (PsA) and Rheumatoid arthritis (RA)). Results A fundamental component of successful data analysis and the collaborative development of novel machine learning methods on these rich data sets has been the construction of a research informatics framework that can capture the data at regular intervals where images could be anonymised and integrated with the de-identified clinical data, quality controlled and compiled into a research-ready relational database which would then be available to multi-disciplinary analysts. The collaborative development from a group of software developers, data wranglers, statisticians, clinicians, and domain scientists across both organisations has been key. This framework is innovative, as it facilitates collaborative data management and makes a complicated clinical trial data set from a pharmaceutical company available to academic researchers who become associated with the project. Conclusions An informatics framework has been developed to capture clinical trial data into a pipeline of anonymisation, quality control, data exploration, and subsequent integration into a database. Establishing this framework has been integral to the development of analytical tools
    corecore