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Using the CUDA platform from NVIDIA, we developed a Multiple Species Data Flocking implementation to be run on the NVIDIA GPU.
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Inputs include environmental and farm management variables which impact on the epidemiology of the disease, e.g. regional weather data; flock stocking rates; initial pasture larval contamination levels and species proportions; lambing dates; timing of flock movements to clean pastures; and removal of lambs during the year.
As a result a unique set of radiological (radiographs combined with EMG, MRI), biomechanical (muscle activation patterns) and 3D motion data (Flock of Birds) will be available on each individual patient, besides usual clinical data and outcome measures (e.g. CS, WORC).
Beyond the business case, data scientists are flocking to the project, according to Databricks.
Whereas diagnosis of Se deficiency is very common in individual samples sent to our lab, comprehensive data at flock level including more than 2 3 samples per flock are currently missing.
So data can flock like birds of a feather.
Adjusted mean proviral concentrations and other summary data by flock are shown in Table 1.
During discussions within the working group of the EU Network of Excellence Neuroprion it became clear that data including flock sizes, age and genotype of animals tested was not available from most countries, and consequently we did not collect these data.
Why collect better job-placement data to provide to prospective students when they're still flocking to mediocre graduate programs?
Patients are flocking in.
multiple species flocking.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com