Herd characteristics and cow-level factors associated with Prototheca mastitis on dairy farms in Ontario, Canada

Prototheca spp. are algae that cause incurable acute or chronic mastitis in dairy cows. The aim of this case-control study was the identification of cow- and herd-level risk factors for this unusual mastitis pathogen. Aseptically collected composite milk samples from 2,428 milking cows in 23 case and 23 control herds were collected between January and May 2011. A questionnaire was administered to the producers, and cow-level production and demographic data were gathered. In 58 of 64 isolates, Prototheca spp. and Prototheca zopfii genotypes were differentiated using PCR and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. All isolates were identified as Prototheca zopfii genotype 2. The mean within-herd prevalence for Prototheca spp. was 5.1% (range 0.0-12.5%). Case herds had a significantly lower herd-level prevalence of Staphylococcus aureus and a higher prevalence of yeasts than did control herds. The final logistic regression model for herd-level risk factors included use of intramammary injections of a non- intramammary drug [odds ratio (OR) = 136.8], the number of different injectable antibiotic products being used (OR = 2.82), the use of any dry cow teat sealant (external OR = 80.0; internal OR = 34.2), and having treated 3 or more displaced abomasums in the last 12 mo OR = 44.7). The final logistic regression model for cow-level risk factors included second or greater lactation (OR = 4.40) and the logarithm of the lactation-average somatic cell count (OR = 2.99). Unsanitary or repeated intramammary infusions, antibiotic treatment, and off-label use of injectable drugs in the udder might promote Prototheca udder infection

Numerical modelling of the separation of complex shaped particles in an optical belt sorter using a DEM-CFD approach and comparison with experiments

In the growing field of bulk solids handling, automated optical sorting systems are of increasing importance. However, the initial sorter calibration is still very time consuming and the precise optical sorting of many materials still remains challenging. In order to investigate the impact of different operating parameters on the sorting quality, a numerical model of an existing modular optical belt sorter is presented in this study. The sorter and particle interaction is described with the Discrete Element Method (DEM) while the air nozzles required for deflecting undesired material fractions are modelled with Computation Fluid Dynamics (CFD). The correct representation of the resulting particle–fluid interaction is realized through a one–way coupling of the DEM with CFD. Complex shaped particle clusters are employed to model peppercorns also used in experimental investigations. To test the correct implementation of the utilized models, the particle mass flow within the sorter is compared between experiment and simulation. The particle separation results of the developed numerical model of the optical sorting system are compared with matching experimental investigations. The findings show that the numerical model is able to predict the sorting quality of the optical sorting system with reasonable accuracy

Improving optical sorting of bulk materials using sophisticated motion models

Visuelle Eigenschaften sind mächtige Merkmale zur Klassifikation von Schüttgütern, auf Basis derer man defekte oder unbrauchbare Teilchen erkennen kann. Die Verwendung optischer Bandsortieranlagen ist eine etablierte Technik zur Sortierung basierend auf diesen Merkmalen. Derartiger Sortierer leiden jedoch unter Verzögerungen zwischen der gleichzeitigen Klassifikation und Lokalisierung und der darauffolgenden Separation. Dadurch entsteht die Notwendigkeit für akkurate Modelle der Teilchenbewegung, mittels derer diese Lücke überbrücktwerden kann. In dieser Veröffentlichung stellen wir unser Konzept vor, mittels hochentwickelter Simulationen genaue Modelle herzuleiten und den Teilchenstrom durch Optimierungen im Design des Sortierers zu verbessern. Dies ermöglicht die Verbesserung der Sortiergüte und Kosteneffizienz. Abschließend präsentieren wir erste Ergebnisse

Real-time multitarget tracking for sensor-based sorting – A new implementation of the auction algorithm for graphics processing units

Utilizing parallel algorithms is an established way of increasing performance in systems that are bound to real-time restrictions. Sensor-based sorting is a machine vision application for which firm real-time requirements need to be respected in order to reliably remove potentially harmful entities from a material feed. Recently, employing a predictive tracking approach using multitarget tracking in order to decrease the error in the physical separation in optical sorting has been proposed. For implementations that use hard associations between measurements and tracks, a linear assignment problem has to be solved for each frame recorded by a camera. The auction algorithm can be utilized for this purpose, which also has the advantage of being well suited for parallel architectures. In this paper, an improved implementation of this algorithm for a graphics processing unit (GPU) is presented. The resulting algorithm is implemented in both an OpenCL and a CUDA based environment. By using an optimized data structure, the presented algorithm outperforms recently proposed implementations in terms of speed while retaining the quality of output of the algorithm. Furthermore, memory requirements are significantly decreased, which is important for embedded systems. Experimental results are provided for two different GPUs and six datasets. It is shown that the proposed approach is of particular interest for applications dealing with comparatively large problem sizes

Unmet needs in the diagnosis and treatment of dyslipidemia in the primary care setting in Germany

Objectives and methods: DETECT is a cross-sectional study of 55,518 unselected consecutive patients in 3188 representative primary care offices in Germany. In a random subset of 7519 patients, an extensive standardized laboratory program was undertaken. The study investigated the prevalence of cardiovascular disease, known risk factors (such as diabetes, hypertension and dyslipidemia and their co-morbid manifestation), as well as treatment patterns. The present analysis of the DETECT laboratory dataset focused on the prevalence and treatment of dyslipidemia in primary medical care in Germany. Coronary artery disease (CAD), risk categories and LDL-C target achievement rates were determined in the subset of 6815 patients according to the National Cholesterol Education Program (NCEP) ATP III Guidelines. Results: Of all patients, 54.3% had dyslipidemia. Only 54.4% of the NCEP-classified dyslipidemic patients were diagnosed as ‘dyslipidemic’ by their physicians. Only 27% of all dyslipidemic patients (and 40.7% of the recognized dyslipidemic patients) were treated with lipid-lowering medications, and 11.1% of all dyslipidemic patients (41.4% of the patients treated with lipid-lowering drugs) achieved their LDL-C treatment goals. In conclusion, 80.3% of patients in the sample with dyslipidemia went undiagnosed, un-treated or under-treated

Multi-messenger searches via IceCube’s high-energy neutrinos and gravitational-wave detections of LIGO/Virgo

We summarize initial results for high-energy neutrino counterpart searches coinciding with gravitational-wave events in LIGO/Virgo\u27s GWTC-2 catalog using IceCube\u27s neutrino triggers. We did not find any statistically significant high-energy neutrino counterpart and derived upper limits on the time-integrated neutrino emission on Earth as well as the isotropic equivalent energy emitted in high-energy neutrinos for each event