Amorimia septentrionalis contains sodium monofluoroactetate (MFA) and can cause acute heart failure in ruminants when ingested in toxic doses. In this study, we demonstrate that resistance to poisoning by A. septentrionalis can be improved in goats by the repeated administration of non-toxic doses of A. septentrionalis. We also show that increased resistance to poisoning by A. septentrionalis can also be achieved by the transfaunation of ruminal content from goats previously conditioned to be resistant to naïve goats. These methods of improving resistance require further study, but appear to provide potential management solutions to mitigate toxicity problems from A. septentrionalis, and perhaps other plant species containing MFA.
The amount of CO2 emitted per kilowatt-hour on an electricity grid varies by time of day and substantially varies by location due to the types of generation. Networked collections of warehouse scale computers, sometimes called Hyperscale Computing, emit more carbon than needed if operated without regard to these variations in carbon intensity. This paper introduces Google's system for global Carbon-Intelligent Compute Management, which actively minimizes electricity-based carbon footprint and power infrastructure costs by delaying temporally flexible workloads. The core component of the system is a suite of analytical pipelines used to gather the next day's carbon intensity forecasts, train day-ahead demand prediction models, and use risk-aware optimization to generate the next day's carbon-aware Virtual Capacity Curves (VCCs) for all datacenter clusters across Google's fleet. VCCs impose hourly limits on resources available to temporally flexible workloads while preserving overall daily capacity, enabling all such workloads to complete within a day with high probability. Data from Google's in-production operation shows that VCCs effectively limit hourly capacity when the grid's energy supply mix is carbon intensive and delay the execution of temporally flexible workloads to "greener" times.
Over the past decade, there has been a global growth in datacenter capacity, power consumption and the associated costs. Accurate mapping of datacenter resource usage (CPU, RAM, etc.) and hardware configurations (servers, accelerators, etc.) to its power consumption is necessary for efficient long-term infrastructure planning and real-time compute load management. This paper presents two types of statistical power models that relate CPU usage of Google's Power Distribution Units (PDUs, commonly referred to as power domains) to their power consumption. The models are deployed in production and are used for cost-and carbon-aware load management, power provisioning and infrastructure rightsizing. They are simple, interpretable and exhibit uniformly high prediction accuracy in modeling power domains with large diversity of hardware configurations and workload types across Google fleet. A multi-year validation of the deployed models demonstrate that they can predict power with less than 5% Mean Absolute Percent Error (MAPE) for more than 95% diverse PDUs across Google fleet. This performance matches the best reported accuracies coming from studies that focus on specific workload types, hardware platforms and, typically, more complex statistical models.
The seroprevalence of Anaplasma marginale, Babesia bigemina, Babesia bovis and Trypanosoma vivax and the risk factors for these infections were investigated in 509 cows on 37 farms in the semiarid region of Paraíba, northeastern Brazil. Cow serum samples were tested by means of immunofluorescence assay (IFA) against each specific antigen. The mean seroprevalence values per farm were 15.0% (range: 0-75%) for A. marginale, 9.5% (range: 0-40%) for B. bigemina and 26.9% (range: 0-73.7%) for B. bovis. All cows tested negative for T. vivax. Higher prevalence for A. marginale was significantly associated with less frequent acaricide spraying per year and with higher use of injectable antihelminthics. Presence of cows positive for B. bigemina was significantly associated with acaricide use and with presence of horse flies on the farm. Both occurrence and higher prevalence of B. bovis were significantly associated with recent observations of ticks on cattle. Overall, the present results indicate that the region investigated is an enzootically unstable area for A. marginale, B. bigemina and B. bovis, since most animals were seronegative to at least one agent.Keywords: Anaplasma marginale, Babesia bigemina, Babesia bovis, cattle, risk factors, Brazil. ResumoA soroprevalência de Anaplasma marginale, Babesia bigemina, Babesia bovis e Trypanosoma vivax, assim como os fatores de risco para estas infecções, foram investigadas em 37 fazendas (total de 509 vacas) da região semiárida da Paraíba, nordeste do Brasil. A presença de anticorpos nos soros dos animais foi detectada pela técnica de imunofluorescência indireta, utilizando antígenos específicos. Os valores médios de soroprevalência por fazenda foram 15,0% (0-75%) para A. marginale, 9,5% (0-40%) para B. bigemina, e 26,9% (0-73,7%) para B. bovis. Todas as vacas foram soronegativas para T. vivax. As maiores prevalências de A. marginale foram significativamente associadas com menor uso de carrapaticidas por ano e com uso mais frequente de antihelmínticos injetáveis. A soroprevalência de B. bigemina foi significativamente associada com o uso de carrapaticidas, e com a presença de mutucas na fazenda. Tanto a ocorrência como a maior soroprevalência para B. bovis nas fazendas foram significativamente associadas com a presença recente de carrapatos nos bovinos. No geral, os resultados indicam que as fazendas amostradas estão situadas em área de instabilidade enzoótica para A. marginale, B. bigemina, e B. bovis, uma vez que a maioria dos animais foi soronegativa para pelo menos um dos agentes.Palavras-chave: Anaplasma marginale, Babesia bigemina, Babesia bovis, bovino, fatores de risco, Brasil.
We propose a solution to improve the confidence on the correctness of applications designed to be executed in heterogeneous environments, like a grid. Our solution is motivated by the observation that the traditional ways to qualify test processes are based on code coverage metrics. We believe that this approach is not adequate when dealing with applications that can (and do) fail when interacting with heterogeneous execution environments. Besides code coverage, tests must also cover possible environments. As a solution we propose the utilization of InGriD to describe and deploy test environments and GridUnit to coordinate and monitor the execution of test sets. By combining these two solutions we provide a cost effective way to introduce environmental coverage to our test suites, which is complementary and orthogonal to traditional code coverage metrics. As a case study, we have shown how our solution could be applied to help testing a grid application called MyPhotoGrid, which uses the grid to parallelize the generation of large photograph albums.
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