Contraintes a l’adoption de la methode de l’hygiene sanitaire des vergers pour la lutte contre les mouches nuisibles aux fruits (Diptera, Tephritidae) par les producteurs de mangues et d’agrumes au Benin

No AbstractMots-clés: Bénin, hygiène sanitaire des vergers, mouches de fruits, producteurs de mangues et d’agrumes 

Effect Of Pasteurization On The Decay Of Mycobacterium Bovis In Milk Cream

Milk cream must be pasteurized in order to be sold in Brazil. However, there are no specific legal requirements for this product, and producers set their own pasteurization parameters using the ones approved for milk as a reference. Considering that fat protects bacteria from heat, that no thermal inactivation studies have been performed on Mycobacterium bovis present in cream, and that bovine tuberculosis is endemic in Brazil, the aim of this study was to evaluate the inactivation of M. bovis in milk cream subjected to commercial parameters of pasteurization. Milk cream samples were contaminated and pasteurized in a water bath at 75, 80, 85, and 90°C for 5 and 15 s. M. bovis cells were plated onto Stonebrink-Leslie medium, incubated at 36°C for 45 days, and quantified; the result was expressed in log CFU mL-1. The fat content of the samples ranged from 34% to 37% and the average initial load of M. bovis was 8.0 Log CFU mL-1. The average decay of the M. bovis populations was 4.0, 4.3, 4.9 and 6.7 log CFU mL-1 when the cream was treated for 15 sec at 75, 80, 85 and 90°C, respectively, showing that the efficiency of the heat treatment was improved by increasing the temperature of the process. Given the lipophilic nature of M. bovis, the cream should be subjected to more intense parameters of pasteurization than those applied to milk.3753737374

Prevalence And Risk Factors For Bovine Tuberculosis In The State Of São Paulo, Brazil

A cross sectional study was carried out between May and November 2011 to investigate the epidemiological situation of bovine tuberculosis (bTB) in the state of São Paulo, Brazil. The state was divided into seven regions. Three hundred farms from each region, with reproductive activity, were randomly chosen and included as primary sample units. A fixed number of bovine females, older than 2 years of age, were randomly selected and tested, using the comparative cervical tuberculin test. An epidemiological questionnaire based survey was conducted in the selected farms. Our results show that in the state of São Paulo, the apparent prevalence of positive farms was 9% (95% confidence interval, 95% CI = 7.8 - 10.5%). The prevalence in the individual regions varied between 3.5% (95% CI = 1.7 - 6.8%) and 13.9% (95% CI = 10.2 - 18.8%). The apparent prevalence of positive animals in the state was 1.3% (95% CI = 0.9 - 1.7%) and varied from 0.3% (95% CI = 0.2 - 0.6%) to 2.5% (95% CI = 1.4 - 4.5%) in the regions. The risk factors associated with tuberculosis in the state were (i) number of adult females in a herd is = 24 (Odds ratio, OR = 1.91, 95% CI = 1.32 - 2.75), (ii) type of farm enterprise (dairy: OR = 2.70, 95% CI = 1.40 - 5.21; mixed: OR = 2.03, 95% CI = 1.08 - 3.82), (iii) milking process (milking parlor: OR = 4.12, 95% CI = 1.46 - 11.64; portable milking machine: OR = 2.94, 95% CI = 1.42 - 6.09), and (iv) pasture sharing (OR = 1.58, 95% CI = 1.07 - 2.33). The state of São Paulo should implement a structured surveillance system to detect and mitigate the disease. Further, an efficient animal health education program, which encourages the farmers to test replacement animals for bTB prior to introduction in their herds and to avoid pasture sharing with farms of unknown sanitary conditions should also be implemented.3753673368

Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients

The authors would like to thank David Kirkby and Connor Sheere for insightful discussions. This work is part of the Recommendation System for Spectroscopic Followup (RESSPECT) project, governed by an inter-collaboration agreement signed between the Cosmostatistics Initiative (COIN) and the LSST Dark Energy Science Collaboration (DESC). This research is supported in part by the HPI Research Center in Machine Learning and Data Science at UC Irvine. EEOI and SS acknowledge financial support from CNRS 2017 MOMENTUM grant under the project Active Learning for Large Scale Sky Surveys. SGG and AKM acknowledge support by FCT under Project CRISP PTDC/FIS-AST-31546/2017. This work was partly supported by the Hewlett Packard Enterprise Data Science Institute (HPE DSI) at the University of Houston. DOJ is supported by a Gordon and Betty Moore Foundation postdoctoral fellowship at the University of California, Santa Cruz. Support for this work was provided by NASA through the NASA Hubble Fellowship grant HF2-51462.001 awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS5-26555. BQ is supported by the International Gemini Observatory, a program of NSF's NOIRLab, which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation, on behalf of the Gemini partnership of Argentina, Brazil, Canada, Chile, the Republic of Korea, and the United States of America. AIM acknowledges support from the Max Planck Society and the Alexander von Humboldt Foundation in the framework of the Max Planck-Humboldt Research Award endowed by the Federal Ministry of Education and Research. L.G. was funded by the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 839090. This work has been partially supported by the Spanish grant PGC2018-095317-B-C21 within the European Funds for Regional Development (FEDER).The recent increase in volume and complexity of available astronomical data has led to a wide use of supervised machine learning techniques. Active learning strategies have been proposed as an alternative to optimize the distribution of scarce labeling resources. However, due to the specific conditions in which labels can be acquired, fundamental assumptions, such as sample representativeness and labeling cost stability cannot be fulfilled. The Recommendation System for Spectroscopic followup (RESSPECT) project aims to enable the construction of optimized training samples for the Rubin Observatory Legacy Survey of Space and Time (LSST), taking into account a realistic description of the astronomical data environment. In this work, we test the robustness of active learning techniques in a realistic simulated astronomical data scenario. Our experiment takes into account the evolution of training and pool samples, different costs per object, and two different sources of budget. Results show that traditional active learning strategies significantly outperform random sampling. Nevertheless, more complex batch strategies are not able to significantly overcome simple uncertainty sampling techniques. Our findings illustrate three important points: 1) active learning strategies are a powerful tool to optimize the label-acquisition task in astronomy, 2) for upcoming large surveys like LSST, such techniques allow us to tailor the construction of the training sample for the first day of the survey, and 3) the peculiar data environment related to the detection of astronomical transients is a fertile ground that calls for the development of tailored machine learning algorithms.HPI Research Center in Machine Learning and Data Science at UC IrvineCNRS 2017 MOMENTUM grant under the project Active Learning for Large Scale Sky SurveysFCT under Project CRISP PTDC/FIS-AST-31546/2017Hewlett Packard Enterprise Data Science Institute (HPE DSI) at the University of HoustonGordon and Betty Moore Foundation postdoctoral fellowship at the University of California, Santa CruzSpace Telescope Science InstituteNational Aeronautics & Space Administration (NASA) HF2-51462.001 NAS5-26555International Gemini Observatory, a program of NSF's NOIRLabNational Science Foundation (NSF)Max Planck SocietyFoundation CELLEXAlexander von Humboldt FoundationEuropean Commission 839090Spanish grant within the European Funds for Regional Development (FEDER) PGC2018-095317-B-C2

Homogeneous cosmologies in generalized modified gravity

Dynamical system methods are used in the study of the stability of spatially flat homogeneous cosmologies within a large class of generalized modified gravity models in the presence of a relativistic matter-radiation fluid. The present approach can be considered as the generalization of previous works in which only $F(R)$ cases were considered. Models described by an arbitrary function of all possible geometric invariants are investigated and general equations giving all critical points are derived.Comment: 13 pages, no figure