Epidemiological analysis of Salmonella enterica from beef sampled in the slaughterhouse and retailers in Dakar (Senegal) using pulsed-field gel electrophoresis and antibiotic susceptibility testing.

Seventy-eight isolates of Salmonella spp. isolated from beef sampled from the official city slaughterhouse and from retailers in Dakar, Senegal were analyzed using serotyping, antimicrobial testing and macrorestriction profiling by Pulsed-Field Gel Electrophoresis (PFGE). These analyses were done to identify clonal relationships and potential transmission routes in beef channel. XbaI macrorestriction allowed defining 17 genotypes among the six main analyzed serotypes: Salmonella bredeney (3 genotypes), S. muenster (6), S. waycross (1), S. corvallis (3), S. kentucky (1) and S. brandenburg (3). The cross analysis of PFGE profiles and origin of the beef samples reveals a wide range of contamination sources in the beef channel in Dakar. Comparison of PFGE and antimicrobial resistance types shows that the Salmonella contamination sources are equally shared by the slaughterhouse (56% of the isolates) and by the distribution channel (44% of the isolates) by handlings and houseflies.

Prevalence and antibiotic-resistance of Salmonella isolated from beef sampled from the slaughterhouse and from retailers in Dakar (Senegal).

A study was made of Salmonella contamination in beef sampled from a slaughterhouse and from retailers in Dakar, Senegal. The serotypes as well as antibiotic-resistance patterns of the Salmonella isolates were determined. A total of 435 meat samples (236 from the slaughterhouse, 199 from retailers) were tested. Among them, 275 (63%) were positive for Salmonella, 43% (101/236) from the slaughterhouse and 87% (174/199) from the retailers. Furthermore, 97% of the investigated retailers had at least one beef sample contaminated by Salmonella. The 286 Salmonella isolates were divided into 51 serotypes. The most prevalent serotypes were Salmonella bredeney (25%), S. muenster (8%), S. waycross (7%), S. corvallis (4%) and S. kentucky (4%). About 62% of the isolates were resistant to nitrofurans. Resistance rates were lower to streptomycin (22%), sulfamethoxazole (15%), spectinomycin (1%), chloramphenicol (1%), and tetracycline (0,4%) while low-level resistance to quinolones was detected. About 16% of the Salmonella strains were multiresistant to two or more antibiotic families. Finally, ten resistance profiles have been identified. This study shows the huge spread of Salmonella in the beef production chain in Dakar, Senegal. Finally, this study provides the very first data about Salmonella prevalence in sub-saharian Africa.

Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy.

Soil organic carbon is a key soil property related to soil fertility, aggregate stability and the exchange of CO2 with the atmosphere. Existing soil maps and inventories can rarely be used to monitor the state and evolution in soil organic carbon content due to their poor spatial resolution, lack of consistency and high updating costs. Visible and Near Infrared diffuse reflectance spectroscopy is an alternative method to provide cheap and high-density soil data. However, there are still some uncertainties on its capacity to produce reliable predictions for areas characterized by large soil diversity. Using a large-scale EU soil survey of about 20,000 samples and covering 23 countries, we assessed the performance of reflectance spectroscopy for the prediction of soil organic carbon content. The best calibrations achieved a root mean square error ranging from 4 to 15 g C kg(-1) for mineral soils and a root mean square error of 50 g C kg(-1) for organic soil materials. Model errors are shown to be related to the levels of soil organic carbon and variations in other soil properties such as sand and clay content. Although errors are ∼5 times larger than the reproducibility error of the laboratory method, reflectance spectroscopy provides unbiased predictions of the soil organic carbon content. Such estimates could be used for assessing the mean soil organic carbon content of large geographical entities or countries. This study is a first step towards providing uniform continental-scale spectroscopic estimations of soil organic carbon, meeting an increasing demand for information on the state of the soil that can be used in biogeochemical models and the monitoring of soil degradation.