Prediction of recreational water safety using Escherichia coli as an indicator: case study of the Passaic and Pompton rivers, New Jersey

2020 ◽  
Vol 714 ◽  
pp. 136814 ◽  
Author(s):  
Alessandra Rossi ◽  
Bernabas T. Wolde ◽  
Lee H. Lee ◽  
Meiyin Wu
Keyword(s):  
Escherichia Coli ◽  
New Jersey ◽  
Recreational Water ◽  
Water Safety

scholarly journals An outbreak of Shiga toxin-producing Escherichia coli O157:H7 associated with contaminated salad leaves: epidemiological, genomic and food trace back investigations

2017 ◽  
Vol 146 (2) ◽  
pp. 187-196 ◽  
Author(s):  
A. F. W. MIKHAIL ◽  
C. JENKINS ◽  
T. J. DALLMAN ◽  
T. INNS ◽  
A. DOUGLAS ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Shiga Toxin ◽  
Recent Common Ancestor ◽  
Outbreak Strain ◽  
The United Kingdom ◽  
Run Off ◽  
Trace Back ◽  
Close Phylogenetic Relationship ◽  
Foodborne Outbreaks

SUMMARYIn August 2015, Public Health England detected an outbreak of Shiga toxin-producing Escherichia coli (STEC) serotype O157:H7 caused by contaminated salad leaves in a mixed leaf prepacked salad product from a national retailer. The implicated leaves were cultivated at five different farms and the zoonotic source of the outbreak strain was not determined. In March 2016, additional isolates from new cases were identified that shared a recent common ancestor with the outbreak strain. A case–case study involving the cases identified in 2016 revealed that ovine exposures were associated with illness (n = 16; AOR 8·24; 95% CI 1·55–39·74). By mapping the recent movement of sheep and lambs across the United Kingdom, epidemiological links were established between the cases reporting ovine exposures. Given the close phylogenetic relationship between the outbreak strain and the isolates from cases with ovine exposures, it is plausible that ovine faeces may have contaminated the salad leaves via untreated irrigation water or run-off from fields nearby. Timely and targeted veterinary and environmental sampling should be considered during foodborne outbreaks of STEC, particularly where ready to eat vegetables and salads are implicated.

scholarly journals Simultaneous Parameters Identifiability and Estimation of anE. coliMetabolic Network Model

2015 ◽  
Vol 2015 ◽  
pp. 1-21 ◽  
Author(s):  
Kese Pontes Freitas Alberton ◽  
André Luís Alberton ◽  
Jimena Andrea Di Maggio ◽  
Vanina Gisela Estrada ◽  
María Soledad Díaz ◽  
...  
Keyword(s):  
Experimental Data ◽  
Escherichia Coli ◽  
Metabolic Networks ◽  
Initial Conditions ◽  
Model Fit ◽  
Real Problem ◽  
Kinetic Rates ◽  
K 12 ◽  
Procedure Model

This work proposes a procedure for simultaneous parameters identifiability and estimation in metabolic networks in order to overcome difficulties associated with lack of experimental data and large number of parameters, a common scenario in the modeling of such systems. As case study, the complex real problem of parameters identifiability of theEscherichia coliK-12 W3110 dynamic model was investigated, composed by 18 differential ordinary equations and 35 kinetic rates, containing 125 parameters. With the procedure, model fit was improved for most of the measured metabolites, achieving 58 parameters estimated, including 5 unknown initial conditions. The results indicate that simultaneous parameters identifiability and estimation approach in metabolic networks is appealing, since model fit to the most of measured metabolites was possible even when important measures of intracellular metabolites and good initial estimates of parameters are not available.

scholarly journals A Statistical Model for Assessing Sample Size for Bacterial Colony Selection: A Case Study of Escherichia Coli and Avian Cellulitis

2000 ◽  
Vol 12 (2) ◽  
pp. 118-125 ◽  
Author(s):  
Randall S. Singer ◽  
Wesley O. Johnson ◽  
Joan S. Jeffrey ◽  
Richard P. Chin ◽  
Tim E. Carpenter ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Statistical Model ◽  
Clinical Sample ◽  
Agar Plate ◽  
Sample Sizes ◽  
Simple Task ◽  
Single Strain ◽  
Bacterial Colony ◽  
E Coli

A general problem for microbiologists is determining the number of phenotypically similar colonies growing on an agar plate that must be analyzed in order to be confident of identifying all of the different strains present in the sample. If a specified number of colonies is picked from a plate on which the number of unique strains of bacteria is unknown, assigning a probability of correctly identifying all of the strains present on the plate is not a simple task. With Escherichia coli of avian cellulitis origin as a case study, a statistical model was designed that would delineate sample sizes for efficient and consistent identification of all the strains of phenotypically similar bacteria in a clinical sample. This model enables the microbiologist to calculate the probability that all of the strains contained within the sample are correctly identified and to generate probability-based sample sizes for colony identification. The probability of cellulitis lesions containing a single strain of E. coli was 95.4%. If one E. coli strain is observed out of three colonies randomly selected from a future agar plate, the probability is 98.8% that only one strain is on the plate. These results are specific for this cellulitis E. coli scenario. For systems in which the number of bacterial strains per sample is variable, this model provides a quantitative means by which sample sizes can be determined.

scholarly journals Spread of Antibiotic Resistance in Aquatic Environments: E. coli as a Case Study

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 693 ◽  
Author(s):  
Maria Adamantia Efstratiou ◽  
Marina Bountouni ◽  
Efthimios Kefalas
Keyword(s):  
Escherichia Coli ◽  
Drinking Water ◽  
Antibiotic Resistance ◽  
Rural Communities ◽  
Ground Water ◽  
Aquatic Environments ◽  
Multiple Resistance ◽  
E Coli ◽  
Of Greece

The aim of this study was to gather information on the spread of antibiotic resistance in Escherichia coli isolates from wells, boreholes and untreated drinking water in islands of Greece. We analyzed for antibiotic resistance 235 E. coli strains isolated from untreated drinking water of small rural communities, and ground water from 4 islands. Resistance was tested against Norfloxacin, Ciprofloxacin, Levofloxacin, Amoxicillin and Cefaclor. More than half (54.9%) were resistant to at least one of the antibiotics tested. Of these 26.3% showed multiple resistance (to two or more antibiotics). Strains from drinking water sources were overall more sensitive. Frequent resistance was observed for Amoxicillin (38.3%) and Levofloxacin (28.5%), low for Norfloxacin (5.5%).

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