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Pereira RV, Fritz HM, Toohey-Kurth K, Clothier KA. Salmonella enterica Serovar Dublin from Cattle in California from 1993 to 2019: Characterization and Analysis of Antimicrobial Resistance Diversity. Antibiotics (Basel) 2023; 13:22. [PMID: 38247581 PMCID: PMC10812445 DOI: 10.3390/antibiotics13010022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 12/18/2023] [Accepted: 12/22/2023] [Indexed: 01/23/2024] Open
Abstract
For this study, antimicrobial susceptibility data for Salmonella enterica subsp. enterica serovar Dublin (S. Dublin)-a well-known cattle-adapted pathogen with current concerns for multidrug resistance-were recovered from cattle at the California Animal Health and Food Safety Laboratory System (CAHFS) over the last three decades (1993-2019) and were evaluated using tools to capture diversity in antimicrobial resistance. For this purpose, minimum inhibitory concentration (MIC) testing was conducted for 247 clinical S. Dublin isolates. Antimicrobial resistance (AMR) profiles revealed a predominant core multidrug-resistant pattern in the three most common AMR profiles observed. Antimicrobial resistance richness, diversity, and similarity analysis revealed patterns for changes in AMR profiles for different age groups. Discriminant analysis using MIC log2-transformed data revealed changes in MIC for year groups, with a time-sequence pattern observed. Drivers for reduced susceptibility were observed for 3rd generation cephalosporins and quinolones observed for more recent year groups (2011-2015 and 2016-2019) when compared to older year groups (1993-1999 and 2000-2005). Together, these results highlight the changes in the diversity of AMR profiles, as well as changes in susceptibility of S. Dublin over time for critical antimicrobials of importance to both animals and humans, and support the need for continued monitoring and efforts that will support judicious use of antimicrobials, especially for these two drug classes.
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Affiliation(s)
- Richard V. Pereira
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, Davis, CA 95616, USA
| | - Heather M. Fritz
- California Animal Health and Food Safety Laboratory, School of Veterinary Medicine, University of California, Davis, CA 95616, USA; (H.M.F.); (K.A.C.)
| | - Kathy Toohey-Kurth
- California Animal Health and Food Safety Laboratory, School of Veterinary Medicine, University of California, San Bernadino, CA 92411, USA;
| | - Kristin A. Clothier
- California Animal Health and Food Safety Laboratory, School of Veterinary Medicine, University of California, Davis, CA 95616, USA; (H.M.F.); (K.A.C.)
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2
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Breen MJ, Williams DR, Abdelfattah EM, Karle BM, Byrne BA, Lehenbauer TW, Aly SS. Effect of Group Housing of Preweaned Dairy Calves: Health and Fecal Commensal Antimicrobial Resistance Outcomes. Antibiotics (Basel) 2023; 12:1019. [PMID: 37370338 DOI: 10.3390/antibiotics12061019] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 05/30/2023] [Accepted: 06/01/2023] [Indexed: 06/29/2023] Open
Abstract
The objectives of this study were to investigate the effects of group housing (three calves per group) on bovine respiratory disease (BRD), diarrhea and antimicrobial resistance (AMR) to fecal commensal Escherichia coli (EC) and enterococci/streptococci (ES). Our study comprised two arms, one experimental and one observational. In the experimental arm, preweaned calves on a California dairy were randomized to either individual (IND; n = 21) or group (GRP; n = 21) housing, using a modified California-style wooden hutch. The study period lasted from birth to 56 days of age, during which calves were health scored daily. Cumulative incidence and hazard ratios were estimated for disease. Antimicrobial resistance outcomes were assessed using a prospective cohort design; feces were collected from each calf three times per week and EC and ES were evaluated for AMR using the broth microdilution method against a panel of 19 antimicrobial drugs (AMD). Analysis of treatment records was used to select calves that had been exposed (EXP) to an AMD-treated calf. In GRP, exposure occurred when a calf was a hutchmate with an AMD-treated calf. In IND, exposure occurred when a calf was a neighbor with an AMD-treated calf (TRT). Age-matched unexposed calves (UNEXP) were then selected for comparison. Proportions of AMR in fecal commensals among EXP, UNEXP, and TRT calves were compared between GRP and IND. Accelerated failure time survival regression models were specified to compare differences in minimum inhibitory concentration (MIC) of fecal commensals between EXP and UNEXP calves within each of GRP and IND calves separately. Group calves had a BRD hazard 1.94 times greater that of IND calves (p = 0.03), using BRD treatment records as the outcome. For AMR in EC isolates, higher resistance to enrofloxacin was detected in enrofloxacin-EXP GRP isolates compared with enrofloxacin-EXP IND isolates, and UNEXP GRP calves had lower resistance to ceftiofur compared with enrofloxacin-EXP and enrofloxacin-TRT calves. A significant housing-by-time interaction was detected for EC ceftiofur MIC in EXP GRP calves at 4-14 days post exposure to enrofloxacin (MIC EXP-UNEXP: µg/mL (95% CI): 10.62 (1.17, 20.07)), compared with UNEXP calves. The findings of this study show an increase in BRD hazard for group-housed calves and an increase in ceftiofur resistance in group-housed calves exposed to an enrofloxacin-treated calf.
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Affiliation(s)
- Martin J Breen
- Veterinary Medicine Teaching and Research Center, School of Veterinary Medicine, University of California Davis, Tulare, CA 93274, USA
| | - Deniece R Williams
- Veterinary Medicine Teaching and Research Center, School of Veterinary Medicine, University of California Davis, Tulare, CA 93274, USA
| | - Essam M Abdelfattah
- Veterinary Medicine Teaching and Research Center, School of Veterinary Medicine, University of California Davis, Tulare, CA 93274, USA
- Department of Animal Hygiene and Veterinary Management, Faculty of Veterinary Medicine, Benha University, Moshtohor 13736, Egypt
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California Davis, Davis, CA 95616, USA
| | - Betsy M Karle
- Cooperative Extension, Division of Agriculture and Natural Resources, University of California, Orland, CA 95963, USA
| | - Barbara A Byrne
- Department of Veterinary Pathology, Microbiology & Immunology, University of California Davis, Davis, CA 95616, USA
| | - Terry W Lehenbauer
- Veterinary Medicine Teaching and Research Center, School of Veterinary Medicine, University of California Davis, Tulare, CA 93274, USA
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California Davis, Davis, CA 95616, USA
| | - Sharif S Aly
- Veterinary Medicine Teaching and Research Center, School of Veterinary Medicine, University of California Davis, Tulare, CA 93274, USA
- Department of Population Health and Reproduction, School of Veterinary Medicine, University of California Davis, Davis, CA 95616, USA
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Aerts M, Tzu-yun Teng K, Jaspers S, Sanchez JA. A multicategory logit model detecting temporal changes in antimicrobial resistance. PLoS One 2022; 17:e0277866. [PMID: 36454890 PMCID: PMC9714861 DOI: 10.1371/journal.pone.0277866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 11/04/2022] [Indexed: 12/05/2022] Open
Abstract
Monitoring and investigating temporal trends in antimicrobial data is a high priority for human and animal health authorities. Timely detection of temporal changes in antimicrobial resistance (AMR) can rely not only on monitoring and analyzing the proportion of resistant isolates based on the use of a clinical or epidemiological cut-off value, but also on more subtle changes and trends in the full distribution of minimum inhibitory concentration (MIC) values. The nature of the MIC distribution is categorical and ordinal (discrete). In this contribution, we developed a particular family of multicategory logit models for estimating and modelling MIC distributions over time. It allows the detection of a multitude of temporal trends in the full discrete distribution, without any assumption on the underlying continuous distribution for the MIC values. The experimental ranges of the serial dilution experiments may vary across laboratories and over time. The proposed categorical model allows to estimate the MIC distribution over the maximal range of the observed experiments, and allows the observed ranges to vary across labs and over time. The use and performance of the model is illustrated with two datasets on AMR in Salmonella.
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Affiliation(s)
- Marc Aerts
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Diepenbeek, Belgium
- Data Science Institute, Hasselt University, Diepenbeek, Belgium
- * E-mail:
| | - Kendy Tzu-yun Teng
- VISAVET Health Surveillance Centre, Universidad Complutense, Madrid, Spain
- Department of Veterinary Medicine, College of Veterinary Medicine, National Chung Hsing University, Taichung City, Taiwan
| | - Stijn Jaspers
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Diepenbeek, Belgium
- Data Science Institute, Hasselt University, Diepenbeek, Belgium
| | - Julio Alvarez Sanchez
- VISAVET Health Surveillance Centre, Universidad Complutense, Madrid, Spain
- Department of Animal Health, Faculty of Veterinary Medicine, Universidad Complutense, Madrid, Spain
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4
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Zhang M, Wang C, O’Connor A. A Bayesian approach to modeling antimicrobial multidrug resistance. PLoS One 2021; 16:e0261528. [PMID: 34965273 PMCID: PMC8716034 DOI: 10.1371/journal.pone.0261528] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 12/03/2021] [Indexed: 11/23/2022] Open
Abstract
Multidrug resistance (MDR) has been a significant threat to public health and effective treatment of bacterial infections. Current identification of MDR is primarily based upon the large proportions of isolates resistant to multiple antibiotics simultaneously, and therefore is a belated evaluation. For bacteria with MDR, we expect to see strong correlations in both the quantitative minimum inhibitory concentration (MIC) and the binary susceptibility as classified by the pre-determined breakpoints. Being able to detect correlations from these two perspectives allows us to find multidrug resistant bacteria proactively. In this paper, we provide a Bayesian framework that estimates the resistance level jointly for antibiotics belonging to different classes with a Gaussian mixture model, where the correlation in the latent MIC can be inferred from the Gaussian parameters and the correlation in binary susceptibility can be inferred from the mixing weights. By augmenting the laboratory measurement with the latent MIC variable to account for the censored data, and by adopting the latent class variable to represent the MIC components, our model was shown to be accurate and robust compared with the current assessment of correlations. Applying the model to Salmonella heidelberg samples isolated from human participants in National Antimicrobial Resistance Monitoring System (NARMS) provides us with signs of joint resistance to Amoxicillin-clavulanic acid & Cephalothin and joint resistance to Ampicillin & Cephalothin. Large correlations estimated from our model could serve as a timely tool for early detection of MDR, and hence a signal for clinical intervention.
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Affiliation(s)
- Min Zhang
- Department of Statistics, Iowa State University, Ames, Iowa, United States of America
| | - Chong Wang
- Department of Statistics, Iowa State University, Ames, Iowa, United States of America
- Department of Veterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, Iowa, United States of America
- * E-mail:
| | - Annette O’Connor
- Department of Veterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, Iowa, United States of America
- Department of Large Animal Clinical Sciences, Michigan State University, East Lansing, Michigan, United States of America
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Zhang M, Wang C, O'Connor AM. A Bayesian latent class mixture model with censoring for correlation analysis in antimicrobial resistance across populations. BMC Med Res Methodol 2021; 21:186. [PMID: 34544374 PMCID: PMC8454148 DOI: 10.1186/s12874-021-01384-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 09/02/2021] [Indexed: 11/28/2022] Open
Abstract
Background The emergence of antimicrobial resistance across populations is a global threat to public health. Surveillance programs often monitor human and animal populations to evaluate trends of emergence in these populations. Many national level antibiotic resistance surveillance programs quantify the proportion of resistant bacteria as a means of monitoring emergence and control measures. The reason for monitoring these different populations are many, including interest in similar changes in resistance which might provide insight into emergence and control options. Methods In this research, we developed a method to quantify the correlation in antimicrobial resistance across populations, for the conventionally unnoticed mean shift of the susceptible bacteria. With the proposed Bayesian latent class mixture model with censoring and multivariate normal hierarchy, we address several challenges associated with analyzing the minimum inhibitory concentration data. Results Application of this approach to the surveillance data from National Antimicrobial Resistance Monitoring System led to a detection of positive correlation in the central tendency of azithromycin resistance of the susceptible populations from Salmonella serotype Typhimurium across food animal and human populations. Conclusions Our proposed approach has been shown to be accurate and superior to the commonly used naïve estimation by simulation studies. Further implementation of this Bayesian model could serve as a useful tool to indicate the co-existence of antimicrobial resistance, and potentially a need of clinical intervention.
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Affiliation(s)
- Min Zhang
- Department of Statistics, Iowa State University, Ames, United States of America
| | - Chong Wang
- Department of Statistics, Iowa State University, Ames, United States of America. .,Department of Veterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, United States of America.
| | - Annette M O'Connor
- Department of Veterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, United States of America.,Department of Large Animal Clinical Sciences, Michigan State University, East Lansing, United States of America
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Hurley JC, Brownridge D. Could simulation methods solve the curse of sparse data within clinical studies of antibiotic resistance? JAC Antimicrob Resist 2021; 3:dlab016. [PMID: 34223093 PMCID: PMC8210330 DOI: 10.1093/jacamr/dlab016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Infectious disease (ID) physicians and ID pharmacists commonly confront therapeutic questions relating to antibiotic resistance. Randomized controlled trial data are few and meta-analytic-based approaches to develop the evidence-base from several small studies that might relate to an antibiotic resistance question are not simple. The overriding challenge is the sparsity of data which is problematic for traditional frequentist methods, being the paradigm underlying the derivation of ‘P value’ inferential statistics. In other sparse data contexts, simulation methods enable answers to key questions that are meaningful, quantitative and potentially relevant. How these simulation methods ‘work’ and how Bayesian-based methods, being not ‘P value based’, can facilitate simulation are reviewed. These methods are becoming increasingly accessible. This review highlights why sparse data is less of an issue within Bayesian versus frequentist paradigms. A fictional pharmacokinetic study with sparse data illustrates a simplistic application of Bayesian and simulation methods to antibiotic dosing. Whether within epidemiological projections or clinical studies, simulation methods are likely to play an increasing role in antimicrobial resistance research within both hospital and community studies of either rare infectious disease or infections within specific population groups.
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Affiliation(s)
- James C Hurley
- Department of Rural Health, Melbourne Medical School, University of Melbourne, Australia.,Division of Internal Medicine, Ballarat Health Services, Ballarat, Victoria, Australia
| | - David Brownridge
- Pharmacy, Ballarat Health Services, Ballarat, Victoria, Australia
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Rovnaghi CR, Rigdon J, Roué JM, Ruiz MO, Carrion VG, Anand KJS. Longitudinal Trajectories of Hair Cortisol: Hypothalamic-Pituitary-Adrenal Axis Dysfunction in Early Childhood. Front Pediatr 2021; 9:740343. [PMID: 34708011 PMCID: PMC8544285 DOI: 10.3389/fped.2021.740343] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 09/10/2021] [Indexed: 11/25/2022] Open
Abstract
The objective of this study was to examine if longitudinal trajectories of hair cortisol concentrations (HCC) measured at two or three yearly time points can identify 1-3 year old children at risk for altered hypothalamic-pituitary-adrenal (HPA)-axis function due to early life stress (ELS). HCC was measured (N = 575) in 265 children using a validated enzyme-linked immunosorbent assay. Hair was sampled in Clinic Visits (CV) centered at years 1, 2, and 3 (n = 45); 1 and 2 (n = 98); 1 and 3 (n = 27); 2 and 3 (n = 95). Log-transformed HCC values were partitioned using latent class mixed models (LCMM) to minimize the Bayesian Information Criterion. Multivariable linear mixed effects models for ln-HCC as a function of fixed effects for age in months and random effects for participants (to account for repeated measures) were generated to identify the factors associated with class membership. Children in Class 1 (n = 69; 9% Black) evidenced declining ln-HCC across early childhood, whereas Class 2 members (n = 196; 43% Black) showed mixed trajectories. LCMM with only Class 2 members revealed Class 2A (n = 17, 82% Black) with sustained high ln-HCC and Class 2B (n = 179, 40% Blacks) with mixed ln-HCC profiles. Another LCMM limited to only Class 2B members revealed Class 2B1 (n = 65, 57% Black) with declining ln-HCC values (at higher ranges than Class 1), and Class 2B2 (n = 113, 30% Black) with sustained high ln-HCC values. Class 1 may represent hair cortisol trajectories associated with adaptive HPA-axis profiles, whereas 2A, 2B1, and 2B2 may represent allostatic load with dysregulated profiles of HPA-axis function in response to varying exposures to ELS. Sequential longitudinal hair cortisol measurements revealed the allostatic load associated with ELS and the potential for developing maladaptive or dysregulated HPA-axis function in early childhood.
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Affiliation(s)
- Cynthia R Rovnaghi
- Pain/Stress Neurobiology Lab, Maternal and Child Health Research Institute, Stanford University School of Medicine, Stanford, CA, United States
| | - Joseph Rigdon
- Quantitative Sciences Unit, Stanford University School of Medicine, Stanford, CA, United States
| | - Jean-Michel Roué
- Department of Pediatrics, University Hospital of Brest, Brest, France.,Laboratory LIEN, University of Brest, Brest, France.,Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, United States
| | - Monica O Ruiz
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, United States
| | - Victor G Carrion
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Kanwaljeet J S Anand
- Pain/Stress Neurobiology Lab, Maternal and Child Health Research Institute, Stanford University School of Medicine, Stanford, CA, United States.,Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, United States
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8
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Michael A, Kelman T, Pitesky M. Overview of Quantitative Methodologies to Understand Antimicrobial Resistance via Minimum Inhibitory Concentration. Animals (Basel) 2020; 10:ani10081405. [PMID: 32806615 PMCID: PMC7459578 DOI: 10.3390/ani10081405] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Revised: 08/05/2020] [Accepted: 08/07/2020] [Indexed: 01/07/2023] Open
Abstract
Simple Summary An emerging threat to human and food animal health is the development of antimicrobial resistance in bacteria associated with food animals. One of the primary tools for assessing resistance levels and monitoring for changes in expressed resistance is the use of minimum inhibitory concentration tests, which expose bacterial isolates to a series of dilutions of an antimicrobial agent to identify the lowest concentration of the antimicrobial that effectively prevents bacterial growth. These tests produce a minimum inhibitory value that falls within a range of concentrations instead of an exact value, a process known as censoring. Analysis of censored data is complex and careful consideration of methods of analysis is necessary. The use of regression methods such as logistic regression that divide the data into two or three categories is relatively easy to implement but may not detect important changes in the distributions of data that occur within the categories. Models that do not simplify the data may be more complex but may detect potentially relevant changes missed when the data is categorized. As a result, the analysis of minimum inhibitory concentration data requires careful consideration to identify the appropriate model for the purpose of the study. Abstract The development of antimicrobial resistance (AMR) represents a significant threat to humans and food animals. The use of antimicrobials in human and veterinary medicine may select for resistant bacteria, resulting in increased levels of AMR in these populations. As the threat presented by AMR increases, it becomes critically important to find methods for effectively interpreting minimum inhibitory concentration (MIC) tests. Currently, a wide array of techniques for analyzing these data can be found in the literature, but few guidelines for choosing among them exist. Here, we examine several quantitative techniques for analyzing the results of MIC tests and discuss and summarize various ways to model MIC data. The goal of this review is to propose important considerations for appropriate model selection given the purpose and context of the study. Approaches reviewed include mixture models, logistic regression, cumulative logistic regression, and accelerated failure time–frailty models. Important considerations in model selection include the objective of the study (e.g., modeling MIC creep vs. clinical resistance), degree of censoring in the data (e.g., heavily left/right censored vs. primarily interval censored), and consistency of testing parameters (e.g., same range of concentrations tested for a given antibiotic).
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Affiliation(s)
- Alec Michael
- Department of Population Health and Reproduction, School of Veterinary Medicine, UC Davis, 1089 Veterinary Medicine Dr., VM3B, Davis, CA 95616, USA;
- Correspondence:
| | - Todd Kelman
- Department of Population Health and Reproduction, School of Veterinary Medicine, UC Davis, 1089 Veterinary Medicine Dr., VM3B, Davis, CA 95616, USA;
| | - Maurice Pitesky
- Department of Population Health and Reproduction, School of Veterinary Medicine-Cooperative Extension, UC Davis, 1089 Veterinary Medicine Dr., VM3B, Davis, CA 95616, USA;
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