Methodological differences between studies confound one-size-fits-all approaches to managing surface waterways for food and water safety

Even though differences in methodology (e.g., sample volume and detection method) have been shown to affect observed microbial water quality, multiple sampling and laboratory protocols continue to be used for water quality monitoring. Research is needed to determine how these differences impact the comparability of findings to generate best management practices and the ability to perform meta-analyses. This study addresses this knowledge gap by compiling and analyzing a data set representing 2,429,990 unique data points on at least one microbial water quality target (e.g., Salmonella presence and Escherichia coli concentration). Variance partitioning analysis was used to quantify the variance in likelihood of detecting each pathogenic target that was uniquely and jointly attributable to non-methodological versus methodological factors. The strength of the association between microbial water quality and select methodological and non-methodological factors was quantified using conditional forest and regression analysis. Fecal indicator bacteria concentrations were more strongly associated with non-methodological factors than methodological factors based on conditional forest analysis. Variance partitioning analysis could not disentangle non-methodological and methodological signals for pathogenic Escherichia coli, Salmonella, and Listeria. This suggests our current perceptions of foodborne pathogen ecology in water systems are confounded by methodological differences between studies. For example, 31% of total variance in likelihood of Salmonella detection was explained by methodological and/or non-methodological factors, 18% was jointly attributable to both methodological and non-methodological factors. Only 13% of total variance was uniquely attributable to non-methodological factors for Salmonella, highlighting the need for standardization of methods for microbiological water quality testing for comparison across studies.IMPORTANCEThe microbial ecology of water is already complex, without the added complications of methodological differences between studies. This study highlights the difficulty in comparing water quality data from projects that used different sampling or laboratory methods. These findings have direct implications for end users as there is no clear way to generalize findings in order to characterize broad-scale ecological phenomenon and develop science-based guidance. To best support development of risk assessments and guidance for monitoring and managing waters, data collection and methods need to be standardized across studies. A minimum set of data attributes that all studies should collect and report in a standardized way is needed. Given the diversity of methods used within applied and environmental microbiology, similar studies are needed for other microbiology subfields to ensure that guidance and policy are based on a robust interpretation of the literature.

Scale and detection method impacted Salmonella prevalence and diversity in ponds

Site-specific approaches for managing food safety hazards in agricultural water require an understanding of foodborne pathogen ecology. This study identified factors associated with Salmonella contamination in Virginia ponds. Grab samples (250 mL, N = 600) were collected from 30 sites across nine ponds. Culture- and culture-independent (CIDT)-based methods were used to detect Salmonella in each sample. Salmonella isolated by culture-based methods were serotyped by Kauffman-White classification. Environmental data were collected for each sample. McNemar's χ2 was used to determine if Salmonella detection differed by testing method. Separate mixed effect models were used to identify environmental factors associated with culture and CIDT-based Salmonella detection. Separate models were built for each pond, and for all ponds combined. Salmonella detection differed significantly (p < 0.001) between CIDT (31 %; 183/600)- and culture (13 %; 77/600)-based methods. Culture-based methods yielded 11 different serovars. All cultured Salmonella samples were confirmed by CIDT; 42.1 % of CIDT Salmonella-positive samples could be cultured. Associations between environmental factors and Salmonella detection also varied substantially by pond and detection method. In the all-pond model, associations were observed for five factors (total coliforms, Escherichia coli, air temperature, UV, rain) for both culture- and CIDT-based Salmonella detection. Rain prior to sampling (24 h) increased odds of Salmonella detection for culture (OR = 5.09) and CIDT (OR = 3.62) in the all-pond model. When all the pond data were used, models masked associations at the individual pond level, as there were noticeable differences between ponds and the odds of isolating Salmonella by environmental factors. Ponds were within a 187-ha area in this study, emphasizing water management needs to be individualized (i.e., assess hazards/risks by pond). Results also highlight detection methods and scale strongly affect observed water quality and should be considered when developing monitoring programs to develop guidance for growers.

Scale of analysis drives the observed ratio of spatial to non-spatial variance in microbial water quality: insights from two decades of citizen science data

AIMS

While fecal indicator bacteria (FIB) testing is used to monitor surface water for potential health hazards, observed variation in FIB levels may depend on the scale of analysis (SOA). Two decades of citizen science data, coupled with random effects models, were used to quantify the variance in FIB levels attributable to spatial versus temporal factors.

METHODS AND RESULTS

Separately, Bayesian models were used to quantify the ratio of spatial to non-spatial variance in FIB levels and identify associations between environmental factors and FIB levels. Separate analyses were performed for three SOA: waterway, watershed, and statewide. As SOA increased (from waterway to watershed to statewide models), variance attributable to spatial sources generally increased and variance attributable to temporal sources generally decreased. While relationships between FIB levels and environmental factors, such as flow conditions (base versus stormflow), were constant across SOA, the effect of land cover was highly dependent on SOA and consistently smaller than the effect of stormwater infrastructure (e.g. outfalls).

CONCLUSIONS

This study demonstrates the importance of SOA when developing water quality monitoring programs or designing future studies to inform water management.

Comparative genomics unveils extensive genomic variation between populations of Listeria species in natural and food-associated environments

Comprehending bacterial genomic variation linked to distinct environments can yield novel insights into mechanisms underlying differential adaptation and transmission of microbes across environments. Gaining such insights is particularly crucial for pathogens as it benefits public health surveillance. However, the understanding of bacterial genomic variation is limited by a scarcity of investigations in genomic variation coupled with different ecological contexts. To address this limitation, we focused on Listeria, an important bacterial genus for food safety that includes the human pathogen L. monocytogenes, and analyzed a large-scale genomic dataset collected by us from natural and food-associated environments across the United States. Through comparative genomics analyses on 449 isolates from the soil and 390 isolates from agricultural water and produce processing facilities representing L. monocytogenes, L. seeligeri, L. innocua, and L. welshimeri, we find that the genomic profiles strongly differ by environments within each species. This is supported by the environment-associated subclades and differential presence of plasmids, stress islands, and accessory genes involved in cell envelope biogenesis and carbohydrate transport and metabolism. Core genomes of Listeria species are also strongly associated with environments and can accurately predict isolation sources at the lineage level in L. monocytogenes using machine learning. We find that the large environment-associated genomic variation in Listeria appears to be jointly driven by soil property, climate, land use, and accompanying bacterial species, chiefly representing Actinobacteria and Proteobacteria. Collectively, our data suggest that populations of Listeria species have genetically adapted to different environments, which may limit their transmission from natural to food-associated environments.

Racial and Ethnic Disparities in Mpox Cases and Vaccination Among Adult Males - United States, May-December 2022

As of December 31, 2022, a total of 29,939 monkeypox (mpox) cases* had been reported in the United States, 93.3% of which occurred in adult males. During May 10-December 31, 2022, 723,112 persons in the United States received the first dose in a 2-dose mpox (JYNNEOS)^† vaccination series; 89.7% of these doses were administered to males (1). The current mpox outbreak has disproportionately affected gay, bisexual, and other men who have sex with men (MSM) and racial and ethnic minority groups (1,2). To examine racial and ethnic disparities in mpox incidence and vaccination rates, rate ratios (RRs) for incidence and vaccination rates and vaccination-to-case ratios were calculated, and trends in these measures were assessed among males aged ≥18 years (males) (3). Incidence in males in all racial and ethnic minority groups except non-Hispanic Asian (Asian) males was higher than that among non-Hispanic White (White) males. At the peak of the outbreak in August 2022, incidences among non-Hispanic Black or African American (Black) and Hispanic or Latino (Hispanic) males were higher than incidence among White males (RR = 6.9 and 4.1, respectively). Overall, vaccination rates were higher among males in racial and ethnic minority groups than among White males. However, the vaccination-to-case ratio was lower among Black (8.8) and Hispanic (16.2) males than among White males (42.5) during the full analytic period, indicating that vaccination rates among Black and Hispanic males were not proportionate to the elevated incidence rates (i.e., these groups had a higher unmet vaccination need). Efforts to increase vaccination among Black and Hispanic males might have resulted in the observed relative increased rates of vaccination; however, these increases were only partially successful in reducing overall incidence disparities. Continued implementation of equity-based vaccination strategies is needed to further increase vaccination rates and reduce the incidence of mpox among all racial and ethnic groups. Recent modeling data (4) showing that, based on current vaccination coverage levels, many U.S. jurisdictions are vulnerable to resurgent mpox outbreaks, underscore the need for continued vaccination efforts, particularly among racial and ethnic minority groups.

Conditional Forest Models Built Using Metagenomic Data Accurately Predicted Salmonella Contamination in Northeastern Streams

The use of water contaminated with Salmonella for produce production contributes to foodborne disease burden. To reduce human health risks, there is a need for novel, targeted approaches for assessing the pathogen status of agricultural water. We investigated the utility of water microbiome data for predicting Salmonella contamination of streams used to source water for produce production. Grab samples were collected from 60 New York streams in 2018 and tested for Salmonella. Separately, DNA was extracted from the samples and used for Illumina shotgun metagenomic sequencing. Reads were trimmed and used to assign taxonomy with Kraken2. Conditional forest (CF), regularized random forest (RRF), and support vector machine (SVM) models were implemented to predict Salmonella contamination. Model performance was assessed using 10-fold cross-validation repeated 10 times to quantify area under the curve (AUC) and Kappa score. CF models outperformed the other two algorithms based on AUC (0.86, CF; 0.81, RRF; 0.65, SVM) and Kappa score (0.53, CF; 0.41, RRF; 0.12, SVM). The taxa that were most informative for accurately predicting Salmonella contamination based on CF were compared to taxa identified by ALDEx2 as being differentially abundant between Salmonella-positive and -negative samples. CF and differential abundance tests both identified Aeromonas salmonicida (variable importance [VI] = 0.012) and Aeromonas sp. strain CA23 (VI = 0.025) as the two most informative taxa for predicting Salmonella contamination. Our findings suggest that microbiome-based models may provide an alternative to or complement existing water monitoring strategies. Similarly, the informative taxa identified in this study warrant further investigation as potential indicators of Salmonella contamination of agricultural water. IMPORTANCE Understanding the associations between surface water microbiome composition and the presence of foodborne pathogens, such as Salmonella, can facilitate the identification of novel indicators of Salmonella contamination. This study assessed the utility of microbiome data and three machine learning algorithms for predicting Salmonella contamination of Northeastern streams. The research reported here both expanded the knowledge on the microbiome composition of surface waters and identified putative novel indicators (i.e., Aeromonas species) for Salmonella in Northeastern streams. These putative indicators warrant further research to assess whether they are consistent indicators of Salmonella contamination across regions, waterways, and years not represented in the data set used in this study. Validated indicators identified using microbiome data may be used as targets in the development of rapid (e.g., PCR-based) detection assays for the assessment of microbial safety of agricultural surface waters.

Spatial Versus Nonspatial Variance in Fecal Indicator Bacteria Differs Within and Between Ponds

Surface water environments are inherently heterogenous, and little is known about variation in microbial water quality between locations. This study sought to understand how microbial water quality differs within and between Virginia ponds. Grab samples were collected twice per week from 30 sampling sites across nine Virginia ponds (n = 600). Samples (100 mL) were enumerated for total coliform (TC) and Escherichia coli (EC) levels, and physicochemical, weather, and environmental data were collected. Bayesian models of coregionalization were used to quantify the variance in TC and EC levels attributable to spatial (e.g., site, pond) versus nonspatial (e.g., date, pH) sources. Mixed-effects Bayesian regressions and conditional inference trees were used to characterize relationships between data and TC or EC levels. Analyses were performed separately for each pond with ≥3 sampling sites (5 intrapond) while one interpond model was developed using data from all sampling sites and all ponds. More variance in TC levels were attributable to spatial opposed to nonspatial sources for the interpond model (variance ratio [VR] = 1.55) while intrapond models were pond dependent (VR: 0.65-18.89). For EC levels, more variance was attributable to spatial sources in the interpond model (VR = 1.62), compared to all intrapond models (VR < 1.0) suggesting that more variance is attributable to nonspatial factors within individual ponds and spatial factors when multiple ponds are considered. Within each pond, TC and EC levels were spatially independent for sites 56-87 m apart, indicating that different sites within the same pond represent different water quality for risk management. Rainfall was positively and pH negatively associated with TC and EC levels in both inter- and intrapond models. For all other factors, the direction and strength of associations varied. Factors driving microbial dynamics in ponds appear to be pond-specific and differ depending on the spatial scale considered.

Epidemiologic Features of the Monkeypox Outbreak and the Public Health Response - United States, May 17-October 6, 2022

On May 17, 2022, the Massachusetts Department of Health announced the first suspected case of monkeypox associated with the global outbreak in a U.S. resident. On May 23, 2022, CDC launched an emergency response (1,2). CDC's emergency response focused on surveillance, laboratory testing, medical countermeasures, and education. Medical countermeasures included rollout of a national JYNNEOS vaccination strategy, Food and Drug Administration (FDA) issuance of an emergency use authorization to allow for intradermal administration of JYNNEOS, and use of tecovirimat for patients with, or at risk for, severe monkeypox. During May 17-October 6, 2022, a total of 26,384 probable and confirmed* U.S. monkeypox cases were reported to CDC. Daily case counts peaked during mid-to-late August. Among 25,001 of 25,569 (98%) cases in adults with information on gender identity,^† 23,683 (95%) occurred in cisgender men. Among 13,997 cisgender men with information on recent sexual or close intimate contact,^§ 10,440 (75%) reported male-to-male sexual contact (MMSC) ≤21 days preceding symptom onset. Among 21,211 (80%) cases in persons with information on race and ethnicity,^¶ 6,879 (32%), 6,628 (31%), and 6,330 (30%) occurred in non-Hispanic Black or African American (Black), Hispanic or Latino (Hispanic), and non-Hispanic White (White) persons, respectively. Among 5,017 (20%) cases in adults with information on HIV infection status, 2,876 (57%) had HIV infection. Prevention efforts, including vaccination, should be prioritized among persons at highest risk within groups most affected by the monkeypox outbreak, including gay, bisexual, and other men who have sex with men (MSM); transgender, nonbinary, and gender-diverse persons; racial and ethnic minority groups; and persons who are immunocompromised, including persons with advanced HIV infection or newly diagnosed HIV infection.

Humans and Hoofed Livestock Are the Main Sources of Fecal Contamination of Rivers Used for Crop Irrigation: A Microbial Source Tracking Approach

Freshwater bodies receive waste, feces, and fecal microorganisms from agricultural, urban, and natural activities. In this study, the probable sources of fecal contamination were determined. Also, antibiotic resistant bacteria (ARB) were detected in the two main rivers of central Chile. Surface water samples were collected from 12 sampling sites in the Maipo (n = 8) and Maule Rivers (n = 4) every 3 months, from August 2017 until April 2019. To determine the fecal contamination level, fecal coliforms were quantified using the most probable number (MPN) method and the source of fecal contamination was determined by Microbial Source Tracking (MST) using the Cryptosporidium and Giardia genotyping method. Separately, to determine if antimicrobial resistance bacteria (AMB) were present in the rivers, Escherichia coli and environmental bacteria were isolated, and the antibiotic susceptibility profile was determined. Fecal coliform levels in the Maule and Maipo Rivers ranged between 1 and 130 MPN/100-ml, and 2 and 30,000 MPN/100-ml, respectively. Based on the MST results using Cryptosporidium and Giardia host-specific species, human, cattle, birds, and/or dogs hosts were the probable sources of fecal contamination in both rivers, with human and cattle host-specific species being more frequently detected. Conditional tree analysis indicated that coliform levels were significantly associated with the river system (Maipo versus Maule), land use, and season. Fecal coliform levels were significantly (p < 0.006) higher at urban and agricultural sites than at sites immediately downstream of treatment centers, livestock areas, or natural areas. Three out of eight (37.5%) E. coli isolates presented a multidrug-resistance (MDR) phenotype. Similarly, 6.6% (117/1768) and 5.1% (44/863) of environmental isolates, in Maipo and Maule River showed and MDR phenotype. Efforts to reduce fecal discharge into these rivers should thus focus on agriculture and urban land uses as these areas were contributing the most and more frequently to fecal contamination into the rivers, while human and cattle fecal discharges were identified as the most likely source of this fecal contamination by the MST approach. This information can be used to design better mitigation strategies, thereby reducing the burden of waterborne diseases and AMR in Central Chile.

Anaerobic soil disinfestation, amendment-type, and irrigation regimen influence Salmonella survival and die-off in agricultural soils

AIMS

This study investigated Salmonella concentrations following combinations of horticultural practices including anaerobic soil disinfestation (ASD), soil amendment type and irrigation regimen.

METHODS AND RESULTS

Sandy-loam soil was inoculated with a five-serovar Salmonella cocktail (5.5 ± 0.2 log CFU per gram) and subjected to one of six treatments: (i) no soil amendment, ASD (ASD control), (ii) no soil amendment, no-ASD (non-ASD control) and (iii-vi) soil amended with pelletized poultry litter, rye, rapeseed or hairy vetch with ASD. The effect of irrigation regimen was determined by collecting samples 3 and 7 days after irrigation. Twenty-five-gram soil samples were collected pre-ASD, post-soil saturation (i.e. ASD-process), and at 14 time-points post-ASD, and Salmonella levels enumerated. Log-linear models examined the effect of amendment type and irrigation regimen on Salmonella die-off during and post-ASD. During ASD, Salmonella concentrations significantly decreased in all treatments (range: -0.2 to -2.7 log CFU per gram), albeit the smallest decrease (-0.2 log CFU per gram observed in the pelletized poultry litter) was of negligible magnitude. Salmonella die-off rates varied by amendment with an average post-ASD rate of -0.05 log CFU per gram day (CI = -0.05, -0.04). Salmonella concentrations remained highest over the 42 days post-ASD in pelletized poultry litter, followed by rapeseed, and hairy vetch treatments. Findings suggested ASD was not able to eliminate Salmonella in soil, and certain soil amendments facilitated enhanced Salmonella survival. Salmonella serovar distribution differed by treatment with pelletized poultry litter supporting S. Newport survival, compared with other serovars. Irrigation appeared to assist Salmonella survival with concentrations being 0.14 log CFU per gram (CI = 0.05, 0.23) greater 3 days, compared with 7 days post-irrigation.

CONCLUSIONS

ASD does not eliminate Salmonella in soil, and may in fact, depending on the soil amendment used, facilitate Salmonella survival.

SIGNIFICANCE AND IMPACT OF THE STUDY

Synergistic and antagonistic effects on food safety hazards of implementing horticultural practices should be considered.

Interpretability Versus Accuracy: A Comparison of Machine Learning Models Built Using Different Algorithms, Performance Measures, and Features to Predict E. coli Levels in Agricultural Water

Since E. coli is considered a fecal indicator in surface water, government water quality standards and industry guidance often rely on E. coli monitoring to identify when there is an increased risk of pathogen contamination of water used for produce production (e.g., for irrigation). However, studies have indicated that E. coli testing can present an economic burden to growers and that time lags between sampling and obtaining results may reduce the utility of these data. Models that predict E. coli levels in agricultural water may provide a mechanism for overcoming these obstacles. Thus, this proof-of-concept study uses previously published datasets to train, test, and compare E. coli predictive models using multiple algorithms and performance measures. Since the collection of different feature data carries specific costs for growers, predictive performance was compared for models built using different feature types [geospatial, water quality, stream traits, and/or weather features]. Model performance was assessed against baseline regression models. Model performance varied considerably with root-mean-squared errors and Kendall's Tau ranging between 0.37 and 1.03, and 0.07 and 0.55, respectively. Overall, models that included turbidity, rain, and temperature outperformed all other models regardless of the algorithm used. Turbidity and weather factors were also found to drive model accuracy even when other feature types were included in the model. These findings confirm previous conclusions that machine learning models may be useful for predicting when, where, and at what level E. coli (and associated hazards) are likely to be present in preharvest agricultural water sources. This study also identifies specific algorithm-predictor combinations that should be the foci of future efforts to develop deployable models (i.e., models that can be used to guide on-farm decision-making and risk mitigation). When deploying E. coli predictive models in the field, it is important to note that past research indicates an inconsistent relationship between E. coli levels and foodborne pathogen presence. Thus, models that predict E. coli levels in agricultural water may be useful for assessing fecal contamination status and ensuring compliance with regulations but should not be used to assess the risk that specific pathogens of concern (e.g., Salmonella, Listeria) are present.

County-Level COVID-19 Vaccination Coverage and Social Vulnerability - United States, December 14, 2020-March 1, 2021

The U.S. COVID-19 vaccination program began in December 2020, and ensuring equitable COVID-19 vaccine access remains a national priority.* COVID-19 has disproportionately affected racial/ethnic minority groups and those who are economically and socially disadvantaged (1,2). Thus, achieving not just vaccine equality (i.e., similar allocation of vaccine supply proportional to its population across jurisdictions) but equity (i.e., preferential access and administra-tion to those who have been most affected by COVID-19 disease) is an important goal. The CDC social vulnerability index (SVI) uses 15 indicators grouped into four themes that comprise an overall SVI measure, resulting in 20 metrics, each of which has national and state-specific county rankings. The 20 metric-specific rankings were each divided into lowest to highest tertiles to categorize counties as low, moderate, or high social vulnerability counties. These tertiles were combined with vaccine administration data for 49,264,338 U.S. residents in 49 states and the District of Columbia (DC) who received at least one COVID-19 vaccine dose during December 14, 2020-March 1, 2021. Nationally, for the overall SVI measure, vaccination coverage was higher (15.8%) in low social vulnerability counties than in high social vulnerability counties (13.9%), with the largest coverage disparity in the socioeconomic status theme (2.5 percentage points higher coverage in low than in high vulnerability counties). Wide state variations in equity across SVI metrics were found. Whereas in the majority of states, vaccination coverage was higher in low vulnerability counties, some states had equitable coverage at the county level. CDC, state, and local jurisdictions should continue to monitor vaccination coverage by SVI metrics to focus public health interventions to achieve equitable coverage with COVID-19 vaccine.

Strain, Soil-Type, Irrigation Regimen, and Poultry Litter Influence Salmonella Survival and Die-off in Agricultural Soils

The use of untreated biological soil amendments of animal origin (BSAAO) have been identified as one potential mechanism for the dissemination and persistence of Salmonella in the produce growing environment. Data on factors influencing Salmonella concentration in amended soils are therefore needed. The objectives here were to (i) compare die-off between 12 Salmonella strains following inoculation in amended soil and (ii) characterize any significant effects associated with soil-type, irrigation regimen, and amendment on Salmonella survival and die-off. Three greenhouse trials were performed using a randomized complete block design. Each strain (~4 log CFU/g) was homogenized with amended or non-amended sandy-loam or clay-loam soil. Salmonella levels were enumerated in 25 g samples 0, 0.167 (4 h), 1, 2, 4, 7, 10, 14, 21, 28, 56, 84, 112, 168, 210, 252, and 336 days post-inoculation (dpi), or until two consecutive samples were enrichment negative. Regression analysis was performed between strain, soil-type, irrigation, and (i) time to last detect (survival) and (ii) concentration at each time-point (die-off rate). Similar effects of strain, irrigation, soil-type, and amendment were identified using the survival and die-off models. Strain explained up to 18% of the variance in survival, and up to 19% of variance in die-off rate. On average Salmonella survived for 129 days in amended soils, however, Salmonella survived, on average, 30 days longer in clay-loam soils than sandy-loam soils [95% Confidence interval (CI) = 45, 15], with survival time ranging from 84 to 210 days for the individual strains during daily irrigation. When strain-specific associations were investigated using regression trees, S. Javiana and S. Saintpaul were found to survive longer in sandy-loam soil, whereas most of the other strains survived longer in clay-loam soil. Salmonella also survived, on average, 128 days longer when irrigated weekly, compared to daily (CI = 101, 154), and 89 days longer in amended soils, than non-amended soils (CI = 61, 116). Overall, this study provides insight into Salmonella survival following contamination of field soils by BSAAO. Specifically, Salmonella survival may be strain-specific as affected by both soil characteristics and management practices. These data can assist in risk assessment and strain selection for use in challenge and validation studies.

Predictive Models May Complement or Provide an Alternative to Existing Strategies for Assessing the Enteric Pathogen Contamination Status of Northeastern Streams Used to Provide Water for Produce Production

While the Food Safety Modernization Act established standards for the use of surface water for produce production, water quality is known to vary over space and time. Targeted approaches for identifying hazards in water that account for this variation may improve growers’ ability to address pre-harvest food safety risks. Models that utilize publicly-available data (e.g., land-use, real-time weather) may be useful for developing these approaches. The objective of this study was to use pre-existing datasets collected in 2017 (N = 181 samples) and 2018 (N = 191 samples) to train and test models that predict the likelihood of detecting Salmonella and pathogenic E. coli markers (eaeA, stx) in agricultural water. Four types of features were used to train the models: microbial, physicochemical, spatial and weather. “Full models” were built using all four features types, while “nested models” were built using between one and three types. Twenty learners were used to develop separate full models for each pathogen. Separately, to assess information gain associated with using different feature types, six learners were randomly selected and used to develop nine, nested models each. Performance measures for each model were then calculated and compared against baseline models where E. coli concentration was the sole covariate. In the methods, we outline the advantages and disadvantages of each learner. Overall, full models built using ensemble (e.g., Node Harvest) and “black-box” (e.g., SVMs) learners out-performed full models built using more interpretable learners (e.g., tree- and rule-based learners) for both outcomes. However, nested eaeA-stx models built using interpretable learners and microbial data performed almost as well as these full models. While none of the nested Salmonella models performed as well as the full models, nested models built using spatial data consistently out-performed models that excluded spatial data. These findings demonstrate that machine learning approaches can be used to predict when and where pathogens are likely to be present in agricultural water. This study serves as a proof-of-concept that can be built upon once larger datasets become available and provides guidance on the learner-data combinations that should be the foci of future efforts (e.g., tree-based microbial models for pathogenic E. coli).

The Composition of Microbial Communities in Six Streams, and Its Association With Environmental Conditions, and Foodborne Pathogen Isolation

Surface water used for produce production is a potential source of pre-harvest contamination with foodborne pathogens. Decisions on how to mitigate food safety risks associated with pre-harvest water use currently rely on generic Escherichia coli-based water quality tests, although multiple studies have suggested that E. coli levels are not a suitable indicator of the food safety risks under all relevant environmental conditions. Hence, improved understanding of spatiotemporal variability in surface water microbiota composition is needed to facilitate identification of alternative or supplementary indicators that co-occur with pathogens. To this end, we aimed to characterize the composition of bacterial and fungal communities in the sediment and water fractions of 68 agricultural water samples collected from six New York streams. We investigated potential associations between the composition of microbial communities, environmental factors and Salmonella and/or Listeria monocytogenes isolation. We found significantly different composition of fungal and bacterial communities among sampled streams and among water fractions of collected samples. This indicates that geography and the amount of sediment in a collected water sample may affect its microbial composition, which was further supported by identified associations between the flow rate, turbidity, pH and conductivity, and microbial community composition. Lastly, we identified specific microbial families that were weakly associated with the presence of Salmonella or Listeria monocytogenes, however, further studies on samples from additional streams are needed to assess whether identified families may be used as indicators of pathogen presence.

A Conceptual Framework for Developing Recommendations for No-Harvest Buffers around In-Field Feces

HIGHLIGHTS

Escherichia coli transfer from simulated wildlife feces to lettuce during foliar irrigation: A field study in the Northeastern United States

Wildlife intrusion has been associated with pathogen contamination of produce. However, few studies have examined pathogen transfer from wildlife feces to pre-harvest produce. This study was performed to calculate transfer coefficients for Escherichia coli from simulated wildlife feces to field-grown lettuce during irrigation. Rabbit feces inoculated with a 3-strain cocktail of non-pathogenic E. coli were placed in a lettuce field 2.5-72 h before irrigation. Following irrigation, the E. coli concentration on the lettuce was determined. After exclusion of an outlier with high E. coli levels (Most Probable Number = 5.94*108), the average percent of E. coli in the feces that transferred to intact lettuce heads was 0.0267% (Standard Error [SE] = 0.0172). Log-linear regression showed that significantly more E. coli transferred to outer leaves compared to inner leaves (Effect = 1.3; 95% Confidence Interval = 0.4, 2.1). Additionally, the percent of E. coli that transferred from the feces to the lettuce decreased significantly with time after fecal placement, and as the distance between the lettuce and the feces, and the lettuce and the sprinklers increased. These findings provide key data that may be used in future quantitative risk assessments to identify potential intervention strategies for reducing food safety risks associated with fresh produce.

Survival of Escherichia coli on Lettuce under Field Conditions Encountered in the Northeastern United States

Although wildlife intrusion and untreated manure have been associated with microbial contamination of produce, relatively few studies have examined the survival of Escherichia coli on produce under field conditions following contamination (e.g., via splash from wildlife feces). This experimental study was performed to estimate the die-off rate of E. coli on preharvest lettuce following contamination with a fecal slurry. During August 2015, field-grown lettuce was inoculated via pipette with a fecal slurry that was spiked with a three-strain cocktail of rifampin-resistant nonpathogenic E. coli. Ten lettuce heads were harvested at each of 13 time points following inoculation (0, 2.5, 5, and 24 h after inoculation and every 24 h thereafter until day 10). The most probable number (MPN) of E. coli on each lettuce head was determined, and die-off rates were estimated. The relationship between sample time and the log MPN of E. coli per head was modeled using a segmented linear model. This model had a breakpoint at 106 h (95% confidence interval = 69, 142 h) after inoculation, with a daily decrease of 0.70 and 0.19 log MPN for 0 to 106 h and 106 to 240 h following inoculation, respectively. These findings are consistent with die-off rates obtained in similar studies that assessed E. coli survival on produce following irrigation. Overall, these findings provide die-off rates for E. coli on lettuce that can be used in future quantitative risk assessments.

Contextualizing the immigrant experience: the role of food and foodways in identity maintenance and formation for first- and second-generation Latinos in Ithaca, New York

This study examines the role of food and foodways in identity maintenance and formation for Latino individuals in Ithaca, New York. Preliminary results indicate that food provides a physical link that connects individuals to their heritage culture and local communities. Despite variability in the importance that immigrants attribute to food, it remains one of the most resilient tools that informants identified as central to identity formation and maintenance. Food can therefore be a useful tool for examining the degree to which immigrants are maintaining their cultural identity and connectedness with their community.

Resistance of morpholino phosphorodiamidate oligomers to enzymatic degradation

Oligomers possessing the Morpholino phosphorodiamidate backbone were evaluated for resistance to a variety of enzymes and biologic fluids. A 25-mer was incubated with nucleases, proteases, esterases, and serum, and the reaction mixtures were directly analyzed by MALDI-TOF mass spectrometry. The 25-mer was completely resistant to 13 different hydrolases and serum and plasma. The excellent resistance of Morpholino phosphorodiamidates to enzymatic attack indicates their suitability for in vivo use.

Preliminary characterization of ribosomes of Entamoeba invadens

The ribosomes of Entamoeba invadens trophozoites have sedimentation coefficients of 77, 53 and 36 S. Most of the ribosomal proteins are basic and their one- and two-dimensional electrophoretic patterns differ from the corresponding patterns of Escherichia coli and Saccharomyces cerevisiae. Two dozen bands were observed in the 10 000 to 100 000 molecular weight range following sodium dodecylsulfate-gel electrophoresis of amoebal ribosomal proteins. Long, thin pronase-sensitive structures were seen in electron micrographs of E. invadens ribosomal preparations.

Ribonuclease of Entamoeba invadens

Two bands of RNAase activity were observed on gel electrophoresis of fractions obtained during partial purification of the RNAase of Entamoeba invadens. The faster migrating band which was responsible for most of the RNAase activity was isolated by gel electrophoresis. A pI of 5.5 was determined for this RNAase activity and estimates of its molecular weight and sedimentation coefficient gave values of about 25 000 and approximately 2.8 S. It exhibited maximum activity around pH 6, and digestion of RNA showed a pattern expected of an endoribonuclease.

A simple procedure for partial purification of an RNAase of Entamoeba invadens

The behavior of an RNAase, present in centrifugally clarified homogenates of axenic trophozoites of Entamoeba invadens, on isoelectric focusing (IEF) and agarose-poly(G) chromatography is described. The results led to a simple two-step procedure for partial purification of the RNAase in which fractionation of the homogenate by IEF is followed by agarose-poly(G) chromatography. Recovery of enzyme activity has ranged from 50 to 80% of that present in homogenates, and increases of 80- to 160-fold in the specific activity have been obtained using the procedure. A single zone of activity was observed on analysis of the partially purified RNAase by sucrose gradient IEF and velocity zonal ultracentrifugation.

Isolation of ribosomes from cysts of entamoeba invadens

A reproducible method for the preparation of 75S ribosomes from cysts of Entamoeba invadens is presented. The method depends on the inactivation of the cyst's ribonuclease by high levels of Bentonite. The ribosomes are found to be extremely sensitive to ribonuclease, and to be stabilized by the addition of manganous and calcium ions to the magnesium customarily employed. Reasons are given for equating these ribosomes with the particles of which the crystalline chromatoid bodies are made.