One and two weeks after inoculation, the roots were carefully removed from the Magenta jars, were rinsed, and were prepared either for GUS-staining or for viewing under a Zeiss Axiophot fluorescent microscope. Nodulation in potting soil. Stem cuttings of the transgenic alfalfa plants were made as described above and allowed to root.One week before inoculation, nitrogen nutrition was withdrawn from the plants, but other macronutrients were supplied. The potting soil was leached with large quantities of tap water four and one days before inoculation. Rm1021 cells were grown in RDM medium , containing 100 mg of streptomycin per liter to an OD600 of 0.11 or 0.13, depending on the experiment. Rhizobia were pelleted in a clinical centrifuge and were suspended in sterile milli-Q water to an OD600 of 0.1 . Rm1021 suspension was placed on the surface of the potting soil of each plant. The plants were grown for 21 dpi. Stems were cut off at the crown, and the potting soil was gently removed from the nodulated roots in standing tap water. The nodules were separated from the roots, were divided into pink and senescent types, and were counted. The external morphology of the nodules was also examined. Nodulation in Turface rooting medium. Rooted cuttings were placed in pots with approximately 400 cm3 of inert Turface rooting medium and were allowed to grow in the presence of a complete, dilute nutrient solution. One day before inoculation, nitrogen was withdrawn from the plants. Rm1021, grown to early stationary phase,barley fodder system was prepared as described above. Rm1021 suspension was inoculated onto each plant. The plants grew for 34 more days, and then, the stems were cut off at the crown. The Turface was removed from the nodulated roots in standing tap water. The external nodule morphology was examined. Nodulation under hydroponic conditions. Stem cuttings were allowed to root as described above.

Fluorescent light grates were covered with aluminum foil, and individual square openings of the grate, five to six squares apart, were cut out for placement of plants. The rooting medium was gently removed from the roots of the cuttings by placing them in standing tap water. The crown of each rooted cutting was wrapped in cotton and firmly wedged into an opening of a fluorescent light grate. Rooted cuttings were spaced evenly, with 30 cuttings per grate. Each grate was placed on top of a tank containing 30 liters of complete 1 /4-strength Hoagland’s medium. Tanks were continuously aerated with aquarium pumps. Six independent vector control, 12 independent LEC1AS, and 12 independent LEC2AS plant lines were used in each of six hydroponic tanks. The entire assembly of 30 plants could be removed and replaced relatively undisturbed from the medium. The complete nutrient solution was replaced with 30 liters of ¼-strength Hoagland’s medium lacking nitrogen. Five days after medium replacement, a suspension of Rm1021, prepared as described above, was uniformly mixed into the medium, and the roots were returned to the solution. Rm1021 inocula from mid-lag, early-exponential, late-exponential, early-stationary, or stationary phase were used. The liquid level of the hydroponic tanks was maintained by adding deionized water. The plants grew for an additional 28 to 37 dpi. Stems of nodulated plants were cut at the crown, were dried under vacuum at 45°C for 2 days, and were weighed. The nodulated roots were pooled for each plant type and were stored at –20°C; the nodules were later separated from the roots. Nodules and roots were dried under vacuum for 2 days at 45°C and were weighed. Some nodulated roots were left intact, were allowed to dry at room temperature under ambient conditions for 14 days, and then were weighed. Uninoculated plants were removed from the hydroponic conditions, were dried in the greenhouse for 1 week, and then, the roots and vegetative tissues were separated at the crowns and weighed. With the growing population and limited freshwater resources, there is increased interest in water conservation practices like using recycled wastewater and hydroponic agriculture. The presence of pathogens in the associated environmental compartments exposes a large fraction of the general populace to infection risks. Therefore, a need of the hour is ensuring that our infrastructure meets the safety requirements designed to protect human health.

Proper disposal and treatment of wastes generated at hospitals, industries and residences help meet this goal by reducing the pathogen loads in the environment. However, complete elimination of pathogens is not an option. Therefore, a framework to quantify the threat to human health is desired. The popularly adopted framework is called Quantitative Microbial Risk Assessment or QMRA. With the growing population and limited freshwater resources, there is increased interest in water conservation practices like using recycled wastewater and hydroponic agriculture. The presence of pathogens in the associated environmental compartments exposes a significant fraction of the general populace to infection risks. Therefore, a need of the hour is ensuring that our infrastructure meets the safety requirements designed to protect human health. Proper disposal and treatment of wastes generated at hospitals, industries, and residences help achieve this goal by reducing the pathogen loads in the environment. However, complete elimination of pathogens is not an option. Therefore, a framework to quantify the threat to human health is desired. The popularly adopted framework is called Quantitative Microbial Risk Assessment or QMRA. The main tenets of QMRA are as follows: 1) hazard identification; 2) exposure assessment; 3) dose-response modeling; 4) risk characterization, and 5) risk management. Hazard identification constitutes deciding on the system of interest and listing out the pathogens present/expected in that system. After identifying the hazard, the interaction of people with the system are modeled to quantify exposure to the pathogen. Suppose the system of interest is a particular lake used for recreation, and the hazard identified is E. coli. Exposure assessment would entail enumerating the E. coli finally ingested by the person . These processes have a lot of associated variability and uncertainty. Therefore, quantities are stratified by groups or represented by distributions rather than point estimates. Estimating the risk while accounting for these variabilities and uncertainties is done by Monte Carlo sampling. Dose-response models relate the number of the pathogen to the probability of a person falling ill . They are constructed with data from clinical trials in which a predetermined dose of pathogens is administered to a cohort of subjects and the number falling ill counted. The latter is then divided by the total number of subjects to reflect the probability of a single person falling ill.

This process is repeated for different pathogen doses to generate data for the models. While these clinical trials may use animals, datasets generated from human trials are preferred since they better reflect the human situation. Popular DRMs are the exponential and beta-Poisson models. DRMs for different pathogens may share the same functional form but differ in the numerical values of model parameters as a consequence of the biological differences between the pathogens. Risk characterization involves calculating the risk posed by the hazard by integrating the output of the exposure assessment with the DRM of choice for that pathogen. One then compares these estimates with guidelines established by the U.S. Environmental Protection Agency or the World Health Organization . Based on these comparisons, risk management measures can be investigated in an iterative process by computing the risk posed by the intervention measures. Understanding the risk posed by ARB has been stymied by the absence of DRMs parameterized for ARB. This difficulty arises from the clinical trials used to parameterize current DRMs, which were performed using antibiotic sensitive bacteria . While we have invitro kinetic information relating ARB to ASB, the biophysical/kinetic interpretation of the parameters of the popular exponential and beta-Poisson DRMs is not straightforward. Moreover,hydroponic barley fodder system the dose-response outcome is potentially complicated by the other processes at play, such as horizontal gene transfer and the differential influence of antibiotics on ASB and ARB death rates. The resulting illness may or may not respond to antibiotic treatment if the ARB sub-population persists. These challenges require a mathematical framework capable of handling the underlying processes, which can then be used to perform risk assessments of ARB and determine the best course of action. A point of longstanding debate in QMRA, and broadly the topic of disease progression, is the hypothesis of independent action. It proposes that pathogens act independently of one another, and each has a probability p of initiating infection. The alternative hypothesis is one of cooperation where infection is expected when more than one organism survives to overwhelm the host’s defenses collectively. DRMs assuming independent action have wider acceptance than DRMs which assume cooperativity. However, DRMs with cooperativity consider the cumulative effects of bacteria but not the potential synergistic interactions between bacterial cells or quorum sensing. I believe that incorporating cell-cell interaction in dose-response is an essential step to developing a better understanding of the development of disease and its treatment.Risk estimates for lettuce grown in the hydroponic tank or soil are presented in Fig. 2.4. Across these systems, the FP model predicted the highest risk while the 1F1 model predicted the lowest risk.

For a given risk model, higher risk was predicted in the hydroponic system than in the soil. This is a consequence of the very low detachment rates in soil compared to the attachment rates. Comparison of results from Sc1 and Sc2 of soil grown lettuce indicated lower risks and disease burdens under Sc1 . Comparing with the safety guidelines, the lowest risk predicted in the hydroponic system is higher than the U.S. EPA defined acceptable annual drinking water risk of 10−4 for each risk model. The annual burdens are also above the 10−6 benchmark recommended by the WHO. In the case of soil grown lettuce, neither Sc1 nor Sc2 met the U.S. EPA safety benchmark. Two risk models predicted borderline disease burden according to the WHO benchmark, for soil grown lettuce in Sc1, but under Sc2 the risk still did not meet the safety guideline. I found that neither increasing holding time of the lettuce to two days after harvesting nor using bigger tanks significantly altered the predicted risk . In comparison, the risk estimates of are higher than range of soil grown lettuce outcomes presented here for 2 of 3 models. The SCSA sensitivity indices are presented in Fig. 2.5. For hydroponically grown lettuce, the top 3 factors influencing daily risk are amount of lettuce consumed, time since last irrigation and the term involving consumption and ρshoot. Also, the risk estimates are robust to the fitted parameters despite low identifiability of some model parameters . For soil grown lettuce, kp appears to be the major influential parameter, followed by the input viral concentration in irrigation water and the lettuce harvest time. Scorr is near zero, suggesting lesser influence of correlation in the input parameters. To predict viral transport in plant tissue, it is necessary to couple mathematical assumptions with an understanding of the underlying biogeochemical processes governing virus removal, plant growth, growth conditions and virus-plant interactions. For example, although a simple transport model without AD could predict the viral load in the lettuce at harvest, it failed to capture the initial curvature in the viral load in the growth medium . An alternative to the AD hypothesis that could capture this curvature is the existence of two populations of viruses as used in, one decaying slower than the other. However, I did not adopt this approach as the double exponential model is not time invariant. This means that the time taken to decay from a concentration C1 to C2 is not unique and depends on the history of the events that occurred . Other viral models, such as the ones used in faced the same issues. Incorporating AD made the model time invariant and always provided the same time for decay between two given concentrations. This model fitting experience showcases how mathematics can guide the understanding of biological mechanisms. The hypothesis of two different NoV populations is less plausible than that of viral attachment and detachment to the hydroponic tank. While it appears that incorporating the AD mechanism does not significantly improve the accuracy of viral load predictions in the lettuce shoot at harvest, this is a consequence of force fitting the model to data under the given conditions.