A Search For More Meaningful Interpretations

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Drivers of Microbial Risk for Direct Potable Reuse and de Facto Reuse Treatment Schemes: The Impacts of Source Water Quality and Blending.

Although reclaimed water for potable applications has many potential benefits, it poses concerns for chemical and microbial risks to consumers. We present a quantitative microbial risk assessment (QMRA) Monte Carlo framework to compare a de facto water reuse scenario (treated wastewater-impacted surface water) with four hypothetical Direct Potable Reuse (DPR) scenarios for Norovirus, Cryptosporidium, and Salmonella. Consumer microbial risks of surface source water quality (impacted by 0-100% treated wastewater effluent) were assessed. Additionally, we assessed risks for different blending ratios (0-100% surface water blended into advanced-treated DPR water) when source surface water consisted of 50% wastewater effluent. De facto reuse risks exceeded the yearly 10-4 infections risk benchmark while all modeled DPR risks were significantly lower. Contamination with 1% or more wastewater effluent in the source water, and blending 1% or more wastewater-impacted surface water into the advanced-treated DPR water drove the risk closer to the 10-4 benchmark. We demonstrate that de facto reuse by itself, or as an input into DPR, drives microbial risks more so than the advanced-treated DPR water. When applied using location-specific inputs, this framework can contribute to project design and public awareness campaigns to build legitimacy for DPR

The dielectric constant of PbTe at 4.2 K and ν~\tilde ν=84.15 cm−1^{-1}, 96.97 cm−1^{-1}, 103.60 cm−1^{-1}

The dielectric constant of a PbTe epitaxial layer has been measured by surface wave spectroscopy using an optically pumped far-infrared laser and the technique of attenuated total reflection

Preparing New Jersey Community College Students for the Mathematics Placement Tests

Although most new college students had to demonstrate algebraic and basic mathematics mastery to earn a high school diploma or the equivalent, the majority of incoming New Jersey community college students are not showing this knowledge on the mathematics placement tests, thus placing into developmental courses, which must be successfully completed before students can attempt any college-level mathematics courses. Guided by Knowles’ theory of andragogy and developmental mathematics as a core concept, the purpose of this study was to determine ways to help incoming New Jersey community college students prepare for the ACCUPLACER mathematics tests. The research questions addressed testing and tutoring administrators’ perceptions of how to help incoming students achieve higher scores on these assessments. This qualitative exploratory case study consisted primarily of interviews with 10 testing and tutoring administrators representing 6 of the 18 New Jersey community colleges. These colleges have programs to prepare students for the mathematics placement tests, and documents related to these programs were also reviewed. Interview transcripts and documents were coded for relevant themes by following the constant comparative method of Glaser and Strauss. Preparation availability, timing, constraint frustrations, student attendance/usage, and minimal intercollege consistency emerged as themes. A position/white paper with the results and recommendations was written and prepared for sharing with the New Jersey testing and tutoring administrator groups. The knowledge gained from this study will engender social change by helping incoming college students avoid developmental mathematics courses, saving the students time, money, and effort, as well as improving their chances of completing college programs and degrees

Seasonality in extra-pulmonary tuberculosis notifications in Germany 2004-2014- a time series analysis

Background Seasonality in tuberculosis (TB) has been found in different parts of the world, showing a peak in spring/summer and a trough in autumn/winter. The evidence is less clear which factors drive seasonality. It was our aim to identify and evaluate seasonality in the notifications of TB in Germany, additionally investigating the possible variance of seasonality by disease site, sex and age group. Methods We conducted an integer-valued time series analysis using national surveillance data. We analysed the reported monthly numbers of started treatments between 2004 and 2014 for all notified TB cases and stratified by disease site, sex and age group. Results We detected seasonality in the extra-pulmonary TB cases (N = 11,219), with peaks in late spring/summer and troughs in fall/winter. For all TB notifications together (N = 51,090) and for pulmonary TB only (N = 39,714) we did not find a distinct seasonality. Additional stratified analyses did not reveal any clear differences between age groups, the sexes, or between active and passive case finding. Conclusion We found seasonality in extra-pulmonary TB only, indicating that seasonality of disease onset might be specific to the disease site. This could point towards differences in disease progression between the different clinical disease manifestations. Sex appears not to be an important driver of seasonality, whereas the role of age remains unclear as this could not be sufficiently investigated.Peer Reviewe

The Effect of Ongoing Exposure Dynamics in Dose Response Relationships

Characterizing infectivity as a function of pathogen dose is integral to microbial risk assessment. Dose-response experiments usually administer doses to subjects at one time. Phenomenological models of the resulting data, such as the exponential and the Beta-Poisson models, ignore dose timing and assume independent risks from each pathogen. Real world exposure to pathogens, however, is a sequence of discrete events where concurrent or prior pathogen arrival affects the capacity of immune effectors to engage and kill newly arriving pathogens. We model immune effector and pathogen interactions during the period before infection becomes established in order to capture the dynamics generating dose timing effects. Model analysis reveals an inverse relationship between the time over which exposures accumulate and the risk of infection. Data from one time dose experiments will thus overestimate per pathogen infection risks of real world exposures. For instance, fitting our model to one time dosing data reveals a risk of 0.66 from 313 Cryptosporidium parvum pathogens. When the temporal exposure window is increased 100-fold using the same parameters fitted by our model to the one time dose data, the risk of infection is reduced to 0.09. Confirmation of this risk prediction requires data from experiments administering doses with different timings. Our model demonstrates that dose timing could markedly alter the risks generated by airborne versus fomite transmitted pathogens

A quantitative microbial risk assessment model for Legionnaires' disease: animal model selection and dose-response modeling

Risk Analysis, 27(6): pp. 1581-1596.Legionnaires’ Disease (LD), first reported in 1976, is an atypical pneumonia caused by bacteria of the genus Legionella, and most frequently by L. pneumophila (Lp). Subsequent research on exposure to the organism employed various animal models, and with Quantitative Microbial Risk Assessment techniques, the animal model data may provide insights on human dose-response for LD. The present report focuses on the rationale for selection of the guinea pig model, comparison of the dose-response model results, comparison of projected low-dose responses for guinea pigs, and risk estimates for humans. Based on both in vivo and in vitro comparisons, the guinea pig (Cavia porcellus) dose-response data were selected for modeling human risk. We completed dose-response modeling for the β-Poisson (approximate and exact), exponential, probit, logistic and Weibull models for Lp inhalation mortality and infection (end point elevated body temperature) in guinea pigs. For mechanistic reasons, including low-dose exposure probability, further work on human risk estimates for LD employed the exponential and β-Poisson models. With an exposure of 10 Colony Forming Units (retained dose), the QMRA model predicted a mild infection risk of 0.4 (as evaluated by seroprevalence) and a clinical severity LD case (e.g., hospitalization and supportive care) risk of 0.0009. The calculated rates based on estimated human exposures for outbreaks used for the QMRA model validation are within an order of magnitude of the reported LD rates. These validation results suggest the LD QMRA animal model selection, dose-response modeling, and extension to human risk projections were appropriate