Extra-intestinal pathogenic E. coli (ExPEC), including avian pathogenic E. coli (APEC), pose a considerable threat to both human and animal health, with illness causing substantial economic loss. APEC strain χ7122 (O78∶K80∶H9), containing three large plasmids [pChi7122-1 (IncFIB/FIIA-FIC), pChi7122-2 (IncFII), and pChi7122-3 (IncI₂)]; and a small plasmid pChi7122-4 (ColE2-like), has been used for many years as a model strain to study the molecular mechanisms of ExPEC pathogenicity and zoonotic potential. We previously sequenced and characterized the plasmid pChi7122-1 and determined its importance in systemic APEC infection; however the roles of the other pChi7122 plasmids were still ambiguous. Herein we present the sequence of the remaining pChi7122 plasmids, confirming that pChi7122-2 and pChi7122-3 encode an ABC iron transport system (eitABCD) and a putative type IV fimbriae respectively, whereas pChi7122-4 is a cryptic plasmid. New features were also identified, including a gene cluster on pChi7122-2 that is not present in other E. coli strains but is found in Salmonella serovars and is predicted to encode the sugars catabolic pathways. In vitro evaluation of the APEC χ7122 derivative strains with the three large plasmids, either individually or in combinations, provided new insights into the role of plasmids in biofilm formation, bile and acid tolerance, and the interaction of E. coli strains with 3-D cultures of intestinal epithelial cells. In this study, we show that the nature and combinations of plasmids, as well as the background of the host strains, have an effect on these phenomena. Our data reveal new insights into the role of extra-chromosomal sequences in fitness and diversity of ExPEC in their phenotypes.

Background: Genetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor segment, which universally recurs. In this study, we use multiple texture analysis and machine learning (ML) algorithms to analyze multi-parametric MRI, and produce new images indicating tumor-rich targets in GBM.

Methods: We recruited primary GBM patients undergoing image-guided biopsies and acquired pre-operative MRI: CE-MRI, Dynamic-Susceptibility-weighted-Contrast-enhanced-MRI, and Diffusion Tensor Imaging. Following image coregistration and region of interest placement at biopsy locations, we compared MRI metrics and regional texture with histologic diagnoses of high- vs low-tumor content (≥80% vs <80% tumor nuclei) for corresponding samples. In a training set, we used three texture analysis algorithms and three ML methods to identify MRI-texture features that optimized model accuracy to distinguish tumor content. We confirmed model accuracy in a separate validation set.

Results: We collected 82 biopsies from 18 GBMs throughout ENH and BAT. The MRI-based model achieved 85% cross-validated accuracy to diagnose high- vs low-tumor in the training set (60 biopsies, 11 patients). The model achieved 81.8% accuracy in the validation set (22 biopsies, 7 patients).

Conclusion: Multi-parametric MRI and texture analysis can help characterize and visualize GBM’s spatial histologic heterogeneity to identify regional tumor-rich biopsy targets.

Predicting the timing of a castrate resistant prostate cancer is critical to lowering medical costs and improving the quality of life of advanced prostate cancer patients. We formulate, compare and analyze two mathematical models that aim to forecast future levels of prostate-specific antigen (PSA). We accomplish these tasks by employing clinical data of locally advanced prostate cancer patients undergoing androgen deprivation therapy (ADT). While these models are simplifications of a previously published model, they fit data with similar accuracy and improve forecasting results. Both models describe the progression of androgen resistance. Although Model 1 is simpler than the more realistic Model 2, it can fit clinical data to a greater precision. However, we found that Model 2 can forecast future PSA levels more accurately. These findings suggest that including more realistic mechanisms of androgen dynamics in a two population model may help androgen resistance timing prediction.

Maricopa County is the home of the Phoenix metropolitan area, an expansive city with serious air quality concerns. To ameliorate air quality in the county, the Maricopa County Air Quality Department developed a website and mobile application called "Clean Air Make More" as a means of outreach and engagement. In doing this, the county has found a way to engender a bilateral relationship between individuals and their government agency. This study analyzes the effectiveness of Clean Air Make More in establishing this relationship and engaging the community in efforts to improve air quality. It concludes that the design of the application effectively meets user needs, but marketing efforts should target populations disposed to taking action regarding air quality.

Background:
Environmental heat exposure is a public health concern. The impacts of environmental heat on mortality and morbidity at the population scale are well documented, but little is known about specific exposures that individuals experience.

Objectives:
The first objective of this work was to catalyze discussion of the role of personal heat exposure information in research and risk assessment. The second objective was to provide guidance regarding the operationalization of personal heat exposure research methods.

Discussion:
We define personal heat exposure as realized contact between a person and an indoor or outdoor environment that poses a risk of increases in body core temperature and/or perceived discomfort. Personal heat exposure can be measured directly with wearable monitors or estimated indirectly through the combination of time–activity and meteorological data sets. Complementary information to understand individual-scale drivers of behavior, susceptibility, and health and comfort outcomes can be collected from additional monitors, surveys, interviews, ethnographic approaches, and additional social and health data sets. Personal exposure research can help reveal the extent of exposure misclassification that occurs when individual exposure to heat is estimated using ambient temperature measured at fixed sites and can provide insights for epidemiological risk assessment concerning extreme heat.

Conclusions:
Personal heat exposure research provides more valid and precise insights into how often people encounter heat conditions and when, where, to whom, and why these encounters occur. Published literature on personal heat exposure is limited to date, but existing studies point to opportunities to inform public health practice regarding extreme heat, particularly where fine-scale precision is needed to reduce health consequences of heat exposure.

Background:
Data assimilation refers to methods for updating the state vector (initial condition) of a complex spatiotemporal model (such as a numerical weather model) by combining new observations with one or more prior forecasts. We consider the potential feasibility of this approach for making short-term (60-day) forecasts of the growth and spread of a malignant brain cancer (glioblastoma multiforme) in individual patient cases, where the observations are synthetic magnetic resonance images of a hypothetical tumor.

Results:
We apply a modern state estimation algorithm (the Local Ensemble Transform Kalman Filter), previously developed for numerical weather prediction, to two different mathematical models of glioblastoma, taking into account likely errors in model parameters and measurement uncertainties in magnetic resonance imaging. The filter can accurately shadow the growth of a representative synthetic tumor for 360 days (six 60-day forecast/update cycles) in the presence of a moderate degree of systematic model error and measurement noise.

Conclusions:
The mathematical methodology described here may prove useful for other modeling efforts in biology and oncology. An accurate forecast system for glioblastoma may prove useful in clinical settings for treatment planning and patient counseling.