Path to autonomous soil sampling and analysis by ground-based robots.

Good site characterization is essential for the selection of remediation alternatives for impacted soils. The value of site characterization is critically dependent on the quality and quantity of the data collected. Current methods for characterizing impacted soils rely on expensive manual sample collection and off-site analysis. However, recent advances in terrestrial robotics and artificial intelligence offer a potentially revolutionary set of tools and methods that will help to autonomously explore natural environments, select sample locations with the highest value of information, extract samples, and analyze the data in real-time without exposing humans to potentially hazardous conditions. A fundamental challenge to realizing this potential is determining how to design an autonomous system for a given investigation with many, and often conflicting design criteria. This work presents a novel design methodology to navigate these criteria. Specifically, this methodology breaks the system into four components - sensing, sampling, mobility, and autonomy - and connects design variables to the investigation objectives and constraints. These connections are established for each component through a survey of existing technology, discussion of key technical challenges, and highlighting conditions where generality can promote multi-application deployment. An illustrative example of this design process is presented for the development and deployment of a robotic platform characterizing salt-impacted oil & gas reserve pits. After calibration, the relationship between the in situ robot chloride measurements and laboratory-based chloride measurements had a good linear relationship (R2-value = 0.861) and statistical significance (p-value = 0.003).

Relationship Between T-Cell Responses to CMV, Markers of Inflammation, and Frailty in HIV-uninfected and HIV-infected Men in the Multicenter AIDS Cohort Study.

Background: Both aging and treated human immunodeficiency virus (HIV)-infected populations exhibit low-level chronic immune activation of unknown etiology, which correlates with morbidity and mortality. Cytomegalovirus (CMV) infection is common in both populations, but its relation to immune activation is unknown. Methods: T cells from men who have sex with men (22 virologically suppressed HIV+, 20 HIV-) were stimulated with peptides spanning 19 CMV open reading frames, and intracellular cytokine responses were assessed. Soluble and cellular inflammatory markers were assessed by multiplex electrochemiluminescence and flow cytometry, respectively. Frailty was assessed by the Fried criteria. Results: All men had responses to CMV. Proportions of CMV-responsive T cells correlated strongly (r ≥ 0.6 or ≤ -0.6; P < .05) with immunologic markers, depending on donor HIV and frailty status. Markers significantly correlated in some groups after adjustment for multiple comparisons included interferon-γ, tumor necrosis factor-α, interleukin-6, and several chemokines in serum, and the proportion of activated T cells. The magnitude of the CD4 IL-2 response significantly predicted onset of frailty in HIV- nonfrail men, but not in HIV+ nonfrail men. Conclusions: T-cell responses to CMV may strongly influence chronic immune activation in HIV-uninfected and virologically suppressed HIV-infected men, and may predict frailty in HIV-uninfected men.