Beyond the Surface: Non-Invasive Low-Field NMR Analysis of Microbially-Induced Calcium Carbonate Precipitation in Shale Fractures - Matthew R. Willett; Kayla Bedey; Dustin Crandall; Joseph D. Seymour; Jonny Rutqvist; Alfred B. Cunningham; Adrienne J. Phillips; Catherine M. Kirkland (Rock Mechanics and Rock Engineering, 2026)
Microbially-induced calcium carbonate precipitation (MICP) is a biological process in which microbially-produced urease enzymes convert urea and calcium into solid calcium carbonate (CaCO 3 ) deposits. MICP has been demonstrated to reduce permeability in shale fractures under elevated pressures, raising the possibility of applying this technology to enhance shale reservoir storage safety. For this and other applications to become a reality, non-invasive tools are needed to determine how effectively MICP seals shale fractures at subsurface temperatures. In this study, two different MICP strategies were tested on 2.54 cm diameter and 5.08 cm long shale cores with a single fracture at 60 ℃. Flow-through, pulsed-flow MICP-treatment was repeatedly applied to Marcellus shale fractures with and without sand (“proppant”) until reaching approximately four orders of magnitude reduction in apparent permeability, while a single application of polymer-based “immersion” MICP-treatment was applied to an Eagle Ford shale fracture with proppant. Low-field nuclear magnetic resonance (LF-NMR) and X-Ray computed microtomography (micro-CT) techniques were used to assess the degree of biomineralization. With the flow-through approach, these tools revealed that while CaCO 3 precipitation occurred throughout the fracture, there was preferential precipitation around proppant. Without proppant, the same approach led to premature sealing at the inlet side of the core. In contrast, immersion MICP-treatment sealed off the fracture edges and showed less mineral precipitation overall. This study highlights the use of LF-NMR relaxometry in characterizing fracture sealing and can help guide NMR logging tools in subsurface remediation efforts.

Predictive models to determine best strategy for metaphylaxis application in cattle at arrival to a feedyard - Lilli Heinen; Phillip A. Lancaster; Robert L. Larson; Dustin L. Pendell; Dannell J. Kopp; Brad J. White (Preventive Veterinary Medicine, 2025)

Effects of snake fungal disease (ophidiomycosis) on the skin microbiome across two major experimental scales - Alexander S. Romer; Matthew Grisnik; Jason W. Dallas; William Sutton; Christopher M. Murray; Rebecca H. Hardman; Tom Blanchard; Ryan J. Hanscom; Rulon W. Clark; Cody Godwin; N. Reed Alexander; Kylie C. Moe; et al. (Conservation Biology, 2025)
Emerging infectious diseases are increasingly recognized as a significant threat to global biodiversity conservation. Elucidating the relationship between pathogens and the host microbiome could lead to novel approaches for mitigating disease impacts. Pathogens can alter the host microbiome by inducing dysbiosis, an ecological state characterized by a reduction in bacterial alpha diversity, an increase in pathobionts, or a shift in beta diversity. We used the snake fungal disease (SFD; ophidiomycosis), system to examine how an emerging pathogen may induce dysbiosis across two experimental scales. We used quantitative polymerase chain reaction, bacterial amplicon sequencing, and a deep learning neural network to characterize the skin microbiome of free‐ranging snakes across a broad phylogenetic and spatial extent. Habitat suitability models were used to find variables associated with fungal presence on the landscape. We also conducted a laboratory study of northern watersnakes to examine temporal changes in the skin microbiome following inoculation with Ophidiomyces ophidiicola . Patterns characteristic of dysbiosis were found at both scales, as were nonlinear changes in alpha and alterations in beta diversity, although structural‐level and dispersion changes differed between field and laboratory contexts. The neural network was far more accurate (99.8% positive predictive value [PPV]) in predicting disease state than other analytic techniques (36.4% PPV). The genus Pseudomonas was characteristic of disease‐negative microbiomes, whereas, positive snakes were characterized by the pathobionts Chryseobacterium , Paracoccus , and Sphingobacterium . Geographic regions suitable for O. ophidiicola had high pathogen loads (>0.66 maximum sensitivity + specificity). We found that pathogen‐induced dysbiosis of the microbiome followed predictable trends, that disease state could be classified with neural network analyses, and that habitat suitability models predicted habitat for the SFD pathogen.

Mehlich III predicts that soils in Wisconsin and Illinois may cause nickel deficiency in crops - Dustin C. Sawyer; Phillip Barak (Plant and Soil, 2024)

Continuous-Flow Left Ventricular Assist Device Support Improves Myocardial Supply:Demand in Chronic Heart Failure - Kevin G. Soucy; Carlo R. Bartoli; Dustin Phillips; Guruprasad A. Giridharan; Michael A. Sobieski; William B. Wead; Robert D. Dowling; Zhongjun J. Wu; Sumanth D. Prabhu; Mark S. Slaughter; Steven C. Koenig (Annals of Biomedical Engineering, 2017)

Spatial Relationships Between Water Quality and Pesticide Application Rates in Agricultural Watersheds - John W. Hunt; Brian S. Anderson; Bryn M. Phillips; Ron S. Tjeerdema; Nancy Richard; Val Connor; Karen Worcester; Mark Angelo; Amanda Bern; Brian Fulfrost; Dustin Mulvaney (Environmental Monitoring and Assessment, 2006)