Bacteroides species are promising indicators for differentiating livestock and human fecal contamination in water because of their high concentration in feces and potential host specificity. In this study, a real-time PCR assay was designed to target Bacteroides species (AllBac) present in human, cattle, and equine feces. Direct PCR amplification (without DNA extraction) using the AllBac assay was tested on feces diluted in water. Fecal concentrations and threshold cycle were linearly correlated, indicating that the AllBac assay can be used to estimate the total amount of fecal contamination in water. Real-time PCR assays were also designed for bovine-associated (BoBac) and human-associated (HuBac) Bacteroides 16S rRNA genes. Assay specificities were tested using human, bovine, swine, canine, and equine fecal samples. The BoBac assay was specific for bovine fecal samples (100% true-positive identification; 0% false-positive identification). The HuBac assay had a 100% true-positive identification, but it also had a 32% false-positive rate with potential for cross-amplification with swine feces. The assays were tested using creek water samples from three different watersheds. Creek water did not inhibit PCR, and results from the AllBac assay were correlated with those from Escherichia coli concentrations ( r ² = 0.85). The percentage of feces attributable to bovine and human sources was determined for each sample by comparing the values obtained from the BoBac and HuBac assays with that from the AllBac assay. These results suggest that real-time PCR assays without DNA extraction can be used to quantify fecal concentrations and provide preliminary fecal source identification in watersheds.

On its revolutionary threshold, quantum sensing is creating potentially transformative opportunities to exploit intricate quantum mechanical phenomena in new ways to make ultrasensitive measurements of multiple parameters. Concurrently, growing interest in quantum sensing has created opportunities for its deployment to improve processes pertaining to energy production, distribution, and consumption. Safe and secure utilization of energy is dependent upon addressing challenges related to material stability and function, secure monitoring of infrastructure, and accuracy in detection and measurement. A summary of well‐established and emerging quantum sensing materials and techniques, as well as the corresponding sensing platforms that have been developed for their deployment is provided here. Specifically, the enhancement of existing advanced sensing technologies with quantum methods and materials is focused on, enabling the realization of an unprecedented level of sensitivity, placing an emphasis on relevance to the energy industry. The review concludes with a discussion on high‐value applications of quantum sensing to the energy sector, as well as remaining barriers to sensor deployment.

Laboratory microcosm experiments were used to assess the effects of environmental parameters on the persistence of the Bacteroides 16S rRNA genes derived from equine fecal samples in stream water to investigate the utility of Bacteroides spp. as fecal indicator organisms. Quantitative real‐time polymerase chain reaction (qPCR) was used to measure gene concentrations over time with treatments designed to compare filtered vs. unfiltered stream water, fecal aggregate size, initial fecal concentrations, and water temperatures. Comparison of Bacteroides16S rRNA genes/mL in microcosms constructed with unfiltered stream water and filtered stream water indicated that stream water filtration to remove indigenous microorganisms followed by temperature had the largest effects on gene persistence. First‐order exponential decay functions were fitted to the data from each microcosm constructed using unfiltered stream water, and the decay constants (k) ranged from 0.0071 h⁻¹ in the microcosms incubated at 5°C to 0.0336 h⁻¹ in a set of microcosms incubated at 25°C. Analysis of k calculated from the 10 experimental treatments indicated that k is more highly correlated to temperature than initial Bacteroides 16S rRNA gene starting concentrations. The equation resulting from graphing k (as the dependent variable) vs. temperature (as the independent variable) best fit a peak, Gaussian, 3 parameter function with a maximum decay at 30°C, a r² of 0.83 and all parameters were significant (P < 0.0015). Thus this data suggest that factors that reduce biological activity, such as physical removal of stream microorganisms by filtration and low temperature, result in slower Bacteroides 16S rRNA gene decay.