The need for the precise and reliable collection of potential biothreat contaminants has motivated research in developing a better understanding of the variability in biological surface sampling methods. In this context, the objective of this work was to determine parameters affecting the efficiency of extracting Bacillus anthracis Sterne spores from commonly used wipe sampling materials and to describe performance using the interfacial energy concept. In addition, surface thermodynamics was applied to understand and predict surface sampling performance. Wipe materials were directly inoculated with known concentrations of B. anthracis spores and placed into extraction solutions, followed by sonication or vortexing. Experimental factors investigated included wipe material (polyester, cotton, and polyester-rayon), extraction solution (sterile deionized water , and physical dissociation method (vortexing or sonication). The most efficient extraction from wipes was observed for solutions containing the nonionic surfactant Tween 80. The increase in extraction efficiency due to surfactant addition was attributed to an attractive interfacial energy between Tween 80 and the centrifuge tube wall, which prevented spore adhesion. Extraction solution significantly impacted the extraction efficiency, as determined by statistical analysis (P < 0.05). Moreover, the extraction solution was the most important factor in extraction performance, followed by the wipe material. Polyester-rayon was the most efficient wipe material for releasing spores into solution by rank; however, no statistically significant difference between polyester-rayon and cotton was observed (P > 0.05). Vortexing provided higher spore recovery in H 2 O and H 2 O-T than sonication, when all three wipe materials and the reference control were considered (P < 0.05).The successful collection of biological contaminants from surfaces is critical to gaining insight into the environmental conditions in which we live and work as well as to ensure public safety in times of biothreat incidents. Traditional methods for biological sample collection have focused on assessing bacterial contamination on surfaces relevant to environmental, clinical, and food safety settings, in which case swabs were the most common adsorptive materials used for sample collection (6,20,21,23,24,29,35,38). Since the anthrax attacks in 2001, sampling methods using wipe and vacuum collection devices have been developed to meet the needs of a broader range of applications, including building characterization and clearance. Data accumulated from sampling of contaminated facilities in 2001 using HEPA vacuum, dry and premoistened swab, and wipe sampling methods demonstrated that sampling efficiency was dependent on surface sampling techniques and sample collection conditions (42,47).Overall recovery efficiency is sensitive to the applied experimental conditions due to a wide range of potential variables in surface sample collection methodologies, such as differences in extraction solution, adsorpti...

ABSTRACTEnvironmental sampling for microbiological contaminants is a key component of hygiene monitoring and risk characterization practices utilized across diverse fields of application. However, confidence in surface sampling results, both in the field and in controlled laboratory studies, has been undermined by large variation in sampling performance results. Sources of variation include controlled parameters, such as sampling materials and processing methods, which often differ among studies, as well as random and systematic errors; however, the relative contributions of these factors remain unclear. The objective of this study was to determine the relative impacts of sample processing methods, including extraction solution and physical dissociation method (vortexing and sonication), on recovery of Gram-positive (Bacillus cereus) and Gram-negative (Burkholderia thailandensisandEscherichia coli) bacteria from directly inoculated wipes. This work showed that target organism had the largest impact on extraction efficiency and recovery precision, as measured by traditional colony counts. The physical dissociation method (PDM) had negligible impact, while the effect of the extraction solution was organism dependent. Overall, however, extraction of organisms from wipes using phosphate-buffered saline with 0.04% Tween 80 (PBST) resulted in the highest mean recovery across all three organisms. The results from this study contribute to a better understanding of the factors that influence sampling performance, which is critical to the development of efficient and reliable sampling methodologies relevant to public health and biodefense.

Human untargeted metabolomics studies annotate only ~10% of molecular features. We introduce reference-data-driven analysis to match metabolomics tandem mass spectrometry (MS/MS) data against metadata-annotated source data as a pseudo-MS/MS reference library. Applying this approach to food source data, we show that it increases MS/MS spectral usage 5.1-fold over conventional structural MS/MS library matches and allows empirical assessment of dietary patterns from untargeted data.Complex sequence data from metagenomic (see Box 1 for definition of terms) or metatranscriptomic experiments require for interpretation both databases of curated genes and reference data, such as whole genomes or other sequence data with carefully curated metadata (developmental stage, tissue location, phenotype, etc.) [1][2][3][4] . Such reference data-driven (RDD) analysis increases understanding of complex communities by using matches between genes or transcripts of known and unknown origin. The RDD strategy is essential for the successful analysis of most metatranscriptomics or metagenomics data. By analogy, interpreting liquid chromatography-tandem mass spectromtery (LC-MS/MS)-based untargeted metabolomics data is performed by searching structural MS/MS libraries. However, leveraging reference data with curated and structured controlled vocabulary metadata to improve insights obtainable from untargeted MS/MS-based metabolomics is not yet done.RDD analysis uses not only annotated MS/MS-spectra but also all unannotated spectra. The gas chromatography-mass spectrometry (GC-MS) BinBase resource has made a step in the direction of RDD. With BinBase one can annotate if a spectrum match has been observed in a non-public GC-MS dataset. However, the metadata is not well controlled and lacks the ability to add contextualized metadata 5,6 . In addition, as we have previously demonstrated, using structural annotations, the source can be determined by literature mining 7 . However, owing to the above mentioned limitations and/ or inability to link related spectra in the case of metabolism, the above strategies to annotate unknowns cannot be used to systematically to interpret the source information at the dataset level. We therefore introduce the RDD approach for metabolomics (Fig. 1), followed by a use case demonstrating empirical food readouts from untargeted human data (Fig. 2).Untargeted MS/MS-based metabolomics experiments involve searching MS/MS structural libraries since the late 1970's 8,9 , or, more recently, for investigating the distribution of a MS/MS spectrum across public untargeted data 10 . Instead of only leveraging a single MS/MS spectrum to obtain an annotation, RDD metabolomics uses all MS/MS spectra from untargeted metabolomics files, which con-