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Spike- and blank-based procedures were applied to estimate the detection limits (DLs) for example analytes from inorganic and organic methods for water samples to compare with the U.S. Environmental Protection Agency's (EPA) Method Detection Limit (MDL) procedures (revisions 1.11 and 2.0). The multi-concentration spike-based procedures ASTM Within-laboratory Critical Level (DQCALC) and EPA's Lowest Concentration Minimum Reporting Level were compared in one application, with DQCALC further applied to many methods. The blank-based DLs, MDL b99 (99th percentile) or MDL bY (= mean blank concentration + s × t), estimated using large numbers (>100) of blank samples often provide DLs that better approach or achieve the desired ≤1% false positive risk level compared to spike-based DLs. For primarily organic methods that do not provide many uncensored blank results, spike-based DQCALC or MDL rev. 2.0 are needed to simulate the blank distribution and estimate the DL. DQCALC is especially useful for estimating DLs for multi-analyte methods having very different analyte response characteristics. Time series plots of DLs estimated using different procedures reveal that DLs are dependent on the applied procedure, should not be expected to be static over time, and seem best viewed as falling over a range versus being a single value. Use of both blank- and spike-based DL procedures help inform this DL range. Data reporting conventions that censor data at a threshold and report "less than" that threshold concentration as the reporting level have unknown and potentially high false negative risk. The U.S. Geological Survey National Water Quality Laboratory's Laboratory Reporting Level (LRL) convention (applied primarily to organic methods) attempts to simultaneously minimize both the false positive and false negative risk when 

Background: Enzyme assays have widespread applications in drug discovery from plants to natural products. The appropriate use of blanks in enzyme assays is important for assay baseline-correction, and the correction of false signals associated with background matrix interferences. However, the blank-correction procedures reported in published literature are highly inconsistent. We investigated the influence of using different types of blanks on the final calculated activity/inhibition results for three enzymes of significance in diabetes and obesity; α-glucosidase, α-amylase, and lipase. This is the first study to examine how different blank-correcting methods affect enzyme assay results. Although assays targeting the above enzymes are common in the literature, there is a scarcity of detailed published protocols. Therefore, we have provided comprehensive, step-by-step protocols for α-glucosidase-, α-amylase- and lipase-inhibition assays that can be performed in 96-well format in a simple, fast, and resource-efficient manner with clear instructions for blank-correction and calculation of results., Results: In the three assays analysed here, using only a buffer blank underestimated the enzyme inhibitory potential of the test sample. In the absorbance-based α-glucosidase assay, enzyme inhibition was underestimated when a sample blank was omitted for the coloured plant extracts. Similarly, in the fluorescence-based α-amylase and lipase assays, enzyme inhibition was underestimated when a substrate blank was omitted. For all three assays, method six [Raw Data - (Substrate + Sample Blank)] enabled the correction of interferences due to the buffer, sample, and substrate without double-blanking, and eliminated the need to add substrate to each sample blank., Conclusion: The choice of blanks and blank-correction methods contribute to the variability of assay results and the likelihood of underestimating the enzyme inhibitory potential of a test sample. This highlights the importance of standardising the use of blanks and the reporting of blank-correction procedures in published studies in order to ensure the accuracy and reproducibility of results, and avoid overlooked opportunities in drug discovery research due to inadvertent underestimation of enzyme inhibitory potential of test samples resulting from unsuitable blank-correction. Based on our assessments, we recommend method six [RD - (Su + SaB)] as a suitable method for blank-correction of raw data in enzyme assays.

Sequencing microbial DNA from deep subsurface environments is complicated by a number of issues ranging from contamination to non-reproducible results. Many samples obtained from these environments - which are of great interest due to the potential to stimulate microbial methane generation - contain low biomass. Therefore, samples from these environments are difficult to study as sequencing results can be easily impacted by contamination. In this case, the low amount of sample biomass may be effectively swamped by the contaminating DNA and generate misleading results. Additionally, performing field work in these environments can be difficult, as researchers generally have limited access to and time on site. Therefore, optimizing a sampling plan to produce the best results while collecting the greatest number of samples over a short period of time is ideal. This study aimed to recommend an adequate sampling plan for field researchers obtaining microbial biomass for 16S rRNA gene sequencing, applicable specifically to low biomass oil and gas-producing environments. Forty-nine different samples were collected by filtering specific volumes of produced water from a hydraulically fractured well producing from the Niobrara Shale. Water was collected in two different sampling events 24 h apart. Four to five samples were collected from 11 specific volumes. These samples along with eight different blanks were submitted for analysis. DNA was extracted from each sample, and quantitative polymerase chain reaction (qPCR) and 16S rRNA Illumina MiSeq gene sequencing were performed to determine relative concentrations of biomass and microbial community composition, respectively. The qPCR results varied across sampled volumes, while no discernible trend correlated contamination to volume of water filtered. This suggests that collecting a larger volume of sample may not result in larger biomass concentrations or better representation of a sampled environment. Researchers could prioritize collecting many low volume samples over few high-volume samples. Our results suggest that there also may be variability in the concentration of microbial communities present in produced waters over short (i.e., hours) time scales, which warrants further investigation. Submission of multiple blanks is also vital to determining how contamination or low biomass effects may influence a sample set collected from an unknown environment., (Copyright © 2020 Shelton, Barnhart, Ruppert, Jubb, Blondes and DeVera.)