Assessment of the Biological Control Potential of Common Carabid Beetle Species for Autumn- and Winter-Active Pests (Gastropoda, Lepidoptera, Diptera: Tipulidae) in Annual Ryegrass in Western Oregon.

While carabid beetles have been shown to feed on a variety of crop pests, little is known about their species assemblages in US annual ryegrass crops, where invertebrate pests, particularly slugs, lepidopteran larvae and craneflies, incur major financial costs. This study assesses the biological control potential of carabid beetles for autumn- and winter-active pests in annual ryegrass grown for seed by: (a) investigating the spatial and temporal overlap of carabids with key pests; and (b) molecular gut content analysis using qPCR. Introduced Nebria brevicollis was the only common carabid that was active during pest emergence in autumn, with 18.6% and 8.3% of N. brevicollis collected between September and October testing positive for lepidopteran and cranefly DNA, respectively, but only 1.7% testing positive for slug DNA. While pest DNA was also detected in the guts of the other common carabid species-Agonum muelleri, Calosoma cancellatum and Poecilus laetulus-these were active only during spring and summer, when crop damage by pests is less critical. None of the four carabid species was affected by disk tilling and only N. brevicollis was significantly associated with a vegetated field margin. However, as its impact on native ecosystems is unknown, we do not recommend managing for this species.

16S rRNA sequencing reveals likely beneficial core microbes within faecal samples of the EU protected slug Geomalacus maculosus.

The EU-protected slug Geomalacus maculosus Allman occurs only in the West of Ireland and in northern Spain and Portugal. We explored the microbial community found within the faeces of Irish specimens with a view to determining whether a core microbiome existed among geographically isolated slugs which could give insight into the adaptations of G. maculosus to the available food resources within its habitat. Faecal samples of 30 wild specimens were collected throughout its Irish range and the V3 region of the bacterial 16S rRNA gene was sequenced using Illumina MiSeq. To investigate the influence of diet on the microbial composition, faecal samples were taken and sequenced from six laboratory reared slugs which were raised on two different foods. We found a widely diverse microbiome dominated by Enterobacteriales with three core OTUs shared between all specimens. While the reared specimens appeared clearly separated by diet in NMDS plots, no significant difference between the slugs fed on the two different diets was found. Our results indicate that while the majority of the faecal microbiome of G. maculosus is probably dependent on the microhabitat of the individual slugs, parts of it are likely selected for by the host.

mQC: A Heuristic Quality-Control Metric for High-Throughput Drug Combination Screening.

Quality control (QC) metrics are critical in high throughput screening (HTS) platforms to ensure reliability and confidence in assay data and downstream analyses. Most reported HTS QC metrics are designed for plate level or single well level analysis. With the advent of high throughput combination screening there is a need for QC metrics that quantify the quality of combination response matrices. We introduce a predictive, interpretable, matrix-level QC metric, mQC, based on a mix of data-derived and heuristic features. mQC accurately reproduces the expert assessment of combination response quality and correctly identifies unreliable response matrices that can lead to erroneous or misleading characterization of synergy. When combined with the plate-level QC metric, Z', mQC provides a more appropriate determination of the quality of a drug combination screen. Retrospective analysis on a number of completed combination screens further shows that mQC is able to identify problematic screens whereas plate-level QC was not able to. In conclusion, our data indicates that mQC is a reliable QC filter that can be used to identify problematic drug combinations matrices and prevent further analysis on erroneously active combinations as well as for troubleshooting failed screens. The R source code of mQC is available at http://matrix.ncats.nih.gov/mQC.