Dual substrate sensor platform for rapid detection and differentiation of coliforms.

In the present work, we modified and improved the previously developed sensor platform (based on screen-printed carbon electrode) for rapid detection and quantification of coliforms. The second substrate (L-alanyl-p-nitroanilide) was included in the sensor platform, which produced the second cathodic voltammetric peak at the new potential (-113 mV) in the defined potential range (-600 to 400 mV). The 106 CFU/mL of 5 different groups of coliforms in 3 h of incubation produced electrochemical signals equal to 960 µA, 970 µA, 988 µA, 950 µA, 956 µA and 920 µA respectively. After 3 h of incubation the two strains of Gram-positive bacteria with concentration of 106 CFU/mL produced voltammetric signals equal to --80 µA and- 200 µA (due to high /-150 µA/ capacitive/background current) respectively. No voltammetric peak occurred with Gram-positive bacteria at the potential equal to -113 mV. The voltammetric signals produced at the new potential range were specific for coliforms.

Authoritative subspecies diagnosis tool for European honey bees based on ancestry informative SNPs.

BACKGROUND: With numerous endemic subspecies representing four of its five evolutionary lineages, Europe holds a large fraction of Apis mellifera genetic diversity. This diversity and the natural distribution range have been altered by anthropogenic factors. The conservation of this natural heritage relies on the availability of accurate tools for subspecies diagnosis. Based on pool-sequence data from 2145 worker bees representing 22 populations sampled across Europe, we employed two highly discriminative approaches (PCA and FST) to select the most informative SNPs for ancestry inference. RESULTS: Using a supervised machine learning (ML) approach and a set of 3896 genotyped individuals, we could show that the 4094 selected single nucleotide polymorphisms (SNPs) provide an accurate prediction of ancestry inference in European honey bees. The best ML model was Linear Support Vector Classifier (Linear SVC) which correctly assigned most individuals to one of the 14 subspecies or different genetic origins with a mean accuracy of 96.2% ± 0.8 SD. A total of 3.8% of test individuals were misclassified, most probably due to limited differentiation between the subspecies caused by close geographical proximity, or human interference of genetic integrity of reference subspecies, or a combination thereof. CONCLUSIONS: The diagnostic tool presented here will contribute to a sustainable conservation and support breeding activities in order to preserve the genetic heritage of European honey bees.