Discovery and profiling of small RNAs responsive to stress conditions in the plant pathogen <i>Pectobacterium atrosepticum</i>

BACKGROUND: Small RNAs (sRNAs) have emerged as important regulatory molecules and have been studied in several bacteria. However, to date, there have been no whole-transcriptome studies on sRNAs in any of the Soft Rot Enterobacteriaceae (SRE) group of pathogens. Although the main ecological niches for these pathogens are plants, a significant part of their life cycle is undertaken outside their host within adverse soil environment. However, the mechanisms of SRE adaptation to this harsh nutrient-deficient environment are poorly understood. RESULTS: In the study reported herein, by using strand-specific RNA-seq analysis and in silico sRNA predictions, we describe the sRNA pool of Pectobacterium atrosepticum and reveal numerous sRNA candidates, including those that are induced during starvation-activated stress responses. Consequently, strand-specific RNA-seq enabled detection of 137 sRNAs and sRNA candidates under starvation conditions; 25 of these sRNAs were predicted for this bacterium in silico. Functional annotations were computationally assigned to 68 sRNAs. The expression of sRNAs in P. atrosepticum was compared under growth-promoting and starvation conditions: 68 sRNAs were differentially expressed with 47 sRNAs up-regulated under nutrient-deficient conditions. Conservation analysis using BLAST showed that most of the identified sRNAs are conserved within the SRE. Subsequently, we identified 9 novel sRNAs within the P. atrosepticum genome. CONCLUSIONS: Since many of the identified sRNAs are starvation-induced, the results of our study suggests that sRNAs play key roles in bacterial adaptive response. Finally, this work provides a basis for future experimental characterization and validation of sRNAs in plant pathogens. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-016-2376-0) contains supplementary material, which is available to authorized users

Computer algorithms show potential for improving dermatologists' accuracy to diagnose cutaneous melanoma: results of the International Skin Imaging Collaboration 2017

Background: Computer vision has promise in image-based cutaneous melanoma diagnosis but clinical utility is uncertain.Objective: To determine if computer algorithms from an international melanoma detection challenge can improve dermatologists' accuracy in diagnosing melanoma.Methods: In this cross-sectional study, we used 150 dermoscopy images (50 melanomas, 50 nevi, 50 seborrheic keratoses) from the test dataset of a melanoma detection challenge, along with algorithm results from 23 teams. Eight dermatologists and 9 dermatology residents classified dermoscopic lesion images in an online reader study and provided their confidence level.Results: The top-ranked computer algorithm had an area under the receiver operating characteristic curve of 0.87, which was higher than that of the dermatologists (0.74) and residents (0.66) (P < .001 for all comparisons). At the dermatologists' overall sensitivity in classification of 76.0%, the algorithm had a superior specificity (85.0% vs. 72.6%, P = .001). Imputation of computer algorithm classifications into dermatologist evaluations with low confidence ratings (26.6% of evaluations) increased dermatologist sensitivity from 76.0% to 80.8% and specificity from 72.6% to 72.8%.Limitations: Artificial study setting lacking the full spectrum of skin lesions as well as clinical metadata.Conclusion: Accumulating evidence suggests that deep neural networks can classify skin images of melanoma and its benign mimickers with high accuracy and potentially improve human performance