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Image segmentation of the corneal endothelium with deep convolutional neural networks (CNN) is challenging due to the scarcity of expert-annotated data. This work proposes a data augmentation technique via warping to enhance the performance of semi-supervised training of CNNs for accurate segmentation. We use a unique augmentation process for images and masks involving keypoint extraction, Delaunay triangulation, local affine transformations, and mask refinement. This approach accurately captures the natural variability of the corneal endothelium, enriching the dataset with realistic and diverse images. The proposed method achieved an increase in the mean intersection over union (mIoU) and Dice coefficient (DC) metrics of 17.2% and 4.8% respectively, for the segmentation task in corneal endothelial images on multiple CNN architectures. Our data augmentation strategy successfully models the natural variability in corneal endothelial images, thereby enhancing the performance and generalization capabilities of semi-supervised CNNs in medical image cell segmentation tasks.

Endophytic bacteria are wide-spread and associated with plant physiological benefits, yet their genomes and secondary metabolites remain largely unidentified. In this study, we explored the genome of the endophyte Streptomyces scabrisporus NF3 for discovery of potential novel molecules as well as genes and metabolites involved in host interactions. The complete genomes of seven Streptomyces and three other more distantly related bacteria were used to define the functional landscape of this unique microbe. The S. scabrisporus NF3 genome is larger than the average Streptomyces genome and not structured for an obligate endosymbiotic lifestyle; this and the fact that can grow in R2YE media implies that it could include a soil-living stage. The genome displays an enrichment of genes associated with amino acid production, protein secretion, secondary metabolite and antioxidants production and xenobiotic degradation, indicating that S. scabrisporus NF3 could contribute to the metabolic enrichment of soil microbial communities and of its hosts. Importantly, besides its metabolic advantages, the genome showed evidence for differential functional specificity and diversification of plant interaction molecules, including genes for the production of plant hormones, stress resistance molecules, chitinases, antibiotics and siderophores. Given the diversity of S. scabrisporus mechanisms for host upkeep, we propose that these strategies were necessary for its adaptation to plant hosts and to face changes in environmental conditions.

Statistical studies of languages have focused on the rank-frequency distribution of words. Instead, we introduce here a measure of how word ranks change in time and call this distribution rank diversity. We calculate this diversity for books published in six European languages since 1800, and find that it follows a universal lognormal distribution. Based on the mean and standard deviation associated with the lognormal distribution, we define three different word regimes of languages: "heads" consist of words which almost do not change their rank in time, "bodies" are words of general use, while "tails" are comprised by context-specific words and vary their rank considerably in time. The heads and bodies reflect the size of language cores identified by linguists for basic communication. We propose a Gaussian random walk model which reproduces the rank variation of words in time and thus the diversity. Rank diversity of words can be understood as the result of random variations in rank, where the size of the variation depends on the rank itself. We find that the core size is similar for all languages studied.

Escherichia coli serogroups O5, O15, O26, O45, O55, O76, O91, O103, O104, O111, O113, O118, O121, O123, O128, O145, O146, O157, O165, O172, and O177 are the O-antigen forms of the most clinically relevant Shiga toxin-producing E. coli (STEC) serotypes. In this study, three multiplex PCR assays able to specifically detect these 21 serogroups were developed and validated. For this purpose, the O-antigen gene clusters of E. coli O5 and O76 were fully sequenced, their associated genes were identified on the basis of homology, and serogroup-specific primers were designed. After preliminary evaluation, these two primer pairs were proven to be highly specific and suitable for the development of PCR assays for O5 and O76 serogroup identification. Specific primers were also designed for serogroups O15, O45, O55, O91, O104, O113, O118, O123, O128, O146, O157, O165, O172, and O177 based on previously published sequences, and previously published specific primers for serogroups O26, O103, O111, O121, and O145 were also included. These 21 primer pairs were shown to be specific for their target serogroup when tested against E. coli type strains representing 169 known O-antigen forms of E. coli and Shigella and therefore suitable for being used in PCR assays for serogroup identification. In order to validate the three multiplex PCR assays, 22 E. coli strains belonging to the 21 covered serogroups and 18 E. coli strains belonging to other serogroups were screened in a double-blind test and their sensitivity was determined as 1 ng chromosomal DNA. The PCR assays developed in this study could be a faster, simpler, and less expensive strategy for serotyping of the most clinically relevant STEC strains in both clinical microbiology and public health laboratories, and so their development could benefit for clinical diagnosis, epidemiological investigations, surveillance, and control of STEC infections.