BackgroundTransposable elements are major evolutionary forces which can cause new genome structure and species diversification. The role of transposable elements in the expansion of nucleotide-binding and leucine-rich-repeat proteins (NLRs), the major disease-resistance gene families, has been unexplored in plants.ResultsWe report two high-quality de novo genomes (Capsicum baccatum and C. chinense) and an improved reference genome (C. annuum) for peppers. Dynamic genome rearrangements involving translocations among chromosomes 3, 5, and 9 were detected in comparison between C. baccatum and the two other peppers. The amplification of athila LTR-retrotransposons, members of the gypsy superfamily, led to genome expansion in C. baccatum. In-depth genome-wide comparison of genes and repeats unveiled that the copy numbers of NLRs were greatly increased by LTR-retrotransposon-mediated retroduplication. Moreover, retroduplicated NLRs are abundant across the angiosperms and, in most cases, are lineage-specific.ConclusionsOur study reveals that retroduplication has played key roles for the massive emergence of NLR genes including functional disease-resistance genes in pepper plants.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-017-1341-9) contains supplementary material, which is available to authorized users.

Background Electronic personal health records (PHRs) are increasingly recognized and used as a tool to address various challenges stemming from the scattered and incompatible personal health information that exists in the contemporary US health care system. Although activity around PHR development and deployment has increased in recent years, little has been reported regarding the use and utility of PHRs among low-income and/or elderly populations.Objective The aim was to assess the use and utility of PHRs in a low-income, elderly population.Methods We deployed a Web-based, institution-neutral PHR system, the Personal Health Information Management System (PHIMS), in a federally funded housing facility for low-income and elderly residents. We assessed use and user satisfaction through system logs, questionnaire surveys, and user group meetings.Results Over the 33-month study period, 70 residents participated; this number was reduced to 44 by the end of the study. Although the PHIMS was available for free and personal assistance and computers with Internet connection were provided without any cost to residents, only 13% (44/330) of the eligible residents used the system, and system usage was limited. Almost one half of the users (47%, 33/70) used the PHIMS only on a single day. Use was also highly correlated with the availability of in-person assistance; 77% of user activities occurred while the assistance was available. Residents’ ability to use the PHR system was limited by poor computer and Internet skills, technophobia, low health literacy, and limited physical/cognitive abilities. Among the 44 PHIMS users, 14 (32%) responded to the questionnaire. In this selected subgroup of survey participants, the majority (82%, 9/11) used the PHIMS three times or more and reported that it improved the quality of overall health care they received.Conclusions Our findings suggest that those who can benefit the most from a PHR system may be the least able to use it. Disparities in access to and use of computers, the Internet, and PHRs may exacerbate health care inequality in the future.

Tracing of left-ventricular epicardial and endocardial borders on echocardiographic sequences is essential for quantification of cardiac function. The authors designed a method based on an extension of active contour models to detect both epicardial and endocardial borders on short-axis cardiac sequences spanning the entire cardiac cycle. They validated the results by comparing the computer-generated boundaries to the boundaries manually outlined by four expert observers on 44 clinical data sets. The mean boundary distance between the computer-generated boundaries and the manually outlined boundaries was 2.80 mm (sigma=1.28 mm) for the epicardium and 3.61 (sigma=1.68 mm) for the endocardium. These distances were comparable to interobserver distances, which had a mean of 3.79 mm (sigma=1.53 mm) for epicardial borders and 2.67 mm (sigma=0.88 mm) for endocardial borders. The correlation coefficient between the areas enclosed by the computer-generated boundaries and the average manually outlined boundaries was 0.95 for epicardium and 0.91 for endocardium. The algorithm is fairly insensitive to the choice of the initial curve. Thus, the authors have developed an effective and robust algorithm to extract left-ventricular boundaries from echocardiographic sequences.

It is possible to distinguish between papillary carcinomas and other lesions with the thyroid stiffness index calculated from US elastography using carotid arterial pulsation.

Automatic prostate segmentation in ultrasound images is a challenging task due to speckle noise, missing boundary segments, and complex prostate anatomy. One popular approach has been the use of deformable models. For such techniques, prior knowledge of the prostate shape plays an important role in automating model initialization and constraining model evolution. In this paper, we have modeled the prostate shape using deformable superellipses. This model was fitted to 594 manual prostate contours outlined by five experts. We found that the superellipse with simple parametric deformations can efficiently model the prostate shape with the Hausdorff distance error (model versus manual outline) of 1.32 +/- 0.62 mm and mean absolute distance error of 0.54 +/- 0.20 mm. The variability between the manual outlinings and their corresponding fitted deformable superellipses was significantly less than the variability between human experts with p-value being less than 0.0001. Based on this deformable superellipse model, we have developed an efficient and robust Bayesian segmentation algorithm. This algorithm was applied to 125 prostate ultrasound images collected from 16 patients. The mean error between the computer-generated boundaries and the manual outlinings was 1.36 +/- 0.58 mm, which is significantly less than the manual interobserver distances. The algorithm was also shown to be fairly insensitive to the choice of the initial curve.