Variability Assessment of Aromatic Rice Germplasm by Pheno-Genomic traits and Population Structure Analysis

While the pleasant scent of aromatic rice is making it more popular, with demand for aromatic rice expected to rise in future, varieties of this have low yield potential. Genetic diversity and population structure of aromatic germplasm provide valuable information for yield improvement which has potential market value and farm profit. Here, we show diversity and population structure of 113 rice germplasm based on phenotypic and genotypic traits. Phenotypic traits showed that considerable variation existed across the germplasm. Based on Shannon-Weaver index, the most variable phenotypic trait was lemma-palea color. Detecting 140 alleles, 11 were unique and suitable as a germplasm diagnostic tool. Phylogenetic cluster analysis using genotypic traits classified germplasm into three major groups. Moreover, model-based population structure analysis divided all germplasm into three groups, confirmed by principal component and neighbors joining tree analyses. An analysis of molecular variance (AMOVA) and pairwise FST test showed significant differentiation among all population pairs, ranging from 0.023 to 0.068, suggesting that all three groups differed. Significant correlation coefficient was detected between phenotypic and genotypic traits which could be valuable to select further improvement of germplasm. Findings from this study have the potential for future use in aromatic rice molecular breeding programs.

Laparoscopic assisted appendicectomy in District Hospital, Joypurhat, Bangladesh

"Laparoscopic assisted appendicectomy" refers to visualization of abdominal cavity, identification of appendix, drawing the appendix out through the port wound and appendicectomy. The objective of this study is to evaluate the outcome of the procedure of laparoscopic assisted appendicectomy. In this prospective study patients with appendicitis were randomly selected for laparoscopic assisted appendicectomy from August 2007 to February 2009 in the Department of Surgery, Modernized District Hospital, Joypurhat, Bangladesh. Out of 73 patients Laparoscopic assisted appendicectomy was performed successfully in 95.89% cases and conversion rate was 4.11%. Male to female ratio was almost 1:2 with mean±SD age 18.62±9.16 years. The wound infection rate was 8.2% and urinary retention 2.7%. Early postoperative feeding was started within 24 hours in 86.3% cases and mean duration of hospital stay was 2 days in 76.71% patients. More than 82% returned to their home and started social activities within 5 days. Duration of surgery was almost similar in emergency and interval appendicectomy group (19.35±10.13 vs. 23.66±9.43) minutes. Postoperative morbidity in emergency appendicectomy group showed significantly higher morbidity than interval appendicectomy group (p=0.003). This study indicates that the laparoscopic assisted appendicectomy is feasible for the majority of the patients with appendicitis in both emergency and interval settings. It reduces the operative time, shortens hospital stay and helps in early resumption of normal activities with good cosmetic outcome and patients' satisfaction.

Automatic detection of informative frames from wireless capsule endoscopy images

Wireless capsule endoscopy (WCE) is a new clinical technology permitting visualization of the small bowel, the most difficult segment of the digestive tract. The major drawback of this technology is the excessive amount of time required for video diagnosis. We therefore propose a method for generating smaller videos by detecting informative frames from original WCE videos. This method isolates useless frames that are highly contaminated by turbid fluids, faecal materials and/or residual foods. These materials and fluids are presented in a wide range of colors, from brown to yellow, and/or have bubble-like texture patterns. The detection scheme therefore consists of two steps: isolating (Step-1) highly contaminated non-bubbled (HCN) frames and (Step-2) significantly bubbled (SB) frames. Two color representations, viz., local color moments in Ohta space and the HSV color histogram, are attempted to characterize HCN frames, which are isolated by a support vector machine (SVM) classifier in Step-1. The rest of the frames go to Step-2, where a Gauss Laguerre transform (GLT) based multiresolution texture feature is used to characterize the bubble structures in WCE frames. GLT uses Laguerre Gauss circular harmonic functions (LG-CHFs) to decompose WCE images into multiresolution components. An automatic method of segmentation was designed to extract bubbled regions from grayscale versions of the color images based on the local absolute energies of their CHF responses. The final informative frames were detected by using a threshold on the segmented regions. An automatic procedure for selecting features based on analyzing the consistency of the energy-contrast map is also proposed. Three experiments, two of which use 14,841 and 37,100 frames from three videos and the rest uses 66,582 frames from six videos, were conducted for justifying the proposed method. The two combinations of the proposed color and texture features showed excellent average detection accuracies (86.42% and 84.45%) with the final experiment, when compared with the same color features followed by conventional Gabor-based (78.18% and 76.29%) and discrete wavelet-based (65.43% and 63.83%) texture features. Although intra-video training-testing cases are typical choices for supervised classification in Step-1, combining a suitable number of training sets using a subset of the input videos was shown to be possible. This mixing not only reduced computation costs but also produced better detection accuracies by minimizing visual-selection errors, especially when processing large numbers of WCE videos.