Role of fine needle aspiration cytology and cytohistopathological co-relation in thyroid lesions: experience at a tertiary care centre of North India

Background: Fine needle aspiration cytology is considered to be simple and cost effective technique for diagnosis of thyroid lesions. However the common limitations which may be encountered in FNAC may be associated with sampling error, dual pathology, cystic change or misinterpretation of morphology. The present study was therefore conducted to study the role of FNAC in diagnosis of thyroid lesions and to study the diagnostic pitfalls which may be encountered that limit the diagnosis of thyroid lesions.Methods: A retrospective study was conducted which included all the cases of thyroid lesions in which FNAC was done either directly or under image guidance over a period of five years. The cytomorphological diagnosis was correlated with histopathology to assess the diagnostic accuracy of FNAC in diagnosis of thyroid lesions.Results: Colloid goitre was the most common benign thyroid lesion while papillary carcinoma was the most common carcinoma constituting 50.2% and 5.2% of total cases. The maximum cyto-histopathological discordance was observed in cases of autoimmune thyroiditis (38%) and papillary carcinoma was most common lesion which was underdiagnosed on FNAC.Conclusions: The study concludes that although FNAC is safe, cost effective and sensitive technique for diagnosis of thyroid lesions but vigilant cyto-morphological interpretation in association with skilful aspiration and clinic-radiological co-relation is essential to avoid diagnostic pitfalls. This is even more important in cases showing focal neoplastic pathology or presence of dual pathology. Repeat image guided FNAC with clinical follow up is recommended in cases with strong clinical suspicion of malignancy

Hadoop and its role in Facebook: An Overview

One of the challenges in storing and processing the data and using the latest internet technologies has resulted in large volumes of data. The technique to manage this massive amount of data and to pull out the value, out of this volume is collectively called Big data. Over the recent years, there has been a rising interest in big data for social media analysis. Online social media have become the important platform across the world to share information. Facebook, one of the largest social media site receives posts in millions every day. One of the efficient technologies that deal with the Big Data is Hadoop. Hadoop, for processing large data volume jobs uses MapReduce programming model. This paper provides a survey on Hadoop and its role in facebook and a brief introduction to HIVE

Image Mosaicing for Wide Angle Panorama

Images are integral part in our daily lives. With a normal camera it is not possible to get a wide angle panorama with high resolution. Image Mosaicing is one of the novel techniques, for combining two or more images of the same scene taken in different views into one image. In the dark areas, the obtained image is a panoramic image with high resolution without mask. But in the case of lighting areas, the resultant image is generating mask. In order to gets wide angle panorama, in the existing system, extracting feature points, finding the best stitching line, Cluster Analysis (CA) and Dynamic Programming (DP) methods are used. Also used Weighted Average (WA) method for smooth stitching results and also eliminate intensity seam effectively. In the proposed system, to get feature extraction and feature matching SIFT (Scaled Invariant Feature Transform) algorithm used. In this process, outliers can be generated. RANSAC (Random Sample Consensus) is used for detecting the outliers from the resultant image. Masking is significantly reduced by using Algebraic Reconstruction Techniques (ART)

An evaluation of automation for flight path management in transport category aircraft

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1989.GRSN 406625Includes bibliographical references (leaves 114-115).by Divya Chandra.M.S

GANav: Group-wise Attention Network for Classifying Navigable Regions in Unstructured Outdoor Environments

We present a new learning-based method for identifying safe and navigable regions in off-road terrains and unstructured environments from RGB images. Our approach consists of classifying groups of terrain classes based on their navigability levels using coarse-grained semantic segmentation. We propose a bottleneck transformer-based deep neural network architecture that uses a novel group-wise attention mechanism to distinguish between navigability levels of different terrains.Our group-wise attention heads enable the network to explicitly focus on the different groups and improve the accuracy. In addition, we propose a dynamic weighted cross entropy loss function to handle the long-tailed nature of the dataset. We show through extensive evaluations on the RUGD and RELLIS-3D datasets that our learning algorithm improves the accuracy of visual perception in off-road terrains for navigation. We compare our approach with prior work on these datasets and achieve an improvement over the state-of-the-art mIoU by 6.74-39.1% on RUGD and 3.82-10.64% on RELLIS-3D

High Performance Liquid Chromatographic Analysis of Almotriptan Malate in Bulk and Tablets

Purpose: A simple RP-HPLC method has been developed and validated for the determination of almotriptan malate (ATM) in bulk and tablets. Methods: Chromatographic separation of ATM was achieved by using a Thermo Scientific C18 column. A Mobile phase containing a mixture of methanol, water and acetic acid (4:8:0.1 v/v) was pumped at the flow rate of 1 mL/min. Detection was performed at 227 nm. According to ICH guidelines, the method was validated. Results: The calibration curve was linear in the concentration range 5–60 μg/mL for the ATM with regression coefficient 0.9999. The method was precise with RSD <1.2%. Excellent recoveries of 99.60 - 100.80% proved the accuracy of the method. The limits of detection and quantification were found to be 0.025 and 0.075 μg/mL, respectively. Conclusion: The method was successfully applied for the quantification of ATM in tablets with acceptable accuracy and precision

Association of ABO blood group and Rh factor in cleft lip and palate patients

Background: One of the most common congenital malformations, with widespread racial and regional variation is an orofacial cleft. The occurrence is attributed to an array of environmental and genetic factors. Blood grouping and Rh factor are genetically determined. Any possible association of clefts with them helps in planning interventional services. Methods: A case control observational study was conducted on 111 samples who were cases presenting with oral clefts in Super speciality hospitals and other 111 samples who came to hospital for their treatment other than for cleft lip or cleft palate, were controls in the study. Cases were evaluated for various phenotypes of clefts. Blood samples of each case and control was collected to elaborate on blood group genotype and Rh typing. SPSS 22.0 version was employed for statistical analysis. Results: The most common blood group noted in cases as well in controls respectively was type ‘B’ in 31.5% and 43.2%, while blood group ‘AB’ was noted the lowest in both cases (14.5%) and controls (7.3%).Rh positive was noted 94.6% in both cases and control population. Clefts were observed more in male population than female counterparts.Cases of cleft lip and palate (CLP) was noted the highest, in 61 (55%) of cases, followed by defects in lip, palate and lastly in soft palate. Conclusions: Though not associated to the biological characteristics of cleft lip and palate in the current study, the functional importance of ABO blood group distribution may be the subject of future research. Identification of any associative traits for clefts assesses individuals with risk so as to help eliminate the chance of occurrence and early identification for better prognosis