Image based Search Engine for Online Shopping

This paper presents a method based on principle of content based image retrieval for online shopping based on color, HSV aiming at efficient retrieval of images from the large database for online shopping specially for fashion shopping. Here, HSV modeling is used for creating our application with a huge image database, which compares image source with the destination components. In this paper, a technique is used for finding items by image search, which is convenient for buyers in order to allow them to see the products. The reason for using image search for items instead of text searches is that item searching by keywords or text has some issues such as errors in search items, expansion in search and inaccuracy in search results. This paper is an attempt to help users to choose the best options among many products and decide exactly what they want with the fast and easy search by image retrieval. This technology is providing a new search mode, searching by image, which will help buyers for finding the same or similar image retrieval in the database store. The image searching results have been made customers buy products quickly. This feature is implemented to identify and extract features of prominent object present in an image. Using different statistical measures, similarity measures are calculated and evaluated. Image retrieval based on color is a trivial task. Identifying objects of prominence in an image and retrieving image with similar features is a complex task. Finding prominent object in an image is difficult in a background image and is the challenging task in retrieving images. We calculated and change the region of interest in order to increase speed of operation as well as accuracy by masking the background content. The Implementation results proved that proposed method is effective in recalling the images of same pattern or texture

D-shaped Left Ventricle, Anatomic, and Physiologic Implications

Right ventricular loading/pressure influences left ventricular function because the two ventricles pump in series and because they are anatomically arranged in parallel, sharing the common ventricular septum. Flattening of the interventricular septum detected during echocardiographic examination is called D-shaped left ventricle. We present a case of an elderly male of African descent, who presented with increased shortness of breath. Transthoracic echocardiogram showed flattening and left sided deviation of interventricular septum causing a decreased size in left ventricle, secondary to volume/pressure overload in the right ventricle. While patient received hemodialysis therapy and intravascular volume was removed, patient blood pressure was noted to increase, paradox. Repeated transthoracic echocardiogram demonstrated less left deviation of interventricular septum compared with previous echocardiogram. We consider that it is important for all physicians to be aware of the anatomic and physiologic implication of D-shaped left ventricle and how right ventricle pressure/volume overload affects its function and anatomy

Dark blood ischemic LGE segmentation using a deep learning approach

The extent of ischemic scar detected by Cardiac Magnetic Resonance (CMR) with late gadolinium enhancement (LGE) is linked with long-term prognosis, but scar quantification is time-consuming. Deep Learning (DL) approaches appear promising in CMR segmentation. Purpose: To train and apply a deep learning approach to dark blood (DB) CMR-LGE for ischemic scar segmentation, comparing results to 4-Standard Deviation (4-SD) semi-automated method. Methods: We trained and validated a dual neural network infrastructure on a dataset of DB-LGE short-axis stacks, acquired at 1.5T from 33 patients with ischemic scar. The DL architectures were an evolution of the U-Net Convolutional Neural Network (CNN), using data augmentation to increase generalization. The CNNs worked together to identify and segment 1) the myocardium and 2) areas of LGE. The first CNN simultaneously cropped the region of interest (RoI) according to the bounding box of the heart and calculated the area of myocardium. The cropped RoI was then processed by the second CNN, which identified the overall LGE area. The extent of scar was calculated as the ratio of the two areas. For comparison, endo- and epi-cardial borders were manually contoured and scars segmented by a 4-SD technique with a validated software. Results: The two U-Net networks were implemented with two free and open-source software library for machine learning. We performed 5-fold cross-validation over a dataset of 108 and 385 labelled CMR images of the myocardium and scar, respectively. We obtained high performance (> ∼0.85) as measured by the Intersection over Union metric (IoU) on the training sets, in the case of scar segmentation. With regards to heart recognition, the performance was lower (> ∼0.7), although improved (∼ 0.75) by detecting the cardiac area instead of heart boundaries. On the validation set, performances oscillated between 0.8 and 0.85 for scar tissue recognition, and dropped to ∼0.7 for myocardium segmentation. We believe that underrepresented samples and noise might be affecting the overall performances, so that additional data might be beneficial. Figure1: examples of heart segmentation (upper left panel: training; upper right panel: validation) and of scar segmentation (lower left panel: training; lower right panel: validation). Conclusion: Our CNNs show promising results in automatically segmenting LV and quantify ischemic scars on DB-LGE-CMR images. The performances of our method can further improve by expanding the data set used for the training. If implemented in a clinical routine, this process can speed up the CMR analysis process and aid in the clinical decision-making

Variabilidade e tendencias interanuais no fitoplancto mariño das costas de Galicia

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Serum Uric Acid as a Predictor for Development of Diabetic Nephropathy in Type 1 Diabetes: An Inception Cohort Study

OBJECTIVE—Experimental and clinical studies have suggested that uric acid may contribute to the development of hypertension and kidney disease. Whether uric acid has a causal role in the development of diabetic nephropathy is not known. The objec-tive of the present study is to evaluate uric acid as a predictor of persistent micro- and macroalbuminuria. RESEARCH DESIGN AND METHODS—This prospective ob-servational follow-up study consisted of an inception cohort of 277 patients followed from onset of type 1 diabetes. Of these, 270 patients had blood samples taken at baseline. In seven cases, uric acid could not be determined; therefore, 263 patients (156 men) were available for analysis. Uric acid was measured 3 years after onset of diabetes and before any patient developed microalbuminuria. RESULTS—During a median follow-up of 18.1 years (rang

Capítulo 7. Biomasa y producción primaria.

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Lack of increases in methylation at three CpG-rich genomic loci in non-mitotic adult tissues during aging

<p>Abstract</p> <p>Background</p> <p>Cell division occurs during normal human development and aging. Despite the likely importance of cell division to human pathology, it has been difficult to infer somatic cell mitotic ages (total numbers of divisions since the zygote) because direct counting of lifetime numbers of divisions is currently impractical. Here we attempt to infer relative mitotic ages with a molecular clock hypothesis. Somatic genomes may record their mitotic ages because greater numbers of replication errors should accumulate after greater numbers of divisions. Mitotic ages will vary between cell types if they divide at different times and rates.</p> <p>Methods</p> <p>Age-related increases in DNA methylation at specific CpG sites (termed "epigenetic molecular clocks") have been previously observed in mitotic human epithelium like the intestines and endometrium. These CpG rich sequences or "tags" start unmethylated and potentially changes in methylation during development and aging represent replication errors. To help distinguish between mitotic versus time-associated changes, DNA methylation tag patterns at 8–20 CpGs within three different genes, two on autosomes and one on the X-chromosome were measured by bisulfite sequencing from heart, brain, kidney and liver of autopsies from 21 individuals of different ages.</p> <p>Results</p> <p>Levels of DNA methylation were significantly greater in adult compared to fetal or newborn tissues for two of the three examined tags. Consistent with the relative absence of cell division in these adult tissues, there were no significant increases in tag methylation after infancy.</p> <p>Conclusion</p> <p>Many somatic methylation changes at certain CpG rich regions or tags appear to represent replication errors because this methylation increases with chronological age in mitotic epithelium but not in non-mitotic organs. Tag methylation accumulates differently in different tissues, consistent with their expected genealogies and mitotic ages. Although further studies are necessary, these results suggest numbers of divisions and ancestry are at least partially recorded by epigenetic replication errors within somatic cell genomes.</p