A unified learning framework for content based medical image retrieval using a statistical model

AbstractThis paper presents a unified learning framework for heterogeneous medical image retrieval based on a Full Range Autoregressive Model (FRAR) with the Bayesian approach (BA). Using the unified framework, the color autocorrelogram, edge orientation autocorrelogram (EOAC) and micro-texture information of medical images are extracted. The EOAC is constructed in HSV color space, to circumvent the loss of edges due to spectral and chromatic variations. The proposed system employed adaptive binary tree based support vector machine (ABTSVM) for efficient and fast classification of medical images in feature vector space. The Manhattan distance measure of order one is used in the proposed system to perform a similarity measure in the classified and indexed feature vector space. The precision and recall (PR) method is used as a measure of performance in the proposed system. Short-term based relevance feedback (RF) mechanism is also adopted to reduce the semantic gap. The Experimental results reveal that the retrieval performance of the proposed system for heterogeneous medical image database is better than the existing systems at low computational and storage cost

Clinical profile of non-alcoholic fatty liver disease and noninvasive analysis of NAFLD fibrosis score among type 2 diabetic patients in a tertiary care hospital.

INTRODUCTION : NAFLD is considered as commonest liver problem of the western world where about 15-40% general population are affected. NAFLD stands as second and fourth cause for liver transplantation in large transplantation centres and in the United States, respectively. Approximately 20-30%and3-10%of Western adults and children are suffering from NAFLD and this value reaches up to 70-80% in the obese population. NAFLD has attained epidemic proportions even in countries at low risk, such as China (15%)and Japan (14%). This alarming increase in NAFLD is because NAFLD progresses from liver failure to cirrhosis to HCC. Many factors contribute to develop NAFLD including diabetes mellitus (T2DM) which can increase its risk and severity. Peripheral insulin resistance is a central mechanism for the pathogenesis of both entities. 10-75% of NAFLD patients have T2DM and 21-72% of diabetic patients are found to have NAFLD. The mortality rate in diabetic patients due to cirrhosis is above 2 times the general population and patients with NAFLD and DM have poorer prognosis in terms of higher rates of cirrhosis and mortality. NAFLD and T2DM are conditions highly dependent on genetic background and dietary factors. NAFLD is a spectrum with, simple steatosis (which remains stable over a period of years without progression in most patients) to steatohepatitis and advanced fibbrosis ( more risk for developing decompensated liver disease with portal hypertension to HCC, or death unless transplantation is done). Hence they need close follow-up and surveillance for esophageal varices and HCC and if required treatment. AIMS AND OBJECTIVES : 1. To study the prevalence of Non-alcoholic fatty liver disease based on ultrasound and study its clinical profile in type 2 diabetic patients attending outpatient clinic and inpatients in the Stanley medical college Hospital. 2. To apply the simple non invasive scoring system (NAFLD FIBROSIS SCORE) which helps in separating NAFLD patients with and without advanced liver fibrosis by using clinical and biochemical variables. 3. To correlate the NAFLD Fibrosis score (Indeterminate and high risk) in patients with high grade fatty liver (ultrasound) with the liver stiffness measured by transient elastography (FIBROSCAN) . CONCLUSIONS : The prevalence of non alcoholic liver disease among the diabetic population in this study was 63.8%higher compared to other series. Majority are females 83.1% in contrast to other series and the common age group was 56-65 years. The mean BMI was 28.04+4.12 kg/m2 and metabolic syndrome was present in 73%. Among the laboratory parameters used in the NAFLD fibrosis score raised AST more than ALT(Ratio >1),low serum albumin, low platelet count, high BMI were statistically significant. The non invasive NAFLD fibrosis score correlates significantly with the different grades of fatty liver detected by ultrasound and also with the liver stiffness measurement by transient elastography (Fibroscan). By comparing the intermediate and high NAFLD fibrosis score with fibroscan liver stiffness, 61% had either low or significant fibrosis and hence an invasive liver biopsy could be avoided in these set of patients to grade the degree of fibrosis. The combination of transient elastography (fibroscan) and NAFLD fibrosis scoring system may provide better performance than each of them used alone, in the non invasive analysis to select patients for whom to do a liver biopsy although this needs to be verified in future studies

Enhancing Rice Plant Disease Recognition and Classification Using Modified Sand Cat Swarm Optimization with Deep Learning

Rice plant diseases play a critical challenge to agricultural productivity and food safety. Timely and accurate recognition and classification of these ailments are vital for efficient management of the disease. Classifying and recognizing rice plant disease by implementing Deep Learning (DL) has emerged as a powerful approach to tackle the challenges associated with automated disease diagnosis in rice crops. DL, a subfield of artificial intelligence, concentrates to train neural networks with several layers for automated learning of the complex patterns and illustrations from data. In the context of rice plant diseases, DL methods can effectually extract meaningful features from images and accurately classify them into different disease categories.  Therefore, this study introduces a new Modified Sand Cat Swarm Optimization with Deep Learning based Rice Plant Disease Detection and Classification (MSCSO-DLRPDC) technique. The main objective of the MSCSO-DLRPDC technique focalize on the automated classification and recognition of rice plant ailments. To achieve this, the MSCSO-DLRPDC methodology involves two levels of pre-processing such as median filter-based noise removal and CLAHE-based contrast enhancement. Besides, Multi-Layer ShuffleNet with Depthwise Separable Convolution (MLS-DSC) methodology is utilized for feature extraction purposes. Moreover, the Multi-Head Attention-based Long Short-Term Memory (MHA-LSTM) methodology is utilized for the process of rice plant disease detection. At last, the MSCSO method is utilized for the tuning process of the MHA-LSTM approach. The MSCSO approach inspired by the collective behaviour of sand cats and the mutation operator, is implemented for optimizing the parameters of the MHA-LSTM network. To demonstrate the enhanced accomplishment of the MSCSO-DLRPDC method, a broad set of simulations were carried out. The extensive outputs show the greater accomplishment of the MSCSO-DLRPDC method over other methods. The proposed approach has the capability in assisting farmers and agricultural stakeholders in effectively managing rice plant diseases, contributing to improved crop yield and sustainable agricultural practices

An efficient method to classify GI tract images from WCE using visual words

The digital images made with the Wireless Capsule Endoscopy (WCE) from the patient's gastrointestinal tract are used to forecast abnormalities. The big amount of information from WCE pictures could take 2 hours to review GI tract illnesses per patient to research the digestive system and evaluate them. It is highly time consuming and increases healthcare costs considerably. In order to overcome this problem, the CS-LBP (Center Symmetric Local Binary Pattern) and the ACC (Auto Color Correlogram) were proposed to use a novel method based on a visual bag of features (VBOF). In order to solve this issue, we suggested a Visual Bag of Features(VBOF) method by incorporating Scale Invariant Feature Transform (SIFT), Center-Symmetric Local Binary Pattern (CS-LBP) and Auto Color Correlogram (ACC). This combination of features is able to detect the interest point, texture and color information in an image. Features for each image are calculated to create a descriptor with a large dimension. The proposed feature descriptors are clustered by K- means referred to as visual words, and the Support Vector Machine (SVM) method is used to automatically classify multiple disease abnormalities from the GI tract. Finally, post-processing scheme is applied to deal with final classification results i.e. validated the performance of multi-abnormal disease frame detection

The Banana stem weevil Odoiporus longicollis

The banana stem weevil Odoiporus longicollis; Le charançon du pseudotronc du bananier Odoiporus longicollis; El barrenador del tallo del banano Odoiporus longicolli

Effect of plant growth regulators on yield parameters, yield and quality of black pepper (Piper nigram L.) variety Panniyur-1

Experiments conducted at Horticultural College and Research Institute, Periyakulam and Horticultural Research Station, Thadiankudisai (TNAU) to study the effect of different plant growth regulators (NAA, GA3, BA and 2,4-D) indicated that spraying NAA (50 ppm) has improved many commercially desirable parameters like number of berries per spike, volume and weight of berries and yield in black pepper (Piper nigrum L.). Benefit cost ratio, however, was the highest in the treatment 2,4-D (10 ppm). The high cost of chemicals outweighed the yield except in the case of 2,4-D. &nbsp

On Binary Matroid Minors and Applications to Data Storage over Small Fields

Locally repairable codes for distributed storage systems have gained a lot of interest recently, and various constructions can be found in the literature. However, most of the constructions result in either large field sizes and hence too high computational complexity for practical implementation, or in low rates translating into waste of the available storage space. In this paper we address this issue by developing theory towards code existence and design over a given field. This is done via exploiting recently established connections between linear locally repairable codes and matroids, and using matroid-theoretic characterisations of linearity over small fields. In particular, nonexistence can be shown by finding certain forbidden uniform minors within the lattice of cyclic flats. It is shown that the lattice of cyclic flats of binary matroids have additional structure that significantly restricts the possible locality properties of $\mathbb{F}_{2}$-linear storage codes. Moreover, a collection of criteria for detecting uniform minors from the lattice of cyclic flats of a given matroid is given, which is interesting in its own right.Comment: 14 pages, 2 figure

Integrated genomic analyses of ovarian carcinoma

A catalogue of molecular aberrations that cause ovarian cancer is critical for developing and deploying therapies that will improve patients’ lives. The Cancer Genome Atlas project has analysed messenger RNA expression, microRNA expression, promoter methylation and DNA copy number in 489 high-grade serous ovarian adenocarcinomas and the DNA sequences of exons from coding genes in 316 of these tumours. Here we report that high-grade serous ovarian cancer is characterized by TP53 mutations in almost all tumours (96%); low prevalence but statistically recurrent somatic mutations in nine further genes including NF1, BRCA1, BRCA2, RB1 and CDK12; 113 significant focal DNA copy number aberrations; and promoter methylation events involving 168 genes. Analyses delineated four ovarian cancer transcriptional subtypes, three microRNA subtypes, four promoter methylation subtypes and a transcriptional signature associated with survival duration, and shed new light on the impact that tumours with BRCA1/2 (BRCA1 or BRCA2) and CCNE1 aberrations have on survival. Pathway analyses suggested that homologous recombination is defective in about half of the tumours analysed, and that NOTCH and FOXM1 signalling are involved in serous ovarian cancer pathophysiology.National Institutes of Health (U.S.) (Grant U54HG003067)National Institutes of Health (U.S.) (Grant U54HG003273)National Institutes of Health (U.S.) (Grant U54HG003079)National Institutes of Health (U.S.) (Grant U24CA126543)National Institutes of Health (U.S.) (Grant U24CA126544)National Institutes of Health (U.S.) (Grant U24CA126546)National Institutes of Health (U.S.) (Grant U24CA126551)National Institutes of Health (U.S.) (Grant U24CA126554)National Institutes of Health (U.S.) (Grant U24CA126561)National Institutes of Health (U.S.) (Grant U24CA126563)National Institutes of Health (U.S.) (Grant U24CA143882)National Institutes of Health (U.S.) (Grant U24CA143731)National Institutes of Health (U.S.) (Grant U24CA143835)National Institutes of Health (U.S.) (Grant U24CA143845)National Institutes of Health (U.S.) (Grant U24CA143858)National Institutes of Health (U.S.) (Grant U24CA144025)National Institutes of Health (U.S.) (Grant U24CA143866)National Institutes of Health (U.S.) (Grant U24CA143867)National Institutes of Health (U.S.) (Grant U24CA143848)National Institutes of Health (U.S.) (Grant U24CA143843)National Institutes of Health (U.S.) (Grant R21CA135877

Metatranscriptomics reveals metabolic adaptation and induction of virulence factors by Haemophilus parasuis during lung infection

International audienceAbstractHaemophilus parasuis is a common inhabitant of the upper respiratory tract of pigs, and the causative agent of Glässer’s disease. This disease is characterized by polyserositis and arthritis, produced by the severe inflammation caused by the systemic spread of the bacterium. After an initial colonization of the upper respiratory tract, H. parasuis enters the lung during the early stages of pig infection. In order to study gene expression at this location, we sequenced the ex vivo and in vivo H. parasuis Nagasaki transcriptome in the lung using a metatranscriptomic approach. Comparison of gene expression under these conditions with that found in conventional plate culture showed generally reduced expression of genes associated with anabolic and catabolic pathways, coupled with up-regulation of membrane-related genes involved in carbon acquisition, iron binding and pathogenesis. Some of the up-regulated membrane genes, including ABC transporters, virulence-associated autotransporters (vtaAs) and several hypothetical proteins, were only present in virulent H. parasuis strains, highlighting their significance as markers of disease potential. Finally, the analysis also revealed the presence of numerous antisense transcripts with possible roles in gene regulation. In summary, this data sheds some light on the scarcely studied in vivo transcriptome of H. parasuis, revealing nutritional virulence as an adaptive strategy for host survival, besides induction of classical virulence factors

LaySeq: A New Representation for Non-Slicing Floorplans

In this paper, we propose LaySeq a new representation for non-slicing floorplans and show its superior properties. Layseq uses only n[lg n] bits for a floorplan of n rectangular blocks. The solution space size of layseq is just O(n!). This is very smaller than that of all recent representations. Given a layseq it takes only linear time to construct the floorplan. Layseq is very simple and easy to implement representation. Based on this new structure a hybrid genetic algorithm for floorplanning is given. We show that layseq is efficient in handling rectilinear blocks too. Experimental results show that layseq results in smaller silicon area than earlier approaches