OVERLAPPING OPTIMIZATION WITH PARSING THROUGH METAGRAMMARS

ABSTRACT This paper describes techniques for improving the performance of meta framework developed by combining C++ and Java language segments through reducing the number of bytecodes generated. Augmented versions of existing languages can be developed by combining good properties of those languages. It increases the flexibility of programmers in using language constructs of those languages. The framework identifies and parses source code with C++ and Java language statements using metagrammar developed and create a unified AST for the hybrid source code. Bytecodes are generated for AST and interpreted. The performance of Bytecodes can be improved through optimization techniques associated with metagrammars, like constant propagation which identifies constant values for variables and propagate it to the place where the variable occurs and replace it with corresponding value. Function inlining and exception optimization greatly improves the execution time performance of Bytecodes. Optimization through metagrammars eliminates rigorous analysis of bytecodes to identify hot spots and optimize them

Feature Extraction Based on ORB- AKAZE for Echocardiogram View Classification

In computer vision, the extraction of robust features from images to construct models that automate image recognition and classification tasks is a prominent field of research. Handcrafted feature extraction and representation techniques become critical when dealing with limited hardware resource settings, low-quality images, and larger datasets. We propose two state-of-the-art handcrafted feature extraction techniques, Oriented FAST and Rotated BRIEF (ORB) and Accelerated KAZE (AKAZE), in combination with Bag of Visual Word (BOVW), to classify standard echocardiogram views using Machine learning (ML) algorithms. These novel approaches, ORB and AKAZE, which are rotation, scale, illumination, and noise invariant methods, outperform traditional methods. The despeckling algorithm Speckle Reduction Anisotropic Diffusion (SRAD), which is based on the Partial Differential Equation (PDE), was applied to echocardiogram images before feature extraction. Support Vector Machine (SVM), decision tree, and random forest algorithms correctly classified the feature vectors obtained from the ORB with accuracy rates of 96.5%, 76%, and 97.7%, respectively. Additionally, AKAZE\u27s SVM, decision tree, and random forest algorithms outperformed state-of-the-art techniques with accuracy rates of 97.7%, 90%, and 99%, respectively

A Enhanced Approach for Identification of Tuberculosis for Chest X-Ray Image using Machine Learning

Lungs are the primary organs affected by the infectious illness tuberculosis (TB). Mycobacterium tuberculosis, often known as Mtb, is the bacterium that causes tuberculosis. When a person speaks, spits, coughs, or breathes in, active tuberculosis can quickly spread through the air. Early TB diagnosis takes some time. Early detection of the bacilli allows for straightforward therapy. Chest X-ray images, sputum images, computer-assisted identification, feature selection, neural networks, and active contour technologies are used to diagnose human tuberculosis. Even when several approaches are used in conjunction, a more accurate early TB diagnosis can still be made. Worldwide, this leads to a large number of fatalities. An efficient technology known as the Deep Learning approach is used to diagnose tuberculosis microorganisms. Because this technology outperforms the present methods for early TB diagnosis, Despite the fact that death cannot be prevented, it is possible to lessen its effects

Study to assess the changing pattern of clinical profile and determine the prognosis in hepatic encephalopathy

Background: Hepatic encephalopathy (HE) is a common complication of liver disease that requires intensive care management. The prevalence of HE is increasing during recent period. The most important factors of HE are alcohol consumption, chronic hepatitis, hepatotoxic drugs and unhealthy changes in life style. There were only relatively few studies from our region on the changing profile of hepatic encephalopathy under the background of life style changes. This study was conducted with the aim to detect the changing pattern of clinical profile, precipitants and to assess the prognosis of patients with hepatic encephalopathy.Methods: This was a prospective study for a period of 18 months since January 2012 at Academy of Medical Sciences, Pariyaram, Kannur, a tertiary care centre situated in the northern part of Kerala. Patients admitted in the medical and gastroenterology wards and intensive care units that fulfilled the inclusion criteria were enrolled in this study.Results: Among the 76 patients with HE, 60 were suffering from CLD and 16 due to acute liver failure. The common etiologies for HE in CLD patients were Alcoholic cirrhosis (63%), Cryptogenic cirrhosis (17%) and cirrhosis due to chronic HBV (10%) and HCV hepatitis (7%) respectively. Among the CLD patients at the start of observation majority were in Child Pugh class B and C. Based on West Haven grading most of them had Grade 2 and 3 HE. Majority with Grade 1, 2 and 3 improved where as those with Grade 4 and Grade 3 in Child Pugh class C worsened. The common precipitants of HE were GI bleed, dyselectrolemia, constipation and infections. Among these precipitants a statistically significant association for a worse outcome was present only with infection. Leptospirosis and deliberate self-harm due to ingestion of hepatotoxic rodenticide and paracetamol were the leading cause of hepatic encephalopathy in acute liver failureConclusions: In present study HE was most commonly seen in patients with alcoholic liver disease. Cryptogenic cirrhosis associated with other life style diseases was the second common condition. Among all precipitating factors infection appeared as a statistically significant factor predicting a worse outcome. Health education among alcoholic patients and life style modifications to prevent cryptogenic cirrhosis are of paramount importance in curtailing the increase in incidence of HE in this region

Analysis of cerebrospinal fluid adenosine deaminase level in tuberculous meningitis and validation of sensitivity and specificity

Background: Tuberculous meningitis is an important cause of morbidity and mortality in developing countries especially in India. The mortality associated with tuberculous meningitis is very high if not detected early and meticulous treatment is not given. CSF analysis and imaging are the most commonly used tools for diagnosis of meningitis. But these are often inadequate in making a definitive diagnosis. CSF Adenosine Deaminase estimation (ADA) is useful in differentiation of tuberculous meningitis from non-tuberculous meningitis. Though few studies have proved efficacy of Adenosine Deaminase level for the diagnosis, studies to assess the sensitivity and specificity of ADA levels were limited. This study was conducted to assess its usefulness and to validate the sensitivity and specificity of ADA level in tuberculous meningitis (TBM).Methods: This was a prospective study conducted at Academy of Medical Sciences, Pariyaram for a period of 18 months from December 2013 to June 2015, Adenosine deaminase level was studied in the cerebrospinal fluid of 50 patients who got admitted with symptoms and signs of meningitis in the medical wards and intensive care units who fulfilled the inclusion criteria.Results: In this study 50 patients were diagnosed clinically and with CSF analysis as meningitis. The mean cerebrospinal fluid adenosine deaminase activity was 23.08+17.5in Tuberculous meningitis 3.8 +1.92U/l in Bacterial meningitis and 4.8+2.3U/l in Viral meningitis. The adenosine deaminase activity in Tuberculous meningitis cases were significantly higher than non-tuberculous meningitis. The sensitivity and specificity of this test for diagnosis of tuberculous meningitis was 90% and 100% respectively with ADA value of more than 10U/L.Conclusions: This study found out that estimation of CSF Adenosine level is a very useful test for the diagnosis of tuberculous meningitis. The sensitivity and specificity attained in this study were comparable to other studies. This study also found out that ADA estimation is very useful in distinguishing tuberculous and viral meningitis

Dissipation of flubendiamide (480 SC) in cardamom [Elettaria cardamomum (L.) Maton]

Insecticide flubendiamide was sprayed thrice at 21 days interval between December-February on cardamom at two concentrations, i.e. 0.72 g a.i. 10 L-1 (X), and 1.44 g a.i. 10 L-1 (2X). Samples of capsules were collected at regular intervals for 15 days after application of the insecticide for residue analysis. The initial deposit of flubendiamide in capsules was 0.42 and 0.60 mg kg-1 for X and 2X treatments, respectively that dissipated with a half-life of 1.25 and 2.53 days, respectively. No residue of des-iodo flubendiamide (the metabolite of flubendiamide) was detected in any of the samples up to 15 days. The limit of quantification (LOQ) of the method was 0.05 mg kg-1, for both flubendiamide and des-iodo flubendiamide. &nbsp

Online prediction of DGA results for intelligent condition monitoring of power transformers

Transformers form a major part of a power system in transmission as well as distribution of power. Considering the criticality, finance, and time involved in repair, periodic condition monitoring and maintenance of transformers are the key to ensure electrical safety as well as stable operation of the large interconnected power system. Dissolved Gas Analysis (DGA) is an established tool used to determine the incipient faults within the transformer by analyzing the concentration of different gases in the transformer oil and giving early warnings and diagnoses. Currently, transformers worldwide utilise online sensors to monitor dissolved gases and moisture content in oil. The online DGA sensor uses a small amount of oil from transformer to perform real-time DGA analysis and gives the ppm content of dissolved gases for further course of action. Considering the large quantity of assets and the huge amount of data produced, it is imperative to develop a tool to aid the operators in assimilating the available data for diagnosis and proactive decision making. The present study improvises AI techniques to predict future dissolved gas concentrations using real time DGA data collected from the transmission utility of the country. The prediction helps to forecast the trend of development of incipient faults in the transformer. The complete project scope is to develop a highly reliable diagnostic tool to emulate the decision-making ability of a human expert in transformer DGA analysis to enhance transformer life. In the present paper, models based on Auto-regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Vector Auto Regression (VAR) are implemented to predict DGA data of three in-service transformers. DGA data is forecasted for up to 8 monthly samples in the future, and the accuracy of results is compared with each other. The LSTM-VAR combined model is seen to provide the best results among them

Annexin A6 improves anti-migratory and anti-invasive properties of tyrosine kinase inhibitors in EGFR overexpressing human squamous epithelial cells

Annexin A6 (AnxA6), a member of the calcium (Ca2+ ) and membrane binding annexins, is known to stabilize and establish the formation of multifactorial signaling complexes. At the plasma membrane, AnxA6 is a scaffold for protein kinase Cα (PKCα) and GTPase-activating protein p120GAP to promote downregulation of epidermal growth factor receptor (EGFR) and Ras/mitogen-activated protein kinase (MAPK) signaling. In human squamous A431 epithelial carcinoma cells, which overexpress EGFR, but lack endogenous AnxA6, restoration of AnxA6 expression (A431-A6) promotes PKCα-mediated threonine 654 (T654)-EGFR phosphorylation, which inhibits EGFR tyrosine kinase activity. This is associated with reduced A431-A6 cell growth, but also decreased migration and invasion in wound healing, matrigel, and organotypic matrices. Here, we show that A431-A6 cells display reduced EGFR activity in vivo, with xenograft analysis identifying increased pT654-EGFR levels, but reduced tyrosine EGFR phosphorylation compared to controls. In contrast, PKCα depletion in A431-A6 tumors is associated with strongly reduced pT654 EGFR levels, yet increased EGFR tyrosine phosphorylation and MAPK activity. Moreover, tyrosine kinase inhibitors (TKIs; gefitinib, erlotinib) more effectively inhibit cell viability, clonogenic growth, and wound healing of A431-A6 cells compared to controls. Likewise, the ability of AnxA6 to inhibit A431 motility and invasiveness strongly improves TKI efficacy in matrigel invasion assays. This correlates with a greatly reduced invasion of the surrounding matrix of TKI-treated A431-A6 when cultured in 3D spheroids. Altogether, these findings implicate that elevated AnxA6 scaffold levels contribute to improve TKI-mediated inhibition of growth and migration, but also invasive properties in EGFR overexpressing human squamous epithelial carcinoma

Annexin A6 and NPC1 regulate LDL-inducible cell migration and distribution of focal adhesions

Cholesterol is considered indispensable for cell motility, but how physiological cholesterol pools enable cells to move forward remains to be clarified. The majority of cells obtain cholesterol from the uptake of Low-Density lipoproteins (LDL) and here we demonstrate that LDL stimulates A431 squamous epithelial carcinoma and Chinese hamster ovary (CHO) cell migration and invasion. LDL also potentiated epidermal growth factor (EGF) -stimulated A431 cell migration as well as A431 invasion in 3-dimensional environments, using organotypic assays. Blocking cholesterol export from late endosomes (LE), using Niemann Pick Type C1 (NPC1) mutant cells, pharmacological NPC1 inhibition or overexpression of the annexin A6 (AnxA6) scaffold protein, compromised LDL-inducible migration and invasion. Nevertheless, NPC1 mutant cells established focal adhesions (FA) that contain activated focal adhesion kinase (pY397FAK, pY861FAK), vinculin and paxillin. Compared to controls, NPC1 mutants display increased FA numbers throughout the cell body, but lack LDL-inducible FA formation at cell edges. Strikingly, AnxA6 depletion in NPC1 mutant cells, which restores late endosomal cholesterol export in these cells, increases their cell motility and association of the cholesterol biosensor D4H with active FAK at cell edges, indicating that AnxA6-regulated transport routes contribute to cholesterol delivery to FA structures, thereby improving NPC1 mutant cell migratory behaviour