Electrodeposition of NiW alloys into deep recesses

Pulse electrodeposition has been investigated as a general technique for the electrodeposition of nickel-tungsten alloys in deep recesses for MEMS. Ni-W exhibits an induced codeposition mechanism, where the Ni reaction rate enhances the codeposition of W. Electrodeposition of this alloy has been achieved into recesses of 500 micron deep. The challenges that have been encountered are those related to gas evolving side reactions, local pH rises, diffusional limitations of the soluble species and long times required for filling the recesses. Electrodeposition on cylinder electrodes at different rotation rates was also carried out in order to obtain data on composition of the alloy and current efficiency of the process, for different baths considered. These studies were carried out to examine the suitability of the bath for microstructure development and to describe better how the codeposition processes is affected by mass transport

HOLISTIC LIFE STYLE AND FOOD HABITS FOR EFFECTIVE MANAGEMENT IN ARSHAS (HAEMORRHOIDS) - AN AYURVEDIC VIEW

Ayurveda places great importance on ones pathya or lifestyle (eating habits and daily routine). Ayurveda provides us with the knowledge of how to prevent disease and how to eliminate its root cause if it occurs. Arshas (haemorrhoids/ piles) are common and unique disease to humans at some time in their life as no other living being suffers with this ailment. Further in the classics of Ayurveda explained that ailment kills the afflicted like an enemy hence it is coined as Arshas. The disease is initiated with Agnimandya (Improper digestion) due to improper food habits and lifestyles. Further Agnimandya leads chronic constipation and manifest the disease. In Ayurveda and contemporary medical science various conservative and surgical methods are described. However haemorrhoids can reoccur even after the good quality of the management hence in Ayurveda it is mentioned as one among Ashtamahagada (diseases difficult to treat). In this regard various reasons are explained for re-manifestation of the disease among those important factors is not maintaining the proper food habits and lifestyle methods. In these conditions Ayurveda is having major role as greater importance has been given in correction of the food habits and modification of the lifestyles as a main factor or acts as a adjuvant to the therapies in treating the diseases. In the management of Arshas various regimens of food habits and lifestyles are described. If these are implemented properly helps in increase appetite, reduces constipation and also prevents the manifestation as well as avoids recurrence of the disease

Voxel selection in fMRI data analysis based on sparse representation

Multivariate pattern analysis approaches toward detection of brain regions from fMRI data have been gaining attention recently. In this study, we introduce an iterative sparse-representation-based algorithm for detection of voxels in functional MRI (fMRI) data with task relevant information. In each iteration of the algorithm, a linear programming problem is solved and a sparse weight vector is subsequently obtained. The final weight vector is the mean of those obtained in all iterations. The characteristics of our algorithm are as follows: 1) the weight vector (output) is sparse; 2) the magnitude of each entry of the weight vector represents the significance of its corresponding variable or feature in a classification or regression problem; and 3) due to the convergence of this algorithm, a stable weight vector is obtained. To demonstrate the validity of our algorithm and illustrate its application, we apply the algorithm to the Pittsburgh Brain Activity Interpretation Competition 2007 functional fMRI dataset for selecting the voxels, which are the most relevant to the tasks of the subjects. Based on this dataset, the aforementioned characteristics of our algorithm are analyzed, and a comparison between our method with the univariate general-linear-model-based statistical parametric mapping is performed. Using our method, a combination of voxels are selected based on the principle of effective/sparse representation of a task. Data analysis results in this paper show that this combination of voxels is suitable for decoding tasks and demonstrate the effectiveness of our method

Character Segmentation for Telugu Image Document using Multiple Histogram Projections

TEXT line segmentation is one of the major component of document image analysis. Text line segmentation is necessary to detect all text regions in the document image. In this paper we propose an algorithm based on multiple histogram projections using morphological operators to extract features of the image. Horizontal projection is performed on the text image, and then line segments are identified by the peaks in the horizontal projection. Threshold is applied to divide the text image into segments. False lines are eliminated using another threshold. Vertical histogram projections are used for the line segments and decomposed into words using threshold and further decomposed to characters. This approach provides best performance based on the experimental results such as Detection rate DR (98%) and Recognition Accuracy RA (98%)

COVID-19: AN APPLIED INTERVENTION THROUGH AYURVEDA

The world is crippling due to pandemic coronavirus disease (COVID-19) which is caused by severe acute respiratory syndrome Coronavirus (SARS-CoV-2). This situation can be considered under Janapadodhwamsa (destructions of states or kingdoms or countries) as mentioned in Ayurveda. Further, COVID-19 infection may be correlated with Vata-Kaphaja Sannipataja Jwara (a type of fever mentioned in classical Ayurvedic texts with severe complications and fatality). The article is mainly intended to interpret direct correlation between pandemics and Janapadodhwamsa, understanding the concept of COVID-19 and its probable management principles based on ethics of Ayurveda. The main aim of management principles includes correction of the Vikruta (contaminated) Vayu (air) and Desha (place/continent etc.) and improving the strength and immunity for prevention of disease as well as the management of COVID 19 patients by various herbal or herbo-mineral combinations based on the stage/severity of the disease along with follow up of recovered patients to avoid the recurrence. Considering above comprehensive aspect in management of COVID-19, the role of Ayurveda intervention may be proved more beneficial in asymptomatic, mild and moderate stages. Further, clinical studies on these drugs need to be conducted to produce evidence for safety and efficacy on COVID-19 for wider acceptance and implementation of Ahara Vidhis, Dinacharya and Sadvritta in National Health Policies for improving disease resistance

Speaker Recognition Based on Mutated Monarch Butterfly Optimization Configured Artificial Neural Network

Speaker recognition is the process of extracting speaker-specific details from voice waves to validate the features asserted by system users; in other words, it allows voice-controlled access to a range of services. The research initiates with extraction features from voice signals and employing those features in Artificial Neural Network (ANN) for speaker recognition. Increasing the number of hidden layers and their associated neurons reduces the training error and increases the computational process\u27s complexity. It is essential to have an optimal number of hidden layers and their corresponding, but attaining those optimal configurations through a manual or trial and the process takes time and makes the process more complex. This urges incorporating optimization approaches for finding optimal hidden layers and their corresponding neurons. The technique involve in configuring the ANN is Mutated Monarch Butterfly Optimization (MMBO). The proposed MMBO employed for configuring the ANN achieves the sensitivity of 97.5% in a real- time database that is superior to contest techniques

Distribution-based measures of tumor heterogeneity are sensitive to mutation calling and lack strong clinical predictive power.

Mutant allele frequency distributions in cancer samples have been used to estimate intratumoral heterogeneity and its implications for patient survival. However, mutation calls are sensitive to the calling algorithm. It remains unknown whether the relationship of heterogeneity and clinical outcome is robust to these variations. To resolve this question, we studied the robustness of allele frequency distributions to the mutation callers MuTect, SomaticSniper, and VarScan in 4722 cancer samples from The Cancer Genome Atlas. We observed discrepancies among the results, particularly a pronounced difference between allele frequency distributions called by VarScan and SomaticSniper. Survival analysis showed little robust predictive power for heterogeneity as measured by Mutant-Allele Tumor Heterogeneity (MATH) score, with the exception of uterine corpus endometrial carcinoma. However, we found that variations in mutant allele frequencies were mediated by variations in copy number. Our results indicate that the clinical predictions associated with MATH score are primarily caused by copy number aberrations that alter mutant allele frequencies. Finally, we present a mathematical model of linear tumor evolution demonstrating why MATH score is insufficient for distinguishing different scenarios of tumor growth. Our findings elucidate the importance of allele frequency distributions as a measure for tumor heterogeneity and their prognostic role

Solvability of iterative systems of three-point boundary value problems

We establish a criterion for the existence of at least one positive solution for the iterative system of three-point boundary value problems by determining the eigenvalues λi, 1 ≤ i ≤ n, using Guo–Krasnosel’skii fixed point theorem.Publisher's Versio

Pan-cancer classifications of tumor histological images using deep learning

Histopathological images are essential for the diagnosis of cancer type and selection of optimal treatment. However, the current clinical process of manual inspection of images is time consuming and prone to intra- and inter-observer variability. Here we show that key aspects of cancer image analysis can be performed by deep convolutional neural networks (CNNs) across a wide spectrum of cancer types. In particular, we implement CNN architectures based on Google Inception v3 transfer learning to analyze 27815 H&E slides from 23 cohorts in The Cancer Genome Atlas in studies of tumor/normal status, cancer subtype, and mutation status. For 19 solid cancer types we are able to classify tumor/normal status of whole slide images with extremely high AUCs (0.995±0.008). We are also able to classify cancer subtypes within 10 tissue types with AUC values well above random expectations (micro-average 0.87±0.1). We then perform a cross-classification analysis of tumor/normal status across tumor types. We find that classifiers trained on one type are often effective in distinguishing tumor from normal in other cancer types, with the relationships among classifiers matching known cancer tissue relationships. For the more challenging problem of mutational status, we are able to classify TP53 mutations in three cancer types with AUCs from 0.65-0.80 using a fully-trained CNN, and with similar cross-classification accuracy across tissues. These studies demonstrate the power of CNNs for not only classifying histopathological images in diverse cancer types, but also for revealing shared biology between tumors. We have made software available at: https://github.com/javadnoorb/HistCNNFirst author draf