Role of immunoturbidimetric plasma fibrin D-dimer test in patients with coronary artery disease as well as ischemic heart disease in emergency medicine

The aim of the present was to assess the value of the ELISA D-dimer (hemostatic marker) assay in patients with coronary artery disease as well as ischemic heart disease presenting to the emergency department with chest pain syndrome. Methods: We measured levels of D-dimers (µg/ml by immunoturbidimetric assay) in 120 patients with angiographically proved CAD, consecutive outpatients with chest pain, arterial fibrillation, acute coronary syndromes and 240 age and sex matched healthy controls. Demographic characteristics were assessed by a standardized questionnaire, and a complete lipid profile was performed for all subjects. In addition to this inflammatory marker C- reactive protein was also measured. Result: The distribution of D-dimer levels skewed to the right, and plasma mean levels were higher in cases than in control (mean: 2.51±3.60 vs .41±.59 µg/ml; p<0.001). In contrast, correlation of D-dimer was found with C-reactive protein (p<0.001) and is higher in cases than controls. Conclusion: Plasma D-dimer levels are strongly and independently associated with the presence of CAD in patients with stable angina. These results support the concept of a contribution of intravascular fibrin to atherothrombogenesis

The Incremental Multiresolution Matrix Factorization Algorithm

Multiresolution analysis and matrix factorization are foundational tools in computer vision. In this work, we study the interface between these two distinct topics and obtain techniques to uncover hierarchical block structure in symmetric matrices -- an important aspect in the success of many vision problems. Our new algorithm, the incremental multiresolution matrix factorization, uncovers such structure one feature at a time, and hence scales well to large matrices. We describe how this multiscale analysis goes much farther than what a direct global factorization of the data can identify. We evaluate the efficacy of the resulting factorizations for relative leveraging within regression tasks using medical imaging data. We also use the factorization on representations learned by popular deep networks, providing evidence of their ability to infer semantic relationships even when they are not explicitly trained to do so. We show that this algorithm can be used as an exploratory tool to improve the network architecture, and within numerous other settings in vision.Comment: Computer Vision and Pattern Recognition (CVPR) 2017, 10 page

Speeding up Permutation Testing in Neuroimaging

Multiple hypothesis testing is a significant problem in nearly all neuroimaging studies. In order to correct for this phenomena, we require a reliable estimate of the Family-Wise Error Rate (FWER). The well known Bonferroni correction method, while simple to implement, is quite conservative, and can substantially under-power a study because it ignores dependencies between test statistics. Permutation testing, on the other hand, is an exact, non-parametric method of estimating the FWER for a given $\alpha$-threshold, but for acceptably low thresholds the computational burden can be prohibitive. In this paper, we show that permutation testing in fact amounts to populating the columns of a very large matrix ${\bf P}$. By analyzing the spectrum of this matrix, under certain conditions, we see that ${\bf P}$ has a low-rank plus a low-variance residual decomposition which makes it suitable for highly sub--sampled --- on the order of $0.5\%$ --- matrix completion methods. Based on this observation, we propose a novel permutation testing methodology which offers a large speedup, without sacrificing the fidelity of the estimated FWER. Our evaluations on four different neuroimaging datasets show that a computational speedup factor of roughly $50\times$ can be achieved while recovering the FWER distribution up to very high accuracy. Further, we show that the estimated $\alpha$-threshold is also recovered faithfully, and is stable.Comment: NIPS 1

Expression of Genetic Variability and Character Association in Raspberry (Rubus ellipticus Smith) Growing Wild in North-Western Himalayas

The present investigation was carried out in various districts of Himachal Pradesh, Jammu&amp;Kashmir and Uttarakhand States falling under north-western Himalayan region of India. As a result of sustained exploration, 170 wild raspberry genotypes were marked and studied for berry quality attributes. Variation ranged from 0.25 g-0.93 g for berry weight. Berry length varied between 6.31 mm and 14.46 mm, while, berry breadth was 7.02 mm to 15.91 mm. Variation in Total Soluble Solids (TSS) in berry ranged between 9.6oB and 18.6oB whereas, acidity in berries ranged between 1.02 and 1.72%. The range of variation was 2-4.90% for reducing sugars, 4.2° - 11.6° for non-reducing sugars and 2.4- 5.2 mg/100 g for ascorbic acid. Berry weight had significant and positive correlation with its length and its breadth. Berry length exhibited positively significant correlation with berry breadth

Designing an Energy Efficient Network Using Integration of KSOM, ANN and Data Fusion Techniques

Energy in a wireless sensor network (WSN) is rendered as the major constraint that affects the overall feasibility and performance of a network. With the dynamic and demanding requirements of diverse applications, the need for an energy efficient network persists. Therefore, this paper proposes a mechanism for optimizing the energy consumption in WSN through the integration of artificial neural networks (ANN) and Kohonen self-organizing map (KSOM) techniques. The clusters are formed and re-located after iteration for effective distribution of energy and reduction of energy depletion at individual nodes. Furthermore, back propagation algorithm is used as a supervised learning method for optimizing the approach and reducing the loss function. The simulation results show the effectiveness of the proposed energy efficient network

A Novel Approach for Enhancement of Blowfish Algorithm by using DES, DCT Methods for Providing, Strong Encryption and Decryption Capabilities

Data safety has evolved into a critical requirement and a duty in modern life. Most of our systems are designed in such a way that it can get hacked, putting our private information at danger. As a result, for numerous safety motives, we utilize various approaches to save as much as possible on this data, regardless of its varied formats, words, photographs, videos, and so on. The data storage capacity of mobile devices is restricted owing to insufficient data storage and processing. In order to develop a safe MCC environment, security concerns must be studied and analysed. This study compares the most widely used symmetric key encryption algorithms, including DES (Data Encryption Standard), Blowfish, TDES (Triple Data Encryption Standard), PRESENT, and KLEIN. The assessment of algorithms is based on attacks, key size, and block size, with the best outcomes in their field