Potential Role of Aromatase over Estrogen Receptor Gene Polymorphisms in Migraine Susceptibility: A Case Control Study from North India

BACKGROUND: The present study was undertaken to find out the role of estrogen pathway related gene polymorphisms in susceptibility to migraine in Northern Indian population. Aromatase, CYP19A1 (rs10046 and rs4646); estrogen receptors, ESR1 (rs2234693, rs1801132, rs2228480 and rs9340799) and ESR2 (rs1271572 and rs1256049) polymorphisms were selected for the present study. METHODOLOGY/PRINCIPAL FINDINGS: The patients were recruited in two cohorts - primary (207) and replicative (127) along with 200 healthy controls and genotyped for various polymorphisms. Logistic regression analysis was applied for statistical analyses. The results were validated in the replicative cohort and pooled by meta analysis using Fisher's and Mantel-Haenszel test. Furthermore, Benjamini - Hochberg false discovery rate test was used to correct for multiple comparisons. CYP19A1 rs10046 and CYP19A1 rs4646 polymorphisms were found to confer risk and protective effect, respectively. Out of four ESR1 polymorphisms, only rs2234693 variant allele was significantly associated in migraine with aura. No significant associations were observed for ESR2 polymorphisms. Significant haplotypes were identified for CYP19A1 and ESR1 polymorphisms. Gene- gene interactions of genotypes as well as haplotypes were observed for CYP19A1- ESR1 showing both risk and protective combinations. CONCLUSION: We strongly suggest CYP19A1 polymorphisms to be the major contributing factors in migraine susceptibility instead of genetic variants of estrogen receptors

Golgi defects enhance APP amyloidogenic processing in Alzheimer's disease

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110763/1/bies201400116.pd

Organ Donation: A Gift of Life

Not Availabl

Second-order polarizabilities of some quinolines

922-927<span style="font-size: 15.0pt;mso-bidi-font-size:8.0pt;font-family:" times="" new="" roman","serif""="">Second order polarizabilities (β) of some quinolines viz. 8-hydroxyquinoline (8-HQ), 8-amimnoquinoline (8-AQ) and 8- hydroxyquinoliuinc (8-HQD) have been experimentally determined by solvatoehromic technique using two-level quantum mechanical model. Comparison has been made with other useful organic molecules and the role of hydrogen bonding has <span style="font-size: 15.0pt;mso-bidi-font-size:8.0pt;font-family:" times="" new="" roman","serif""="">been discussed . </span

A STUDY TO ASSESS THE AWARENESS AND ATTITUDE REGARDING COPPER-T INSERTION AMONG POSTNATAL MOTHER WITH A VIEW TO DEVELOP INFORMATION BOOKLET IN SELECTED HOSPITAL OF DEHRADUN, UTTRAKHAND

&lt;p&gt;A study to assess the awareness and attitude regarding copper-T insertion among post-natal mothers with a view to develop a information booklet in selected hospital of Dehradun, Uttarakhand.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;1)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;To assess the awareness and attitude of copper-T insertion among postnatal mothers.&lt;/p&gt;&lt;p&gt;2)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;To find out the association between awareness of copper-T insertion with their demographic variables.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methodology: &lt;/strong&gt;Quantitative research approach with descriptive research design was used in the study.The study was conducted in Coronation Hospital Dehradun, Uttarakhand. Total enumeration sampling was used to collect data from 100 subjects by using Demographic profile and rating scale.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Result: &lt;/strong&gt;Majority (45%) of the study participants were between 26-30 years of age, remaining (25%) are of the age 31-35 years, (22%) are of the age group 20-25 years and (8%) are of the age group 36-45 years.The majority of the women i.e, 74% of women had moderate awareness and 78% of the women had a neutral attitude.And there is no significant association between the score level and demographic variables.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;It can be concluded that there is a need to focus on the postnatal mothers to motivate for&nbsp;copper-T insertion and majority of the women had moderate awareness and neutral attitude.&lt;/p&gt

Neural Network Fusion Processing and Inverse Mapping to Combine Multisensor Satellite Data and Analyze the Prominent Features

In the last decade, the increase of active and passive earth observation satellites has provided us with more remote sensing data. This fact has led to enhanced interest in the fusion of different satellite data since some of the satellites have properties complementary to others. Fusion techniques can improve the estimation in areas of interest by using the complementary information and inferring unknown parameters. They also have the potential to provide high-resolution detailed classification maps. Thus, we propose a neural network, which combines and analyzes the data obtained from synthetic aperture radar (SAR) and optical sensors to provide high-resolution classification maps. The neural network employs a novel activation function to construct a neural network explainability method termed as inverse mapping for prominent feature analysis. By applying inverse mapping to the data fusion neural network, we can understand which input features are the prominent contributors for which classification outputs. Inverse mapping realizes backward signal flow based on teacher-signal backpropagation dynamics, which is consistent with its forward processing. It performs the contribution analysis of the data pixel by pixel and class by class. In this article, we focus on earthquake damage detection by dealing with SAR and optical sensor data of the 2018 Sulawesi earthquake in Indonesia. The fusion-based results show increased classification accuracy compared to the results of independent sensors. Moreover, we observe that inverse mapping shows reasonable explanations in a consistent manner. It also indicates the contributions of features different from straightforward counterparts, namely, pre- and post-seismic features, in the detection of particular classes

Application of Inverse Mapping for Automated Determination of Normalized Indices Useful for Land Surface Classification

Precise surface classification is essential for glacial health monitoring, where normalized indices have traditionally been used. These indices are created empirically for a specific sensor. The transferability of these indices to other sensors can be affected by differences in spectral and spatial resolution. Thus, it is essential to evaluate the transferability of an index before applying it to a new sensor to ensure accuracy and reliability. However, as the number of satellites, sensors, and observation bands increases, there is a need for automated methods for determining application-specific normalized indices. In this article, we propose using all the bands of multispectral optical sensors to generate multiple normalized indices and determining application-specific indices using inverse mapping. We use these normalized indices for pixel-by-pixel surface classification using neural networks. First, we employ all the bands for generating normalized indices and then eliminate low-spatial-resolution bands to evaluate classification performance by using only high-spatial-resolution indices. We apply this method to a glacial region and observe 81.98&#x0025; and 84.81&#x0025; overall accuracy compared to the ground truth data for the two classifications, respectively. We then apply inverse mapping dynamics to the classification results to determine prominent indices useful for glacier classification. The results show that although some of the determined indices are not traditional indices, they are still useful for classification due to the relative differences between various land types. The proposed method has the potential to automate normalized index determination, thereby eliminating the need for empirical band assessment methods and making the index development process more efficient

Analysis of critical drivers affecting implementation of agent technology in a manufacturing system

Abstract Technological advancement in the manufacturing system in current scenario is inevitable due to today’s customer-driven and volatile nature of the market. Implementation of agent technology in a manufacturing system increases flexibility which handles uncertainty generated due to advance technology. Therefore, in this paper, the critical drivers affecting implementation of agent technology are identified and the relationships among them are analysed for a case study of a manufacturing system in an Indian steering wheel manufacturing company. Interpretive structural modelling (ISM) is used to provide binary relationships among identified critical drivers (CDs), while MICMAC approach describes sensitive analysis of driving and dependence behaviour of CDs. The classification of the drivers affecting agent technology and their relationships according to ISM-MICMAC approach provides importance to this study. A structural model is developed for providing rank to the identified critical drivers, and driving-dependent power diagram is presented for analysing the behaviour of different critical drivers with respect to others. The identification of the most influential CDs that lead to increase the effect of other drivers is the major finding of this study. Finally, the implication of this research for the industries is also described