Identification of cardiomyopathy-related core genes through human metabolic networks and expression data

Background Cardiomyopathy is a complex type of myocardial disease, and its incidence has increased significantly in recent years. Dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are two common and indistinguishable types of cardiomyopathy. Results Here, a systematic multi-omics integration approach was proposed to identify cardiomyopathy-related core genes that could distinguish normal, DCM and ICM samples using cardiomyopathy expression profile data based on a human metabolic network. First, according to the differentially expressed genes between different states (DCM/ICM and normal, or DCM and ICM) of samples, three sets of initial modules were obtained from the human metabolic network. Two permutation tests were used to evaluate the significance of the Pearson correlation coefficient difference score of the initial modules, and three candidate modules were screened out. Then, a cardiomyopathy risk module that was significantly related to DCM and ICM was determined according to the significance of the module score based on Markov random field. Finally, based on the shortest path between cardiomyopathy known genes, 13 core genes related to cardiomyopathy were identified. These core genes were enriched in pathways and functions significantly related to cardiomyopathy and could distinguish between samples of different states. Conclusion The identified core genes might serve as potential biomarkers of cardiomyopathy. This research will contribute to identifying potential biomarkers of cardiomyopathy and to distinguishing different types of cardiomyopathy.

Reclaiming the Multilingual Narrative of Children in the Borderlands Using a Critical Integration Approach

By joining together different methods and curriculum delivery in an elementary school setting, the author defined a unique critical integration approach to address questions of inclusive multilingual literacy practices. The author encouraged students to build upon their prior knowledge, ways in which to show that knowledge, and specifically, their linguistic cultural wealth, which generated a respect for the linguistic diversity of all students. The author created a collaborative pedagogical space in which the students constructed an innovative curriculum by co-mingling student experiences, their cultural and linguistic resources, and their interpretive frameworks. The teacher-research project involved a Funds of Knowledge orientation, the use of a variety of pedagogical tools influenced by the theory of Multiple Intelligences, gifted strategies, community cultural wealth, emancipatory education, critical and culturally responsive pedagogy, and visual arts aesthetics.

Polling India via regression and post-stratification of non-probability online samples

Recent technological advances have facilitated the collection of large-scale administrative data and the online surveying of the Indian population. Building on these we propose a strategy for more robust, frequent and transparent projections of the Indian vote during the campaign. We execute a modified MrP model of Indian vote preferences that proposes innovations to each of its three core components: stratification frame, training data, and a learner. For the post-stratification frame we propose a novel Data Integration approach that allows the simultaneous estimation of counts from multiple complementary sources, such as census tables and auxiliary surveys. For the training data we assemble panels of respondents from two unorthodox online populations: Amazon Mechanical Turks workers and Facebook users. And as a modeling tool, we replace the Bayesian multilevel regression learner with Random Forests. Our 2019 pre-election forecasts for the two largest Lok Sahba coalitions were very close to actual outcomes: we predicted 41.8% for the NDA, against an observed value of 45.0% and 30.8% for the UPA against an observed vote share of just under 31.3%. Our uniform-swing seat projection outperforms other pollsters—we had the lowest absolute error of 89 seats (along with a poll from ‘Jan Ki Baat’); the lowest error on the NDA-UPA lead (a mere 8 seats), and we are the only pollster that can capture real-time preference shifts due to salient campaign events.

Identification and Mitigation of Fraudulent Transaction using Deep Autoencoder

In an ancient era, physical resources used to apply for transacting messages, treaty content, monarchy schemes, and policies and associated national or territorial currency which consumes time duration in the heavy count with negligible security. But as time passes, technological advancement has tendered its valuable and qualitative inputs to make the conventional transaction more better at its highest level of the extent, and as a qualitative and progressive resultant, the world is breathing in the current era of the digital environment with high-security priority. The responsibility of researchers and concerned authorities is to protect the online digital transaction under the safe digital environment. Therefore continuous enhancement is required in the upgrade of the security of the transaction system to handle digital transaction fraud. This research study suggests an approach of deep autoencoder for identifying fraudulent payment card transactions. To assess the outcome and validity of the projected approach of deep autoencoder for identifying fraudulent payment card transactions, testing was executed with the help of two datasets. The first dataset is a real credit card fraud dataset that is public available in world and the second dataset are generated by collecting the data using payment card transaction including genuine transaction and fraudulent transactions. A comparative analysis performed which is based on a comparison with different method and used first dataset. The proposed integration approach performed exceptionally with the different method and accomplished the maximum performance with respect to area under receiver operating characteristic curve (AUC) (95.66%).

Error characteristics of a model-based integration approach for fixed-wing unmanned aerial vehicles

The paper presents the error characteristics of a vehicle dynamic model (VDM)-based integration architecture for fixed-wing unmanned aerial vehicles. Global navigation satellite system (GNSS) and inertial measurement unit measurements are fused in an extended Kalman filter (EKF) which uses the VDM as the main process model. Control inputs from the autopilot system are used to drive the navigation solution. Using a predefined trajectory with segments of both high and low dynamics and a variable wind profile, Monte Carlo simulations reveal a degrading performance in varying periods of GNSS outage lasting 10 s, 20 s, 30 s, 60 s and 90 s, respectively. These are followed by periods of re-acquisition where the navigation solution recovers. With a GNSS outage lasting less than 60 s, the position error gradually grows to a maximum of 8⋅4 m while attitude errors in roll and pitch remain bounded, as opposed to an inertial navigation system (INS)/GNSS approach in which the navigation solution degrades rapidly. The model-based approach shows improved navigation performance even with parameter uncertainties over a conventional INS/GNSS integration approach.