Coding roles of long non-coding RNAs in breast cancer: Emerging molecular diagnostic biomarkers and potential therapeutic targets with special reference to chemotherapy resistance

Dysregulation of epigenetic mechanisms have been depicted in several pathological consequence such as cancer. Different modes of epigenetic regulation (DNA methylation (hypomethylation or hypermethylation of promotor), histone modifications, abnormal expression of microRNAs (miRNAs), long non-coding RNAs, and small nucleolar RNAs), are discovered. Particularly, lncRNAs are known to exert pivot roles in different types of cancer including breast cancer. LncRNAs with oncogenic and tumour suppressive potential are reported. Differentially expressed lncRNAs contribute a remarkable role in the development of primary and acquired resistance for radiotherapy, endocrine therapy, immunotherapy, and targeted therapy. A wide range of molecular subtype specific lncRNAs have been assessed in breast cancer research. A number of studies have also shown that lncRNAs may be clinically used as non-invasive diagnostic biomarkers for early detection of breast cancer. Such molecular biomarkers have also been found in cancer stem cells of breast tumours. The objectives of the present review are to summarize the important roles of oncogenic and tumour suppressive lncRNAs for the early diagnosis of breast cancer, metastatic potential, and chemotherapy resistance across the molecular subtypes

An efficient machine learning model based on improved features selections for early and accurate heart disease predication

Coronary heart disease has an intense impact on human life. Medical history-based diagnosis of heart disease has been practiced but deemed unreliable. Machine learning algorithms are more reliable and efficient in classifying, e.g., with or without cardiac disease. Heart disease detection must be precise and accurate to prevent human loss. However, previous research studies have several shortcomings, for example, take enough time to compute while other techniques are quick but not accurate. This research study is conducted to address the existing problem and to construct an accurate machine learning model for predicting heart disease. Our model is evaluated based on five feature selection algorithms and performance assessment matrix such as accuracy, precision, recall, F1-score, MCC, and time complexity parameters. The proposed work has been tested on all of the dataset's features as well as a subset of them. The reduction of features has an impact on the performance of classifiers in terms of the evaluation matrix and execution time. Experimental results of the support vector machine, K-nearest neighbor, and logistic regression are 97.5%,95 %, and 93% (accuracy) with reduced computation times of 4.4, 7.3, and 8seconds respectively

Knowledge Guided Non-Uniform Rational B-Spline (NURBS) for Supporting Design Intent in Computer Aided Design (CAD) Modeling

For many years, incompatible computer-aided design (CAD) packages that are based on Non-uniform Rational B-Spline (NURBS) technology carried out the exchange of models and data through either neutral file formats (IGES or STEP) or proprietary formats that have been accepted as quasi industry standards. Although it is the only available solution at the current time, the exchange process most often produces unsatisfactory results. Models that are impeccable in the original modeling system usually end up with gaps or intersections between surfaces on another incompatible system. Issues such as loss of information, change of data accuracy, inconsistent tolerance, and misinterpretation of the original design intent are a few examples of problems associated with migrating models between different CAD systems. While these issues and drawbacks are well known and cost the industry billions of dollars every year, a solution to eradicate problems from their sources has not been developed. Meanwhile, researchers along with the industries concerned with these issues have been trying to resolve such problems by finding means to repair the migrated models either manually or by using specialized software. Designing in recent years is becoming more knowledge intensive and it is essential for NURBS to take its share of the ever increasing use of knowledge. NURBS are very powerful modeling tools and have become the de facto standard in modeling. If we stretch their strength and make them knowledge driven, benefits beyond current expectations can be achieved easily. This dissertation introduces knowledge guided NURBS with theoretical and practical foundations for supporting design intent capturing, retrieval, and exchange among dissimilar CAD systems. It shows that if NURBS entities are tagged with some knowledge, we can achieve seamless data exchange, increase robustness, and have more reliable computations, all of which are ultimate objectives many researchers in the field of CAD have been trying to accomplish for decades. Establishing relationships between a NURBS entity and its origin and destinations can aid with seamless CAD model migration. The type of the NURBS entity and the awareness of any irregularities can lead to more intelligent decisions on how to proceed with many computations to increase robustness and achieve a high level of reliability. As a result, instead of having models that are hardly modifiable because of migrating raw numerical data in isolation, the knowledge driven migration process will produce models that are editable and preserve design intent. We have addressed the issues not only theoretically but also by developing a prototype system that can serve as a test bed. The developed system shows that a click of a button can regenerate a migrated model instead of repairing it, avoiding delay and corrective processes that only limit the effective use of such models

Knowledge Guided Non-Uniform Rational B-Spline (NURBS) for Supporting Design Intent in Computer Aided Design (CAD) Modeling

For many years, incompatible computer-aided design (CAD) packages that are based on Non-uniform Rational B-Spline (NURBS) technology carried out the exchange of models and data through either neutral file formats (IGES or STEP) or proprietary formats that have been accepted as quasi industry standards. Although it is the only available solution at the current time, the exchange process most often produces unsatisfactory results. Models that are impeccable in the original modeling system usually end up with gaps or intersections between surfaces on another incompatible system. Issues such as loss of information, change of data accuracy, inconsistent tolerance, and misinterpretation of the original design intent are a few examples of problems associated with migrating models between different CAD systems. While these issues and drawbacks are well known and cost the industry billions of dollars every year, a solution to eradicate problems from their sources has not been developed. Meanwhile, researchers along with the industries concerned with these issues have been trying to resolve such problems by finding means to repair the migrated models either manually or by using specialized software. Designing in recent years is becoming more knowledge intensive and it is essential for NURBS to take its share of the ever increasing use of knowledge. NURBS are very powerful modeling tools and have become the de facto standard in modeling. If we stretch their strength and make them knowledge driven, benefits beyond current expectations can be achieved easily. This dissertation introduces knowledge guided NURBS with theoretical and practical foundations for supporting design intent capturing, retrieval, and exchange among dissimilar CAD systems. It shows that if NURBS entities are tagged with some knowledge, we can achieve seamless data exchange, increase robustness, and have more reliable computations, all of which are ultimate objectives many researchers in the field of CAD have been trying to accomplish for decades. Establishing relationships between a NURBS entity and its origin and destinations can aid with seamless CAD model migration. The type of the NURBS entity and the awareness of any irregularities can lead to more intelligent decisions on how to proceed with many computations to increase robustness and achieve a high level of reliability. As a result, instead of having models that are hardly modifiable because of migrating raw numerical data in isolation, the knowledge driven migration process will produce models that are editable and preserve design intent. We have addressed the issues not only theoretically but also by developing a prototype system that can serve as a test bed. The developed system shows that a click of a button can regenerate a migrated model instead of repairing it, avoiding delay and corrective processes that only limit the effective use of such models

New Hybrid Features Selection Method: A Case Study on Websites Phishing

Phishing is one of the serious web threats that involves mimicking authenticated websites to deceive users in order to obtain their financial information. Phishing has caused financial damage to the different online stakeholders. It is massive in the magnitude of hundreds of millions; hence it is essential to minimize this risk. Classifying websites into “phishy” and legitimate types is a primary task in data mining that security experts and decision makers are hoping to improve particularly with respect to the detection rate and reliability of the results. One way to ensure the reliability of the results and to enhance performance is to identify a set of related features early on so the data dimensionality reduces and irrelevant features are discarded. To increase reliability of preprocessing, this article proposes a new feature selection method that combines the scores of multiple known methods to minimize discrepancies in feature selection results. The proposed method has been applied to the problem of website phishing classification to show its pros and cons in identifying relevant features. Results against a security dataset reveal that the proposed preprocessing method was able to derive new features datasets which when mined generate high competitive classifiers with reference to detection rate when compared to results obtained from other features selection methods

Heterogeneous modeling of medical image data using B-spline functions

Biomedical data visualization and modeling rely predominately on manual processing and utilization of voxel- and facet-based homogeneous models. Biological structures are naturally heterogeneous and it is important to incorporate properties, such as material composition, size and shape, into the modeling process. A method to approximate image density data with a continuous B-spline surface is presented. The proposed approach generates a density point cloud, based on medical image data to reproduce heterogeneity across the image, through point densities. The density point cloud is ordered and approximated with a set of B-spline curves. A B-spline surface is lofted through the cross-sectional B-spline curves preserving the heterogeneity of the point cloud dataset. Preliminary results indicate that the proposed methodology produces a mathematical representation capable of capturing and preserving density variations with high fidelity

Evaluation of the Fast Synchrophasors Estimation Algorithm Based on Physical Signals

The goal of this study is to evaluate the performance of the fast algorithm for synchrophasor estimation proposed on the basis of a physical system. The test system is represented by a physical model of a power system with four synchronous generators (15 and 5 kVA). Three synchronous machines represent steam turbine generators, while the fourth machine represents a hydro generator. The proposed method of accuracy assessment is based on comparison of the original and the recovered signals, using values of amplitude and phase angle. The experiments conducted in the study include three-phase faults, two-phase faults and single-phase faults at various buses of the test model. Functional dependencies of initial signal standard deviation from the recovered signal are obtained, as well as those for sampling rate and window width. Based on the results, the following requirements for measurement system and window width are formulated: sampling rate of analog-to-digital converter should be 10 kHz; and window width should start from 5 ms. In addition, the fast algorithm of synchrophasor estimation was tested on event recorder signals. The sampling rate of these signals was 2 kHz. Acceptable window width for event recorder signals is 8 ms. The algorithm was implemented using programming language Python 3 for the testing purposes. The proposed fast algorithm of synchrophasor estimation can be applied in methods for emergency control and equipment state monitoring with short time response

Implementation of Virtual Training: The Example of a Faculty of Computer Science during COVID-19 for Sustainable Development in Engineering Education

Research on faculty engagement in computer science and e-learning environments is limited. Students in computer science majors and courses often cite the lack of engagement of their faculty as a reason for their decision to switch majors, drop out or perform poorly. With the shift to e-learning associated with the current global pandemic, reports of faculty engagement across countries and higher education systems converged to indicate a reduced level of interactivity. Using a cross-sectional sample of 39 lecturers and professors from a southern public university in Saudi Arabia, this manuscript documents empirically the low levels of computer science faculty engagement during the 2020 spring semester (March–May). The study found support for the hypotheses linking higher levels of empathetic instruction, an exhibition of exemplary performance traits, utilization of community building strategies and use of storytelling and students’ engagement. The study also found that many faculties need immediate and significant training on making their online instruction more interactive and exciting. Theoretically, the evidence presented confirms the importance of faculty engagement as the main predictor of desirable students’ outcomes across e-learning, as well as computer science learning environments

Implementation of Virtual Training: The Example of a Faculty of Computer Science during COVID-19 for Sustainable Development in Engineering Education

Research on faculty engagement in computer science and e-learning environments is limited. Students in computer science majors and courses often cite the lack of engagement of their faculty as a reason for their decision to switch majors, drop out or perform poorly. With the shift to e-learning associated with the current global pandemic, reports of faculty engagement across countries and higher education systems converged to indicate a reduced level of interactivity. Using a cross-sectional sample of 39 lecturers and professors from a southern public university in Saudi Arabia, this manuscript documents empirically the low levels of computer science faculty engagement during the 2020 spring semester (March–May). The study found support for the hypotheses linking higher levels of empathetic instruction, an exhibition of exemplary performance traits, utilization of community building strategies and use of storytelling and students’ engagement. The study also found that many faculties need immediate and significant training on making their online instruction more interactive and exciting. Theoretically, the evidence presented confirms the importance of faculty engagement as the main predictor of desirable students’ outcomes across e-learning, as well as computer science learning environments