Effects of Xin-Ji-Er-Kang on Anticardiovascular Remodeling in L

Background. Xin-Ji-Er-Kang (XJEK) shows protective effects on the myocardial ischemic diseases in our previous reports. We hypothesized that XJEK may exert preventing effects on L-NAME induced hypertensive mice by ameliorating oxidative stress (OS) and endothelial dysfunction (ED). Methods. After treatment with XJEK for four weeks, cardiac function and cardiovascular pathology changes were evaluated. Then, endothelial-dependent vascular relaxation and serum NO, eNOS, AMDA, SOD, MDA content, and cardiac tissue eNOS expression were detected. Results. The hypertensive mice displayed distinct cardiovascular remodeling including increased HW/BW index (4.7 ± 0.33 versus 5.2 ± 0.34), cross-section area, and collagen deposition. In addition, ED was found manifested by decreased serum NO (20.54 ± 8.05 versus 6.29 ± 2.33), eNOS (28.34 ± 2.36 versus 20.37 ± 2.30), content, and decreased eNOS expression in cardiac tissue and damaged endothelium-dependent diastolic function. Moreover, OS was detected confirmed by decreased SOD activity and increased MDA content in serum. However, treatment with XJEK for 4 wk could reverse cardiovascular remodeling (HW/BW index normalized from 5.2 ± 0.34 to 4.59 ± 0.25), ameliorate and preserve endothelial function (NO: 16.67 ± 7.24 versus 6.29 ± 2.33; eNOS: 16.67 ± 7.24 versus 6.29 ± 2.33), and suppress OS. Conclusion. XJEK has protective effects against cardiovascular remodeling in L-NAME induced hypertensive mice

Multidimensional ontology modeling of human digital ecosystems affected by social behavioural data patterns

Relational and hierarchical data modeling studies are carried out, using simple and explicit comparison based ontology. The comparison is basically performed on relationally and hierarchically structured data entities/dimensions.This methodology is adopted to understand the human ecosystem that is affected by human behavioural and social disorder data patterns. For example, the comparison may be made among human systems, which could be between male and female, fat and slim, disabled and normal (physical impairment), again normal and abnormal (psychological), smokers and non-smokers and among different age group domains.There could be different hierarchies among which, different super-type dimensions are conceptualized into several subtype dimensions and integrated them by connecting the interrelated several common data attributes. Domain ontologies are built based on the known-knowledge mining and thus unknownrelationships are modeled that are affected by social behaviour data patterns. This study is useful in understanding human situations, behavioral patterns and social ecology that can facilitate health and medical practitioners, social workers and psychologists, while treating their patients and clients

On data integration workflows for an effective management of multidimensional petroleum digital ecosystems in Arabian Gulf Basins

Data integration of multiple heterogeneous datasets from multidimensional petroleum digital ecosystems is an effective way, for extracting information and adding value to knowledge domain from multiple producing onshore and offshore basins. At present, data from multiple basins are scattered and unusable for data integration, because of scale and format differences. Ontology based warehousing and mining modeling are recommended for resolving the issues of scaling and formatting of multidimensional datasets, in which case, seismic and well-domain datasets are described. Issues, such as semantics among different data dimensions and their associated attributes are also addressed by Ontology modeling.Intelligent relationships are built among several petroleum system domains (structure, reservoir, source and seal, for example) at global scale and facilitated the integration process among multiple dimensions in a data warehouse environment. For this purpose, integrated workflows are designed for capturing and modeling unknown relationships among petroleum system data attributes in interpretable knowledge domains.This study is an effective approach in mining and interpreting data views drawn from warehoused exploration and production metadata, with special reference to Arabian onshore and offshore basins

A Framework for Credit Risk Prediction Using the Optimized-FKSVR Machine Learning Classifier

Transparency is influenced by several crucial factors, such as credit risk (CR) predictions, model reliability, efficient loan processing, etc. The emergence of machine learning (ML) techniques provides a promising solution to address these challenges. However, it is the responsibility of banking or nonbanking organizations to control their approach to incorporate this innovative methodology to mitigate human preferences in loan decision-making. The research article presents the Optimized-Feature based Kernel Support Vector Regression (O-FKSVR) model which is an ML-based CR analysis model in the digital banking. This proposal aims to compare several ML methods to identify a precise model for CR assessment using real credit database information. The goal is to introduce a classification model that uses a hybrid of Stochastic Gradient Descent (SGD) and firefly optimization (FFO) methods with Support Vector Regression (SVR) to predict credit risks in the form of probability, loss given, and exposure at defaults. The proposed  O-FKSVR model extracts features and predicts outcomes based on data gathered from online credit analysis. The proposed O-FKSVR model has increased the accuracy rate and resolved the existing problems. The experimental study is conducted in Python, and the results demonstrate improvements in accuracy, precision, and reduced error rates compared to previous ML methods. The proposed O-FKSVR model has achieved a maximum accuracy rate value of 0.955%, precision value of 0.96%, and recall value of 0.952%, error rate value of 4.4 when compared with the existing models such as SVR, DT, RF, and AdaBoost.&nbsp

A Resource Aware MapReduce Based Parallel SVM for Large Scale Image Classifications

Machine learning techniques have facilitated image retrieval by automatically classifying and annotating images with keywords. Among them support vector machines (SVMs) are used extensively due to their generalization properties. However, SVM training is notably a computationally intensive process especially when the training dataset is large. This paper presents RASMO, a resource aware MapReduce based parallel SVM algorithm for large scale image classifications which partitions the training data set into smaller subsets and optimizes SVM training in parallel using a cluster of computers. A genetic algorithm based load balancing scheme is designed to optimize the performance of RASMO in heterogeneous computing environments. RASMO is evaluated in both experimental and simulation environments. The results show that the parallel SVM algorithm reduces the training time significantly compared with the sequential SMO algorithm while maintaining a high level of accuracy in classifications.National Basic Research Program (973) of China under Grant 2014CB34040

Nonlinear feature extraction through manifold learning in an electronic tongue classification task

A nonlinear feature extraction-based approach using manifold learning algorithms is developed in order to improve the classification accuracy in an electronic tongue sensor array. The developed signal processing methodology is composed of four stages: data unfolding, scaling, feature extraction, and classification. This study aims to compare seven manifold learning algorithms: Isomap, Laplacian Eigenmaps, Locally Linear Embedding (LLE), modified LLE, Hessian LLE, Local Tangent Space Alignment (LTSA), and t-Distributed Stochastic Neighbor Embedding (t-SNE) to find the best classification accuracy in a multifrequency large-amplitude pulse voltammetry electronic tongue. A sensitivity study of the parameters of each manifold learning algorithm is also included. A data set of seven different aqueous matrices is used to validate the proposed data processing methodology. A leave-one-out cross validation was employed in 63 samples. The best accuracy (96.83%) was obtained when the methodology uses Mean-Centered Group Scaling (MCGS) for data normalization, the t-SNE algorithm for feature extraction, and k-nearest neighbors (kNN) as classifier.Peer ReviewedPostprint (published version

Reverse Skyline Computation over Sliding Windows

Reverse skyline queries have been used in many real-world applications such as business planning, market analysis, and environmental monitoring. In this paper, we investigated how to efficiently evaluate continuous reverse skyline queries over sliding windows. We first theoretically analyzed the inherent properties of reverse skyline on data streams and proposed a novel pruning technique to reduce the number of data points preserved for processing continuous reverse skyline queries. Then, an efficient approach, called Semidominance Based Reverse Skyline (SDRS), was proposed to process continuous reverse skyline queries. Moreover, an extension was also proposed to handle n-of-N and (n1,n2)-of-N reverse skyline queries. Our extensive experimental studies have demonstrated the efficiency as well as effectiveness of the proposed approach with various experimental settings

Thermodynamics of Layered Double Hydroxides for Removal of Iodine Species from Aqueous Media

Nuclear energy has the potential to provide sufficient clean energy to replace fossil fuels and reduce carbon dioxide emissions. However, significant health and environmental concerns surround nuclear power plants. Radioiodine is a major fission product of uranium (~3 wt%) iodine-131 (131I) has a half-life of 1.6 x 107 years and iodine-129 (129I) has a half-life of 8 days and is a high energy gamma radiator (364 KeV). Furthermore, iodine is highly environmentally mobile, specifically as the iodide (I-) and iodate (IO3-) anions, and therefore poses a great risk to humans. Although the iodate anion makes up the majority of iodine species (~70.6 %) at the Hanford Tank Waste Treatment and Immobilization Plant in Washington, United States, the focus for iodine capture and detection studies has mainly been on elemental iodine (I2) and organoiodides. Layered materials such as layered double hydroxides (LDHs) have great potential to both adsorb and detect iodine from aqueous waste. The positively charged layers of LDHs require charge balancing anions in the interlayer, thus we can use the interlayer space to confine iodine anions within the LDH structure. LDHs are battery-like capacitor materials with redox by an insertion mechanism, they possess pseudocapacitive charge storage capabilities. Their electrochemical properties open the door to sensing applications that could aid in the detection of radioiodine from the environment. This work will outline the following main findings (i) structural degradation is observed upon insertion of iodine into the interlayer of NiAl-LDH structure, (ii) iodide confined in the interlayer of NiAl-LDH, causes the formation to become more energetically stable by 46.16 kJ/mol LDH, whereas the formation of iodate confined NiAl-LDH becomes less energetically stable by 121.47 kJ/mol LDH compared to the NiAl-LDH with no iodine confined, (iii) the mechanism for binding iodine within the interlayer of NiAl-LDH differs from I- to IO3- yet both interfacial bonding mechanisms present high thermal stabilities above 340℃ under oxidative gas, and (iv) the specific capacitance of NiAl-LDH is observed to decrease by 274.13 Fg-1 and 427.13 Fg-1 when I- and IO3- respectively are present in the interlayer

Challenge of material haemocompatibility for microfluidic blood-contacting applications

Biological applications of microfluidics technology is beginning to expand beyond the original focus of diagnostics, analytics and organ-on-chip devices. There is a growing interest in the development of microfluidic devices for therapeutic treatments, such as extra-corporeal haemodialysis and oxygenation. However, the great potential in this area comes with great challenges. Haemocompatibility of materials has long been a concern for blood-contacting medical devices, and microfluidic devices are no exception. The small channel size, high surface area to volume ratio and dynamic conditions integral to microchannels contribute to the blood-material interactions. This review will begin by describing features of microfluidic technology with a focus on blood-contacting applications. Material haemocompatibility will be discussed in the context of interactions with blood components, from the initial absorption of plasma proteins to the activation of cells and factors, and the contribution of these interactions to the coagulation cascade and thrombogenesis. Reference will be made to the testing requirements for medical devices in contact with blood, set out by International Standards in ISO 10993-4. Finally, we will review the techniques for improving microfluidic channel haemocompatibility through material surface modifications—including bioactive and biopassive coatings—and future directions