A New Ensemble Learning Method for Temporal Pattern Identification

AbstractIn this paper we present a method for identification of temporal patterns predictive of significant events in a dynamic data system. A new hybrid model using Reconstructed Phase Space (MRPS) and Hidden Markov Model (HMM) is applied to identify temporal patterns. This method constructs phase space embedding by using individual embedding of each variable sequences. We also employ Hidden Markov Models (HMM) to the multivariate sequence data to categorize multi-dimensional data into three states, e.g. normal, patterns and events. A support vector machine optimization method is used to search an optimal classifier to identify temporal patterns that are predictive of future events. We performed two experimental applications using chaotic time series and natural gas usage series related to the natural gas usage forecasting problem. Experiments show that the new method significantly outperforms the original RPS framework and neural network method

Detecting Temporal Patterns using Reconstructed Phase Space and Support Vector Machine in the Dynamic Data System

In this paper we present a method for detecting dynamic temporal patterns that are characteristic and predictive of significant events in a dynamic data system. We employ the Gaussian Mixture Model (GMM) to cluster the data sequence into three categories of signals, e.g. normal, patterns and events. The data sequence is then embedded into a Reconstructed Phase Space (RPS) which is topologically equivalent to the dynamics of the original system. We apply a hybrid method using Support Vector Machines (SVM) and Maximum a Posterior (MAP) to classify temporal pattern signals based on the event function. We performed two experimental applications using chaotic time series and Sludge Volume Index (SVI) series related to the Sludge Bulking problem. The proposed hybrid GMM-SVM phase space approach effectively detects temporal patterns and achieves higher predictive accuracy compared with the original RPS framework

Modeling Temporal Pattern and Event Detection using Hidden Markov Model with Application to a Sludge Bulking Data

This paper discusses a method of modeling temporal pattern and event detection based on Hidden Markov Model (HMM) for a continuous time series data. We also provide methods for checking model adequacy and predicting future events. These methods are applied to a real example of sludge bulking data for detecting sludge bulking for a water plant in Chicago

TRES: An R Package for Tensor Regression and Envelope Algorithms

Recently, there has been a growing interest in tensor data analysis, where tensor regression is the cornerstone of statistical modeling for tensor data. The R package TRES provides the standard least squares estimators and the more efficient envelope estimators for the tensor response regression (TRR) and the tensor predictor regression (TPR) models. Envelope methodology provides a relatively new class of dimension reduction techniques that jointly models the regression mean and covariance parameters. Three types of widely applicable envelope estimation algorithms are implemented and applied to both TRR and TPR models

Glutamate Excitotoxicity Inflicts Paranodal Myelin Splitting and Retraction.

Paranodal myelin damage is observed in white matter injury. However the culprit for such damage remains unknown. By coherent anti-Stokes Raman scattering imaging of myelin sheath in fresh tissues with sub-micron resolution, we observed significant paranodal myelin splitting and retraction following glutamate application both ex vivo and in vivo. Multimodal multiphoton imaging further showed that glutamate application broke axo-glial junctions and exposed juxtaparanodal K+ channels, resulting in axonal conduction deficit that was demonstrated by compound action potential measurements. The use of 4-aminopyridine, a broad-spectrum K+ channel blocker, effectively recovered both the amplitude and width of compound action potentials. Using CARS imaging as a quantitative readout of nodal length to diameter ratio, the same kind of paranodal myelin retraction was observed with applications of Ca2+ ionophore A23187. Moreover, exclusion of Ca2+ from the medium or application of calpain inhibitor abolished paranodal myelin retraction during glutamate exposure. Examinations of glutamate receptor agonists and antagonists further showed that the paranodal myelin damage was mediated by NMDA and kainate receptors. These results suggest that an increased level of glutamate in diseased white matter could impair paranodal myelin through receptor-mediated Ca2+ overloading and subsequent calpain activation

Developmental Trend of the World Crude Oil Trade Spatial Pattern

As per the data of world crude oil exploitation-reserve ratio, this paper analyzes the short, middle and long-term supply situation of the world oil trade; based on the demand forecasts by the International Energy Agency and US Department of Energy, proposes that North America, Asia and Europe will remain the world oil importing regions, while China, India, the United States and Korea will become the world’s largest importers; Finally, based on the supply and demand analysis, concludes the developmental trend of the world crude oil spatial pattern. Key words: Crude oil; Trade pattern; Ratio of exploitation and reserve; Deman

Disulfiram, a ferroptosis inducer, triggers lysosomal membrane permeabilization by up-regulating ROS in glioblastoma

Introduction: Disulfiram (DSF), a drug used in the treatment of alcoholism since 1948, has been shown to have antitumor properties in various tumor types possibly due to the induction of a type cell death, ferroptosis, and the sensitization of cells to chemo- and radiotherapy. In this study, we explored the antitumor properties of DSF in glioblastoma (GBM) and investigated the underlying molecular mechanisms. Methods: GBM cell lines U251 and LN229 were treated with DSF to assess cytotoxicity and activity of the molecule in vitro. Response of cells to treatment was examined using cell viability, flow cytometry, LDH release assay, immunofluorescence and Western blot analysis. Results: DSF inhibited cell growth of GBM U251 and LN229 cell lines in vitro in a concentration-dependent manner. Flow cytometry demonstrated that DSF caused G0-G1 growth arrest. DSF treatment led to increased ROS and lipid peroxidation levels relative to controls indicating the involvement of ferroptosis. Furthermore, DSF triggered lysosomal membrane permeabilization (LMP), a critical mechanism promoting cell death, in a ROS-dependent manner. Finally, DSF enhanced radiosensitivity of U251 and LN229 cells. Discussion: Our findings indicated that DSF induced ferroptosis and LMP and enhanced the radiosensitivity of GBM cells. Therefore, DSF might have efficient antitumor activity in the treatment of human GBM.publishedVersio