Generalized linear mixed model for monitoring autocorrelated logistic regression profiles

Profile monitoring is used to monitor the regression relationship between a response variable and one or more explanatory variables over time. Many researches have been done in this area, but in most of them, the distribution of the response variable is assumed to be normal. However, this assumption is violated in many real case problems. In these instances, classic methods cannot be used for monitoring the profiles. For example, when the response variable is binary, logistic regression methods should be used rather than ordinary least square or other classic regression methods. There are some methods for monitoring logistic profiles in the literature, but the basic assumption of these methods is the independency of the consecutive observations, while this assumption is violated in some instances for example when the successive samples are taken in short intervals. This paper considers the effect of autocorrelation presence between the observations in different levels of the independent variable in a logistic regression profile on the monitoring procedure (T ² control chart) and proposes two remedies to account for the autocorrelation within logistic profiles. In one of the remedies, upper control limit of the traditional T ² control chart is modified. In the second one, we use a generalized linear mixed model (GLMM) to estimate the regression parameters and then use the T ² control chart for monitoring autocorrelated logistic regression profiles. Simulation studies show the better performance of T ² control chart when the regression parameters are estimated by the GLMM method under both step shifts and drifts.     

Identifying the time of step change in binary profiles

Control charts are intended to aid quality practitioners in monitoring whether a change has occurred in a process. When a control chart indicates an out-of-control signal, it means that the process has changed. However, control chart signals do not indicate the real time of process changes; so estimators are applied to indicate the time when a change in the process takes place, which is referred to as the change point. This paper provides a maximum likelihood estimator to identify the real time of a step change in phase II monitoring of binary profiles, in which the quality of a process is characterized by a logistic regression between the response and predictor variables. Simulation studies are provided to evaluate the effectiveness of the change point estimator.     

Application of classification and regression trees for sensitivity analysis of the Escherichia coli O157:H7 food safety process risk model

Microbial food safety process risk models are simplifications of the real world that help risk managers in their efforts to mitigate food safety risks. An important tool in these risk assessment endeavors is sensitivity analysis, a systematic method used to quantify the effect of changes in input variables on model outputs. In this study, a novel sensitivity analysis method called classification and regression trees was applied to safety risk assessment with the use of portions of the Slaughter Module and Preparation Module of the E. coli O157:H7 microbial food safety process risk as an example. Specifically, the classification and regression trees sensitivity analysis method was evaluated on the basis of its ability to address typical characteristics of microbial food safety process risk models such as nonlinearities, interaction, thresholds, and categorical inputs. Moreover, this method was evaluated with respect to identification of high exposure scenarios and corresponding key inputs and critical limits. The results from the classification and regression trees analysis applied to the Slaughter Module confirmed that the process of chilling carcasses is a critical control point. The method identified a cutoff value of a 2.2-log increase in the number of organisms during chilling as a critical value above which high levels of contamination would be expected. When classification and regression trees analysis was applied to the cooking effects part of the Preparation Module, cooking temperature was found to be the most sensitive input, with precooking treatment (i.e., raw product storage conditions) ranked second in importance. This case study demonstrates the capabilities of classification and regression trees analysis as an alternative to other statistically based sensitivity analysis methods, and one that can readily address specific characteristics that are common in microbial food safety process risk models.     

Training a Hidden Markov Model with a Bayesian Spiking Neural Network

It is of some interest to understand how statistically based mechanisms for signal processing might be integrated with biologically motivated mechanisms such as neural networks. This paper explores a novel hybrid approach for classifying segments of sequential data, such as individual spoken works. The approach combines a hidden Markov model (HMM) with a spiking neural network (SNN). The HMM, consisting of states and transitions, forms a fixed backbone with nonadaptive transition probabilities. The SNN, however, implements a biologically based Bayesian computation that derives from the spike timing-dependent plasticity (STDP) learning rule. The emission (observation) probabilities of the HMM are represented in the SNN and trained with the STDP rule. A separate SNN, each with the same architecture, is associated with each of the states of the HMM. Because of the STDP training, each SNN implements an expectation maximization algorithm to learn the emission probabilities for one HMM state. The model was studied on synthesized spike-train data and also on spoken word data. Preliminary results suggest its performance compares favorably with other biologically motivated approaches. Because of the model’s uniqueness and initial promise, it warrants further study. It provides some new ideas on how the brain might implement the equivalent of an HMM in a neural circuit.     

Spectral analysis of GFDM modulated signal under nonlinear behavior of power amplifier

Wireless Networks - Generalized frequency division multiplexing (GFDM) is a flexible non-orthogonal waveform candidate for 5G which can offer some advantages such as low out-of-band emission and...     

Evaluating multimedia features and fusion for example-based event detection

Multimedia event detection (MED) is a challenging problem because of the heterogeneous content and variable quality found in large collections of Internet videos. To study the value of multimedia features and fusion for representing and learning events from a set of example video clips, we created SESAME, a system for video SEarch with Speed and Accuracy for Multimedia Events. SESAME includes multiple bag-of-words event classifiers based on single data types: low-level visual, motion, and audio features; high-level semantic visual concepts; and automatic speech recognition. Event detection performance was evaluated for each event classifier. The performance of low-level visual and motion features was improved by the use of difference coding. The accuracy of the visual concepts was nearly as strong as that of the low-level visual features. Experiments with a number of fusion methods for combining the event detection scores from these classifiers revealed that simple fusion methods, such as arithmetic mean, perform as well as or better than other, more complex fusion methods. SESAME’s performance in the 2012 TRECVID MED evaluation was one of the best reported.     

Sustainable approach toward synthesis of green functional carbonaceous 3-D micro/nanostructures from biomass

This study proposes a novel technique to synthesize functional carbonaceous three-dimensional (3-D) micro/nanocompounds from agricultural by-products using femtosecond laser irradiation. Biowastes of rice husk and wheat straw are value-engineered to carbonaceous structures in a single-step process under ambient conditions. Our results demonstrate that by controlling the laser fluence, structures with a variety of different morphologies from nanostructures to microstructures can be achieved. Also, the results indicate that altering the laser processing parameters influences the chemical composition of the synthesized structures. This sustainable approach presents an important step towards synthesizing 3-D micro/nanofibrous compounds from biowaste materials. These structures, as-synthesized or as nanocomposite fillers, can have practical uses in electronic, sensing, biological, and environmental applications.     

Preparation,characterization and in vitro biological response of simultaneous co-substitution of Zr+4/Sr+2 58S bioactive glass powder

In the present study, structure of zirconium-containing bioactive glass (58S-BG (Zr-BG)) with optimal fixed Zr content (5 mol.%) was modified by incorporation of strontium (Sr). These Zr and Sr-containing BGs (ZS-BGs) were synthesized by sol-gel method and substitution of Ca with modifier ions (Sr content = 0, 3, 6, 9, and 12 mol.%). The results obtained from characterization by X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier-transform infrared (FTIR), and energy-dispersive X-ray spectroscopy (EDS) techniques from surface of all the ZS-BGs revealed formation of hydroxyapatite (HA) after 7 days of immersion in the simulated body fluid (SBF) solution. Evaluation of changes in the SBF solution, by monitoring pH variations and ions? concentration, was in agreement with the results of morphological and structural investigations. The in-vitro biological function of synthesized BGs was studied through (MTT) assay and alkaline phosphatase (ALP) activity analysis. The results showed that all the specimens significantly stimulated proliferation and viability of MC3T3 osteoblast-like cells. Furthermore, antibacterial studies confirmed less resistance of methicillin-resistant Staphylococcus aureus (MRSA) bacteria against ZS-BGs. Eventually, the results of in-vitro bio-analysis were clarified and confirmed by two cell staining techniques of Live/Dead and Dapi/Actin. This confirmation was achieved by observing the increased quantity of live cells and their nuclei as well as the decreased number of dead cells after co-culturing with all ZS-BGs.     

Foam Cells as Therapeutic Targets in Atherosclerosis with a Focus on the Regulatory Roles of Non-Coding RNAs

Atherosclerosis is a major cause of human cardiovascular disease, which is the leading cause of mortality around the world. Various physiological and pathological processes are involved, including chronic inflammation, dysregulation of lipid metabolism, development of an environment characterized by oxidative stress and improper immune responses. Accordingly, the expansion of novel targets for the treatment of atherosclerosis is necessary. In this study, we focus on the role of foam cells in the development of atherosclerosis. The specific therapeutic goals associated with each stage in the formation of foam cells and the development of atherosclerosis will be considered. Processing and metabolism of cholesterol in the macrophage is one of the main steps in foam cell formation. Cholesterol processing involves lipid uptake, cholesterol esterification and cholesterol efflux, which ultimately leads to cholesterol equilibrium in the macrophage. Recently, many preclinical studies have appeared concerning the role of non-encoding RNAs in the formation of atherosclerotic lesions. Non-encoding RNAs, especially microRNAs, are considered regulators of lipid metabolism by affecting the expression of genes involved in the uptake (e.g., CD36 and LOX1) esterification (ACAT1) and efflux (ABCA1, ABCG1) of cholesterol. They are also able to regulate inflammatory pathways, produce cytokines and mediate foam cell apoptosis. We have reviewed important preclinical evidence of their therapeutic targeting in atherosclerosis, with a special focus on foam cell formation.     

Studying the catechol binding cavity in comparative models of human dopamine D2 receptor

Obtaining more structural information of human dopamine D(2) receptor may help in the design of better therapeutic agents against diseases such as Parkinson. In this study attempts have been made to develop a functional model for the catechol binding site of the human dopamine D(2) receptor, with two primary models being postulated based on the presence of a disulfide bridge in the second extracellular loop. The models have been subjected to subsequent molecular dynamics simulation and receptor based virtual screening of catechol structures. During steady state of the simulations, representative models with the reduced disulfide bridge were more capable of discriminating between active and inactive catechol structures. It is postulated that similar conformational changes of the second extracellular loop observed in 5-HT4 and β-adrenergic receptors, might also take place in the human D(2) receptor during its interaction with agonist ligands.