A simple reduction method without the need for high-temperature annealing is proposed for highly conductive and dispersible graphene sheets. This method consists of the grafting of graphene oxide (GO) with 1-pyrenecarboxylic acid (PCA) and the exothermic reduction of the PCA-grafted GO, followed by an endothermic decarboxylation with refluxing hot water. The PCA-grafted reduced graphene oxide (...

p2x 7 antagonist activity for a set of 49 molecules of the p2x 7 receptor antagonists, derivatives of purine, was modeled with the aid of chemometric and artificial intelligence techniques. the activity of these compounds was estimated by means of combination of principal component analysis (pca), as a well-known data reduction method, genetic algorithm (ga), as a variable selection technique, ...

A new method that combines (1)H-NMR and principal component analysis (PCA) was employed to obtain the quality evaluation of biopharmaceuticals, with regard to their quality, consistency, and differences in protein modification patterns. To assess the feasibility of the method, three (1)H-NMR spectra of oxytocin (OXT) were collected every 7 d (at Day 0, 7 and 14), and time-dependent changes in t...

Many applications in computer vision use Principal Components Analysis (PCA), for example, in camera calibration, stereo, localization and motion estimation. We present a new and fast PCA-based method to analyze optical snow. Optical snow is a complex form of visual motion that occurs when an observer moves through a highly cluttered 3D scene. For this category of motion field, no spatial or de...

Due to being so effective to mitigate the fading effect of wireless channels relay networks have received so much attention recently. Especially because relays are typically small, power limited and low cost and also can remove the problem of attenuation of signal due to propagation loss. Moreover increasing the number of relays improves the system performance and also using more power. The sys...

We present a method for simultaneous dimension reduction, model fitting and metastability analysis of high dimensional time series. The approach is based on the combination of hidden Markov models (HMMs) with localized principal component analysis (PCA) and fitting of multidimensional stochastic differential equations (SDE). We derive explicit estimators for PCA-SDE model parameters and employ ...

BACKGROUND Principal component analysis (PCA) is a powerful mathematical method able to analyze data sets containing a large number of variables. To our knowledge, this method is applied here for the first time in the field of medical laboratory analysis. METHODS PCA was used to evaluate the results of a blind comparative study of 21 carcinoembryonic antigen (CEA) reagent kits used to determi...

Eigenvoice speaker adaptation has been shown to be effective when only a small amount of adaptation data is available. At the heart of the method is principal component analysis (PCA) employed to find the most important eigenvoices. In this paper, we postulate that nonlinear PCA, in particular kernel PCA, may be even more effective. One major challenge is to map the feature-space eigenvoices ba...

The nonlinear factor analysis (NFA) method by Lappalainen and Honkela (2000) [2] is initialised with linear principal component analysis (PCA). Because of the multilayer perceptron (MLP) network used to model the nonlinearity, the method is susceptible to local minima and therefore sensitive to the initialisation used. As the method is used for nonlinear separation, the linear initialisation ma...

BACKGROUND AND OBJECTIVES Procoagulant activity (PCA) of monocytes is known to play a pivotal role in a variety of physiologic and pathophysiologic processes, such as disseminated intravascular coagulation, atherosclerosis, arterial and venous thromboembolism, cancer-related hypercoagulability and immunopathologies. Until now, PCA has been studied by clotting assays of a whole cell population o...