Relationships between morphological types of plaque in Alzheimer’s disease as revealed in silver and immunostained preparations

The objective of this study was to determine the possible relationships between the morphological types of plaque revealed in silver and immunostained sections of Alzheimer’s disease (AD) tissue. The density of cored and uncored senile plaques in Glees and Marsland preparations, and of diffuse, primitive, classic and compact beta/A4 deposits in immunostained preparations were estimated. A principal components analysis (PCA) of the data suggested that three uncorrelated principal components accounted for 80% of the variation in lesion density in the tissues. This suggested that thee processes lead independently to the formation of: (1) the uncored Glees plaques; (2) the primitive beta/A4 deposits and most of the classic beta/A4 deposits and (3) the compact beta/A4 deposits and the remaining classic deposits. Hence, the uncored plaques revealed by the Glees stain and the primitive beta/A4 deposits represented distinct plaque populations. In addition, the classic beta/A4 deposits did not appear to represent a uniform plaque population but to originate from at least two pathological processes. The uncored Glees plaques appeared to the only plaque population closely related to the diffuse beta/A4 deposits.

TDP-43-Immunoreactive pathology in frontotemporal lobar degeneration with TDP proteinopathy (FTLD-TDP) with and without associated motor neuron disease

A proportion of patients with motor neuron disease (MND) exhibit frontotemporal dementia (FTD) and some patients with FTD develop the clinical features of MND. Frontotemporal lobar degeneration (FTLD) is the pathological substrate of FTD and some forms of this disease (referred to as FTLD-U) share with MND the common feature of ubiquitin-immunoreactive, tau-negative cellular inclusions in the cerebral cortex and hippocampus. Recently, the transactive response (TAR) DNA-binding protein of 43 kDa (TDP-43) has been found to be a major protein of the inclusions of FTLD-U with or without MND and these cases are referred to as FTLD with TDP-43 proteinopathy (FTLD-TDP). To clarify the relationship between MND and FTLD-TDP, TDP-43 pathology was studied in nine cases of FTLD-MND and compared with cases of familial and sporadic FTLD–TDP without associated MND. A principal components analysis (PCA) of the nine FTLD-MND cases suggested that variations in the density of surviving neurons in the frontal cortex and neuronal cytoplasmic inclusions (NCI) in the dentate gyrus (DG) were the major histological differences between cases. The density of surviving neurons in FTLD-MND was significantly less than in FTLD-TDP cases without MND, and there were greater densities of NCI but fewer neuronal intranuclear inclusions (NII) in some brain regions in FTLD-MND. A PCA of all FTLD-TDP cases, based on TDP-43 pathology alone, suggested that neuropathological heterogeneity was essentially continuously distributed. The FTLD-MND cases exhibited consistently high loadings on PC2 and overlapped with subtypes 2 and 3 of FTLD-TDP. The data suggest: (1) FTLD-MND cases have a consistent pathology, variations in the density of NCI in the DG being the major TDP-43-immunoreactive difference between cases, (2) there are considerable similarities in the neuropathology of FTLD-TDP with and without MND, but with greater neuronal loss in FTLD-MND, and (3) FTLD-MND cases are part of the FTLD-TDP ‘continuum’ overlapping with FTLD-TDP disease subtypes 2 and 3.

Quantitative pathological changes in the cerebellum of multiple system atrophy

Multiple system atrophy (MSA) is a rare neurodegenerative disorder associated with parkinsonism, ataxia, and autonomic dysfunction. Its pathology is primarily subcortical comprising vacuolation, neuronal loss, gliosis, and α-synuclein-immunoreactive glial cytoplasmic inclusions (GO). To quantify cerebellar pathology in MSA, the density and spatial pattern of the pathological changes were studied in α-synuclein-immunolabelled sections of the cerebellar hemisphere in 10 MSA and 10 control cases. In MSA, densities of Purkinje cells (PC) were decreased and vacuoles in the granule cell layer (GL) increased compared with controls. In six MSA cases, GCI were present in cerebellar white matter. In the molecular layer (ML) and GL of MSA, vacuoles were clustered, the clusters exhibiting a regular distribution parallel to the edge of the folia. Purkinje cells were randomly or regularly distributed with large gaps between surviving cells. Densities of glial cells and surviving neurons in the ML and surviving cells and vacuoles in the GL were negatively correlated consistent with gliosis and vacuolation in response to neuronal loss. Principal components analysis (PCA) suggested vacuole densities in the ML and vacuole density and cell losses in the GL were the main source of neuropathological variation among cases. The data suggest that: (1) cell losses and vacuolation of the GCL and loss of PC were the most significant pathological changes in the cases studied, (2) pathological changes were topographically distributed, and (3) cerebellar pathology could influence cerebral function in MSA via the cerebello-dentato-thalamic tract.

TDP-43-immunoreactive pathology in frontotemporal lobar degeneration with TDP proteinopathy (FTLD-TDP) with and without associated motor neuron disease (MND)

A proportion of patients with motor neuron disease (MND) exhibit frontotemporal dementia (FTD) and some patients with FTD develop the clinical features of MND. Frontotemporal lobar degeneration (FTLD) is the pathological substrate of FTD and some forms of this disease (referred to as FTLD-U) share with MND the common feature of ubiquitin-immunoreactive, tau-negative cellular inclusions in the cerebral cortex and hippocampus. Recently, the transactive response (TAR) DNA-binding protein of 43 kDa (TDP-43) has been found to be a major protein of the inclusions of FTLD-U with or without MND and these cases are referred to as FTLD with TDP-43 proteinopathy (FTLD-TDP). To clarify the relationship between MND and FTLD-TDP, TDP-43 pathology was studied in nine cases of FTLD-MND and compared with cases of familial and sporadic FTLD-TDP without associated MND. A principal components analysis (PCA) of the nine FTLD-MND cases suggested that variations in the density of surviving neurons in the frontal cortex and neuronal cytoplasmic inclusions (NCI) in the dentate gyrus (DG) were the major histological differences between cases. The density of surviving neurons in FTLD-MND was significantly less than in FTLD-TDP cases without MND, and there were greater densities of NCI but fewer neuronal intranuclear inclusions (NII) in some brain regions in FTLD-MND. A PCA of all FTLD-TDP cases, based on TDP-43 pathology alone, suggested that neuropathological heterogeneity was essentially continuously distributed. The FTLD-MND cases exhibited consistently high loadings on PC2 and overlapped with subtypes 2 and 3 of FTLD-TDP. The data suggest: (1) FTLD-MND cases have a consistent pathology, variations in the density of NCI in the DG being the major TDP-43-immunoreactive difference between cases, (2) there are considerable similarities in the neuropathology of FTLD-TDP with and without MND, but with greater neuronal loss in FTLD-MND, and (3) FTLD-MND cases are part of the FTLD-TDP 'continuum' overlapping with FTLD-TDP disease subtypes 2 and 3. © 2012 Nova Science Publishers, Inc. All rights reserved.

A quantitative study of tau pathology in eleven cases of chronic traumatic encephalopathy

AIMS: To quantify tau pathology of chronic traumatic encephalopathy (CTE) and investigate influence of dot-like lesions (DL), brain region, co-morbidity, and sporting career length. METHODS: Densities of neurofibrillary tangles (NFT), astrocytic tangles (AT), DL, oligodendroglial inclusions (GI), neuropil threads (NT), vacuoles, neurons, and enlarged neurons (EN) were measured in tau-immunoreactive sections of upper cortical laminae of frontal and temporal lobe, hippocampus (HC), amygdala, and substantia nigra (SN) of eleven cases of CTE. RESULTS: DL were a consistent finding in CTE. Densities of NFT, NT and DL were greatest in sectors CA1 and CA2 of the HC. Densities of AT were lower than NFT, small numbers of GI were recorded in temporal lobe, and low densities of vacuoles and EN were consistently present. β-amyloid containing neuritic plaques (NP) also occurred at low density. Densities of NFT, NT, DL, and AT were greater in sulci than gyri while vacuole density was greater in gyri. Principal components analysis (PCA) suggested that sporting career length and densities of NFT in entorhinal cortex, NT in CA2 and SN, and vacuolation in the DG were significant sources of variation among cases. CONCLUSION: DL are frequent in CTE suggesting affinity with argyrophilic grain disease (AGD) and Parkinson's disease dementia (PD-Dem). Densities of AT in all regions and NT/DL in sectors CA2/4 were consistent features of CTE. The eleven cases are neuropathologically heterogeneous which may result from genetic diversity, and variation in anatomical pathways subjected to trauma. This article is protected by copyright. All rights reserved.

Relationships Between Hydrology and Soils Describe Vegetation Patterns in Seasonally Flooded Tree Islands of the Southern Everglades, Florida

Tree island ecosystems are important and distinct features of Florida Everglades wetlands. We described the inter-relationships among abiotic factors describing seasonally flooded tree islands and characterized plant–soil relationships in tree islands occurring in a relatively unimpacted area of the Everglades. We used Principal Components Analysis (PCA) to reduce our multi-factor dataset, quantified forest structure and vegetation nutrient dynamics, and related these vegetation parameters to PCA summary variables using linear regression analyses. We found that, of the 21 abiotic parameters used to characterize the ecosystem structure of seasonally flooded tree islands, 13 parameters were significantly correlated with four principal components, and they described 78% of the variance among the study islands. Most variation was described by factors related to soil oxidation and hydrology, exemplifying the sensitivity of tree island structure to hydrologic conditions. PCA summary variables describing tree island structure were related to variability in Chrysobalanus icaco (L.) canopy cover, Ilex cassine (L.) and Salix caroliniana (Michx.) canopy cover, Myrica cerifera (L.) plot frequency, litter turnover, % phosphorus resorption of co-dominant species, and nitrogen nutrient-use efficiency. This study supported findings that vegetation characteristics can be sensitive indicators of variability in tree island ecosystem structure. This study produced valuable, information which was used to recommend ecological targets (i.e. restoration performance measures) for seasonally flooded tree islands in more impacted regions of the Everglades landscape.

Comprehensive Characterization of Measurement Data Gathered by the Pressure Tube to Calandria Tube Gap Probe

Multi-frequency eddy current measurements are employed in estimating pressure tube (PT) to calandria tube (CT) gap in CANDU fuel channels, a critical inspection activity required to ensure fitness for service of fuel channels. In this thesis, a comprehensive characterization of eddy current gap data is laid out, in order to extract further information on fuel channel condition, and to identify generalized applications for multi-frequency eddy current data. A surface profiling technique, generalizable to multiple probe and conductive material configurations has been developed. This technique has allowed for identification of various pressure tube artefacts, has been independently validated (using ultrasonic measurements), and has been deployed and commissioned at Ontario Power Generation. Dodd and Deeds solutions to the electromagnetic boundary value problem associated with the PT to CT gap probe configuration were experimentally validated for amplitude response to changes in gap. Using the validated Dodd and Deeds solutions, principal components analysis (PCA) has been employed to identify independence and redundancies in multi-frequency eddy current data. This has allowed for an enhanced visualization of factors affecting gap measurement. Results of the PCA of simulation data are consistent with the skin depth equation, and are validated against PCA of physical experiments. Finally, compressed data acquisition has been realized, allowing faster data acquisition for multi-frequency eddy current systems with hardware limitations, and is generalizable to other applications where real time acquisition of large data sets is prohibitive.

Face description for perceptual user interfaces

[EN]We investigate mechanisms which can endow the computer with the ability of describing a human face by means of computer vision techniques. This is a necessary requirement in order to develop HCI approaches which make the user feel himself/herself perceived. This paper describes our experiences considering gender, race and the presence of moustache and glasses. This is accomplished comparing, on a set of 6000 facial images, two di erent face representation approaches: Principal Components Analysis (PCA) and Gabor lters. The results achieved using a Support Vector Machine (SVM) based classi er are promising and particularly better for the second representation approach.

Socioeconomic Status and Health Behavior in Nepal

Thesis (Master's)--University of Washington, 2016-07

Characterization of the methanol recovery process at Prio Biocombustíveis S. A.

In a industrial environment, to know the process one is working with is crucial to ensure its good functioning. In the present work, developed at Prio Biocombustíveis S.A. facilities, using process data, collected during the present work, and historical process data, the methanol recovery process was characterized, having started with the characterization of key process streams. Based on the information retrieved from the stream characterization, Aspen Plus® process simulation software was used to replicate the process and perform a sensitivity analysis with the objective of accessing the relative importance of certain key process variables (reflux/feed ratio, reflux temperature, reboiler outlet temperature, methanol, glycerol and water feed compositions). The work proceeded with the application of a set of statistical tools, starting with the Principal Components Analysis (PCA) from which the interactions between process variables and their contribution to the process variability was studied. Next, the Design of Experiments (DoE) was used to acquire experimental data and, with it, create a model for the water amount in the distillate. However, the necessary conditions to perform this method were not met and so it was abandoned. The Multiple Linear Regression method (MLR) was then used with the available data, creating several empiric models for the water at distillate, the one with the highest fit having a R2 equal to 92.93% and AARD equal to 19.44%. Despite the AARD still being relatively high, the model is still adequate to make fast estimates of the distillate’s quality. As for fouling, its presence has been noticed many times during this work. Not being possible to directly measure the fouling, the reboiler inlet steam pressure was used as an indicator of the fouling growth and its growth variation with the amount of Used Cooking Oil incorporated in the whole process. Comparing the steam cost associated to the reboiler’s operation when fouling is low (1.5 bar of steam pressure) and when fouling is high (reboiler’s steam pressure of 3 bar), an increase of about 58% occurs when the fouling increases.

Uso da técnica de análise de componentes principais na redefinição do parâmetro BLA

Dissertação (mestrado)—Universidade de Brasília, Instituto de Física, Programa de Pós-Graduação em Física, 2016.

Professor gender, age, and “hotness” in influencing college students’ generation and interpretation of professor ratings

Undergraduate psychology students rated expectations of a bogus professor (randomly designated a man or woman and hot versus not hot) based on an online rating and sample comments as found on RateMyProfessors.com (RMP). Five professor qualities were derived using principal components analysis (PCA): dedication, attractiveness, enhancement, fairness, and clarity. Participants rated current psychology professors on the same qualities. Current professors were divided based on gender (man or woman), age (under 35 or 35 and older), and attractiveness (at or below the median or above the median). Using multivariate analysis of covariance (MANCOVA), students expected hot professors to be more attractive but lower in clarity. They rated current professors as lowest in clarity when a man and 35 or older. Current professors were rated significantly lower in dedication, enhancement, fairness, and clarity when rated at or below the median on attractiveness. Results, with previous research, suggest numerous factors, largely out of professors’ control, influencing how students interpret and create professor ratings. Caution is therefore warranted in using online ratings to select courses or make hiring and promotion decisions.

Generalized power method for sparse principal component analysis

In this paper we develop a new approach to sparse principal component analysis (sparse PCA). We propose two single-unit and two block optimization formulations of the sparse PCA problem, aimed at extracting a single sparse dominant principal component of a data matrix, or more components at once, respectively. While the initial formulations involve nonconvex functions, and are therefore computationally intractable, we rewrite them into the form of an optimization program involving maximization of a convex function on a compact set. The dimension of the search space is decreased enormously if the data matrix has many more columns (variables) than rows. We then propose and analyze a simple gradient method suited for the task. It appears that our algorithm has best convergence properties in the case when either the objective function or the feasible set are strongly convex, which is the case with our single-unit formulations and can be enforced in the block case. Finally, we demonstrate numerically on a set of random and gene expression test problems that our approach outperforms existing algorithms both in quality of the obtained solution and in computational speed. © 2010 Michel Journée, Yurii Nesterov, Peter Richtárik and Rodolphe Sepulchre.

A Near-Infrared Reflectance Spectroscopy Method for Direct Analysis of Several Chemical Components and Properties of Fruit, for Example, Chinese Hawthorn

Near-infrared spectroscopy (NIRS) calibrations were developed for the discrimination of Chinese hawthorn (Crataegus pinnatifida Bge. var. major) fruit from three geographical regions as well as for the estimation of the total sugar, total acid, total phenolic content, and total antioxidant activity. Principal component analysis (PCA) was used for the discrimination of the fruit on the basis of their geographical origin. Three pattern recognition methods, linear discriminant analysis, partial least-squares-discriminant analysis, and back-propagation artificial neural networks, were applied to classify and compare these samples. Furthermore, three multivariate calibration models based on the first derivative NIR spectroscopy, partial least-squares regression, back-propagation artificial neural networks, and least-squares-support vector machines, were constructed for quantitative analysis of the four analytes, total sugar, total acid, total phenolic content, and total antioxidant activity, and validated by prediction data sets.

Frequency response function based damage identification using principal component analysis and pattern recognition technique

Pattern recognition is a promising approach for the identification of structural damage using measured dynamic data. Much of the research on pattern recognition has employed artificial neural networks (ANNs) and genetic algorithms as systematic ways of matching pattern features. The selection of a damage-sensitive and noise-insensitive pattern feature is important for all structural damage identification methods. Accordingly, a neural networks-based damage detection method using frequency response function (FRF) data is presented in this paper. This method can effectively consider uncertainties of measured data from which training patterns are generated. The proposed method reduces the dimension of the initial FRF data and transforms it into new damage indices and employs an ANN method for the actual damage localization and quantification using recognized damage patterns from the algorithm. In civil engineering applications, the measurement of dynamic response under field conditions always contains noise components from environmental factors. In order to evaluate the performance of the proposed strategy with noise polluted data, noise contaminated measurements are also introduced to the proposed algorithm. ANNs with optimal architecture give minimum training and testing errors and provide precise damage detection results. In order to maximize damage detection results, the optimal architecture of ANN is identified by defining the number of hidden layers and the number of neurons per hidden layer by a trial and error method. In real testing, the number of measurement points and the measurement locations to obtain the structure response are critical for damage detection. Therefore, optimal sensor placement to improve damage identification is also investigated herein. A finite element model of a two storey framed structure is used to train the neural network. It shows accurate performance and gives low error with simulated and noise-contaminated data for single and multiple damage cases. As a result, the proposed method can be used for structural health monitoring and damage detection, particularly for cases where the measurement data is very large. Furthermore, it is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy under varying levels of damage.