Statistical interpretation of medical data of patients with alcohol abuse

Open Medicine ◽  
2012 ◽  
Vol 7 (4) ◽  
pp. 465-474 ◽  
Author(s):  
Ventzislav Bardarov ◽  
Pavlina Simeonova ◽  
Ludmila Neikova ◽  
Krum Bardarov ◽  
Vasil Simeonov ◽  
...  
Keyword(s):  
Alcohol Abuse ◽  
Liver Function ◽  
Ethanol Metabolism ◽  
Statistical Interpretation ◽  
Anthropometric Data ◽  
Multivariate Statistical ◽  
Data Set ◽  
Components Analysis ◽  
Significant Patterns

AbstractAn attempt is made to assess a set of biochemical, kinetic and anthropometric data for patients suffering from alcohol abuse (alcoholics) and healthy patients (non-alcoholics). The main goal is to identify the data set structure, finding groups of similarity among the clinical parameters or among the patients. Multivariate statistical methods (cluster analysis and principal components analysis) were used to assess the data collection. Several significant patterns of related parameters were found to be representative of the role of the liver function, kinetic and anthropometric indicators (conditionally named “liver function factor”, “ethanol metabolism factor”, “body weight factor”, and “acetaldehyde metabolic factor”). An effort is made to connect the role of kinetic parameters for acetaldehyde metabolism with biochemical, ethanol kinetic and anthropometric data in parallel.

Chemometric exploratory data analysis for patients with diabetes type 2 and diabetic complications

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Miroslava Nedyalkova ◽  
Ralitsa Robeva ◽  
Atanaska Elenkova ◽  
Vasil Simeonov
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Diabetes Type 2 ◽  
Latent Factors ◽  
Multivariate Statistical ◽  
Patients With Diabetes ◽  
Exploratory Data ◽  
Components Analysis

Abstract The present study deals with the interpretation and modeling of clinical data for patients with diabetes mellitus type 2 (DMT2) additionally diagnosed with complications of the disease by the use of multivariate statistical methods. The major goal is to determine some specific clinical descriptors characterizing each health problem by applying the options of the exploratory data analysis. The results from the statistical analysis are commented in details by medical reasons for each of the complications. It was found that each of the complications is characterized by specific medical descriptors linked into each one of the five latent factors identified by factor and principal components analysis. Such an approach to interpret concomitant to DMT2 complications is original and allows a better understanding of the role of clinical parameters for diagnostic and prevention goals.

Adaptive multiscale principal components analysis for online monitoring of wastewater treatment

2002 ◽  
Vol 45 (4-5) ◽  
pp. 227-235 ◽  
Author(s):  
J. Lennox ◽  
C. Rosen
Keyword(s):  
Wastewater Treatment ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Fault Detection And Isolation ◽  
Scale Model ◽  
Multivariate Statistical ◽  
Data Set ◽  
Significant Difference ◽  
Components Analysis ◽  
Adaptive Pca

Fault detection and isolation (FDI) are important steps in the monitoring and supervision of industrial processes. Biological wastewater treatment (WWT) plants are difficult to model, and hence to monitor, because of the complexity of the biological reactions and because plant influent and disturbances are highly variable and/or unmeasured. Multivariate statistical models have been developed for a wide variety of situations over the past few decades, proving successful in many applications. In this paper we develop a new monitoring algorithm based on Principal Components Analysis (PCA). It can be seen equivalently as making Multiscale PCA (MSPCA) adaptive, or as a multiscale decomposition of adaptive PCA. Adaptive Multiscale PCA (AdMSPCA) exploits the changing multivariate relationships between variables at different time-scales. Adaptation of scale PCA models over time permits them to follow the evolution of the process, inputs or disturbances. Performance of AdMSPCA and adaptive PCA on a real WWT data set is compared and contrasted. The most significant difference observed was the ability of AdMSPCA to adapt to a much wider range of changes. This was mainly due to the flexibility afforded by allowing each scale model to adapt whenever it did not signal an abnormal event at that scale. Relative detection speeds were examined only summarily, but seemed to depend on the characteristics of the faults/disturbances. The results of the algorithms were similar for sudden changes, but AdMSPCA appeared more sensitive to slower changes.

Multivariate statistical approaches as applied to environmental physics studies

Open Physics ◽  
2006 ◽  
Vol 4 (2) ◽  
Author(s):  
Vasil Lovchinov ◽  
Stefan Tsakovski
Keyword(s):  
Surface Waters ◽  
Principal Components ◽  
Coastal Sediments ◽  
Principal Components Regression ◽  
Latent Factors ◽  
Multivariate Statistical ◽  
Data Set ◽  
Components Analysis ◽  
Substantial Interest ◽  
Environmental Physics

AbstractThe present communication deals with the application of the most important environmetric approaches like cluster analysis, principal components analysis and principal components regression (apportioning models) to environmental systems which are of substantial interest for environmental physics — surface waters, aerosols, and coastal sediments. Using various case studies we identify the latent factors responsible for the data set structure and construct models showing the contribution of each identified source (anthropogenic or natural) to the total measure of the pollution. In this way the information obtained by the monitoring data becomes broader and more intelligent, which help in problem solving in environmental physics.

Molecular Views of Ice-Embedded Lumbricus Terrestris Erythrocruorin Obtained By Invariant Classification

1990 ◽  
Vol 48 (1) ◽  
pp. 450-451
Author(s):  
Michael schatz ◽  
Joachim Jäger ◽  
Marin van Heel
Keyword(s):  
Image Interpretation ◽  
Lumbricus Terrestris ◽  
Reference Image ◽  
Large Set ◽  
Multivariate Statistical ◽  
Data Set ◽  
Low Contrast ◽  
Negative Stain ◽  
Reference Images ◽  
Statistical Resolution

Lumbricus terrestris erythrocruorin is a giant oxygen-transporting macromolecule in the blood of the common earth worm (worm "hemoglobin"). In our current study, we use specimens (kindly provided by Drs W.E. Royer and W.A. Hendrickson) embedded in vitreous ice (1) to avoid artefacts encountered with the negative stain preparation technigue used in previous studies (2-4).Although the molecular structure is well preserved in vitreous ice, the low contrast and high noise level in the micrographs represent a serious problem in image interpretation. Moreover, the molecules can exhibit many different orientations relative to the object plane of the microscope in this type of preparation. Existing techniques of analysis requiring alignment of the molecular views relative to one or more reference images often thus yield unsatisfactory results.We use a new method in which first rotation-, translation- and mirror invariant functions (5) are derived from the large set of input images, which functions are subsequently classified automatically using multivariate statistical techniques (6). The different molecular views in the data set can therewith be found unbiasedly (5). Within each class, all images are aligned relative to that member of the class which contributes least to the classes′ internal variance (6). This reference image is thus the most typical member of the class. Finally the aligned images from each class are averaged resulting in molecular views with enhanced statistical resolution.

Ethanol Metabolism in Vivo and the Role of Hepatic Microsomal Ethanol Oxidation

1972 ◽  
Vol 33 (3) ◽  
pp. 751-755 ◽  
Author(s):  
Mary K. Roach ◽  
Myrna Khan ◽  
Marguerite Knapp ◽  
W. N. Reese
Keyword(s):  
Ethanol Oxidation ◽  
Ethanol Metabolism

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

Engagement Through the Diaspora Channel

2017 ◽  
Author(s):  
Michael S. Danielson
Keyword(s):  
Civil Society ◽  
Statistical Models ◽  
State Level ◽  
The State ◽  
Multivariate Statistical ◽  
Political Factors ◽  
Data Set ◽  
Level Data ◽  
Municipal Level

The first empirical task is to identify the characteristics of municipalities which US-based migrants have come together to support financially. Using a nationwide, municipal-level data set compiled by the author, the chapter estimates several multivariate statistical models to compare municipalities that did not benefit from the 3x1 Program for Migrants with those that did, and seeks to explain variation in the number and value of 3x1 projects. The analysis shows that migrants are more likely to contribute where migrant civil society has become more deeply institutionalized at the state level and in places with longer histories as migrant-sending places. Furthermore, the results suggest that political factors are at play, as projects have disproportionately benefited states and municipalities where the PAN had a stronger presence, with fewer occurring elsewhere.

Vocational Development in Emerging Adulthood

2018 ◽  
Author(s):  
Michael W. Pratt ◽  
M. Kyle Matsuba
Keyword(s):  
Turning Point ◽  
Life Stories ◽  
Life Story ◽  
Emerging Adult ◽  
Vocational Development ◽  
Data Set ◽  
The World ◽  
Key Concepts

Chapter 6 reviews research on the topic of vocational/occupational development in relation to the McAdams and Pals tripartite personality framework of traits, goals, and life stories. Distinctions between types of motivations for the work role (as a job, career, or calling) are particularly highlighted. The authors then turn to research from the Futures Study on work motivations and their links to personality traits, identity, generativity, and the life story, drawing on analyses and quotes from the data set. To illustrate the key concepts from this vocation chapter, the authors end with a case study on Charles Darwin’s pivotal turning point, his round-the-world voyage as naturalist for the HMS Beagle. Darwin was an emerging adult in his 20s at the time, and we highlight the role of this journey as a turning point in his adult vocational development.

scholarly journals An Algorithm for the Removal of Cosmic Ray Artifacts in Spectral Data Sets

2019 ◽  
Vol 73 (8) ◽  
pp. 893-901
Author(s):  
Sinead J. Barton ◽  
Bryan M. Hennelly
Keyword(s):  
Cosmic Ray ◽  
Data Sets ◽  
Biological Cells ◽  
Statistical Classification ◽  
Signal To Noise ◽  
Multivariate Statistical ◽  
Data Set ◽  
Artefact Removal ◽  
Single Capture ◽  
Acquisition Method

Cosmic ray artifacts may be present in all photo-electric readout systems. In spectroscopy, they present as random unidirectional sharp spikes that distort spectra and may have an affect on post-processing, possibly affecting the results of multivariate statistical classification. A number of methods have previously been proposed to remove cosmic ray artifacts from spectra but the goal of removing the artifacts while making no other change to the underlying spectrum is challenging. One of the most successful and commonly applied methods for the removal of comic ray artifacts involves the capture of two sequential spectra that are compared in order to identify spikes. The disadvantage of this approach is that at least two recordings are necessary, which may be problematic for dynamically changing spectra, and which can reduce the signal-to-noise (S/N) ratio when compared with a single recording of equivalent duration due to the inclusion of two instances of read noise. In this paper, a cosmic ray artefact removal algorithm is proposed that works in a similar way to the double acquisition method but requires only a single capture, so long as a data set of similar spectra is available. The method employs normalized covariance in order to identify a similar spectrum in the data set, from which a direct comparison reveals the presence of cosmic ray artifacts, which are then replaced with the corresponding values from the matching spectrum. The advantage of the proposed method over the double acquisition method is investigated in the context of the S/N ratio and is applied to various data sets of Raman spectra recorded from biological cells.

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