Principal component and decomposition analysis of multicomponent mixtures of carcinogenic fluorophores

1980 ◽  
Vol 122 (2) ◽  
pp. 171-178 ◽  
Author(s):  
Harvey S. Gold ◽  
Gregory T. Rasmussen ◽  
Janet A. Mercer-Smith ◽  
David G. Whitten ◽  
Richard P. Buck
Keyword(s):  
Decomposition Analysis ◽  
Principal Component ◽  
Multicomponent Mixtures

scholarly journals Extended-range predictability of sudden stratospheric warming events suggested by mode decomposition

2021 ◽  
Author(s):  
Zheng Wu ◽  
Bernat Jiménez-Esteve ◽  
Raphaël de Fondeville ◽  
Enikő Székely ◽  
Guillaume Obozinski ◽  
...  
Keyword(s):  
Decomposition Analysis ◽  
Polar Vortex ◽  
Principal Component ◽  
Empirical Orthogonal Functions ◽  
Rate Of Change ◽  
The Arctic ◽  
Lead Times ◽  
Main Contributor ◽  
Budget Analysis ◽  
Mode Decomposition

Abstract. Major sudden stratospheric warmings (SSWs) are extreme wintertime circulation events of the Arctic stratosphere that are accompanied by a breakdown of the polar vortex and are considered an important source of predictability of tropospheric weather on subseasonal to seasonal time scales over the Northern Hemisphere mid- and high- latitudes. However, SSWs themselves are difficult to forecast, with a predictability limit of around one to two weeks. The predictability limit for determining the type of event, i.e., wave-1 or wave-2 events, is even shorter. Here we analyze the dynamics of the vortex breakdown and look for early signs of the vortex deceleration process with lead times beyond the current predictability limit of SSWs. To this end, we employ a mode decomposition analysis to analyze potential vorticity (PV) equation on the 850 K isentropic surface by decomposing each term in the PV equation using the empirical orthogonal functions of the PV. The first principal component (PC) is an indicator of the strength of the polar vortex and starts to increase from around 25 days before the onset of SSWs, indicating a deceleration of the polar vortex. We then use a budget analysis based on the mode decomposition to characterize the contribution of the linear and the nonlinear PV advection terms to the rate of change (tendency) of the first PC. The linear PV advection is the main contributor to the PC tendency at 15 to 25 days before the onset of both types of SSW events. The nonlinear PV advection becomes important between 1 to 15 days before the onset of wave-2 events, while the linear PV advection continues to be the main contributor for wave-1 events. By linking the PV advection to the PV flux, we find that the linear PV flux is important for both types of SSWs from 15 to 25 days before the events but with different wave-2 spatial patterns, while the nonlinear PV flux displays a wave-3 wave pattern, which finally leads to a split of the polar vortex. The signals found here indicate that both the lead times for predicting the SSW onset and the lead times for predicting the type of the SSW event could potentially be extended beyond the current predictability limit of one to two weeks.

scholarly journals Supervised and Unsupervised Classification of Cocoa Bean Origin and Processing using Liquid Chromatography-Mass Spectrometry

2020 ◽  
Author(s):  
Santhust Kumar ◽  
Roy N. D’Souza ◽  
Britta Behrends ◽  
Marcello Corno ◽  
Matthias S. Ullrich ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Liquid Chromatography ◽  
Geographic Origin ◽  
Decomposition Analysis ◽  
Principal Component ◽  
Unsupervised Classification ◽  
Nonlinear Dependence ◽  
Cocoa Bean ◽  
Liquid Chromatography Mass Spectrometry ◽  
Chromatography Mass Spectrometry

ABSTRACTLiquid Chromatography-Mass Spectrometry (LC-MS) provides an unprecedented wealth of metabolomics information for food products, including insights into compositional changes during food processing. Here, we employed the largest available LC-MS dataset of around 300 cocoa bean samples to assess the capability of two popular multivariate classification methods, principal component analysis (PCA) and linear decomposition analysis (LDA), for studying bean geographic origin and responsible characteristic compounds.The unsupervised method, PCA, only provides a limited separation in bean origin. Expectedly, the supervised method, LDA, provides a better origin clustering. However, it suffers from a strong, nonlinear dependence on the set of compounds used in the analysis. We show that for LDA a compound filtering criterion based on Gaussian intensity distributions dramatically enhances origin clustering of samples, thus increasing its predictive efficiency. In this form, the supervised method of LDA holds the possibility to identify potential markers of a specific origin.

scholarly journals Socioeconomic inequality in congenital heart diseases in Iran

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Mostafa Amini-Rarani ◽  
Sajad Vahedi ◽  
Maryam Borjali ◽  
Mehdi Nosratabadi
Keyword(s):  
Congenital Heart ◽  
Socioeconomic Inequality ◽  
Heart Diseases ◽  
Decomposition Analysis ◽  
Principal Component ◽  
Congenital Heart Diseases ◽  
Chi Square ◽  
Cross Sectional ◽  
Biological Variables ◽  
The Social

Abstract Introduction Social-economic factors have an important role in shaping inequality in congenital heart diseases. The current study aimed to assess and decompose the socio-economic inequality in Congenital Heart Diseases (CHDs) in Iran. Methods This is a cross-sectional research conducted at Shahid Rajaie Cardiovascular Medical and Research Center in Tehran, Iran, as one of the largest referral heart hospitals in Asia. Data were collected primarily from 600 mothers who attended in pediatric cardiology department in 2020. The polychoric principal component analysis (PCA) and Errygers corrected CI (ECI) were used to construct household socioeconomic status and to assess inequality in CHDs, respectively. A regression-based decomposition analysis was also applied to explain socioeconomic-related inequalities. To select the explanatory social, medical/biological, and lifestyle variables, the chi-square test was first used. Results There was a significant pro-rich inequality in CHDs (ECI = -0.65, 95% CI, − 0.72 to − 0.58). The social, medical/biological, and lifestyle variables accounted for 51.47, 43.25, and 3.92% of inequality in CHDs, respectively. Among the social variables, family SES (about 50%) and mother’s occupation (21.05%) contributed the most to CHDs’ inequality. Besides, in the medical/biological group, receiving pregnancy care (22.06%) and using acid folic (15.70%) had the highest contribution. Conclusion We concluded that Iran suffers from substantial socioeconomic inequality in CHDs that can be predominantly explained by social and medical/biological variables. It seems that distributional policies aim to reduce income inequality while increasing access of prenatal care and folic acid for disadvantaged mothers could address this inequality much more strongly in Iran.

scholarly journals Liquid Chromatography and Chemometric-Assisted Spectrophotometric Methods for the Analysis of Two Multicomponent Mixtures Containing Cough Suppressant Drugs

2005 ◽  
Vol 88 (4) ◽  
pp. 1069-1080 ◽  
Author(s):  
Alaa El-Gindy ◽  
Samy Emara ◽  
Mostafa K Mesbah ◽  
Ghada M Hadad
Keyword(s):  
Liquid Chromatography ◽  
Principal Component Regression ◽  
Principal Component ◽  
Uv Spectra ◽  
Chemometric Methods ◽  
Multicomponent Mixtures ◽  
Chlorpheniramine Maleate ◽  
Phenylephrine Hydrochloride ◽  
Spectrophotometric Methods ◽  
Acid Sodium Salt

Abstract Three methods were applied for the analysis of 2 multicomponent mixtures containing dextromethorphan hydrobromide, phenylephrine hydrochloride, chlorpheniramine maleate, methylparaben, and propylparaben, together with either sodium benzoate (Mix 1) or ephedrine hydrochloride and benzoic acid (Mix 2). In the first method, liquid chromatography was used for their simultaneous determination using an ODS column with a mobile phase consisting of acetonitrile–phosphate buffer, pH 2.7 (40 + 60, v/v), containing 5mM heptanesulfonic acid sodium salt and ultraviolet (UV) detection at 214 nm. Also, 2 chemometric methods, principal component regression, and partial least squares were used. For both chemometric calibrations, a concentration set of the mixture consisting of each compound in each mixture was prepared in distilled water. The absorbance data in the UV spectra were measured for the 76 or 71 wavelength points in the spectral region 210–240 or 210–224 nm considering the intervals of Δλ = 0.4 or 0.2 nm for Mix 1 and Mix 2, respectively. The 2 chemometric methods did not require any separation step. These methods were successfully applied for the analysis of the 2 multicomponent combinations in synthetic mixtures and in commercial syrups, and the results were compared with each other.

On the Physics of Conversion of Longitudinal Elastic Waves Into Extended Vibrations in a Suspended Aluminum Rod

2013 ◽  
Author(s):  
Ioannis T. Georgiou
Keyword(s):  
Wave Propagation ◽  
Elastic Waves ◽  
High Performance ◽  
Decomposition Analysis ◽  
Principal Component ◽  
Piezoelectric Sensor ◽  
Characteristic Time Scale ◽  
Two Stage ◽  
Pulse Waves ◽  
Time Frames

This work concerns a two stage decomposition analysis of a dynamics phenomenon due to reflection of longitudinal pulse elastic waves in long elastic rods. Elastic pulse waves are induced by impacting a miniature modal hammer at one of its free ends whereas its dynamics is recorded by a high performance piezoelectric sensor at the other end. An underlining characteristic time scale leads to a natural decomposition of experimental time series of wave acceleration into a sequence of time frames. The signals are viewed as a sequence of time frames and thus are analyzed globally and locally by further decomposing them into their intrinsic Proper Orthogonal Decomposition Modes (POD) or Principal Component Analysis (PCA) modes. It is found that experimental signals of acceleration during propagation of elastic pulse waves are governed by a small number of POD modes; one of the modes is dominant. It is conjectured that these intrinsic modes of the time frame-arranged signals represent physical modes of pulse wave propagation. The introduced method of the two-stage decomposition analysis is potentially useful for data-driven analysis in wave propagation-based damage detection.

Enhancing the detection of buried anthropogenic features thought a cloud-computing Python procedure

2021 ◽  
Author(s):  
Filippo Brandolini ◽  
Guillem Domingo-Ribas ◽  
Andrea Zerboni ◽  
Sam Turner
Keyword(s):  
Sustainable Development ◽  
Satellite Imagery ◽  
Environmental Changes ◽  
Scientific Information ◽  
Decomposition Analysis ◽  
Principal Component ◽  
Bare Soil ◽  
Google Earth ◽  
Po Plain ◽  
The World

<p>The necessity of sustainable development for landscapes has emerged as an important theme in recent decades. Moreover, past landscape reconstruction enables a better understanding of human resilience to climatic and environmental changes in different periods and locations, and illustrates examples of sustainable development in the past. Free and open-source (FOSS) datasets of satellite imagery offer considerable opportunities for landscape heritage stakeholders both for recording and monitoring activities. In this research, a completely FOSS-cloud procedure to enhance the detection of palaeo-landscape features is presented. Sentinel - 2 satellite imagery has been retrieved in the Google Earth Engine dataset collection and analysed through a Python script code realized in Google Colaboratory. A multi-temporal approach has been adopted to investigate the potential of satellite imagery to detect buried features along with Spectral Index (i.e., RGB, False Short Wave Infrared Colour and Bare Soil Index) and Spectral Decomposition analysis (i.e., Hue, Saturation and Value, Tasselled Cap Transformation and Principal Component Analysis). This procedure has been tested in the Po Plain (Northern Italy), chosen because it is characterized by human-landscape interaction since the Mid-Holocene. Thanks to its complex settlement and land-management history, the Po Plain represents an ideal laboratory to assess the potentiality of satellite imagery to enhance riverscapes’ palaeo-features.  The outputs obtained can be visualized directly in the Google Colaboratory browser or downloaded via Google Drive for further graphical applications or spatial analysis. The buried features detected have been checked through the available geomorphological and archaeological literature; published case studies interpreting the occurrence of buried features served as a benchmark to validate the script code developed. This research represents one of the first applications of the GEE Python API in landscape studies. The main advantages of this procedure consist of: i) being FOSS, all the software used here are open-licensed; ii) working in cloud, no powerful hardware is necessary to run the script code; iii) high adaptability, changing the ROI is possible to calculate SI and SD outputs for any area of the world; iv) very basic coding skills are required to adapt the code to a ROI with different environmental characteristics. The development of FOSS-cloud procedures could support the identification, conservation and management of cultural and natural heritage anywhere around the world. In remote areas or where local heritage is threatened as a result of political instability, climate change or other factors, FOSS-cloud protocols can facilitate the application of new scientific methods and enable the dissemination of and access to scientific information.</p>

Growth equity and sectoral decompositions of aggregate poverty changes in Cameroon

2018 ◽  
Vol 9 (1) ◽  
pp. 56-71
Author(s):  
Johannes Tabi Atemnkeng ◽  
Daniel Mbu Tambi
Keyword(s):  
Poverty Reduction ◽  
Methodological Approach ◽  
Decomposition Analysis ◽  
Principal Component ◽  
Well Being ◽  
Income Poverty ◽  
Household Asset ◽  
Content Type ◽  
Household Assets ◽  
Physical Assets

Purpose The purpose of this paper is to provide insight to policy-makers into a framework for action, which is needed to effectively reduce poverty in its monetary and non-monetary dimensions. Design/methodology/approach Specifically, an exact decomposition analysis is conducted that is based on the Shapley value method, and investigated the growth and redistribution effects as well as changes due to mobility and sector-specific effects of the variation in both income/expenditure and non-income poverty dimensions. Findings Growth in mean consumption and household assets accounted for the bulk of the improvement in poverty reduction and the results complement the evidence obtained from the “sectoral decomposition” of poverty in Cameroon which may indeed have a strong bearing on the sectoral shares of poverty. The temptation is resisted, however, not to deny that redistribution also has an important role to play, yet there must be severe limits to what can be achieved by growth neutral redistribution. The redistribution effect had an ameliorating tendency in household asset deprivation among farming households. Originality/value This paper is a well-written piece using quite rigorous and interesting methodological approach. To obtain a measure of non-income dimensions of well-being, the authors constructed composite indices on household assets reflecting household access to a range of physical assets and services including human capital by polychoric principal component analysis method.

scholarly journals Socio-economic inequality in unhealthy snacks consumption among adolescent students in Iran: a concentration index decomposition analysis

2019 ◽  
Vol 22 (12) ◽  
pp. 2179-2188 ◽  
Author(s):  
Vahid Yazdi-Feyzabadi ◽  
Arash Rashidian ◽  
Mostafa Amini Rarani
Keyword(s):  
Concentration Index ◽  
Economic Status ◽  
Economic Inequality ◽  
Decomposition Analysis ◽  
Principal Component ◽  
Cross Sectional Study ◽  
Future Health ◽  
Cross Sectional ◽  
Adolescent Students ◽  
Unhealthy Snacks

AbstractObjective:The present study aimed to assess and decompose the socio-economic inequality in unhealthy snacks consumption among adolescent students in Kerman, Iran.Design:The data were obtained from a cross-sectional study. Principal component analysis was done to measure the socio-economic status (SES) of the adolescents’ families and the normalized concentration index (NCI) was used to measure the inequality in unhealthy snacks consumption among adolescent students of different SES. The contributions of environmental and individual explanatory variables to inequality were assessed by decomposing the concentration index.Setting:Forty secondary schools of Kerman Province in Iran in 2015.Participants:Eighth-grade adolescent students (n 1320).Results:The data of 1242 adolescent students were completed for the current study. Unhealthy snacks consumption was unequally distributed among adolescent students and was concentrated mainly among the high-SES adolescents (NCI = 0·179; 95 % CI 0·056, 0·119). The decomposition showed that higher SES (62 %) and receiving pocket money allowance (31 %), as environmental variables, had the highest positive contributions to the measured inequality in unhealthy snacks consumption. Taste and sensory perception (7 %) as well as cost sensitivity (5 %), as individual variables, followed them in terms of their contribution importance.Conclusions:It is highly suggested that both environmental and individual factors should be addressed at different settings including schools, families and suppliers of unhealthy snacks. These findings can help future health promotion strategies in Iran to tackle the observed inequality in unhealthy snacks consumption.

Digital Processing of STEM Images

1981 ◽  
Vol 39 ◽  
pp. 236-237
Author(s):  
A. V. Crewe ◽  
M. Ohtsuki
Keyword(s):  
High Resolution ◽  
Low Dose ◽  
Assembly Language ◽  
Processing System ◽  
Principal Component ◽  
Digital Processing ◽  
Image Analysis System ◽  
Black And White ◽  
Analysis System ◽  
Dose Imaging

We have assembled an image processing system for use with our high resolution STEM for the particular purpose of working with low dose images of biological specimens. The system is quite flexible, however, and can be used for a wide variety of images.The original images are stored on magnetic tape at the microscope using the digitized signals from the detectors. For low dose imaging, these are “first scan” exposures using an automatic montage system. One Nova minicomputer and one tape drive are dedicated to this task.The principal component of the image analysis system is a Lexidata 3400 frame store memory. This memory is arranged in a 640 x 512 x 16 bit configuration. Images are displayed simultaneously on two high resolution monitors, one color and one black and white. Interaction with the memory is obtained using a Nova 4 (32K) computer and a trackball and switch unit provided by Lexidata.The language used is BASIC and uses a variety of assembly language Calls, some provided by Lexidata, but the majority written by students (D. Kopf and N. Townes).

Scanning x-ray fluorescence microscopy and principal component analysis

1992 ◽  
Vol 50 (2) ◽  
pp. 1752-1753
Author(s):  
Brian Cross
Keyword(s):  
Principal Component Analysis ◽  
Fluorescence Microscopy ◽  
Spatial Resolution ◽  
High Speed ◽  
Image Acquisition ◽  
Principal Component ◽  
Fluorescence Microscope ◽  
Acquisition Rate ◽  
X Ray ◽  
Speed Up

A relatively new entry, in the field of microscopy, is the Scanning X-Ray Fluorescence Microscope (SXRFM). Using this type of instrument (e.g. Kevex Omicron X-ray Microprobe), one can obtain multiple elemental x-ray images, from the analysis of materials which show heterogeneity. The SXRFM obtains images by collimating an x-ray beam (e.g. 100 μm diameter), and then scanning the sample with a high-speed x-y stage. To speed up the image acquisition, data is acquired "on-the-fly" by slew-scanning the stage along the x-axis, like a TV or SEM scan. To reduce the overhead from "fly-back," the images can be acquired by bi-directional scanning of the x-axis. This results in very little overhead with the re-positioning of the sample stage. The image acquisition rate is dominated by the x-ray acquisition rate. Therefore, the total x-ray image acquisition rate, using the SXRFM, is very comparable to an SEM. Although the x-ray spatial resolution of the SXRFM is worse than an SEM (say 100 vs. 2 μm), there are several other advantages.

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