Chromatographic Fingerprints Combined with Chemometric Methods Reveal the Chemical Features of Authentic Radix Polygalae

2017 ◽  
Vol 100 (1) ◽  
pp. 30-37 ◽  
Author(s):  
Zhongquan Xin ◽  
Dabing Ren ◽  
Xiaojuan Zhang ◽  
Zhibiao Yi ◽  
Lunzhao Yi
Keyword(s):  
Factor Analysis ◽  
Cross Validation ◽  
Permutation Tests ◽  
Predictive Ability ◽  
Volatile Components ◽  
Chemometric Methods ◽  
Chromatographic Fingerprints ◽  
Discrimination Model ◽  
Subwindow Factor Analysis ◽  
Pls Discriminant Analysis

Abstract GC-MS fingerprints of Radix Polygalae (RP) were measured for deliberately collected samples. A total of 88 volatile components were identified and quantified by subwindow factor analysis, heuristic evolving latent projection, and retention index. Next, an efficient discrimination model based on partial least-squares (PLS) discriminant analysis (DA) was developed to distinguish the superior RP samples from the inferior ones, and the reliability and predictive ability of the model was evaluated by cross-validation and permutation tests. Furthermore, four components (1-octanol, shyobunone, isobornyl acetate, and α-asarone) were screened by coefficient β of PLS-DA. They represented the important chemical features of authentic RP and could be applied to the accurate discrimination and QC of RP in the future. Our results suggest that chromatographic fingerprints coupled with chemometric methods provide an effective and convenient strategy for QC of RP and are helpful for revealing the chemical features of a complex analytical sample.

scholarly journals The Comprehensive Autistic Trait Inventory (CATI): development and validation of a new measure of autistic traits in the general population

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Michael C. W. English ◽  
Gilles E. Gignac ◽  
Troy A. W. Visser ◽  
Andrew J. O. Whitehouse ◽  
James T. Enns ◽  
...  
Keyword(s):  
Factor Analysis ◽  
General Population ◽  
Measurement Invariance ◽  
High Reliability ◽  
Predictive Ability ◽  
Autistic Traits ◽  
Autism Spectrum ◽  
Autism Spectrum Quotient ◽  
Sensory Sensitivity ◽  
Wide Range

Abstract Background Traits and characteristics qualitatively similar to those seen in diagnosed autism spectrum disorder can be found to varying degrees in the general population. To measure these traits and facilitate their use in autism research, several questionnaires have been developed that provide broad measures of autistic traits [e.g. Autism-Spectrum Quotient (AQ), Broad Autism Phenotype Questionnaire (BAPQ)]. However, since their development, our understanding of autism has grown considerably, and it is arguable that existing measures do not provide an ideal representation of the trait dimensions currently associated with autism. Our aim was to create a new measure of autistic traits that reflects our current understanding of autism, the Comprehensive Autism Trait Inventory (CATI). Methods In Study 1, 107 pilot items were administered to 1119 individuals in the general population and exploratory factor analysis of responses used to create the 42-item CATI comprising six subscales: Social Interactions, Communication, Social Camouflage, Repetitive Behaviours, Cognitive Rigidity, and Sensory Sensitivity. In Study 2, the CATI was administered to 1068 new individuals and confirmatory factor analysis used to verify the factor structure. The AQ and BAPQ were administered to validate the CATI, and additional autistic participants were recruited to compare the predictive ability of the measures. In Study 3, to validate the CATI subscales, the CATI was administered to 195 new individuals along with existing valid measures qualitatively similar to each CATI subscale. Results The CATI showed convergent validity at both the total-scale (r ≥ .79) and subscale level (r ≥ .68). The CATI also showed superior internal reliability for total-scale scores (α = .95) relative to the AQ (α = .90) and BAPQ (α = .94), consistently high reliability for subscales (α > .81), greater predictive ability for classifying autism (Youden’s Index = .62 vs .56–.59), and demonstrated measurement invariance for sex. Limitations Analyses of predictive ability for classifying autism depended upon self-reported diagnosis or identification of autism. The autistic sample was not large enough to test measurement invariance of autism diagnosis. Conclusions The CATI is a reliable and economical new measure that provides observations across a wide range of trait dimensions associated with autism, potentially precluding the need to administer multiple measures, and to our knowledge, the CATI is also the first broad measure of autistic traits to have dedicated subscales for social camouflage and sensory sensitivity.

Abstract W MP37: Novel Prognostic Scores for Early Prediction of Outcome Following Aneurysmal Subarachnoid Hemorrhage

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
Blessing Jaja ◽  
Hester Lingsma ◽  
Ewout Steyerberg ◽  
R. Loch Macdonald ◽  
Keyword(s):  
Subarachnoid Hemorrhage ◽  
Cross Validation ◽  
Prediction Models ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Model Performance ◽  
Predictor Variable ◽  
Predictive Ability ◽  
Prognostic Scores ◽  
Operating Characteristics ◽  
Fisher Grade

Background: Aneurysmal subarachnoid hemorrhage (SAH) is a cerebrovascular emergency. Currently, clinicians have limited tools to estimate outcomes early after hospitalization. We aimed to develop novel prognostic scores using large cohorts of patients reflecting experience from different settings. Methods: Logistic regression analysis was used to develop prediction models for mortality and unfavorable outcomes according to 3-month Glasgow outcome score after SAH based on readily obtained parameters at hospital admission. The development cohort was derived from 10 prospective studies involving 10936 patients in the Subarachnoid Hemorrhage International Trialists (SAHIT) repository. Model performance was assessed by bootstrap internal validation and by cross validation by omission of each of the 10 studies, using R2 statistic, Area under the receiver operating characteristics curve (AUC), and calibration plots. Prognostic scores were developed from the regression coefficients. Results: Predictor variable with the strongest prognostic strength was neurologic status (partial R2 = 12.03%), followed by age (1.91%), treatment modality (1.25%), Fisher grade of CT clot burden (0.65%), history of hypertension (0.37%), aneurysm size (0.12%) and aneurysm location (0.06%). These predictors were combined to develop 3 sets of hierarchical scores based on the coefficients of the regression models. The AUC at bootstrap validation was 0.79-0.80, and at cross validation was 0.64-0.85. Calibration plots demonstrated satisfactory agreement between predicted and observed probabilities of the outcomes. Conclusions: The novel prognostic scores have good predictive ability and potential for broad application as they have been developed from prospective cohorts reflecting experience from different centers globally.

scholarly journals Consistency and overfitting of multi-omics methods on experimental data

2019 ◽  
Vol 21 (4) ◽  
pp. 1277-1284 ◽  
Author(s):  
Sean D McCabe ◽  
Dan-Yu Lin ◽  
Michael I Love
Keyword(s):  
Experimental Data ◽  
Factor Analysis ◽  
Canonical Correlation ◽  
Cross Validation ◽  
Small Sample Size ◽  
R Package ◽  
Small Sample ◽  
Ready Availability ◽  
Variation Explained ◽  
The Relationship

Abstract Knowledge on the relationship between different biological modalities (RNA, chromatin, etc.) can help further our understanding of the processes through which biological components interact. The ready availability of multi-omics datasets has led to the development of numerous methods for identifying sources of common variation across biological modalities. However, evaluation of the performance of these methods, in terms of consistency, has been difficult because most methods are unsupervised. We present a comparison of sparse multiple canonical correlation analysis (Sparse mCCA), angle-based joint and individual variation explained (AJIVE) and multi-omics factor analysis (MOFA) using a cross-validation approach to assess overfitting and consistency. Both large and small-sample datasets were used to evaluate performance, and a permuted null dataset was used to identify overfitting through the application of our framework and approach. In the large-sample setting, we found that all methods demonstrated consistency and lack of overfitting; however, in the small-sample size setting, AJIVE provided the most stable results. We provide an R package so that our framework and approach can be applied to evaluate other methods and datasets.

scholarly journals Detection of Cyanuric Acid and Melamine in Infant Formula Powders by Mid-FTIR Spectroscopy and Multivariate Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Edwin García-Miguel ◽  
Ofelia Gabriela Meza-Márquez ◽  
Guillermo Osorio-Revilla ◽  
Darío Iker Téllez-Medina ◽  
Cristian Jiménez-Martínez ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Ftir Spectroscopy ◽  
Least Squares ◽  
Infant Formula ◽  
Cyanuric Acid ◽  
Principal Component Regression ◽  
Predictive Ability ◽  
Principal Component ◽  
Infant Formulas ◽  
Chemometric Methods

Chemometric methods using mid-FTIR spectroscopy were developed in order to reduce the time of study of melamine and cyanuric acid in infant formulas. Chemometric models were constructed using the algorithms Partial Least Squares (PLS1, PLS2) and Principal Component Regression (PCR) in order to correlate the IR signal with the levels of melamine or cyanuric acid in the infant formula samples. Results showed that the best correlations were obtained using PLS1 (R2: 0.9998, SEC: 0.0793, and SEP: 0.5545 for melamine and R2: 0.9997, SEC: 0.1074, and SEP: 0.5021 for cyanuric acid). Also, the SIMCA model was studied to distinguish between adulterated formulas and nonadulterated samples, giving optimum discrimination and good interclass distances between samples. Results showed that chemometric models demonstrated a good predictive ability of melamine and cyanuric acid concentrations in infant formulas, showing that this is a rapid and accurate technique to be used in the identification and quantification of these adulterants in infant formulas.

A Confirmatory Factor Analysis of the WISC-III in a Clinical Sample With Cross-validation in the Standardization Sample

2001 ◽  
Vol 7 (2) ◽  
pp. 104-116 ◽  
Author(s):  
D. Bradley Burton ◽  
Arash Sepehri ◽  
Fred Hecht ◽  
Anekke VandenBroek ◽  
Joseph J. Ryan ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Cross Validation ◽  
Clinical Sample ◽  
Standardization Sample ◽  
Confirmatory Factor

Factor analysis using column cross-validation

1989 ◽  
Vol 13 (1) ◽  
pp. 39-44 ◽  
Author(s):  
Marius D'Amboise ◽  
Benoit Lagarde
Keyword(s):  
Factor Analysis ◽  
Cross Validation
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