Principal Component Analysis Scores Research Articles

Use of a hybrid method to derive dietary patterns in 7 years olds with explanatory ability of body mass index at age 10.

The usual definition of dietary patterns only accounts for the explanation of dietary choices and not a specific health outcome. This could partially explain the lack of consistent associations between diet and related diseases. This study aims to identify dietary patterns in 7 years olds explaining body mass index (BMI) at age 10 and to assess their association with early-life factors (sociodemographic, birth, and infancy characteristics). Children from the birth cohort Generation XXI at ages 7 and 10 were included (n = 4698). Diet was assessed by a validated food-frequency questionnaire. Measured BMI z-scores (zBMI) were calculated. Principal component analysis (PCA) and partial least squares (PLS) were run to derive dietary patterns. The component scores of PCA was able to explain 13.0% of food groups and only 0.2% of zBMI, while the PLS scores explained the variance of both food groups (10.1%) and zBMI at age 10 (4.2%). By using PLS, two dietary patterns were derived, but only one, higher in processed meats and energy-dense foods and lower in vegetable soup consumption, was significantly associated with an increased zBMI in 10 years olds (adjusted β̂ 0.032; 95% CI:0.017; 0.047). It was more likely followed by children from younger and less educated mothers and who were born heavier. A dietary pattern higher in processed and energy-dense foods and with lower vegetable soup intake in 7 years olds significantly explained zBMI of 10 years olds, and was predicted by early-life characteristics. The other dietary patterns were not significantly associated with zBMI at age 10.

Electronic Eye Based on RGB Analysis for the Identification of Tequilas.

The present work reports the development of a biologically inspired analytical system known as Electronic Eye (EE), capable of qualitatively discriminating different tequila categories. The reported system is a low-cost and portable instrumentation based on a Raspberry Pi single-board computer and an 8 Megapixel CMOS image sensor, which allow the collection of images of Silver, Aged, and Extra-aged tequila samples. Image processing is performed mimicking the trichromatic theory of color vision using an analysis of Red, Green, and Blue components (RGB) for each image’s pixel. Consequently, RGB absorbances of images were evaluated and preprocessed, employing Principal Component Analysis (PCA) to visualize data clustering. The resulting PCA scores were modeled with a Linear Discriminant Analysis (LDA) that accomplished the qualitative classification of tequilas. A Leave-One-Out Cross-Validation (LOOCV) procedure was performed to evaluate classifiers’ performance. The proposed system allowed the identification of real tequila samples achieving an overall classification rate of 90.02%, average sensitivity, and specificity of 0.90 and 0.96, respectively, while Cohen’s kappa coefficient was 0.87. In this case, the EE has demonstrated a favorable capability to correctly discriminated and classified the different tequila samples according to their categories.

Parameter Analysis of Pulsed Eddy Current Sensor Using Principal Component Analysis

Pulsed eddy current (PEC) technique provides a means to inspect structures without surface contact. It is particularly useful when the structure's surface is rough or inaccessible, such as insulated pipes in pipeline. Probe parameters of a PEC system, especially the sensing and excitation coil diameters, can significantly affect the PEC system's performance. Thus, detailed analysis of these parameters is paramount in developing a PEC system. Currently, this is accomplished by establishing the trend of features with respect to the analyzed variables, e.g. sample thicknesses. However, prior to extracting these features, a number of configuration parameters have to be determined. For this reason, analyzing PEC performance over a range of coil diameter values is rather time-consuming as both the sensing and excitation coil diameters significantly affect the received signals. Principal component analysis (PCA) is proposed as an alternative to the feature extraction. The work here analyzes the trends contributed by the PCA scores for different values of sensing and excitation coil parameters. Results from both numerical simulations and experiments suggest that the sensitivity of the PEC probe is highly correlated with the excitation coil diameter, while the excitation-sensing coil distance is not significant in determining the sensitivity of the PEC probe. These findings are consistent with those reported in the literature, suggesting the potential of adopting PCA for an automated PEC performance analysis process.

Gender discrimination of Populus tomentosa barks by HPLC fingerprint combined with multivariate statistics.

A high‐performance liquid chromatography (HPLC) fingerprint method with multivariate statistical analyses was applied to discriminate the male and female barks of Populus tomentosa for the first time. The samples of 11 male and 13 female barks of mature P. tomentosa were collected in Beijing. The chemical fingerprint of methanol extract was established by HPLC method with diode array detector (DAD). The principal component analysis (PCA), hierarchical clustering analysis (HCA), and supervised orthogonal partial least squares discriminant analysis (OPLS‐DA) were applied to discriminate male and female barks based on the area of common peaks identified in HPLC fingerprints. A clear grouping trend (R 2 X, 0.83; Q 2, 0.595) among the male and female samples was exhibited by PCA score plot. Two groups were clearly divided into male and female samples by HCA. Both male and female samples were well discriminated with OPLS‐DA (R 2 X, 0.775; Q 2, 0.795). Seven potential chemical markers were screened by variable importance in projection (VIP values >1.0) of OPLS‐DA model and four of them were identified as micranthoside, siebolside B, sakuranin, and isosakuranin. The HPLC fingerprint combined with multivariate statistical analyses could be used to discriminate the gender of barks of P. tomentosa and revealed the differences in chemical components, which enriched the basic studies on dioecious plant.

Comparative metabolic study of Citrus sinensis leaves cultivars based on GC–MS and their cytotoxic activity

Citrus sinensis L. Osbeck, (family Rutaceae), known as sweet orange, is a fruit-bearing shrub that widely cultivated all over the world due to its nutritive value, nutraceutical attributes and economic importance. In the present study, a comparative metabolic profile study of the essential oils of the leaves of nine cultivars of Citrus sinensis cultivated in Egypt was carried out based on GC–MS analysis coupled to chemometrics. A total of 47 compounds were identified, where monoterpenes hydrocarbons (61.39 %–78.26 %) represented the main recognized class in the essential oils. Sabinene (8.25 %–28.81 %), 2-carene (11.25 %–16.72 %) and cis-β-ocimene (10.22 %–13.93 %) were the major components identified in different cultivars. Chemometric analyses comprising Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were experienced for their ability to discriminate between closely related cultivars. Both PCA score plot and HCA dendrogram could successfully segregate different cultivars based on their metabolic profiles. The MTT assay was used to evaluate the cytotoxicity of essential oils of Citrus sinensis leaves cultivars on different cell lines; MCF-7, HepG-2, HeLa, were all essential oils showed moderate cytotoxic activity. The MCF-7 cell lines were the most resistant cells with IC50 range from 243 to 343 μg/mL, while HeLa cells were the most sensitive cell lines IC50 range from 203 to 283 μg/mL. This may be attributed to the synergistic effects of different chemical components.

Underground coal fire emission of spontaneous combustion, Sandaoba coalfield in Xinjiang, China: Investigation and analysis.

Long-term spontaneous combustion of coal has caused serious ecological and environmental problems. Only in recent years has it received growing popularity to undertake relevant researches. In order to study the impact of combustion by-products on atmosphere in the Sandaoba fire field, Xinjiang, a region-scale field survey was firstly conducted to investigate the gaseous-solid emissions in separated fire sections. The evaluation method and model have been proposed to describe the underground combustion and the related air pollution. Every year, the total estimates of the gaseous emission are approximately 4030t of CO2, 113.6t of SO2 and 57.3t of CO. The emission pollution varies considerably from regions, and is substantially attenuated with the advancement of fire control. Principal component analysis (PCA) refines the thermophysical parameters into three attributions: the intrinsic thermophysical property, atmospheric dynamics, and combustion degree. PCA score distribution shows that thermophysical parameter is dominated by the combustion condition at severely polluted areas. Factor Analysis is used to extract four contaminant indicators, which suggests the local air suffers sulfur oxides pollution the most. The air quality index of the eight study sections calculated are all below 60, ranging from 24 to 58. It indicates that coal fire air pollution is in the medium-to-severe stage. By Canonical Correlation Analysis, it is noted that thermophysical indicator performs outstanding explanatory for contaminant variates. On the whole, the higher the level of thermophysical properties in the fire area, the greater the intensity of pollutant emission. Underground coalfield fire is dominated by smoldering, and the overall combustion efficiency is lower than 0.8 which generally declines as the excess air coefficient increasing.

Parental preconception exposure to phenol and phthalate mixtures and the risk of preterm birth

BackgroundParental preconception exposure to select phenols and phthalates was previously associated with increased risk of preterm birth in single chemical analyses. However, the joint effect of phenol and phthalate mixtures on preterm birth is unknown. MethodsWe included 384 female and 211 male (203 couples) participants seeking infertility treatment in the Environment and Reproductive Health (EARTH) Study who gave birth to 384 singleton infants between 2005 and 2018. Mean preconception urinary concentrations of bisphenol A (BPA), parabens, and eleven phthalate biomarkers, including di(2-ethylhexyl) phthalate (DEHP) metabolites, were examined. We used principal component analysis (PCA) with log-Poisson regression and Probit Bayesian Kernel Machine Regression (BKMR) with hierarchical variable selection to examine maternal and paternal phenol and phthalate mixtures in relation to preterm birth. Couple-based BKMR model was fit to assess couples’ joint mixtures in relation to preterm birth. ResultsPCA identified the same four factors for maternal and paternal preconception mixtures. Each unit increase in PCA scores of maternal (adjusted Risk Ratio (aRR): 1.36, 95%CI: 1.00, 1.84) and paternal (aRR: 1.47, 95%CI: 0.90, 2.42) preconception DEHP-BPA factor was positively associated with preterm birth. Maternal and paternal BKMR models consistently presented the DEHP-BPA factor with the highest group Posterior Inclusion Probability (PIP). BKMR models further showed that maternal preconception BPA and mono(2-ethyl-5-hydroxyhexyl) phthalate, and paternal preconception mono(2-ethylhexyl) phthalate were positively associated with preterm birth when the remaining mixture components were held at their median concentrations. Couple-based BKMR models showed a similar relative contribution of paternal (PIP: 61%) and maternal (PIP: 77%) preconception mixtures on preterm birth. We found a positive joint effect on preterm birth across increasing quantiles of couples’ total mixture concentrations. ConclusionIn this prospective cohort of subfertile couples, maternal BPA and DEHP, and paternal DEHP exposure before conception were positively associated with preterm birth. Both parental windows jointly contributed to the outcome. These results suggest that preterm birth may be a couple-based pregnancy outcome.

Fast diagnosis of men's fertility using Raman spectroscopy combined with chemometric methods: An experimental study.

BackgroundIdiopathic infertile men suffer from unexplained male infertility; they are infertile despite having a normal semen analysis, a normal history, and physical examination, and when female infertility factor has been ruled out.ObjectiveThe present study aimed to develop a metabolic fingerprinting methodology using Raman spectroscopy combined with Chemometrics to detect idiopathic infertile men vs. fertile ones by seminal plasma.Materials and Methods In this experimental study, the seminal plasma of 26 men including 13 fertile and 13 with unexplained infertility who reffered to, Avicenna Infertility Clinic, 2018, Tehran, Iran, have been investigated. The seminal metabolomic fingerprinting was evaluated using Raman spectrometer from 100 to 4250 cm-1. The principal component analysis and discriminate analysis methods were used.ResultsThe total of 26 samples were divided into 20 training and 6 test sets. The Principal component analysis score plot of the training set showed that the data were perfectly divided into two sides of the plot, which statistically approves the direct effect of semen metabolome changes on the Raman spectra. A classification model was constructed by linear discriminant analysis using the training set and evaluated by the test group which resulted in completely correct classification. While three of the six test samples appeared in the fertile group, the rest appeared in the infertile as expected.ConclusionMetabolic fingerprinting of seminal plasma using Raman spectroscopy combined with chemometric classification methods accurately discriminated between the idiopathic infertile men and the fertile ones and predicted their fertility type.

1H-NMR metabolomics-based classification of Japanese sake and comparative metabolome analysis by gas chromatography–mass spectrometry

Six categories of Japanese sake have been established by the National Tax Agency of Japan. In this system, the rice polishing ratio and the addition of alcohol are the main criteria for classification. The most common nuclear magnetic resonance (NMR) spectrometry method is 1H-NMR, and has higher throughput than gas chromatography-mass spectrometry (GC-MS) or liquid chromatography-mass spectrometry (LC-MS) analysis due to its short measurement time, easy sample preparation, and high reproducibility. However, owing to the production of dominant ethanol signals, metabolome analyses have not been used for classifying Japanese sake using 1H-NMR. In this study, a technique to selectively suppress ethanol signals was used to classify Japanese sake by 1H-NMR, and a model was constructed to predict the rice polishing ratio. The results were compared to those obtained by GC-MS. The suppression of ethanol signals enabled the detection of trace components by 1H-NMR. In a principal component analysis (PCA) score plot of 1H-NMR spectra with ethanol signal suppression, PC1 was associated with both the addition of alcohol and the rice polishing ratio. Additionally, the separation of samples observed was similar when PCA score plots of 1H-NMR and GC-MS data were compared. Similarly, to predict the rice polishing ratio using partial least squares regression analysis, a model was constructed using 1H-NMR data, and showed nearly similar values for precision and predictive performance with the model constructed using GC-MS data. These results suggest that metabolomic analyses of Japanese sake based on 1H-NMR spectral patterns may be useful for classification.

Generic multicriteria approach to determine the best precipitation agent for removal of biomacromolecules prior to non-targeted metabolic analysis

The removal of biomacromolecules from biofluids decreases the sample complexity and lower electrospray suppression effects. Furthermore, it can increase the analysis sensitivity, precision, and selectivity. Often removal approaches evaluate the model based on a single criterion, like protein removed or response of one of few specific metabolites. In this study, we used a multicriteria approach to test the effect of using the solvents methanol and acetonitrile (organic solvent precipitation), trichloroacetic acid (acidic precipitation) and ammonium sulphate (salting out) to remove biomacromolecules from a downstream recovery process from a bacillus fermentation. The downstream recovery process intermediates were analysed using reversed-phase ultra-high-pressure liquid chromatography with electrospray ionisation and high-resolution time-of-flight mass spectrometry detection. To evaluate the pre-treatment agents the following multicriteria was applied i) practical considerations, ii) total amino acid in the precipitated pellet, iii) putative identification of the molecules removed or created by the different treatments, iv) coherence between high quality extracted ion chromatograms (repeatability of DW-CODA) and v) replicate consistency from principal component analysis score values obtained by using the CHEMometric analysis of sections of Selected Ion Chromatograms (CHEMSIC) method. This study presents a generic workflow to find the best pre-treatment for removing bio-macromolecules from biofluids with a multicriteria approach. In our case, the best protein removal strategy for downstream recovery intermediates was acetonitrile precipitation. This method showed high precision, created few artefact peaks compared to simple sample dilution, and mainly removed small peptides.

ATR-FTIR Spectroscopy, HPLC Chromatography, and Multivariate Analysis for Controlling Bee Pollen Quality in Some Algerian Regions.

Bee pollen collected by honeybees (Apis mellifera) is one of the bee products, and it is as valuable as honey, propolis, royal jelly, or beebread. Its quality varies according to its geographic location or plant sources. This study aimed to apply rapid, simple, and accurate analytical methods such as attenuated total reflectance Fourier transform infrared spectroscopy (ATR–FTIR) and high-performance liquid chromatography (HPLC) along with chemometrics analysis to construct a model aimed at discriminating between different pollen samples. In total, 33 samples were collected and analyzed using principal component analysis (PCA), hierarchical clustering analysis (HCA), and partial least squares regression (PLS) to assess the differences and similarities between them. The PCA score plot based on both HPLC and ATR–FTIR revealed the same discriminatory pattern, and the samples were divided into four major classes depending on their total content of polyphenols. The results revealed that spectral data obtained from ATR–FTIR acquired in the region (4000–500 cm–1) were further subjected to a standard normal variable (SNV) method that removes scattering effects from spectra. However, PCA, HCA, and PLS showed that the best PLS model was obtained with a regression coefficient (R2) of 0.9001, root-mean-square estimation error (RMSEE) of 0.0304, and root-mean-squared error cross-validation (RMSEcv) of 0.036. Discrimination between the three species has also been possible by combining the pre-processed ATR–FTIR spectra with PCA and PLS. Additionally, the HPLC chromatograms after pre-treatment (SNV) were subjected to unsupervised analysis (PCA–HCA) and supervised analysis (PLS). The PLS model confers good results by factors (R2 = 0.98, RMSEE = 8.22, and RMSEcv = 27.86). Prospects for devising bee pollen quality assessment methods include utilizing ATR–FTIR and HPLC in combination with multivariate methods for rapid authentication of the geographic location or plant sources of bee pollen.

A Multivariate Approach for the Determination of the Optimal Mixing Ratio of the Non-Strong Interacting Co-Amorphous System Carvedilol-Tryptophan.

Converting crystalline compounds into co-amorphous systems is an effective way to improve the solubility of poorly water-soluble drugs. It is, however, of critical importance for the physical stability of co-amorphous systems to find the optimal mixing ratio of the drug with the co-former. In this study, a novel approach for this challenge is presented, exemplified with the co-amorphous system carvedilol–tryptophan (CAR–TRP). Following X-ray powder diffraction (XRPD) and differential scanning calorimetry (DSC) of the ball-milled samples to confirm their amorphous form, Fourier-transform infrared spectroscopy (FTIR) and principal component analysis (PCA) were applied to investigate intermolecular interactions. A clear deviation from a purely additive spectrum of CAR and TRP was visualized in the PCA score plot, with a maximum at around 30% drug (mol/mol). This deviation was attributed to hydrogen bonds of CAR with TRP ether groups. The sample containing 30% drug (mol/mol) was also the most stable sample during a stability test. Using the combination of FTIR with PCA is an effective approach to investigate the optimal mixing ratio of non-strong interacting co-amorphous systems.

Use of 13C-NMR Chemical Shifts; Application of Principal Component Analysis for Categorizing Structurally Similar Methoxyflavones and Correlation Analysis between Chemical Shifts and Cytotoxicity.

The 13C-NMR spectral data for the 15-carbon flavonoid skeleton in eleven methoxyflavones isolated from Kaempferia parviflora (Zingiberaceae) were processed by principal component analysis (PCA). Based on the PCA score plots, the methoxyflavones were categorized into three groups according to their structural features. The cytotoxicities of the methoxyflavones toward 3T3-L1 murine preadipocyte cells were evaluated by 3-(4,5-dimethylthiazole-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTT) assay and found to differ according to structure. The relationship between the 13C-NMR chemical shifts of the methoxyflavones and their cytotoxicities was investigated using Pearson's correlation analysis. The 13C-NMR signal at C-10, a quaternary carbon, was correlated with cytotoxicity. Based on these results, a structural design which lowers the 13C-NMR chemical shift at C-10 would be important for the development of cytotoxic compounds. Although quantitative structure-activity and structure-property relationships are well established paradigms for predicting trends among a series of compounds, quantitative property-activity relationships have been relatively unstudied. This approach offers a new strategy for directing structure-activity relationship research.

Principal component and multivariate factor analysis of detailed sheep milk fatty acid profile

Fatty acid (FA) profile is one of the most important aspects of the nutritional properties of milk. The FA content in milk is affected by several factors such as diet, physiology, environment, and genetics. Recently, principal component analysis (PCA) and multivariate factor analysis (MFA) have been used to summarize the complex correlation pattern of the milk FA profile by extracting a reduced number of new variables. In this work, the milk FA profile of a sample of 993 Sarda breed ewes was analyzed with PCA and MFA to compare the ability of these 2 multivariate statistical techniques in investigating the possible existence of latent substructures, and in studying the influence of physiological and environmental effects on the new extracted variables. Individual scores of PCA and MFA were analyzed with a mixed model that included the fixed effects of parity, days in milking, lambing month, number of lambs born, altitude of flock location, and the random effect of flock nested within altitude. Both techniques detected the same number of latent variables (9) explaining 80% of the total variance. In general, PCA structures were difficult to interpret, with only 4 principal components being associated with a clear meaning. Principal component 1 in particular was the easiest to interpret and agreed with the interpretation of the first factor, with both being associated with the FA of mammary origin. On the other hand, MFA was able to identify a clear structure for all the extracted latent variables, confirming the ability of this technique to group FA according to their function or metabolic origin. Key pathways of the milk FA metabolism were identified as mammary gland de novo synthesis, ruminal biohydrogenation, desaturation performed by stearoyl-coenzyme A desaturase enzyme, and rumen microbial activity, confirming previous findings in sheep and in other species. In general, the new extracted variables were mainly affected by physiological factors as days in milk, parity, and lambing month; the number of lambs born had no effect on the new variables, and altitude influenced only one principal component and factor. Both techniques were able to summarize a larger amount of the original variance into a reduced number of variables. Moreover, factor analysis confirmed its ability to identify latent common factors clearly related to FA metabolic pathways.

Evaluating the effects of temperature and time on heterocyclic aromatic amine profiles in roasted pork using combined UHPLC-MS/MS and multivariate analysis

In this study, ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS) combined with principal component analysis (PCA) were used to investigate the effects of process conditions on the profiles of carcinogenic and mutagenic heterocyclic aromatic amine (HAA) in the pork roasted at 175 °C, 200 °C, 225 °C and 250 °C for 10, 15, 20, 25, 30, 35 and 40 min. Twelve HAAs from four categories, including carboline (Norharman, Harman, and Phe-p-1), imidazopyridine (PhIP, 4′-OH-PhIP, DMIP, and 1,5,6-TMIP), imidazoquinoline (IQ, IQ [4,5-b], and MeIQ), and imidazoquinoxaline (MeIQx and 4,8-DiMeIQx), were detected, quantified and used to compose the HAA profiles in roasted pork. After being Analyzed by PCA, the distributions of HAA profiles from different temperature on the PCA score plot demonstrated that there are significant differences among the HAA profiles from different temperatures. The loading plot also showed that PhIP, 4′-OH-PhIP, IQ[4,5-b], and MeIQ were mainly responsible for the difference. The profiles from higher temperature distribute more scattered than the lower ones, illustrating that the time effects on the HAA profiles from higher temperature are stronger than the lower ones. Comparing the score and loading plots of different heating times, the diversities of the HAA profiles at different temperatures increased under prolonged heating because of the changingpyridines levels. The results of PCA that comparing the HAA from different categories displayed that the formation features of four categories HAAs were significantly differed because of their formation discrepancy under low temperatures and short–term roasting. Using HAA profiles as an entirety, these findings obtained in this study are more close to the real process of HAA formation in roasted pork, and make the complex effects of temperature and time on multiple HAA formations more simply to be concluded.

Principal Component Analysis Applications in COVID-19 Genome Sequence Studies.

RNA genomes from coronavirus have a length as long as 32 kilobases, and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that caused the outbreak of coronavirus disease 2019 (COVID-19) pandemic has long sequences which made the analysis difficult. Over 20,000 sequences have been submitted to GISAID, and the number is growing fast each day which increased the difficulties in data analysis; however, genome sequence analysis is critical in understanding the COVID-19 and preventing the spread of the disease. In this study, a principal component analysis (PCA) was applied to the aligned large size genome sequences and the numerical numbers were converted from the letters using a published method designed for protein sequence cluster analysis. The study initialized with a shortlist sequence testing, and the PCA score plot showed high tolerance with low-quality data, and the major virus sequences from humans were separated from the pangolin and bat samples. Our study also successfully built a model for a large number of sequences with more than 20,000 sequences which indicate the potential mutation directions for the COVID-19 which can be served as a pretreatment method for detailed studies such as decision tree-based methods. In summary, our study provided a fast tool to analyze the high-volume genome sequences such as the COVID-19 and successfully applied to more than 20,000 sequences which may provide mutation direction information for COVID-19 studies.

GC-MS Based Identification of the Volatile Components of Six Astragalus Species from Uzbekistan and Their Biological Activity.

The compositions of volatile components in the aerial parts of six Astragalus species, namely A. campylotrichus (Aca), A. chiwensis (Ach), A. lehmannianus (Ale), A. macronyx (Ama), A. mucidus (Amu) and A. sieversianus (Asi), were investigated using gas chromatograph-mass spectrometry (GC-MS) analysis. Ninety-seven metabolites were identified, accounting for 73.28, 87.03, 74.38, 87.93, 85.83, and 91.39% of Aca, Ach, Ale, Ama, Amu and Asi whole oils, respectively. Sylvestrene was the most predominant component in Asi, Amu and Ama, with highest concentration in Asi (64.64%). In addition, (E)-2-hexenal was present in a high percentage in both Ale and Ach (9.97 and 10.1%, respectively). GC-MS based metabolites were subjected to principal component analysis (PCA) and hierarchal cluster analysis (HCA) to explore the correlations between the six species. The PCA score plot displayed clear differentiation of all Astragalus species and a high correlation between the Amu and Ama species. The antioxidant activity was evaluated in vitro using various assays, phosphomolybdenum (PM), 2,2 diphenyl-1-picryl-hydrazyl-hydrate (DPPH), 2,2-azino bis (3-ethylbenzothiazoline-6-sulphonic acid) (ABTS), cupric reducing antioxidant capacity (CUPRAC), ferric reducing power (FRAP) and ferrous ion chelation (FIC) assays. In addition, the potential for the volatile samples to inhibit both acetyl/butyrylcholinesterases (AChE, BChE), α- amylase, α-glucosidase and tyrosinase was assessed. Most of the species showed considerable antioxidant potential in the performed assays. In the DPPH assay, Ama exhibited the maximum activity (24.12 ± 2.24 mg TE/g sample), and the volatiles from Amu exhibited the highest activity (91.54 mgTE/g oil) in the ABTS radical scavenging assay. The effect was more evident in both CUPRAC and FRAP assays, where both Ale and Ama showed the strongest activity in comparison with the other tested species (84.06, 80.28 mgTE/g oil for CUPRAC and 49.47, 49.02 mgTE/g oil for FRAP, respectively). Asi demonstrated the strongest AChE (4.55 mg GALAE/g oil) and BChE (3.61 mg GALAE/g oil) inhibitory effect. Furthermore, the best tyrosinase inhibitory potential was observed for Ale (138.42 mg KAE/g). Accordingly, Astragalus species can be utilized as promising natural sources for many medicinally important components that could be tested as drug candidates for treating illnesses such as Alzheimer’s disease, diabetes mellitus and oxidative stress-related diseases.

Metabolomics approach to biomarkers of dry eye disease using 1H-NMR in rats

ABSTRACT Dry eye disease (DED) is a chronic and progressive lesion on the ocular surface and induces symptoms, such as burning sensation, itchy eyes, heavy eyes, tired eyes, dry feeling, facial flushing, and blurred vision. The present study was performed to develop DED biomarkers using metabolomics in a rat model. DED was induced by injecting scopolamine and exposing rats to a dry condition. Scopolamine (12 mg/kg/day for 7 days) was subcutaneously injected to male Sprague–Dawley rats. The rats were placed in dry condition with air-flow and dehumidifier. Tear volume and tear breakup time (TBUT) were measured, and eyes were examined through fluorescein staining to assess DED. Mucosal damage and immune reactions were also determined. Plasma and urinary endogenous metabolites were determined using 1H-NMR analysis. Compared with control tear and TBUT levels were significantly decreased in the DED group whereas corneal damage was significantly increased. The levels of interleukins (IL-6) and IL-1β significantly elevated in the cornea and lacrimal glands in the DED group. TNF-α was numerically increased but not significantly different between groups. Pattern recognition using principal component analysis (PCA) and orthogonal projections to latent structure-discriminant analysis (OPLS-DA) of the NMR spectra in global profiling revealed different clusters between DED and control groups. Target profiling demonstrated that PCA and OPLS-DA score plots were separated between DED and controls in plasma and urine. Subsequently, 9 plasma metabolites were selected to examine different clustering between groups, and 26 urinary metabolites were also selected. Plasma metabolites showed a non-significant rising tendency in the DED group. Urinary phenylalanine, phenylacetate, pantothenate, glycine, succinate, methanol, valine, propylene glycol, histidine, threonine, lactate, and acetate were significantly different between control and DED rats. These results may contribute to understanding the metabolic regulation that is involved in DED and might be useful for potential biomarkers related to DED in rats.

Evidence-Based Statistical Evaluation of Japanese L2-Learners’ Proficiency using Principal Component Analysis

This paper aims at an automatic evaluation of second language (L2) learners’ proficiencies and tries to analyze English conversation data having 94 statistics and Global Scale scores of the Common European Framework of Reference (CEFR) given to each participant. The CEFR defines Range, Accuracy, Fluency, Interaction and Coherence as 5 subcategories, which constitute the CEFR Global Scale score. The statistics were classified into the CEFR’s 5 subcategories. We used the Principal Component Analysis (PCA), an unsupervised machine learning method, on each subcategory and obtained the participants’ principal component scores (PC scores) of the 5 subcategories for estimation parameters. We predicted the participants’ CEFR Global scores using the Multiple Regression Analysis (MRA). The proposed prediction method using the PC scores was compared with conventional methods with the 94 statistics. Based on the coefficients of determination (R2), the value of the proposed method (0.82) was nearly equivalent to one of values obtained by the conventional methods. Meanwhile, as for standard deviation, the proposed method showed the smallest value in the comparison. The results indicated usability of the PCA and PC scores calculated from the CEFR subcategory data for objective evaluation of L2 learners’ English proficiencies.

A vicious cycle of fear of falling avoidance behavior in Parkinson’s disease: A path analysis

BackgroundPostural instability (PI) in Parkinson’s disease (PD) is associated with several negative downstream consequences. ObjectiveThe purpose was to explore the validity of a theoretical model of these downstream consequences arranged in a vicious cycle wherein PI leads to decreased balance confidence, which in turn leads to increased fear of falling (FOF) avoidance behavior, which in turn leads to decreased physical conditioning, which then feeds back and negatively affects PI. MethodsA path analysis of cross-sectional data from 55 participants with PD was conducted. The four constructs in the model connected in succession were: 1. PI (principal components analysis (PCA) composite of the Unified Parkinson’s Disease Rating Scale PI and Gait Difficulty score, Timed Up and Go test, and Berg Balance Scale); 2. balance confidence (Activities-Specific Balance Confidence Scale); 3. FOF avoidance behavior (PCA composite of the FOF Avoidance Behavior Questionnaire and average number of steps per day); and, 4. physical conditioning (2-Minute Step Test). ResultsThe path model was an excellent fit to the data, χ2 (7) = 7.910, p = .341, CFI = 0.985, TLI = 0.968, RMSEA = 0.049 (90% CI: 0.000 to 0.179). The moderate to strong and uniformly significant parameter estimates were −0.519, −0.651, −0.653, and −0.570, respectively (ps < 0.01). ConclusionsPI directly and inversely predicted balance confidence, which in turn directly and inversely predicted FOF avoidance behavior. Furthermore, FOF avoidance behavior directly and inversely predicted physical conditioning, which directly and inversely predicted PI, thereby closing the cycle. These findings highlight the downstream consequences of PI in PD and support the notion of a vicious cycle of FOF avoidance behavior.