Principal Component Analysis Research Articles

Determination of Element Concentration of Brewed Tea Consumed in Iran Using ICP-OES: A Risk Assessment Study.

Tea is a popular drink enjoyed by many people around the world. However, it is important to note that impurities and contaminants in tea can potentially threaten human health when consumed. The main objective of this research is to assess the concentration of 16 trace elements (As, Fe, Al, Co, B, Li, Cd, Ba, Pb, Cr, Zn, Hg, Ni, Mn, Sb, and Sn) present in different types brewed of tea by using the ICP-OES (the inductively coupled plasma optical emission spectrometer) device, and the human health risks related to its use were evaluated. In this study, manganese (Mn) indicated the highest mean concentration in black (917.64µg/kg) and green tea (912.89µg/kg), respectively. Our study showed that the highest mean concentration of element boron (B) was (44.36µg/kg) in Stachys lavandulifolia brewed tea. Among different packaging, tea bag samples had the highest concentration of Mn (1025.65µg/kg) and aluminum (Al) (396.63µg/kg). However, those unpacked posed the lowest content of Mn (188.13µg/kg) and Al (100.47µg/kg). The lead (Pb), mercury (Hg), and cadmium (Cd) concentrations in all samples were lower than the maximum limit of the Iranian standard and the WHO. In general, the amount of tea metal in Iranian samples was higher than in imported samples. Also, increasing the brewing time (10 min) can be effective in the solubility and extraction of metals such as B, Hg, cobalt (Co), iron (Fe), and lithium (Li). Further, the correlation between the amount of metals and type in tea samples was evaluated with principal component analysis. Based on the conducted non-carcinogenic risk assessment by the Monte Carlo simulation, the hazard index (HI), due to ingestion of heavy metals via tea in the 5-min brewing of tea, was 7.39E - 03 and 2.09E - 03, and in the 10-min brewing of tea, it was 3.20E - 02 and 9.07E - 03 for children and adults, respectively. Therefore, there was no significant non-carcinogenic risk from tea consumption.

Bioguided Fractionation of Phyllanthus spp.: Unveiling Anticancer Potential through Metabolomic Correlation and ADMETox Insights.

Cancer remains a significant global health concern, with mortality rates steadily rising and prompting an urgent search for effective treatments. This study focuses on the medicinal properties of plants from the Phyllanthus genus, specifically Phyllanthus amarus and Phyllanthus niruri, which have shown promise in traditional medicine. Through bioguided fractionation using preparative high-performance liquid chromatography (HPLC), bioactive compounds were isolated and identified using ultra-performance liquid chromatography coupled to time-of-flight mass spectrometry (UHPLC-QTOF-MSE) and nuclear magnetic resonance (NMR) spectroscopy. Chemometric analyses such as principal component analysis (PCA) aided in understanding metabolite distribution. Biological assays demonstrated cytotoxic activities of specific fractions against cancer cell lines, notably the PhyN 4n fraction from P. niruri, which induced S-phase cell cycle arrest and apoptosis in HL60 cells. These findings underscore the anticancer potential of Phyllanthus species and lay the groundwork for future drug development efforts. The study's integration of advanced analytical techniques, chemometrics, and biological assays provides valuable insights for harnessing natural products in the fight against cancer.

Unravelling the metabolomic diversity of pigmented and non-pigmented traditional rice from Tamil Nadu, India

Rice metabolomics is widely used for biomarker research in the fields of pharmacology. As a consequence, characterization of the variations of the pigmented and non-pigmented traditional rice varieties of Tamil Nadu is crucial. These varieties possess fatty acids, sugars, terpenoids, plant sterols, phenols, carotenoids and other compounds that plays a major role in achieving sustainable development goal 2 (SDG 2). Gas-chromatography coupled with mass spectrometry was used to profile complete untargeted metabolomics of Kullkar (red colour) and Milagu Samba (white colour) for the first time and a total of 168 metabolites were identified. The metabolite profiles were subjected to data mining processes, including principal component analysis (PCA), Orthogonal Partial Least Square Discrimination Analysis (OPLS-DA) and Heat map analysis. OPLS-DA identified 144 differential metabolites between the 2 rice groups, variable importance in projection (VIP) ≥ 1 and fold change (FC) ≥ 2 or FC ≤ 0.5. Volcano plot (64 down regulated, 80 up regulated) was used to illustrate the differential metabolites. OPLS-DA predictive model showed good fit (R2X = 0.687) and predictability (Q2 = 0.977). The pathway enrichment analysis revealed the presence of three distinct pathways that were enriched. These findings serve as a foundation for further investigation into the function and nutritional significance of both pigmented and non-pigmented rice grains thereby can achieve the SDG 2.

An effective role-oriented binary Walrus Grey Wolf approach for feature selection in early-stage chronic kidney disease detection.

In clinical decision-making for chronic disorders like chronic kidney disease, high variability often leads to uncertainty and negative outcomes. Deep learning techniques have been developed as useful tools for minimizing the chance and improving clinical decision-making. Moreover, traditional techniques for chronic kidney disease recognition frequently the accuracy is compromised as it relies on limited sets of biological attributes. Therefore, in the proposed work, a combination of deep radial bias network and the puma optimization algorithm is suggested for precised chronic kidney disease classification. Initially, the accessed data undergo preprocessing using Spectral Z score Bag Boost K-Means SMOTE transformation, which includes robust scaling, data cleaning, balancing, encoding, handling missing values, min-max scaling, and z-standardization. Feature selection is then conducted using the hybrid methodology of Role-oriented Binary Walrus Grey Wolf Algorithm to choose discriminative features for improving classification accuracy. Then, Auto Encoder with Patch-Based Principal Component Analysis is employed for dimensionality reduction to minimize the processing time. Finally, the proposed classification method utilizes deep radial bias and the puma optimization search algorithm for effective chronic kidney disease classification. The introduced scheme is tested on two datasets: the risk factor prediction of chronic kidney disease dataset and chronic kidney disease dataset, which provides accuracies of 99.02%, and 99.15%, respectively. Experiments demonstrate that the proposed model identifies chronic kidney disease more accurately than the existing approaches.

Morphological and biochemical variation of Ajuga chamaecistus Ging. ex Benth. in different habitats of Markazi province in the center of Iran

BackgroundMedicinal plants, such as Ajuga chamaecistus Ging. ex Benth. are a natural and available source of treatment for a wide range of diseases. The objective of the present study was to assess the morphological and biochemical variation of 70 accessions of this species collected from seven geographical areas of Markazi province in the center of Iran.ResultsThe measured traits exhibited considerable variability across the populations. Positive correlations were observed between antioxidant activity and total phenolic content, as well as total flavonoid content. Principal component analysis showed six components explaining 72.15% of the total variance, and the PC1 explained 20.68% of the total variance. The Ward dendrogram based on morphological variables identified two main clusters. Morphological analysis of A. chamaecistus showed a high variation between qualitative and quantitative traits that help the breeders for selecting the desired genotypes. The accessions collected from the Robat-Mil area showed the highest values for the recorded morphological characteristics. Also, the populations of Robat-Mil, Hassanabad, and Khaneh-Miran were characterized by high values of total phenolic content, total flavonoid content, and antioxidant activity, which can be used in various industries, including pharmaceuticals, cosmetics, and food.ConclusionsOverall, the present results showed that the best place for the growth of A. chamaecistus with the production of significant contents of phenol and flavonoid is in Robat-Mil area.

Investigation of odor pollution by utilizing selected ion flow tube mass spectrometry (SIFT-MS) and principal component analysis (PCA).

Odor pollution, also referred to as odor nuisance, is a growing environmental concern that is significantly associated with mental health. Once emitted into the air, the concentration of odorous substances varies considerably with wind conditions, leading to difficulties in timely sampling. In the present study, we employed selected ion flow tube mass spectrometry (SIFT-MS) to measure 22 odor-producing molecules continuously in an urban-rural complex city. In addition, we applied statistical analyses, principal component analysis (PCA), and a conditional probability function (CPF) to the datasets obtained from SIFT-MS to identify the odor characteristics at two study sites. At site A, odorants related to livestock farming and industry showed high factor loadings on principal components (PCs) from the PCA. In contrast, we estimated that the odorous gaseous chemicals affecting site B were closely related to sewage treatment and municipal solid waste disposal. Similar CPF patterns of grouped substances from the PCA supported the association between potential odor sources and specific odorants at site B, which helped estimate possible source locations. Consequently, our findings indicate that continuous monitoring of odorous substances using SIFT-MS can be an effective way to provide sufficient information on odor-producing molecules, leading to the clear identification of odor characteristics despite the high variability of odorous substances.

FT-Raman and FTIR spectroscopy as a tools showing marker of platinum-resistant phenomena in women suffering from ovarian cancer

Platinum-resistant phenomena in ovarian cancer is very dangerous for women suffering from this disease, because reduces the chances of complete recovery. Unfortunately, until now there are no methods to verify whether a woman with ovarian cancer is platinum-resistant. Importantly, histopathology images also were not shown differences in the ovarian cancer between platinum-resistant and platinum-sensitive tissues. Therefore, in this study, Fourier Transform InfraRed (FTIR) and FT-Raman spectroscopy techniques were used to find chemical differences between platinum-resistant and platinum-sensitive ovarian cancer tissues. Furthermore, Principal Component Analysis (PCA) and machine learning methods were performed to show if it possible to differentiate these two kind of tissues as well as to propose spectroscopy marker of platinum-resistant. Indeed, obtained results showed, that in platinum-resistant ovarian cancer tissues higher amount of phospholipids, proteins and lipids were visible, however when the ratio between intensities of peaks at 1637 cm−1 (FTIR) and at 2944 cm−1 (Raman) and every peaks in spectra was calculated, difference between groups of samples were not noticed. Moreover, structural changes visible as a shift of peaks were noticed for C–O–C, C–H bending and amide II bonds. PCA clearly showed, that PC1 can be used to differentiate platinum-resistant and platinum-sensitive ovarian cancer tissues, while two-trace two-dimensional correlation spectra (2T2D-COS) showed, that only in amide II, amide I and asymmetric CH lipids vibrations correlation between two analyzed types of tissues were noticed. Finally, machine learning algorithms showed, that values of accuracy, sensitivity and specificity were near to 100% for FTIR and around 95% for FT-Raman spectroscopy. Using decision tree peaks at 1777 cm−1, 2974 cm−1 (FTIR) and 1714 cm−1, 2817 cm−1 (FT-Raman) were proposed as spectroscopy marker of platinum-resistant.

Potatoes (Solanum tuberosum L.) grown at “Patata dell'alto Viterbese” PGI have different quality characteristics and storage responses

Four pre-selected potato cultivars (Solanum tuberosum L.) from the “Patata dell’Alto Viterbese” PGI area underwent physico-chemical and enzymatic characterization to assess their suitability for specific processing line. Key chemical parameters (reducing sugar content (RSC), total phenol content (TPC), dry matter (DM), physical attributes (firmness), physico-chemical aspects (color, browning development), and enzymatic factors (polyphenol oxidase enzyme activity, PPO) were analyzed at harvest and monthly during a 6-month storage period over two production seasons (2020 and 2021). The results of one-way within-subject ANOVA and Principal Component Analysis (PCA) highlighted the superior attributes of the ‘Fontane’ cultivar for the processing industry, particularly for frying applications (e.g., chips, French fries, etc.). In comparison to other tested cultivars, ‘Fontane’ exhibited: i) the highest DM (> 200 g kg−1), ii) the lowest TPC and RSC, iii) the greatest firmness, iv) low susceptibility to browning, and v) high tolerance to Low-Temperature Sweetening (LTS). The ‘Jelly’ cultivar displayed favorable attributes for versatile use, suitable for both fresh market uses (roasting, homemade “gnocchi”, etc.) and industrial applications (fresh-cut or dried potato). It demonstrated high DM content (200 g kg−1) and firmness, the lowest PPO activity, good TPC, but higher RSC than ‘Fontane’. However, elevated RSC for ‘Jelly’ renders it less suitable for industrial frying. ‘Constance’ and ‘Gaudi’ exhibited positive traits for fresh market use such as boiling and steaming, with moderate to low DM content and firmness. Nonetheless, both cultivars displayed low tolerance to LTS, and ‘Gaudi’ exhibited the poorest processing properties, showing the highest susceptibility to browning.

Identification of Microbial Strains via 2D Cross-Correlation of LC-MS Data.

Mass spectrometry is commonly used in the identification of species present in microbial samples, but the high similarity in the peptide composition between strains of a single species has made analysis at the subspecies level challenging. Prior research in this area has employed methods such as Principal Component Analysis (PCA), the k-Nearest Neighbors' (kNN) algorithm, and Pearson correlation. Previously, 1D cross-correlation of mass spectra has been shown to be useful in the classification of small molecule compounds as well as in the identification of peptide sequences via the SEQUEST algorithm and its variants. While direct application of cross-correlation to mass spectral data has been shown to aid in the identification of many other types of compounds, this type of analysis has not been demonstrated in the literature for the purpose of LC-MS based identification of microbial strains. A method of identifying microbial strains is presented here that applies the principle of 2D cross-correlation to LC-MS data. For a set of N = 30 yeast isolate samples representing 5 yeast strains (K-97, S-33, T-58, US-05, WB-06), high-resolution LC-MS-Orbitrap data were collected. Reference spectra were then generated for each strain from the combined data of each sample of that strain. Sample strains were then predicted by computing the 2D cross-correlation of each sample against the reference spectra, followed by application of correction factors measuring the asymmetry of the 2D correlation functions.

Groundwater vulnerability to fluoride pollution and health risk assessment in the western part of Odisha, India.

The fluoride dynamics of the Dharmagarh Block of Kalahandi District, Odisha, India, and associated health risk assessment have been studied. Complex data matrices were evaluated using groundwater quality index, fluoride pollution index, and principal component analysis to understand the geological evolution and identify potential sources for fluoride pollution. The study region comprises granite, granitic gneiss, and khondalite of hard and compact rock of Precambrian Eon, which supplies mostly the fluoride-bearing minerals. Altogether thirty-four (34) groundwater samples across the entire study area were collected and subjected to various physico-chemical analyses. The majority of the groundwater in the proposed region is hard to very hard type with Mg-HCO3 and Na-HCO3 being the two dominant facies. Groundwater contains fluoride in concentrations ranging from 0.21 to 2.26mg/L. The statistical analysis of the quality parameters reveals the moderate positive correlation of fluoride with sodium (0.392) and pH (0.313) and week positive correlation with EC, TDS, TH, TA, Mg2+, and HCO3-, which directly depicts the initiation of fluoride problem within the study area. Based on the water quality index, 23.53% samples are good, 73.53% are poor, and 2.94% are very poor in nature. With respect to fluoride pollution index, 5.88% samples show high pollution, 55.88% samples show medium pollution, and 38.24% of samples show low pollution index. Human health risk assessment has also been carried out using the hazard quotient of fluoride. Altogether 70.59% of samples show Total Hazard Index (THI) values < 1 suggesting low risk of cancer and within the permissible range, whereas 29.41% of samples show THI > 1 suggesting the non-carcinogenic risk of pollutants, which exceeds the allowable limit for all the classes of male, female and children.

Childhood obesity accelerates biological ageing: is oxidative stress a link?

Obesity is multifactorial pathophysiological condition with an imbalance in biochemical, immunochemical, redox status and genetic parameters values. We aimed estimate connection between relative leukocyte telomere lengths (rLRL) - biomarker of cellular aging with metabolic and redox status biomarkers values in a group of obese and lean children. The study includes 110 obese and 42 lean children and adolescents, both genders. The results suggested that rLTL are significantly shorter in obese, compared to lean group (p<0,01). Negative correlation of rLTL with total oxidant status, TOS (Spearman's ρ = -0.365, p<0.001) as well as with C reactive protein (Spearman's ρ= -0,363, p<0.001) were observed. Principal component analysis (PCA) extracted three distinct factors (i.e. principal components) entitled as: prooxidant factor with 35% of total variability; antioxidant factor with 30% of total variability and lipid antioxidant - biological ageing factor with 12% of the total variability. The most important predictor of body mass index (BMI) >30kg/m2 according to logistic regression analysis was PCA derived antioxidant factor's score (OR: 1.66, 95th Cl: 1.05-2.6, p=0.029). PCA analysis confirmed oxidative stress importance in biological ageing caused by obesity and its multiple consequences related to prooxidants augmentation and antioxidants exhaustion and gave us clear signs of disturbed cellular homeostasis deepness, even before any overt disease occurrence.

Integrating CoDA and PCA for enhanced characterization of fluvial depositional processes: a case study of the Shendi formation, Sudan

Traditional approaches to fluvial sedimentary analysis often face challenges in deciphering complex, multivariate datasets. This study combines compositional data analysis (CoDA) with principal component analysis (PCA) to enhance the characterization of depositional processes and sub-environments within the Shendi Formation. The PCA applied to centered log ratio (clr)-transformed grain size distributions, reveals three principal components with ~ 91.86% of the data variance explained, representing distinct processes: bedload-dominated channel-bar dynamics, overbank deposition, and high-energy flood events. Specific lithofacies associations strongly correlate to each principal component. This integrated approach enables the identification of subtle yet significant patterns within the complex sedimentological record. The Shendi Formation exhibits characteristics of a dynamic fluvial setting with variations in flow energy, channel migration, and periodic flooding. Our findings demonstrate the power of CoDA-PCA in refining the understanding of fluvial depositional systems and highlight its potential for broader applications.

Water Quality and Environmental Health of Lakes in Coimbatore, India: A Comprehensive Study of the Physicochemical Characteristics

The deterioration of lake water quality is a major national issue, particularly in regions with industrial activity. This study assesses the water quality of five lakes in Coimbatore, India: Krishnampathi, Ukkadam, Kurichi, Sulur, and Singanallur, between December 2016 and April 2017. Twenty physicochemical parameters were analysed following standard procedures outlined by the American Public Health Association. Twelve heavy metal elements concentration in the lakes was analysed using inductively coupled plasma-mass spectroscopy. The results of this study revealed that the values of 17 physicochemical parameters fell beyond the WHO recommended pollution levels in all the study sites, except Sodium, Nitrate, and Sulphate. While, the concentration of 10 heavy metal elements determined were within acceptable limits of WHO-recommended standards for drinking water across all lakes, except Fe and Pb. F-test revealed that the concentration of all the physicochemical parameters varied significantly across all the study sites, except for pH (P = .749). Statistical analysis such as principal component analysis was adopted, and the results were discussed on the multivariate relationships of the physiochemical parameters and heavy metal concentrations of the five study sites. Overall, the findings highlight the urgent need for continuous monitoring and comprehensive management strategies to mitigate the deteriorating water quality in the lakes of Coimbatore district, emphasizing the critical importance of addressing the multitude of factors contributing to this environmental challenge.

Qualitative analysis of licorice and strychnine decoction before and after combination using UPLC-QE-Orbitrap-MS.

Glycyrrhiza glabra L. (GG) and Strychnos nux-vomica L. (NV) are traditional Chinese medicines (TCMs). Changes in the chemical composition may occur before and after the GG-NV compatibility. Ultra-performance liquid chromatography Q-exactive Orbitrap mass spectrometry (UPLC-QE-Orbitrap-MS) was applied here to study the difference in the components of the GG and NV decoctions before and after they were combined. The changes in the chemical composition of GG and NV before and after the combination were determined. The precise molecular weight, retention time, and fragment ion peak of the different components of the decoctions before and after compatibility were obtained through UPLC-QE-Orbitrap-MS. Differential analysis methods, such as principal component analysis, were used for comparison. In the positive ion mode, 200 new components were added, whereas six components were lost. In the negative ion mode, 144 new compounds were identified, whereas three components were missing. The compatibility difference between GG and NV was studied through UPLC-QE-Orbitrap-MS. The chemical composition of GG and NV changed before and after compatibility, and a class of compounds different from GG and NV was identified in the co-decoction. This study provides an experimental basis for subsequent research into detoxification mechanisms of the GG-NV combination and offers a new analytical method for investigating the compatibility of various other TCM pairs.

Principle Component Analysis for Forecasting of Pre-harvest Rapeseed and Mustard Yield Based on Meteorological Parameters

The pre-harvest forecast model was developed using time series data on Rapeseed and Mustard yields as well as weekly data on six weather variables from the 40th SMW of one year to the 8th SMW of the next year, which covers the years 1990–1991 to 2014–2015. Multiple regression and principal component analysis are two statistical techniques that have been reported for creating pre-harvest forecast models. The most accurate model found by applying step-by-step regression analysis to weekly weather data was based on adj R2, RMSE, and%SE. One and a half months prior to harvest, these models can be utilised to obtain a trustworthy forecast of the yield of Rapeseed and Mustard.

In-vitro induced drought and heat stress on seed germination of diverse African sorghum germplasm

Abstract One of the critical growth and development stages that is vulnerable to drought and heat stress even in hardy staple crops is germination. Sorghum is a critically essential, resilient, and diverse crop that displays genotypic variations in its ability to withstand such harsh conditions, limiting crop stand and ultimately leading to yield losses. Therefore, the goal of this study was to evaluate the performance of 50 high potential genotypes of African sorghum, including landraces, breeding lines and check varieties to simulated drought and heat stress at germination stage. The study used a split plot arrangement for temperature treatments, laid in a completely randomized design with three replications. Final germination percentage, mean germination time, germination index and coefficient variation of germination time (CVt) were determined. Data was subjected to generalized linear model, principal component analysis, hierarchical agglomerative cluster analysis and principal coordinate analysis to determine statistical differences in genotypes and visualize groups of genotypes according to their overall performance in assessed germination parameters. Drought stress and supra-optimal temperatures suppressed and delayed germination. The genotypes were grouped into six distinct clusters based on their performance. Genotypes NPGRC1593, NPGRC1782, NPGRC1476 and IS224426 performed exceptionally well under both stressors and outperformed check varieties in almost all parameters assessed. To improve crop establishment and increase agricultural yields, breeding and crop improvement programs should focus on genotypes that can withstand both stresses.

A Rapid and Accurate UHPLC Method for Determination of Monosaccharides in Polysaccharides of Different Sources of Radix Astragali and Its Immune Activity Analysis

With the escalating demand for Astragalus polysaccharides products developed from Radix Astragali (RA), the necessity for quality control of polysaccharides in RA has become increasingly urgent. In this study, a specific method for the simultaneous determination of seven monosaccharides in polysaccharides extracted from Radix Astragali (RA) has been developed and validated using ultra-performance liquid chromatography equipped with an ultraviolet detector (UHPLC-UV) for the first time. The 1-phenyl-3-methyl-5-pyrazolone (PMP) derivatizations were separated on a C18 column (Waters ACQUITYTM, Milfor, MA, USA, 1.8 µm, 2.1 × 100 mm) using gradient elution with a binary system of 5 mm ammonium formate (0.1% formic acid)-acetonitrile for 24 min. Additionally, seven monosaccharides showed good linear relationships (R2, 0.9971–0.9995), adequate precision (RSD &lt; 4.21%), and high recoveries (RSD &lt; 4.70%). The established method was used to analyze 109 batches of RA. Results showed that the Astragalus polysaccharides (APSs) mainly consist of mannose (Man), rhamnose (Rha), glucose (Glu), galactose (Gal), arabinose (Ara), xylose (Xyl); and fucose (Fuc); however, their composition was different among RA samples from different growth patterns, species, growth years, and origins, and the growth mode of RA and the age of wild-simulated RA can be accurately distinguished by principal component analysis (PCA). In addition, the immunological activity of APSs were also evaluated jointly by measurement of the NO release with RAW264.7, with the results showing that APSs have a promoting effect on the release of NO and exhibit a significant correlation with Man, Glu, Xyl, and Fuc contents. Accordingly, the new established monosaccharides analytical method and APS-immune activity determination in this study can provide a reference for quality evaluation and the establishment of quality standards for RA.

Adoption and use of MOOCs by undergraduates in selected universities in Ibadan, Nigeria

This research examines the utilisation of Massive Open Online Courses (MOOCs) by university undergraduates in Ibadan, Nigeria, for digital skill development. A survey of 378 undergraduates from the University of Ibadan, Lead City University, and Dominican University is conducted. Principal Component Analysis (PCA) simplifies UTAUT variables, while regression analysis explores factors influencing MOOC usage. Results show a substantial 77.8% adoption rate of MOOCs, indicating a growing interest in online learning. Performance expectancy, effort expectancy, social influence, facilitating conditions, and internet accessibility significantly influence MOOC usage, though higher values for performance expectancy and facilitating conditions unexpectedly correspond to reduced utilisation. Demographic factors like age and gender do not significantly impact adoption. Limitations include the focus on Ibadan university students, potentially limiting generalizability, and the study’s cross-sectional nature, which may not capture long-term trends. Nonetheless, the research sheds light on digital education in Nigeria, emphasising MOOC utilisation among students. Rigorous statistical analyses and incorporating the UTAUT framework provide a robust foundation for interpretation. The results highlight prevalent MOOC usage and offer insights into adoption factors. Implications extend to educational institutions, instructors, digital literacy initiatives, and policy-making, stressing the importance of enhancing MOOC adoption for digital skill development among Nigerian university students.

Analysis of the Role of Human Capital on Economic Growth in the Beijing-Tianjin-Hebei

Abstract This paper uses some human capital related data in the Beijing-Tianjin-Hebei region as empirical data to study the impact of human capital on the economic growth of the Beijing-Tianjin-Hebei region; based on the selected data, the principal component analysis method is used to analyze health human capital, education human capital, and comprehensive human capital. Measure and compare the human capital differences of the three places in Beijing, Tianjin and Hebei. Finally, based on the economic growth model, the three places are regression analysis, and the results of different types of human capital on the three places are obtained. The analysis results show that different types of human capital measurement values in the Beijing-Tianjin-Hebei region are indeed different. In general, the human capital in the Beijing-Tianjin-Hebei region contributes more to economic growth than physical capital, and the contribution of health human capital is better than that of education human capital. Keywords: Human capital, Economic growth, Regression analysis, Principal component analysis, Health; Education.

Using principal component analysis to determine which vestibular stimuli provide best biomarkers for separating Alzheimer's from mixed Alzheimer's disease.

Alzheimer's disease (AD) is often mixed with cerebrovascular disease (AD-CVD). Heterogeneity of dementia etiology and the overlapping of neuropathological features of AD and AD-CVD make feature identification of the two challenging. Separation of AD from AD-CVD is important as the optimized treatment for each group may differ. Recent studies using vestibular responses recorded from electrovestibulography (EVestG™) have offered promising results for separating these two pathologies. An EVestG measurement records responses to several different physical stimuli (called tilts). In previous research, the number of EVestG features from different tilts was selected based on physiological intuition to classify AD from AD-CVD. As the number of potential characteristic features from all tilts can be very large, in this study, we used an algorithm based on principal component analysis (PCA) to rank the most effective vestibular stimuli for differentiating AD from AD-CVD. Analyses were performed on the EVestG signals of 28 individuals with AD and 24 with AD-CVD. The results of this study showed that tilts simulating the otolithic organs (utricle and saccule) generated the most characteristic features for separating AD from AD-CVD.