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Independent Component Analysis (ICA) has proven to be an effective data driven method for analyzing EEG data, separating signals from temporally and functionally independent brain and non-brain source processes and thereby increasing their definition. Dimension reduction by Principal Component Analysis (PCA) has often been recommended before ICA decomposition of EEG data, both to minimize the amount of required data and computation time. Here we compared ICA decompositions of fourteen 72-channel single subject EEG data sets obtained (i) after applying preliminary dimension reduction by PCA, (ii) after applying no such dimension reduction, or else (iii) applying PCA only. Reducing the data rank by PCA (even to remove only 1% of data variance) adversely affected both the numbers of dipolar independent components (ICs) and their stability under repeated decomposition. For example, decomposing a principal subspace retaining 95% of original data variance reduced the mean number of recovered ‘dipolar’ ICs from 30 to 10 per data set and reduced median IC stability from 90% to 76%. PCA rank reduction also decreased the numbers of near-equivalent ICs across subjects. For instance, decomposing a principal subspace retaining 95% of data variance reduced the number of subjects represented in an IC cluster accounting for frontal midline theta activity from 11 to 5. PCA rank reduction also increased uncertainty in the equivalent dipole positions and spectra of the IC brain effective sources. These results suggest that when applying ICA decomposition to EEG data, PCA rank reduction should best be avoided. •It is currently a common practice to apply dimension reduction to EEG data using PCA before performing ICA decomposition.•We tested the quality of Independent Components (ICs) after different levels of rank reduction to a principal subspace.•PCA rank reduction adversely affected dipolarity and stability of ICs accounting for brain and known non-brain processes.•PCA rank reduction also increased inter-subject variance in IC source locations (by equivalent dipole fitting) and spectra.•For EEG data at least, PCA rank reduction should be avoided or carefully tested before applying it as a preprocessing step.

COVID-19 pandemic led to major life changes. We assessed the psychological impact of COVID-19 on dental academics globally and on changes in their behaviors. We invited dental academics to complete a cross-sectional, online survey from March to May 2020. The survey was based on the Theory of Planned Behavior (TPB). The survey collected data on participants' stress levels (using the Impact of Event Scale), attitude (fears, and worries because of COVID-19 extracted by Principal Component Analysis (PCA), perceived control (resulting from training on public health emergencies), norms (country-level COVID-19 fatality rate), and personal and professional backgrounds. We used multilevel regression models to assess the association between the study outcome variables (frequent handwashing and avoidance of crowded places) and explanatory variables (stress, attitude, perceived control and norms). 1862 academics from 28 countries participated in the survey (response rate = 11.3%). Of those, 53.4% were female, 32.9% were <46 years old and 9.9% had severe stress. PCA extracted three main factors: fear of infection, worries because of professional responsibilities, and worries because of restricted mobility. These factors had significant dose-dependent association with stress and were significantly associated with more frequent handwashing by dental academics (B = 0.56, 0.33, and 0.34) and avoiding crowded places (B = 0.55, 0.30, and 0.28). Low country fatality rates were significantly associated with more handwashing (B = -2.82) and avoiding crowded places (B = -6.61). Training on public health emergencies was not significantly associated with behavior change (B = -0.01 and -0.11). COVID-19 had a considerable psychological impact on dental academics. There was a direct, dose-dependent association between change in behaviors and worries but no association between these changes and training on public health emergencies. More change in behaviors was associated with lower country COVID-19 fatality rates. Fears and stresses were associated with greater adoption of preventive measures against the pandemic.

Existing approaches for multivariate functional principal component analysis are restricted to data on the same one-dimensional interval. The presented approach focuses on multivariate functional data on different domains that may differ in dimension, such as functions and images. The theoretical basis for multivariate functional principal component analysis is given in terms of a Karhunen-Loève Theorem. For the practically relevant case of a finite Karhunen-Loève representation, a relationship between univariate and multivariate functional principal component analysis is established. This offers an estimation strategy to calculate multivariate functional principal components and scores based on their univariate counterparts. For the resulting estimators, asymptotic results are derived. The approach can be extended to finite univariate expansions in general, not necessarily orthonormal bases. It is also applicable for sparse functional data or data with measurement error. A flexible R implementation is available on CRAN. The new method is shown to be competitive to existing approaches for data observed on a common one-dimensional domain. The motivating application is a neuroimaging study, where the goal is to explore how longitudinal trajectories of a neuropsychological test score covary with FDG-PET brain scans at baseline. Supplementary material, including detailed proofs, additional simulation results, and software is available online.

We develop the necessary methodology to conduct principal component analysis at high frequency. We construct estimators of realized eigenvalues, eigenvectors, and principal components, and provide the asymptotic distribution of these estimators. Empirically, we study the high-frequency covariance structure of the constituents of the S&P 100 Index using as little as one week of high-frequency data at a time, and examines whether it is compatible with the evidence accumulated over decades of lower frequency returns. We find a surprising consistency between the low- and high-frequency structures. During the recent financial crisis, the first principal component becomes increasingly dominant, explaining up to 60% of the variation on its own, while the second principal component drives the common variation of financial sector stocks. Supplementary materials for this article are available online.

Background High throughput RNA sequencing is a powerful approach to study gene expression. Due to the complex multiple-steps protocols in data acquisition, extreme deviation of a sample from samples of the same treatment group may occur due to technical variation or true biological differences. The high-dimensionality of the data with few biological replicates make it challenging to accurately detect those samples, and this issue is not well studied in the literature currently. Robust statistics is a family of theories and techniques aim to detect the outliers by first fitting the majority of the data and then flagging data points that deviate from it. Robust statistics have been widely used in multivariate data analysis for outlier detection in chemometrics and engineering. Here we apply robust statistics on RNA-seq data analysis. Results We report the use of two robust principal component analysis (rPCA) methods, PcaHubert and PcaGrid , to detect outlier samples in multiple simulated and real biological RNA-seq data sets with positive control outlier samples. PcaGrid achieved 100% sensitivity and 100% specificity in all the tests using positive control outliers with varying degrees of divergence. We applied rPCA methods and classical principal component analysis (cPCA) on an RNA-Seq data set profiling gene expression of the external granule layer in the cerebellum of control and conditional SnoN knockout mice. Both rPCA methods detected the same two outlier samples but cPCA failed to detect any. We performed differentially expressed gene detection before and after outlier removal as well as with and without batch effect modeling. We validated gene expression changes using quantitative reverse transcription PCR and used the result as reference to compare the performance of eight different data analysis strategies. Removing outliers without batch effect modeling performed the best in term of detecting biologically relevant differentially expressed genes. Conclusions rPCA implemented in the PcaGrid function is an accurate and objective method to detect outlier samples. It is well suited for high-dimensional data with small sample sizes like RNA-seq data. Outlier removal can significantly improve the performance of differential gene detection and downstream functional analysis.

The exploration of carbonate-hosted Pb-Zn mineralization is challenging due to the complex structural-geological settings and costly using geophysical and geochemical techniques. Hydrothermal alteration minerals and structural features are typically associated with this type of mineralization. Application of multi-sensor remote sensing satellite imagery as a fast and inexpensive tool for mapping alteration zones and lithological units associated with carbonate-hosted Pb-Zn deposits is worthwhile. Multiple sources of spectral data derived from different remote sensing sensors can be utilized for detailed mapping a variety of hydrothermal alteration minerals in the visible near infrared (VNIR) and the shortwave infrared (SWIR) regions. In this research, Landsat-8, Sentinel-2, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and WorldView-3 (WV-3) satellite remote sensing sensors were used for prospecting Zn-Pb mineralization in the central part of the Kashmar–Kerman Tectonic Zone (KKTZ), the Central Iranian Terrane (CIT). The KKTZ has high potential for hosting Pb-Zn mineralization due to its specific geodynamic conditions (folded and thrust belt) and the occurrence of large carbonate platforms. For the processing of the satellite remote sensing datasets, band ratios and principal component analysis (PCA) techniques were adopted and implemented. Fuzzy logic modeling was applied to integrate the thematic layers produced by image processing techniques for generating mineral prospectivity maps of the study area. The spatial distribution of iron oxide/hydroxides, hydroxyl-bearing and carbonate minerals and dolomite were mapped using specialized band ratios and analyzing eigenvector loadings of the PC images. Subsequently, mineral prospectivity maps of the study area were generated by fusing the selected PC thematic layers using fuzzy logic modeling. The most favorable/prospective zones for hydrothermal ore mineralizations and carbonate-hosted Pb-Zn mineralization in the study region were particularly mapped and indicated. Confusion matrix, field reconnaissance and laboratory analysis were carried out to verify the occurrence of alteration zones and highly prospective locations of carbonate-hosted Pb-Zn mineralization in the study area. Results indicate that the spectral data derived from multi-sensor remote sensing satellite datasets can be broadly used for generating remote sensing-based prospectivity maps for exploration of carbonate-hosted Pb-Zn mineralization in many metallogenic provinces around the world.

A prototype gas chromatography-ion mobility spectrometry (GC-IMS) system, hyphenating temperature-ramped headspace GC to a modified drift time IMS cell, was evaluated and compared to a conventional, isothermal capillary column (CC)-IMS system on the example of the geographical differentiation of extra virgin olive oils (EVOO) from Spain and Italy. It allows orthogonal, 2D separation of complex samples and individual detection of compounds in robust and compact benchtop systems. The information from the high-resolution 3D fingerprints of volatile organic compound (VOC) fractions of EVOO samples were extracted by specifically developed chemometric MATLAB® routines to differentiate between the different olive oil provenances. A combination of unsupervised principal component analysis (PCA) with two supervised procedures, linear discriminant analysis (LDA) and k -nearest neighbors (kNN), was applied to the experimental data. The results showed very good discrimination between oils of different geographical origins, featuring 98 and 92% overall correct classification rate for PCA-LDA and kNN classifier, respectively. Furthermore, the results showed that the higher resolved 3D fingerprints obtained from the GC-IMS system provide superior resolving power for non-targeted profiling of VOC fractions from highly complex samples such as olive oil. Graphical abstract Principle of the determination of geographic origins of olive oils by chemometric analysis of three-dimensional HS-GC-IMS fingerprints

High content of citric acid in lemon juice leads to poor sensory experience. The study aimed to investigate the dynamics changes in organic acids, phenolic compounds, and antioxidant activities of lemon juice fermented with Issatchenkia terricola WJL-G4. The sensory evaluation of fermented lemon juice was conducted as well. Issatchenkia terricola WJL-G4 exhibited a potent capability of reducing the contents of citric acid (from 51.46 ± 0.11 g/L to 8.09 ± 0.05 g/L within 60 h fermentation) and increasing total phenolic level, flavonoid contents, and antioxidant activities compared to those of unfermented lemon juice. A total of 20 bioactive substances, including 10 phenolic acids and 10 flavonoid compounds, were detected both in fermented and unfermented lemon juice. The lemon juice fermented for 48 h had better sensory characteristics. Our findings demonstrated that lemon juice fermented with Issatchenkia terricola exhibited reduced citric acid contents, increased levels of health-promoting phenolic compounds, and enhanced antioxidant activities.

Public health quality in Gilgit Baltistan (GB) is at threat due to multiple water-borne diseases. Anthropogenic activities are accelerating the burden of pollution load on the glacio-fluvial streams and surface water resources of Basho Valley in Skardu district of GB. The present research has investigated the drinking water quality of the Basho Valley that is being used for domestic purposes. The study also comprehends public health status by addressing the basis drinking water quality parameters. A total of 23 water samples were collected and then analyzed to elucidate the current status of physico-chemical, metals, and microbial parameters. Principal component analysis (PCA) was applied and three principal components were obtained accounting 53.04% of the total variance, altogether. PCA identified that metallic and microbial parameters are the major factor to influence the water quality of the valley. Meanwhile, water quality index (WQI) was also computed and it was observed that WQI of the valley is characterized as excellent in terms of physico-chemical characteristics; however, metals and microbial WQI shows most of the samples are unfit for drinking purpose. Spatial distribution is also interpolated using the Inverse distance weight (IDW) to anticipate the results of mean values of parameters and WQI scores. The study concludes that water quality is satisfactory in terms of physico-chemical characteristics; however, analysis of metals shows that the concentrations of copper (Cu) (0.40 ± 0.16 mg/L), lead (Pb) (0.24 ± 0.10 mg/L), zinc (Zn) (6.77 ± 27.1 mg/L), manganese (Mn) (0.19 ± 0.05), and molybdenum (Mo) (0.07 ± 0.02 mg/L) are exceeding the maximum permissible limit as set in the WHO guidelines for drinking water. Similarly, the results of the microbial analysis indicate that the water samples are heavily contaminated with fecal pollution (TCC, TFC, and TFS > 3 MPN/100 mL). On the basis of PCA, WQI, and IDW, the main sources of pollution are most likely to be concluded as the anthropogenic activities including incoming pollution load from upstream channels. A few underlying sources by natural process of weathering and erosion may also cause release of metals in surface and groundwater. This study recommends ensuring public health with regular monitoring and assessment of water resources in the valley.

Background Principal component analysis (PCA) is commonly applied to the atomic trajectories of biopolymers to extract essential dynamics that describe biologically relevant motions. Although application of PCA is straightforward, specialized software to facilitate workflows and analysis of molecular dynamics simulation data to fully harness the power of PCA is lacking. The Java Essential Dynamics inspector (JEDi) software is a major upgrade from the previous JED software. Results Employing multi-threading, JEDi features a user-friendly interface to control rapid workflows for interrogating conformational motions of biopolymers at various spatial resolutions and within subregions, including multiple chain proteins. JEDi has options for Cartesian-based coordinates (cPCA) and internal distance pair coordinates (dpPCA) to construct covariance (Q), correlation (R), and partial correlation (P) matrices. Shrinkage and outlier thresholding are implemented for the accurate estimation of covariance. The effect of rare events is quantified using outlier and inlier filters. Applying sparsity thresholds in statistical models identifies latent correlated motions. Within a hierarchical approach, small-scale atomic motion is first calculated with a separate local cPCA calculation per residue to obtain eigenresidues. Then PCA on the eigenresidues yields rapid and accurate description of large-scale motions. Local cPCA on all residue pairs creates a map of all residue-residue dynamical couplings. Additionally, kernel PCA is implemented. JEDi output gives high quality PNG images by default, with options for text files that include aligned coordinates, several metrics that quantify mobility, PCA modes with their eigenvalues, and displacement vector projections onto the top principal modes. JEDi provides PyMol scripts together with PDB files to visualize individual cPCA modes and the essential dynamics occurring within user-selected time scales. Subspace comparisons performed on the most relevant eigenvectors using several statistical metrics quantify similarity/overlap of high dimensional vector spaces. Free energy landscapes are available for both cPCA and dpPCA. Conclusion JEDi is a convenient toolkit that applies best practices in multivariate statistics for comparative studies on the essential dynamics of similar biopolymers. JEDi helps identify functional mechanisms through many integrated tools and visual aids for inspecting and quantifying similarity/differences in mobility and dynamic correlations.