scholarly journals Robust linear modelling in ElectroEncephaloGraphy can be obtained using single weights reflecting each single trials’ dynamics

2021 ◽  
Author(s):  
Cyril Pernet ◽  
Guillaume Rousselet ◽  
Ignacio Suay Mas ◽  
Ramon Martinez ◽  
Rand Wilcox ◽  
...  
Keyword(s):  
Linear Models ◽  
Synthetic Data ◽  
Principal Component ◽  
Least Square ◽  
Data Availability ◽  
Family Wise Error Rate ◽  
Eeg Data ◽  
Linear Modelling ◽  
Outlier Data

AbstractBeing able to remove or weigh down the influence of outlier data is desirable for any statistical models. While Magnetic and ElectroEncephaloGraphic (MEEG) data used to average trials per condition, it is now becoming common practice to use information from all trials to build linear models. Individual trials can, however, have considerable weight and thus bias inferential results. Here, rather than looking for outliers independently at each data point, we apply the principal component projection (PCP) method at each channel, deriving a single weight per trial at each channel independently. Using both synthetic data and open EEG data, we show (1) that PCP is efficient at detecting a large variety of outlying trials; (2) how PCP derived weights can be implemented in the context of the general linear model with accurate control of type 1 family-wise error rate; and (3) that our PCP-based Weighted Least Square (WLS) approach leads to in increase in power at the group results comparable to a much slower Iterative Reweighted Least Squares (IRLS), although the weighting scheme is markedly different. Together, results show that WLS based on PCP weights derived upon whole trial profiles is an efficient method to weigh down the influence of outlier data in linear models.Data availabilityall data used are publicly available (CC0), all code (simulations and data analyzes) is also available online in the LIMO MEEG GitHub repository (MIT license).

scholarly journals A Robust System for Detection of Artifacts from EEG Brain Recordings

2019 ◽  
Vol 9 (2) ◽  
pp. 3947-3951
Keyword(s):  
Background Noise ◽  
Roc Analysis ◽  
Detection Method ◽  
Synthetic Data ◽  
Principal Component ◽  
Robust Principal Component Analysis ◽  
Identification Process ◽  
Chronic Disorder ◽  
Eeg Data ◽  
Subspace Detection

Epilepsy is a chronic disorder and has the propensity of two or more brain. Analysis of EEG is the primary method for the diagnosis of epilepsy. Contamination of eye movement and blink artifacts presence in EEG data becomes more complicated to the doctors during the diagnosis period. Earlier detection of these artifacts gives a significant advantage of refining the Epilepsy identification process. In this regard, a robust subspace detection method is applied to detect the target signal in noise with possible interference-artifacts, then a dimensionality reduction model, with the combination of fast Independent and Robust Principal Component Analysis (FICA and rPCA) is implemented for identification of artifacts from EEG brain recordings. To perform this the proposed detection method uses synthetic data and artifact contaminated data. The extracted target subspace signal is considered as the input for rPCA and FICA. The ROC analysis is developed as a standard methodology to quantify the detectors' ability to correctly distinguish the target of interest (artifacts) from the background noise in the system.

Predicting Exercise-Related Changes in Glucose in People with Type 1 Diabetes Using Linear Models and Incorporating Knowledge of Prior Exercise

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 751-P
Author(s):  
NICHOLE S. TYLER ◽  
RAVI REDDY ◽  
JOSEPH EL YOUSSEF ◽  
JESSICA R. CASTLE ◽  
PETER G. JACOBS
Keyword(s):  
Type 1 Diabetes ◽  
Linear Models ◽  
Prior Exercise

Pemanfaatan CFSRv2 untuk Statistical Downscaling menggunakan Principal Component Regression dan Partial Least Square

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Khairunnisa Khairunnisa ◽  
Rizka Pitri ◽  
Victor P Butar-Butar ◽  
Agus M Soleh
Keyword(s):  
Statistical Downscaling ◽  
General Circulation ◽  
Principal Component Regression ◽  
Circulation Model ◽  
Principal Component ◽  
Meteorological Station ◽  
Grid Size ◽  
Partial Least Square ◽  
Least Square ◽  
Output Data

This research used CFSRv2 data as output data general circulation model. CFSRv2 involves some variables data with high correlation, so in this research is using principal component regression (PCR) and partial least square (PLS) to solve the multicollinearity occurring in CFSRv2 data. This research aims to determine the best model between PCR and PLS to estimate rainfall at Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station by comparing RMSEP value and correlation value. Size used was 3×3, 4×4, 5×5, 6×6, 7×7, 8×8, 9×9, and 11×11 that was located between (-40) N - (-90) S and 1050 E -1100 E with a grid size of 0.5×0.5 The PLS model was the best model used in stastistical downscaling in this research than PCR model because of the PLS model obtained the lower RMSEP value and the higher correlation value. The best domain and RMSEP value for Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station is 9 × 9 with 100.06, 6 × 6 with 194.3, 8 × 8 with 117.6, and 6 × 6 with 108.2, respectively.

scholarly journals Association of Serum Levels of Zinc, Copper, and Iron with Risk of Metabolic Syndrome

Nutrients ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 548
Author(s):  
Chia-Wen Lu ◽  
Yi-Chen Lee ◽  
Chia-Sheng Kuo ◽  
Chien-Hsieh Chiang ◽  
Hao-Hsiang Chang ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Metabolic Syndrome ◽  
Linear Models ◽  
Serum Zinc ◽  
Serum Levels ◽  
Least Square ◽  
Model Assessment ◽  
Serum Concentrations ◽  
Metabolic Factors ◽  
Homeostatic Model Assessment

The association between serum concentrations of zinc, copper, or iron and the risk of metabolic syndrome are inconclusive. Therefore, we conduct a case-control study to explore the relationship between serum levels of zinc, copper, or iron and metabolic syndrome as well as each metabolic factor and insulin resistance. We enrolled 1165 adults, aged ≥ 40 (65.8 ± 10) years in a hospital-based population to compare the serum levels of zinc, copper, and iron between subjects with and without metabolic syndrome by using multivariate logistic regression analyses. The least square means were computed by general linear models to compare serum concentrations of zinc, copper, and iron in relation to the number of metabolic factors. The mean serum concentrations of zinc, copper, and iron were 941.91 ± 333.63 μg/L, 1043.45 ± 306.36 μg/L, and 1246.83 ± 538.13 μg/L, respectively. The odds ratios (ORs) of metabolic syndrome for the highest versus the lowest quartile were 5.83 (95% CI: 3.35–10.12; p for trend < 0.001) for zinc, 2.02 (95% CI: 1.25–3.25; p for trend: 0.013) for copper, and 2.11 (95% CI: 1.24–3.62; p for trend: 0.021) for iron after adjusting for age, sex, personal habits, body mass index, and homeostatic model assessment insulin resistance. Additionally, the serum zinc, copper, and iron concentrations increased as the number of metabolic factors rose (p for trend < 0.001). This was the first study to clearly demonstrate that higher serum levels of zinc, copper, and iron were associated with the risk of metabolic syndrome and the number of metabolic factors independent of BMI and insulin resistance.

scholarly journals A Novel Validated Injectable Colistimethate Sodium Analysis Combining Advanced Chemometrics and Design of Experiments

Molecules ◽  
2021 ◽  
Vol 26 (6) ◽  
pp. 1546
Author(s):  
Ioanna Dagla ◽  
Anthony Tsarbopoulos ◽  
Evagelos Gikas
Keyword(s):  
Design Of Experiments ◽  
High Performance ◽  
Similarity Index ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Distance Measures ◽  
Linear Regression Models ◽  
Colistimethate Sodium

Colistimethate sodium (CMS) is widely administrated for the treatment of life-threatening infections caused by multidrug-resistant Gram-negative bacteria. Until now, the quality control of CMS formulations has been based on microbiological assays. Herein, an ultra-high-performance liquid chromatography coupled to ultraviolet detector methodology was developed for the quantitation of CMS in injectable formulations. The design of experiments was performed for the optimization of the chromatographic parameters. The chromatographic separation was achieved using a Waters Acquity BEH C8 column employing gradient elution with a mobile phase consisting of (A) 0.001 M aq. ammonium formate and (B) methanol/acetonitrile 79/21 (v/v). CMS compounds were detected at 214 nm. In all, 23 univariate linear-regression models were constructed to measure CMS compounds separately, and one partial least-square regression (PLSr) model constructed to assess the total CMS amount in formulations. The method was validated over the range 100–220 μg mL−1. The developed methodology was employed to analyze several batches of CMS injectable formulations that were also compared against a reference batch employing a Principal Component Analysis, similarity and distance measures, heatmaps and the structural similarity index. The methodology was based on freely available software in order to be readily available for the pharmaceutical industry.

scholarly journals Successful Aging Perception in Middle-Aged Korean Men: A Q Methodology Approach

2021 ◽  
Vol 18 (6) ◽  
pp. 3095
Author(s):  
Mi Hyeon Seong ◽  
Eunyoung Shin ◽  
Sohyune Sok
Keyword(s):  
Successful Aging ◽  
Q Methodology ◽  
Principal Component ◽  
Middle Aged ◽  
Practice Nurses ◽  
Mature Type ◽  
Type 3 ◽  
Family Centered

The purpose of this study is to identify the types of perception of successful aging in middle-aged men and to analyze and describe the characteristics of each type of successful aging perception of middle-aged men in South Korea. This study uses an exploratory study design, applying the Q methodology, which is a subjective research method. The participants were 25 middle-aged men (40 to 60 years old) living in C, Y, and B cities, which were P-samples that were judged to best reveal the successful aging of middle-aged men. In this study, principal component analysis of the PQ method program was used. The five perception types of successful aging among middle-aged men are Type 1 for the “leisure type”, Type 2 for the “mature type”, Type 3 for the “health-oriented type”, Type 4 for the “patriarchal type”, and Type 5 for the “family-centered type”. The mature type had the highest variance among the five types, and the leisure type was the type that showed the second-highest variance. In nursing practice, nurses need to pay attention to the successful aging perceptions of middle-aged Korean men for their successful aging in the future.

scholarly journals The inflammatory profile of cerebrospinal fluid, plasma, and saliva from patients with severe neuropathic pain and healthy controls-a pilot study

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Mika Jönsson ◽  
Björn Gerdle ◽  
Bijar Ghafouri ◽  
Emmanuel Bäckryd
Keyword(s):  
Cerebrospinal Fluid ◽  
Neuropathic Pain ◽  
Pain Intensity ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Therapeutic Interventions ◽  
Healthy Controls ◽  
Inflammatory Profile

Abstract Background Neuropathic pain (NeuP) is a complex, debilitating condition of the somatosensory system, where dysregulation between pro- and anti-inflammatory cytokines and chemokines are believed to play a pivotal role. As of date, there is no ubiquitously accepted diagnostic test for NeuP and current therapeutic interventions are lacking in efficacy. The aim of this study was to investigate the ability of three biofluids - saliva, plasma, and cerebrospinal fluid (CSF), to discriminate an inflammatory profile at a central, systemic, and peripheral level in NeuP patients compared to healthy controls. Methods The concentrations of 71 cytokines, chemokines and growth factors in saliva, plasma, and CSF samples from 13 patients with peripheral NeuP and 13 healthy controls were analyzed using a multiplex-immunoassay based on an electrochemiluminescent detection method. The NeuP patients were recruited from a clinical trial of intrathecal bolus injection of ziconotide (ClinicalTrials.gov identifier NCT01373983). Multivariate data analysis (principal component analysis and orthogonal partial least square regression) was used to identify proteins significant for group discrimination and protein correlation to pain intensity. Proteins with variable influence of projection (VIP) value higher than 1 (combined with the jack-knifed confidence intervals in the coefficients plot not including zero) were considered significant. Results We found 17 cytokines/chemokines that were significantly up- or down-regulated in NeuP patients compared to healthy controls. Of these 17 proteins, 8 were from saliva, 7 from plasma, and 2 from CSF samples. The correlation analysis showed that the most important proteins that correlated to pain intensity were found in plasma (VIP > 1). Conclusions Investigation of the inflammatory profile of NeuP showed that most of the significant proteins for group separation were found in the less invasive biofluids of saliva and plasma. Within the NeuP patient group it was also seen that proteins in plasma had the highest correlation to pain intensity. These preliminary results indicate a potential for further biomarker research in the more easily accessible biofluids of saliva and plasma for chronic peripheral neuropathic pain where a combination of YKL-40 and MIP-1α in saliva might be of special interest for future studies that also include other non-neuropathic pain states.

scholarly journals EEG Data Quality: Determinants and Impact in a Multicenter Study of Children, Adolescents, and Adults with Attention-Deficit/Hyperactivity Disorder (ADHD)

2021 ◽  
Vol 11 (2) ◽  
pp. 214
Author(s):  
Anna Kaiser ◽  
Pascal-M. Aggensteiner ◽  
Martin Holtmann ◽  
Andreas Fallgatter ◽  
Marcel Romanos ◽  
...  
Keyword(s):  
Data Quality ◽  
Large Scale ◽  
Linear Models ◽  
Spectral Power ◽  
Control Group ◽  
Continuous Performance Task ◽  
Related Data ◽  
Eyes Closed ◽  
Eeg Data ◽  
Eyes Open

Electroencephalography (EEG) represents a widely established method for assessing altered and typically developing brain function. However, systematic studies on EEG data quality, its correlates, and consequences are scarce. To address this research gap, the current study focused on the percentage of artifact-free segments after standard EEG pre-processing as a data quality index. We analyzed participant-related and methodological influences, and validity by replicating landmark EEG effects. Further, effects of data quality on spectral power analyses beyond participant-related characteristics were explored. EEG data from a multicenter ADHD-cohort (age range 6 to 45 years), and a non-ADHD school-age control group were analyzed (ntotal = 305). Resting-state data during eyes open, and eyes closed conditions, and task-related data during a cued Continuous Performance Task (CPT) were collected. After pre-processing, general linear models, and stepwise regression models were fitted to the data. We found that EEG data quality was strongly related to demographic characteristics, but not to methodological factors. We were able to replicate maturational, task, and ADHD effects reported in the EEG literature, establishing a link with EEG-landmark effects. Furthermore, we showed that poor data quality significantly increases spectral power beyond effects of maturation and symptom severity. Taken together, the current results indicate that with a careful design and systematic quality control, informative large-scale multicenter trials characterizing neurophysiological mechanisms in neurodevelopmental disorders across the lifespan are feasible. Nevertheless, results are restricted to the limitations reported. Future work will clarify predictive value.

scholarly journals The relationship between empathy and altruistic motivations in nursing studies: a multi-method study

BMC Nursing ◽  
2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Linda Messineo ◽  
Luciano Seta ◽  
Mario Allegra
Keyword(s):  
Nursing Education ◽  
Nursing Students ◽  
Health Professionals ◽  
Linear Models ◽  
Principal Component ◽  
Motivational Factors ◽  
Care Process ◽  
First Year ◽  
Young Males ◽  
Young Females

Abstract Background The efficient management of relational competences in healthcare professionals is crucial to ensuring that a patient’s treatment and care process is conducted positively. Empathy is a major component of the relational skills expected of health professionals. Knowledge of undergraduate healthcare students’ empathic abilities is important for educators in designing specific and efficient educational programmes aimed at supporting or enhancing such competences. In this study, we measured first-year undergraduate nursing students’ attitudes towards professional empathy in clinical encounters. The students’ motivations for entering nursing education were also evaluated. This study takes a multi-method approach based on the use of qualitative and quantitative tools to examine the association between students’ positive attitudes towards the value of empathy in health professionals and their prosocial and altruistic motivations in choosing to engage in nursing studies. Methods A multi-method study was performed with 77 first-year nursing students. The Jefferson Scale of Empathy (JSE) – Health Professions Student Version was administered. Students’ motivations for choosing nursing studies were detected through an open question and thematically analysed. Using explorative factor analysis and principal component analysis, a dimensional reduction was conducted to identify subjects with prosocial and altruistic motivations. Finally, linear models were tested to examine specific associations between motivation and empathy. Results Seven distinct themes distinguishing internal and external motivational factors were identified through a thematic analysis of students’ answers regarding their decision to enter a nursing degree course. Female students gained higher scores on the empathy scale than male ones. When students’ age was considered, this difference was only observed for younger students, with young females’ total scores being higher than young males'. High empathy scores were positively associated with altruistic motivational factors. A negative correlation was found between external motivational factors and the scores of the Compassionate Care subscale of the JSE. Conclusions Knowing the level of nursing students’ empathy and their motivational factors for entering nursing studies is important for educators to implement training paths that enhance students’ relational attitudes and skills and promote the positive motivational aspects that are central to this profession.

scholarly journals A fast and low-cost approach to quality control of alcohol-based hand sanitizer using portable near infrared spectrometer and chemometrics

2021 ◽  
pp. 096703352098731
Author(s):  
Adenilton C da Silva ◽  
Lívia PD Ribeiro ◽  
Ruth MB Vidal ◽  
Wladiana O Matos ◽  
Gisele S Lopes
Keyword(s):  
Quality Control ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Chemical Components ◽  
Linear Discriminant ◽  
Hand Sanitizer

The use of alcohol-based hand sanitizers is recommended as one of several strategies to minimize contamination and spread of the COVID-19 disease. Current reports suggest that the virucidal potential of ethanol occurs at concentrations close to 70%. Traditional methods of verifying the ethanol concentration in such products invite potential errors due to the viscosity of chemical components or may be prohibitively expensive to undertake in large demand. Near infrared (NIR) spectroscopy and chemometrics have already been used for the determination of ethanol in other matrices and present an alternative fast and reliable approach to quality control of alcohol-based hand sanitizers. In this study, a portable NIR spectrometer combined with classification chemometric tools, i.e., partial least square discriminant analysis (PLS–DA) and linear discriminant analysis with successive algorithm projection (SPA–LDA) were used to construct models to identify conforming and non-conforming commercial and laboratory synthesized hand sanitizer samples. Principal component analysis (PCA) was applied in an exploratory data study. Three principal components accounted for 99% of data variance and demonstrate clustering of conforming and non-conforming samples. The PLS–DA and SPA–LDA classification models presented 77 and 100% of accuracy in cross/internal validation respectively and 100% of accuracy in the classification of test samples. A total of 43% commercial samples evaluated using the PLS–DA and SPA–LDA presented ethanol content non-conforming for hand sanitizer gel. These results indicate that use of NIR spectroscopy and chemometrics is a promising strategy, yielding a method that is fast, portable, and reliable for discrimination of alcohol-based hand sanitizers with respect to conforming and non-conforming ethanol concentrations.

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