Principal Component Approach Research Articles

Leveraging crop yield forecasts using satellite information for early warning in Senegal

Agricultural losses driven by climate variability and anthropogenic pressures have severely impacted food security in Senegal. There is a crucial need to generate early warning signals for the upcoming season to enhance food security in response to the sudden climate shocks like drought. In this study, we investigated the spatial distribution of maize and groundnut using factor analysis with a principal component approach. We aimed to identify suitable predictors of crop yields for the development of a seasonal yield prediction model. Subsequently, multi-regression analysis was performed to predict crop yield based on various combinations of satellite-derived vegetation and climate (rainfall) datasets as well as agronomic data from Senegal's 40 districts between 2010 and 2021. Studies revealed a strong correlation between seasonal rainfall (May to September) and crop yield: a 10–20% decline in rainfall can lead to crop losses. The accuracy of the yield prediction model, built on the best performing scenarios for each district based on monsoon onset, duration, and planting time, exceeded 0.5 (R-squared) for all districts when combining rainfall and normalized difference vegetation index (NDVI) data. The model prediction accuracy varied between 0.6 and 0.8 for major crop growing areas. The study emphasizes that refining the yield prediction model using machine learning techniques can improve its accuracy and enable its implementation in early warning systems. This enhanced capability could bolster Senegal's resilience to climate change by aiding decision-makers and planners in developing more effective strategies to ensure food security.

Improvement of simultaneous prediction using principal components approach

Prediction has a fundamental role both in linear regression analysis and in many applied sciences. The predictions of actual values or mean values of the response variable were obtained separately, earlier than defined the target function. It has been attracting a lot of interest because obtaining simultaneous predictions both of the mean values and actual values is more reasonable in many applied studies since the target function was reported. This paper purposes to improve the simultaneous prediction with the target function when the problem of multicollinearity exists in the linear regression model by defining new predictors based on the principal components estimator, which is more resistant to multicollinearity than the ordinary least squares estimator. The comparisons of proposed predictors are done theoretically by using the mean squared error of predictions. Then theoretical results are supported by an exhaustive simulation study. Also, performance comparisons of the newly suggested predictors are made for two real-life data sets, real estate valuation and cruise ship info. The findings show that the use of the proposed predictors will make a crucial contribution to applied studies in obtaining better predictions.

Principal Component Approach and Relationship between Nomination Scale for Identification of Football Talent and Physical Fitness in Young Soccer Players

The present study aimed to investigate the relationship between the physical capabilities of young soccer players and their performance in game-related variables as assessed through the Nomination Scale for Identifying Football Talent (NSIFT) questionnaire. A total of 80 young soccer players, with an average age of 10.70 ± 1.02 years, participated in the research. Each player underwent a comprehensive assessment session that included the 5-0-5 Change of Direction (COD) test, the Illinois Agility Test, and the Countermovement Jump (CMJ) test. These assessments were selected to evaluate critical physical attributes essential for soccer performance such as agility, explosive strength, and the ability to change direction rapidly. To analyze the data, Principal Component Analysis (PCA), a statistical technique that reduces the dimensionality of large datasets while retaining as much variance as possible, was employed. The PCA results indicated strong sample validity as confirmed by the Kaiser–Meyer–Olkin (KMO) measurement index, which assesses the adequacy of the sample size for factor analysis. The analysis revealed two principal components: development and disposition, which together accounted for 73% of the total variance in the data. The development component encompasses various physical attributes that contribute to a player’s growth and improvement, including strength, speed, and agility. Conversely, the disposition component reflects innate qualities and cognitive skills that predispose players to excel in soccer such as decision making and game awareness. This research highlights the importance of incorporating physical assessments into talent identification processes, providing objective measures that complement subjective evaluations. This study contributed to the literature on talent identification in soccer, emphasizing the need for a multidisciplinary approach to nurture young athletes effectively. Future research should continue to explore the interplay between physical and cognitive skills in soccer to enhance player development and success in competitive environments.

Temporal assessment of Gross alpha emissions from the petroleum industry in Tenerife, Canary Islands (2001–2022)

A ca. 76% decrease in gross alpha activity levels, measured in surface aerosols collected in the city of Santa Cruz de Tenerife (Spain), has been explained in the present study in connection with the reduction of activities, and eventual closure, of an oil refinery in the city. Gross Alpha in surface aerosols, collected at weekly intervals over a period of 22 years (2001–2022), was used for the analysis. The dynamic behaviour of the gross alpha time series was studied using statistical wavelet, multifractal analysis, empirical decomposition method, multivariate analysis, principal component, and cluster analyses approaches. This was performed to separate the impact of other sources of alpha emitting radionuclides influencing the gross alpha levels at this site. These in-depth analyses revealed a noteworthy shift in the dynamic behaviour of the gross alpha levels following the refinery's closure in 2013. This analysis also attributed fluctuations and trends in the gross alpha levels to factors such as the 2008 global economic crisis and the refinery's gradual reduction of activity leading up to its closure. The mixed-model approach, incorporating multivariate regression and autoregressive integrated moving average methods, explained approximately 84% of the variance of the gross alpha levels. Finally, this work underscored the marked reduction in alpha activity levels following the refinery's closure, alongside the decline of other pollutants (CO, SO2, NO, NO2, Benzene, Toluene and Xylene) linked to the primary industrial activity in the municipality of Santa Cruz de Tenerife.

Analyzing Key Factors on Training Days within a Standard Microcycle for Young Sub-Elite Football Players: A Principal Component Approach.

Utilizing techniques for reducing multivariate data is essential for comprehensively understanding the variations and relationships within both biomechanical and physiological datasets in the context of youth football training. Therefore, the objective of this study was to identify the primary factors influencing training sessions within a standard microcycle among young sub-elite football players. A total of 60 male Portuguese youth sub-elite footballers (15.19 ± 1.75 years) were continuous monitored across six weeks during the 2019-2020 in-season, comprising the training days from match day minus (MD-) 3, MD-2, and MD-1. The weekly training load was collected by an 18 Hz global positioning system (GPS), 1 Hz heart rate (HR) monitors, the perceived exertion (RPE) and the total quality recovery (TQR). A principal component approach (PCA) coupled with a Monte Carlo parallel analysis was applied to the training datasets. The training datasets were condensed into three to five principal components, explaining between 37.0% and 83.5% of the explained variance (proportion and cumulative) according to the training day (p < 0.001). Notably, the eigenvalue for this study ranged from 1.20% to 5.21% within the overall training data. The PCA analysis of the standard microcycle in youth sub-elite football identified that, across MD-3, MD-2, and MD-1, the first was dominated by the covered distances and sprinting variables, while the second component focused on HR measures and training impulse (TRIMP). For the weekly microcycle, the first component continued to emphasize distance and intensity variables, with the ACC and DEC being particularly influential, whereas the second and subsequent components included HR measures and perceived exertion. On the three training days analyzed, the first component primarily consisted of variables related to the distance covered, running speed, high metabolic load, sprinting, dynamic stress load, accelerations, and decelerations. The high intensity demands have a high relative weight throughout the standard microcycle, which means that the training load needs to be carefully monitored and managed.

Impact of antenatal exposure to a mixture of persistent organic pollutants on intellectual development

ObjectiveStrong experimental evidence exists that several endocrine disrupting chemicals (EDCs) have neurobehavioral toxicity. However, evidence of associations between prenatal exposure and child's cognitive development is inconsistent. Moreover, toxicants are generally analyzed one by one without considering aggregate effects. We examined here the impact of a prenatal exposure to a mixture of persistent organic pollutants (POPs) on intellectual abilities in preschool children, and compared their effects to those described in the literature. MethodsSixty-two children were included in a longitudinal cohort. Four organochlorine pesticides, four polychlorinated biphenyls (PCBs) and seven perfluorinated compounds (PFCs) were measured in cord blood. Intellectual abilities were assessed at 6 years of age using the Wechsler Preschool and Primary Scale of Intelligence 4th ed. (WPPSI-IV). We examined the associations between a mixture of POPs and cognitive performances using principal components approach (PCA) and weighted quantile sum (WQS) regression taking sex difference into account. ResultsNo negative correlation was found when analyses were performed on boys and girls together. In sex-stratified analyses, lower scores in full scale intelligence quotient (FSIQ) and fluid reasoning index (FRI) were observed in boys most exposed to a mixture of POPs. Increase of the WQS index was also associated with lower verbal comprehension index (VCI) scores in girls only. No other negative correlation was found using both WQS and PCA models. ConclusionOur study suggests deleterious associations between antenatal exposure to a mixture of POPs and sex-specific cognitive level, clarifying some trends described in the literature.

Impact of Integrated Pest Management Technology Input and Yield Disparities on Cotton Crop in Vidarbha Region of Maharashtra: Principal Component Approach

Impact of Integrated Pest Management Technology Input and Yield Disparities on Cotton Crop in Vidarbha Region of Maharashtra: Principal Component Approach

The combination of kidney function variables with cell cycle arrest biomarkers identifies distinct subphenotypes of sepsis-associated acute kidney injury: a post-hoc analysis (the PHENAKI study)

Background The severity and course of sepsis-associated acute kidney injury (SA-AKI) are correlated with the mortality rate. Early detection of SA-AKI subphenotypes might facilitate the rapid provision of individualized care. Patients and methods In this post-hoc analysis of a multicenter prospective study, we combined conventional kidney function variables with serial measurements of urine (tissue inhibitor of metalloproteinase-2 [TIMP-2])* (insulin-like growth factor-binding protein [IGFBP7]) at 0, 6, 12, and 24 h) and then using an unsupervised hierarchical clustering of principal components (HCPC) approach to identify different phenotypes of SA-AKI. We then compared the subphenotypes with regard to a composite outcome of in-hospital death or the initiation of renal replacement therapy (RRT). Results We included 184 patients presenting SA-AKI within 6 h of the initiation of catecholamines. Three distinct subphenotypes were identified: subphenotype A (99 patients) was characterized by a normal urine output (UO), a low SCr and a low [TIMP-2]*[IGFBP7] level; subphenotype B (74 patients) was characterized by existing chronic kidney disease (CKD), a higher SCr, a low UO, and an intermediate [TIMP-2]*[IGFBP7] level; and subphenotype C was characterized by very low UO, a very high [TIMP-2]*[IGFBP7] level, and an intermediate SCr level. With subphenotype A as the reference, the adjusted hazard ratio (aHR) [95%CI] for the composite outcome was 3.77 [1.92–7.42] (p < 0.001) for subphenotype B and 4.80 [1.67–13.82] (p = 0.004) for subphenotype C. Conclusions Combining conventional kidney function variables with urine measurements of [TIMP-2]*[IGFBP7] might help to identify distinct SA-AKI subphenotypes with different short-term courses and survival rates.

Clustering based on renal and inflammatory admission parameters in critically ill patients admitted to the ICU.

The COVID-19 pandemic has been associated with significant variability in acute kidney injury (AKI) incidence, leading to concerns regarding patient heterogeneity. The study's primary objective was a cluster analysis, to identify homogeneous subgroups of patients (clusters) using baseline characteristics, including inflammatory biomarkers. The secondary objectives were the comparisons of MAKE-90 and mortality between the different clusters at three months. This retrospective single-center study was conducted in the Medical Intensive Care Unit of the University Hospital of Clermont-Ferrand, France. Baseline data, clinical and biological characteristics on ICU admission, and outcomes at day 90 were recorded. The primary outcome was the risk of major adverse kidney events at 90 days (MAKE-90). Clusters were determined using hierarchical clustering on principal components approach based on admission characteristics, biomarkers and serum values of immune dysfunction and kidney function. It included consecutive adult patients admitted between March 20, 2020 and February 28, 2021 for severe COVID-19. A total of 149 patients were included in the study. Three clusters were identified of which two were fully described (cluster 3 comprising 2 patients). Cluster 1 comprised 122 patients with fewer organ dysfunctions, moderate immune dysfunction, and was associated with reduced mortality and a lower incidence of MAKE-90. Cluster 2 comprised 25 patients with greater disease severity, immune dysfunction, higher levels of suPAR and L-FABP/U Creat, and greater organ support requirement, incidence of AKI, day-90 mortality and MAKE-90. This study identified two clusters of severe COVID-19 patients with distinct biological characteristics and renal event risks. Such clusters may help facilitate the identification of targeted populations for future clinical trials. Also, it may help to understand the significant variability in AKI incidence observed in COVID-19 patients.

Factor Sufficiency in Asset Pricing: An Application for the Brazilian Market

The multifactor asset pricing model derived from the Fama–French approach is extensively used in asset risk premium estimation procedures. Even including a considerable number of factors, it is still possible that omitted factors affect the estimation of this model. In this work, we compare estimators robust to the presence of omitted factors in estimating the risk premium in the Brazilian market. Initially, we analyze the panel of asset returns using the mean group and common correlated effect estimators to detect the presence of omitted factors. We then compare the results with those obtained by a estimator robust to omitted variables, which uses a principal components approach to correct the estimation in the case of the omission of latent factors. We conclude that there is evidence of omitted factors, and the best predictor for the expect returns is the common correlated effects estimator.

Quantitation of olefins in sustainable aviation fuel intermediates using principal component analysis coupled with vacuum ultraviolet spectroscopy

Olefins, a common intermediate from biomass conversion processes, are undesirable in jet fuel because of their poor thermal stability. This paper presents an approach for olefin quantitation using 2D gas chromatography coupled with vacuum ultraviolet spectroscopy. Principal component analysis was used to reduce the dimensionality of the spectroscopic data from a highly olefinic fuel intermediate. A principal component template was created that enabled olefin quantitation, which was compared to the existing GCxGC-VUV approach from the literature. The principal component method was able to identify and quantify trace amounts of cyclodienes, which were present at only 0.01 wt% in the fuel sample. The principal component approach also identifies species that fall outside of the GCxGC template. For instance, quantitation with the literature method resulted in an olefin concentration of 0.95 times that of the principal component method due to olefins falling outside of the expected GCxGC regions. The principal component results were compared with 13C and 1H NMR data, which confirmed that the fuel had a high concentration of olefins and alkanes with little aromatic content.

Remote sensing-based drought hazard monitoring and assessment in a coastal plain: A principal component approach

Accurate drought information is essential for preventing agricultural and societal losses. The indicators of how severe a drought is the deficiency in precipitation, soil moisture, and vegetation stress. The indicators were evaluated using the Precipitation Condition Index (PCI), Vegetation Condition Index (VCI), and Temperature Condition Index (TCI).The indices were combined using Principal Component Analysis to create the Synthetic Drought Index (SDI) for the evaluation of drought severity. The indices were estimated using multi-source remote sensing data from the Tropical Rainfall Measuring Mission (TRMM) and Operational Land Imager (OLI) of various years. Temporal analysis showed that the district is drought-prone and deficiency of 65% of precipitation in northeast monsoon of 2016 and below average non-monsoon rainfall in 2017, caused drought and affected 223.5 Km2 in 2017. Below average precipitation in northeast monsoon of 2018 and below average non-monsoon rainfall in 2019, caused drought and affected 423 Km2 in 2019. The northeast coastal regions of Ottapidaram, Thoothukudi, and Vilathikulam taluks of the district were more severely prone to drought. Failure of monsoon is the root cause of water deficit in water bodies. The semi-arid coastal climate accelerates the evaporation of water in water bodies and causes soil moisture deficit that leads to drought in the coastal district. A sequential evaluation of this index can be used to identify the onset of drought and mitigate the effect of drought.

Principal components approach of physiological and physical metrics collected through GPS technology in soccer referees

The aim of this study was to define and classify the different physical and physiological performance metrics on national and non-national soccer referees (SRs) through GPS technology. Sixty SRs (age: 28.9 ± 4.9 years; weight: 70.2 ± 8.0 kg; height: 175.7 ± 5.5 cm) were tested using the 18 Hz global positioning system (GPS) (WimuPro, Realtrack, Almería, Spain). An exploratory factor analysis was performed using principal component analysis (PCA) on performance indicators with orthogonal rotation (VariMax). Thirteen metrics (total distance, distance covered at different speeds, maximum heart rate, and heart rate recorded in different zones) were integrated in three principal components, activation (PCA1), recovery (PCA2), and high intensity (PCA3). They represented 86% and 89% of total explained variance of the competition performance in national and non-national SRs, respectively. Distance covered at very high speed and heart rate between 80% and 89.9% of the maximum integrated the high-intensity principal component and were higher in non-national SRs. The main finding of this study was that it defined for the first time in the scientific literature the main components that discriminate physical performance between national and non-national referees, where high intensity metrics have greater weight in non-national referees. In addition, GPS technology together with PCA analysis were presented as useful methods to discriminate the most representative physical and physiological performance metrics in sports competition in SRs.

Singular Points of the Tremor of the Earth’s Surface

A method for studying properties of the Earth’s surface tremor, measured by means of GPS, is proposed. The following tremor characteristics are considered: the entropy of wavelet coefficients, the Donoho–Johnston wavelet index, and two estimates of the spectral slope. The anomalous areas of tremor are determined by estimating the probability densities of extreme values of the studied properties. The criteria for abnormal tremor behavior are based on the proximity to, or the difference between, tremor properties and white noise. The greatest deviation from the properties of white noise is characterized by entropy minima and spectral slope and DJ index maxima. This behavior of the tremor is called “active”. The “passive” tremor behavior is characterized by the maximum proximity to the properties of white noise. The principal components approach provides weighted averaged density maps of these two variants of extreme distributions of parameters in a moving time window of 3 years. Singular points are the points of maximum average densities. The method is applied to the analysis of daily time series from a GPS network in California during the period 2009–2022. Singular points of tremor form well-defined clusters were found. The passive tremor could be caused by the activation of movement in fragments of the San Andreas fault.

Resultant equations for training load monitoring during a standard microcycle in sub-elite youth football: a principal components approach.

Applying data-reduction techniques to extract meaningful information from electronic performance and tracking systems (EPTS) has become a hot topic in football training load (TL) monitoring. The aim of this study was to reduce the dimensionality of the internal and external load measures, by a principal component approach, to describe and explain the resultant equations for TL monitoring during a standard in-season microcycle in sub-elite youth football. Additionally, it is intended to identify the most representative measure for each principal component. A principal component analysis (PCA) was conducted with a Monte Carlo parallel analysis and VariMax rotation to extract baseline characteristics, external TL, heart rate (HR)-based measures and perceived exertion. Training data were collected from sixty sub-elite young football players during a 6-week training period using 18 Hz global positioning system (GPS) with inertial sensors, 1 Hz short-range telemetry system, total quality recovery (TQR) and rating of perceived exertion (RPE). Five principal components accounted for 68.7% of the total variance explained in the training data. Resultant equations from PCA was subdivided into: (1) explosiveness, accelerations and impacts (27.4%); (2) high-speed running (16.2%); (3) HR-based measures (10.0%); (4) baseline characteristics (8.3%); and (5) average running velocity (6.7%). Considering the highest factor in each principal component, decelerations (PCA 1), sprint distance (PCA 2), average HR (PCA 3), chronological age (PCA 4) and maximal speed (PCA 5) are the conditional dimension to be considered in TL monitoring during a standard microcycle in sub-elite youth football players. Current research provides the first composite equations to extract the most representative components during a standard in-season microcycle in sub-elite youth football players. Futures research should expand the resultant equations within training days, by considering other well-being measures, technical-tactical skills and match-related contextual factors.

Biological risk factors for dementia in India

AbstractBackgroundMany clinical biomarkers have been associated with dementia. This paper uses a novel data set from an understudied population – a national sample of older Indians ‐ to assess the relative importance of 41 biomarkers using a variety of approaches. The value of these data is the extensiveness of the biomarker collection, the validated classification of dementia, and the uniqueness of the population with low average education. Many approaches to both summarizing and reducing data complexity have been suggested. Some of these are based on theory about biological mechanisms and some are agnostic and based on machine‐learning approaches. The results are informative for others collecting and analyzing biomarker data in population samples.MethodWe use 6 approaches to assessing the value of the biomarkers in explaining variance in dementia diagnosis and cognitive functioning – some traditional social science approaches and some based on machine learning. The aim of the different approaches is to determine how to best characterize the influence of biology as well as how to trim and combine the data. The five approaches to measuring biological risk: 41 individual biomarkers; identification of subsets of biomarkers with elastic net; support vector machine learning; factors developed with factor analysis; a principal components approach; and a factor classification based on a theoretical approach.ResultsResults indicate if the aim is explain variance in dementia, all the biomarkers or a reduced set of biomarkers identified by elastic net do the best job at explaining variability. However, biological markers chosen as most important do not fit well our understanding of biological mechanisms.ConclusionIf the aim is understanding the relative importance of biological systems, traditional social science approaches, e.g. the factor analysis and principal components approach, allow better understanding and interpretation of the biomarkers associated with cognitive performance.

The impacts of economic policy uncertainty, energy consumption, sustainable innovation, and quality of governance on green growth in emerging economies

This study investigates the impacts of economic policy uncertainty (EPU), energy consumption (EC), sustainable technological innovation (STI), and quality of governance on green growth (GG). Besides, it examines the moderating effect of STI and governance quality on the association between EPU and GG. It applies a Pooled Mean Group Autoregressive Distributed Lag estimator for 25 emerging economies for periods 1991–2019. For the robustness test, we utilize the asymptotic distribution of the Cross-section Augmented Distributed Lag to control for cross-sectional dependence concerns. We drive two indicators for GG by applying the principal component approach and directional distance function. The findings imply that STI and quality of governance have significant positive impacts on GG. However, EPU and EC adversely impact GG in emerging economies. Besides, quality of governance and STI positively moderate EPU's influence on GG, implying that countries with better quality of governance and promote STI mitigate the detrimental effects of high EPU on GG. Moreover, we run the causality analysis to investigate the causal relationship between target variables and GG. Policy suggestions are proposed based on the results.

Reducing waste management scenario space for developing countries: A hierarchical clustering on principal components approach.

The complexity of waste management (WM) problems resulted in the explosion of scenarios that challenge focused discussion among stakeholders and hinder the integrity of policy responses in developing countries. Hence, drawing similarities is essential to reduce the number of scenarios to simplify the WM efforts. To extract similarities, measuring WM performance is not enough, but the background factors related to this performance should be incorporated. These factors form a unique system characteristic that facilitates or hinders WM functions. Thus, this study applied multivariate statistical analysis to clarify underlying characteristics that facilitate efficient WM scenario developments for developing countries. The study first analysed drivers associated with improved WM system performance using bivariate correlation analysis. As a result, twelve significant drivers associated with controlled solid waste were identified. Then, it mapped the countries based on their WM system characteristics using the combined principal component analysis and hierarchical clustering approach. Thirteen variables were examined to extract similarities between the countries. The results identified three homogenous clusters. The clusters were found considerably parallel to the global classifications based on income and human development index. Hence, the presented approach is efficient in explaining similarities that reduce WM scenarios and favours cooperation among countries.

Psychometric Properties of the Persian Version of the Five-Factor Narcissism Inventory-Short Form (FFNI-SF).

Objective: For a long time, it was held that narcissism had two aspects: narcissistic grandiosity and narcissistic fragility. The extraversion, neuroticism, and antagonism elements of the three-factor narcissism paradigm, on the other hand, have gained popularity in recent years. Based on the three-factor framework of narcissism, the Five-Factor Narcissism Inventory-short form (FFNI-SF) is a relatively recent invention. Therefore, this research aimed to assess the validity and reliability of the FFNI-SF in Persian among Iranians. Method : Ten specialists (with Ph.D.s in psychology) were enlisted in this research to translate and evaluate the reliability of the Persian version of the FFNI-SF. The Content Validity Index (CVI) and the Content Validity Ratio (CVR) were then used to assess face and content validity. It was given to 430 students at Azad University, Tehran Medical Branch, once the Persian form was completed. The available sampling technique was used to choose the participants. Cronbach's alpha and the test-retest correlation coefficient were used to assess the reliability of the FFNI-SF. In addition, concept validity was obtained using exploratory factor analysis. In addition, correlations with NEO Five-Factor Inventory (NEO-FFI) and Pathological Narcissism Inventory (PNI) were employed to establish the convergent validity of the FFNI-SF. Results: According to professional opinions, the face and content validity indices met expectations. With Cronbach's alpha and test-retest reliability, the questionnaire's reliability was also established. Cronbach's alphas varied between 0.7 and 0.83 for the FFNI-SF components. According to test-retest reliability coefficients, the components' values varied from 0.7 to 0.86. Additionally, three factors (extraversion, neuroticism, and antagonism) were recovered using the principal components approach and a straight oblimin rotation. According to an analysis of the eigenvalues, the three-factor solution accounted for 49.01 of the variation in the FFNI-SF. The eigenvalues for the three variables were 2.95 (M = 1.39), 2.51 (M = 1.3), and 1.88 (M = 1.24) respectively. The FFNI-SF Persian form's convergent validity was further verified by the association between its results and those from the NEO-FFI and PNI tests and the FFNI-SF. There was a substantial positive association between FFNI-SF Extraversion and NEO Extraversion (r = 0.51, P ≤ 0.001), as well as a strong negative correlation between FFNI-SF antagonism and NEO agreeableness (r = -0.59, P ≤ 0.001). As well as this, PNI grandiose narcissism (r = 0.37, P ≤ 0.001) was shown to be significantly associated with FFNI-SF grandiose narcissism (r = 0.48, P ≤ 0.001), as it was with PNI vulnerable narcissism (r = 0.48, P < 0.001). Conclusion: with its solid psychometric qualities, we may utilize the Persian FFNI-SF to test the three-factor model of narcissism as an effective tool for research.

Phenotypes of hemodynamic response to fluid challenge during anesthesia: a cluster analysis.

The fluid challenge (FC) response is usually evaluated as binary, which may be inadequate to describe the complex interactions between heart function and vascular tone response after fluid administration. We applied a clustering approach to assess the different phenotypes of cardiovascular responses to FC administration, considering the associations of all the baseline variables potentially influencing pressure and flow response to a FC. Secondarily, we evaluated the reliability of baseline hemodynamic variables in discriminating fluid responsiveness, which is considered the standard approach at the bedside. Five merged datasets from elective surgical patients receiving a FC dose ≥4 mL/kg, infused over 10 minutes. In a principal component approach, hierarchical clustering was used to define hemodynamic phenotypes of response to FC administration. Hierarchical cluster analysis with Ward linkage was carried out to define similar patient groups using the Gower distance for the mixed combination of continuous and categorical variables. No a priori criteria of fluid responsiveness were applied. The area (AUC) under the pre-FC variables' receiver operating characteristic curves (ROC) was also built to predict fluid responsiveness, defined as SVI ≥10% after FC. We analyzed 223 patients. The cluster analysis identified three hemodynamic clusters of patients: cluster 1 (98 patients, 44.0%) showed an average increase of mean arterial pressure (MAP) and Stroke Volume Index (SVI) of 17.3% (11.9-23.1) and 13.1% (0.5-23.4) at the end of FC, respectively. These patients showed baseline flow and pressure variables slightly below physiological ranges, with high pulse pressure variation (PPV). Cluster 2 (68 patients, 30.5%) showed no increase of MAP and SVI at the end of FC. These patients showed baseline flow and pressure variables within physiological ranges, with low hear rate (HR) and PPV. Cluster 3 (57 patients, 25.5%) showed no MAP increase and an SVI increase of 13.1 (2.1-19.6). These patients showed baseline pressure variables within physiological ranges, low flow variables associated to high HR and PPV. The pulse pressure variation (PPV) showed an AUC of 0.82 (0.03), with a grey zone ranging from 6% to 12%, including 86 (38.5%) patients. Clustering analysis identified three hemodynamic clusters with different response phenotypes to FC. This promising approach may enhance the ability to detect fluid responsiveness at the bedside, by considering the specific association of parameters and not the presence of a single one, such as the PPV. In fact, in our cohort the reliability of the PPV was limited, showing high sensibility and specificity only above 12% and below 6%, respectively, and a grey zone including 38.5% of patients.