MPI-parallelization of the grid inhomogeneous solvation theory calculation.

The grid inhomogeneous solvation theory (GIST) method requires the often time-consuming calculation of water-water and water-solute energy on a grid. Previous efforts to speed up this calculation include using OpenMP, GPUs, and particle mesh Ewald. This article details how the speed of this calculation can be increased by parallelizing it with MPI, where trajectory frames are divided among multiple processors. This requires very little communication between individual processes during trajectory processing, meaning the calculation scales well to large processor counts. This article also details how the entropy calculation, which must happen after trajectory processing since it requires information from all trajectory frames, is parallelized via MPI. This parallelized GIST method has been implemented in the freely-available CPPTRAJ analysis software.

Development and validation of the Unmet Needs Questionnaire for young stroke survivors.

OBJECTIVE: Owing to the lack of a suitable tool for detecting the unmet needs of young stroke survivors, this study aims to develop a validated questionnaire for evaluating these unmet needs. DESIGN: A cross-sectional, observational research design. SETTING: Chang Gung Memorial Hospital Linkou and Taoyuan branches in Taiwan. PARTICIPANTS: A total of 211 participants (average age 53 years; within 6 months post-stroke) completed the questionnaire. MAIN MEASURES: A qualitative approach was used to create an item pool. Experts verified item suitability, and content validity was evaluated using the item content validity index. Item analysis was applied to determine item quality, and factor analysis was used to explore construct validity. In addition, parallel analysis was employed to ascertain the optimal number of factors. RESULTS: The scale development procedure resulted in a 27-item questionnaire that assesses the unmet needs of young stroke survivors after a stroke. The item content validity index was 1.0. The Unmet Needs Questionnaire has five factors: restoring prestroke abilities and life, rehabilitation-related resources, social support and self-adjustment, economic and post-stroke life adjustment, and stroke-related information. These five factors accounted for 54% of the variance. Cronbach's alpha for the total scale was 0.91, while the alpha for the subscales ranged from 0.74 to 0.88. CONCLUSIONS: The Unmet Needs Questionnaire showed acceptable reliability and validity. It can help clinical professionals and government agencies identify stroke survivors' unmet needs and develop tailored care plans. Future research should explore the trajectory of post-stroke unmet needs using this tool.

Dimensionality assessment in the presence of wording effects: A network psychometric and factorial approach.

This study proposes a procedure for substantive dimensionality estimation in the presence of wording effects, the inconsistent response to regular and reversed self-report items. The procedure developed consists of subtracting an approximate estimate of the wording effects variance from the sample correlation matrix and then estimating the substantive dimensionality on the residual correlation matrix. This is achieved by estimating a random intercept factor with unit loadings for all the regular and unrecoded reversed items. The accuracy of the procedure was evaluated through an extensive simulation study that manipulated nine relevant variables and employed the exploratory graph analysis (EGA) and parallel analysis (PA) retention methods. The results indicated that combining the proposed procedure with EGA or PA achieved high accuracy in estimating the substantive latent dimensionality, but that EGA was superior. Additionally, the present findings shed light on the complex ways that wording effects impact the dimensionality estimates when the response bias in the data is ignored. A tutorial on substantive dimensionality estimation with the R package EGAnet is offered, as well as practical guidelines for applied researchers.

Assessing Dimensionality in Non-Positive Definite Tetrachoric Correlation Matrices: Does Matrix Smoothing Help?

We performed two simulation studies that investigated dimensionality recovery in NPD tetrachoric correlation matrices using parallel analysis. In each study, the NPD matrices were rehabilitated by three smoothing algorithms. In Study 1, we replicated the work by Debelak and Tran on the assessment of dimensionality in one- or two-dimensional common factor models. In Study 2, we extended the Debelak and Tran design in three important ways. Specifically, we investigated: (a) a wider range of factors; (b) models with varying amounts of model error; and (c) models generated from more realistic population item parameters. Our results indicated that matrix smoothing of NPD tetrachoric correlation matrices improves the performance of parallel analysis with binary data. However, these improvements were modest and often of trivial size. To demonstrate the effect of matrix smoothing on an empirical data set, we applied parallel analysis and factor analysis to Adjective Checklist data from the California Twin Registry.

Global Analysis of the Human RNA Degradome Reveals Widespread Decapped and Endonucleolytic Cleaved Transcripts.

RNA decay is an important regulatory mechanism for gene expression at the posttranscriptional level. Although the main pathways and major enzymes that facilitate this process are well defined, global analysis of RNA turnover remains under-investigated. Recent advances in the application of next-generation sequencing technology enable its use in order to examine various RNA decay patterns at the genome-wide scale. In this study, we investigated human RNA decay patterns using parallel analysis of RNA end-sequencing (PARE-seq) data from XRN1-knockdown HeLa cell lines, followed by a comparison of steady state and degraded mRNA levels from RNA-seq and PARE-seq data, respectively. The results revealed 1103 and 1347 transcripts classified as stable and unstable candidates, respectively. Of the unstable candidates, we found that a subset of the replication-dependent histone transcripts was polyadenylated and rapidly degraded. Additionally, we identified 380 endonucleolytically cleaved candidates by analyzing the most abundant PARE sequence on a transcript. Of these, 41.4% of genes were classified as unstable genes, which implied that their endonucleolytic cleavage might affect their mRNA stability. Furthermore, we identified 1877 decapped candidates, including HSP90B1 and SWI5, having the most abundant PARE sequences at the 5'-end positions of the transcripts. These results provide a useful resource for further analysis of RNA decay patterns in human cells.

Brain network profiling defines functionally specialized cortical networks.

Neuroimaging research made rapid advances in the study of the functional architecture of the brain during the past decade. Many proposals endorsed the relevance of large-scale brain networks, defined as ensembles of brain regions that exhibit highly correlated signal fluctuations. However, analysis methods need further elaboration to define the functional and anatomical extent of specialized subsystems within classical networks with a high reliability. We present a novel approach to characterize and examine the functional proprieties of brain networks. This approach, labeled as brain network profiling (BNP), considers similarities in task-evoked activity and resting-state functional connectivity across biologically relevant brain subregions. To combine task-driven activity and functional connectivity features, principal components were extracted separately for task-related beta values and resting-state functional connectivity z-values (data available from the Human Connectome Project), from 360 brain parcels. Multiple clustering procedures were employed to assess if different clustering methods (Gaussian mixtures; k-means) and/or data structures (task and rest data; only rest data) led to improvements in the replication of the brain architecture. The results indicated that combining information from resting-state functional connectivity and task-evoked activity and using Gaussian mixtures models for clustering produces more reliable results (99% replication across data sets). Moreover, the findings revealed a high-resolution partition of the cerebral cortex in 16 networks with unique functional connectivity and/or task-evoked activity profiles. BNP potentially offers new approaches to advance the investigation of the brain functional architecture.

Psychometric properties of the Resilience Scale - 14 in a sample of college students from France.

INTRODUCTION: In recent years, the integration of resilience in several psychological and medical studies underscores a need for resilience assessment measures with robust psychometric properties. This study aimed to evaluate the underlying structure of the French version of the Resilience Scale (RS-14), a widely used measure to assess resilience both in general and clinical population. METHOD: A sample of 2195 college students from France (18.68% of male; Mean age=20.09 years old (±1.21) completed the RS-14, the Child and Youth Resilience Measure, the Social Support Questionnaire and the Kessler Psychological Distress Scale. EFA with parallel analysis was conducted to assess the factorial structure of the RS-14 while CFA was performed to investigate the goodness-of-fit. Internal consistency, concurrent and convergent validity were evaluated. RESULTS: A one-dimensional-factorial-solution emerged from the EFA, its goodness-of-fit was adequate and it presented good internal consistency. As expected, the RS-14 score correlated positively to the CYRM and SSQ scores and negatively to the psychological distress score, supporting the validity of the scale. CONCLUSION: The one-dimensional-factor corroborates the initial and many languages versions of the RS-14. The results showed that the French version of the RS-14 presents adequate psychometric properties and that is a reliable and valid scale in evaluating resilience.

Development of a Microsoft Excel tool for applying a factor retention criterion of a dimension coefficient to a survey on patient safety culture.

BACKGROUND: Many quality-of-life studies have been conducted in healthcare settings, but few have used Microsoft Excel to incorporate Cronbach's α with dimension coefficient (DC) for describing a scale's characteristics. To present a computer module that can report a scale's validity, we manipulated datasets to verify a DC that can be used as a factor retention criterion for demonstrating its usefulness in a patient safety culture survey (PSC). METHODS: Microsoft Excel Visual Basic for Applications was used to design a computer module for simulating 2000 datasets fitting the Rasch rating scale model. The datasets consisted of (i) five dual correlation coefficients (correl. = 0.3, 0.5, 0.7, 0.9, and 1.0) on two latent traits (i.e., true scores) following a normal distribution and responses to their respective 1/3 and 2/3 items in length; (ii) 20 scenarios of item lengths from 5 to 100; and (iii) 20 sample sizes from 50 to 1000. Each item containing 5-point polytomous responses was uniformly distributed in difficulty across a ± 2 logit range. Three methods (i.e., dimension interrelation ≥0.7, Horn's parallel analysis (PA) 95% confidence interval, and individual random eigenvalues) were used for determining one factor to retain. DC refers to the binary classification (1 as one factor and 0 as many factors) used for examining accuracy with the indicators sensitivity, specificity, and area under receiver operating characteristic curve (AUC). The scale's reliability and DC were simultaneously calculated for each simulative dataset. PSC real data were demonstrated with DC to interpret reports of the unit-based construct validity using the author-made MS Excel module. RESULTS: The DC method presented accurate sensitivity (=0.96), specificity (=0.92) with a DC criterion (≥0.70), and AUC (=0.98) that were higher than those of the two PA methods. PA combined with DC yielded good sensitivity (=0.96), specificity (=1.0) with a DC criterion (≥0.70), and AUC (=0.99). CONCLUSIONS: Advances in computer technology may enable healthcare users familiar with MS Excel to apply DC as a factor retention criterion for determining a scale's unidimensionality and evaluating a scale's quality.

Dysexecutive Questionnaire (DEX): Unrestricted structural analysis in large clinical and non-clinical samples.

The factorial structure of the Dysexecutive Questionnaire (DEX) is an unresolved issue in scientific literature. One-to-five-factor solutions have been found in several studies by applying different research methods. Only a few of these studies used appropriate analysis procedures to suit a Likert scale-type of answer or investigated large enough samples to ensure the stability of factorial solutions. The present study examines a sample of 2151 subjects, 1482 from the general population and 669 from a clinical population. An unrestricted factorial analysis was carried out on both samples. The results unequivocally point to a single-factor solution in both samples. This means that only one latent variable is displayed in the DEX, which accounts for symptoms of oversight malfunction in activities of daily living. It is concluded that the diversity of results previously obtained in other studies may be due to using research methods that depict Likert-type scales on a continuum when they are actually ordinal categorical measures. In conclusion, the DEX should be considered a screening test that reports symptoms of prefrontal malfunction, although it is unable to specify what areas or functions have been affected, as previous studies have claimed.

Validity and reliability testing of the Swedish version of Melbourne Decision Making Questionnaire.

BACKGROUND: The Melbourne Decision-Making Questionnaire (MDMQ) is an attempt to capture and measure coping strategies that people use. The instrument had not previously been translated into Swedish. The aim of this study was to evaluate validity and reliability of the Swedish version of the MDMQ. METHOD: A Swedish translation was performed and back-translated. A group of five pilot readers evaluated content validity. The translated questionnaire was tested among 735 patients, healthcare workers, healthcare students and teachers. A parallel analysis (PA), exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were performed. RESULT: An initial EFA with a four-factor solution showed a low concordance with the original 22-item four-factor model with a very low Cronbach's alpha in one of the dimensions. However, a second EFA with a three-factor solution showed a good model fit for the Swedish translation of the Melbourne Decision-Making Questionnaire (MDMQ-S) with a satisfactory Cronbach's alpha. A CFA showed a goodness of fit after deleting six items. CONCLUSION: After testing the MDMQ-S, we found support for validity and reliability of the instrument. We found the 16-item version of MDMQ-S to be satisfactory concerning the subscales vigilance, procrastination and buck-passing. However, we found no support that the hypervigilance dimension could be measured by the MDMQ-S.

An evaluation of the independence of the Health of the Nation Outcome Scales.

OBJECTIVE: In this study the independence of the scales/items in the Health of the Nation Outcome Scales (HoNOS) was empirically investigated. METHOD: Parallel analysis using random column permutation and bootstrapping were used to compare the factor structure, intercorrelations and Cronbach's alpha from the original HoNOS study and also recently collected HoNOS ratings. Random permutation ensures that the data has the same distributions as the data it is based on, but that the variables are now independent. RESULTS: It is shown that both of the real HoNOS data sets are significantly different to the independent items data in many ways. An examination of fit statistics from confirmatory factor analysis is also used to show that the independence model is a very poor fit to the data. CONCLUSIONS: It is clear that the 12 HoNOS scales are unlikely to be independent. There is a need for more research to clarify the appropriate structure of HoNOS, and also to consider whether some of the items need either replacing or augmenting.

Using Multiple Imputation to Account for the Uncertainty Due to Missing Data in the Context of Factor Retention.

Although parallel analysis has been found to be an accurate method for determining the number of factors in many conditions with complete data, its application under missing data is limited. The existing literature recommends that, after using an appropriate multiple imputation method, researchers either apply parallel analysis to every imputed data set and use the number of factors suggested by most of the data copies or average the correlation matrices across all data copies, followed by applying the parallel analysis to the average correlation matrix. Both approaches for pooling the results provide a single suggested number without reflecting the uncertainty introduced by missing values. The present study proposes the use of an alternative approach, which calculates the proportion of imputed data sets that result in k (k = 1, 2, 3 . . .) factors. This approach will inform applied researchers of the degree of uncertainty due to the missingness. Results from a simulation experiment show that the proposed method can more likely suggest the correct number of factors when missingness contributes to a large amount of uncertainty.

Assessing Dimensionality of IRT Models Using Traditional and Revised Parallel Analyses.

Determining the number of dimensions is extremely important in applying item response theory (IRT) models to data. Traditional and revised parallel analyses have been proposed within the factor analysis framework, and both have shown some promise in assessing dimensionality. However, their performance in the IRT framework has not been systematically investigated. Therefore, we evaluated the accuracy of traditional and revised parallel analyses for determining the number of underlying dimensions in the IRT framework by conducting simulation studies. Six data generation factors were manipulated: number of observations, test length, type of generation models, number of dimensions, correlations between dimensions, and item discrimination. Results indicated that (a) when the generated IRT model is unidimensional, across all simulation conditions, traditional parallel analysis using principal component analysis and tetrachoric correlation performs best; (b) when the generated IRT model is multidimensional, traditional parallel analysis using principal component analysis and tetrachoric correlation yields the highest proportion of accurately identified underlying dimensions across all factors, except when the correlation between dimensions is 0.8 or the item discrimination is low; and (c) under a few combinations of simulated factors, none of the eight methods performed well (e.g., when the generation model is three-dimensional 3PL, the item discrimination is low, and the correlation between dimensions is 0.8).

On the Detection of the Correct Number of Factors in Two-Facet Models by Means of Parallel Analysis.

Methods for optimal factor rotation of two-facet loading matrices have recently been proposed. However, the problem of the correct number of factors to retain for rotation of two-facet loading matrices has rarely been addressed in the context of exploratory factor analysis. Most previous studies were based on the observation that two-facet loading matrices may be rank deficient when the salient loadings of each factor have the same sign. It was shown here that full-rank two-facet loading matrices are, in principle, possible, when some factors have positive and negative salient loadings. Accordingly, the current simulation study on the number of factors to extract for two-facet models was based on rank-deficient and full-rank two-facet population models. The number of factors to extract was estimated from traditional parallel analysis based on the mean of the unreduced eigenvalues as well as from nine other rather traditional versions of parallel analysis (based on the 95th percentile of eigenvalues, based on reduced eigenvalues, based on eigenvalue differences). Parallel analysis based on the mean eigenvalues of the correlation matrix with the squared multiple correlations of each variable with the remaining variables inserted in the main diagonal had the highest detection rates for most of the two-facet factor models. Recommendations for the identification of the correct number of factors are based on the simulation results, on the results of an empirical example data set, and on the conditions for approximately rank-deficient and full-rank two-facet models.

Exploring the Impact of Missing Data on Residual-Based Dimensionality Analysis for Measurement Models.

Researchers frequently use Rasch models to analyze survey responses because these models provide accurate parameter estimates for items and examinees when there are missing data. However, researchers have not fully considered how missing data affect the accuracy of dimensionality assessment in Rasch analyses such as principal components analysis (PCA) of standardized residuals. Because adherence to unidimensionality is a prerequisite for the appropriate interpretation and use of Rasch model results, insight into the impact of missing data on the accuracy of this approach is critical. We used a simulation study to examine the accuracy of standardized residual PCA with various proportions of missing data and multidimensionality. We also explored an adaptation of modified parallel analysis in combination with standardized residual PCA as a source of additional information about dimensionality when missing data are present. Our results suggested that missing data impact the accuracy of PCA on standardized residuals, and that the adaptation of modified parallel analysis provides useful supplementary information about dimensionality when there are missing data.

Incorporating Uncertainty Into Parallel Analysis for Choosing the Number of Factors via Bayesian Methods.

A number of psychometricians have suggested that parallel analysis (PA) tends to yield more accurate results in determining the number of factors in comparison with other statistical methods. Nevertheless, all too often PA can suggest an incorrect number of factors, particularly in statistically unfavorable conditions (e.g., small sample sizes and low factor loadings). Because of this, researchers have recommended using multiple methods to make judgments about the number of factors to extract. Implicit in this recommendation is that, when the number of factors is chosen based on PA, uncertainty nevertheless exists. We propose a Bayesian parallel analysis (B-PA) method to incorporate the uncertainty with decisions about the number of factors. B-PA yields a probability distribution for the various possible numbers of factors. We implement and compare B-PA with a frequentist approach, revised parallel analysis (R-PA), in the contexts of real and simulated data. Results show that B-PA provides relevant information regarding the uncertainty in determining the number of factors, particularly under conditions with small sample sizes, low factor loadings, and less distinguishable factors. Even if the indicated number of factors with the highest probability is incorrect, B-PA can show a sizable probability of retaining the correct number of factors. Interestingly, when the mode of the distribution of the probabilities associated with different numbers of factors was treated as the number of factors to retain, B-PA was somewhat more accurate than R-PA in a majority of the conditions.

Systematic Characterization of MicroRNA Processing Modes in Plants With Parallel Amplification of RNA Ends.

In plants, the RNase III-type enzyme Dicer-like 1 (DCL1) processes most microRNAs (miRNAs) from their primary transcripts called pri-miRNAs. Four distinct processing modes (i.e., short base to loop, sequential base to loop, short loop to base, and sequential loop to base) have been characterized in Arabidopsis, mainly by the Specific Parallel Amplification of RNA Ends (SPARE) approach. However, SPARE is a targeted cloning method which requires optimization of cloning efficiency and specificity for each target. PARE (Parallel Amplification of RNA Ends) is an untargeted method per se and is widely used to identify miRNA mediated target slicing events. A major concern with PARE in characterizing miRNA processing modes is the potential contamination of mature miRNAs. Here, we provide a method to estimate miRNA contamination levels and showed that most publicly available PARE libraries have negligible miRNA contamination. Both the numbers and processing modes detected by PARE were similar to those identified by SPARE in Arabidopsis. PARE also determined the processing modes of 36 Arabidopsis miRNAs that were unexplored by SPARE, suggesting that it can complement the SPARE approach. Using publicly available PARE datasets, we identified the processing modes of 36, 91, 90, and 54 miRNAs in maize, rice, soybean, and tomato, respectively, and demonstrated that the processing mode was conserved overall within each miRNA family. Through its power of tracking miRNA processing remnants, PARE also facilitated miRNA characterization and annotation.

Determining the Number of Factors When Population Models Can Be Closely Approximated by Parsimonious Models.

Despite the existence of many methods for determining the number of factors, none outperforms the others under every condition. This study compares traditional parallel analysis (TPA), revised parallel analysis (RPA), Kaiser's rule, minimum average partial, sequential χ2, and sequential root mean square error of approximation, comparative fit index, and Tucker-Lewis index under a realistic scenario in behavioral studies, where researchers employ a closing-fitting parsimonious model with K factors to approximate a population model, leading to a trivial model-data misfit. Results show that while traditional and RPA both stand out when zero population-level misfits exist, the accuracy of RPA substantially deteriorates when a K-factor model can closely approximate the population. TPA is the least sensitive to trivial misfits and results in the highest accuracy across most simulation conditions. This study suggests the use of TPA for the investigated models. Results also imply that RPA requires further revision to accommodate a degree of model-data misfit that can be tolerated.

Elucidation of the MicroRNA Transcriptome in Western Corn Rootworm Reveals Its Dynamic and Evolutionary Complexity.

Diabrotica virgifera virgifera (western corn rootworm, WCR) is one of the most destructive agricultural insect pests in North America. It is highly adaptive to environmental stimuli and crop protection technologies. However, little is known about the underlying genetic basis of WCR behavior and adaptation. More specifically, the involvement of small RNAs (sRNAs), especially microRNAs (miRNAs), a class of endogenous small non-coding RNAs that regulate various biological processes, has not been examined, and the datasets of putative sRNA sequences have not previously been generated for WCR. To achieve a comprehensive collection of sRNA transcriptomes in WCR, we constructed, sequenced, and analyzed sRNA libraries from different life stages of WCR and northern corn rootworm (NCR), and identified 101 conserved precursor miRNAs (pre-miRNAs) in WCR and other Arthropoda. We also identified 277 corn rootworm specific pre-miRNAs. Systematic analyses of sRNA populations in WCR revealed that its sRNA transcriptome, which includes PIWI-interacting RNAs (piRNAs) and miRNAs, undergoes a dynamic change throughout insect development. Phylogenetic analysis of miRNA datasets from model species reveals that a large pool of species-specific miRNAs exists in corn rootworm; these are potentially evolutionarily transient. Comparisons of WCR miRNA clusters to other insect species highlight conserved miRNA-regulated processes that are common to insects. Parallel Analysis of RNA Ends (PARE) also uncovered potential miRNA-guided cleavage sites in WCR. Overall, this study provides a new resource for studying the sRNA transcriptome and miRNA-mediated gene regulation in WCR and other Coleopteran insects.

Determining the Number of Attributes in Cognitive Diagnosis Modeling.

Cognitive diagnosis models (CDMs) allow classifying respondents into a set of discrete attribute profiles. The internal structure of the test is determined in a Q-matrix, whose correct specification is necessary to achieve an accurate attribute profile classification. Several empirical Q-matrix estimation and validation methods have been proposed with the aim of providing well-specified Q-matrices. However, these methods require the number of attributes to be set in advance. No systematic studies about CDMs dimensionality assessment have been conducted, which contrasts with the vast existing literature for the factor analysis framework. To address this gap, the present study evaluates the performance of several dimensionality assessment methods from the factor analysis literature in determining the number of attributes in the context of CDMs. The explored methods were parallel analysis, minimum average partial, very simple structure, DETECT, empirical Kaiser criterion, exploratory graph analysis, and a machine learning factor forest model. Additionally, a model comparison approach was considered, which consists in comparing the model-fit of empirically estimated Q-matrices. The performance of these methods was assessed by means of a comprehensive simulation study that included different generating number of attributes, item qualities, sample sizes, ratios of the number of items to attribute, correlations among the attributes, attributes thresholds, and generating CDM. Results showed that parallel analysis (with Pearson correlations and mean eigenvalue criterion), factor forest model, and model comparison (with AIC) are suitable alternatives to determine the number of attributes in CDM applications, with an overall percentage of correct estimates above 76% of the conditions. The accuracy increased to 97% when these three methods agreed on the number of attributes. In short, the present study supports the use of three methods in assessing the dimensionality of CDMs. This will allow to test the assumption of correct dimensionality present in the Q-matrix estimation and validation methods, as well as to gather evidence of validity to support the use of the scores obtained with these models. The findings of this study are illustrated using real data from an intelligence test to provide guidelines for assessing the dimensionality of CDM data in applied settings.