Internal Metrics Research Articles

Benchmarking data-driven filtering for denoising of TCRpMHC single-cell data

Pairing of the T cell receptor (TCR) with its cognate peptide-MHC (pMHC) is a cornerstone in T cell-mediated immunity. Recently, single-cell sequencing coupled with DNA-barcoded MHC multimer staining has enabled high-throughput studies of T cell specificities. However, the immense variability of TCR-pMHC interactions combined with the relatively low signal-to-noise ratio in the data generated using current technologies are complicating these studies. Several approaches have been proposed for denoising single-cell TCR-pMHC specificity data. Here, we present a benchmark evaluating two such denoising methods, ICON and ITRAP. We applied and evaluated the methods on publicly available immune profiling data provided by 10x Genomics. We find that both methods identified approximately 75% of the raw data as noise. We analyzed both internal metrics developed for the purpose and performance on independent data using machine learning methods trained on the raw and denoised 10x data. We find an increased signal-to-noise ratio comparing the denoised to the raw data for both methods, and demonstrate an overall superior performance of the ITRAP method in terms of both data consistency and performance. In conclusion, this study demonstrates that Improving the data quality from high throughput studies of TCRpMHC-specificity by denoising is paramount in increasing our understanding of T cell-mediated immunity.

AI-assisted analysis of content, structure, and sentiment in MOOC discussion forums

Discussion forums are a key component of online learning platforms, allowing learners to ask for help, provide help to others, and connect with others in the learning community. Analyzing patterns of forum usage and their association with course outcomes can provide valuable insight into how learners actually use discussion forums, and suggest strategies for shaping forum dynamics to improve learner experiences and outcomes. However, the fine-grained coding of forum posts required for this kind of analysis is a manually intensive process that can be challenging for large datasets, e.g., those that result from popular MOOCs. To address this issue, we propose an AI-assisted labeling process that uses advanced natural language processing techniques to train machine learning models capable of labeling a large dataset while minimizing human annotation effort. We fine-tune pretrained transformer-based deep learning models on category, structure, and emotion classification tasks. The transformer-based models outperform a more traditional baseline that uses support vector machines and a bag-of-words input representation. The transformer-based models also perform better when we augment the input features for an individual post with additional context from the post's thread (e.g., the thread title). We validate model quality through a combination of internal performance metrics, human auditing, and common-sense checks. For our Python MOOC dataset, we find that annotating approximately 1% of the forum posts achieves performance levels that are reliable for downstream analysis. Using labels from the validated AI models, we investigate the association of learner and course attributes with thread resolution and various forms of forum participation. We find significant differences in how learners of different age groups, gender, and course outcome status ask for help, provide help, and make posts with emotional (positive or negative) sentiment.

A unified pipeline for FISH spatial transcriptomics

High-throughput spatial transcriptomics has emerged as a powerful tool for investigating the spatial distribution of mRNA expression and its effects on cellular function. There is a lack of standardized tools for analyzing spatial transcriptomics data, leading many groups to write their own in-house tools that are often poorly documented and not generalizable. To address this, we have expanded and improved the starfish library and used those tools to create PIPEFISH, a semi-automated and generalizable pipeline that performs transcript annotation for fluorescence in situ hybridization (FISH)-based spatial transcriptomics. We used this pipeline to annotate transcript locations from three real datasets from three different common types of FISH image-based experiments, MERFISH, seqFISH, and targeted in situ sequencing (ISS), and verified that the results were high quality using the internal quality metrics of the pipeline and also a comparison with an orthogonal method of measuring RNA expression. PIPEFISH is a publicly available and open-source tool.

ADME characterization and PBK model development of 3 highly protein-bound UV filters through topical application.

Estimating human exposure in the safety assessment of chemicals is crucial. Physiologically based kinetic (PBK) models which combine information on exposure, physiology, and chemical properties, describing the absorption, distribution, metabolism, and excretion (ADME) processes of a chemical, can be used to calculate internal exposure metrics such as maximum concentration and area under the concentration-time curve in plasma or tissues of a test chemical in next-generation risk assessment. This article demonstrates the development of PBK models for 3 UV filters, specifically octyl methoxycinnamate, octocrylene, and 4-methylbenzylidene camphor. The models were parameterized entirely based on data obtained from in vitro and/or in silico methods in a bottom-up modeling approach and then validated based on human dermal pharmacokinetic (PK) data. The 3 UV filters are "difficult to test" in in vitro test systems due to high lipophilicity, high binding affinity for proteins, and nonspecific binding, for example, toward plastic. This research work presents critical considerations in ADME data generation, interpretation, and parameterization to assure valid PBK model development to increase confidence in using PBK modeling to help make safety decisions in the absence of human PK data. The developed PBK models of the 3 chemicals successfully simulated the plasma concentration profiles of clinical PK data following dermal application, indicating the reliability of the ADME data generated and the parameters determined. The study also provides insights and lessons learned for characterizing ADME and developing PBK models for highly lipophilic and protein-bound chemicals in the future.

A novel 6-day cycle surgical pathology rotation improves resident satisfaction and maintains Accreditation Council for Graduate Medical Education (ACGME) milestone performance

Surgical pathology residency training in the United States lags behind other specialties in quality control and graduated responsibility to train independent pathologists capable of seamlessly entering practice after training. We observed that our traditional 3-day-cycle surgical pathology cycle (day 1–grossing; day 2 –biopsies/frozens/preview; day 3 – sign-out) consistently and negatively impacted resident education by reducing preview time, case follow-up, immunohistochemical stain (IHC) interpretation, and molecular study integration. We aimed to create a modern surgical pathology rotation that improved performance and outcomes. We innovated our rotation to enhance resident education and ensure graduated responsibility. A novel 6-day cycle was created composed of 2 grossing days, 1 frozens/biopsies/preview days, 2 dedicated sign-out days, and 1 frozens/biopsies/case completion day. Residents completed surveys before implementing the new rotation and 6 months after implementation to track self-assessment of Accreditation Council for Graduate Medical Education (ACGME) milestone performance and internal quality control metrics. Clinical Competency Committee (CCC) annual evaluations were assessed in paired PGY levels pre- and post-intervention. After implementation, there was a statistically significant improvement in self-assessment of levels 4 and 5 of ACGME milestones and improved satisfaction of quality metrics, including time for previewing, reviewing IHC, graduated responsibility, and perceived readiness for independent practice. CCC evaluations showed overall maintained performance levels, with trends towards improvements in junior resident classes. Our 6-day cycle adequately fulfills the current demands of our sizeable academic center's surgical pathology training and can be a model for pathology residencies looking to modernize their surgical pathology rotations and resident education.

Predictive Quantitative Read-Across Structure-Property Relationship Modeling of the Retention Time (Log tR) of Pesticide Residues Present in Foods and Vegetables.

The retention time (log tR) of pesticidal compounds in a reverse-phase high-performance liquid chromatography (HPLC) analysis has a direct relationship with lipophilicity, which could be related to the ecotoxicity potential of the compounds. The novel quantitative read-across structure-property relationship (q-RASPR) modeling approach uses similarity-based descriptors for predictive model generation. These models have been shown to enhance external predictivity in previous studies for several end points. The current study describes the development of a q-RASPR model using experimental retention time data (log tR) in the HPLC experiments of 823 environmentally significant pesticide residues collected from a large compound database. To model the retention time (log tR) end point, 0D-2D descriptors have been used along with the read-across-derived similarity descriptors. The developed partial least squares (PLS) model was rigorously validated by various internal and external validation metrics as recommended by the Organization for Economic Co-operation and Development (OECD). The final q-RASPR model is proven to be a good fit, robust, and externally predictive (ntrain = 618, R2 = 0.82, Q2LOO = 0.81, ntest = 205, and Q2F1 = 0.84) that literally outperforms the external predictivity of the previously reported quantitative structure-property relationship (QSPR) model. From the insights of modeled descriptors, lipophilicity is found to be the most important chemical property, which positively correlates with the retention time (log tR). Various other characteristics, such as the number of multiple bonds (nBM), graph density (GD), etc., have a substantial and inversely proportionate relationship with the retention time end point. The software tools utilized in this study are user-friendly, and most of them are free, which makes our methodology quite cost-effective when compared to experimentation. In a nutshell, to obtain better external predictivity, interpretability, and transferability, q-RASPR is an efficient technique that has the potential to be employed as a good alternative approach for retention time prediction and ecotoxicity potential identification.

K-means variations analysis for translation of English Tafseer Al-Quran text

Text mining is a powerful modern technique used to obtain interesting information from huge datasets. Text clustering is used to distinguish between documents that have the same themes or topics. The absence of the datasets ground truth enforces the use of clustering (unsupervised learning) rather than others, such as classification (supervised learning). The “no free lunch” (NFL) theorem supposed that no algorithm outperformed the other in a variety of conditions (several datasets). This study aims to analyze the k-means cluster algorithm variations (three algorithms (k-means, mini-batch k-means, and k-medoids) at the clustering process stage. Six datasets were used/analyzed in chapter Al-Baqarah English translation (text) of 286 verses at the preprocessing stage. Moreover, feature selection used the term frequency–inverse document frequency (TF-IDF) to get the weighting term. At the final stage, five internal cluster validations metrics were implemented silhouette coefficient (SC), Calinski-Harabasz index (CHI), C-index (CI), Dunn’s indices (DI) and Davies Bouldin index (DBI) and regarding execution time (ET). The experiments proved that k-medoids outperformed the other two algorithms in terms of ET only. In contrast, no algorithm is superior to the other in terms of the clustering process for the six datasets, which confirms the NFL theorem assumption.

First report on ecotoxicological QSTR and i-QSTR modeling for the prediction of acute ecotoxicity of diverse organic chemicals against three protozoan species

The recent years have witnessed an upsurge of interest to assess the toxicity of organic chemicals exhibiting harmful impacts on the environment. In this investigation, we have developed regression-based quantitative structure-toxicity relationship (QSTR) models against three protozoan species (Entosiphon sulcantum, Uronema parduczi, and Chilomonas paramecium) using three sets of descriptor combinations such as ETA indices only, non-ETA descriptors only, and both ETA and non-ETA descriptors to examine the key structural features that determine the toxic properties of protozoa. The interspecies QSTR models (i-QSTRs) were also generated for efficient data gap-filling of toxicity databases. The statistical results of the validated models in terms of both internal and external validation metrics suggested that the models are statistically reliable and robust. Additionally, using these validated models, we screened the DrugBank database containing 11,300 pharmaceuticals for assessing the ecotoxicological properties. The features appearing in the models suggested that non-polar characteristics, electronegativity, hydrogen bonding, π–π, and hydrophobic interactions are responsible for chemical toxicity toward protozoan. The validated models may be utilized for the development of eco-friendly drugs & chemicals, data gap-filling of toxicity databases for regulatory purposes and research, as well as to decrease the use of toxic and hazardous chemicals in the environment.

Detecting hypoglycemia-induced electrocardiogram changes in a rodent model of type 1 diabetes using shape-based clustering.

Sudden death related to hypoglycemia is thought to be due to cardiac arrhythmias. A clearer understanding of the cardiac changes associated with hypoglycemia is needed to reduce mortality. The objective of this work was to identify distinct patterns of electrocardiogram heartbeat changes that correlated with glycemic level, diabetes status, and mortality using a rodent model. Electrocardiogram and glucose measurements were collected from 54 diabetic and 37 non-diabetic rats undergoing insulin-induced hypoglycemic clamps. Shape-based unsupervised clustering was performed to identify distinct clusters of electrocardiogram heartbeats, and clustering performance was assessed using internal evaluation metrics. Clusters were evaluated by experimental conditions of diabetes status, glycemic level, and death status. Overall, shape-based unsupervised clustering identified 10 clusters of ECG heartbeats across multiple internal evaluation metrics. Several clusters demonstrating normal ECG morphology were specific to hypoglycemia conditions (Clusters 3, 5, and 8), non-diabetic rats (Cluster 4), or were generalized among all experimental conditions (Cluster 1). In contrast, clusters demonstrating QT prolongation alone or a combination of QT, PR, and QRS prolongation were specific to severe hypoglycemia experimental conditions and were stratified heartbeats by non-diabetic (Clusters 2 and 6) or diabetic status (Clusters 9 and 10). One cluster demonstrated an arrthymogenic waveform with premature ventricular contractions and was specific to heartbeats from severe hypoglycemia conditions (Cluster 7). Overall, this study provides the first data-driven characterization of ECG heartbeats in a rodent model of diabetes during hypoglycemia.

A Model-Based Approach Machine Learning to Scalable Portfolio Selection

This study proposes a scalable asset selection and allocation approach using machine learning that integrates clustering methods into portfolio optimization models. The methodology applies the Uniform Manifold Approximation and Projection method and ensemble clustering techniques to preselect assets from the Ibovespa and S&P 500 indices. The research compares three allocation models and finds that the Hierarchical Risk Parity model outperformed the others, with a Sharpe ratio of 1.11. Despite the pandemic's impact on the portfolios, with drawdowns close to 30%, they recovered in 111 to 149 trading days. The portfolios outperformed the indices in cumulative returns, with similar annual volatilities of 20%. Preprocessing with UMAP allowed for finding clusters with higher discriminatory power, evaluated through internal cluster validation metrics, helping to reduce the problem's size during optimal portfolio allocation. Overall, this study highlights the potential of machine learning in portfolio optimization, providing a useful framework for investment practitioners.

The impact of a two-year long COVID-19 public health restriction program on mechanical thrombectomy outcomes in a stroke network

ObjectiveThe purpose of this study was to evaluate the impact of COVID-19 pandemic public health restrictions on our drip and ship mechanical thrombectomy program in Santiago Chile. Materials and methodsThis was a retrospective analysis of a prospectively collected database comparing two cohorts, one during a two-year period before COVID-19 and the second during the two years of the pandemic at our metropolitan stroke program. ResultsA total of 100 patients were included in the pre COVID-19 cohort (cohort 1) and 121 in the COVID-19 cohort (cohort 2). There was a significant difference between cohorts, with older patients, different occlusion sites and higher door to arterial puncture time during the COVID-19 period. A non-significant trend for worse 90-day outcomes and higher mortality was present in cohort 2. There were no statistical differences in safety treatment parameters. ConclusionsCOVID-19 pandemic has had a measurable impact on our mechanical thrombectomy program. Results showed similarities to other reported Latin American series, where less robust health systems could adapt less efficiently compared to developed countries. After two years of public health restrictions, there were changes in the treatment population characteristics, delay in some internal management metrics and a non-significant trend to worse 90-day outcomes and higher mortality.

Machine-learning-based similarity meets traditional QSAR: “q-RASAR” for the enhancement of the external predictivity and detection of prediction confidence outliers in an hERG toxicity dataset

Recently, the concept of quantitative Read-Across Structure-Activity Relationship (q-RASAR) has been introduced by using various Machine Learning (ML) - derived similarity functions in the traditional quantitative structure-activity relationship (QSAR) modeling framework with the objective of enhancing the external predictivity of models while using the same available chemical information content. The present study uses the hERG K+ channel inhibition cardiotoxicity, a pharmaceutically relevant endpoint, as the modeling set for making predictions using the novel q-RASAR approach, as the approach combines the merits of QSAR and Read-Across, and generates simple and interpretable models using various similarity and error-based measures as descriptors. The cardiotoxicity data (in terms of pIC50 values) were collected from the literature. The curated data set was then divided into training and test sets using the sorted response-based division algorithm. The important set of features was identified based on the internal validation metrics of initial genetic algorithm models. Based on the features selected in the final Multiple Linear Regression (MLR) model, RASAR descriptors were computed using a tool available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home. The RASAR descriptors were then merged with the previously selected features, and an MLR q-RASAR model was generated using the grid search approach. The prediction outliers were then identified using the novel DTC Applicability Domain Plot, and the q-RASAR models were used for predictions after the removal of prediction outliers. A final Partial Least Squares (PLS) q-RASAR model was generated to obviate inter-correlation among descriptors. Various other Machine Learning approaches were also employed with the optimization of relevant hyperparameters based on the cross-validation approach, and the final test set prediction results were compared. Based on the performance in the test set predictions and interpretability, the PLS q-RASAR model was chosen as the final model which provided enhanced predictivity in comparison to previously reported models even without using 3-D descriptors. This model can thus be used for the quick screening of molecules, even before their synthesis, to estimate their cardiotoxic potential thus prioritizing molecules for further experimental testing in the drug discovery pipeline. A Java-based prediction tool has also been developed for the quick screening of cardiotoxic properties of query compounds and made available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home.

Prediction of soil ecotoxicity against Folsomia candida using acute and chronic endpoints

ABSTRACT Soil invertebrates serve as great biological indicators of soil quality. However, there are very few in silico models developed so far on the soil toxicity of chemicals against soil invertebrates due to paucity of data. In this study, three available soil ecotoxicity data (pLC50, pLOEL and pNOEL) against the soil invertebrate Folsomia candida were collected from the ECOTOX database (cfpub.epa.gov/ecotox) and subjected to quantitative structure-activity relationship (QSAR) analysis using 2D descriptors. The collected data for each endpoint were initially curated and used to develop a partial least squares (PLS) regression model based on the features selected through a genetic algorithm followed by the best subset selection. Both internal and external validation metrics of the models’ predictions are well-balanced and within the acceptable range as per the Organization for the Economic Cooperation and Development (OECD) criteria. From the developed models, it has been found that molecular weight and presence of phosphate group, electron donor groups, and polyhalogen substitution have a significant impact on the soil ecotoxicity. The soil ecotoxicological risk assessment of organic chemicals can therefore be prioritized by these features. With the availability of additional data in the future, the models may be further refined for more precise predictions.

Gene Self-Expressive Networks as a Generalization-Aware Tool to Model Gene Regulatory Networks

Self-expressiveness is a mathematical property that aims at characterizing the relationship between instances in a dataset. This property has been applied widely and successfully in computer-vision tasks, time-series analysis, and to infer underlying network structures in domains including protein signaling interactions and social-networks activity. Nevertheless, despite its potential, self-expressiveness has not been explicitly used to infer gene networks. In this article, we present Generalizable Gene Self-Expressive Networks, a new, interpretable, and generalization-aware formalism to model gene networks, and we propose two methods: GXN•EN and GXN•OMP, based respectively on ElasticNet and OMP (Orthogonal Matching Pursuit), to infer and assess Generalizable Gene Self-Expressive Networks. We evaluate these methods on four Microarray datasets from the DREAM5 benchmark, using both internal and external metrics. The results obtained by both methods are comparable to those obtained by state-of-the-art tools, but are fast to train and exhibit high levels of sparsity, which make them easier to interpret. Moreover we applied these methods to three complex datasets containing RNA-seq informations from different mammalian tissues/cell-types. Lastly, we applied our methodology to compare a normal vs. a disease condition (Alzheimer), which allowed us to detect differential expression of genes' sub-networks between these two biological conditions. Globally, the gene networks obtained exhibit a sparse and modular structure, with inner communities of genes presenting statistically significant over/under-expression on specific cell types, as well as significant enrichment for some anatomical GO terms, suggesting that such communities may also drive important functional roles.

Hybrid Genetic Algorithm for Clustering IC Topographies of EEGs

Clustering of independent component (IC) topographies of Electroencephalograms (EEG) is an effective way to find brain-generated IC processes associated with a population of interest, particularly for those cases where event-related potential features are not available. This paper proposes a novel algorithm for the clustering of these IC topographies and compares its results with the most currently used clustering algorithms. In this study, 32-electrode EEG signals were recorded at a sampling rate of 500 Hz for 48 participants. EEG signals were pre-processed and IC topographies computed using the AMICA algorithm. The algorithm implements a hybrid approach where genetic algorithms are used to compute more accurate versions of the centroids and the final clusters after a pre-clustering phase based on spectral clustering. The algorithm automatically selects the optimum number of clusters by using a fitness function that involves local-density along with compactness and separation criteria. Specific internal validation metrics adapted to the use of the absolute correlation coefficient as the similarity measure are defined for the benchmarking process. Assessed results across different ICA decompositions and groups of subjects show that the proposed clustering algorithm significantly outperforms the (baseline) clustering algorithms provided by the software EEGLAB, including CORRMAP.

Development and Validation of Predictive Quantitative Structure-Activity Relationship Models for Estrogenic Activities of Hydroxylated Polychlorinated Biphenyls.

Disruption of the endocrine system by hydroxylated polychlorinated biphenyls (OH-PCBs) is hypothesized, among other potential mechanisms, to be mediated via nuclear receptor binding. Due to the high cost and lengthy time required to produce high-quality experimental data, empirical data to support the nuclear receptor binding hypothesis are in short supply. In the present study, two quantitative structure-activity relationship models were developed for predicting the estrogenic activities of OH-PCBs. Findings revealed that model I (for the estrogen receptor α dataset) contained five two-dimensional (2D) descriptors belonging to the classes autocorrelation, Burden modified eigenvalues, chi path, and atom type electrotopological state, whereas model II (for the estrogen receptor β dataset) contained three 2D and three 3D descriptors belonging to the classes autocorrelation, atom type electrotopological state, and Radial Distribution Function descriptors. The internal and external validation metrics reported for models I and II indicate that both models are robust, reliable, and suitable for predicting the estrogenic activities of untested OH-PCB congeners. Environ Toxicol Chem 2023;42:823-834. © 2023 SETAC.

Computational modeling of aquatic toxicity of polychlorinated naphthalenes (PCNs) employing 2D-QSAR and chemical read-across

Polychlorinated naphthalenes (PCNs) are produced from a variety of industrial sources, and they reach the aquatic ecosystems by the dry-wet deposition from the atmosphere and also by the drainage from the land surfaces. Then the PCNs can be transmitted through the food chain to humans and show toxic effects on different aquatic animals as well as humans. Considering this scenario, it is an obligatory task to explore the toxicity data of PCNs more deeply for the species of an aquatic ecosystem (green algae-Daphnia magna-fish), and to extrapolate those data for humans. But the toxicity data for different aquatic species are quite limited. The laboratory experimentations are complicated and ethically troublesome to fill toxicity data gaps; therefore, different in silico methods (e.g., QSAR, quantitative read-across predictions) are emerging as crucial ways to fill the data gaps and hazard assessments. In the present study, we developed individual toxicity models as well as interspecies models from the 75 PCN toxicity data against three aquatic species (green algae-Daphnia magna-fish) by employing easily interpretable 2D descriptors; these models were validated rigorously employing different globally accepted internal and external validation metrics. Then we interpreted the modelled descriptors mechanistically with the endpoint values for better understanding. And finally, we endeavored to improve the prediction quality in terms of external validation metrics by employing a novel quantitative read-across approach by pooling the descriptors from the developed individual QSAR models.

TFSFB: Two-stage feature selection via fusing fuzzy multi-neighborhood rough set with binary whale optimization for imbalanced data

Obtaining informative features is crucial in imbalanced classification. However, existing neighborhood rough set-based feature selection approaches easily overlook the diversity and complexity of data distributions, and it is difficult to obtain this global optimal feature subset from imbalanced and high-dimensional datasets. To tackle these limitations, we construct a new two-stage feature subset selection scheme by fusing the fuzzy multi-neighborhood rough set (FMRS) with the binary whale optimization algorithm (BWOA) for imbalanced data. First, to evaluate those distributions of different features, this standard deviation coefficient is introduced to construct a fuzzy multi-neighborhood radius set. Then, the fuzzy multi-neighborhood granule and fuzzy membership degree are presented to establish the novel FMRS, and the feature significance measure in the view of algebra is developed to balance the approximate properties and influences of different features in the negative and positive classes. Second, fuzzy multi-neighborhood conditional entropy is defined to maximize information quantity of class- imbalanced data from the information view, and then by fusing the two evaluation perspectives above, this mixed metric is provided to fully assess this uncertainty of class-imbalanced datasets. These internal and external significant metrics are designed to obtain this preselected candidate set of features based on the filter FMRS model at this first stage. Third, a control factor can be developed to dominate the whale position update, and a novel fitness function will be constructed when fusing the dependency degree and entropy measure with the reduction ratio to evaluate this optimal subset of features. Adopting population partitioning and local interference schemes can prevent the BWOA from becoming trapped within a local optimum. To reduce this search space of evolution, the dynamic bitmask is used to improve the BWOA, and then an optimal subset of features is acquired through continuous iterations of this wrapper BWOA at this second stage. Finally, a new two-stage algorithm for feature subset selection by fusing FMRS and BWOA is provided to process class-imbalanced data, where this particle swarm optimization algorithm confirms those optimized parameters. Experiments on 31 datasets show that our algorithm is efficient and can achieve excellent classification efficiency for binary and multiclass imbalanced data.

Alternative Structure Models of the Traumatic Brain Injury Rehabilitation Needs Survey: A Veterans Affairs TBI Model Systems Study

ObjectiveTo explore the factor structure of the Rehabilitation Needs Survey (RNS). DesignSecondary analysis of observational cohort study who were 5-years post-traumatic brain injury (TBI). SettingFive Inpatient Rehabilitation Facilities. ParticipantsVeterans enrolled in the TBI Model Systems longitudinal study who completed the RNS at 5-year follow-up (N=378). Main Outcome Measure(s)RNS. ResultsRNS factor structure was examined with exploratory factor analysis (EFA) with oblique rotation. Analyses returned 2- and 3-factor solutions with Cronbach alphas ranging from 0.715 to 0.905 and corrected item-total correlations that ranged from 0.279 to 0.732. The 2-factor solution accounted for 61.7% of the variance with ≥3 exclusively loading items on each factor with acceptable internal consistency metrics and was selected as the most parsimonious and clinically applicable model. Ad hoc analysis found the RNS structure per the EFA corresponded with elements of the International Classification of Functioning, Disability and Health (ICF) conceptual framework. All factors had adequate internal consistency (α≥0.70) and 20 of the 21 demonstrated good discrimination (corrected item-total correlations≥0.40). ConclusionsThe 2-factor solution of the RNS appears to be a useful model for enhancing its clinical interpretability. Although there were cross-loading items, they refer to complex rehabilitation needs that are likely influenced by multiple factors. Alternatively, there are items that may require alteration and redundant items that should be considered for elimination.

Relationship between internal and external load metrics in professional male basketball players

Relationship between internal and external load metrics in professional male basketball players