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The success and sustainability of U.S. EPA efforts to reduce, refine, and replace in vivo animal testing depends on the ability to translate toxicokinetic and toxicodynamic data from in vitro and in silico new approach methods (NAMs) to human-relevant exposures and health outcomes. Organotypic culture models employing primary human cells enable consideration of human health effects and inter-individual variability but present significant challenges for test method standardization, transferability, and validation. Increasing confidence in the information provided by these in vitro NAMs requires setting appropriate performance standards and benchmarks, defined by the context of use, to consider human biology and mechanistic relevance without animal data. The human thyroid microtissue (hTMT) assay utilizes primary human thyrocytes to reproduce structural and functional features of the thyroid gland that enable testing for potential thyroid-disrupting chemicals. As a variable-donor assay platform, conventional principles for assay performance standardization need to be balanced with the ability to predict a range of human responses. The objectives of this study were to (1) define the technical parameters for optimal donor procurement, primary thyrocyte qualification, and performance in the hTMT assay, and (2) set benchmark ranges for reference chemical responses. Thyrocytes derived from a cohort of 32 demographically diverse euthyroid donors were characterized across a battery of endpoints to evaluate morphological and functional variability. Reference chemical responses were profiled to evaluate the range and chemical-specific variability of donor-dependent effects within the cohort. The data-informed minimum acceptance criteria for donor qualification and set benchmark parameters for method transfer proficiency testing and validation of assay performance. [ABSTRACT FROM AUTHOR]

Background The LactoSens® R method was previously shown to have acceptable accuracy and repeatability precision as required by AOAC Standard Method Performance Requirements (SMPR®) 2018.009 for determination of lactose in low-lactose or lactose-free milk, milk products, and products containing dairy ingredients and was awarded Official Method of Analysis SM (OMA) First Action status in 2020. Objective The method was subjected to a multilaboratory validation (MLV) study to evaluate the reproducibility precision of the method. Methods Fourteen validation materials were provided to 15 laboratories in seven countries as blind duplicates. The materials ranged from 0 to 173 mg/100 g lactose. Each laboratory analyzed the blind duplicates according to OMA 2020.01. The data were analyzed for repeatability and reproducibility precision. Results RSDr values varied from 2.81 to 8.76%, and RSDR values varied from 4.25 to 12.5%. When sorted by category and concentration range, these results met the repeatability and reproducibility criteria required by SMPR 2018.009. Conclusions The data generated in the MLV support the adoption of OMA 2020.01 as Final Action status. Highlights The LactoSens R method, as described by OMA 2020.01 , provides an accurate and precise determination of lactose in a variety of low-lactose and lactose-free milk, milk products, and products containing dairy ingredients in minutes. [ABSTRACT FROM AUTHOR]

Motivation The steady increment of Whole Genome/Exome sequencing and the development of novel Next Generation Sequencing-based gene panels requires continuous testing and validation of variant calling (VC) pipelines and the detection of sequencing-related issues to be maintained up-to-date and feasible for the clinical settings. State of the art tools are reliable when used to compute standard performance metrics. However, the need for an automated software to discriminate between bioinformatic and sequencing issues and to optimize VC parameters remains unmet. Results The aim of the current work is to present RecallME, a bioinformatic suite that tracks down difficult-to-detect variants as insertions and deletions in highly repetitive regions, thus providing the maximum reachable recall for both single nucleotide variants and small insertion and deletions and to precisely guide the user in the pipeline optimization process. Availability and implementation Source code is freely available under MIT license at https://github.com/mazzalab-ieo/recallme. RecallME web application is available at https://translational-oncology-lab.shinyapps.io/recallme/. To use RecallME, users must obtain a license for ANNOVAR by themselves. [ABSTRACT FROM AUTHOR]

Epidemiologists are often interested in estimating the effect of functions of time‐varying exposure histories in relation to continuous outcomes, for example, cognitive function. However, the individual exposure measurements that constitute the history upon which an exposure history function is constructed are usually mismeasured. To obtain unbiased estimates of the effects for mismeasured functions in longitudinal studies, a method incorporating main and validation studies was developed. Simulation studies under several realistic assumptions were conducted to assess its performance compared to standard analysis, and we found that the proposed method has good performance in terms of finite sample bias reduction and nominal confidence interval coverage. We applied it to a study of long‐term exposure to PM2.5$\text{PM}_{2.5}$, in relation to cognitive decline in the Nurses' Health Study Previously, it was found that the 2‐year decline in the standard measure of cognition was 0.018 (95% CI, −0.034 to −0.001) units worse per 10 μg/m3$\mu \text{g/m}^3$ increase in PM2.5$\text{PM}_{2.5}$ exposure. After correction, the estimated impact of PM2.5$\text{PM}_{2.5}$ on cognitive decline increased to 0.027 (95% CI, −0.059 to 0.005) units lower per 10 μg/m3$\mu \text{g/m}^3$ increase. To put this into perspective, effects of this magnitude are about 2/3 of those found in our data associated with each additional year of aging: 0.044 (95% CI, −0.047 to −0.040) units per 1 year older after applying our correction method. [ABSTRACT FROM AUTHOR]

Appropriately representing elements in a database so that queries may be accurately matched is a central task in information retrieval; recently, this has been achieved by embedding the graphical structure of the database into a manifold in a hierarchy-preserving manner using a variety of metrics. Persistent homology is a tool commonly used in topological data analysis that is able to rigorously characterize a database in terms of both its hierarchy and connectivity structure. Computing persistent homology on a variety of embedded datasets reveals that some commonly used embeddings fail to preserve the connectivity. We show that those embeddings which successfully retain the database topology coincide in persistent homology by introducing two dilation-invariant comparative measures to capture this effect: in particular, they address the issue of metric distortion on manifolds. We provide an algorithm for their computation that exhibits greatly reduced time complexity over existing methods. We use these measures to perform the first instance of topology-based information retrieval and demonstrate its increased performance over the standard bottleneck distance for persistent homology. We showcase our approach on databases of different data varieties including text, videos and medical images. [ABSTRACT FROM AUTHOR]

Motivation Sequence-based deep learning approaches have been shown to predict a multitude of functional genomic readouts, including regions of open chromatin and RNA expression of genes. However, a major limitation of current methods is that model interpretation relies on computationally demanding post hoc analyses, and even then, one can often not explain the internal mechanics of highly parameterized models. Here, we introduce a deep learning architecture called totally interpretable sequence-to-function model (tiSFM). tiSFM improves upon the performance of standard multilayer convolutional models while using fewer parameters. Additionally, while tiSFM is itself technically a multilayer neural network, internal model parameters are intrinsically interpretable in terms of relevant sequence motifs. Results We analyze published open chromatin measurements across hematopoietic lineage cell-types and demonstrate that tiSFM outperforms a state-of-the-art convolutional neural network model custom-tailored to this dataset. We also show that it correctly identifies context-specific activities of transcription factors with known roles in hematopoietic differentiation, including Pax5 and Ebf1 for B-cells, and Rorc for innate lymphoid cells. tiSFM's model parameters have biologically meaningful interpretations, and we show the utility of our approach on a complex task of predicting the change in epigenetic state as a function of developmental transition. Availability and implementation The source code, including scripts for the analysis of key findings, can be found at https://github.com/boooooogey/ATAConv , implemented in Python. [ABSTRACT FROM AUTHOR]

Motivation Imaging Spatial Transcriptomics techniques characterize gene expression in cells in their native context by imaging barcoded probes for mRNA with single molecule resolution. However, the need to acquire many rounds of high-magnification imaging data limits the throughput and impact of existing methods. Results We describe the Joint Sparse method for Imaging Transcriptomics, an algorithm for decoding lower magnification Imaging Spatial Transcriptomics data than that used in standard experimental workflows. Joint Sparse method for Imaging Transcriptomics incorporates codebook knowledge and sparsity assumptions into an optimization problem, which is less reliant on well separated optical signals than current pipelines. Using experimental data obtained by performing Multiplexed Error-Robust Fluorescence in situ Hybridization on tissue from mouse brain, we demonstrate that Joint Sparse method for Imaging Transcriptomics enables improved throughput and recovery performance over standard decoding methods. Availability and implementation Software implementation of JSIT, together with example files, is available at https://github.com/jpbryan13/JSIT. [ABSTRACT FROM AUTHOR]

Logical character dependency is a major conceptual and methodological problem in phylogenetic inference of morphological data sets, as it violates the assumption of character independence that is common to all phylogenetic methods. It is more frequently observed in higher-level phylogenies or in data sets characterizing major evolutionary transitions, as these represent parts of the tree of life where (primary) anatomical characters either originate or disappear entirely. As a result, secondary traits related to these primary characters become "inapplicable" across all sampled taxa in which that character is absent. Various solutions have been explored over the last three decades to handle character dependency, such as alternative character coding schemes and, more recently, new algorithmic implementations. However, the accuracy of the proposed solutions, or the impact of character dependency across distinct optimality criteria, has never been directly tested using standard performance measures. Here, we utilize simple and complex simulated morphological data sets analyzed under different maximum parsimony optimization procedures and Bayesian inference to test the accuracy of various coding and algorithmic solutions to character dependency. This is complemented by empirical analyses using a recoded data set on palaeognathid birds. We find that in small, simulated data sets, absent coding performs better than other popular coding strategies available (contingent and multistate), whereas in more complex simulations (larger data sets controlled for different tree structure and character distribution models) contingent coding is favored more frequently. Under contingent coding, a recently proposed weighting algorithm produces the most accurate results for maximum parsimony. However, Bayesian inference outperforms all parsimony-based solutions to handle character dependency due to fundamental differences in their optimization procedures—a simple alternative that has been long overlooked. Yet, we show that the more primary characters bearing secondary (dependent) traits there are in a data set, the harder it is to estimate the true phylogenetic tree, regardless of the optimality criterion, owing to a considerable expansion of the tree parameter space. [Bayesian inference, character dependency, character coding, distance metrics, morphological phylogenetics, maximum parsimony, performance, phylogenetic accuracy.] [ABSTRACT FROM AUTHOR]

Introduction Airway obstruction is the third most common cause of preventable death on the battlefield, accounting for 1%–2% of total combat fatalities. No previous surgical cricothyroidotomy (SC) studies have analyzed the learning curve required to obtain proficiency despite being studied in numerous other surgical technique training experiments. The aims of this study were to establish expert SC performance criteria, develop a novel standardized SC curriculum, and determine the necessary number of practice iterations required by a novice to reach this pre-determined performance goal. Materials and Methods A standardized checklist and SC performance standards were established based on the performance of 12 board certified Military Health System surgeons with prior experience on performing a SC using a simulated trauma mannequin. Expert-level criteria were defined as a SC time to completion of 40 s or less and checklist score of at least 9/10, including all critical steps. Study subjects included 89 novice providers (54 active-duty first- and second-year medical students and 35 Navy corpsmen). Subjects received instruction on performing a SC using the principles of mastery learning and performed a final test of SC proficiency on a trauma mannequin within a realistic simulated MEDEVAC helicopter. The total number of subject practice attempts, checklist scores, and time to completion were measured and/or blindly scored. Learning curve and exponential plateau equations were used to characterize their improvement in mean time to SC completion and checklist scores. Results Mean pre-test knowledge scores for the entire group were 11.8 ± 3.1 out of 24 points. Total mean practice learning plateaued at checklist scores of 9.9/10 after 7 iterations and at a mean completion time of 30.4 s after 10 iterations. During the final test performance in the helicopter, 67.4% of subjects achieved expert-level performance on the first attempt. All subjects achieved expert-level performance by the end of two additional attempts. While a significantly larger proportion of medical students (79.9%) successfully completed the helicopter test on the first attempt compared to corpsmen (54.3%), there were no statistically significant differences in mean SC completion times and checklist scores between both groups (P  > 0.05). Medical students performed a SC only 1.3 s faster and scored only 0.16 points higher than corpsmen. The effect size for differences were small to negligible (Cohen's d range 0.18–0.33 for SC completion time; Cohen's d range 0.45–0.46 for checklist scores). Conclusion This study successfully defined SC checklist scores and completion times based on the performance of experienced surgeons on a simulator. Using these criteria and the principles of mastery learning, novices with little knowledge and experience in SC were successfully trained to the level of experienced providers. All subjects met performance targets after training and overall performance plateaued after approximately seven iterations. Over two-thirds of subjects achieved the performance target on the first testing attempt in a simulated helicopter environment. Performance was comparable between medical student and corpsmen subgroups. Further research will assess the durability of maintaining SC skills and the timing for introducing refresher courses after initial skill acquisition. [ABSTRACT FROM AUTHOR]

Background: In April 2020, the BIOMILK 300 LAC method for the quantification of lactose in lactose-free and low-lactose dairy products was adopted as First Action Official Method of AnalysisSM 2020.09 by AOAC INTERNATIONAL. To test the reproducibility of the method, as the last step toward the Final Action status, a collaborative study was organized by BIOLAN Microbiosensores. Objective: Fifteen collaborators within the European Union took part in this study, where nine different samples were sent as duplicate blind test portions for lactose determination. The data obtained were used to determine method repeatability and reproducibility and also to validate the new version of the biosensor. Method: The test method is based on the direct enzymatic recognition–electrochemical detection of trace levels of lactose over a wide range of dairy samples by means of the BIOMILK 300 biosensor, in less than 5 min, and without intricate sample pretreatments. Results: All samples resulted in a repeatability relative standard deviation (RSDr) of <8.1% and a reproducibility relative standard deviation (RSDR) of 14% maximum, meeting requirements from Standard Method Performance Requirement (SMPRVR ) 2018.09. Conclusions: The method has proved to give reproducible results for the quantification of lactose in lactose-free and lowlactose dairy products. Highlights: On the basis of these results, the enzymatic amperometric biosensor method developed by BIOLAN Microbiosensores was adopted as a Final Action Official Method in July 2022. [ABSTRACT FROM AUTHOR]

Risk prediction models are a crucial tool in healthcare. Risk prediction models with a binary outcome (i.e. binary classification models) are often constructed using methodology which assumes the costs of different classification errors are equal. In many healthcare applications, this assumption is not valid, and the differences between misclassification costs can be quite large. For instance, in a diagnostic setting, the cost of misdiagnosing a person with a life-threatening disease as healthy may be larger than the cost of misdiagnosing a healthy person as a patient. In this article, we present Tailored Bayes (TB), a novel Bayesian inference framework which "tailors" model fitting to optimize predictive performance with respect to unbalanced misclassification costs. We use simulation studies to showcase when TB is expected to outperform standard Bayesian methods in the context of logistic regression. We then apply TB to three real-world applications, a cardiac surgery, a breast cancer prognostication task, and a breast cancer tumor classification task and demonstrate the improvement in predictive performance over standard methods. [ABSTRACT FROM AUTHOR]

Circular RNAs (circRNAs) are covalently closed transcripts involved in critical regulatory axes, cancer pathways and disease mechanisms. CircRNA expression measured with RNA-seq has particular characteristics that might hamper the performance of standard biostatistical differential expression assessment methods (DEMs). We compared 38 DEM pipelines configured to fit circRNA expression data's statistical properties, including bulk RNA-seq, single-cell RNA-seq (scRNA-seq) and metagenomics DEMs. The DEMs performed poorly on data sets of typical size. Widely used DEMs, such as DESeq2, edgeR and Limma-Voom, gave scarce results, unreliable predictions or even contravened the expected behaviour with some parameter configurations. Limma-Voom achieved the most consistent performance throughout different benchmark data sets and, as well as SAMseq, reasonably balanced false discovery rate (FDR) and recall rate. Interestingly, a few scRNA-seq DEMs obtained results comparable with the best-performing bulk RNA-seq tools. Almost all DEMs' performance improved when increasing the number of replicates. CircRNA expression studies require careful design, choice of DEM and DEM configuration. This analysis can guide scientists in selecting the appropriate tools to investigate circRNA differential expression with RNA-seq experiments. [ABSTRACT FROM AUTHOR]

Background: The 3M™ Molecular Detection Assay 2--Salmonella method is based on real-time loop-mediated isothermal amplification when used with the 3M Molecular Detection System for the rapid and specific detection of Salmonella in enriched products. The 3M Molecular Detection Assay 2--Salmonella was approved as AOAC INTERNATIONAL (AOAC) Performance Tested MethodSM (PTM) Certificate No. 091501 and as AOAC Official Method of AnalysisSM 2016.01. Objective: This matrix extension study evaluated the 3M Molecular Detection Assay 2--Salmonella for detection of Salmonella in dried cannabis flower [>0.3% delta 9-tetrahydrocannabinol (THC)] and dried hemp flower (≤0.3% THC) at a 10 g test portion size. Method: Matrix studies in dried cannabis and hemp flowers followed procedures outlined in 3M Molecular Detection Assay 2--Salmonella product instructions and Standard Method Performance Requirement (SMPR®) for Detection of Salmonella species in Cannabis and Cannabis Products (AOAC SMPR 2020.002). The method was evaluated at low, high, and non-contaminated levels. Results: Results showed no statistically significant difference between the presumptive positive 3M Molecular Detection Assay 2--Salmonella results and the SMPR 2020.002 recommended cultural confirmations. Conclusions: This study demonstrates that the 3M Molecular Detection Assay 2--Salmonella is a reliable method for the rapid and specific detection of Salmonella in dried cannabis flower and dried hemp flower. Highlights: The 3M Molecular Detection Assay 2--Salmonella method is suitable for the rapid and specific detection of Salmonella in dried cannabis flower and dried hemp flower. [ABSTRACT FROM AUTHOR]

Background: Since the publication of Standard Method Performance Requirements (SMPRVR ) for vitamin D in infant formula (SMPR 2011.004) by AOAC INTERNATIONAL, revised vitamin D limits have been recommended by the European Food Safety Authority (EFSA) for infant formula and adopted in Commission Delegated Regulation (EU) 2019/828. The vitamin D range introduced, 2-2.5 lg/100 kcal, is significantly narrower than previous limits specified by Codex Standard 72-1981 and requires lower method reproducibility metrics to adequately assess regulatory compliance. The narrower limits for vitamin D present a significant challenge for current-generation reference analytical methods that comply with SMPR 2011.004. Objective: We evaluate the impact of Delegated Regulation (EU) 2019/828 on the demonstrated performance of AOAC Method 2016.05/ISO 20636:2018 to assess the likelihood that vitamin D results produced by the method would be found outside the EU limits when testing infant formula that is compliant as manufactured. Methods: AOAC Method 2016.05/ISO 20636:2018, specifically data generated during multi-laboratory study, was used as a basis for statistical evaluation of the impact of the narrower EU vitamin D limits. Results: The review of AOAC Method 2016.05/ISO 20636:2018 method performance against the vitamin D regulatory range introduced in (EU) 2019/828 indicates methods capable of performing in alignment with SMPR 2011.004 are likely to produce results that fail to meet EU requirements. Conclusions: Our assessment illustrates the high probability that a well-manufactured product with vitamin D levels within the EU regulatory range would fail to meet the regulatory requirements due to analytical method variability when tested using fit-for-purpose methods. Further, required method performance cannot be expected with the future development of new methods. To avoid this, consideration should be given to aligning proposed regulatory limits with method performance metrics of current-generation compendial methods. Highlights: Current, state-of-the-art methods cannot consistently verify infant formula product compliance for vitamin D in accordance with (EU) 2019/828. [ABSTRACT FROM AUTHOR]

When a ranking of institutions such as medical centers or universities is based on a numerical measure of performance provided with a standard error, confidence intervals (CIs) should be calculated to assess the uncertainty of these ranks. We present a novel method based on Tukey's honest significant difference test to construct simultaneous CIs for the true ranks. When all the true performances are equal, the probability of coverage of our method attains the nominal level. In case the true performance measures have no exact ties, our method is conservative. For this situation, we propose a rescaling method to the nominal level that results in shorter CIs while keeping control of the simultaneous coverage. We also show that a similar rescaling can be applied to correct a recently proposed Monte‐Carlo based method, which is anticonservative. After rescaling, the two methods perform very similarly. However, the rescaling of the Monte‐Carlo based method is computationally much more demanding and becomes infeasible when the number of institutions is larger than 30–50. We discuss another recently proposed method similar to ours based on simultaneous CIs for the true performance. We show that our method provides uniformly shorter CIs for the same confidence level. We illustrate the superiority of our new methods with a data analysis for travel time to work in the United States and on rankings of 64 hospitals in the Netherlands. [ABSTRACT FROM AUTHOR]

Background Beyond their specific experiment, video records of behavior have future value—for example, as inputs for new experiments or for yet unknown types of analysis of behavior—similar to tissue or blood sample banks in life sciences where clinically derived or otherwise well-described experimental samples are stored to be available for some unknown potential future purpose. Findings Research using an animal model of obsessive-compulsive disorder employed a standardized paradigm where the behavior of rats in a large open field was video recorded for 55 minutes on each test. From 43 experiments, there are 19,976 such trials that amount to over 2 years of continuous recording. In addition to videos, there are 2 video-derived raw data objects: XY locomotion coordinates and plots of animal trajectory. To motivate future use, the 3 raw data objects are annotated with a general schema—one that abstracts the data records from their particular experiment while providing, at the same time, a detailed list of independent variables bearing on behavioral performance. The raw data objects are deposited as 43 datasets but constitute, functionally, a library containing 1 large dataset. Conclusions Size and annotation schema give the library high reuse potential: in applications using machine learning techniques, statistical evaluation of subtle factors, simulation of new experiments, or as educational resource. Ultimately, the library can serve both as the seed and as the test bed to create a machine-searchable virtual library of linked open datasets for behavioral performance in defined conditions. [ABSTRACT FROM AUTHOR]

Background: The AOAC Stakeholder Panel on Strategic Food Analytical Methods issued a call for methods for the measurement of lactose in low-lactose and lactose-free products under Standard Method Performance Requirement (SMPRVR) 2018.009. Megazyme's Lactose Assay Kit (K-LOLAC) was developed specifically to address the need for accurate enzymatic testing in lactose-free samples. Objective: K-LOLAC was validated for measurement of lactose in low-lactose and lactose-free milk, milk products, and products containing dairy ingredients. A single-laboratory validation (SLV) of the method is reported. Method: K-LOLAC is an accurate and sensitive enzymatic method for the rapid measurement of lactose in low-lactose or lactose-free products. Validation analysis was performed on a sample set of 36 commercial food and beverage products and a set of 10 certified reference materials. Parameters examined during the validation included working range and linear range, selectivity, LOD, LOQ, trueness (bias), precision (repeatability and intermediate precision), robustness, and stability. Results: For all samples tested within the lower range (10-100 mg/100 g or mL), recoveries varied from 93.21-114.10%. Recoveries obtained for samples in the higher range (>100 mg/100 g or mL) varied from 94.44-108.28%. All materials had repeatability relative standard deviations (RSDr and RSDir) of <9%. Conclusions: The commercial K-LOLAC assay kit, as developed by Megazyme, meets the requirements set out under SMPR 2018.009. Highlights: K-LOLAC is a robust, quick, and easy method for analysis of lactose in foodstuffs and beverages. [ABSTRACT FROM AUTHOR]

Background: AOAC Method 2015.06 is a Final Action Official MethodSM for the determination of 12 elements (Na, Mg, P, K, Ca, Cr, Mn, Fe, Cu, Zn, Se, and Mo) in infant formula and adult nutritional products, based on inductively coupled plasma (ICP)- MS. Currently, its scope does not include certain kinds of formulations used in India. The method would likely be used more in Indian laboratories if its performance were characterized on Indian matrixes. Objective: In this study we describe a typical single-laboratory validation (SLV) exercise designed to characterize the precision and accuracy of AOAC Method 2015.06 for common Indian nutritional matrixes so that the scope of the method can be extended to include them. Method: Six matrixes specific to the Indian market were carried through an SLV and the Standard Method Performance Requirements (SMPRsVR) previously published for this method were used to evaluate the results. Results: The method demonstrated typical repeatability (<5% RSD), and intermediate precision (5-8% RSD) on the Indian matrixes, with very few exceptions. Accuracy was demonstrated by overspike recoveries in the range of 90-110% over 3 days for the Indian matrixes, as well as excellent agreement with previously published results for three additional matrixes tested. Some of the new Indian matrixes required alternate sample preparation procedures versus the usual reconstitution prescribed by the SMPRs. Conclusions: The SLV results showed that AOAC Method 2015.06 can be extended to include these Indian matrices. Highlights: The two special sample preparation procedures can now be considered validated. [ABSTRACT FROM AUTHOR]

Objective: Workload is a useful construct in human factors and neuroergonomics research that describes "the perceived relationship between the amount of mental processing capability or resources and the amount required by the task". We assessed workload in relation to performance on two standard neuropsychological tests, the Trails Making Test and the Grooved Pegboard. Method: Eight college students completed the Trail Making Test (TMT) and the Grooved Pegboard (GP). Workload was assessed with the NASA-Task Load Index (NASA-TLX), a self-report measure including several subscales (we focused on Mental Demand and Physical Demand). Specifically, the NASA-TLX was completed immediately after each portion of each test. Results: For the TMT, completion time was greater for Part B versus A, as usual. Mental demand was greater in Part B (M = 44.38, SD = 21.45) versus Part A (M = 10.63, SD = 7.76) (p =.003). There was no difference in physical demand. For the GP, no difference in completion time was evident between DH and NDH. While there was no difference in mental workload, as expected, physical demand did differ between DH (M = 31.88, SD = 27.51) and NDH (M = 43.13, SD = 27.38) (p =.035). Conclusion(s): We validated the NASA-TLX with two standard neuropsychological tests. As expected, mental workload was greater in the more difficult TMT condition. A similar pattern was found with physical workload with the GP. We propose that workload, not normally considered in neuropsychology, might be a valuable tool in the assessment of cognitive status. [ABSTRACT FROM AUTHOR]