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OBJECTIVES: Quantitative bias analysis (QBA) methods evaluate the impact of biases arising from systematic errors on observational study results. This systematic review aimed to summarize the range and characteristics of QBA methods for summary-level data published in the peer-reviewed literature. STUDY DESIGN AND SETTING: We searched MEDLINE, Embase, Scopus, and Web of Science for English-language articles describing QBA methods. For each QBA method, we recorded key characteristics, including applicable study designs, bias(es) addressed; bias parameters, and publicly available software. The study protocol was preregistered on the Open Science Framework (https://osf.io/ue6vm/). RESULTS: Our search identified 10,249 records, of which 53 were articles describing 57 QBA methods for summary-level data. Of the 57 QBA methods, 53 (93%) were explicitly designed for observational studies, and 4 (7%) for meta-analyses. There were 29 (51%) QBA methods that addressed unmeasured confounding, 19 (33%) misclassification bias, 6 (11%) selection bias, and 3 (5%) multiple biases. Thirty-eight (67%) QBA methods were designed to generate bias-adjusted effect estimates and 18 (32%) were designed to describe how bias could explain away observed findings. Twenty-two (39%) articles provided code or online tools to implement the QBA methods. CONCLUSION: In this systematic review, we identified a total of 57 QBA methods for summary-level epidemiologic data published in the peer-reviewed literature. Future investigators can use this systematic review to identify different QBA methods for summary-level epidemiologic data. PLAIN LANGUAGE SUMMARY: Quantitative bias analysis (QBA) methods can be used to evaluate the impact of biases on observational study results. However, little is known about the full range and characteristics of available methods in the peer-reviewed literature that can be used to conduct QBA using information reported in manuscripts and other publicly available sources without requiring the raw data from a study. In this systematic review, we identified 57 QBA methods for summary-level data from observational studies. Overall, there were 29 methods that addressed unmeasured confounding, 19 that addressed misclassification bias, six that addressed selection bias, and three that addressed multiple biases. This systematic review may help future investigators identify different QBA methods for summary-level data.

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Measuring the fitnesses of genetic variants is a fundamental objective in evolutionary biology. A standard approach for measuring microbial fitnesses in bulk involves labeling a library of genetic variants with unique sequence barcodes, competing the labeled strains in batch culture, and using deep sequencing to track changes in the barcode abundances over time. However, idiosyncratic properties of barcodes can induce nonuniform amplification or uneven sequencing coverage that causes some barcodes to be over- or under-represented in samples. This systematic bias can result in erroneous read count trajectories and misestimates of fitness. Here, we develop a computational method, named REBAR (Removing the Effects of Bias through Analysis of Residuals), for inferring the effects of barcode processing bias by leveraging the structure of systematic deviations in the data. We illustrate this approach by applying it to two independent data sets, and demonstrate that this method estimates and corrects for bias more accurately than standard proxies, such as GC-based corrections. REBAR mitigates bias and improves fitness estimates in high-throughput assays without introducing additional complexity to the experimental protocols, with potential applications in a range of experimental evolution and mutation screening contexts.

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Metacognitive systematic bias impairs human learning efficiency, which is characterized by the inconsistency between predicted and actual memory performance. However, the underlying mechanism of metacognitive systematic bias remains unclear in existing studies. In this study, we utilized judgments of learning task in human participants to compare the neural mechanism difference in metacognitive systematic bias. Participants encoded words in fMRI sessions that would be tested later. Immediately after encoding each item, participants predicted how likely they would remember it. Multivariate analyses on fMRI data demonstrated that working memory and uncertainty decisions are represented in patterns of neural activity in metacognitive systematic bias. The available information participants used led to overestimated bias and underestimated bias. Effective connectivity analyses further indicate that information about the metacognitive systematic bias is represented in the dorsolateral prefrontal cortex and inferior parietal cortex. Different neural patterns were found underlying overestimated bias and underestimated bias. Specifically, connectivity regions with the dorsolateral prefrontal cortex, anterior cingulate cortex, and supramarginal gyrus form overestimated bias, while less regional connectivity forms underestimated bias. These findings provide a mechanistic account for the construction of metacognitive systematic bias.

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Unprecedented technological advances in digitization and the steadily expanding open-access digital repositories are yielding new opportunities to quickly and efficiently measure morphological traits without transportation and advanced/expensive microscope machinery. A prime example is the AntWeb.org database, which allows researchers from all over the world to study taxonomic, ecological, or evolutionary questions on the same ant specimens with ease. However, the reproducibility and reliability of morphometric data deduced from AntWeb compared to traditional microscope measurements has not yet been tested. Here, we compared 12 morphological traits of 46 Temnothorax ant specimens measured either directly by stereomicroscope on physical specimens or via the widely used open-access software tpsDig utilizing AntWeb digital images. We employed a complex statistical framework to test several aspects of reproducibility and reliability between the methods. We estimated (i) the agreement between the measurement methods and (ii) the trait value dependence of the agreement, then (iii) compared the coefficients of variation produced by the different methods, and finally, (iv) tested for systematic bias between the methods in a mixed modeling-based statistical framework. The stereomicroscope measurements were extremely precise. Our comparisons showed that agreement between the two methods was exceptionally high, without trait value dependence. Furthermore, the coefficients of variation did not differ between the methods. However, we found systematic bias in eight traits: apart from one trait where software measurements overestimated the microscopic measurements, the former underestimated the latter. Our results shed light on the fact that relying solely on the level of agreement between methods can be highly misleading. In our case, even though the software measurements predicted microscope measurements very well, replacing traditional microscope measurements with software measurements, and especially mixing data collected by the different methods, might result in erroneous conclusions. We provide guidance on the best way to utilize virtual specimens (2D z-stacked images) as a source of morphometric data, emphasizing the method's limitations in certain fields and applications.

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Entropy indicates irregularity or randomness of a dynamic system. Over the decades, entropy calculated at different scales of the system through subsampling or coarse graining has been used as a surrogate measure of system complexity. One popular multi-scale entropy analysis is the multi-scale sample entropy (MSE), which calculates entropy through the sample entropy (SampEn) formula at each time scale. SampEn is defined by the "logarithmic likelihood" that a small section (within a window of a length m) of the data "matches" with other sections will still "match" the others if the section window length increases by one. "Match" is defined by a threshold of r times standard deviation of the entire time series. A problem of current MSE algorithm is that SampEn calculations at different scales are based on the same matching threshold defined by the original time series but data standard deviation actually changes with the subsampling scales. Using a fixed threshold will automatically introduce systematic bias to the calculation results. The purpose of this paper is to mathematically present this systematic bias and to provide methods for correcting it. Our work will help the large MSE user community avoiding introducing the bias to their multi-scale SampEn calculation results.

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BACKGROUND: Quantitative bias analysis (QBA) measures study errors in terms of direction, magnitude and uncertainty. This systematic review aimed to describe how QBA has been applied in epidemiological research in 2006-19. METHODS: We searched PubMed for English peer-reviewed studies applying QBA to real-data applications. We also included studies citing selected sources or which were identified in a previous QBA review in pharmacoepidemiology. For each study, we extracted the rationale, methodology, bias-adjusted results and interpretation and assessed factors associated with reproducibility. RESULTS: Of the 238 studies, the majority were embedded within papers whose main inferences were drawn from conventional approaches as secondary (sensitivity) analyses to quantity-specific biases (52%) or to assess the extent of bias required to shift the point estimate to the null (25%); 10% were standalone papers. The most common approach was probabilistic (57%). Misclassification was modelled in 57%, uncontrolled confounder(s) in 40% and selection bias in 17%. Most did not consider multiple biases or correlations between errors. When specified, bias parameters came from the literature (48%) more often than internal validation studies (29%). The majority (60%) of analyses resulted in >10% change from the conventional point estimate; however, most investigators (63%) did not alter their original interpretation. Degree of reproducibility related to inclusion of code, formulas, sensitivity analyses and supplementary materials, as well as the QBA rationale. CONCLUSIONS: QBA applications were rare though increased over time. Future investigators should reference good practices and include details to promote transparency and to serve as a reference for other researchers.

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OBJECTIVES: To investigate the effect of compressed SENSE (CS), an acceleration technique combining parallel imaging and compressed sensing, on potential bias and precision of brain volumetry and evaluate it in the context of normative brain volumetry. MATERIALS AND METHODS: In total, 171 scans from scan-rescan experiments on three healthy subjects were analyzed. Each subject received 3D-T1-weighted brain MRI scans at increasing degrees of acceleration (CS-factor = 1/4/8/12/16/20/32). Single-scan acquisition times ranged from 00:41 min (CS-factor = 32) to 21:52 min (CS-factor = 1). Brain segmentation and volumetry was performed using two different software tools: md.brain, a proprietary software based on voxel-based morphometry, and FreeSurfer, an open-source software based on surface-based morphometry. Four sub-volumes were analyzed: brain parenchyma (BP), total gray matter, total white matter, and cerebrospinal fluid (CSF). Coefficient of variation (CoV) of the repeated measurements as a measure of intra-subject reliability was calculated. Intraclass correlation coefficient (ICC) with regard to increasing CS-factor was calculated as another measure of reliability. Noise-to-contrast ratio as a measure of image quality was calculated for each dataset to analyze the association between acceleration factor, noise and volumetric brain measurements. RESULTS: For all sub-volumes, there is a systematic bias proportional to the CS-factor which is dependent on the utilized software and subvolume. Measured volumes deviated significantly from the reference standard (CS-factor = 1), e.g. ranging from 1 to 13% for BP. The CS-induced systematic bias is driven by increased image noise. Except for CSF, reliability of brain volumetry remains high, demonstrated by low CoV (< 1% for CS-factor up to 20) and good to excellent ICC for CS-factor up to 12. CONCLUSION: CS-acceleration has a systematic biasing effect on volumetric brain measurements.

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Ladybirds (family Coccinellidae) are one of the most diverse groups of beetles and globally comprise over 6000 species. Despite their scientific and economic significance, the taxonomy of Coccinellidae remains unstable, and we still know little about their evolutionary history. By using a small number of genes, previous phylogenetic analyses have not reliably resolved the relationships among major ladybird lineages. In this study, we sequenced 94 nuclear protein-coding genes for 214 species of Coccinellidae and 14 outgroups, covering 90 genera and 35 tribes. We found that nucleotide compositional heterogeneity is present among ladybird tribes so that phylogenetic inference at the amino acid level is more reliable than at the DNA level. Based on the maximum likelihood analyses of the amino acid dataset, we recognize three subfamilies in Coccinellidae: Microweiseinae, Monocoryninae stat. nov., and Coccinellinae. The subfamily relationships are strongly supported as (Microweiseinae, (Monocoryninae stat. nov., Coccinellinae)). The tribes of ladybirds are mostly monophyletic, except Ortaliini, Sticholotidini, Scymnini, and Coccidulini. The phylogenetic relationships among tribes of Coccinellinae are still not well resolved, with many nodes weakly supported. Our divergence time analysis suggests that the crown group of extant lady beetles arose in the Early Cretaceous ~ 143 million years ago (Mya) and experienced a rapid diversification during the Late Cretaceous (120-70 Mya). We hypothesize that the boom of angiosperms in the Late Cretaceous promoted the diversification of herbivorous Sternorrhyncha insects, especially aphids, which in turn drove the rapid radiation of predatory lady beetles. In summary, our work provides a comprehensive time-calibrated phylogeny of Coccinellidae that provides a sound framework for revising their classification and understanding the origin of their biodiversity.

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BACKGROUND: In diploid cells, it is important to construct maternal and paternal Hi-C contact maps respectively since the two homologous chromosomes can differ in chromatin three-dimensional (3D) organization. Though previous softwares could construct diploid (maternal and paternal) Hi-C contact maps by using phased genetic variants, they all neglected the systematic biases in diploid Hi-C contact maps caused by variable genetic variant density in the genome. In addition, few of softwares provided quantitative analyses on allele-specific chromatin 3D organization, including compartment, topological domain and chromatin loop. RESULTS: In this work, we revealed the feature of allele-assignment bias caused by the variable genetic variant density, and then proposed a novel strategy to correct the systematic biases in diploid Hi-C contact maps. Based on the bias correction, we developed an integrated tool, called HiCHap, to perform read mapping, contact map construction, whole-genome identification of compartments, topological domains and chromatin loops, and allele-specific testing for diploid Hi-C data. Our results show that the correction on allele-assignment bias in HiCHap does significantly improve the quality of diploid Hi-C contact maps, which subsequently facilitates the whole-genome identification of diploid chromatin 3D organization, including compartments, topological domains and chromatin loops. Finally, HiCHap also supports the data analysis for haploid Hi-C maps without distinguishing two homologous chromosomes. CONCLUSIONS: We provided an integrated package HiCHap to perform the data processing, bias correction and structural analysis for diploid Hi-C data. The source code and tutorial of software HiCHap are freely available at https://pypi.org/project/HiCHap/ .

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Accumulating evidence demonstrates the impact of bias that reflects social inequality on the performance of machine learning (ML) models in health care. Given their intended placement within healthcare decision making more broadly, ML tools require attention to adequately quantify the impact of bias and reduce its potential to exacerbate inequalities. We suggest that taking a patient safety and quality improvement approach to bias can support the quantification of bias-related effects on ML. Drawing from the ethical principles underpinning these approaches, we argue that patient safety and quality improvement lenses support the quantification of relevant performance metrics, in order to minimize harm while promoting accountability, justice, and transparency. We identify specific methods for operationalizing these principles with the goal of attending to bias to support better decision making in light of controllable and uncontrollable factors.

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Measurement error is common in epidemiology, but few studies use quantitative methods to account for bias due to mismeasurement. One potential barrier is that some intuitive approaches that readily combine with methods to account for other sources of bias, like multiple imputation for measurement error (MIME), rely on internal validation data, which are rarely available. Here, we present a reparameterized imputation approach for measurement error (RIME) that can be used with internal or external validation data. We illustrate the advantages of RIME over a naive approach that ignores measurement error and MIME using a hypothetical example and a series of simulation experiments. In both the example and simulations, we combine MIME and RIME with inverse probability weighting to account for confounding when estimating hazard ratios and counterfactual risk functions. MIME and RIME performed similarly when rich external validation data were available and the prevalence of exposure did not vary between the main study and the validation data. However, RIME outperformed MIME when validation data included only true and mismeasured versions of the exposure or when exposure prevalence differed between the data sources. RIME allows investigators to leverage external validation data to account for measurement error in a wide range of scenarios.

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In phylogenetic inference, we commonly use models of substitution which assume that sequence evolution is stationary, reversible, and homogeneous (SRH). Although the use of such models is often criticized, the extent of SRH violations and their effects on phylogenetic inference of tree topologies and edge lengths are not well understood. Here, we introduce and apply the maximal matched-pairs tests of homogeneity to assess the scale and impact of SRH model violations on 3,572 partitions from 35 published phylogenetic data sets. We show that roughly one-quarter of all the partitions we analyzed (23.5%) reject the SRH assumptions, and that for 25% of data sets, tree topologies inferred from all partitions differ significantly from topologies inferred using the subset of partitions that do not reject the SRH assumptions. This proportion increases when comparing trees inferred using the subset of partitions that rejects the SRH assumptions, to those inferred from partitions that do not reject the SRH assumptions. These results suggest that the extent and effects of model violation in phylogenetics may be substantial. They highlight the importance of testing for model violations and possibly excluding partitions that violate models prior to tree reconstruction. Our results also suggest that further effort in developing models that do not require SRH assumptions could lead to large improvements in the accuracy of phylogenomic inference. The scripts necessary to perform the analysis are available in https://github.com/roblanf/SRHtests, and the new tests we describe are available as a new option in IQ-TREE (http://www.iqtree.org).

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PURPOSE: Measurement error discussions often assume classification errors of key variables are independent. Yet, small amounts of dependent error can create large biases in effect estimates. The purpose of this review was to evaluate frequency of measurement error discussions and potential for dependent error in the observational literature. METHODS: Two samples of articles analyzing exposure-outcome contrasts were collected: a random sample (n = 100) from high-impact epidemiology and medical journals (June 2015-July 2016), and a citation-based sample (n = 39) of studies citing one of two prominent dependent misclassification articles (through July 2016). We extracted study details, recorded measurement error mentions, and qualitatively assessed dependent error potential. RESULTS: Measurement error was often discussed. No random sample articles explicitly mentioned dependent error, compared with 59% of the citation-based sample. The random sample was found to be at low risk of exposure-outcome (15% plausible/probable) but increased risk for exposure-confounder (38% plausible/probable) dependency. The citation-based sample was at higher risk for dependent error (exposure-outcome: 46% plausible/probable; exposure-confounder: 61% plausible/probable). CONCLUSIONS: Although measurement error was frequently mentioned, potential impact on observed results was rarely discussed in-depth or quantified. Dependent error mentions were rare, even among studies deemed susceptible. Further education and steps to avoid dependent error are needed.

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Knowledge of the internal phylogeny and evolutionary history of ants (Formicidae), the world's most species-rich clade of eusocial organisms, has dramatically improved since the advent of molecular phylogenetics. A number of relationships at the subfamily level, however, remain uncertain. Key unresolved issues include placement of the root of the ant tree of life and the relationships among the so-called poneroid subfamilies. Here we assemble a new data set to attempt a resolution of these two problems and carry out divergence dating, focusing on the age of the root node of crown Formicidae. For the phylogenetic analyses we included data from 110 ant species, including the key species Martialis heureka. We focused taxon sampling on non-formicoid lineages of ants to gain insight about deep nodes in the ant phylogeny. For divergence dating we retained a subset of 62 extant taxa and 42 fossils in order to approximate diversified sampling in the context of the fossilized birth-death process. We sequenced 11 nuclear gene fragments for a total of ∼7.5 kb and investigated the DNA sequence data for the presence of among-taxon compositional heterogeneity, a property known to mislead phylogenetic inference, and for its potential to affect the rooting of the ant phylogeny. We found sequences of the Leptanillinae and several outgroup taxa to be rich in adenine and thymine (51% average AT content) compared to the remaining ants (45% average). To investigate whether this heterogeneity could bias phylogenetic inference we performed outgroup removal experiments, analysis of compositionally homogeneous sites, and a simulation study. We found that compositional heterogeneity indeed appears to affect the placement of the root of the ant tree but has limited impact on more recent nodes. Our findings have implications for outgroup choice in phylogenetics, which should be made not only on the basis of close relationship to the ingroup, but should also take into account sequence divergence and other properties relative to the ingroup. We put forward a hypothesis regarding the rooting of the ant phylogeny, in which Martialis and the Leptanillinae together constitute a clade that is sister to all other ants. After correcting for compositional heterogeneity this emerges as the best-supported hypothesis of relationships at deep nodes in the ant tree. The results of our divergence dating under the fossilized birth-death process and diversified sampling suggest that the crown Formicidae originated during the Albian or Aptian ages of the Lower Cretaceous (103-124 Ma). In addition, we found support for monophyletic poneroids comprising the subfamilies Agroecomyrmecinae, Amblyoponinae, Apomyrminae, Paraponerinae, Ponerinae, and Proceratiinae, and well-supported relationships among these subfamilies except for the placement of Proceratiinae and (Amblyoponinae + Apomyrminae). Our phylogeny also highlights the non-monophyly of several ant genera, including Protanilla and Leptanilla in the Leptanillinae, Proceratium in the Proceratiinae, and Cryptopone, Euponera, and Mesoponera within the Ponerinae.

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Case-crossover designs have been widely applied to epidemiological and medical investigations of associations between short-term exposures and risk of acute adverse health events. Much effort has been made in literature on understanding source of confounding and reducing systematic bias by reference-select strategies. In this paper, we explored the nature of bias in the ambi-directional and time-stratified case-crossover designs via simulation using actual air pollution data from urban Edmonton, Alberta, Canada. We further proposed a calibration approach for eliminating systematic bias in estimates (coefficient estimate, 95% confident interval, and p-value). Bias check for coefficient estimation, size check and power check for significance test were done via simulation experiments to show advantages of the calibrated case-crossover studies over the ones without calibration. An application was done to investigate associations between air pollutants and acute myocardial infarction hospitalizations in urban Edmonton. In conclusion, systematic bias in a case-crossover design is often unavoidable, leading to an obvious bias in the estimated effect and an unreliable p value in the significance test. The proposed calibration technique provides an efficient approach to eliminating systematic bias in a case-crossover study.

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BACKGROUND: A new functional movement assessment, known as the Fusionetics- Movement Efficiency (ME) Test, has recently been introduced in the literature. Before the potential clinical utility of the ME Test can be examined, the reliability of this assessment must be established. PURPOSE: To examine the intra-rater test-retest reliability of the Fusionetics- ME Test. STUDY DESIGN: Cross-sectional. METHODS: ME Test data were collected among 23 (6 males, 17 females) university students (mean ± SD, age = 25.96 ± 3.16 yrs; height = 170.70 ± 9.96 cm; weight = 66.89 ± 12.67 kg) during sessions separated by 48 hours (Day 1, Day 2). All participants completed the seven sub-tests of the ME Test: 2-Leg Squat, 2-Leg Squat with Heel Lift, 1-Leg Squat, Push-Up, Shoulder Movements, Trunk Movements, and Cervical Movements. Overall ME Test scores and ME Test scores for each individual sub-test were calculated on a scale of 0 - 100 (worst - best) based on commonly observed movement compensations associated with each sub-test. RESULTS: Intraclass correlation coefficients (ICC3,1) statistics indicated that the intra-rater test-retest reliability of the Overall ME Test and individual sub-tests ranged from fair-to-excellent (ICC3,1 range = 0.55 - 0.84). Statistically significant differences in ME Test scores were identified between Day 1 and Day 2 among the 2-Leg Squat with Heel Lift (p = 0.015) and Cervical Movements (p = 0.005) sub-tests. In addition, a large range in the standard error of the measure (SEM) and minimal detectable change values (MDC90% & MDC95%) were identified within individual sub-tests of the ME Test (SEM range = 7.05 - 13.44; MDC90% range = 16.40 - 31.27; MDC95% range = 19.53 - 37.25), suggesting that the response stability varies among these individual sub-tests. Prevalence-adjusted bias-adjusted kappa statistics (κPABA) suggest that 55 of the 60 (92%) individual movement compensations hold moderate-to-almost perfect intra-rater test-retest reliability (κPABA range = 0.30 - 1.00). CONCLUSIONS: Excellent intra-rater test-retest reliability of the Overall ME Test score was identified, and thus, clinicians can reliably utilize the Fusionetics- ME Test to assess change in functional movement quality across time. However, caution should be taken if utilizing an individual sub-test to assess functional movement quality over time. LEVEL OF EVIDENCE: 2b.

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The spatial updating and memory systems are employed during updating in both the immediate and retrieved environments. However, these dual systems seem to work differently, as the difference of pointing latency and absolute error between the two systems vary across environments. To verify this issue, the present study employed the bias analysis of signed errors based on the hypothesis that the transformed representation will bias toward the original one. Participants learned a spatial layout and then either stayed in the learning location or were transferred to a neighboring room directly or after being disoriented. After that, they performed spatial judgments from perspectives aligned with the learning direction, aligned with the direction they faced during the test, or a novel direction misaligned with the two above-mentioned directions. The patterns of signed error bias were consistent across environments. Responses for memory aligned perspectives were unbiased, whereas responses for sensorimotor aligned perspectives were biased away from the memory aligned perspective, and responses for misaligned perspectives were biased toward sensorimotor aligned perspectives. These findings indicate that the spatial updating system is consistently independent of the spatial memory system regardless of the environments, but the updating system becomes less accessible as the environment changes from immediate to a retrieved one.

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[Purpose] To determine the validity of knee extension muscle strength measurements using belt-stabilized hand-held dynamometry with and without body stabilization compared with the gold standard isokinetic dynamometry in healthy adults. [Subjects and Methods] Twenty-nine healthy adults (mean age, 21.3 years) were included. Study parameters involved right side measurements of maximal isometric knee extension strength obtained using belt-stabilized hand-held dynamometry with and without body stabilization and the gold standard. Measurements were performed in all subjects. [Results] A moderate correlation and fixed bias were found between measurements obtained using belt-stabilized hand-held dynamometry with body stabilization and the gold standard. No significant correlation and proportional bias were found between measurements obtained using belt-stabilized hand-held dynamometry without body stabilization and the gold standard. The strength identified using belt-stabilized hand-held dynamometry with body stabilization may not be commensurate with the maximum strength individuals can generate; however, it reflects such strength. In contrast, the strength identified using belt-stabilized hand-held dynamometry without body stabilization does not reflect the maximum strength. Therefore, a chair should be used to stabilize the body when performing measurements of maximal isometric knee extension strength using belt-stabilized hand-held dynamometry in healthy adults. [Conclusion] Belt-stabilized hand-held dynamometry with body stabilization is more convenient than the gold standard in clinical settings.

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Measurement errors of medico-technical devices can be separated into systematic bias and random error. We propose a new method to address both simultaneously via generalized additive models for location, scale and shape (GAMLSS) in combination with permutation tests. More precisely, we extend a recently proposed boosting algorithm for GAMLSS to provide a test procedure to analyse potential device effects on the measurements. We carried out a large-scale simulation study to provide empirical evidence that our method is able to identify possible sources of systematic bias as well as random error under different conditions. Finally, we apply our approach to compare measurements of skin pigmentation from two different devices in an epidemiological study.

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