Measurement error and the replication crisis

Artificial intelligence in gastric cancer: Application and future perspectives

Gastric cancer is the fourth leading cause of cancer-related mortality across the globe, with a 5-year survival rate of less than 40%. In recent years, several applications of artificial intelligence (AI) have emerged in the gastric cancer field based on its efficient computational power and learning capacities, such as image-based diagnosis and prognosis prediction. AI-assisted diagnosis includes pathology, endoscopy, and computerized tomography, while researchers in the prognosis circle focus on recurrence, metastasis, and survival prediction. In this review, a comprehensive literature search was performed on articles published up to April 2020 from the databases of PubMed, Embase, Web of Science, and the Cochrane Library. Thereby the current status of AI-applications was systematically summarized in gastric cancer. Moreover, future directions that target this field were also analyzed to overcome the risk of overfitting AI models and enhance their accuracy as well as the applicability in clinical practice.

Searching for the Backfire Effect: Measurement and Design Considerations

One of the most concerning notions for science communicators, fact-checkers, and advocates of truth, is the backfire effect; this is when a correction leads to an individual increasing their belief in the very misconception the correction is aiming to rectify. There is currently a debate in the literature as to whether backfire effects exist at all, as recent studies have failed to find the phenomenon, even under theoretically favorable conditions. In this review, we summarize the current state of the worldview and familiarity backfire effect literatures. We subsequently examine barriers to measuring the backfire phenomenon, discuss approaches to improving measurement and design, and conclude with recommendations for fact-checkers. We suggest that backfire effects are not a robust empirical phenomenon, and more reliable measures, powerful designs, and stronger links between experimental design and theory could greatly help move the field ahead.

Testing repeatability, measurement error and species differentiation when using geometric morphometrics on complex shapes: a case study of Patagonian lizards of the genus Liolaemus (Squamata: Liolaemini)

The repeatability of findings is the key factor behind scientific reliability, and the failure to reproduce scientific findings has been termed the ‘replication crisis’. Geometric morphometrics is an established tool in evolutionary biology. However, different operators (and/or different methods) could act as large sources of variation in the data obtained. Here, we investigated inter-operator error in geometric morphometric protocols on complex shapes of Liolaemus lizards, as well as measurement error in three taxa varying in their difficulty of digitalization. We also examined the potential for these protocols to discriminate among complex shapes in closely related species. We found a wide range of inter-operator error, contributing between 19.5% and 60% to the total variation. Moreover, measurement error increased with the complexity of the quantified shape. All protocols were able to discriminate between species, but the use of more landmarks did not imply better performance. We present evidence that complex shapes reduce repeatability, highlighting the need to explore different sources of variation that could lead to such low repeatability. Lastly, we suggest some recommendations to improve the repeatability and reliability of geometric morphometrics results.

Can hypnotic suggestibility be measured online?

Hypnosis and hypnotic suggestions are gradually gaining popularity within the consciousness community as established tools for the experimental manipulation of illusions of involuntariness, hallucinations and delusions. However, hypnosis is still far from being a widespread instrument; a crucial hindrance to taking it up is the amount of time needed to invest in identifying people high and low in responsiveness to suggestion. In this study, we introduced an online assessment of hypnotic response and estimated the extent to which the scores and psychometric properties of an online screening differ from an offline one. We propose that the online screening of hypnotic response is viable as it reduces the level of responsiveness only by a slight extent. The application of online screening may prompt researchers to run large-scale studies with more heterogeneous samples, which would help researchers to overcome some of the issues underlying the current replication crisis in psychology.

Understanding event-related potentials (ERPs) in clinical and basic language and communication disorders research: a tutorial

BACKGROUND

Event-related potentials (ERPs), which are electrophysiological neural responses time-locked to a stimulus, have become an increasingly common tool in language and communication disorders research. They can provide complementary evidence to behavioural measures as well as unique perspectives on communication disorders. ERPs have the distinct advantage of providing precise information about the timing of neural processes and can be used in cases where it is difficult to obtain responses from participants, such as infants or individuals who are minimally verbal. However, clinicians and clinician-scientists rarely receive training in how to interpret ERP research.

AIMS

To provide information that allows readers to better understand, interpret and evaluate research using ERPs. We focus on research related to communication sciences and disorders and the information that is most relevant to interpreting research articles.

METHOD

We explain what ERPs are and how ERP data are collected, referencing key texts and primary research articles. Potential threats to validity, guidelines for interpreting data, and the pros and cons using of ERPs are discussed. Research in the area of paediatric language disorders is used as a model; common paradigms such as the semantic incongruity N400 and auditory mismatch negativity are used as tangible examples. With this foundation of understanding ERPs, the state of the field in terms of how ERPs are used and the ways they may inform the field are discussed.

MAIN CONTRIBUTION

To date, no review has focused on ERPs as they relate to clinical or communication research. The main contribution of this review is that it provides practical information geared toward understanding ERP research.

CONCLUSIONS

ERPs offer insights into neural processes supporting communication and can both complement behaviour and provide information that behavioural measures cannot. We encourage readers to evaluate articles using ERPs critically, effectively pushing the field forward through increased understanding and rigor. What this paper adds ERPs have become more prevalent in research relevant to communication sciences and disorders. In order for clinicians to review and evaluate this research, an understanding of ERPs is needed. This review adds to the field by providing an accessible description of what ERPs are, a description of what ERP components are, and the most relevant commonly used components, as well as how ERP data are recorded and processed. With this foundational understanding of how ERPs work, guidelines for the interpretation of ERP data are given. Though few ERP studies currently have direct implications for clinical practice, we discuss several ways through which ERPs can impact clinical practice in future, by providing information that cannot be obtained by behaviour alone about the aetiology of disorders, and as potential biomarkers of disorder or treatment response.

The Eight Steps of Data Analysis: A Graphical Framework to Promote Sound Statistical Analysis

Data analysis is a risky endeavor, particularly among people who are unaware of its dangers. According to some researchers, "statistical conclusions validity" threatens all research subjected to the dark arts of statistical magic. Although traditional statistics classes may advise against certain practices (e.g., multiple comparisons, small sample sizes, violating normality), they may fail to cover others (e.g., outlier detection and violating linearity). More common, perhaps, is that researchers may fail to remember them. In this article, rather than rehashing old warnings and diatribes against this practice or that, I instead advocate a general statistical-analysis strategy. This graphic-based eight-step strategy promises to resolve the majority of statistical traps researchers may fall into-without having to remember large lists of problematic statistical practices. These steps will assist in preventing both false positives and false negatives and yield critical insights about the data that would have otherwise been missed. I conclude with an applied example that shows how the eight steps reveal interesting insights that would not be detected with standard statistical practices.

Reproducibility of animal research in light of biological variation

Context-dependent biological variation presents a unique challenge to the reproducibility of results in experimental animal research, because organisms' responses to experimental treatments can vary with both genotype and environmental conditions. In March 2019, experts in animal biology, experimental design and statistics convened in Blonay, Switzerland, to discuss strategies addressing this challenge. In contrast to the current gold standard of rigorous standardization in experimental animal research, we recommend the use of systematic heterogenization of study samples and conditions by actively incorporating biological variation into study design through diversifying study samples and conditions. Here we provide the scientific rationale for this approach in the hope that researchers, regulators, funders and editors can embrace this paradigm shift. We also present a road map towards better practices in view of improving the reproducibility of animal research.

A modern method of multiple working hypotheses to improve inference in ecology

Science provides a method to learn about the relationships between observed patterns and the processes that generate them. However, inference can be confounded when an observed pattern cannot be clearly and wholly attributed to a hypothesized process. Over-reliance on traditional single-hypothesis methods (i.e. null hypothesis significance testing) has resulted in replication crises in several disciplines, and ecology exhibits features common to these fields (e.g. low-power study designs, questionable research practices, etc.). Considering multiple working hypotheses in combination with pre-data collection modelling can be an effective means to mitigate many of these problems. We present a framework for explicitly modelling systems in which relevant processes are commonly omitted, overlooked or not considered and provide a formal workflow for a pre-data collection analysis of multiple candidate hypotheses. We advocate for and suggest ways that pre-data collection modelling can be combined with consideration of multiple working hypotheses to improve the efficiency and accuracy of research in ecology.

Interindividual variability and lateralization of μ-opioid receptors in the human brain

Alterations in the brain's μ-opioid receptor (MOR) system have been associated with several neuropsychiatric disorders. Central MOR availability also varies considerably in healthy individuals. Multiple epidemiological factors have been proposed to influence the MOR system, but due to small sample sizes the magnitude of their influence remains inconclusive. We compiled [¹¹C]carfentanil positron emission tomography scans from 204 individuals with no neurologic or psychiatric disorders, and estimated the effects of sex, age, body mass index (BMI) and smoking on [¹¹C]carfentanil binding potential using between-subject regression analysis. We also examined hemispheric differences in MOR availability. Older age was associated with increase in MOR availability in frontotemporal areas but decrease in amygdala, thalamus, and nucleus accumbens. The age-dependent increase was stronger in males. MOR availability was globally lowered in smokers but independent of BMI. Finally, MOR availability was higher in the right versus the left hemisphere. The presently observed variation in MOR availability may explain why some individuals are prone to develop MOR-linked pathological states, such as chronic pain or psychiatric disorders. Lateralized MOR system may reflect hemispheric work specialization in central emotion and pain processes.

Robust data and power in infant research: A case study of the effect of number of infants and number of trials in visual preference procedures

As in many areas of science, infant research suffers from low power. The problem is further compounded in infant research because of the difficulty in recruiting and testing large numbers of infant participants. Researchers have been searching for a solution and, as illustrated by this special section, have been focused on getting the most out of infant data. We illustrate one solution by showing how we can increase power in visual preference tasks by increasing the amount of data obtained from each infant. We discuss issues of power and present work examining how, under some circumstances, power is increased by increasing the precision of measurement. We report the results of a series of simulations based on a sample of visual preference task data collected from three infant laboratories showing how more powerful research designs can be achieved by including more trials per infant. Implications for infant procedures in general are discussed.

Influence of Processing Pipeline on Cortical Thickness Measurement

In recent years, replicability of neuroscientific findings, specifically those concerning correlates of morphological properties of gray matter (GM), have been subject of major scrutiny. Use of different processing pipelines and differences in their estimates of the macroscale GM may play an important role in this context. To address this issue, here, we investigated the cortical thickness estimates of three widely used pipelines. Based on analyses in two independent large-scale cohorts, we report high levels of within-pipeline reliability of the absolute cortical thickness-estimates and comparable spatial patterns of cortical thickness-estimates across all pipelines. Within each individual, absolute regional thickness differed between pipelines, indicating that in-vivo thickness measurements are only a proxy of actual thickness of the cortex, which shall only be compared within the same software package and thickness estimation technique. However, at group level, cortical thickness-estimates correlated strongly between pipelines, in most brain regions. The smallest between-pipeline correlations were observed in para-limbic areas and insula. These regions also demonstrated the highest interindividual variability and the lowest reliability of cortical thickness-estimates within each pipeline, suggesting that structural variations within these regions should be interpreted with caution.

Reassessing the observational evidence for nitrogen deposition impacts in acid grassland: spatial Bayesian linear models indicate small and ambiguous effects on species richness

Nitrogen deposition (Ndep) is considered a significant threat to plant diversity in grassland ecosystems around the world. The evidence supporting this conclusion comes from both observational and experimental research, with “space-for-time” substitution surveys of pollutant gradients a significant portion of the former. However, estimates of regression coefficients for Ndep impacts on species richness, derived with a focus on causal inference, are hard to locate in the observational literature. Some influential observational studies have presented estimates from univariate models, overlooking the effects of omitted variable bias, and/or have used P-value-based stepwise variable selection (PSVS) to infer impacts, a strategy known to be poorly suited to the accurate estimation of regression coefficients. Broad-scale spatial autocorrelation has also generally been unaccounted for. We re-examine two UK observational datasets that have previously been used to investigate the relationship between Ndep and plant species richness in acid grasslands, a much-researched habitat in this context. One of these studies (Stevens et al., 2004, Science, 303: 1876–1879) estimated a large negative impact of Ndep on richness through the use of PSVS; the other reported smaller impacts (Maskell et al., 2010, Global Change Biology, 16: 671–679), but did not explicitly report regression coefficients or partial effects, making the actual size of the estimated Ndep impact difficult to assess. We reanalyse both datasets using a spatial Bayesian linear model estimated using integrated nested Laplace approximation (INLA). Contrary to previous results, we found similar-sized estimates of the Ndep impact on plant richness between studies, both with and without bryophytes, albeit with some disagreement over the most likely direction of this effect. Our analyses suggest that some previous estimates of Ndep impacts on richness from space-for-time substitution studies are likely to have been over-estimated, and that the evidence from observational studies could be fragile when confronted with alternative model specifications, although further work is required to investigate potentially nonlinear responses. Given the growing literature on the use of observational data to estimate the impacts of pollutants on biodiversity, we suggest that a greater focus on clearly reporting important outcomes with associated uncertainty, the use of techniques to account for spatial autocorrelation, and a clearer focus on the aims of a study, whether explanatory or predictive, are all required.

Machine Learning and Psychological Research: The Unexplored Effect of Measurement

Machine learning (i.e., data mining, artificial intelligence, big data) has been increasingly applied in psychological science. Although some areas of research have benefited tremendously from a new set of statistical tools, most often in the use of biological or genetic variables, the hype has not been substantiated in more traditional areas of research. We argue that this phenomenon results from measurement errors that prevent machine-learning algorithms from accurately modeling nonlinear relationships, if indeed they exist. This shortcoming is showcased across a set of simulated examples, demonstrating that model selection between a machine-learning algorithm and regression depends on the measurement quality, regardless of sample size. We conclude with a set of recommendations and a discussion of ways to better integrate machine learning with statistics as traditionally practiced in psychological science.

Methods of Detecting Insufficient Effort Responding: Comparisons and Practical Recommendations

Insufficient effort responding (IER) affects many forms of assessment in both educational and psychological contexts. Much research has examined different types of IER, IER's impact on the psychometric properties of test scores, and preprocessing procedures used to detect IER. However, there is a gap in the literature in terms of practical advice for applied researchers and psychometricians when evaluating multiple sources of IER evidence, including the best strategy or combination of strategies when preprocessing data. In this study, we demonstrate how the use of different IER detection methods may affect psychometric properties such as predictive validity and reliability. Moreover, we evaluate how different data cleansing procedures can detect different types of IER. We provide evidence via simulation studies and applied analysis using the ACT's Engage assessment as a motivating example. Based on the findings of the study, we provide recommendations and future research directions for those who suspect their data may contain responses reflecting careless, random, or biased responding.

Psychophysiological modelling and the measurement of fear conditioning

Quantification of fear conditioning is paramount to many clinical and translational studies on aversive learning. Various measures of fear conditioning co-exist, including different observables and different methods of pre-processing. Here, we first argue that low measurement error is a rational desideratum for any measurement technique. We then show that measurement error can be approximated in benchmark experiments by how closely intended fear memory relates to measured fear memory, a quantity that we term retrodictive validity. From this perspective, we discuss different approaches commonly used to quantify fear conditioning. One of these is psychophysiological modelling (PsPM). This builds on a measurement model that describes how a psychological variable, such as fear memory, influences a physiological measure. This model is statistically inverted to estimate the most likely value of the psychological variable, given the measured data. We review existing PsPMs for skin conductance, pupil size, heart period, respiration, and startle eye-blink. We illustrate the benefit of PsPMs in terms of retrodictive validity and translate this into sample size required to achieve a desired level of statistical power. This sample size can differ up to a factor of three between different observables, and between the best, and the current standard, data pre-processing methods.

Improving the Rigor and Replicability of Applied Psychophysiology Research: Sample Size, Standardization, Transparency, and Preregistration

Scientific research across a number of areas, including applied psychophysiology, biofeedback, and neurofeedback, is facing considerable scrutiny for poor replication rates, high numbers of false-positive findings, and insufficient scientific rigor. There are many factors underlying this replication crisis in scientific research; yet incentives for more rigorous research practices at the institutional and editorial levels lag behind the need for improvement. The author provides examples of replication and rigor difficulties in scientific research with an eye toward psychophysiological research, including researcher flexibility in data analysis, “p-hacking,” insufficient sample sizes, and lack of availability and implementation of rigorous methodological and publication guidelines. Subsequently, the author highlights examples and opportunities for improvement, including decreasing researcher flexibility, reporting sample size information, increasing sample sizes through collaboration, improving reporting standards/following established guidelines for reporting psychophysiological data, and increasing adoption of preregistration and registered reports. The author concludes that the Association for Applied Psychophysiology and Biofeedback (AAPB) can improve clinical practice and perception of public and scientific credibility by implementing rigorous and transparent research practices with a focus on replicability and clear methodological and reporting techniques and standards.

Reflection on modern methods: five myths about measurement error in epidemiological research

Epidemiologists are often confronted with datasets to analyse which contain measurement error due to, for instance, mistaken data entries, inaccurate recordings and measurement instrument or procedural errors. If the effect of measurement error is misjudged, the data analyses are hampered and the validity of the study's inferences may be affected. In this paper, we describe five myths that contribute to misjudgments about measurement error, regarding expected structure, impact and solutions to mitigate the problems resulting from mismeasurements. The aim is to clarify these measurement error misconceptions. We show that the influence of measurement error in an epidemiological data analysis can play out in ways that go beyond simple heuristics, such as heuristics about whether or not to expect attenuation of the effect estimates. Whereas we encourage epidemiologists to deliberate about the structure and potential impact of measurement error in their analyses, we also recommend exercising restraint when making claims about the magnitude or even direction of effect of measurement error if not accompanied by statistical measurement error corrections or quantitative bias analysis. Suggestions for alleviating the problems or investigating the structure and magnitude of measurement error are given.

Learning from the past and considering the future of chemicals in the environment

Knowledge of the hazards and associated risks from chemicals discharged to the environment has grown considerably over the past 40 years. This improving awareness stems from advances in our ability to measure chemicals at low environmental concentrations, recognition of a range of effects on organisms, and a worldwide growth in expertise. Environmental scientists and companies have learned from the experiences of the past; in theory, the next generation of chemicals will cause less acute toxicity and be less environmentally persistent and bioaccumulative. However, researchers still struggle to establish whether the nonlethal effects associated with some modern chemicals and substances will have serious consequences for wildlife. Obtaining the resources to address issues associated with chemicals in the environment remains a challenge.

Obtaining Unbiased Results in Meta-Analysis: The Importance of Correcting for Statistical Artifacts

Most published meta-analyses address only artifactual variance due to sampling error and ignore the role of other statistical and psychometric artifacts, such as measurement error variance (due to factors including unreliability of measurements, group misclassification, and variable treatment strength) and selection effects (including range restriction or enhancement and collider biases). These artifacts can have severe biasing effects on the results of individual studies and meta-analyses. Failing to account for these artifacts can lead to inaccurate conclusions about the mean effect size and between-studies effect-size heterogeneity, and can influence the results of meta-regression, publication-bias, and sensitivity analyses. In this article, we provide a brief introduction to the biasing effects of measurement error variance and selection effects and their relevance to a variety of research designs. We describe how to estimate the effects of these artifacts in different research designs and correct for their impacts in primary studies and meta-analyses. We consider meta-analyses of correlations, observational group differences, and experimental effects. We provide R code to implement the corrections described.

Enhancing Statistical Inference in Psychological Research via Prospective and Retrospective Design Analysis

In the past two decades, psychological science has experienced an unprecedented replicability crisis, which has uncovered several issues. Among others, the use and misuse of statistical inference plays a key role in this crisis. Indeed, statistical inference is too often viewed as an isolated procedure limited to the analysis of data that have already been collected. Instead, statistical reasoning is necessary both at the planning stage and when interpreting the results of a research project. Based on these considerations, we build on and further develop an idea proposed by Gelman and Carlin (2014) termed "prospective and retrospective design analysis." Rather than focusing only on the statistical significance of a result and on the classical control of type I and type II errors, a comprehensive design analysis involves reasoning about what can be considered a plausible effect size. Furthermore, it introduces two relevant inferential risks: the exaggeration ratio or Type M error (i.e., the predictable average overestimation of an effect that emerges as statistically significant) and the sign error or Type S error (i.e., the risk that a statistically significant effect is estimated in the wrong direction). Another important aspect of design analysis is that it can be usefully carried out both in the planning phase of a study and for the evaluation of studies that have already been conducted, thus increasing researchers' awareness during all phases of a research project. To illustrate the benefits of a design analysis to the widest possible audience, we use a familiar example in psychology where the researcher is interested in analyzing the differences between two independent groups considering Cohen's d as an effect size measure. We examine the case in which the plausible effect size is formalized as a single value, and we propose a method in which uncertainty concerning the magnitude of the effect is formalized via probability distributions. Through several examples and an application to a real case study, we show that, even though a design analysis requires significant effort, it has the potential to contribute to planning more robust and replicable studies. Finally, future developments in the Bayesian framework are discussed.

Improving the Reliability of Computational Analyses: Model-Based Planning and Its Relationship With Compulsivity

BACKGROUND

Computational models show great promise in mapping latent decision-making processes onto dissociable neural substrates and clinical phenotypes. One prominent example in reinforcement learning is model-based planning, which specifically relates to transdiagnostic compulsivity. However, the reliability of computational model-derived measures such as model-based planning is unclear. Establishing reliability is necessary to ensure that such models measure stable, traitlike processes, as assumed in computational psychiatry. Although analysis approaches affect validity of reinforcement learning models and reliability of other task-based measures, their effect on reliability of reinforcement learning models of empirical data has not been systematically studied.

METHODS

We first assessed within- and across-session reliability and effects of analysis approaches (model estimation, parameterization, and data cleaning) of measures of model-based planning in patients with compulsive disorders (n = 38). The analysis approaches affecting test-retest reliability were tested in 3 large generalization samples (healthy participants: n = 541 and 111; people with a range of compulsivity: n = 1413).

RESULTS

Analysis approaches greatly influenced reliability: reliability of model-based planning measures ranged from 0 (no concordance) to above 0.9 (acceptable for clinical applications). The largest influence on reliability was whether model-estimation approaches were robust and accounted for the hierarchical structure of estimated parameters. Improvements in reliability generalized to other datasets and greatly reduced the sample size needed to find a relationship between model-based planning and compulsivity in an independent dataset.

CONCLUSIONS

These results indicate that computational psychiatry measures such as model-based planning can reliably measure latent decision-making processes, but when doing so must assess the ability of methods to estimate complex models from limited data.

Legitimacy for Sustainability: A Case of A Strategy Change for An Oil and Gas Company

This study introduces and validates a measure of a company’s institutional profile for sustainability. It uses institutional theory as a lens to understand the factors that legitimize the adoption of renewable energy activities in an oil and gas company. The three institutional dimensions used in this study are regulative, normative and cognitive, which aimed to measure legitimacy in an oil and gas company under a sustainable change. Survey-based research was carried out among employees to test this theory. Moreover, structural equation modeling was used to test the model fit, validate the measures, and test the four hypotheses. The results showed that regulative and normative pillars play the main role in legitimating renewable energy practices in our case company. The findings provide researchers and companies with a valuable resource for exploring legitimacy in order to understand what makes companies legitimize new sustainable activities that are outside the companies’ core business.

The Role of the Striatum in Learning to Orthogonalize Action and Valence: A Combined PET and 7 T MRI Aging Study

Pavlovian biases influence instrumental learning by coupling reward seeking with action invigoration and punishment avoidance with action suppression. Using a probabilistic go/no-go task designed to orthogonalize action (go/no-go) and valence (reward/punishment), recent studies have shown that the interaction between the two is dependent on the striatum and its key neuromodulator dopamine. Using this task, we sought to identify how structural and neuromodulatory age-related differences in the striatum may influence Pavlovian biases and instrumental learning in 25 young and 31 older adults. Computational modeling revealed a significant age-related reduction in reward and punishment sensitivity and marked (albeit not significant) reduction in learning rate and lapse rate (irreducible noise). Voxel-based morphometry analysis using 7 Tesla MRI images showed that individual differences in learning rate in older adults were related to the volume of the caudate nucleus. In contrast, dopamine synthesis capacity in the dorsal striatum, assessed using [18F]-DOPA positron emission tomography in 22 of these older adults, was not associated with learning performance and did not moderate the relationship between caudate volume and learning rate. This multiparametric approach suggests that age-related differences in striatal volume may influence learning proficiency in old age.

Statistical noise from recombinant plasmids can be abated via complementation of a ribosomal protein gene deletion

The phenotypes conferred by recombinant plasmids upon host cells often exhibit variability between replicate populations. This statistical noise is mostly a consequence of adaptive evolution in response to fitness burdens imposed by the plasmids themselves. We developed a novel strategy, 'ribosome pegging', to exclude common unwanted mutations that benefit host cells at the expense of heterologous gene expression. Plasmids that constitutively co-expressed the fluorescent reporter tagRFP and ribosomal protein L23 (rplW) were used to transform Escherichia coli cells that lacked the essential chromosomal rplW gene. Cells within the population that expressed too little L23, or too much, were evidently inviable. Ribosome pegging obviates the need for antibiotics, thus facilitating the deployment of recombinant bacteria in uncontrolled environments. We show that ribosome-pegged E. coli carrying a plasmid that constitutively expresses L23 and an artificially evolved enzyme protects fruit flies from otherwise toxic doses of the insecticide malathion.

Endometrial cancer and its cell lines

Endometrial cancer is one of the most common gynaecological malignancies worldwide. One type of research in this field is the growing of cell lines (CLs) and cultures, which can be used to explore the biological mechanisms of cancer. The purpose of this review is to offer an overview of the current literature and highlight the importance of correct CL studies. We carried out a literature analysis of more than 60 articles from the Pubmed, Medline databases that were almost exclusively published in indexed journals in the last 10 years as well as the primary originating scientific studies of specific CLs. We then summarized the newest findings and recommendations. Cell lines are becoming widely used as in vitro tumour models. Recent work has shown inconsistencies in nomenclature and culturing of CLs. Their genomic evolution leads to a high degree of variation across CL strains therefore it is of the utmost importance to recognize the variability within laboratory cancer models. Laboratories must adapt, incorporate additional characterisation techniques and view this situation as an opportunity to improve the reproducibility of pre-clinical cancer research. The authors offer a comprehensive literature review about endometrial cancer CLs, a review of the current literature and advice on culturing CLs.

When siblings matter: Commentary on Golub and Sobin's 'Statistical modeling of litter as a random effect in mixed models to manage "intralitter likeness"'

Responsible data practices include acknowledging and properly dealing with data structures in which the assumption of independence of observations is violated. Golub and Sobin uncover a common statistical faux pas in neurodevelopmental research - inconsistent and inadequate handling of the clustered variability associated with the use of multiple littermates - and propose a simple mixed model solution in which litter is included as a random factor.

New insights in the link between malocclusion and periodontal disease

AIM

We aimed to investigate associations between malocclusions and periodontal disease by comparing it to that of smoking in subjects recruited from the population-based cross-sectional study "Study of Health in Pomerania."

MATERIALS AND METHODS

Sagittal intermaxillary relationship, variables of malocclusion and socio-demographic parameters of 1,202 dentate subjects, 20-39 years of age, were selected. Probing depth (PD) and attachment loss (AL) were assessed at four sites by tooth in a half-mouth design. Analyses were performed with multilevel models on subject, jaw and tooth level.

RESULTS

Distal occlusion determined in the canine region, ectopic position of canines, anterior spacing, deep anterior overbite and increased sagittal overjet were associated with AL (p-value <0.05). Associations between malocclusions and PD: deep anterior overbite with gingival contact (odds ratio [OR] = 1.40, 95% CI: 1.08-1.82; p-value = 0.0101) and anterior crossbite (OR = 1.75, 95% CI: 1.29-2.38; p-value = 0.0003). Regarding crowding, only severe anterior crowding was compatible with a moderate to large association with PD (OR = 1.93, 95% CI: 0.89-4.20). Compared to smoking, the overall effect of malocclusions was about one half for AL and one-third for PD.

CONCLUSION

Malocclusions or morphologic parameters were associated with periodontal disease.

A mortality study on male subjects exposed to polyfluoroalkyl acids with high internal dose of perfluorooctanoic acid

OBJECTIVES

The aim of the present study was to examine the association between exposure to polyfluoroalkyl substances (PFASs) and mortality (1970-2018) in a cohort of 462 male employees who had worked at least six months before 2009 for a factory (14,658 person-years; 107 deaths, average follow-up time 31.7 years), which had been producing perfluorooctanoic acid (PFOA), perfluorooctanesulfonyl fluoride (PFOS) and other chemicals since 1968.

METHODS

Employees were classified as follows: 1) by probability of exposure to PFASs; 2) by tertiles of PFOA serum concentrations. In a fraction (n = 120) of workers measurements of internal PFOA serum concentration were used to predict a cumulative serum PFOA concentration of each cohort member. Mortality rates were compared to that of the regional population using the standardized mortality ratio (SMR), and to that of the workers of a nearby metalworking factory in terms of risk ratio (RR), across categories of probability of PFASs exposure and tertiles of cumulative serum PFOA concentrations.

RESULTS

Internal PFOA serum concentration among 120 workers in the 2000-2013 period was very high (Geometric Mean: 4048 ng/mL; range 19-91,900 ng/mL). The mortality of the chemical cohort was increased for liver cancer (SMR: 2.32; CI: 1.11-4.87), malignant neoplasm of lymphatic and haematopoietic tissue (SMR: 2.26; CI: 1.08-4.73). In the comparison with the cohort of workers from the metalworking factory, the RRs for mortality of the cohort were increased for overall mortality (RR: 1.42; CI: 1.12-1.79), diabetes (RR: 5.95; CI: 1.08-32.8), liver cancer (RR: 6.69; CI: 1.71-26.2) and liver cirrhosis (RR: 3.87; CI: 1.18-12.7). Mortality for these causes increased in association with probability of PFASs exposure and with tertiles of cumulative PFOA serum concentrations.

CONCLUSION

The present is a small observational study with limited control over confounding factors. The cohort showed increased mortality for all causes and subjects in the highest cumulative internal dose of PFOA had a statistically significant increase for mortality of liver cancer, liver cirrhosis, diabetes, malignant neoplasms of lymphatic and haematopoietic tissue in both comparisons. Toxicological studies on PFOA and PFOS provide support for causality for the observed association with the risk for liver cirrhosis and liver cancer.

Is Empathy the Default Response to Suffering? A Meta-Analytic Evaluation of Perspective Taking’s Effect on Empathic Concern

We conducted a series of meta-analytic tests on experiments in which participants read perspective-taking instructions—that is, written instructions to imagine a distressed persons’ point of view (“imagine-self” and “imagine-other” instructions), or to inhibit such actions (“remain-objective” instructions)—and afterwards reported how much empathic concern they experienced upon learning about the distressed person. If people spontaneously empathize with others, then participants who receive remain-objective instructions should report less empathic concern than do participants in a “no-instructions” control condition; if people can deliberately increase how much empathic concern they experience, then imagine-self and imagine-other instructions should increase empathic concern relative to not receiving any instructions. Random-effects models revealed that remain-objective instructions reduced empathic concern, but “imagine” instructions did not significantly increase it. The results were robust to most corrections for bias. Our conclusions were not qualified by the study characteristics we examined, but most relevant moderators have not yet been thoroughly studied.

Automated Study Challenges the Existence of a Foundational Statistical-Learning Ability in Newborn Chicks

What mechanisms underlie learning in newborn brains? Recently, researchers reported that newborn chicks use unsupervised statistical learning to encode the transitional probabilities (TPs) of shapes in a sequence, suggesting that TP-based statistical learning can be present in newborn brains. Using a preregistered design, we attempted to reproduce this finding with an automated method that eliminated experimenter bias and allowed more than 250 times more data to be collected per chick. With precise measurements of each chick's behavior, we were able to perform individual-level analyses and substantially reduce measurement error for the group-level analyses. We found no evidence that newborn chicks encode the TPs between sequentially presented shapes. None of the chicks showed evidence for this ability. Conversely, we obtained strong evidence that newborn chicks encode the shapes of individual objects, showing that this automated method can produce robust results. These findings challenge the claim that TP-based statistical learning is present in newborn brains.

How does the peak-end rule smell? Tracing hedonic experience with odours

The peak-end rule predicts that retrospective evaluations of affective events heavily depend on their most intense and last moment and imply duration neglect. It was originally proposed for negative experiences such as painful medical procedures. It is unclear, however, to what degree it also applies to positive experiences. Previously, rigorous comparisons between the two domains were limited due to the use of qualitatively different stimuli. Hence, it is not clear if the peak-end rule holds for short positive and negative experiences alike. To address these questions in a genuinely emotional domain, we conducted two experiments (n = 48 each) in which we used odours as stimuli. Participants repeatedly evaluated continuous odour sequences delivered into their noses via an olfactometer. The sequences differed in valence (positive vs. negative), length (36 vs. 72 s), and trajectory (increasing, decreasing, U-shaped, and inverse U-shaped). Results provide evidence for the peak-end rule for both positive and negative experiences alike. Results further show an overweighting of intense negative experiences for sequences that contain both pleasant and unpleasant episodes but provide little evidence for an effect of the trajectory manipulation.

Grounds for Ambiguity: Justifiable Bases for Engaging in Questionable Research Practices

The current study sought to determine research scientists' sensitivity to various justifications for engaging in behaviors typically considered to be questionable research practices (QRPs) by asking them to evaluate the appropriateness and ethical defensibility of each. Utilizing a within-subjects design, 107 National Institutes of Health principal investigators responded to an invitation to complete an online survey in which they read a series of research behaviors determined, in prior research, to either be ambiguous or unambiguous in their ethical defensibility. Additionally, each behavior was paired with either an ostensibly sound or unsound reason for the behavior. Consistent with hypotheses, the results indicated that scientists perceived QRPs as more appropriate and defensible when paired with a justifiable motive relative to when paired with a clearly unethical motive, particularly for QRPs that are more ambiguous in their ethicality. In fact, ambiguous QRPs were perceived as categorically defensible when given a justifiable motive. This suggests scientists are sensitive to contextual factors related to QRPs' appropriateness, which could inform how institutions develop appropriate training modules for research integrity.

Handling Multiplicity in Neuroimaging Through Bayesian Lenses with Multilevel Modeling

Here we address the current issues of inefficiency and over-penalization in the massively univariate approach followed by the correction for multiple testing, and propose a more efficient model that pools and shares information among brain regions. Using Bayesian multilevel (BML) modeling, we control two types of error that are more relevant than the conventional false positive rate (FPR): incorrect sign (type S) and incorrect magnitude (type M). BML also aims to achieve two goals: 1) improving modeling efficiency by having one integrative model and thereby dissolving the multiple testing issue, and 2) turning the focus of conventional null hypothesis significant testing (NHST) on FPR into quality control by calibrating type S errors while maintaining a reasonable level of inference efficiency. The performance and validity of this approach are demonstrated through an application at the region of interest (ROI) level, with all the regions on an equal footing: unlike the current approaches under NHST, small regions are not disadvantaged simply because of their physical size. In addition, compared to the massively univariate approach, BML may simultaneously achieve increased spatial specificity and inference efficiency, and promote results reporting in totality and transparency. The benefits of BML are illustrated in performance and quality checking using an experimental dataset. The methodology also avoids the current practice of sharp and arbitrary thresholding in the p-value funnel to which the multidimensional data are reduced. The BML approach with its auxiliary tools is available as part of the AFNI suite for general use.

Variance-Preserving Estimation of Intensity Values Obtained From Omics Experiments

Faced with the lack of reliability and reproducibility in omics studies, more careful and robust methods are needed to overcome the existing challenges in the multi-omics analysis. In conventional omics data analysis, signal intensity values (denoted by M and values) are estimated neglecting pixel-level uncertainties, which may reflect noise and systematic artifacts. For example, intensity values from two-color microarray data are estimated by taking the mean or median of the pixel intensities within the spot and then subjected to a within-slide normalization by LOWESS. Thus, focusing on estimation and normalization of gene expression profiles, we propose a spot quantification method that takes into account pixel-level variability. Also, to preserve relevant variation that may be removed in LOWESS normalization with poorly chosen parameters, we propose a parameter selection method that is parsimonious and considers intrinsic characteristics of microarray data, such as heteroskedasticity. The usefulness of the proposed methods is illustrated by an application to real intestinal metaplasia data. Compared with the conventional approaches, the analysis is more robust and conservative, identifying fewer but more reliable differentially expressed genes. Also, the variability preservation allowed the identification of new differentially expressed genes. Using the proposed approach, we have identified differentially expressed genes involved in pathways in cancer and confirmed some molecular markers already reported in the literature.

Should We Say Goodbye to Latent Constructs to Overcome Replication Crisis or Should We Take Into Account Epistemological Considerations?

This paper discusses theoretical and epistemological problems concerning validity of psychological science in the context of latent constructs. I consider the use of latent constructs as one reason for the replicability crisis. At the moment, there exist different constructs describing the same psychological phenomena side by side, and different psychological phenomena that are reflected by the same latent construct. Hagger called them déjà-variables, which lead to a decreasing validity of measurements and inhibit a deeper understanding of psychological phenomena. To overcome this problem, I suggest a shift of theoretical and epistemological perspective on latent constructs. One main point is the explicit consideration of latent constructs as mental representations, which change objects and are changed by objects via assimilative and accommodative processes. The explicit orientation toward assimilation and accommodation allows the control of normally automatized processes that influence our understanding of psychological phenomena and their corresponding latent constructs. I argue that assimilation and accommodation are part of our research practice anyway and cause the mentioned problems. For example, taking a measurement is an assimilative process, and thus a high measurement error should lead to an increase of accommodative processes. Taking into account these considerations, I suggest consequences for research practices, for individual researchers and for the philosophy of science.

Robust maximum marginal likelihood (RMML) estimation for item response theory models

Self-report data are common in psychological and survey research. Unfortunately, many of these samples are plagued with careless responses, due to unmotivated participants. The purpose of this study was to propose and evaluate a robust estimation method to detect careless or unmotivated responders, while leveraging item response theory (IRT) person-fit statistics. First, we outlined a general framework for robust estimation specific for IRT models. Subsequently, we conducted a simulation study covering multiple conditions in order to evaluate the performance of the proposed method. Ultimately, we showed that robust maximum marginal likelihood (RMML) estimation significantly improves detection rates for careless responders and reduces bias in item parameters across conditions. Furthermore, we applied our method to a real data set, to illustrate the utility of the proposed method. Our findings suggest that robust estimation coupled with person-fit statistics offers a powerful procedure to identify careless respondents for further review and to provide more accurate item parameter estimates in the presence of careless responses.

Occupation and Parkinson disease in the Women's Health Initiative Observational Study

INTRODUCTION

There is a lack of consistent study findings on associations between workplace exposures and the risk of Parkinson disease (PD) and a paucity of such data on women. We assessed PD risk among occupational groups to derive insights about potential occupation-specific exposures in a large cohort of women.

METHODS

The Women's Health Initiative Observational Study (WHI-OS) is a prospective cohort that enrolled 91 627 postmenopausal women, 50 to 79 years of age, from 01 October 1993 through 31 December 1998, at 40 clinical centers across the United States, with average follow-up interval of 11 years. These women reported up to three paid jobs, held the longest since age 18; these jobs were coded and duration of employment calculated. We defined a case by self-report of doctor-diagnosed PD (at baseline or follow-up), death attributed to PD, or taking medication consistent with PD.

RESULTS

Among 2590 PD cases, we found evidence of excess risk among "counselors, social workers, and other community and social service specialists," and there was a suggestion of increased in risk among postsecondary teachers, and "building and grounds cleaning and maintenance" workers. There was also evidence of a deficit in risk among women who worked in sales. Results according to ever-employed and job duration were similar, except for evidence of excess risk among "health technologists and technicians" with more than 20 years of employment. Longer duration of life on a farm was associated with higher risk.

CONCLUSION

Our findings paint a largely reassuring picture of occupational risks for PD among US women.

Adolescent Health Risk Behaviors: Convergent, Discriminant and Predictive Validity of Self-Report and Cognitive Measures

Self-report and cognitive tasks of reward sensitivity and self-regulation have influenced several developmental models that may explain the heightened engagement in risk behaviors during adolescence. Despite some inconsistencies across studies, few studies have explored the convergent, discriminant, and predictive validity of self-report and cognitive measures of these psychological characteristics in adolescence. The present study evaluated the convergent and discriminant validity of self-report and cognitive measures of reward sensitivity and self-regulation among 2017 adolescents (age M = 16.8, SD = 1.1; 56% female; 55% White, 22% Black, 8% Hispanic, 15% other race/ethnic; 49% 10th grade and 51% 12th grade). This study compared the predictive validity of an omnibus measure and specific measures of risk engagement. Convergent and discriminant validity from self-report to cognitive tasks were as predicted, although with weak convergent relationships. As hypothesized, compared to cognitive tasks, self-report measures consistently predicted risky behaviors and explained more variance in the models. These results demonstrate that while cognitive tasks can significantly predict certain risk behaviors, they require increased power to find the very small effects, raising questions about their use as implicit proxies for real world risk behavior.

Reproducibility via coordinated standardization: a multi-center study in a Shank2 genetic rat model for Autism Spectrum Disorders

Inconsistent findings between laboratories are hampering scientific progress and are of increasing public concern. Differences in laboratory environment is a known factor contributing to poor reproducibility of findings between research sites, and well-controlled multisite efforts are an important next step to identify the relevant factors needed to reduce variation in study outcome between laboratories. Through harmonization of apparatus, test protocol, and aligned and non-aligned environmental variables, the present study shows that behavioral pharmacological responses in Shank2 knockout (KO) rats, a model of synaptic dysfunction relevant to autism spectrum disorders, were highly replicable across three research centers. All three sites reliably observed a hyperactive and repetitive behavioral phenotype in KO rats compared to their wild-type littermates as well as a dose-dependent phenotype attenuation following acute injections of a selective mGluR1 antagonist. These results show that reproducibility in preclinical studies can be obtained and emphasizes the need for high quality and rigorous methodologies in scientific research. Considering the observed external validity, the present study also suggests mGluR1 as potential target for the treatment of autism spectrum disorders.

The reliability of the isotropic fractionator method for counting total cells and neurons

BACKGROUND

The Isotropic Fractionator (IF) is a method to determine the cellular composition of nervous tissue. It has been mostly applied to assess variation across species, where differences are expected to be large enough not to be masked by methodological error. However, understanding the sources of variation in the method is important if the goal is to detect smaller differences, for example, in same-species comparisons. Comparisons between different mice strains suggest that the IF is consistent enough to detect these differences. Nevertheless, the reliability of the method has not yet been examined directly.

METHOD

In this study, we evaluate the reliability of the method for the determination of cellular and neuronal numbers of Swiss mice. We performed repeated cell counts of the same material by different experimenters to quantify different sources of variation.

RESULTS

In total cell counts, we observed that for the cerebral cortex most of the variance was at the counter level. For the cerebellum, most of the variance is attributed to the sample itself. As for neurons, random error along with the immunostaining correspond to most of the variation, both in the cerebral cortex and in the cerebellum. Test-retest reliability coefficients were relatively high, especially for cell counts.

CONCLUSIONS

Although biases between counters and random variation in staining could be problematic when aggregating data from different sources, we offer practical suggestions to improve the reliability of the method. While small, this study is a most needed step towards more precise measurement of the brain's cellular composition.