Tutorial review of error evaluation in experimental water research at the example of membrane filtration.

Experimental water research lacks clear methodology to estimate experimental error. Especially when natural waters are involved, the characterization tools bear method-specific artifacts while the varying environmental conditions prevent regular repeats. This tutorial review identifies common mistakes, and proposes a practical procedure to determine experimental errors at the example of membrane filtration. Statistical analysis is often applied to an insufficient number of repeated measurements, while not all error sources and contributions are considered. This results in an underestimation of the experimental error. Variations in relevant experimental parameters need to be investigated systematically, and the related errors are quantified as a half of the variation between the max and min values when standard deviation is not applicable. Error of calculated parameters (e.g. flux, pollutant removal and mass loss) is estimated by applying error propagation, where weighing contributions of the experimental parameters are considered. Appropriate judgment and five-time repetition of a selected experiment under identical conditions are proposed to validate the propagated experimental error. For validation, the five repeated data points should lie within the estimated error range of the error bar. The proposed error evaluation procedure is adaptable in experimental water research and intended for researchers to identify the contributing factors of an experimental error and carry out appropriate error quantification and validation. The most important aim is to raise awareness of the necessity to question error methodology and reproducibility of experimental data, to produce and publish high quality research.

Revisiting host identification of bacteriophage Enc34: from biochemical to molecular.

In our 2012 genome announcement (J Virol 86:11403-11404, 2012, https://doi.org/10.1128/JVI.01954-12), we initially identified the host bacterium of bacteriophage Enc34 as Enterobacter cancerogenus using biochemical tests. However, later in-house DNA sequencing revealed that the true host is a strain of Hafnia alvei. Capitalizing on our new DNA-sequencing capabilities, we also refined the genomic termini of Enc34, confirming a 60,496-bp genome with 12-nucleotide 5' cohesive ends. IMPORTANCE: Our correction reflects the evolving landscape of bacterial identification, where molecular methods have supplanted traditional biochemical tests. This case underscores the significance of revisiting past identifications, as seemingly known bacterial strains may yield unexpected discoveries, necessitating essential updates to the scientific record. Despite the host identity correction, our genome announcement retains importance as the first complete genome sequence of a Hafnia alvei bacteriophage.

Experimental error analysis of biomechanical phenotyping for stalk lodging resistance in maize.

Stalk lodging destroys between 5 and 25% of grain crops annually. Developing crop varieties with improved lodging resistance will reduce the yield gap. Field-phenotyping equipment is critical to develop lodging resistant crop varieties, but current equipment is hindered by measurement error. Relatively little research has been done to identify and rectify sources of measurement error in biomechanical phenotyping platforms. This study specifically investigated sources of error in bending stiffness and bending strength measurements of maize stalks acquired using an in-field phenotyping platform known as the DARLING. Three specific sources of error in bending stiffness and bending strength measurements were evaluated: horizontal device placement, vertical device placement and incorrect recordings of load cell height. Incorrect load cell heights introduced errors as large as 130% in bending stiffness and 50% in bending strength. Results indicated that errors on the order of 15-25% in bending stiffness and 1-10% in bending strength are common in field-based measurements. Improving the design of phenotyping devices and associated operating procedures can mitigate this error. Reducing measurement error in field-phenotyping equipment is crucial for advancing the development of improved, lodging-resistant crop varieties. Findings have important implications for reducing the yield gap.

A Critical Analysis of the Evidence for the SARS-CoV-2 Origin Hypotheses.

When humans experience a new, devastating viral infection such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), significant challenges arise. How should individuals as well as societies respond to the situation? One of the primary questions concerns the origin of the SARS-CoV-2 virus that infected and was transmitted efficiently among humans, resulting in a pandemic. At first glance, the question appears straightforward to answer. However, the origin of SARS-CoV-2 has been the topic of substantial debate primarily because we do not have access to some relevant data. At least two major hypotheses have been suggested: a natural origin through zoonosis followed by sustained human-to-human spread or the introduction of a natural virus into humans from a laboratory source. Here, we summarize the scientific evidence that informs this debate to provide our fellow scientists and the public with the tools to join the discussion in a constructive and informed manner. Our goal is to dissect the evidence to make it more accessible to those interested in this important problem. The engagement of a broad representation of scientists is critical to ensure that the public and policy-makers can draw on relevant expertise in navigating this controversy.

Manipulação e fabricação de dados em pesquisa científica: as consequências são tão simples como pensamos? / Data manipulation and fabrication in scientific research: are the consequences as simple as we think?

A má conduta científica vem sendo observada ao longo da história da ciência, entretanto, nas últimas décadas teve um crescimento exponencial, e um exemplo disso foi a época da pandemia da COVID-19. Ficamos a refletir sobre o potencial impacto que uma evidência frágil pode gerar a partir de um convencimento de uma prática ou tomada de decisão profissional. Isso pode ocorrer devido a falhas no sistema educacional, na formação de pesquisadores e até mesmo a desvios morais e éticos.

Scientific misconduct has been observed throughout the history of science. However, it has grown exponentially in recent decades, an example of which was the time of the COVID-19 pandemic. We reflect on the potential impact of weak evidence from a convincing practice or professional decision-making. This situation can occur due to educational system failures, training of researchers, and even moral and ethical deviations.

Non-normal Data in Repeated Measures ANOVA: Impact on Type I Error and Power / Datos no Normales en el ANOVA de Medidas Repetidas: Impacto en el Error Tipo I y Potencia

Background: Repeated measures designs are commonly used in health and social sciences research. Although there are other, more advanced, statistical analyses, the F-statistic of repeated measures analysis of variance (RM-ANOVA) remains the most widely used procedure for analyzing differences in means. The impact of the violation of normality has been extensively studied for between-subjects ANOVA, but this is not the case for RM-ANOVA. Therefore, studies that extensively and systematically analyze the robustness of RM-ANOVA under the violation of normality are needed. This paper reports the results of two simulation studies aimed at analyzing the Type I error and power of RM-ANOVA when the normality assumption is violated but sphericity is fulfilled. Method: Study 1 considered 20 distributions, both known and unknown, and we manipulated the number of repeated measures (3, 4, 6, and 8) and sample size (from 10 to 300). Study 2 involved unequal distributions in each repeated measure. The distributions analyzed represent slight, moderate, and severe deviation from normality. Results: Overall, the results show that the Type I error and power of the F-statistic are not altered by the violation of normality. Conclusions: RM-ANOVA is generally robust to non-normality when the sphericity assumption is met.(AU)

Antecedentes: El diseño de medidas repetidas es uno de los más usados en ciencias sociales y de la salud. Aunque hay otras alternativas más avanzadas, el análisis de varianza de medidas repetidas (ANOVA-MR) sigue siendo el procedimiento más empleado para analizar las diferencias de medias. El impacto de la violación de la normalidad ha sido muy estudiado en el ANOVA intersujeto, pero los estudios son muy escasos en el ANOVA-MR. Por ello, el objetivo de este trabajo es realizar dos estudios de simulación Monte Carlo para analizar el error de Tipo I y la potencia cuando se incumple este supuesto bajo el cumplimiento de la esfericidad. Método: El estudio 1 incluye 20 distribuciones, tanto conocidas como desconocidas, manipulando el número de medidas repetidas (3, 4, 6 y 8) y el tamaño muestral (de 10 a 300). El estudio 2 incluye diferentes distribuciones en cada medida repetida. Las distribuciones analizadas representan desviación leve, moderada y severa de la normalidad. Resultados: En general, los resultados muestran que tanto el error Tipo I como la potencia del estadístico F no se alteran con la violación de la normalidad. Conclusiones: El ANOVA-MR es generalmente robusto a la no normalidad cuando la esfericidad se satisface.(AU)

Identification of human gene research articles with wrongly identified nucleotide sequences.

Nucleotide sequence reagents underpin molecular techniques that have been applied across hundreds of thousands of publications. We have previously reported wrongly identified nucleotide sequence reagents in human research publications and described a semi-automated screening tool Seek & Blastn to fact-check their claimed status. We applied Seek & Blastn to screen >11,700 publications across five literature corpora, including all original publications in Gene from 2007 to 2018 and all original open-access publications in Oncology Reports from 2014 to 2018. After manually checking Seek & Blastn outputs for >3,400 human research articles, we identified 712 articles across 78 journals that described at least one wrongly identified nucleotide sequence. Verifying the claimed identities of >13,700 sequences highlighted 1,535 wrongly identified sequences, most of which were claimed targeting reagents for the analysis of 365 human protein-coding genes and 120 non-coding RNAs. The 712 problematic articles have received >17,000 citations, including citations by human clinical trials. Given our estimate that approximately one-quarter of problematic articles may misinform the future development of human therapies, urgent measures are required to address unreliable gene research articles.

Quantifying the short-term effects of air pollution on health in the presence of exposure measurement error: a simulation study of multi-pollutant model results.

BACKGROUND: Most epidemiological studies estimate associations without considering exposure measurement error. While some studies have estimated the impact of error in single-exposure models we aimed to quantify the effect of measurement error in multi-exposure models, specifically in time-series analysis of PM2.5, NO2, and mortality using simulations, under various plausible scenarios for exposure errors. Measurement error in multi-exposure models can lead to effect transfer where the effect estimate is overestimated for the pollutant estimated with more error to the one estimated with less error. This complicates interpretation of the independent effects of different pollutants and thus the relative importance of reducing their concentrations in air pollution policy. METHODS: Measurement error was defined as the difference between ambient concentrations and personal exposure from outdoor sources. Simulation inputs for error magnitude and variability were informed by the literature. Error-free exposures with their consequent health outcome and error-prone exposures of various error types (classical/Berkson) were generated. Bias was quantified as the relative difference in effect estimates of the error-free and error-prone exposures. RESULTS: Mortality effect estimates were generally underestimated with greater bias observed when low ratios of the true exposure variance over the error variance were assumed (27.4% underestimation for NO2). Higher ratios resulted in smaller, but still substantial bias (up to 19% for both pollutants). Effect transfer was observed indicating that less precise measurements for one pollutant (NO2) yield more bias, while the co-pollutant (PM2.5) associations were found closer to the true. Interestingly, the sum of single-pollutant model effect estimates was found closer to the summed true associations than those from multi-pollutant models, due to cancelling out of confounding and measurement error bias. CONCLUSIONS: Our simulation study indicated an underestimation of true independent health effects of multiple exposures due to measurement error. Using error parameter information in future epidemiological studies should provide more accurate concentration-response functions.

Behavioural science is unlikely to change the world without a heterogeneity revolution.

In the past decade, behavioural science has gained influence in policymaking but suffered a crisis of confidence in the replicability of its findings. Here, we describe a nascent heterogeneity revolution that we believe these twin historical trends have triggered. This revolution will be defined by the recognition that most treatment effects are heterogeneous, so the variation in effect estimates across studies that defines the replication crisis is to be expected as long as heterogeneous effects are studied without a systematic approach to sampling and moderation. When studied systematically, heterogeneity can be leveraged to build more complete theories of causal mechanism that could inform nuanced and dependable guidance to policymakers. We recommend investment in shared research infrastructure to make it feasible to study behavioural interventions in heterogeneous and generalizable samples, and suggest low-cost steps researchers can take immediately to avoid being misled by heterogeneity and begin to learn from it instead.

Imputing intracluster correlation coefficients from a posterior predictive distribution is a feasible method of dealing with unit of analysis errors in a meta-analysis of cluster RCTs.

BACKGROUND: Incorporating cluster randomized trials (CRTs) into meta-analyses is challenging because appropriate standard errors of study estimates accounting for clustering are not always reported. Systematic reviews of CRTs often use a single constant external estimate of the intraclass correlation coefficient (ICC) to adjust study estimate standard errors and facilitate meta-analyses; an approach that fails to account for possible variation of ICCs among studies and the imprecision with which they are estimated. Using a large systematic review of the effects of diabetes quality improvement interventions, we investigated whether we could better account for ICC variation and uncertainty in meta-analyzed effect estimates by imputing missing ICCs from a posterior predictive distribution constructed from a database of relevant ICCs. METHODS: We constructed a dataset of ICC estimates from applicable studies. For outcomes with two or more available ICC estimates, we constructed posterior predictive ICC distributions in a Bayesian framework. For a selected continuous outcome, glycosylated hemoglobin (HbA1c), we compared the impact of incorporating a single constant ICC versus imputing ICCs drawn from the posterior predictive distribution when estimating the effect of intervention components on post treatment mean in a case study of diabetes quality improvement trials. RESULTS: Using internal and external ICC estimates, we were able to construct a database of 59 ICCs for 12 of the 13 review outcomes (range 1-10 per outcome) and estimate the posterior predictive ICC distribution for 11 review outcomes. Synthesized results were not markedly changed by our approach for HbA1c. CONCLUSION: Building posterior predictive distributions to impute missing ICCs is a feasible approach to facilitate principled meta-analyses of cluster randomized trials using prior data. Further work is needed to establish whether the application of these methods leads to improved review inferences for different reviews based on different factors (e.g., proportion of CRTs and CRTs with missing ICCs, different outcomes, variation and precision of ICCs).

Diez errores estadísticos frecuentes que tener en cuenta al escribir o revisar un artículo / Ten common statistical mistakes to watch out for when writing or reviewing a manuscript

Inspirados por esfuerzos más amplios para hacer más sólidas las conclusiones de la investigación científica, hemos recopilado una lista de algunos de los errores estadísticos más comunes que aparecen en la literatura científica. Los errores tienen su origen en diseños experimentales ineficaces, análisis inapropiados y/o razonamientos erróneos. Proporcionamos asesoramiento sobre la forma en que los autores, revisores y lectores pueden identificar y resolver estos errores y esperamos evitarlos en el futuro. Todos los errores pueden ser identificados en los distintos apartados de una publicación principalmente en material y métodos, resultados o conclusiones

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