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.

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).

Addressing Measurement Error in Random Forests Using Quantitative Bias Analysis.

Although variables are often measured with error, the impact of measurement error on machine-learning predictions is seldom quantified. The purpose of this study was to assess the impact of measurement error on the performance of random-forest models and variable importance. First, we assessed the impact of misclassification (i.e., measurement error of categorical variables) of predictors on random-forest model performance (e.g., accuracy, sensitivity) and variable importance (mean decrease in accuracy) using data from the National Comorbidity Survey Replication (2001-2003). Second, we created simulated data sets in which we knew the true model performance and variable importance measures and could verify that quantitative bias analysis was recovering the truth in misclassified versions of the data sets. Our findings showed that measurement error in the data used to construct random forests can distort model performance and variable importance measures and that bias analysis can recover the correct results. This study highlights the utility of applying quantitative bias analysis in machine learning to quantify the impact of measurement error on study results.

Misleading meta-analyses of observational studies may generate unjustified alarms: The case of medications for nausea and vomiting in pregnancy.

OBJECTIVES: Because observational studies often use imperfect measurements, results are prone to misclassification errors. We used as a motivating example the possible teratogenic risks of antiemetic agents in pregnancy since a large observational study recently showed that first-trimester exposure to doxylamine-pyridoxine was associated with significantly increased risk of congenital malformations as a whole, as well as central nervous system defects, and previous observational studies did not show such associations. A meta-analysis on this issue was carried out with the aim to illustrate how differential exposure and outcome misclassifications may lead to uncertain conclusions. METHODS: Medline, searched to October 2019 for full text papers in English. Summary Odds Ratios (ORs) with confidence intervals (CIs) were calculated using random-effect models. Probabilistic sensitivity analyses were performed for evaluating the extension of differential misclassification required to account for the exposure-outcome association. RESULTS: Summary ORs were 1.02 (95 % CI, 0.92-1.15), 0.99 (0.82-1.19) and 1.25 (1.08-1.44) for overall congenital, cardiocirculatory, and central nervous system malformations respectively. By assuming exposure and outcome bias factor respectively of 0.95 (i.e., newborns with congenital defects had exposure specificity 5% lower than healthy newborns) and 1.12 (i.e., exposed newborns had outcome sensitivity 12 % higher than unexposed newborns), summary OR of central nervous system defects became 1.13 (95 % CI, 0.99-1.29) and 1.17 (95 % CI, 0.99-1.38). CONCLUSION: Observational investigations and meta-analyses of observational studies need cautious interpretations. Their susceptibility to several, often sneaky, sources of bias should be carefully evaluated.

Decoding the epitranscriptional landscape from native RNA sequences.

Traditional epitranscriptomics relies on capturing a single RNA modification by antibody or chemical treatment, combined with short-read sequencing to identify its transcriptomic location. This approach is labor-intensive and may introduce experimental artifacts. Direct sequencing of native RNA using Oxford Nanopore Technologies (ONT) can allow for directly detecting the RNA base modifications, although these modifications might appear as sequencing errors. The percent Error of Specific Bases (%ESB) was higher for native RNA than unmodified RNA, which enabled the detection of ribonucleotide modification sites. Based on the %ESB differences, we developed a bioinformatic tool, epitranscriptional landscape inferring from glitches of ONT signals (ELIGOS), that is based on various types of synthetic modified RNA and applied to rRNA and mRNA. ELIGOS is able to accurately predict known classes of RNA methylation sites (AUC > 0.93) in rRNAs from Escherichiacoli, yeast, and human cells, using either unmodified in vitro transcription RNA or a background error model, which mimics the systematic error of direct RNA sequencing as the reference. The well-known DRACH/RRACH motif was localized and identified, consistent with previous studies, using differential analysis of ELIGOS to study the impact of RNA m6A methyltransferase by comparing wild type and knockouts in yeast and mouse cells. Lastly, the DRACH motif could also be identified in the mRNA of three human cell lines. The mRNA modification identified by ELIGOS is at the level of individual base resolution. In summary, we have developed a bioinformatic software package to uncover native RNA modifications.