Bootstrapping for Estimating the Conservative Kill Ratio of the Surrogate to the Pathogen for Use in Thermal Process Validation at the Industrial Scale

A surrogate is commonly used for process validations. The industry often uses the target log cycle reduction for the test (LCRTest) microorganism (surrogate) to be equal to the desired log cycle reduction for the target (LCRTarget) microorganism (pathogen). When the surrogate is too conservative with far greater resistance than the pathogen, the food may be overprocessed with quality and cost consequences. In aseptic processing, the Institute for Thermal Processing Specialists recommends using relative resistance (DTarget)/(DTest) to calculate LCRTest (product of LCRTarget and relative resistance). This method uses the mean values of DTarget and DTest and does not consider the estimating variability. We defined kill ratio (KR) as the inverse of relative resistance.The industry uses an extremely conservative KR of 1 in the validation of food processes for low-moisture foods, which ensures an adequate reduction of LCRTest, but can result in quality degradation. This study suggests an approach based on bootstrap sampling to determine conservative KR, leading to practical recommendations considering experimental and biological variability in food matrices. Previously collected thermal inactivation kinetics data of Salmonella spp. (target organism) and Enterococcus faecium (test organism) in Non-Fat Dried Milk (NFDM) and Whole Milk Powder (WMP) at 85, 90, and 95°C were used to calculate the mean KR. Bootstrapping was performed on mean inactivation rates to get a distribution of 1000 bootstrap KR values for each of the treatments. Based on minimum temperatures used in the industrial process and acceptable level of risk (e.g., 1, 5, or 10% of samples that would not achieve LCRTest), a conservative KR value can be estimated. Consistently, KR increased with temperature and KR for WMP was higher than NFDM. Food industries may use this framework based on the minimum processing temperature and acceptable level of risk for process validations to minimize quality degradation.

Application of bootstrap f2 to dissolution data from biorelevant media and evidence of the conservative nature of bootstrap f2

f2 with or without bootstrapping is the most common method to compare in vitro dissolution profiles, but methods to compare dissolution profiles have become less harmonized. The objective was to compare outcomes from bootstrap f2 and f2 (i.e. not-bootstrapped f2) using a large set of in vitro dissolution data. Non-parametric bootstrapping was performed on the 104 profile comparisons that did not meet the percent coefficient of variation (CV%) criteria to use average dissolution data. Bootstrap f2 was taken as the lower 90 % confidence interval of bootstrapped samples. There was concordance between bootstrap f2 and f2 in 92 of the 104 comparisons (88 %). There were no false positives. However, 12 % were false negative. Inspection of these discordance pairs suggests that bootstrap f2 serves as a conservative approach to assess profile similarity, particularly when a value of 50 is being used as decision criteria.

Young children with higher verbal intelligence are less likely to cheat

Cheating is a pervasive unethical behavior. Existing research involving young children has mainly focused on contextual factors affecting cheating behavior, whereas cognitive factors have been relatively understudied. This study investigated the unique role of verbal and performance intelligence on young children's cheating behavior (N = 50; mean age = 5.73 years; 25 boys). Bootstrapping hierarchical logistic regression showed that children's Verbal IQ scores were significantly and negatively correlated with their cheating behavior above and beyond the contributions of age, gender, and Performance IQ scores. Children with higher Verbal IQ scores were less inclined to cheat. However, neither children's Performance IQ nor their Total IQ scores had a significant and unique correlation with cheating. These findings suggest that intelligence plays a significant role in children's cheating but that this role is limited to verbal intelligence only. In addition, this study highlights the need for researchers to go beyond the contextual factors to study the early development of cheating behavior.

How many motor units is enough? An assessment of the influence of the number of motor units on firing rate calculations

The number of motor units included in calculations of mean firing rates varies widely in the literature. It is unknown how the number of decomposed motor units included in the calculation of firing rate per participant compares to the total number of active motor units in the muscle, and if this is different for males and females. Bootstrapped distributions and confidence intervals (CI) of mean motor unit firing rates decomposed from the tibialis anterior were used to represent the total number of active motor units for individual participants in trials from 20 to 100 % of maximal voluntary contraction. Bootstrapped distributions of mean firing rates were constructed using different numbers of motor units, from one to the maximum number for each participant, and compared to the CIs. A probability measure for each number of motor units involved in firing rate was calculated and then averaged across all individuals. Motor unit numbers required for similar levels of probability increased as contraction intensity increased (p < 0.001). Increased levels of probability also required higher numbers of motor units (p < 0.001). There was no effect of sex (p ≥ 0.97) for any comparison. This methodology should be repeated in other muscles, and aged populations.

Noise-dependent bias in quantitative STEM-EMCD experiments revealed by bootstrapping

Electron magnetic circular dichroism (EMCD) is a powerful technique for estimating element-specific magnetic moments of materials on nanoscale with the potential to reach atomic resolution in transmission electron microscopes. However, the fundamentally weak EMCD signal strength complicates quantification of magnetic moments, as this requires very high precision, especially in the denominator of the sum rules. Here, we employ a statistical resampling technique known as bootstrapping to an experimental EMCD dataset to produce an empirical estimate of the noise-dependent error distribution resulting from application of EMCD sum rules to bcc iron in a 3-beam orientation. We observe clear experimental evidence that noisy EMCD signals preferentially bias the estimation of magnetic moments, further supporting this with error distributions produced by Monte-Carlo simulations. Finally, we propose guidelines for the recognition and minimization of this bias in the estimation of magnetic moments.

Multilevel mediation analysis in R: A comparison of bootstrap and Bayesian approaches

Mediation analysis in repeated measures studies can shed light on the mechanisms through which experimental manipulations change the outcome variable. However, the literature on interval estimation for the indirect effect in the 1-1-1 single mediator model is sparse. Most simulation studies to date evaluating mediation analysis in multilevel data considered scenarios that do not match the expected numbers of level 1 and level 2 units typically encountered in experimental studies, and no study to date has compared resampling and Bayesian methods for constructing intervals for the indirect effect in this context. We conducted a simulation study to compare statistical properties of interval estimates of the indirect effect obtained using four bootstrap and two Bayesian methods in the 1-1-1 mediation model with and without random effects. Bayesian credibility intervals had coverage closest to the nominal value and no instances of excessive Type I error rates, but lower power than resampling methods. Findings indicated that the pattern of performance for resampling methods often depended on the presence of random effects. We provide suggestions for selecting an interval estimator for the indirect effect depending on the most important statistical property for a given study, as well as code in R for implementing all methods evaluated in the simulation study. Findings and code from this project will hopefully support the use of mediation analysis in experimental research with repeated measures.

Establishing performance metrics for quantitative non-targeted analysis: a demonstration using per- and polyfluoroalkyl substances

Non-targeted analysis (NTA) is an increasingly popular technique for characterizing undefined chemical analytes. Generating quantitative NTA (qNTA) concentration estimates requires the use of training data from calibration "surrogates," which can yield diminished predictive performance relative to targeted analysis. To evaluate performance differences between targeted and qNTA approaches, we defined new metrics that convey predictive accuracy, uncertainty (using 95% inverse confidence intervals), and reliability (the extent to which confidence intervals contain true values). We calculated and examined these newly defined metrics across five quantitative approaches applied to a mixture of 29 per- and polyfluoroalkyl substances (PFAS). The quantitative approaches spanned a traditional targeted design using chemical-specific calibration curves to a generalizable qNTA design using bootstrap-sampled calibration values from "global" chemical surrogates. As expected, the targeted approaches performed best, with major benefits realized from matched calibration curves and internal standard correction. In comparison to the benchmark targeted approach, the most generalizable qNTA approach (using "global" surrogates) showed a decrease in accuracy by a factor of ~4, an increase in uncertainty by a factor of ~1000, and a decrease in reliability by ~5%, on average. Using "expert-selected" surrogates (n = 3) instead of "global" surrogates (n = 25) for qNTA yielded improvements in predictive accuracy (by ~1.5×) and uncertainty (by ~70×) but at the cost of further-reduced reliability (by ~5%). Overall, our results illustrate the utility of qNTA approaches for a subclass of emerging contaminants and present a framework on which to develop new approaches for more complex use cases.

Measurement uncertainty for < 905 > Uniformity of Dosage Units tests using Monte Carlo and bootstrapping methods - Uncertainties arising from sampling and analytical steps

Uniformity of dosage unit (UDU) test is widely used to assess the quality, safety, and effectiveness of dosage forms in unit doses. An increased variability of the amount of drug (API - Active Pharmaceutical Ingredient) in each dose unit may lead to low quality, unsafety, and ineffective medicines. The aim of this work was to evaluate the measurement uncertainty associated with the acceptance value (AV) using the Monte Carlo and Bootstrapping methods, as well as to estimate the risk of false decisions regarding compliance/non-compliance due to uncertainty. Haloperidon 5 mg tablets and ofloxacin 200 mg tablets were subject to content uniformity (CU) and weight variation (WV) tests, respectively. Measurement uncertainty evaluation of UDU tests considered both uncertainties arising from sampling analytical steps. Uncertainty values were quantified using Monte Carlo (sampling) or bootstrapping (resampling) methods. Confidence intervals at 95% confidence level (CI95%) for AV value obtained for haloperidol 5 mg tablets were found to between 8.1 and15.8 and between 8.1 and 16.9 for Boostrapping and Monte Carlo methods, respectively. There is an increased risk of false conformity assessment for haloperidol UDU test (6.5% and 12.1% risk values for Bootstrapping and Monte Carlo methods). Considering the ofloxacin 200 mg tablets, the CI95% for AV value were found to be between 4.0 and 11.3 and between 4.9 and 11.4 for Bootstrapping and Monte Carlo methods, respectively. Uncertainty arising from sampling and analytical steps were both relevant to the overall uncertainty of AV values. Measurement uncertainty evaluation provided relevant information to support conformity assessments with a reduced risk of false decision making.

Clinical prediction models and the multiverse of madness

BACKGROUND

Each year, thousands of clinical prediction models are developed to make predictions (e.g. estimated risk) to inform individual diagnosis and prognosis in healthcare. However, most are not reliable for use in clinical practice.

MAIN BODY

We discuss how the creation of a prediction model (e.g. using regression or machine learning methods) is dependent on the sample and size of data used to develop it-were a different sample of the same size used from the same overarching population, the developed model could be very different even when the same model development methods are used. In other words, for each model created, there exists a multiverse of other potential models for that sample size and, crucially, an individual's predicted value (e.g. estimated risk) may vary greatly across this multiverse. The more an individual's prediction varies across the multiverse, the greater the instability. We show how small development datasets lead to more different models in the multiverse, often with vastly unstable individual predictions, and explain how this can be exposed by using bootstrapping and presenting instability plots. We recommend healthcare researchers seek to use large model development datasets to reduce instability concerns. This is especially important to ensure reliability across subgroups and improve model fairness in practice.

CONCLUSIONS

Instability is concerning as an individual's predicted value is used to guide their counselling, resource prioritisation, and clinical decision making. If different samples lead to different models with very different predictions for the same individual, then this should cast doubt into using a particular model for that individual. Therefore, visualising, quantifying and reporting the instability in individual-level predictions is essential when proposing a new model.

Blood bank programs and transfusion sustainability. A serial mediating model

INTRODUCTION

Researchers establish that the current challenges of blood safety and shortage are based on relationship between master production scheduling and blood transfusion sustainability of blood banks.

OBJECTIVE

The objective of this study is to evaluate program relationship between master production scheduling and blood transfusion sustainability through total quality management and blood production.

METHODS

A survey questionnaire was adopted with staff from regional and government university teaching hospital blood banks. Simple random sampling was used to collect data from respondents. Preliminary and main data analysis was done using SPSS AMOS23.

RESULTS

The results revealed that master production scheduling influence blood transfusion sustainability when serially mediated by total quality management and blood production with 34% variation change at 95% confidence interval. Again, the results obtained show that master production scheduling influence total quality management significantly. Furthermore, total quality management influence blood production significantly. Finally, blood production influence blood transfusion sustainability significantly.

CONCLUSION AND RECOMMENDATIONS

Master production scheduling program actions has a positive significant relationship with blood transfusion sustainability through total quality management and blood production programs as serial mediators. This research contributes to the management of blood banks and suggests to have a greater relational management of total quality management, blood production and master production scheduling program actions in order to achieve high levels of blood transfusion sustainability, and in general, a greater benefit for society.

Local Search and the Evolution of World Models

An open question regarding how people develop their models of the world is how new candidates are generated for consideration out of infinitely many possibilities. We discuss the role that evolutionary mechanisms play in this process. Specifically, we argue that when it comes to developing a global world model, innovation is necessarily incremental, involving the generation and selection among random local mutations and recombinations of (parts of) one's current model. We argue that, by narrowing and guiding exploration, this feature of cognitive search is what allows human learners to discover better theories, without ever grappling directly with the problem of finding a "global optimum," or best possible world model. We suggest this aspect of cognitive processing works analogously to how blind variation and selection mechanisms drive biological evolution. We propose algorithms developed for program synthesis provide candidate mechanisms for how human minds might achieve this. We discuss objections and implications of this perspective, finally suggesting that a better process-level understanding of how humans incrementally explore compositional theory spaces can shed light on how we think, and provide explanatory traction on fundamental cognitive biases, including anchoring, probability matching, and confirmation bias.

manymome: An R package for computing the indirect effects, conditional effects, and conditional indirect effects, standardized or unstandardized, and their bootstrap confidence intervals, in many (though not all) models

Mediation, moderation, and moderated mediation are common in behavioral research models. Several tools are available for estimating indirect effects, conditional effects, and conditional indirect effects and forming their confidence intervals. However, there are no simple-to-use tools that can appropriately form the bootstrapping confidence interval for standardized conditional indirect effects. Moreover, some tools are restricted to a limited type of models. We developed an R package, manymome, which can be used to estimate and form confidence intervals for indirect effects, conditional effects, and conditional indirect effects, standardized or not, using a two-step approach: model parameters are estimated either by structural equation modeling using lavaan or by a set of linear regression models using lm, and then the coefficients are used to compute the requested effects and form confidence intervals. It can be used when there are missing data if the model is fitted by structural equation modeling. There are only a few limitations on some aspects of a model, and no inherent limitations on the number of predictors, the number of independent variables, or the number of moderators and mediators. The goal is to have a tool that allows researchers to focus on model fitting first and worry about estimating the effects later. The use of the model is illustrated using a few numerical examples, and the limitations of the package are discussed.

Bodily Pain in Physical Function and the Role of Physical Activity Among Community-Dwelling Adults Aged 50-69 Years in Ghana

BACKGROUND

Older adults who experience pain are likely to report higher functional limitations (FL) and lower physical activity (PA) levels. However, the extent to which PA explains the association of pain with FL is largely unknown, particularly in low- and middle-income countries. This study estimates whether and how much pain in FL is mediated by PA engagement.

METHODS

We used cross-sectional data from 770 adults aged 50-69 years who participated in the Aging, Health, Psychological Well-being, and Health-seeking Behavior Study in Ghana. FL and pain characteristics were defined using the Medical Outcomes Study Short Form-36. PA was assessed by the International Physical Activity Questionnaire-Short Form. Bootstrapped mediation analyses estimated the direct and indirect hypothesized associations. The control variables included age, sex, residential type, level of education, monthly income, social isolation, emotional distress, multimorbidity, and self-rated health.

RESULTS

After adjusting for potential confounders, higher pain interference (β = 0.091, p < .05) and higher pain severity (β = 0.075, p < .05) were associated with greater FL. The bootstrapping analyses showed that PA mediated the pain interference-FL association, accounting for approximately 58% (β = 0.124, Boots 95% confidence interval (CI): 0.078-0.175) of the total effect (β = 0.215, Boots 95% CI: 0.095-0.335). Similarly, PA mediated the association between pain severity with FL, accounting for approximately 37% (β = 0.044, Boots 95% CI: 0.001-0.094) of the overall effect (β = 0.119, Boots 95% CI: -0.011 to 0.249).

CONCLUSIONS

Our data suggest that the higher pain severity and pain interference may lead to higher FL in middle and old age, and the associations are partially explained by PA. Effective and low-cost PA participation could be targeted in efforts to reduce the effect of pain on physical functioning among middle-aged and older adults.

Finite mixtures of mean-parameterized Conway-Maxwell-Poisson models

For modeling count data, the Conway-Maxwell-Poisson (CMP) distribution is a popular generalization of the Poisson distribution due to its ability to characterize data over- or under-dispersion. While the classic parameterization of the CMP has been well-studied, its main drawback is that it is does not directly model the mean of the counts. This is mitigated by using a mean-parameterized version of the CMP distribution. In this work, we are concerned with the setting where count data may be comprised of subpopulations, each possibly having varying degrees of data dispersion. Thus, we propose a finite mixture of mean-parameterized CMP distributions. An EM algorithm is constructed to perform maximum likelihood estimation of the model, while bootstrapping is employed to obtain estimated standard errors. A simulation study is used to demonstrate the flexibility of the proposed mixture model relative to mixtures of Poissons and mixtures of negative binomials. An analysis of dog mortality data is presented.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00362-023-01452-x.

Model-Based Estimation of Expected Time to Cholera Extinction in Lusaka, Zambia

The developing world has been facing a significant health issue due to cholera as an endemic communicable disease. Lusaka was Zambia's worst affected province, with 5414 reported cases of cholera during the outbreak from late October 2017 to May 12, 2018. To explore the epidemiological characteristics associated with the outbreak, we fitted weekly reported cholera cases with a compartmental disease model that incorporates two transmission routes, namely environment-to-human and human-to-human. Estimates of the basic reproduction number show that both transmission modes contributed almost equally during the first wave. In contrast, the environment-to-human transmission appears to be mostly dominating factor for the second wave. Our study finds that a massive abundance of environmental vibrio's with a huge reduction in water sanitation efficacy triggered the secondary wave. To estimate the expected time to extinction (ETE) of cholera, we formulate the stochastic version of our model and find that cholera can last up to 6.5-7 years in Lusaka if any further outbreak occurs at a later time. Results indicate that a considerable amount of attention is to be paid to sanitation and vaccination programs in order to reduce the severity of the disease and to eradicate cholera from the community in Lusaka.

Genome-wide gene expression responses to experimental manipulation of Saccharomyces cerevisiae repressor activator protein 1 (Rap1) expression level

Precise regulation of transcription in gene expression is critical for all aspects of normal organism form, fitness, and function and even minor alterations in the level, location, and timing of gene expression can result in phenotypic variation within and between species including evolutionary innovations and human disease states. Eukaryotic transcription is regulated by a complex interplay of multiple factors working both at a physical and molecular levels influencing this process. In Saccharomyces cerevisiae, the TF with the greatest number of putative regulatory targets is the essential gene Repressor Activator Protein 1 (RAP1). While much is known about the roles of Rap1 in gene regulation and numerous cellular processes, the response of Rap1 target genes to systematic titration of RAP1 expression level remains unknown. To fill this knowledge gap, we used a strain with a tetracycline-titratable promoter replacing wild-type regulatory sequences of RAP1 to systematically reduce the expression level of RAP1 and followed this with RNA sequencing (RNA-seq) to measure genome-wide gene expression responses. Previous research indicated that Rap1 plays a significant regulatory role in particular groups of genes including telomere-proximal genes, homothallic mating (HM) loci, glycolytic genes, DNA repair genes, and ribosomal protein genes; therefore, we focused our analyses on these groups and downstream targets to determine how they respond to reductions in RAP1 expression level. Overall, despite being known as both an activator and as a repressor of its target genes, we found that Rap1 acts as an activator for more target genes than as a repressor. Additionally, we found that Rap1 functions as an activator of ribosomal protein genes and a repressor of the silent mating locus genes consistent with predictions from the literature. Unexpectedly, we found that Rap1 functions as a repressor of glycolytic enzyme genes contrary to prior reports of it having the opposite effect. We also compared the expression of RAP1 to five different genes related to DNA repair pathway and found that decreasing RAP1 downregulated four of those five genes. Finally, we found no effect of RAP1 depletion on telomere-proximal genes despite its functioning to silence telomeric repeat-containing RNAs. Together our results enrich our understanding of this important transcriptional regulator. The graphical abstract is provided as a supplementary fig. (S-Fig 1).

Network analysis of body-related complaints in patients with neurotic or personality disorders referred to psychotherapy

Background

Psychopathology theory and clinical practice require the most complex knowledge about patients' complaints. In patients seeking for psychotherapy, body-related symptoms often complicate treatment.

Aim

This study aimed at examining connections between body-related symptoms, and identification of symptoms which may be responsible for emergency and sustaining of anxiety, somatoform and personality disorders with the use of network analysis.

Methods

In our retrospective research we used data from a sample of 4616 patients of the Department of Psychotherapy, University Hospital in Cracow, diagnosed with anxiety, somatoform or personality disorders. We constructed the Triangulated Maximally Filtered Graph (TMFG) networks of 44 somatoform symptoms endorsed in the symptom checklist "O" (SCL-O) and identified the most central symptoms within the network for all patients and in subgroups of women vs. men, older vs. younger, and diagnosed in 1980-2000 vs. 2000-2015. We used bootstrap to determine the accuracy and stability of five networks' parameters: strength, expected influence, eigenvector, bridge strength and hybrid centrality.

Results

The most central symptoms within the overall network, and in six subnetworks were dyspnea and migratory pains. We identified some gender-related differences, but no differences were observed for the age and time of diagnosis.

Conclusions

Self-reported dyspnea and migratory pains are potential important targets for treatment procedures.

A Statistical Approach for Assessing the Compliance of Integrated Continuous Glucose Monitoring Systems with Food and Drug Administration Accuracy Requirements

To assess the compliance of "integrated" continuous glucose monitoring (CGM) systems with U.S. Food and Drug Administration requirements, the calculation of confidence intervals (CIs) on agreement rates (ARs), that is, the percentage of CGM measurements lying within a certain deviation of a comparator method, is stipulated. However, despite the existence of numerous approaches that could yield different results, a specific procedure for calculating CIs is not described anywhere. This report, therefore, proposes a suitable statistical procedure to allow transparency and comparability between CGM systems. Three existing methods were applied to six data sets from different CGM performance studies. The results indicate that a bootstrap-based method that accounts for the clustered structure of CGM data is reliable and robust. We thus recommend its use for the estimation of CIs of ARs. A software implementation of the proposed method is freely available (https://github.com/IfDTUlm/CGM_Performance_Assessment).

Bayesian Techniques in Predicting Frailty among Community-Dwelling Older Adults in the Netherlands

Background Frailty is a syndrome that is defined as an accumulation of deficits in physical, psychological, and social domains. On a global scale, there is an urgent need to create frailty-ready healthcare systems due to the healthcare burden that frailty confers on systems and the increased risk of falls, healthcare utilization, disability, and premature mortality. Several studies have been conducted to develop prediction models for predicting frailty. Most studies used logistic regression as a technique to develop a prediction model. One area that has experienced significant growth is the application of Bayesian techniques, partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. Objective We compared ten different Bayesian networks as proposed by ten experts in the field of frail elderly people to predict frailty with a choice from ten dichotomized determinants for frailty. Methods We used the opinion of ten experts who could indicate, using an empty Bayesian network graph, the important predictors for frailty and the interactions between the different predictors. The candidate predictors were age, sex, marital status, ethnicity, education, income, lifestyle, multimorbidity, life events, and home living environment. The ten Bayesian network models were evaluated in terms of their ability to predict frailty. For the evaluation, we used the data of 479 participants that filled in the Tilburg Frailty indicator (TFI) questionnaire for assessing frailty among community-dwelling older people. The data set contained the aforementioned variables and the outcome "frail". The model fit of each model was measured using the Akaike information criterion (AIC) and the predictive performance of the models was measured using the area under the curve (AUC) of the receiver operator characteristic (ROC). The AUCs of the models were validated using bootstrapping with 100 repetitions. The relative importance of the predictors in the models was calculated using the permutation feature importance algorithm (PFI). Results The ten Bayesian networks of the ten experts differed considerably regarding the predictors and the connections between the predictors and the outcome. However, all ten networks had corrected AUCs >0.700. Evaluating the importance of the predictors in each model, "diseases or chronic disorders" was the most important predictor in all models (10 times). The predictors "lifestyle" and "monthly income" were also often present in the models (both 6 times). One or more diseases or chronic disorders, an unhealthy lifestyle, and a monthly income below 1800 euro increased the likelihood of frailty. Conclusions Although the ten experts all made different graphs, the predictive performance was always satisfying (AUCs >0.700). While it is true that the predictor importance varied all the time, the top three of the predictor importance consisted of "diseases or chronic disorders", "lifestyle" and "monthly income". All in all, asking for the opinion of experts in the field of frail elderly to predict frailty with Bayesian networks may be more rewarding than a data-driven forecast with Bayesian networks because they have expert knowledge regarding interactions between the different predictors.

DIY bootstrapping: Getting the nonparametric bootstrap confidence interval in SPSS for any statistics or function of statistics (when this bootstrapping is appropriate)

Researchers can generate bootstrap confidence intervals for some statistics in SPSS using the BOOTSTRAP command. However, this command can only be applied to selected procedures, and only to selected statistics in these procedures. We developed an extension command and prepared some sample syntax files based on existing approaches from the Internet to illustrate how researchers can (a) generate a large number of nonparametric bootstrap samples, (b) do desired analysis on all these samples, and (c) form the bootstrap confidence intervals for selected statistics using the OMS commands. We developed these tools to help researchers apply nonparametric bootstrapping to any statistics for which this method is appropriate, including statistics derived from other statistics, such as standardized effect size measures computed from the t test results. We also discussed how researchers can extend the tools for other statistics and scenarios they encounter.

Gdaphen, R pipeline to identify the most important qualitative and quantitative predictor variables from phenotypic data

BACKGROUND

In individuals or animals suffering from genetic or acquired diseases, it is important to identify which clinical or phenotypic variables can be used to discriminate between disease and non-disease states, the response to treatments or sexual dimorphism. However, the data often suffers from low number of samples, high number of variables or unbalanced experimental designs. Moreover, several parameters can be recorded in the same test. Thus, correlations should be assessed, and a more complex statistical framework is necessary for the analysis. Packages already exist that provide analysis tools, but they are not found together, rendering the decision method and implementation difficult for non-statisticians.

RESULT

We present Gdaphen, a fast joint-pipeline allowing the identification of most important qualitative and quantitative predictor variables to discriminate between genotypes, treatments, or sex. Gdaphen takes as input behavioral/clinical data and uses a Multiple Factor Analysis (MFA) to deal with groups of variables recorded from the same individuals or anonymize genotype-based recordings. Gdaphen uses as optimized input the non-correlated variables with 30% correlation or higher on the MFA-Principal Component Analysis (PCA), increasing the discriminative power and the classifier's predictive model efficiency. Gdaphen can determine the strongest variables that predict gene dosage effects thanks to the General Linear Model (GLM)-based classifiers or determine the most discriminative not linear distributed variables thanks to Random Forest (RF) implementation. Moreover, Gdaphen provides the efficacy of each classifier and several visualization options to fully understand and support the results as easily readable plots ready to be included in publications. We demonstrate Gdaphen capabilities on several datasets and provide easily followable vignettes.

CONCLUSIONS

Gdaphen makes the analysis of phenotypic data much easier for medical or preclinical behavioral researchers, providing an integrated framework to perform: (1) pre-processing steps as data imputation or anonymization; (2) a full statistical assessment to identify which variables are the most important discriminators; and (3) state of the art visualizations ready for publication to support the conclusions of the analyses. Gdaphen is open-source and freely available at https://github.com/munizmom/gdaphen , together with vignettes, documentation for the functions and examples to guide you in each own implementation.

A hybrid method based on semi-supervised learning for relation extraction in Chinese EMRs

Background

Building a large-scale medical knowledge graphs needs to automatically extract the relations between entities from electronic medical records (EMRs) . The main challenges are the scarcity of available labeled corpus and the identification of complexity semantic relations in text of Chinese EMRs. A hybrid method based on semi-supervised learning is proposed to extract the medical entity relations from small-scale complex Chinese EMRs.

Methods

The semantic features of sentences are extracted by a residual network and the long dependent information is captured by bidirectional gated recurrent unit. Then the attention mechanism is used to assign weights for the extracted features respectively, and the output of two attention mechanisms is integrated for relation prediction. We adjusted the training process with manually annotated small-scale relational corpus and bootstrapping semi-supervised learning algorithm, and continuously expanded the datasets during the training process.

Results

We constructed a small corpus of Chinese EMRs relation extraction based on the EMR datasets released at the China Conference on Knowledge Graph and Semantic Computing. The experimental results show that the best F1-score of the proposed method on the overall relation categories reaches 89.78%, which is 13.07% higher than the baseline CNN.

Calibration of sightseeing tour choices considering multiple decision criteria with diminishing reward

For an increasing number of cities, managing tourism becomes an important task and accordingly better understanding of touristic travel patterns is required. We model the sightseeing-tour choice within a city as a utility maximization problem. For this, attractions and their intrinsic utilities as well as tourists' preferences are evaluated over multiple dimensions in order to explain the variance in tourists' choice of POIs (points of interest) including the visiting order. Furthermore, the choice of destinations is considered "history-dependent" in that there is diminishing marginal utility gained by visiting additional POIs. Given the many potential sights, this leads to a large combinatorial problem. We solve this with a variant of a TTDP (tourist trip design problem) with the modified distance that evaluates omitted POIs and geographical distance between estimated and observed tours. The approach is applied to revealed-preference survey data from Kyoto, Japan, where tourists stated their visited attractions among 37 touristic areas. We discuss model fit and scenarios with the existing and a modified transport network.

Topologically directed confocal Raman imaging (TD-CRI): Advanced Raman imaging towards compositional and micromeritic profiling of a commercial tablet components

Particle size distribution (PSD), spatial location and particle cluster size of ingredients, polymorphism, compositional distribution of a pharmaceutical product are few of the most important attributes in establishing the drug release-controlling microstructural and solid state properties that would be used to (re)design or reproduce similar products. There are numerous solid-state techniques available for PSD analysis. Laser diffraction (LD) is mostly used to study PSD of raw materials. However, a constraint of LD is the interference between the active pharmaceutical ingredients (API) and excipients, where it is very challenging to measure API size in a tablet. X-ray powder diffraction (XRPD) is widely employed in establishing the polymorphism of API and excipients. This research examined a commercial osmotic tablet in terms of extracting solid state properties of API and functional excipient by Raman Imaging. Establishing repeatability, reproducibility, and sample representativeness when the samples are non-uniform and inhomogeneous necessitates multiple measurements. In such scenarios, when employing imaging-based techniques, it can be time-consuming and tedious. Advanced statistical methodologies are used to overcome these disadvantages and expedite the characterization process. Overall, this study demonstrates that Raman imaging can be employed as a non-invasive and effective offline method for assessing the solid-state characteristics of API and functional excipients in complex dosage forms like osmotic tablets.

Constructing Statistical Intervals for Small Area Estimates Based on Generalized Linear Mixed Model in Health Surveys

Generalized Linear Mixed Model (GLMM) has been widely used in small area estimation for health indicators. Bayesian estimation is usually used to construct statistical intervals, however, its computational intensity is a big challenge for large complex surveys. Frequentist approaches, such as bootstrapping, and Monte Carlo (MC) simulation, are also applied but not evaluated in terms of the interval magnitude, width, and the computational time consumed. The 2013 Florida Behavioral Risk Factor Surveillance System data was used as a case study. County-level estimated prevalence of three health-related outcomes was obtained through a GLMM; and their 95% confidence intervals (CIs) were generated from bootstrapping and MC simulation. The intervals were compared to 95% credential intervals through a hierarchial Bayesian model. The results showed that 95% CIs for county-level estimates of each outcome by using MC simulation were similar to the 95% credible intervals generated by Bayesian estimation and were the most computationally efficient. It could be a viable option for constructing statistical intervals for small area estimation in public health practice.

Neuro-cognitive development of semantic and syntactic bootstrapping in 6- to 7.5-year-old children

The present study examined the longitudinal relations of brain and behavior from ages 6-7.5 years old to test the bootstrapping account of language development. Prior work suggests that children's vocabulary development is foundational for acquiring grammar (e.g., semantic bootstrapping) and that children rely on the syntactic context of sentences to learn the meaning of new words (e.g., syntactic bootstrapping). Yet, little is known about the dynamics underlying semantic and syntactic development as children enter elementary school. In a series of preregistered and exploratory analyses, we tested how semantic and syntactic behavioral skills may influence the development of brain regions implicated in these processes, i.e. left posterior middle temporal gyrus (pMTG) and inferior frontal gyrus (pars opercularis, IFGop), respectively. Vice-a-versa, we tested how these brain regions may influence the development of children's semantic and syntactic behavioral skills. We assessed semantic (N = 26) and syntactic (N = 30) processes behaviorally and in the brain when children were ages 5.5-6.5 years old (Time 1) and again at 7-8 years old (Time 2). All brain-behavior analyses controlled for T1 autoregressive effects and phonological memory. Exploratory hierarchical regression analyses suggested bi-directional influences, but with greater support for syntactic bootstrapping. Across the analyses, there was a small to medium effect of change in variance in models where semantics predicted syntax. Conversely, there was medium to large change in variance in models where syntax predicted semantics. In line with prior literature, results suggest a close relationship between lexical and grammatical development in children ages 6-7.5 years old. However, there was more robust evidence for syntactic bootstrapping, suggesting that acquisition of phrase structure in school age children may allow for more effective learning of word meanings. This complements prior behavioral studies and suggests a potential shift in the early reliance on semantics to later reliance on syntax in development.

Docking-generated multiple ligand poses for bootstrapping bioactivity classifying Machine Learning: Repurposing covalent inhibitors for COVID-19-related TMPRSS2 as case study

In the present work we introduce the use of multiple docked poses for bootstrapping machine learning-based QSAR modelling. Ligand-receptor contact fingerprints are implemented as descriptor variables. We implemented this method for the discovery of potential inhibitors of the serine protease enzyme TMPRSS2 involved the infectivity of coronaviruses. Several machine learners were scanned, however, Xgboost, support vector machines (SVM) and random forests (RF) were the best with testing set accuracies reaching 90%. Three potential hits were identified upon using the method to scan known untested FDA approved drugs against TMPRSS2. Subsequent molecular dynamics simulation and covalent docking supported the results of the new computational approach.

Development and validation of a risk score for predicting postoperative delirium after major abdominal surgery by incorporating preoperative risk factors and surgical Apgar score

STUDY OBJECTIVE

To develop and validate a simple delirium-predicting scoring system in patients undergoing major abdominal surgery by incorporating preoperative risk factors and intraoperative surgical Apgar score (SAS).

DESIGN

Observational retrospective cohort study.

SETTING

A tertiary general hospital in China.

PATIENTS

1055 patients who received major abdominal surgery from January 2015 to December 2019.

MEASUREMENTS

We collected data on preoperative and intraoperative variables, and postoperative delirium. A risk scoring system for postoperative delirium in patients after major open abdominal surgery was developed and validated based on traditional logistic regression model. The elastic net algorithm was further developed and evaluated.

MAIN RESULTS

The incidence of postoperative delirium was 17.8% (188/1055) in these patients. They were randomly divided into the development (n = 713) and validation (n = 342) cohorts. Both the logistic regression model and the elastic net regression model identified that advanced age, arrythmia, hypoalbuminemia, coagulation dysfunction, mental illness or cognitive impairments and low surgical Apgar score are related with increased risk of postoperative delirium. The elastic net algorithm has an area under the receiver operating characteristic curve (AUROC) of 0.842 and 0.822 in the development and validation cohorts, respectively. A prognostic score was calculated using the following formula: Prognostic score = Age classification (0 to 3 points) + arrythmia + 2 * hypoalbuminemia + 2 * coagulation dysfunction + 4 * mental illness or cognitive impairments + (10-surgical Apgar score). The 22-point risk scoring system had good discrimination and calibration with an AUROC of 0.823 and 0.834, and a non-significant Hosmer-Lemeshow test P = 0.317 and P = 0.853 in the development and validation cohorts, respectively. The bootstrapping internal verification method (R = 1000) yielded a C-index of 0.822 (95% CI: 0.759-0.857).

CONCLUSION

The prognostic scoring system, which used both preoperative risk factors and surgical Apgar score, serves as a good first step toward a clinically useful predictive model for postoperative delirium in patients undergoing major open abdominal surgery.

Cost-effectiveness of adding Sativex® spray to spasticity care in Belgium: using bootstrapping instead of Monte Carlo simulation for probabilistic sensitivity analyses

BACKGROUND

Uncertainty in model-based cost-utility analyses is commonly assessed in a probabilistic sensitivity analysis. Model parameters are implemented as distributions and values are sampled from these distributions in a Monte Carlo simulation. Bootstrapping is an alternative method that requires fewer assumptions and incorporates correlations between model parameters.

METHODS

A Markov model-based cost-utility analysis comparing oromucosal spray containing delta-9-tetrahidrocannabinol + cannabidiol (Sativex®, nabiximols) plus standard care versus standard spasticity care alone in the management of multiple sclerosis spasticity was performed over a 5-year time horizon from the Belgian healthcare payer perspective. The probabilistic sensitivity analysis was implemented using a bootstrap approach to ensure that the correlations present in the source clinical trial data were incorporated in the uncertainty estimates.

RESULTS

Adding Sativex® spray to standard care was found to dominate standard spasticity care alone, with cost savings of €6,068 and a quality-adjusted life year gain of 0.145 per patient over the 5-year analysis. The probability of dominance increased from 29% in the first year to 94% in the fifth year, with the probability of QALY gains in excess of 99% for all years considered.

CONCLUSIONS

Adding Sativex® spray to spasticity care was found to dominate standard spasticity care alone in the Belgian healthcare setting. This study showed the use of bootstrapping techniques in a Markov model probabilistic sensitivity analysis instead of Monte Carlo simulations. Bootstrapping avoided the need to make distributional assumptions and allowed the incorporation of correlating structures present in the original clinical trial data in the uncertainty assessment.

Mechanisms of mindfulness for professional golfers: the mediating role of basic psychological needs

The relationship between mindfulness and well-being and ill-being has been demonstrated to a great extent. In sports, the fulfillment of individuals' basic psychological needs depends mostly on support from others, such as that from a coach in a sports team context. However, a possible way for individuals to fulfill their basic psychological needs is by enhancing mindfulness rather than depending on others. Therefore, building on SDT and mindfulness, this study examines the mediating effect of basic psychological needs on mindfulness to predict subjective vitality and athlete burnout in professional golfers. The participants were 120 golfers (47% females), with a mean age and golf experience of 22.28 and 9.48 years, respectively. The association between mindfulness and subjective vitality was partially mediated by the need for autonomy and relatedness. By contrast, the association between mindfulness and burnout was partially mediated by the need for competence and relatedness, thus supporting our mediation analysis. Overall, the study highlights mindfulness as a potential mechanism to ensure the fulfillment of basic psychological needs in golf training, which could enhance golfers' subjective vitality and reduce the occurrence of burnout.

Application of ATR-FTIR spectroscopy for profiling of non-structural carbohydrates in onion (Allium cepa L.) bulbs

Qualitative and quantitative composition of non-structural carbohydrates comprising glucose, fructose, sucrose and fructooligosaccharides (FOS) is one of the key determinants of market suitability, storability and technological processability of onions. To develop a cost-effective and rapid tool for carbohydrate profiling, applicability of attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy of onion juice was investigated with special regard to FOS patterns. As reference, detailed carbohydrate profiles of onion juices were generated by high-performance liquid chromatography coupled with evaporative light scattering detection (HPLC-ELSD). Hierarchical cluster analysis (HCA) of ATR-FTIR spectra was successfully applied for classifying onions into fresh market, storage and dehydrator type according to HPLC-ELSD profiles. A bootstrapping method for automatized test-set validation by projection to latent structures (PLS) algorithms using HPLC and ATR-FTIR spectroscopy data was developed. Model statistics showed promising perspectives for reliable quantification of individual saccharides and sum parameters. The presented methodology allows estimating the nutritional and pre-biotic value directly during cultivation and processing.

Using data envelopment analysis and the bootstrap method to evaluate organ transplantation efficiency in Brazil

Brazil has the most extensive public program for organ transplantation in the world, and the Brazilian National Health System (SUS) provides full coverage of all costs involved in organ donation, transplants, and post-transplant. Despite the relevance of the subject and the shortage of organs for transplants, transplantation process efficiency assessments are still uncommon in Brazil and abroad. This study aims to evaluate the efficiency of the Brazilian states and the Federal District in transforming potential organ donors into actual donations. We applied data envelopment analysis (DEA) in conjunction with the bootstrap technique, using organ transplantation data from 2018. The bootstrap methods applied (bootstrap technique, the bootstrap-biased scores of efficiency, and the bootstrap bias-corrected scores of efficiency) allow to obtain a confidence interval for DEA scores and provide greater robustness to studies based on DEA methodology. The bootstrap bias-corrected model indicates that there is significant room for improvement in terms of converting potential donors into actual donors. The mean corrected score is 0.55, signalizing that altogether the Brazilian states could maximize in 45% the number of transplanted organs without necessarily increasing the pool of potential donors. The study provides insights into the Brazilian processes of organ donation and transplantation, helping to identify locations in need of resource allocation improvements. Given the scarcity of studies with a joint application of DEA and bootstrap techniques in this crucial health activity, we also intend to methodologically contribute to this type of benchmark analysis, emphasizing the importance of considering measurement errors, randomness, and bias at DEA models.

Factors associated with daily weight gain in preweaned calves on dairy farms

The preweaning period is vital in the development of calves on dairy farms and improving daily liveweight gain (DLWG) is important to both financial and carbon efficiency; minimising rearing costs and improving first lactation milk yields. In order to improve DLWG, veterinary advisors should provide advice that has both a large effect size as well as being consistently important on the majority of farms. Whilst a variety of factors have previously been identified as influencing the DLWG of preweaned calves, it can be challenging to determine their relative importance, which is essential for optimal on-farm management decisions. Regularised regression methods such as ridge or lasso regression provide a solution by penalising variable coefficients unless there is a proportional improvement in model performance. Elastic net regression incorporates both lasso and ridge penalties and was used in this research to provide a sparse model to accommodate strongly correlated predictors and provide robust coefficient estimates. Sixty randomly selected British dairy farms were enrolled to collect weigh tape data from preweaned calves at birth and weaning, resulting in data being available for 1014 calves from 30 farms after filtering to remove poor quality data, with a mean DLWG of 0.79 kg/d (range 0.49-1.06 kg/d, SD 0.13). Farm management practices (e.g. colostrum, feeding, hygiene protocols), building dimensions, temperature/humidity and colostrum quality/bacteriology data were collected, resulting in 293 potential variables affecting farm level DLWG. Bootstrapped elastic net regression models identified 17 variables as having both a large effect size and high stability. Increasing the maximum preweaned age within the first housing group (0.001 kg/d per 1d increase, 90 % bootstrap confidence interval (BCI): 0.000-0.002), increased mean environmental temperature within the first month of life (0.012 kg/d per 1 °C increase, 90 % BCI: 0.002-0.037) and increased mean volume of milk feeding (0.012 kg/d per 1 L increase, 90 % BCI: 0.001-0.024) were associated with increased DLWG. An increase in the number of days between the cleaning out of calving pen (-0.001 kg/d per 1d increase, 90 % BCI: -0.001-0.000) and group housing pens (-0.001 kg/d per 1d increase, 90 % BCI: -0.002-0.000) were both associated with decreased DLWG. Through bootstrapped elastic net regression, a small number of stable variables have been identified as most likely to have the largest effect size on DLWG in preweaned calves. Many of these variables represent practical aspects of management with a focus around stocking demographics, milk/colostrum feeding, environmental hygiene and environmental temperature; these variables should now be tested in a randomised controlled trial to elucidate causality.

A Heterogeneous Ensemble Forecasting Model for Disease Prediction

The manuscript presents a bragging-based ensemble forecasting model for predicting the number of incidences of a disease based on past occurrences. The objectives of this research work are to enhance accuracy, reduce overfitting, and handle overdrift; the proposed model has shown promising results in terms of error metrics. The collated dataset of the diseases is collected from the official government site of Hong Kong from the year 2010 to 2019. The preprocessing is done using log transformation and z score transformation. The proposed ensemble model is applied, and its applicability to a specific disease dataset is presented. The proposed ensemble model is compared against the ensemble models, namely dynamic ensemble for time series, arbitrated dynamic ensemble, and random forest using different error metrics. The proposed model shows the reduced value of MAE (mean average error) by 27.18%, 3.07%, 11.58%, 13.46% for tuberculosis, dengue, food poisoning, and chickenpox, respectively. The comparison drawn between the proposed model and the existing models shows that the proposed ensemble model gives better accuracy in the case of all the four-disease datasets.

Long-Term Studies of Fish Health before and after the Closure of a Bleached Kraft Pulp Mill in Northern Ontario, Canada

Changes in ecosystems after the removal of stress provide a rich source of information for conservation science. We used a long-term regional data set on the performance of white sucker (Catostomus commersonii) collected before and after the closure of a pulp mill to explore recovery in fish. Physiological indicators, including liver enzymes and plasma steroids, showed some compelling changes after the closure of the mill consistent with reduced exposure to pulp mill effluent but did not unequivocally demonstrate recovery. However, persistent signals in these fish may indicate effects of impoundment or discharge of sewage. We also used quantile regression with environmental covariates and bootstrap iteration to determine if systematic variation remained in relative body weight, liver weight, and gonad weight. In fish formerly exposed to pulp mill effluent, we found evidence of improvements (male gonad weight and liver weight of males and females), degradation (gonad weight of females), and no change (body weight). Although the observed patterns may be associated with closure of the mill, some differences were also found at regional locations, suggesting roles of additional stressors and challenging the clear association of change at the Mattagami River exposure site with the closure of the mill. However, fish captured at this location show responses consistent with regional locations, suggesting no residual impacts and highlights the challenges of identifying changes in fish even after large and known interventions. Environ Toxicol Chem 2021;40:162-176. © 2020 SETAC.

A nonparametric framework for inferring orders of categorical data from category-real pairs

Given a dataset of careers and incomes, how large a difference of incomes between any pair of careers would be? Given a dataset of travel time records, how long do we need to spend more when choosing a public transportation mode A instead of B to travel? In this paper, we propose a framework that is able to infer orders of categories as well as magnitudes of difference of real numbers between each pair of categories using an estimation statistics framework. Our framework not only reports whether an order of categories exists, but it also reports magnitudes of difference of each consecutive pair of categories in the order. In a large dataset, our framework is scalable well compared with existing frameworks. The proposed framework has been applied to two real-world case studies: 1) ordering careers by incomes from 350,000 households living in Khon Kaen province, Thailand, and 2) ordering sectors by closing prices from 1,060 companies in NASDAQ stock market between years 2000 and 2016. The results of careers ordering demonstrate income inequality among different careers. The stock market results illustrate dynamics of sector domination that can change over time. Our approach is able to be applied in any research area that has category-real pairs. Our proposed Dominant-Distribution Network provides a novel approach to gain new insight of analyzing category orders. A software of this framework is available for researchers or practitioners in an R CRAN package: EDOIF.

Bootstrapping of compliance to anti-Tuberculosis medication: South Africa, 2014

BACKGROUND

Although, Tuberculosis (TB) is curable if the treatment is adhered to and completed it is still a major cause of death globally including South Africa. The success rate for TB treatment was 77.2% in 2014, of which more than 37 000 lives were lost because of it in South Africa. Several studies have been carried out on this subject, but the difference between the present study and the previous work done is the methodology proposed to establish the determinants of anti-TB medication compliance. Understanding the determinant of anti-TB medication compliance will help the policymakers on the appropriate decision to reduce the menace of the disease.

METHODS

In this study, we proposed logistic regression to a sample of individuals taken from the National Income Dynamics Survey data that self-reported to have been TB diagnosed. A comparison of the classical logistic regression and parametric bootstrap estimation methods was done for this data to determine the model that best describes the data.

RESULTS

The results obtained from the two methods were similar and identified gender, language, alcohol, English literacy, belief in religion and household SES as the determinants of TB patients on medication. The standard errors for the bootstrap logistic model were bigger than the standard errors of the classical model.

CONCLUSION

We conclude that the classical model is better and for this scenario, there was no need to resample. The outcome of this study supports the existing findings that controlling the social and economic determinants of health will help eradicate TB.

Normative ability of young females to control the lumbopelvic curvature during active knee extension in sitting

[Purpose] Active unilateral knee extension in sitting (AUKEiSit) is a clinical test for lumbopelvic control during limb movements. We aimed to identify the normal upper limit for the angle of lumbopelvic sagittal alignment (θ) during AUKEiSit in young females. [Participants and Methods] The primary inclusion criteria of the participant included asymptomatic females 18-44 years of age. Lumbopelvic curvature from T12 to S2 was traced on paper using a flexible ruler during right AUKEiSit. The θ value was calculated using 2 methods: 1) 2-point-method, calculating the angle between 2 tangential lines at T12 and S2 on a trace line using Image J software; and 2) max-method, calculating θ by measuring the distance between T12 and S2 and the maximum depth of the curvature. A negative value of θ indicated lumbar lordosis. The mean and 95% confidence intervals (CIs) were computed with bootstrapping. [Results] The data of 121 participants (mean age: 20.6 years) were analyzed. The mean and 95% confidence intervals of θ were -12.06° (-14.03° to -9.40°) with the 2-point-method and -5.40° (-7.62° to -2.73°) with the max-method. [Conclusion] In asymptomatic young females, the 95% confidence intervals of θ during AUKEiSit are negative regardless of the 2 different methods.

Introduction to clinical research based on modern epidemiology

Over the past 20 years, recent advances in science technologies have dramatically changed the styles of clinical research. Currently, it has become more popular to use recent modern epidemiological techniques, such as propensity score, instrumental variable, competing risks, marginal structural modeling, mixed effects modeling, bootstrapping, and missing data analyses, than before. These advanced techniques, also known as modern epidemiology, may be strong tools for performing good clinical research, especially in large-scale observational studies, along with relevant research questions, good databases, and the passion of researchers. However, to use these methods effectively, we need to understand the basic assumptions behind them. Here, I will briefly introduce the concepts of these techniques and their implementation. In addition, I would like to emphasize that various types of clinical studies, not only large database studies but also small studies on rare and intractable diseases, are equally important because clinicians always do their best to take care of many kinds of patients who suffer from various kidney diseases and this is our most important mission.

Integrated machine learning methods with resampling algorithms for flood susceptibility prediction

Flood susceptibility projections relying on standalone models, with one-time train-test data splitting for model calibration, yields biased results. This study proposed novel integrative flood susceptibility prediction models based on multi-time resampling approaches, random subsampling (RS) and bootstrapping (BT) algorithms, integrated with machine learning models: generalized additive model (GAM), boosted regression tree (BTR) and multivariate adaptive regression splines (MARS). RS and BT algorithms provided 10 runs of data resampling for learning and validation of the models. Then the mean of 10 runs of predictions is used to produce the flood susceptibility maps (FSM). This methodology was applied to Ardabil Province on coastal margins of the Caspian Sea which faced destructive floods. The area under curve (AUC) of receiver operating characteristic (ROC) and true skill statistic (TSS) and correlation coefficient (COR) were utilized to evaluate the predictive accuracy of the proposed models. Results demonstrated that resampling algorithms improved the performance of Standalone GAM, MARS and BRT models. Results also revealed that Standalone models had better performance with the BT algorithm compared to the RS algorithm. BT-GAM model attained superior performance in terms of statistical measures (AUC = 0.98, TSS = 0.93, COR = 0.91), followed by BT-MARS (AUC = 0.97, TSS = 0.91, COR = 0.91) and BT-BRT model (AUC = 0.95, TSS = 0.79, COR = 0.79). Results demonstrated that the proposed models outperformed the benchmark models such as Standalone GAM, MARS, BRT, multilayer perceptron (MLP) and support vector machine (SVM). Given the admirable performance of the proposed models in a large scale area, the promising results can be expected from these models for other regions.

Determinants of soft budget constraints: How public debt affects hospital performance in Austria

Soft budget constraints (SBCs) undermine reforms to increase hospital service efficiency when hospital management can count on being bailed out by (subnational) governments in case of deficits. Using cost accounting data on publicly financed, non-profit hospitals in Austria from 2002 to 2015, we analyse the association between SBCs and hospital efficiency change in a setting with negligible risk of hospital closure in a two-stage study design based on bias-corrected non-radial input-oriented data envelopment analysis and ordinary least squares regression. We find that the European debt crisis altered the pattern of hospital efficiency development: after the economic crisis, hospitals in low-debt states had a 1.1 percentage point lower annual efficiency change compared to hospitals in high-debt states. No such systematic difference is found before the economic crisis. The results suggest that sudden exogenous shocks to public finances can increase the budgetary pressure on publicly financed institutions, thereby counteracting a pre-existing SBC.

How many streamlines are required for reliable probabilistic tractography? Solutions for microstructural measurements and neurosurgical planning

Diffusion MRI tractography is commonly used to delineate white matter tracts. These delineations can be used for planning neurosurgery or for identifying regions of interest from which microstructural measurements can be taken. Probabilistic tractography produces different delineations each time it is run, potentially leading to microstructural measurements or anatomical delineations that are not reproducible. Generating a sufficiently large number of streamlines is required to avoid this scenario, but what constitutes "sufficient" is difficult to assess and so streamline counts are typically chosen in an arbitrary or qualitative manner. This work explores several factors influencing tractography reliability and details two methods for estimating this reliability. The first method automatically estimates the number of streamlines required to achieve reliable microstructural measurements, whilst the second estimates the number of streamlines required to achieve a reliable binarised trackmap than can be used clinically. Using these methods, we calculated the number of streamlines required to achieve a range of quantitative reproducibility criteria for three anatomical tracts in 40 Human Connectome Project datasets. Actual reproducibility was checked by repeatedly generating the tractograms with the calculated numbers of streamlines. We found that the required number of streamlines varied strongly by anatomical tract, image resolution, number of diffusion directions, the degree of reliability desired, the microstructural measurement of interest, and/or the specifics on how the tractogram was converted to a binary volume. The proposed methods consistently predicted streamline counts that achieved the target reproducibility. Implementations are made available to enable the scientific community to more-easily achieve reproducible tractography.

A Simulation Study of Bootstrap Approaches to Estimate Confidence Intervals in DeFries-Fulker Regression Models (with Application to the Heritability of BMI Changes in the NLSY)

The univariate bootstrap is a relatively recently developed version of the bootstrap (Lee and Rodgers in Psychol Methods 3(1): 91, 1998). DeFries-Fulker (DF) analysis is a regression model used to estimate parameters in behavioral genetic models (DeFries and Fulker in Behav Genet 15(5): 467-473, 1985). It is appealing for its simplicity; however, it violates certain regression assumptions such as homogeneity of variance and independence of errors that make calculation of standard errors and confidence intervals problematic. Methods have been developed to account for these issues (Kohler and Rodgers in Behav Genet 31(2): 179-191, 2001), however the univariate bootstrap represents a unique means of doing so that is presaged by suggestions from previous DF research (e.g., Cherny et al. in Behav Genet 22(2): 153-162, 1992). In the present study we use simulations to examine the performance of the univariate bootstrap in the context of DF analysis. We compare a number of possible bootstrap schemes as well as more traditional confidence interval methods. We follow up with an empirical demonstration, applying results of the simulation to models estimated to investigate changes in body mass index in adults from the National Longitudinal Survey of Youth 1979 data.

Intracluster correlation coefficients in the Greater Mekong Subregion for sample size calculations of cluster randomized malaria trials

BACKGROUND

Sample size calculations for cluster randomized trials are a recognized methodological challenge for malaria research in pre-elimination settings. Positively correlated responses from the participants in the same cluster are a key feature in the estimated sample size required for a cluster randomized trial. The degree of correlation is measured by the intracluster correlation coefficient (ICC) where a higher coefficient suggests a closer correlation hence less heterogeneity within clusters but more heterogeneity between clusters.

METHODS

Data on uPCR-detected Plasmodium falciparum and Plasmodium vivax infections from a recent cluster randomized trial which aimed at interrupting malaria transmission through mass drug administrations were used to calculate the ICCs for prevalence and incidence of Plasmodium infections. The trial was conducted in four countries in the Greater Mekong Subregion, Laos, Myanmar, Vietnam and Cambodia. Exact and simulation approaches were used to estimate ICC values for both the prevalence and the incidence of parasitaemia. In addition, the latent variable approach to estimate ICCs for the prevalence was utilized.

RESULTS

The ICCs for prevalence ranged between 0.001 and 0.082 for all countries. The ICC from the combined 16 villages in the Greater Mekong Subregion were 0.26 and 0.21 for P. falciparum and P. vivax respectively. The ICCs for incidence of parasitaemia ranged between 0.002 and 0.075 for Myanmar, Cambodia and Vietnam. There were very high ICCs for incidence in the range of 0.701 to 0.806 in Laos during follow-up.

CONCLUSION

ICC estimates can help researchers when designing malaria cluster randomized trials. A high variability in ICCs and hence sample size requirements between study sites was observed. Realistic sample size estimates for cluster randomized malaria trials in the Greater Mekong Subregion have to assume high between cluster heterogeneity and ICCs. This work focused on uPCR-detected infections; there remains a need to develop more ICC references for trials designed around prevalence and incidence of clinical outcomes. Adequately powered trials are critical to estimate the benefit of interventions to malaria in a reliable and reproducible fashion.

TRIAL REGISTRATION

ClinicalTrials.govNCT01872702. Registered 7 June 2013. Retrospectively registered. https://clinicaltrials.gov/ct2/show/NCT01872702.

A bootstrapping approach for generating an inverse distance weight matrix when multiple observations have an identical location in large health surveys

Spatial weight matrices play a key role in econometrics to capture spatial effects. However, these constructs are prone to clustering and can be challenging to analyse in common statistical packages such as STATA. Multiple observations of survey participants in the same location (or cluster) have traditionally not been dealt with appropriately by statistical packages. It is common that participants are assigned Geographic Information System (GIS) data at a regional or district level rather than at a small area level. For example, the Demographic Health Survey (DHS) generates GIS data at a cluster level, such as a regional or district level, rather than providing coordinates for each participant. Moreover, current statistical packages are not suitable for estimating large matrices such as 20,000 × 20,000 (reflective of data within large health surveys) since the statistical package limits the N to a smaller number. In addition, in many cases, GIS information is offered at an aggregated level of geographical areas. To alleviate this problem, this paper proposes a bootstrap approach that generates an inverse distance spatial weight matrix for application in econometric analyses of health survey data. The new approach is illustrated using DHS data on uptake of HIV testing in low and middle income countries.

Long-term trends of wet inorganic nitrogen deposition in Rocky Mountain National Park: Influence of missing data imputation methods and associated uncertainty

Excess reactive nitrogen (Nr) deposition is occurring in Rocky Mountain National Park and impacting sensitive ecosystems. In 2006, the National Park Service, State of Colorado, and Environmental Protection Agency established the goal to reduce Nr deposition to below the ecosystem critical load by 2032. Progress is tracked using 5-year averages of annual wet inorganic nitrogen (IN) deposition measured at Loch Vale, Colorado, by the National Atmospheric Deposition Program (NADP). This remote high alpine site is challenging to operate, and large fractions of the annual precipitation, at times >40%, had invalid IN concentrations. Annual wet IN deposition is calculated using the NADP protocol, which replaces missing concentrations with the annual precipitation-weighted mean (PWM) concentration of valid samples. This protocol does not account for seasonal variations in IN concentrations and the inverse relationship between concentration and precipitation amounts. Invalid samples occurred more frequently in the winter and at high and low precipitation amounts, and the NADP protocol generally overestimated annual deposition rates, by as much as 20%. Here, a new method for imputing missing weekly IN concentrations that accounts for their seasonal and precipitation dependence is introduced. Using a bootstrapping analysis shows that the new method reduced the errors in the annual deposition rates by about 30% compared to the NADP protocol and the biases were near zero. The overall trend in the wet IN deposition rates was found to be flat from 1990 to 2017, but the nitrate contribution decreased about 33%, which was offset by a nearly equal increase in ammonium wet deposition. These trends are consistent with known changes in nitrate and ammonium precursor emissions. The long-term trends in the annual IN deposition rates were similar using both data imputation methods, but the 2013-2017 average was about 10% smaller using the new method.

Response to letter to the editor: "comment on unplanned out-of-hospital birth and risk factors of adverse perinatal outcome: findings from a prospective cohort"

The aim of this Letter to the Editor was to respond to a comment highlighting potential statistical biases in an analysis of our recently published article. We therefore specified the method for selecting the model variables in order to limit overfitting, then we used the Firth method to control the sparse data bias, and finally for checking internal validity we used bootstrapping methods. In total, the conclusions of our model were not changed by these new analyses.

Energetics of Storage and Diffusion of Water and Cyclo-Octasulfur for a Nonpolar Cavity of RHCC Tetrabrachion by Molecular Dynamics Simulations

Tetrabrachion forms the key component of the S-layer of Staphylothermus marinus. Molecular dynamics simulations have been used to study the energetics of occupancy of cavity 3 of the right-handed coiled-coil stalk of tetrabrachion by both water molecules and cyclooctasulfur S₈ crowns, as well as to determine possible pathways and free energy barriers for the diffusion of both water and cyclooctasulfur through the peptide walls of RHCC tetrabrachion between cavity 3 and bulk solvent. Calculations of the transfer free energy from solvent to cavity show that clusters of six, seven and eight water molecules are marginally stable in cavity 3, but that occupancy of the cavity by a cyclooctasulfur ring is favoured significantly over water clusters of all sizes. Thermal activation simulations at T = 400K revealed that water molecules diffusing through the wall pass through a sequence of metastable configurations where they are temporarily immobilized by forming networks of hydrogen bonds with specific wall residues. Calculations of the free energy of these metastable configurations using multi-configurational thermodynamic integration yielded a free energy profile with a principal free energy maximum ∆G~50 kJ/mol and a slight activation asymmetry in favour of the direction from cavity to solvent. Potential exit pathways for cyclooctasulfur were investigated with the methods of steered molecular dynamics and umbrella sampling. The cyclooctasulfur was steered through a gap in the tetrabrachion wall along a linear path from cavity 3 into the solvent and the resulting trajectory was subdivided into 22 sampling windows. The free energy profile created for the trajectory by umbrella sampling showed a sharp principal maximum as a function of the reaction coordinate with asymmetric free energy barriers ∆G_exit~220 kJ/mol and ∆G_entrance~100 kJ/mol for cavity exit and entrance, respectively.

Discourse Coherence as a Cue to Reference in Word Learning: Evidence for Discourse Bootstrapping

Word learning depends critically on the use of linguistic context to constrain the likely meanings of words. However, the mechanisms by which children infer word meaning from linguistic context are still poorly understood. In this study, we asked whether adults (n = 58) and 2- to 6-year-old children (n = 180) use discourse coherence relations (i.e., the meaningful relationships between elements within a discourse) to constrain their interpretation of novel words. Specifically, we showed participants videos of novel animals exchanging objects. These videos were accompanied by a linguistic description of the events in which we manipulated a single word within a sentence (and vs. because) in order to alter the causal and temporal relations between the events in the discourse (e.g., "One animal handed the baby to the other animal [and/because] the baby started crying in the talfa's arms"). We then asked participants which animal (the giver or the receiver) was the referent of the novel word. Across two experiments, we found evidence that young children used the causal and temporal relations in each discourse to constrain their interpretations of novel words.

Contrast and entailment: Abstract logical relations constrain how 2- and 3-year-old children interpret unknown numbers

Do children understand how different numbers are related before they associate them with specific cardinalities? We explored how children rely on two abstract relations - contrast and entailment - to reason about the meanings of 'unknown' number words. Previous studies argue that, because children give variable amounts when asked to give an unknown number, all unknown numbers begin with an existential meaning akin to some. In Experiment 1, we tested an alternative hypothesis, that because numbers belong to a scale of contrasting alternatives, children assign them a meaning distinct from some. In the "Don't Give-a-Number task", children were shown three kinds of fruit (apples, bananas, strawberries), and asked to not give either some or a number of one kind (e.g. Give everything, but not [some/five] bananas). While children tended to give zero bananas when asked to not give some, they gave positive amounts when asked to not give numbers. This suggests that contrast - plus knowledge of a number's membership in a count list - enables children to differentiate the meanings of unknown number words from the meaning of some. Experiment 2 tested whether children's interpretation of unknown numbers is further constrained by understanding numerical entailment relations - that if someone, e.g. has three, they thereby also have two, but if they do not have three, they also do not have four. On critical trials, children saw two characters with different quantities of fish, two apart (e.g. 2 vs. 4), and were asked about the number in-between - who either has or doesn't have, e.g. three. Children picked the larger quantity for the affirmative, and the smaller for the negative prompts even when all the numbers were unknown, suggesting that they understood that, whatever three means, a larger quantity is more likely to contain that many, and a smaller quantity is more likely not to. We conclude by discussing how contrast and entailment could help children scaffold the exact meanings of unknown number words.