Descriptive statistics, mean ± SD, range, median and interquartile range (IQR).

Code Abstract We provide R statistical software code (“CWVS_LMC.txt”) to fit the linear model of coregionalization (LMC) version of the Critical Window Variable Selection (CWVS) method developed in the manuscript. We also provide R code (“Results_Summary.txt”) to summarize/plot the estimated critical windows and posterior marginal inclusion probabilities. Description “CWVS_LMC.txt”: This code is delivered to the user in the form of a .txt file that contains R statistical software code. Once the “Simulated_Dataset.RData” workspace has been loaded into R, the text in the file can be used to identify/estimate critical windows of susceptibility and posterior marginal inclusion probabilities. “Results_Summary.txt”: This code is also delivered to the user in the form of a .txt file that contains R statistical software code. Once the “CWVS_LMC.txt” code is applied to the simulated dataset and the program has completed, this code can be used to summarize and plot the identified/estimated critical windows and posterior marginal inclusion probabilities (similar to the plots shown in the manuscript). Optional Information (complete as necessary) Required R packages: • For running “CWVS_LMC.txt”: • msm: Sampling from the truncated normal distribution • mnormt: Sampling from the multivariate normal distribution • BayesLogit: Sampling from the Polya-Gamma distribution • For running “Results_Summary.txt”: • plotrix: Plotting the posterior means and credible intervals Instructions for Use Reproducibility (Mandatory) What can be reproduced: The data and code can be used to identify/estimate critical windows from one of the actual simulated datasets generated under setting E4 from the presented simulation study. How to use the information: • Load the “Simulated_Dataset.RData” workspace • Run the code contained in “CWVS_LMC.txt” • Once the “CWVS_LMC.txt” code is complete, run “Results_Summary.txt”. Format: Below is the replication procedure for the attached data set for the portion of the analyses using a simulated data set: Data The data used in the application section of the manuscript consist of geocoded birth records from the North Carolina State Center for Health Statistics, 2005-2008. In the simulation study section of the manuscript, we simulate synthetic data that closely match some of the key features of the birth certificate data while maintaining confidentiality of any actual pregnant women. Availability Due to the highly sensitive and identifying information contained in the birth certificate data (including latitude/longitude and address of residence at delivery), we are unable to make the data from the application section publically available. However, we will make one of the simulated datasets available for any reader interested in applying the method to realistic simulated birth records data. This will also allow the user to become familiar with the required inputs of the model, how the data should be structured, and what type of output is obtained. While we cannot provide the application data here, access to the North Carolina birth records can be requested through the North Carolina State Center for Health Statistics, and requires an appropriate data use agreement. Description Permissions: These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. This dataset is associated with the following publication: Warren, J., W. Kong, T. Luben, and H. Chang. Critical Window Variable Selection: Estimating the Impact of Air Pollution on Very Preterm Birth. Biostatistics. Oxford University Press, OXFORD, UK, 1-30, (2019).

This product was developed as part of the project supported by the grant from and the National Oceanic and Atmospheric Administration’s Ocean Acidification Program under award NA18OAR0170430 to the Virginia Institute of Marine Science. The data product consists of water quality data for tidal 98 stations for 1984–2018. The source data used to generate this product were downloaded from the Chesapeake Bay Program’s (CBP) data hub. Out of the total of 255 monitoring stations in the Tidal Monitoring Program, we selected 98 with the long monitoring record (30 years or longer). The following variables were downloaded from the data hub at the native temporal and vertical resolution (between one and four cruises per month and approximately 10 depth levels sampled between 0 and 37 m) for 1984–2018: water temperature (T), salinity (S), pH, total alkalinity (TA), dissolved oxygen (DO) , and chlorophyll (Chl). All pH data prior to 1998 were removed because of the data quality concerns (Herrmann et al., 2020). Briefly, we found a dramatic difference in long-term trends between stations measured by institutions in the state of Virginia and stations measured by the state of Maryland, particularly from late spring to early fall. The boundary between the station groups runs east–west within the mesohaline portion of the bay, where the Potomac River estuary intersects the mainstem bay. The boundary separates strong negative linear trends to the south (Virginia stations) from neutral and weakly positive linear trends to the north (Maryland stations). For all variables, data entries marked with CBP’s “Problem†and “Qualifier†flags were removed. Additionally, all variables were scanned for extreme outliers: for each variable, data from all stations, depths, and times were combined into a single composite sample for which the 75th and 25th percentiles (i.e., the upper and lower quantiles) and the interquartile range (the difference between the upper and lower quantiles) were calculated. Extreme outliers were defined as the values falling outside of a certain number (censoring criterion) of interquartile ranges from the upper and lower quantiles.

This product was developed as part of the project supported by the grant from and the National Oceanic and Atmospheric Administrationâ s Ocean Acidification Program under award NA18OAR0170430 to the Virginia Institute of Marine Science. The data product consists of water quality data for tidal 98 stations for 1984ÂÂâ 2018. The source data used to generate this product were downloaded from the Chesapeake Bay Programâ s (CBP) data hub. Out of the total of 255 monitoring stations in the Tidal Monitoring Program, we selected 98 with the long monitoring record (30 years or longer). The following variables were downloaded from the data hub at the native temporal and vertical resolution (between one and four cruises per month and approximately 10 depth levels sampled between 0 and 37 m) for 1984â 2018: water temperature (T), salinity (S), pH, total alkalinity (TA), dissolved oxygen (DO) , and chlorophyll (Chl). All pH data prior to 1998 were removed because of the data quality concerns (Herrmann et al., 2020). Briefly, we found a dramatic difference in long-term trends between stations measured by institutions in the state of Virginia and stations measured by the state of Maryland, particularly from late spring to early fall. The boundary between the station groups runs eastâ west within the mesohaline portion of the bay, where the Potomac River estuary intersects the mainstem bay. The boundary separates strong negative linear trends to the south (Virginia stations) from neutral and weakly positive linear trends to the north (Maryland stations). For all variables, data entries marked with CBPâ s â Problemâ and â Qualifierâ flags were removed. Additionally, all variables were scanned for extreme outliers: for each variable, data from all stations, depths, and times were combined into a single composite sample for which the 75th and 25th percentiles (i.e., the upper and lower quantiles) and the interquartile range (the difference between the upper and lower quantiles) were calculated. Extreme outliers were defined as the values falling outside of a certain number (censoring criterion) of interquartile ranges from the upper and lower quantiles.

Clinical trials increasingly employ medical imaging data in conjunction with supervised classifiers, where the latter require large amounts of training data to accurately model the system. Yet, a classifier selected at the start of the trial based on smaller and more accessible datasets may yield inaccurate and unstable classification performance. In this paper, we aim to address two common concerns in classifier selection for clinical trials: (1) predicting expected classifier performance for large datasets based on error rates calculated from smaller datasets and (2) the selection of appropriate classifiers based on expected performance for larger datasets. We present a framework for comparative evaluation of classifiers using only limited amounts of training data by using random repeated sampling (RRS) in conjunction with a cross-validation sampling strategy. Extrapolated error rates are subsequently validated via comparison with leave-one-out cross-validation performed on a larger dataset. The ability to predict error rates as dataset size increases is demonstrated on both synthetic data as well as three different computational imaging tasks: detecting cancerous image regions in prostate histopathology, differentiating high and low grade cancer in breast histopathology, and detecting cancerous metavoxels in prostate magnetic resonance spectroscopy. For each task, the relationships between 3 distinct classifiers (k-nearest neighbor, naive Bayes, Support Vector Machine) are explored. Further quantitative evaluation in terms of interquartile range (IQR) suggests that our approach consistently yields error rates with lower variability (mean IQRs of 0.0070, 0.0127, and 0.0140) than a traditional RRS approach (mean IQRs of 0.0297, 0.0779, and 0.305) that does not employ cross-validation sampling for all three datasets.

Post-Stroke Recovery Data Repository

This repository contains various resources related to the study on post-stroke recovery in a mouse model, focusing on the application of the Proportional Recovery Rule (PRR).

Repository Structure

• code/: Contains all the code used for the analysis in this study. Detailed information is available in the README within the code folder.
• input/: This folder contains all datasets used in the publication.
• output/: This directory includes the final results generated for each dataset. Detailed information for each dataset's output can be found in their respective subfolders.
• docs/: Additional documentation related to this project, including extra resources in the form of a README file within this folder.

Methodology Overview

Introduction

The Fugl-Meyer upper extremity score is a widely used assessment tool in clinical settings to evaluate motor function in stroke patients. With a maximum score of 66, higher values indicate better motor performance, while lower values signify greater deficits.

The Proportional Recovery Rule (PRR) suggests that the magnitude of recovery from nonsevere upper limb motor impairment after stroke is approximately 0.7 times the initial impairment. This rule, proposed in 2008, has been applied to various motor and nonmotor impairments, leading to inconsistencies in its formulation and application across studies.

Translating PRR to Deficit Score

In this study, we translated the Fugl-Meyer upper extremity score into a deficit score suitable for use in a mouse model. The PRR posits that the change in impairment can be predicted as 0.7 times the initial impairment, plus an error term. We adapted this rule by fitting a linear regression model without an intercept to relate the initial impairment to the change in impairment.

Data Analysis

1. Initial Impairment Calculation:

   • Initial impairment (d-score) is calculated as the difference between the deficit score at day 3 post-stroke and the baseline deficit score.

2. Change Observed and Predicted:

   • Change observed: Initial impairment minus deficit score on day 28.
   • Change predicted: 0.7 times the initial impairment plus an error term.

3. Cluster Analysis:

   • Data were plotted with initial impairment on the x-axis and change observed on the y-axis.
   • A linear fit was applied to generate two lines: one based on the proportional recovery rule and one from the data fit.
   • Subjects were clustered based on their proximity to these lines, iterating the process until convergence.

4. Outlier Removal:

   • Outliers were identified and removed based on the interquartile range rule both initially and during each iteration of the clustering process.

Results

1. Cluster Characteristics:

   • The final clustering resulted in 65 subjects following the PRR, with a fixed slope of 0.7 and an intercept of -0.42.
   • The other cluster contained 21 subjects with a distinct recovery pattern, characterized by a slope of 0.84.

2. Statistical Analysis:

   • The slope of the overall linear fit was found to be 0.93.
   • Approximately 75.58% of the subjects adhered to the PRR, indicating the potential relevance of the PRR in the mouse model.

Additional Information

This structured dataset was created with reference to the following publication:

DOI:10.1038/s41597-023-02242-8

If you have any questions or require further assistance, please do not hesitate to reach out to us. Contact us via email at markus.aswendtATuk-koeln.de or aref.kalantari-sarcheshmehATuk-koeln.de.

This 5 km resolution grid indicates what borehole yields (in l/s) can reasonably be expected in different hydrogeological units. The ranges indicate the approximate interquartile range of the yield of boreholes that have been sited and drilled using appropriate techniques. Groundwater productivity is given in liters per second.

Detailed description of the methodology, and a full list of data sources used to develop the layer can be found in the peer-reviewed paper available here: http://iopscience.iop.org/article/10.1088/1748-9326/7/2/024009/pdf

The raster and a high resolution PDF file are available for download on the website of British Geological Survey (BGS): http://www.bgs.ac.uk/research/groundwater/international/africanGroundwater/mapsDownload.html

This product was developed as part of the project supported by the grant from and the National Oceanic and Atmospheric Administration’s Ocean Acidification Program under award NA18OAR0170430 to the Virginia Institute of Marine Science. The data product consists of water quality data for tidal 98 stations for 1984–2018. The source data used to generate this product were downloaded from the Chesapeake Bay Program’s (CBP) data hub. Out of the total of 255 monitoring stations in the Tidal Monitoring Program, we selected 98 with the long monitoring record (30 years or longer). The following variables were downloaded from the data hub at the native temporal and vertical resolution (between one and four cruises per month and approximately 10 depth levels sampled between 0 and 37 m) for 1984–2018: water temperature (T), salinity (S), pH, total alkalinity (TA), dissolved oxygen (DO) , and chlorophyll (Chl). All pH data prior to 1998 were removed because of the data quality concerns (Herrmann et al., 2020). Briefly, we found a dramatic difference in long-term trends between stations measured by institutions in the state of Virginia and stations measured by the state of Maryland, particularly from late spring to early fall. The boundary between the station groups runs east–west within the mesohaline portion of the bay, where the Potomac River estuary intersects the mainstem bay. The boundary separates strong negative linear trends to the south (Virginia stations) from neutral and weakly positive linear trends to the north (Maryland stations). For all variables, data entries marked with CBP’s “Problem†and “Qualifier†flags were removed. Additionally, all variables were scanned for extreme outliers: for each variable, data from all stations, depths, and times were combined into a single composite sample for which the 75th and 25th percentiles (i.e., the upper and lower quantiles) and the interquartile range (the difference between the upper and lower quantiles) were calculated. Extreme outliers were defined as the values falling outside of a certain number (censoring criterion) of interquartile ranges from the upper and lower quantiles.

BackgroundEvidence is mounting that the biopsychosocial paradigm is more accurate and useful than the biomedical paradigm of care. Habits of thought can hinder the implementation of this knowledge into daily care strategies. To understand and lessen these potential barriers, we asked: 1) What is the relative implicit and explicit attitudes of musculoskeletal surgeons towards the biomedical or biopsychosocial paradigms of medicine? 2) What surgeon factors are associated with these attitudes?MethodsAn online survey-based experiment was distributed to members of the Science of Variation Group (SOVG) with a total of 163 respondents. Implicit bias towards the biomedical or biopsychosocial paradigms was measured using an Implicit Association Test (IAT) designed by our team using open-source software; explicit preferences were measured using ordinal scales.ResultsOn average, surgeons demonstrated a moderate implicit bias towards the biomedical paradigm (d-score: -0.21; Interquartile range [IQR]: -0.56 to 0.19) and a moderate explicit preference towards the biopsychosocial paradigm (mean: 14; standard deviation: 14). A greater implicit bias towards the biomedical paradigm was associated with male surgeons (d-score: -0.30; IQR: -0.57 to 0.14; P = 0.005). A greater explicit preference towards the biomedical paradigm was independently associated with a European practice location (Regression coefficient: -9.1; 95% CI: -14 to -4.4; P

Descriptive statistics of variables (Occ. = Occurrences, Medn. = Median, IQR = Interquartile Range).

*P-values for two-way comparisons were obtianed by Wilcoxon rank sum tests, and p-values for the trend across the three groups were made by Kruskal-Wallis tests.†Values represent the measurement taken in the sample used for measurement of plasma free adducts. These values differ from those presented in Table 1 as the latter represent the mean values over the two-year baseline interval used for classification of new onset microalbuminuria.‡AGEs: Advanced Glycation endproducts.NA, Normoalbuminuria. MA, Microalbuminuria. GFR, glomerular filtration rate.FL, Nε-Fructosyl-lysine. CML, Nε-Carboxymethyl-lysine. CEL, Nε-(1-Carboxyethyl)lysine. PENT, Pentosidine. G-H1, Nδ-(5-hydro-4-imidazolon-2-yl) ornithine. MG-H1, Nδ-(5-hydro-5-methyl-4-imidazolon-2-yl)ornithine. 3DG-H, Nδ-(5-hydro-5-(2,3,4-trihydroxybutyl)-4-imidazolon-2-yl)ornithine and related structural isomers, .CMA, Nω-carboxymethylarginine. MetSO, methionine sulfoxide, NFK, N-formylkynurenine. DT, o,o′-dityrosine. 3NT, 3-nitrotyrosine.

BackgroundIn women scheduled for cancer treatment, oocytes cryopreservation is a well-established procedure. Random start protocols have been a substantial improvement in this setting, allowing to prevent delay in the initiation of cancer treatments. However, there is still the need to optimize the regimen of ovarian stimulation, to make treatments more patient-friendly and to reduce costs.MethodsThis retrospective study compares two periods (2019 and 2020), corresponding to two different ovarian stimulation regimens. In 2019, women were treated with corifollitropin, recombinant FSH and GnRH antagonists. Ovulation was triggered with GnRH agonists. In 2020, the policy changed, and women were treated with a progestin-primed ovarian stimulation (PPOS) protocol with human menopausal gonadotropin (hMG) and dual trigger (GnRH agonist and low dose hCG) Continuous data are reported as median [Interquartile Range]. To overcome expected changes in baseline characteristics of the women, the primary outcome was the ratio between the number of mature oocytes retrieved and serum anti-mullerian hormone (AMH) in ng/ml.ResultsOverall, 124 women were selected, 46 in 2019 and 78 in 2020. The ratio between the number of mature oocytes retrieved and serum AMH in the first and second period was 4.0 [2.3–7.1] and 4.0 [2.7–6.8], respectively (p = 0.80). The number of scans was 3 [3–4] and 3 [2–3], respectively (p

GA: Gaze Alternation.ML: Mouth Licking.*Corrected regarding relative size of the areas.Obs: Gaze Alternation was only measured in absolute frequency, and variables that involved areas were only measured in duration.Significant differences are in bold.Medians (Interquartile ranges-IQR) for the variables and two-sample Wilcoxon Signed-rank tests for comparisons regarding the effect of the owner’s direction of attention.

Guardian’s response to postprocedural questionnaires.

Characteristics of FHSIS before and after the update.