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

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 not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: File format: R workspace file; “Simulated_Dataset.RData”. Metadata (including data dictionary) • y: Vector of binary responses (1: adverse outcome, 0: control) • x: Matrix of covariates; one row for each simulated individual • z: Matrix of standardized pollution exposures • n: Number of simulated individuals • m: Number of exposure time periods (e.g., weeks of pregnancy) • p: Number of columns in the covariate design matrix • alpha_true: Vector of “true” critical window locations/magnitudes (i.e., the ground truth that we want to estimate) 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 dataset is a collection of 1, 2, or 3 images from: BIPED, BSDS500, BSDS300, DIV2K, WIRE-FRAME, CID, CITYSCAPES, ADE20K, MDBD, NYUD, THANGKA, PASCAL-Context, SET14, URBAN10, and the camera-man image. The image selection process consists on computing the Inter-Quartile Range (IQR) intensity value on all the images, images larger than 720×720 pixels were not considered. In dataset whose images are in HR, they were cut. We thank all the datasets owners to make them public. This dataset is just for Edge Detection not contour nor Boundary tasks.

Dataset from the article Secchi F, Monti CB, Alì M, Carbone FS, Cannaò PM, Sardanelli F. Diagnostic Value of Global Cardiac Strain in Patients With Myocarditis. J Comput Assist Tomogr. 2020 Jul/Aug;44(4):591-598. doi: 10.1097/RCT.0000000000001062. PMID: 32697530.

Abstract

Background: Cardiac strain represents an imaging biomarker of contractile dysfunction.

Purpose: The purpose of this study was to investigate the diagnostic value of cardiac strain obtained by feature-tracking cardiac magnetic resonance (MR) in acute myocarditis.

Materials and methods: Cardiac MR examinations of 46 patients with myocarditis and preserved ejection fraction at acute phase and follow-up were analyzed along with cardiac MR of 46 healthy age- and sex-matched controls. Global circumferential strain and global radial strain were calculated for each examination, along with myocardial edema and late gadolinium enhancement, and left ventricle functional parameters, through manual contouring of the myocardium. Correlations were assessed using Spearman ρ. Wilcoxon and Mann-Whitney U test were used to assess differences between data. Receiver operating characteristics curves and reproducibility were obtained to assess the diagnostic role of strain parameters.

Results: Global circumferential strain was significantly lower in controls (median, -20.4%; interquartile range [IQR], -23.4% to -18.7%) than patients in acute phase (-18.4%; IQR, -21.0% to -16.1%; P = 0.001) or at follow-up (-19.2%; IQR, -21.5% to -16.1%; P = 0.020). Global radial strain was significantly higher in controls (82.4%; IQR, 62.8%-104.9%) than in patients during the acute phase (65.8%; IQR, 52.9%-79.5%; P = 0.001). Correlations were found between global circumferential strain and global radial strain in all groups (acute, ρ = -0.580, P < 0.001; follow-up, ρ = -0.399, P = 0.006; controls, ρ = -0.609, P < 0.001), and between global circumferential strain and late gadolinium enhancement only in myocarditis patients (acute, ρ = 0.035, P = 0.024; follow-up, ρ = 0.307, P = 0.038).

Conclusions: Cardiac strain could potentially have a role in detecting acute myocarditis in low-risk acute myocarditis patients where cardiac MR is the main diagnosing technique.

A live version of the data record, which will be kept up-to-date with new estimates, can be downloaded from the Humanitarian Data Exchange: https://data.humdata.org/dataset/covid-19-mobility-italy.

If you find the data helpful or you use the data for your research, please cite our work:

Pepe, E., Bajardi, P., Gauvin, L., Privitera, F., Lake, B., Cattuto, C., & Tizzoni, M. (2020). COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown. Scientific Data 7, 230 (2020).

The data record is structured into 4 comma-separated value (CSV) files, as follows:

id_provinces_IT.csv. Table of the administrative codes of the 107 Italian provinces. The fields of the table are:

COD_PROV is an integer field that is used to identify a province in all other data records;

SIGLA is a two-letters code that identifies the province according to the ISO_3166-2 standard (https://en.wikipedia.org/wiki/ISO_3166-2:IT);

DEN_PCM is the full name of the province.

OD_Matrix_daily_flows_norm_full_2020_01_18_2020_04_17.csv. The file contains the daily fraction of users’ moving between Italian provinces. Each line corresponds to an entry of matrix (i, j). The fields of the table are:

p1: COD_PROV of origin,

p2: COD_PROV of destination,

day: in the format yyyy-mm-dd.

median_q1_q3_rog_2020_01_18_2020_04_17.csv. The file contains median and interquartile range (IQR) of users’ radius of gyration in a province by week. Each entry of the table fields of the table are:

COD_PROV of the province;

SIGLA of the province;

DEN_PCM of the province;

week: median value of the radius of gyration on week week, with week in the format dd/mm-DD/MM where dd/mm and DD/MM are the first and the last day of the week, respectively.

week Q1 first quartile (Q1) of the distribution of the radius of gyration on week week,

week Q3 third quartile (Q3) of the distribution of the radius of gyration on week week,

average_network_degree_2020_01_18_2020_04_17.csv. The file contains daily time-series of the average degree 〈k〉 of the proximity network. Each entry of the table is a value of 〈k〉 on a given day. The fields of the table are:

COD_PROV of the province;

SIGLA of the province;

DEN_PCM of the province;

day in the format yyyy-mm-dd.

ESRI shapefiles of the Italian provinces updated to the most recent definition are available from the website of the Italian National Office of Statistics (ISTAT): https://www.istat.it/it/archivio/222527.

Background: Severe pneumonia is pathological manifestation of Coronavirus Disease 2019 (COVID-19), however complications have been reported in COVID-19 patients with a worst prognosis. Aim of this study was to evaluate the role of high sensitivity cardiac troponin I (hs-TnI) in patients with SARS-CoV-2 infection.

Methods: we retrospectively analysed hs-TnI values measured in 523 patients (median age 64 years, 68% men) admitted to a university hospital in Milan, Italy, and diagnosed COVID-19.

Results: A significant difference in hs-TnI concentrations was found between deceased patients (98 patients) vs discharged (425 patients) [36.05 ng/L IQR 16.5-94.9 vs 6.3 ng/L IQR 2.6-13.9, p < 0.001 respectively]. Hs-TnI measurements were independent predictors of mortality at multivariate analysis adjusted for confounding parameters such as age (HR 1.004 for each 10 point of troponin, 95% CI 1.002-1.006, p < 0.001). The survival rate, after one week, in patients with hs-TnI values under 6 ng/L was 97.94%, between 6 ng/L and the normal value was 90.87%, between the normal value and 40 ng/L was 86.98, and 59.27% over 40 ng/L.

Conclusion: Increase of hs-TnI associated with elevated mortality in patients with COVID-19. Troponin shows to be a useful biomarker of disease progression and worse prognosis in COVID-19 patients.

Context

Our target was to predict gender, age and emotion from audio. We found audio labeled datasets on Mozilla and RAVDESS. So by using R programming language 20 statistical features were extracted and then after adding the labels these datasets were formed. Audio files were collected from "Mozilla Common Voice" and “Ryerson AudioVisual Database of Emotional Speech and Song (RAVDESS)”.

Content

Datasets contains 20 feature columns and 1 column for denoting the label. The 20 statistical features were extracted through the Frequency Spectrum Analysis using R programming Language. They are: 1) meanfreq - The mean frequency (in kHz) is a pitch measure, that assesses the center of the distribution of power across frequencies. 2) sd - The standard deviation of frequency is a statistical measure that describes a dataset’s dispersion relative to its mean and is calculated as the variance’s square root. 3) median - The median frequency (in kHz) is the middle number in the sorted, ascending, or descending list of numbers. 4) Q25 - The first quartile (in kHz), referred to as Q1, is the median of the lower half of the data set. This means that about 25 percent of the data set numbers are below Q1, and about 75 percent are above Q1. 5) Q75 - The third quartile (in kHz), referred to as Q3, is the central point between the median and the highest distributions. 6) IQR - The interquartile range (in kHz) is a measure of statistical dispersion, equal to the difference between 75th and 25th percentiles or between upper and lower quartiles. 7) skew - The skewness is the degree of distortion from the normal distribution. It measures the lack of symmetry in the data distribution. 8) kurt - The kurtosis is a statistical measure that determines how much the tails of distribution vary from the tails of a normal distribution. It is actually the measure of outliers present in the data distribution. 9) sp.ent - The spectral entropy is a measure of signal irregularity that sums up the normalized signal’s spectral power. 10) sfm - The spectral flatness or tonality coefficient, also known as Wiener entropy, is a measure used for digital signal processing to characterize an audio spectrum. Spectral flatness is usually measured in decibels, which, instead of being noise-like, offers a way to calculate how tone-like a sound is. 11) mode - The mode frequency is the most frequently observed value in a data set. 12) centroid - The spectral centroid is a metric used to describe a spectrum in digital signal processing. It means where the spectrum’s center of mass is centered. 13) meanfun - The meanfun is the average of the fundamental frequency measured across the acoustic signal. 14) minfun - The minfun is the minimum fundamental frequency measured across the acoustic signal 15) maxfun - The maxfun is the maximum fundamental frequency measured across the acoustic signal. 16) meandom - The meandom is the average of dominant frequency measured across the acoustic signal. 17) mindom - The mindom is the minimum of dominant frequency measured across the acoustic signal. 18) maxdom - The maxdom is the maximum of dominant frequency measured across the acoustic signal 19) dfrange - The dfrange is the range of dominant frequency measured across the acoustic signal. 20) modindx - the modindx is the modulation index, which calculates the degree of frequency modulation expressed numerically as the ratio of the frequency deviation to the frequency of the modulating signal for a pure tone modulation.

Acknowledgements

Gender and Age Audio Data Souce: Link: https://commonvoice.mozilla.org/en Emotion Audio Data Souce: Link : https://smartlaboratory.org/ravdess/

For each of the 13 variables, the article count, average, standard deviation (SD), median, interquartile range (IQR), and minimum and maximum values are presented. All descriptive statistics are calculated for all journals and within the distinct journal quartiles. As many variables have been transformed, a description of the transformation performed (if any) is also included.

BackgroundTuberculosis (TB) remains a significant public health challenge, particularly among vulnerable populations like children. This is especially true in Sub-Saharan Africa, where the burden of TB in children is substantial. Zambia ranks 21st among the top 30 high TB endemic countries globally. While studies have explored TB in adults in Zambia, the prevalence and associated factors in children are not well documented. This study aimed to determine the prevalence and sociodemographic, and clinical factors associated with active TB disease in hospitalized children under the age of 15 years at Livingstone University Teaching Hospital (LUTH), the largest referral center in Zambia’s Southern Province.MethodsThis retrospective cross-sectional study of 700 pediatric patients under 15 years old, utilized programmatic data from the Pediatrics Department at LUTH. A systematic sampling method was used to select participants from medical records. Data on demographics, medical conditions, anthropometric measurements, and blood tests were collected. Data analysis included descriptive statistics, chi-square tests, and multivariable logistic regression to identify factors associated with TB.ResultsThe median age was 24 months (interquartile range (IQR): 11, 60) and majority were male (56.7%, n = 397/700). Most participants were from urban areas (59.9%, n = 419/700), and 9.2% (n = 62/675) were living with HIV. Malnutrition and comorbidities were present in a significant portion of the participants (19.0% and 25.1%, respectively). The prevalence of active TB cases was 9.4% (n = 66/700) among hospitalized children. Persons living with HIV (Adjusted odds ratio (AOR) of 6.30; 95% confidence interval (CI) of 2.85, 13.89, p< 0.001), and those who were malnourished (AOR: 10.38, 95% CI: 4.78, 22.55, p< 0.001) had a significantly higher likelihood of developing active TB disease.ConclusionThis study revealed a prevalence 9.4% active TB among hospitalized children under 15 years at LUTH. HIV status and malnutrition emerged as significant factors associated with active TB disease. These findings emphasize the need for pediatric TB control strategies that prioritize addressing associated factors to effectively reduce the burden of tuberculosis in Zambian children.

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

This is a dataset of 43 pediatric acute lymphoblastic leukemia patients in which we analysed consecutive methotrexate and folate levels. Background After High-Dose Methotrexate (HD-MTX), folinic acid rescue therapy (Leucovorin) is administered to reduce side effects in pediatric acute lymphoblastic leukemia (ALL) patients. Leucovorin and MTX are structural analogues, possibly competing for cellular transport and intracellular metabolism. We hypothesize that Leucovorin accumulates during consecutive courses, which might result in a lower MTX uptake.

Methods We prospectively measured red blood cell (RBC) folate and MTX levels during four HD-MTX and Leucovorin courses in 43 patients treated according the DCOG ALL-11 protocol with 2-weekly HD-MTX (5 g/m2/dose) and Leucovorin (15 mg/m2/dose) using LC-MS/MS. We estimated a linear mixed model to assess the relationship between these variables over time.

Results Both RBC MTX-PG and folate levels increased significantly during protocol M. MTX-PG2-5 levels increased most substantially after the first two HD-MTX courses (until median 113.0 nmol/L, IQR 76.8-165.2) after which levels plateaued during the 3d and 4th course (until median 141.3 nmol/L, IQR 100.2-190.2). In parallel, folate levels increased most substantially after the first two HD-MTX courses (until median 401.6 nmol/L, IQR 163.3-594.2) after which levels plateaued during the 3d and 4th course (until median 411.5 nmol/L, IQR 240.3-665.6). The ratio folate/MTX-PG decreased significantly over time, which was mostly due to the relatively higher increase (delta) of MTX-PG.

Conclusion These results suggest that the increase in RBC folate levels does not seem to have a large effect on RBC MTX levels. Future studies, assessing competition of Leucovorin and MTX on other cellular mechanisms which might negatively affect treatment efficacy, are necessary.

BackgroundBirthweight has an impact on newborn’s future health outcomes. Maternal factors, including age, delivery mode, HIV status, gestational age, parity and obstetric complications (preeclampsia or eclampsia [PE], antepartum hemorrhage [APH] and sepsis), however, have been shown as risk factors of low birthweight (LBW) elsewhere. For data-guided interventions, we aimed to identify predictors of LBW and compare newborn birthweights between different groups of maternal factors at Rev. John Chilembwe Hospital in Phalombe district, Malawi.MethodsUsing a retrospective record review study design, we extracted data from maternity registers of 1244 women and their newborns from October, 2022 to March, 2023. Data were skewed. Median test was used to compare median birthweights. Chi-square or Fisher’s exact tests were used to compare proportions of LBW among different groups of maternal factors. Multivariable logistic regression with stepwise, forward likelihood method was performed to identify predictors of LBW.ResultsMedian birthweight was 2900.00g (interquartile range [IQR]: 2600.00g to 3200.00g). Prevalence of LBW was 16.7% (n = 208). Proportions of LBW infants were higher in women with PE, APH, including women with sepsis than controls (10 [47.6%] of 21 vs 7 [58.3%] of 12 vs 191 [15.8%] of 1211, p < .001). Lower in term and postterm than preterm (46 [5.5%] of 835 vs 2 [3.7%] of 54 vs 160 [45.1%] of 355, p < .001). The odds of LBW infants were higher in preterm than term (AOR = 13.76, 95%CI: 9.54 to 19.84, p < .001), women with PE (AOR = 3.88, 95%CI: 1.35 to 11.18, p = .012), APH, including women with sepsis (AOR = 6.25, 95%CI: 1.50 to 26.11, p = .012) than controls.ConclusionPrevalence of LBW was high. Its predictors were prematurity, PE, APH and sepsis. Interventions aimed to prevent these risk factors should be prioritized to improve birthweight outcomes.

Relationship between common exonic deletion/duplication and clinical phenotypes in Korean patients with dystrophinopathy.

Clinical characteristics of 227 Korean patients with dystrophinopathy.

Non-genetic exposures—including nutrients, lifestyle factors, pollutants, and infections—substantially contribute to phenotypic variation. Most studies assess only a few exposures or phenotypes, yielding fragmented exposome-phenome relationships. Systematic approaches are needed to quantify how the exposome—the totality of environmental exposures—relates broadly to clinically relevant phenotypes. We developed a resource benchmarking the exposome’s role using data from the National Health and Nutrition Examination Survey (NHANES), cataloging 619 exposures and 278 phenotypes, and systematically testing associations (Phenotype-exposure-wide association study [P-ExWAS]). Among ~119k associations, 5% (n=5,661) were Bonferroni significant, and 40% replicated across independent population samples. Single exposures explained modest variance (median R²=0.5%; interquartile range [IQR]: 0.27–1.10%). Twenty simultaneous exposome factors increased median variance explained to 3.5% (IQR: 1.8–7.8%), comparable to 1M genetic variants. The exposome-phenome atlas is freely available at: http://apps.chiragjpgroup.org/pe_atlas/.

a : Median absolute percent bias of σ2u was calculated for each simulation scenario, then summarized across scenarios.b : This is the number of simulation scenarios used to calculate the information.

Detailed information on data used to obtain the results presented in this article. (XLSX)