Wildlife disease surveillance programs and research studies track infection and identify risk factors for wild populations, humans, and agriculture. Often, several types of samples are collected from individuals to provide more complete information about an animal's infection history. Methods that jointly analyze multiple data streams to study disease emergence and drivers of infection via epidemiological process models remain underdeveloped. Joint-analysis methods can more thoroughly analyze all available data, more precisely quantifying epidemic processes, outbreak status, and risks. We contribute a paired data modeling approach that analyzes multiple samples from individuals. We use "characterization maps" to link paired data to epidemiological processes through a hierarchical statistical observation model. Our approach can provide both Bayesian and frequentist estimates of epidemiological parameters and states. Our approach can also incorporate test sensitivity and specificity, and ..., , , # A method for characterizing disease emergence curves from paired pathogen detection and serology data

Reproducibility strategy

The targets workflow manager for R organizes the analysis. A thorough tutorial and a quick overview are available to learn targets. The targets package can make it easier to create and store project artifacts, such as pre-processed datasets, fitted models, diagnostic and predictive output, and tables and figures. However, the tutorial describes ideal workflows that do not necessarily scale well to very large projects with many computationally expensive steps. So, the repository's use of the targets package will occasionally deviate from the tutorial's demonstration workflows.

The targets package creates and manip...

In statistics and chemometrics education for chemistry and STEAM (Science, Technology, Engineering, Arts, and Mathematics) students, gone are the days of laborious calculations and conclusions based solely on p values. Free and graphical user interface (GUI) software like JAMOVI streamlines calculations, generates informative plots, and fosters a deeper understanding beyond just p values. This article serves as a comprehensive guide for instructors on utilizing JAMOVI to teach essential parametric and nonparametric tests commonly encountered in these fields. Our focus centers on a paired samples t test, Wilcoxon rank sum test, repeated measures ANOVA (RMANOVA), and Friedman test. We also delve into principal component analysis (PCA) using the MEDA plugin, which generates high-quality colored plots that visually elucidate trends between groups. Through 21 guided questions, students will assess data normality, compare dependent groups using both parametric and nonparametric tests, explore comparisons between dependent groups, and observe trends using PCA. These STEAM-contextualized questions and practical examples empower educators to seamlessly integrate JAMOVI into their teaching, enhancing the learning experience for students.

dataset and Octave/MatLab codes/scripts for data analysis Background: Methods for p-value correction are criticized for either increasing Type II error or improperly reducing Type I error. This problem is worse when dealing with thousands or even hundreds of paired comparisons between waves or images which are performed point-to-point. This text considers patterns in probability vectors resulting from multiple point-to-point comparisons between two event-related potentials (ERP) waves (mass univariate analysis) to correct p-values, where clusters of signiticant p-values may indicate true H0 rejection. New method: We used ERP data from normal subjects and other ones with attention deficit hyperactivity disorder (ADHD) under a cued forced two-choice test to study attention. The decimal logarithm of the p-vector (p') was convolved with a Gaussian window whose length was set as the shortest lag above which autocorrelation of each ERP wave may be assumed to have vanished. To verify the reliability of the present correction method, we realized Monte-Carlo simulations (MC) to (1) evaluate confidence intervals of rejected and non-rejected areas of our data, (2) to evaluate differences between corrected and uncorrected p-vectors or simulated ones in terms of distribution of significant p-values, and (3) to empirically verify rate of type-I error (comparing 10,000 pairs of mixed samples whit control and ADHD subjects). Results: the present method reduced the range of p'-values that did not show covariance with neighbors (type I and also type-II errors). The differences between simulation or raw p-vector and corrected p-vectors were, respectively, minimal and maximal for window length set by autocorrelation in p-vector convolution. Comparison with existing methods: Our method was less conservative while FDR methods rejected basically all significant p-values for Pz and O2 channels. The MC simulations, gold-standard method for error correction, presented 2.78±4.83% of difference (all 20 channels) from p-vector after correction, while difference between raw and corrected p-vector was 5,96±5.00% (p = 0.0003). Conclusion: As a cluster-based correction, the present new method seems to be biological and statistically suitable to correct p-values in mass univariate analysis of ERP waves, which adopts adaptive parameters to set correction.

Above-threshold regions in ALE meta-analyses of the Hit > Miss contrast.

Collection description

Functional neuroimaging data on paired associate recollection have expanded over the years, raising the need for an integrative understanding of the literature. The present study performed a quantitative meta-analysis of the data to fulfill that need. The meta-analysis focused on the three most widely used types of activation contrast: Hit > Miss, Intact > Rearranged, and Memory > Perception.

Subject species

homo sapiens

Modality

fMRI-BOLD

Analysis level

meta-analysis

Cognitive paradigm (task)

episodic recall

Map type

R

The Paired Air and Stream Temperature Analysis (PASTA) web application allows users to calculate and plot metrics from paired air and stream temperature annual signal analysis or linear regression, which can help to inform hydrologic processes of a stream reach. Users can upload their stream temperature data or select from specific publically-available data sources (e.g. NWIS or HydroShare) and then compare these stream temperatures to available air temperature data within North America or upload their air temperature data.

Supplemental Data Sets 1-7Supplemental Data Sets 1-7tpc125617_SupplementalDatasets.zip

Analysis of ‘Percentage distribution of the pair victim/reported person according to age of both. VGD (API identifier: 28231)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/urn-ine-es-tabla-t3-312-28231 on 08 January 2022.

--- Dataset description provided by original source is as follows ---

Table of INEBase Percentage distribution of the pair victim/reported person according to age of both. Annual. National. Statistics on Domestic Violence and Gender Violence

--- Original source retains full ownership of the source dataset ---

Biology students’ understanding of statistics is incomplete due to poor integration of these two disciplines. In some cases, students fail to learn statistics at the undergraduate level due to poor student interest and cursory teaching of concepts, highlighting a need for new and unique approaches to the teaching of statistics in the undergraduate biology curriculum. The most effective method of teaching statistics is to provide opportunities for students to apply concepts, not just learn facts. Opportunities to learn statistics also need to be prevalent throughout a student’s education to reinforce learning. The purpose of developing and implementing curriculum that integrates a topic in biology with an emphasis on statistical analysis was to improve students’ quantitative thinking skills. Our lesson focuses on the change in the richness of native species for a specified area with the aid of iNaturalist and the capacity for analysis afforded by Google Sheets. We emphasized the skills of data entry, storage, organization, curation and analysis. Students then had to report their findings, as well as discuss biases and other confounding factors. Pre- and post-lesson assessment revealed students’ quantitative thinking skills, as measured by a paired-samples t test, improved. At the end of the lesson, students had an increased understanding of basic statistical concepts, such as bias in research and making data-based claims, within the framework of biology.

Primary Image: Website screenshot of an iNaturalist observation (Clasping Milkweed – Asclepias amplexicalis). This image is an example of a data entry on iNaturalist. The data students export from iNaturalist is made up of hundreds, or even thousands, of observations like this one. This image is licensed under Creative Commons Attribution - Share Alike 4.0 International license. Source: Observation by cassi saari, 2014.

Data summarized by city, includes the number of city-pair markets in the top 1,000 in either comparison period that involve each city, the number of passengers traveling to and from each city, the average fare, average fare per mile (yield), and average distance traveled. All records are aggregated as directionless city pair markets. All traffic traveling in both directions is added together. https://www.transportation.gov/policy/aviation-policy/competition-data-analysis/research-reports

Dataset contains 70 paired-end ATAC-seq samples from 8 patients.

Nonadditivity in protein–ligand affinity data represents highly instructive structure–activity relationship (SAR) features that indicate structural changes and have the potential to guide rational drug design. At the same time, nonadditivity is a challenge for both basic SAR analysis as well as many ligand-based data analysis techniques such as Free-Wilson Analysis and Matched Molecular Pair analysis, since linear substituent contribution models inherently assume additivity and thus do not work in such cases. While structural causes for nonadditivity have been analyzed anecdotally, no systematic approaches to interpret and use nonadditivity prospectively have been developed yet. In this contribution, we lay the statistical framework for systematic analysis of nonadditivity in a SAR series. First, we develop a general metric to quantify nonadditivity. Then, we demonstrate the non-negligible impact of experimental uncertainty that creates apparent nonadditivity, and we introduce techniques to handle experimental uncertainty. Finally, we analyze public SAR data sets for strong nonadditivity and use recourse to the original publications and available X-ray structures to find structural explanations for the nonadditivity observed. We find that all cases of strong nonadditivity (ΔΔpKi and ΔΔpIC50 > 2.0 log units) with sufficient structural information to generate reasonable hypothesis involve changes in binding mode. With the appropriate statistical basis, nonadditivity analysis offers a variety of new attempts for various areas in computer-aided drug design, including the validation of scoring functions and free energy perturbation approaches, binding pocket classification, and novel features in SAR analysis tools.

Ulcerative colitis (UC) is a serious chronic intestinal inflammatory disease, with an increased incidence in recent years. The intestinal microbiota plays a key role in the pathogenesis of UC. However, there is no unified conclusion on how the intestinal microbiota changes. Most studies focus on the change between UC patients and healthy individuals, rather than the active and remission stage of the same patient. To minimize the influences of genetic differences, environmental and dietary factors, we studied the intestinal microbiota of paired fecal samples from 42 UC patients at the active and remission stages. We identified 175 species of microbes from 11 phyla and found no difference of the alpha and beta diversities between the active and remission stages. Paired t-test analysis revealed differential microbiota at levels of the phyla, class, order, family, genus, and species, including 13 species with differential abundance. For example, CAG-269 sp001916005, Eubacterium F sp003491505, Lachnospira sp000436475, et al. were downregulated in the remission, while the species of Parabacteroides distasonis, Prevotellamassilia sp900540885, CAG-495 sp001917125, et al. were upregulated in the remission. The 13 species can effectively distinguish the active and remission stages. Functional analysis showed that the sporulation and biosynthesis were downregulated, and the hydrogen peroxide catabolic process was upregulated in remission of UC. Our study suggests that the 13 species together may serve as a biomarker panel contributing to identify the active and remission stages of UC, which provides a valuable reference for the treatment of UC patients by FMT or other therapeutic methods.

Usage notes

The data file includes data collected from survey research conducted across three national contexts (Ireland, Malta and the UK) and events in seven cities between 2016 and 2019 (n=1590). This questionnaire used closed-ended multiple choice questions (e.g. demographic data and Likert scales about attitudes towards research). The research used a software solution designed for paired samples with matching between pre-visit and post-visit responses at the individual level, as well as automated email invitations and reminders for the post-visit questionnaire and real-time data analysis and automatic visualizations for event organizers (see qualiaanalytics.org).

Abstract
Extra-pair reproduction is widely hypothesised to allow females to avoid inbreeding with related socially-paired males. Consequently, numerous field studies have tested the key predictions that extra-pair offspring are less inbred than females’ alternative within-pair offspring, and that the probability of extra-pair reproduction increases with a female's relatedness to her socially-paired male. However such studies rarely measure inbreeding or relatedness sufficiently precisely to detect subtle effects, or consider biases stemming from failure to observe inbred offspring that die during early development. Analyses of multi-generational song sparrow (Melospiza melodia) pedigree data showed that most females had opportunity to increase or decrease the coefficient of inbreeding of their offspring through extra-pair reproduction with neighbouring males. In practice, observed extra-pair offspring had lower inbreeding coefficients than females’ within-pair offspring on average, while the probability of extra-pair reproduction increased substantially with the coefficient of kinship between a female and her socially-paired male. However, simulations showed that such effects could simply reflect bias stemming from inbreeding depression in early offspring survival. The null hypothesis that extra-pair reproduction is random with respect to kinship therefore cannot be definitively rejected in song sparrows, and existing general evidence that females avoid inbreeding through extra-pair reproduction requires re-evaluation given such biases.

1. One hypothesis explaining extra-pair reproduction is that socially monogamous females mate with extra-pair males to adjust the coefficient of inbreeding (f) of extra-pair offspring (EPO) relative to that of within-pair offspring (WPO) they would produce with their socially paired male. Such adjustment of offspring f requires non-random extra-pair reproduction with respect to relatedness, which is in turn often assumed to require some mechanism of explicit pre-copulatory or post-copulatory kin discrimination. 2. We propose three demographic processes that could potentially cause mean f to differ between individual females' EPO and WPO given random extra-pair reproduction with available males without necessarily requiring explicit kin discrimination. Specifically, such a difference could arise if social pairings formed non-randomly with respect to relatedness or persisted non-randomly with respect to relatedness, or if the distribution of relatedness between females and their sets of p...

Available only on the web, provides information for airport pair markets rather than city pair markets. This table only lists airport markets where the origin or destination airport is an airport that has other commercial airports in the same city. Midway Airport (MDW) and O'Hare (ORD) are examples of this. All records are aggregated as directionless markets. The combination of Airport_1 and Airport_2 define the airport pair market. All traffic traveling in both directions is added together. https://www.transportation.gov/policy/aviation-policy/competition-data-analysis/research-reports

The set of all 803 originally identified orthologous target pairs (OTPs) and the subset of 222 OTPs with at least 10 shared compounds are provided herein. For each OTP both organisms, the target, the number of shared compounds,the OTP category, and the number of reference articles is reported. In addtion, the list of all 1149 candidate compounds and their human target assignments is provided.

Abstract
Understanding the evolutionary dynamics of inbreeding and inbreeding depression requires unbiased estimation of inbreeding depression across diverse mating systems. However, studies estimating inbreeding depression often measure inbreeding with error, for example, based on pedigree data derived from observed parental behavior that ignore paternity error stemming from multiple mating. Such paternity error causes error in estimated coefficients of inbreeding (f) and reproductive success and could bias estimates of inbreeding depression. We used complete “apparent” pedigree data compiled from observed parental behavior and analogous “actual” pedigree data comprising genetic parentage to quantify effects of paternity error stemming from extra-pair reproduction on estimates of f, reproductive success, and inbreeding depression in free-living song sparrows (Melospiza melodia). Paternity error caused widespread error in estimates of f and male reproductive success, causing inbreeding depression in male and female annual and lifetime reproductive success and juvenile male survival to be substantially underestimated. Conversely, inbreeding depression in adult male survival tended to be overestimated when paternity error was ignored. Pedigree error stemming from extra-pair reproduction therefore caused substantial and divergent bias in estimates of inbreeding depression that could bias tests of evolutionary theories regarding inbreeding and inbreeding depression and their links to variation in mating system.

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