b and b_se are respectively the point estimate and the standard error of b obtained from the census data. b_boot_withinyear_lower (or upper) gives respectively the 95% lower (or upper) bound of b estimated from 500 samples bootstrapped within each year. b_boot_withinyearstate_lower (or upper) gives respectively the 95% lower (or upper) bound of b estimated from 500 samples bootstrapped within each combination of year and state. (XLSX)

Descriptive statistics, correlation matrix, Cronbach’s α, AVE, CR, and square root of AVE.

Wildlife microbiome studies are being used to assess microbial links with animal health and habitat. The gold standard of sampling microbiomes directly from captured animals is ideal for limiting potential abiotic influences on microbiome composition, yet fails to leverage the many benefits of non-invasive sampling. Application of microbiome-based monitoring for rare, endangered, or elusive species creates a need to non-invasively collect scat samples shed into the environment. Since controlling sample age is not always possible, the potential influence of time-associated abiotic factors was assessed. To accomplish this, we analyzed partial 16S rRNA genes of fecal metagenomic DNA sampled non-invasively from Rocky Mountain elk (Cervus canadensis) near Yellowstone National Park. We sampled pellet piles from four different elk, then aged them in a natural forest plot for 1, 3, 7, and 14 days, with triplicate samples at each time point (i.e., a blocked, repeat measures (longitudinal) study design). We compared microbiomes of each elk through time with point estimates of diversity, bootstrapped hierarchical clustering of samples, and a version of ANOVA–simultaneous components analysis (ASCA) with PCA (LiMM-PCA) to assess the variance contributions of time, individual and sample replication. Our results showed community stability through days 0, 1, 3 and 7, with a modest but detectable change in abundance in only 2 genera (Bacteroides and Sporobacter) at day 14. The total variance explained by time in our LiMM-PCA model across the entire 2-week period was not statistically significant (p>0.195) and the overall effect size was small (<10% variance) compared to the variance explained by the individual animal (p<0.0005; 21% var.). We conclude that non-invasive sampling of elk scat collected within one week during winter/early spring provides a reliable approach to characterize microbiome composition in a 16S rDNA survey and that sampled individuals can be directly compared across unknown time points with minimal bias. Further, point estimates of microbiome diversity were not mechanistically affected by sample age. Our assessment of samples using bootstrap hierarchical clustering produced clustering by animal (branches) but not by sample age (nodes). These results support greater use of non-invasive microbiome sampling to assess ecological patterns in animal systems. Methods 1.1 Sample collection Scat samples from 4 elk were collected near the northern boundary of Yellowstone National Park in Montana in March 2016. Animal sampling was conducted non-invasively within 15 minutes of defecation. Elk sex and age could not be accurately determined due to these samples being collected after observing the elk defecating from a distance using binoculars. Based on our observations, they were most likely adult females or young males. Fecal samples from each scat pile (i.e., individual) were collected from the ground with sterile gloves and forceps and placed in sterile whirl-pak sample bags. Sample whirl-paks were placed on wet ice in a cooler in the field for transportation to the experimental site. The experimental site was located on a sparsely forested plot near Evaro, MT with conditions known to be suitable as elk habitat, at approximately 4000 ft elevation. Three pellets from each animal were frozen at -20° C after arriving at the experimental site approximately 6 hours post-defecation. This initial subsample represents time-point zero samples (and technical replicates) with minimal exposure to ambient conditions typical of a direct or capture-based sampling scheme. The remaining pellets from each elk were placed in square plastic culture plates (25 cm x 25 cm) with a grid backing using sterile gloves and forceps. Each culture plate had a larger glass plate suspended above it at a height of 4 cm using a cork stopper in each corner to allow air flow and prevent direct contact with incidental precipitation (although no precipitation occurred on-site during the study), and the group of culture plates was surrounded by protective wire fencing. One plate was used for each technical replicate, with each replicate plate containing samples from all four individuals (for photos of the enclosure and a schematic of the experimental layout see Supplemental Figure 1). The samples were exposed to ambient conditions from March 27th through April 9th (14 days). Three samples from each elk were removed from the replicate plates after 1 day, 3 days, 7 days, and 14 days and immediately frozen at ‑20° C after removal from ambient conditions. A total of 60 elk pellets were experimentally collected. Temperature was logged in 10-minute increments during the study using Thermocron temperature loggers (OnSolution Pty Ltd, Australia) distributed above and below the culture plates and shielded from direct sunlight. The temperature data were aggregated into hourly oscillations, daily max and minimum, and a smoothed average temperature. Additional temperature recordings were obtained from a NOAA weather station (Point 6, MT) 3.5 miles and 4000 ft above our site as reference. 1.1 Sample preparation, DNA extraction and sequencing Frozen elk fecal pellets (stored frozen at -20° C) were prepared for DNA extractions by separating a standard weight (250 mg) cross-section from each pellet using a sterile petri dish (10 cm) and sterile safety razor blade for each sample. This fraction was placed into a designated sample tube from the Qiagen PowerSoil DNA extraction kit (Qiagen Inc., Germantown, MD) and processed using the manufacturer’s recommended protocol. The resulting purified metagenomic DNA was eluted with 100 µL PCR-grade water and stored at -20° C prior to further analysis. To assess the bacterial community present in the fecal DNA extraction, we used a generally-conserved (i.e., “universal”) 16S/18S barcoded primer set (536F and 907R) designed to amplify the V4 and V5 variable regions of the rRNA gene (Holben et al., 2004) and PCR using 1mL of elk fecal sample metagenomic DNA standardized to 25ng/mL as template. Once amplified, samples were gel purified using the QIAGEN Gel Purification kit (QIAGEN, Germantown, MD) following the manufacturer’s recommended protocol for downstream direct sequencing. An Illumina MiSeq platform (San Diego, CA, USA) was used to obtain 300 base-pair (bp) paired-end sequencing using the Illumina MiSeq Reagent Kit.

Regression analyses and Shapley value decomposition.

Formatted dataset corresponding to the design in Fig 2.

While Hierarch cannot produce p-values and confidence intervals for interaction effects, it can account for interactions when estimating main effects.

Hierarchical linear regression model (n = 289) with anxiety as dependent variable with 95% bias corrected and accelerated confidence intervals based on 1000 bootstrap samples.

Mediation analysis of principals’ perceived trust by teachers on the relationships between leadership practice components and PLCs.

For experiments with several factors on the same level (left side of table), exact permutation tests can be constructed by concatenating all but one of the factors and treating the leftover factor as nested within the others (right side of table).

File List

   BootstrapSES.R: R code demonstrating how to compute diversity effects as standardized effect size (SES) following Potvin and Gotelli (2008) for both (i) plot-level overyielding and (ii) comparison among canopy statuses.
   (md5: 0b9c6fc3752d601e7660515eb0505159)
    MultilevelModeling.R: R code for fitting and comparing the relative support for a hierarchy of linear-mixed models of individual tree growth. R code demonstrating how to compute confidence intervals with a bias-corrected bootstrap method.
   (md5: c73e3dec4e1059c7c518fe1a07a416fa)
Description

   BootstrapSES.R generates random data conforming to our experimental design (part 0) and tests for overyielding (part 1) using our modified version of the bootstrap methods developed by Potvin and Gotelli (2008). The bootstrap procedure is then used to compare the magnitude of overyielding among canopy statuses (part 2). Part 1 and part 2 could be directly applied to any data.frame containing columns similar to that of “data”, which is fully described at the end of part 0.
    MultilevelModeling.R requires the lme4 package to fit linear-mixed models. First, methods are defined to generate random tree growth data (simulateData()), to perform bootstrap likelihood ratio tests (bootLRT()), and to compute confidence intervals following a bias-corrected percentile method (see Efron and Tibishrani, 1986; BCconf()). The rest of the code generates random data, fits a hierarchy of linear-mixed model of tree growth, compares the models based on AIC and computes confidence intervals for the most complex example model.

Neural demyelination and brain damage accumulated in white matter appear as hyperintense areas on T2-weighted MRI scans in the form of lesions. Modeling binary images at the population level, where each voxel represents the existence of a lesion, plays an important role in understanding aging and inflammatory diseases. We propose a scalable hierarchical Bayesian spatial model, called BLESS, capable of handling binary responses by placing continuous spike-and-slab mixture priors on spatially varying parameters and enforcing spatial dependency on the parameter dictating the amount of sparsity within the probability of inclusion. The use of mean-field variational inference with dynamic posterior exploration, which is an annealing-like strategy that improves optimization, allows our method to scale to large sample sizes. Our method also accounts for underestimation of posterior variance due to variational inference by providing an approximate posterior sampling approach based on Bayesian bootstrap ideas and spike-and-slab priors with random shrinkage targets. Besides accurate uncertainty quantification, this approach is capable of producing novel cluster size based imaging statistics, such as credible intervals of cluster size, and measures of reliability of cluster occurrence. Lastly, we validate our results via simulation studies and an application to the UK Biobank, a large-scale lesion mapping study with a sample size of 40,000 subjects. Supplementary materials for this article are available online.

The characterization of the metabolome is a critical aspect in basic research and plant breeding. In this work, the putative application of metabolomics for phenotyping closely related genotypes has been tested. Crude extracts were profiled by LC-MS and GC-MS, and mass data extraction was performed with XCMS software. Result validation was achieved with principal component analysis (PCA). The ability of the profiling methodologies to discriminate plant genotypes was assessed after hierarchical clustering analysis (HCA). Cluster robustness was assessed by a multiscale bootstrap resampling method. A better performance of LC-MS profiling over GC-MS was evidenced in terms of phenotype demarcation after PCA and HCA. Citrus demarcation was similarly achieved independently of the environmental conditions used to grow plants. In addition, when all different locations were pooled in a single experimental design, it was still possible to differentiate the three closely related genotypes. The presented methodology provides a fast and nontargeted workflow as a powerful tool to discriminate related plant phenotypes. The novelty of the technique relies on the use of mass signals as markers for phenotype demarcation independent of putative metabolite identities and the relatively simple analytical strategy that can be applicable to a wide range of plant matrices with no previous optimization.

Makerspaces gather a large number of entrepreneurial entities and resources, playing a vital role in the survival and development of user entrepreneurial enterprises. Based on network embeddedness theory and resource-based theory, this study employs hierarchical regression analysis and bootstrap method to examine the relationship between makerspace network embeddedness, business model innovation, and user entrepreneurial performance, as well as the moderating role of environmental dynamics. Using empirical data from 245 valid samples of user entrepreneurial firms in Chinese makerspaces, the results reveal that makerspace network embeddedness significantly promotes business model innovation and user entrepreneurial performance. Moreover, business model innovation has a significantly positive impact on user entrepreneurial performance, and partially mediates the relationship between makerspace network embeddedness and user entrepreneurial performance. Furthermore, environmental dynamics exhibit a significant positive moderating effect on the relationship between makerspace network embeddedness and business model innovation. These findings effectively reveal the mechanism for enhancing user entrepreneurial performance and offer practical insights for user entrepreneurial enterprises to leverage network embeddedness for improved entrepreneurial performance.

Gestational weight gain (GWG) estimates for the year 2020 by regions and national income level derived from hierarchical modeling.

The present study aims to explore the mechanism for the impact of job embeddedness on safety performance, the mediating role of perceived insider status and the cross-level moderating role of safety climate among miners. The questionnaire data used for analysis in this study were collected from 310 miners in 38 coal mine production teams in China. Bootstrap analysis was performed to explore the mediating role of perceived insider status, and multilevel linear analysis was performed to explore the cross-level moderating role of safety climate. The results showed that job embeddedness was positively related to miners’ safety performance; perceived insider status mediating the relationship between job embeddedness and miners’ safety performance; and safety climate moderating the relationship between perceived insider status and miners’ safety performance across levels.

Direct, indirect, and total effects of social support on autonomous physical learning with bootstrap confidence intervals.

Combinatorial gene perturbations provide rich information for a systematic exploration of genetic interactions. Despite successful applications to bacteria and yeast, the scalability of this approach remains a major challenge for higher organisms such as humans. Here, we report a novel experimental and computational framework to efficiently address this challenge by limiting the ‘search space’ for important genetic interactions. We propose to integrate rich phenotypes of multiple single gene perturbations to robustly predict functional modules, which can subsequently be subjected to further experimental investigations such as combinatorial gene silencing. We present posterior association networks (PANs) to predict functional interactions between genes estimated using a Bayesian mixture modelling approach. The major advantage of this approach over conventional hypothesis tests is that prior knowledge can be incorporated to enhance predictive power. We demonstrate in a simulation study and on biological data, that integrating complementary information greatly improves prediction accuracy. To search for significant modules, we perform hierarchical clustering with multiscale bootstrap resampling. We demonstrate the power of the proposed methodologies in applications to Ewing's sarcoma and human adult stem cells using publicly available and custom generated data, respectively. In the former application, we identify a gene module including many confirmed and highly promising therapeutic targets. Genes in the module are also significantly overrepresented in signalling pathways that are known to be critical for proliferation of Ewing's sarcoma cells. In the latter application, we predict a functional network of chromatin factors controlling epidermal stem cell fate. Further examinations using ChIP-seq, ChIP-qPCR and RT-qPCR reveal that the basis of their genetic interactions may arise from transcriptional cross regulation. A Bioconductor package implementing PAN is freely available online at http://bioconductor.org/packages/release/bioc/html/PANR.html.

BackgroundThe aging population has led to a marked increase in the prevalence of chronic diseases among the elderly, significantly impacting their physical and mental health, as well as their overall quality of life. In rural regions of Western China, these challenges are exacerbated by limited access to medical insurance, low living standards, and inadequate mental health services. Consequently, the physical and mental well-being of elderly individuals with chronic conditions in these areas warrants focused attention. This study aims to investigate the interrelationships between depression, social support, and subjective well-being, with particular emphasis on the mediating role of social support.MethodsThis cross-sectional study involved a survey of 2,156 elderly individuals aged 60 and above, all living with chronic diseases in the rural areas of Qiannan, Guizhou, China. Pearson correlation and hierarchical linear regression analyses were employed to explore the relationships between the variables. A structural equation model was then constructed using Amos 23.0, based on the identified correlations between depression, social support, and subjective well-being. The bootstrap estimation method was applied to assess the mediating effect of social support in the depression-subjective well-being relationship.ResultsThe analysis revealed a significant negative correlation between depression and subjective well-being, while social support showed a strong positive association with subjective well-being. Mediation analysis confirmed that social support significantly mediates the relationship between depression and subjective well-being, accounting for 10.23% of the total effect. Notably, the influence of subjective support on depression and subjective well-being was found to be more pronounced than that of objective support or social support utilization.ConclusionsThe findings highlight the necessity of strengthening the social support system for elderly individuals with chronic diseases in rural Western China, particularly by enhancing psychological and emotional support. This approach is crucial for mitigating depressive symptoms and improving subjective well-being in this population.

Results of multiple hierarchical regression analyses of sample 1 and sample 2.

ContentThis work is a derivative from the Cambridge sample found in the 1000 functional connectome project (Liu et al., 2009), originally released under Creative Commons -- Attribution Non-Commercial. It includes group brain parcellations generated from resting-state functional magnetic resonance images for about 200 young healthy subjects. Multiple scales (number of networks) are available, and includes 7, 12, 20, 36, 64, 122, 197, 325, 444. The brain parcellations have been generated using a method called bootstrap analysis of stable clusters (BASC, Bellec et al., 2010) and the scales have been selected using a data-driven method called MSTEPS (Bellec, 2013).This release more specifically contains the following files:* README.md: a markdown (text) description of the release.* brain_parcellation_cambridge_basc_multiscale_(sym,asym)_scale(NNN).(mnc,nii).gz: a 3D volume (either in .mnc or .nii format) at 3 mm isotropic resolution, in the MNI non-linear 2009a space (http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009). Region number I is filled with Is (background is filled with 0s).

Note that four versions of the template are available, ending with either mnc_sym, mnc_asym, nii_sym or nii_asym. The mnc flavor contains files in the minc format, while the nii flavor has files in the nifti format. The asym flavor contains brain images that have been registered in the asymmetric version of the MNI brain template (reflecting that the brain is asymmetric), while with the sym flavor they have been registered in the symmetric version of the MNI template. The symmetric template has been forced to be symmetric anatomically, and is therefore ideally suited to study homotopic functional connections in fMRI: finding homotopic regions simply consists of flipping the x-axis of the template.

PreprocessingThe datasets were analysed using the NeuroImaging Analysis Kit (NIAK https://github.com/SIMEXP/niak) version 0.12.14, under CentOS version 6.3 with Octave (http://gnu.octave.org) version 3.8.1 and the Minc toolkit (http://www.bic.mni.mcgill.ca/ServicesSoftware/ServicesSoftwareMincToolKit) version 0.3.18.Each fMRI dataset was corrected for inter-slice difference in acquisition time and the parameters of a rigid-body motion were estimated for each time frame. Rigid-body motion was estimated within as well as between runs, using the median volume of the first run as a target. The median volume of one selected fMRI run for each subject was coregistered with a T1 individual scan using Minctracc (Collins and Evans, 1998), which was itself non-linearly transformed to the Montreal Neurological Institute (MNI) template (Fonov et al., 2011) using the CIVET pipeline (Ad-Dabbagh et al., 2006). The MNI symmetric template was generated from the ICBM152 sample of 152 young adults, after 40 iterations of non-linear coregistration. The rigid-body transform, fMRI-to-T1 transform and T1-to-stereotaxic transform were all combined, and the functional volumes were resampled in the MNI space at a 3 mm isotropic resolution. The “scrubbing” method of (Power et al., 2012), was used to remove the volumes with excessive motion (frame displacement greater than 0.5 mm). A minimum number of 60 unscrubbed volumes per run, corresponding to ~180 s of acquisition, was then required for further analysis. The following nuisance parameters were regressed out from the time series at each voxel: slow time drifts (basis of discrete cosines with a 0.01 Hz high-pass cut-off), average signals in conservative masks of the white matter and the lateral ventricles as well as the first principal components (95% energy) of thesix rigid-body motion parameters and their squares (Giove et al., 2009). The fMRI volumes were finally spatially smoothed with a 6 mm isotropic Gaussian blurring kernel.

Bootstrap Analysis of Stable ClustersBrain parcellations were derived using BASC (Bellec et al. 2010). A region growing algorithm was first applied to reduce the brain into regions of roughly equal size, set to 1000 mm3. The BASC used 100 replications of a hierarchical clustering with Ward's criterion on resampled individual time series, using circular block bootstrap. A consensus clustering (hierarchical with Ward's criterion) was generated across all the individual clustering replications pooled together, hence generating group clusters. The generation of group clusters was itself replicated by bootstraping subjects 500 times, and a (final) consensus clustering (hierarchical Ward's criterion) was generated on the replicated group clusters. The MSTEPS procedure (Bellec et al., 2013) was implemented to select a data-driven subset of scales in the range 5-500, approximating the group stability matrices up to 5\% residual energy, through linear interpolation over selected scales. Note that the number of scales itself was selected by the MSTEPS procedure in a data-driven fashion, and that the number of individual, group and final (consensus) number of clusters were not necessarily identical.

References

Ad-Dab’bagh, Y., Einarson, D., Lyttelton, O., Muehlboeck, J. S., Mok, K.,Ivanov, O., Vincent, R. D., Lepage, C., Lerch, J., Fombonne, E., Evans, A. C.,2006. The CIVET Image-Processing Environment: A Fully Automated Comprehensive Pipeline for Anatomical Neuroimaging Research. In: Corbetta, M. (Ed.), Proceedings of the 12th Annual Meeting of the Human Brain MappingOrganization. Neuroimage, Florence, Italy. Bellec, P., Rosa-Neto, P., Lyttelton, O. C., Benali, H., Evans, A. C., Jul. 2010. Multi-level bootstrap analysis of stable clusters in resting-state fMRI. NeuroImage 51 (3), 1126–1139.URL http://dx.doi.org/10.1016/j.neuroimage.2010.02.082 Bellec, P., Jun. 2013. Mining the Hierarchy of Resting-State Brain Networks: Selection of Representative Clusters in a Multiscale Structure. In: Pattern Recognition in Neuroimaging (PRNI), 2013 International Workshop on. pp.54–57. Collins, D. L., Evans, A. C., 1997. Animal: validation and applications of nonlinear registration-based segmentation. International Journal of Pattern Recognition and Artificial Intelligence 11, 1271–1294. Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., Collins,D. L., Jan. 2011. Unbiased average age-appropriate atlases for pediatric studies. NeuroImage 54 (1), 313–327.URL http://dx.doi.org/10.1016/j.neuroimage.2010.07.033 Giove, F., Gili, T., Iacovella, V., Macaluso, E., Maraviglia, B., Oct. 2009.Images-based suppression of unwanted global signals in resting-state functional connectivity studies. Magnetic resonance imaging 27 (8), 1058–1064.URL http://dx.doi.org/10.1016/j.mri.2009.06.004 Liu, H., Stufflebeam, S. M., Sepulcre, J., Hedden, T., Buckner, R. L., Dec.2009. Evidence from intrinsic activity that asymmetry of the human brainis controlled by multiple factors. Proceedings of the National Academy ofSciences 106 (48), 20499–20503.URL http://dx.doi.org/10.1073/pnas.0908073106 Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., Petersen, S. E.,Feb. 2012. Spurious but systematic correlations in functional connectivityMRI networks arise from subject motion. NeuroImage 59 (3), 2142–2154.URL http://dx.doi.org/10.1016/j.neuroimage.2011.10.018

Other derivatives

This dataset was used in a publication, see the link below.https://github.com/SIMEXP/glm_connectome