dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Categorical variables are presented as counts and percentages, continuous variables as mean ± standard deviation. The unpaired and paired (serial analysis) t test, the chi square test (or Fishers' exact test) and the Wilcoxon test were used for comparison of means and percentages. OCT intra-and interobserver variability in the analysis of lumen area and stent area were assessed calculating the mean relative differences (as a percentage) with the standard deviation. All statistical tests were two-sided and the p value of 0. 05 was considered statistically significant. Statistical analyses were performed using SPSS, version 21 (IL, US).
Morphology measurements Atomic force microscopy (PicoPlus from Molecular Imaging, Agilent Technologies) was used to investigate the morphology of nanoparticle arrays in the tapping mode. The average diameter was determined by analyzing the grain distribution of nanoparticle arrays using the free Gwyddion software. Additionally, scanning electron microscopy (Zeiss Supra 40 VP SEM) was employed to acquire complementary images.
Acknowledgements NGQ acknowledges funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  642787 ,  Marie Sklodowska - Curie Innovative Training Network BIOGEL, ML  and  PV  from the project jointly funded by  Agence Nationale de la Recherche (ANR)  and  Austrian Science Fund (FWF)  under the grant agreements  ANR-15-CE29-0026  and I 2647, respectively. IK is grateful for the support from the  FWF  through the project  TRP 304-N20.
In addition, frequentist implementation of SCR does not offer the same flexibility and accessibility to build complex models that have made Bayesian SCR models popular with many ecologists. Here, we describe an approach for local evaluation of the individual state-space (LESS) for computationally efficient fitting of large-scale SCR models in a Bayesian framework. We use simulations to assess the performance of the LESS approach, both in terms of computation speed and the performance of the estimators. We then use the LESS approach to estimate wolverine density throughout Norway using noninvasive genetic sampling data from the national monitoring program (Figure  1 ) on a standard desktop computer, a task that we were unable to execute using SCR without LESS. We provide example R codes and functions for implementing our approach in JAGS  (Plummer, 2003).
This trick consists of determining whether an individual AC was located within suitable habitat or not pOK i (1/0) and rejecting the proposed AC location if pOK i = 0 using: where OK is a set of ones. Bayesian analysis by Markov Chain Monte Carlo (MCMC) with data augmentation can be used to analyze SCR models  (Royle, Dorazio, & Link, 2007; Royle, Karanth, Gopalaswamy, & Kumar, 2009). Data augmentation involves (a) augmentation of the dataset with an arbitrary (but sufficiently large) number of individuals that were never detected, and (b) use of a latent binary variable z i reflecting the realization of the inclusion probability ψ, which is an estimate of the proportion of individuals truly present in the population. Estimated abundance ( N) is then derived by summing over the inclusion vector z, and density d is derived by dividing N by the area of S.
| Model fitting We fitted Bayesian SCR models using Markov chain Monte Carlo (MCMC) with JAGS  (Plummer, 2003)  and rjags  (Plummer, 2016)  in R version 3. 3. 3 (R Core Team, 2017). After an adaptive phase of 1,000 iterations, we ran 3,000 iterations of three chains thinned by three. We considered models as converged when the Gelman-Rubin statistics (gelman. diag function in coda package  (Plummer, Best, Cowles, & Vines, 2006) )  (Brooks & Gelman, 1998; Gelman & Rubin, 1992)  was ≤1. 1 and after visually inspecting trace plots for all monitored parameters. R and JAGS code for the different SCR models and simulations used are provided in Supporting Information Appendix S1 and S2, and list of priors used in Supporting Information Appendix S3. (7) MRE = � � � � � � D j ∑ h i Dj - D j ∑ h i D j � � � � � � F I G U R E.
Ultimately, the width of the windows should be specific to the study system; an optimal trade-off between computation speed and reliability of prediction can be determined using sensitivity analysis. Customizable R code for simulation and model fitting is provided in the Supplementary information Appendix S1. The use of data augmentation to estimate abundance in SCR is specific to Bayesian inferences  (Royle et al. , 2007 (Royle et al. , , 2009)). Although proven to be a robust tool to estimate abundance, dealing with data augmentation can be a challenge with more complex model formulation. For example, the correction that we applied to the inclusion parameter (ψ i ) as a function of the size of available habitat for each individual is specific to augmented individuals. In cases where individuals show a high degree of spatial aggregation within the study area, density of augmented individuals should be larger than the maximum observed density, to ensure that enough AC windows are available for augmented individuals to estimate density in areas with high individual density.
Additionally, in the case of open population SCR models  (Bischof, Brøseth, et al. , 2016) , steps would have to be taken to deal with individual movement. Under an open population SCR model where a dispersal kernel is explicitly defined, or for nonstationary activity centers  (Royle, Fuller, & Sutherland, 2016) , there exists a spatial parameter similar to σ (dispersal σ) to redistribute potential ACs. The general rule of the LESS approach that restricts calculation where detection probability is >0 would also apply to where the relocation probability of an individual's AC is >0 (i. e. , based on dispersal kernel function). As it stands, our approach does not accommodate for moving individuals because we defined static individual evaluation windows. Ecological studies have an increasing need for methodological developments to analyze the ever-increasing size of spatially explicit datasets and produce landscape, regional, and even global inferences about population state variables  (Sutherland, Brambilla, Pedrini, & Tenan, 2016; Tenan, Brambilla, Pedrini, & Sutherland, 2017).
such a reduction in the number of detectors used for each individual likely explains the major speed gains with LESS. This means that increasing the size of the problem (e. g. , higher number of detector, larger spatial extent, or greater population size) will amplify the gains even further as demonstrated in the wolverine example. While we were unable to fit the wolverine SCR model for the entire Norwegian range of the species (>200,000 km 2 ), the LESS approach allowed us F I G U R E 7 Simulation-based comparison of true and SCR predicted densities. (a) Example of a true density (du 2 ) from a simulated dataset (N = 100). (b) Predicted density maps obtained with a SCR model with and without local evaluation of the individual state-space (LESS). The local evaluation was performed using an AC evaluation window of 10σ with a 2σ extension of detector windows beyond the edges of AC windows.
| Land-use intensity In order to rank our sites in terms of land-use intensity, we calculated the community-weighted means (CWM,  Lavorel et al. , 2008)  for specific leaf area (SLA; leaf area per unit leaf mass). We used SLA because this leaf trait is strongly correlated with soil nutrient fertility  (Ordoñez et al. , 2009)  and plant nutrient economy  (Reich, 2014) ; high SLA and fast nutrient uptake is associated with exploitative species which are characteristic for sites with intense land use and high nutrient inputs  (Allan et al. , 2015). We obtained species SLA values from the TRY database  (Kattge et al. , 2011) , which contains trait data of many authors (Appendix S1). We averaged SLA for all species in a community, weighted by their frequency among 72 subplots per site in 2010 (prior to precipitation exclusion).
Biomass harvests in 2010 were used to assess direct effects of drought, whereas biomass harvests in 2011 were used to assess legacy effects of drought. The biomass of the first and second harvests in 2011 was sorted to grasses (including all graminoids) and forbs (nongramineous herbs and woody dwarf shrubs), and the sum of both harvests was used as a measure of postdrought functioning of these groups in tests of drought legacies. S1 ). Richness was also calculated separately for perennial grass, perennial forb, and annual species. In order to better distinguish effects of two drivers of vegetation dynamics with overlapping impacts postdrought, we calculated abundance-weighted means (AWM) of maximum rooting depth and AWM of SLA using species data 2010 as predictors of the abilities to survive under water-limiting conditions and to compete for resources (Figure  S2 ).
We obtained maximum rooting depth for grass and forb species from  Kutschera and Lichtenegger (1982, 1992) , from references therein, and from the TRY database and SLA from the TRY database (Appendix S1, Figure  S4 ). Seed rain produced by species was sampled in 2011 in the first growth period, which normally contributes most to reproductive output in hay meadows  (Zeiter et al. , 2016). We used seed traps of 3. 6 cm in diameter and 2. 5 cm in height, filled with sterilized sand, in spaces between subplot pairs in the centre of plots, following Zei-.
This highlights the importance of the choice of metric; choosing one or the other is likely to affect our perception of ecosystem resilience, including the complementary aspects of resistance and recovery  (Oliver et al. , 2015). General predictive understanding of ecosystem function sensitivity to climate change requires standardized data from multisite experimental networks  (Fraser et al. , 2013). In the present study, the severity of drought was scaled across sites using soil water potentials in order to avoid erroneous interpretation of drought responses, and sites were ranked using community-level leaf trait data (SLA) as an indicator of land-use intensity. Trait-based approaches are an effective tool for scaling up from plants to ecosystem functioning and indicating environmental change  (Dwyer, Hobbs, & Mayfield, 2014; Lavorel & Garnier, 2002; Poorter, Niinemets, Poorter, Wright, & Villar, 2009; Wellstein et al.
, 2017). Recent work has also shown that SLA is a satisfactory proxy of land-use intensity assessed as the sum of mowing, grazing and N addition  (Allan et al. , 2015). Of course, we recognize that the use of plant trait data from a global database rather than from our field sites is a limitation of the present study as this may underestimate in situ trait variation in response to underlying site fertility and environmental conditions  (Siefert et al. , 2015). Nevertheless, community-weighted means calculated with species trait values from databases can accurately reflect site-scale variation in plant community traits  (Cordlandwehr et al. , 2013). In our study, database information allowed for a functional distinction between graminoids and forbs across our wide productivity gradient and also provides useful baseline information for future cross-study comparisons.
ACKNOWLEDGEMENTS This work was supported by the  Swiss National Science Foundation  (grant numbers  127246 ,  149862 ). The synthesis across sites was facilitated by data from the TRY initiative on plant traits (http:// www. try-db. org),both initiative and database being hosted, developed and maintained by  J. Kattge  and  G. B€ onisch  ( Max Planck Institute for Biogeochemistry, Jena, Germany ). TRY is currently supported by  DIVERSITAS/Future Earth  and the  German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig. We thank  J. Preukschas ,  M. Benincore ,  R. H€ uppi  and  S. Schulz  for support in the field,  A. Salcedo  for help with CN analyses,  S. Burgos  for empirical information on growth-limiting water potentials and  E. Steele  for commenting the draft. O R C I D Andreas Stampfli  http://orcid.
Detailed reviews on multi-modal methods and investigations for psychopathology can be found in  [24, 29, 30]. Due to the challenges of combining multiple modalities in a single framework  [24, 30] , defining a multi-modal brain fingerprinting remains to this day an elusive task. Morphometry-based approaches, such as Brainprint  [8] , could potentially be extended to other modalities like dMRI. However, this requires solving non-trivial problems such as the cross-modality alignment of images with different resolutions, the segmentation and correspondence of neuroanatomical structures, etc. Computational efficiency is another important issue when dealing with large-scale, multi-subject and multi-modal datasets like the Human Connectome Project (HCP)  [16]  and UK Biobank  [17]. In this work, we propose a multi-modal brain fingerprinting that overcomes these challenges using manifold approximation.
Modality comparisons: Quantifying contribution of individual modalities for fingerprint and validation of hypothesis that each modality provides certain complimentary information, using a common task of twin/sibling identification. In addition, the current study presents a comprehensive analysis of the proposed fingerprint using a large-scale dataset from the Human Connectome Project (HCP), where numerous properties/factors are investigated, including the impact of various fingerprint parameters (e. g. , manifold dimensionality and proximity graph connectivity), the contribution of individual modalities and/or their combination to the fingerprint's discriminativeness, the fingerprint's robustness to image alignment and scan resolution, and the reproducibility of results using re-test or corrupted scans.
Using the genetically verified Zygosity labels from the HCP twin dataset, we analyze the proposed fingerprint's ability to identify genetically-related subjects (i. e. , monozygotic twins, dizygotic twins and non-twin siblings) from a large cohort, and show our multi-modal fingerprint to outperform single-modality approaches or fingerprints based on raw images. In an analysis of local feature correspondences, we identify for each modality the neuroanatomical regions having the most significant differences across groups of genetically-related subjects, as well as between males and females. Lateral asymmetry is also considered in this analysis by comparing the distribution of features correspondences across hemispheres. To our knowledge, this study constitutes the most in-depth investigation of a multi-modal brain fingerprint.
In Section 3, we then conduct an extensive experimental validation using the T1-weighted, T2-weighted, diffusion-weighted MRI, and resting state fMRI data of 945 subjects from the HCP dataset. Finally, we conclude with a summary of our contributions and a discussion of possible extensions. In the third step, the multi-modal fingerprints of subjects are computed using manifold approximation. Towards this goal, a subject-level proximity graph is first constructed by matching the features of each modality across images, and identifying pairs of subjects with a high number of correspondences. Fingerprints are then obtained by embedding this graph in a low-dimensional subspace. In the last step, we perform various analyses on the subject fingerprints. The informativeness of individual modalities and their link to genetic proximity are first measured in a twin/sibling identification analysis.
We used the hcp2blocks. m script (described in the HCP1200 These pairs are used for twin/sibling identification task in the following sections. For structural MRI we considered T1-weighted (0. 7 mm) and T2-weighted (0. 7 mm), with and without skull. The images are in native space and skull stripped, unless explicitly specified. In the case of dMRI data, signal reconstruction was performed with the freely available DSI Studio toolbox  [55]  using the Diffusion Tensor Imaging (DTI) and Generalized Q-Ball Imaging (GQI) reconstruction options. Four widely used DTI based measures were extracted to characterize white matter micro-structure: fractional anisotropy (FA), axial diffusivity (AD), mean diffusivity (MD) and radial diffusivity (RD). The interpretation of these measures are discussed in  [56].
In addition, to utilize the multi-shell information and high angular resolution of the HCP data, General- ized Q-Ball Imaging (GQI)  [55]  based measures including generalized fractional anisotropy (GFA) and Quantitative Anisotropy (QA) were also obtained. For resting state fMRI, we used the connectivity matrices (netmats), provided by the HCP 1200 release, derived using the FSLNets toolbox, either via full correlation or the partial correlation  [57] , the latter being calculated by inverting the covariance matrix. For analyzing the impact of alignment, we also used the MNI space aligned data for T1-weighted (0. 7 mm) and T2-weighted (0. 7 mm) provided by the HCP 1200 release. In addition, to combine structural modalities with dMRI, and to analyze impact of scan resolution, we re-sampled T1and T2-weighted images to a 1.
The extended embedding is then obtained following Nystrom's method as X ext =   U P UΛ -1   , ( 6 ) where UΛU is the eigendecomposition of S, and P is the normalized submatrix in S ext corresponding to P. Hence, the embedding of base subjects is the same as in Section 2. 2. 3, and new subjects are embedded as P UΛ -1. Once more, a fingerprint of size k is obtained by considering only the k leading eigenvectors in matrix U, ignoring the constant eigenvector. where N is the number of images, and because extending the manifold embedding can be done efficiently using the Nystrom method, our framework is scalable to large datasets.
Evaluation measures To measure the link between fingerprint similarity and genetic proximity, we performed a rank retrieval analysis using the sibling information provided in the HCP dataset. In this analysis, we try to identify the twins/siblings of a given subject by comparing its fingerprint with that of all other subjects in the group. Another goal of this analysis is to provide a common platform for the quantitative comparison of individual modalities and their combination. Two standard performance metrics for rank retrieval are used to evaluate the fingerprints: mean recall@k and mean average precision (MAP)  [65]. Given a subject i, we rank all other subjects by the similarity (i. e. , inverse of Euclidean distance) of their fingerprint to that of subject i. Denote as T i the set of target siblings/twins of subject i.
Identification of genetically-related subjects In this section, we use genetically verified labels of the HCP dataset to determine whether fingerprints generated using different modality combinations can identify genetically-related individuals within a group of subjects. For combining structural and diffusion modalities, we considered data at 1. 25mm resolution. For resting state fMRI, we utilize the connectivity matrices (netmats) as functional connectivity fingerprints, and obtain the subject proximity graph (manifold approximation) by computing pairwise Pearson correlation. The idea is to closely follow the functional connectivity fingerprint and similarity computation described in Finn et al. [9]  (the parcellation and dataset sizes are different). The multi-modal combinations involving rfMRI are obtained by linear combination of rfMRI subject proximity graph with other modality/combination based subject proximity graph.
Subject age In twin studies, the age of subjects can be a confound when comparing between different sibling types. For instance, DZ twins and FS siblings share the same amount of genetic material, yet DZ twins could be more similar due to their same age. The HCP data used in this study was acquired in the age range of 22-36, which corresponds to the plateau/saturation in brain and white matter development  [52, 51]. Nevertheless, we analyze whether age differences in non-twin siblings is a contributing factor on performance. Toward this goal, we divided FS sibling pairs in two groups based on the median age difference of 3 years, and measured the MAP in each group, for fingerprints generated from T1w, T2w, and FA. Similarly, we also evaluated the impact of absolute age on performance of MZ/DZ.
Zygosity label correction The Q3 release of the HCP dataset contained self-reported zygosity labels for twins. In the HCP 1200 release, which contains genetically verified zyosity labels, it was found that many self-reported DZ twins were actually MZ twins. In light of this problem, we first evaluate if the proposed framework can be used to identify the twins in large dataset whose self-reported zygosity differs from their true zygosity. In earlier experiments, we found higher MAP values for MZ and that the MZ twin of subjects was within the 10 nearest neighbors of a subject (i. e. , a mean recall@k of 100% was obtained for k ≤ 10, supplement material Table  8 ), regardless of the modality combination used for the fingerprint. Conversely, a lower percentage of DZ twins could be identified in these lists of nearest neighbors.
Retest and duplicate scan identification To analyze our fingerprint's ability to detect repeat scans of the same subjects (acquired after a time gap), we used the data of 945 subjects + 42 retest subjects, and considered the task of identifying repeat scan in a rank retrieval analysis. Following the same evaluation protocol as for identifying MZ/DZ/FS siblings, we obtained a MAP value of 1 for fingerprints generated from T1w, T2w or FA. Thus, in all cases, the single most similar fingerprint to that of a subject corresponded to this subject's retest data. Moreover, when considering the number of local feature correspondences in the subject similarity (i. e. , m∈M |B m i ∩B m j | in Eq (1)), we observed more correspondences for the retest data of a subject than for the subject's MZ twin. Duplicate scans in a dataset, for example resulting from noise corruption, renaming or other manual errors, can introduce bias in analyses.
Another potential application could be to provide physicians with related cases in clinical settings like MCI diagnostic assistance  [71]. While various twin studies have analyzed genetic influences based on volume, cortical thickness, surface area, and morphometry  [8] , this is the first work to use local features and manifold approximation for this problem. Analyzing the distribution of features correspondences across brain regions, in images of different modalities, reveals many interesting insights. Results identify various neuroanatomical regions (e. g. , cerebellum, lateral ventricles, ventral diencephalon, hippocampus and thalamus proper) having significantly different match counts in MZ twins than DZ twins or full siblings. These findings relate to those reported in  [8] , which were obtained on a different dataset (mean subject of age of 56 years, compared to a median of 29 years in the HCP dataset).
However, a deeper analysis could be carried out to investigate false feature correspondences and correlation between features correspondences across modality. As mentioned before, other manifold embedding methods like Locally Linear Embedding (LLE)  [33]  could also be employed for this step. In this study we analyzed data from sMRI, dMRI and rfMRI. However, the proposed framework is generic and could be extended to other modalities like task-fMRI, PET-MRI and quantitative T1/T2 maps. Finally, this study focused on comparing and combining different modalities for identifying geneticallyrelated subjects, misreported zygosity labels and duplicate/restest scans. An interesting extension of this work would to be to assess whether our fingerprint can be used as a biomarker to identify subjects with cognitive or neurological disorders.