dataesr/scientific-paragraphs-categorization huggingface dataset:
COVIZ relies on QDS, a novel data structure that encodes and indexes various data distributions to enable their efficient retrieval. Additionally, COVIZ visualizes air quality data in the regions where patients live to help with data interpretations. We demonstrate two key scenarios. In the ecological scenario, we show how COVIZ can be used to explore patient data to generate hypotheses on the health evolution of cohorts. In the case cross-over scenario, we show how COVIZ can be used to generate hypotheses on cohort health and pollution data. A video demonstration of COVIZ is accessible via http://bit. ly/video-coviz.
Medical experts do not necessarily know what to look for in the data, which cohorts are most insightful, and how to make sense of some observations. Cohort analysis can greatly benefit from a visual tool that helps them walk through their data to identify cohorts of interest and generate hypotheses. An essential aspect of that process is the ability to enrich observations with exogenous data that can be used to make sense of some phenomenon. For instance, analyzing data about patients suffering from respiratory problems would benefit from visualizing air quality data in the regions where those patients live. We propose to demonstrate COVIZ, a system that acts as a visual enabler for cohort formation and exploration. COVIZ lets medical experts form cohorts, obtain their various statistics, examine their health condition and treatments, visualize how their health evolves over time, and compare cohorts.
To do that, COVIZ relies on two principles: aggregated analytics and interactivity. Aggregated analytics refers to forming groups of patients (aka cohorts) in an exploratory fashion and observe their collective behavior. Cohorts can be formed with common demographics, health markers, and treatments. The visual interface helps medical experts examine different possibilities of forming cohorts, verifying members of cohorts, and examining differences in their health status. Interactivity requires fast iterations so that the train of thought of the analyst is not lost during the formation and exploration of cohorts. To ensure that, COVIZ relies on QDS  [12] , a novel data cube structure that encodes various distributions of health-care data and indexes them to enable their efficient retrieval.
QDS index construc/on Health-care data Visual cohort explora/on SQL-like language which operates on top of a visual algebra to show results. Vexus  [1]  provides native support for visualizing and exploring groups of users. Vizdom  [3]  enables an interactive whiteboard to compose complex workflows of data analysis and statistics. It exploits approximation and partial refinement techniques to deliver visualizations interactively. Also SeeDB  [14]  and Voyager  [17]  are visualization recommendation tools that explore the space of visualizations, and recommend interesting ones. While interactivity has been the focus of these systems, there has been less attention towards aggregated analytics (i. e. , analysis of cohorts). COVIZ is a single destination system that visually enables the formation and exploration of medical cohorts without the burden of formalizing queries.
SYSTEM DESIGN The overall architecture of COVIZ is illustrated in Figure  1. Initially an index is built offline to boost online cohort formation and exploration. COVIZ displays healthcare data and other exogenous data sources as separate layers over a geographical map. A set of filters is provided in the visual interface to facilitate the visual formation of cohorts. Once a cohort is formed, COVIZ provides a succinct representation of the cohort's health trajectory which helps analysts comprehend the health evolution of cohort's members and compare cohorts (i. e. , cohort exploration). COVIZ is a web service whose front-end is implemented in the Angular framework and back-end in C++ (the index) and Python (cohort exploration). The implementation of COVIZ is publicly available under GPL-3.
Datasets Health-care data. We use a dataset from our medical partner which contains events of 56, 284 patients with respiratory problems between the years 2000 and 2017. Patient events are: treatment, compliance, etiology, fatigue marker, BMI marker, sleepiness marker, and hospitalization. The dataset has 1, 536, 516 records in the following schema patient id , lat, lon, date, marker , value, treatment durations. Each record reports the value of a marker (fatigue, BMI, and sleepiness) for a specific patient identified by patient id. Also treatment durations reports the duration of treatments (with a month-level precision) which co-occurred with the marker for the patient patient id. Examples of treatments are Aerosoltherapy (AERO) and Oxygenotherapy (OXY). Each patient is also associated with a set of demographics such as gender, age, and life status.
Pollution data. High air pollution levels can cause serious respiratory problems. We consider a dataset of air pollution as an exogenous resource to enable potential explanations of observations in the health status of the formed cohorts. 1 he dataset contains values of different air pollutants (NO2, Ozone, PM2. 5, and PM10) for all of France in the period of 2009 to 2013. The exposure models, developed in the context of a European project EU-FP7 SysCLAD  [6] , have a fine spatial resolution (1km × 1km) and temporal resolution (on a daily basis). The dataset has 2,671,128,000 records in the following schema lat, lon, date, pollutant, value.
We store such representation as a payload of numeric dimensions, which also support on-the-fly aggregations and merging of distribution functions. QDS supports the selection of predicates for cohort formation. On-the-fly aggregations build a data view which constitutes a cohort. Hence cohorts are directly indexed in QDS. In COVIZ, aggregated values of cohorts are not limited to averages but distributions within different quantiles. For instance, instead of indexing a single average value of BMI marker for the cohort of females under AERO treatment in Grenoble, QDS stores its quantiles. As a result, all aggregations can be computed on-the-fly, such as average, quantile, max, and min. Our experiments in  [4]  shows that spatial extensions of PostgreSQL, SQLite, and MonetDB fail to render an interactive performance for filtering and combining spatial, temporal, and categorical dimensions.
We also observe that the progression of the sleepiness marker decreased until late 2015 and then it increased again. Cohort exploration. Experts can examine the health evolution of cohort's members using cohort trajectories. Figure  3 -D shows the cohort trajectory of females under AERO treatment in Grenoble (the x-axis is the timeline). The trajectory shows that the cohort's members started their treatment with AERO and OXY. Then they had a series of OXY treatments in four consecutive months. Additionally, multiple cohorts can be formed and compared visually. Figure  3 -D right shows the cohort trajectory of males under AERO treatment in Grenoble. We observe that both female and male cohorts received AERO right after their admission to the hospital. Sensemaking with pollution data. COVIZ uses another instance of QDS for pollution data to enable an interactive exploration of that data over different regions and in different granularities.
Plain Language Summary Mountain ecosystems and the human communities that inhabit them deliver critical resources-such as fresh water and timber-to over half the planet's human population. Despite their importance, there has been no global assessment of threats to mountain systems, even as they face unprecedented challenges to their sustainability. With survey data from 57 mountain sites worldwide, we test our understanding of the types of stresses that are threatening mountain systems as well as the resources and benefits that come from mountains. We find that mountain systems worldwide are experiencing both gradual and abrupt climatic, economic, and governance changes. One of the most ubiquitous challenges facing mountain systems is that policies directly affecting mountain systems are being made by those living outside of the mountains themselves.
Analyses We empirically confronted the expert-based MtSES characteristics, paradoxes, and conceptual model with the survey data at three levels. First, we considered the global scale, using all survey responses (n = 57). Second, we compared subsistence-oriented (n = 32) versus market-oriented (n = 25) MtSES, based on respondent selections. Third, we split the MtSES into five land use groups using hierarchical clustering in SAS (v. 9. 3). To obtain the groups, we created a dissimilarity matrix for all sites based on squared   S1 ). NTFP = nontimber forest products. 10. 1029/2018EF001024 Earth's Future Euclidean distances between land use importance scores and applied Ward's clustering algorithm to the matrix to identify MtSES groups with similar land uses. We assessed whether importance scores differed significantly between subsistence and market-oriented sites and among land use groups using Wilcoxon rank-sum tests.
Acknowledgments We thank the  Mountain Sentinels Collaborative Network  for their contributions to this work. Individuals who contributed site-level data and their affiliations are listed Table  S1. Ideas presented in this paper were first developed at a workshop supported by the  Mountain Research Initiative (MRI) , the  Colorado State University (CSU) Warner College of Natural Resources , and the  CSU Office of International Programs. Further support was provided by the  National Science Foundation ,  NSF   DEB 1414106. R. M. was supported under the  Climate Change Impacts on Ecosystem Services and Food Security in Eastern Africa (CHIESA)  funded by the  Ministry of Foreign Affairs of Finland.
Diffusion MR images are prone to severe geometric distortions induced by head movement, eddy-current and inhomogeneity of magnetic susceptibility. Various correction methods have been proposed that depend on the choice of the acquisition settings and potentially provide highly different data quality. However, the impact of this choice has not been evaluated in terms of the ratio between scan time and preprocessed data quality. This study aims at investigating the impact of six wellknown preprocessing methods, each associated to specific acquisition settings, on the outcome of diffusion analyses. For this purpose, we developed a comprehensive toolbox called Diffuse which automatically guides the user to the best preprocessing pipeline according to the input data. Using MR images of 20 subjects from the HCP dataset, we compared the six pre-processing pipelines regarding the following criteria: the ability to recover brain's true geometry, the tensor model estimation and derived indices in the white matter, and finally the spatial dispersion of six well known connectivity pathways.
This choice -usually driven by the time constraint typically different between clinical and research contexts -should also be done in the light of analyses comparing its influence on final diffusion measurements. For instance, for an equivalent time cost, it is yet debatable whether the acquisition of a B0 field map image would conduct to a better data quality than the acquisition of a single b0 volume with reversed phase encoding direction. Conversely, in a context of uncontrolled external acquisition such as for public or multicentric datasets, the limitations inherent to the quality of the associated pre-treatments are still poorly documented. To date, quantitative comparisons of preprocessing techniques have only been performed in a clinical (e. g. ,  Cusack et al. , 2003; Wang et al. , 2017)  or a research context (e.
For this purpose, we developed a dedicated and comprehensive toolbox called Diffuse which, out of six different preprocessing pipelines, automatically selects the one most adapted to the acquisition settings. Diffuse also includes registration methods as well as post-processing methods to perform local signal modeling and tractography. Six different types of acquisition settings were chosen because they are widely used in the literature ranging from clinical to research contexts, and each of them matches to a known dedicated preprocessing pipeline. We selected MRI data of 20 subjects from the HCP database  (Van Essen et al. , 2013)  which is, to our knowledge, the only database that includes all the data necessary to conduct this work. To relate the same experiments in a clinical context, we also acquired a similar acquisition set for one healthy subject in a standard 3T scanner and with lower spatial resolution.
HCP Dataset The HCP dataset  (Van Essen et al. , 2013)  was found as the only publicly available database including every MRI sequences and acquisition settings necessary to pre-process images through the six pipelines. MRI data of 20 participants were used in this study (subject IDs are listed in the Section "Annexe"). All individuals were right-handed males (age range 25-30). Images were acquired using a modified version of Siemens Skyra 3T scanner (Siemens, Erlangen, Germany) with a maximum gradient strength of 100 mT/m, slew rate of 200 T/m/s and a 32channel head coil. T1-weighted images were acquired using 3D MPRAGE sequence (TR/TE = 2400/2. 14 ms, flip angle = 8 • , FOV = 224 × 224 mm 2 , resolution = 0. 7 mm isotropic). Diffusion-weighted images were acquired with a spin-echo EPI sequence consisting of 3 shells of 90 diffusion-weighted volumes each (b = 1000, 2000, and 3000 s/mm 2 ) and 6 interleaved b0 volumes each (TR/TE = 5520/89.
Data Processing: The Diffuse Toolbox Diffuse is a BrainVISA toolbox, written in the Python language dedicated to diffusion MRI processing and publicly available on Github 1. Diffuse relies on algorithms from FSL 2  (Jenkinson et al. , 2012) , Dipy 3. (Garyfallidis et al. , 2014) , Niftyreg 4. (Modat et al. , 2010)  and on functionalities provided by the BrainVISA software platform 5 for neuroimaging  (Geffroy et al. , 2011). This platform already offers several processing pipelines for other modalities such as structural and functional MRI. In particular, an anatomical pipeline gives access to segmented T1w images, cortical surface meshes and a number of tools providing morphometric and functional measurements on the surface. BrainVISA includes the Anatomist software  (Rivière et al. , 2011)  for visualization and interaction with all associated data formats.
Example for the HCP dataset. Note that HS and FS pipelines use the same data subset with 3 shells of 90 diffusion gradients directions and 6 b0 volumes each. 3 × 90 DW images (L -> R) • • • • • • 3 × 6 b = 0 images (L -> R) • • • • • • 3 × 90 DW images (R -> L) • 3 × 6 b = 0 images (R -> L) • • B0 field map image All processes can be operated under a unified graphical user interface or as batch and using parallel distribution for processing groups of subjects. T1w MR images were processed using BrainVISA's Morphologist pipeline, dedicated to the processing of anatomical images  (Fischer et al. , 2012) , to obtain bias corrected T1w images as well as brain extraction, gray and white matter masks and cortical surface meshes. Diffusion-weighted images were processed through the Diffuse workflow described in Figure  1.
Importation and Reorientation of Data Data files are stored into a database to facilitate filesystem organization and indexation. This is an important practical aspect in the management of diffusion data with various complex data types and, in our case, for testing multiple processing using varying parameters. While input files are imported into the BrainVISA database, the storage orientation of DWI data and gradients vectors are changed to the neurological convention (RAS+) 6  which is supported by both FSL and Dipy tools.
With a sufficient number of samples, images with quasi-opposed gradients directions can be considered with opposed distortions. This method implemented in Diffuse calls the 'eddy' tool from the FSL software  (Andersson and Sotiropoulos, 2016). Using pairs of volumes with close orientation but quasi-opposed polarity of diffusion gradients, the algorithm applies a non-parametric Gaussian Process to estimate a higher order distortion field caused by both eddy-currents and motion and recover the midway geometry in the image. During the final resampling, a spline interpolation is combined with a Jacobian modulation to account for signal dilution in areas with stretching. This single correction step constitutes the preprocessing pipeline called hereafter "FS pipeline" (Full-Sphere) and could be applied to the same first subset.
B0 susceptibility-induced distortions In Diffuse, two approaches were implemented to correct for magnetic susceptibility-induced distortions. The first procedure uses the acquired B0 field map magnitude and phase images to correct the data through the workflow described in  Cusack et al. (2003)  involving the 'fugue' command from FSL  (Jenkinson et al. , 2012). This correction step is applied after eddy-current and motion correction to ensure that volumes, and thus head-dependent distortion fields, are aligned. This yield two other preprocessing pipelines called "HSfmap pipeline" and "FSfmap pipeline. " The second method uses non-diffusion weighted volumes acquired with reversed phase-encode direction (FSb0RPE)  (Andersson et al. , 2003). In the Diffuse toolbox, this approach is implemented via the use of the 'topup' tool from FSL  (Smith et al.
EXPERIMENTS AND RESULTS In this section, we investigated the performance of the six preprocessing pipelines on the HCP data in four different experiments, regarding: (1) their capacity to recover brain geometry, (2) their influence on whole-brain diffusivity measurements; (3) their influence on diffusivity in central white-matter regions; (4) their influence on tractography measurements. Experiments 1 and 2 were reproduced on the clinical data, for the multi-shell subset as well as for the 3 separated b-values. Note that this dataset was not included in the statistical analyses. For each experiment, complementary analysis was also performed to compare the data corrected through the six preprocessing pipelines with the raw uncorrected data. Results can be found in Supplementary Materials and interpretations will be drawn in the "Discussion" Section.
Reported effect sizes correspond to partial eta-squared (η p 2 ) of the within-subject design, defined as follows: η 2 p = SS effect SS effect + SS error (1) with SS effect the sum of squares of the effect and SS error the sum of squares of the error associated with the effect. Then, the differences between pipelines of distortion correction were assessed using Student's paired samples t-tests. The significance threshold was set to 0. 003 (0. 05/15pairs) to account for multiple comparisons. Figure  2A  illustrates the results of linear registration of the average b0 image onto the T1w image, for one subject (see Supplementary Material S2 for the results on clinical dataset). Our results show that the brain geometry in the frontal and temporal lobes (red arrows) are recovered only after explicit correction for susceptibility-induced distortions (HSfmap, FSfmap, FSb0RPE, and FSfullRPE pipelines).
, 2018). Here, we only evaluated its interest as a complement to the initial pipeline to improve alignment between anatomical and diffusion spaces. After the initial linear registration, the high SNR b0 image was non-linearly registered into the T1w image referential using 'reg f3d' as described in FIGURE 3 | Quantitative assessment of distortion correction methods using the similarity between diffusion and structural images. The Mattes Mutual Information was computed as a similarity measure between the T1w image and the average b0 image registered into structural space, using affine transformation (A) or non-linear transformation (B left). The black line with red dots corresponds to the clinical dataset. Student's paired t-tests were performed to compare the registration accuracy between linear and non-linear transformation for each pipeline separately (B right).
DISCUSSION In this article, we studied the influence of preprocessing distortion correction pipelines on diffusivity metrics and tractography measurements. For this purpose, we developed the Diffuse toolbox for DWI data processing which provides, in a guided user interface, the adapted preprocessing pipeline according to the data acquisition settings. Six different distortion correction pipelines are available, compatible with most acquisition type from clinical to research context. Two diffusion models and three tractography algorithms constitute the post-processing steps. Embedded in the BrainVISA open-source platform, the toolbox comes with an automatic indexation of data into a database organization as well as a visualization tool, and an access to processed anatomical data. This software configuration was well suited to investigate the impact of preprocessing methods on diffusivity measurements and tractography.
Conversely, the field map-based correction is performed as a second step, involving a second resampling and interpolation of signal intensity which is likely to induce smoothing in the corrected images  (Wang et al. , 2017). Indeed, ROI-based analyses showed that, compared to HS and FS pipeline, the additional use of a field map resulted in a highly homogeneous reduction of all tensor-derived metrics, while we expected the effect to be higher in regions prone to severe geometric artifacts, as reported in  Wu et al. (2008). To understand this artificial decrease of tensor-derived metrics, one has to understand the effects that a 3D smoothing has on the 4th dimension of DWI data (i. e. , across gradient directions). In fact, we can imagine that the smoothing would flatten the ellipsoid of the tensor model by removing high frequency fluctuations in the signal.
Relevance Toward Clinical Data To relate our observations with the clinical context we reproduced the same experiments on a similar dataset but acquired on a Siemens Prisma 3T MR-system with similar maximum gradient strength and slew rate as for the HCP scanner but using different acquisition settings. In particular, the T1w image had a lower resolution which, as we found, did not alter neither the capacity nor the interest of using non-linear registration to improve the alignment between T1 and diffusion weighted images. Importantly, the performance differences that we observed between pipelines is still valid for the dataset acquired in a context closer to the clinical environment. Although we found differences between the two datasets, common to all pipelines, as a global shift. For instance, the tensor-fitting quality was highly different from the HCP data which could be attributed to a higher SNR in the images, relative to the lower spatial resolution of DW images and the lower acceleration factor.
With this clinical dataset, we also highlighted the impact of b-value on the tensor fitting performance. Indeed, we found a better tensor fitting for lower b-values. Also, the FA and MD values measured in the white matter showed an important sensitivity to the b-value. This finding is probably related to the amount of SNR as well as the quality of distortion correction. Indeed, lower b-values involve lower gradient amplitudes and thus less eddy-currents. Apart from the amount of distortions, a higher SNR in the images could imply that conventional image registration algorithms perform better  (Andersson and Sotiropoulos, 2016). It has been reported in the literature that the amount of noise in the raw image can have an influence on the tensor-derived metrics  (Manjón et al. , 2013; Hutchinson et al.