dataesr/scientific-paragraphs-categorization huggingface dataset:
However, it is worth noting that of the 47 landslides correctly timed across the two events using the C1 and C2 techniques combined, only 3 had already been timed using the combined amplitude-based techniques in Sect. 3. 1, suggesting that the incorporation of coherence techniques could increase sensitivity if these could be made more reliable. Currently, only the Sentinel-1 SAR constellation acquires SAR data with sufficient coverage and acquisition frequency for widespread use in landslide timing studies. These data are acquired at the C-band, which usually has low coherence in vegetated areas. L-band data are better suited to InSARcoherence-based landslide detection in vegetated areas  (Burrows et al. , 2020). The planned NASA-ISRO SAR (NISAR) mission has a similar acquisition strategy to Sentinel and will acquire L-band SAR data.
Acknowledgements. We thank  Robert Emberson  for sharing some landslide inventories. Financial support. This research has been supported by the  Centre National d'Etudes Spatiales  (post-doctoral fellowship -Characterising the temporal evolution of rainfall-triggered  landslides using radar and optical satellite data ). Review statement. This paper was edited by  Ugur Ozturk  and reviewed by  Magdalena Stefanova Vassileva  and three anonymous referees.
com/ KABurrows/Supplement-to-nhess-2022-21 (last access: July 2022) and https://doi. org/10. 5281/zenodo. 6984291  (Burrows, 2022). Images were produced using Python Matplotlib  (Hunter, 2007)  (https://doi. org/10. 1109/MCSE. 2007. 55) and PyGMT  (Uieda et al. , 2021)  (https://doi. org/10. 5281/zenodo. 5607255) software. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/nhess-22-2637-2022-supplement.
Despite the increasing computerization of hospital information systems, segments of patient care are still in paper format. Data extracted automatically from the hospital databases for one specific project are thus supplemented by data collected manually. Data collection tools are usually developed entirely, which requires computer knowledge and is tedious, or automatically from metadata or drag and drop controls, which is limiting in terms of functionality. To facilitate this manual collection, we developed a free and open-source tool for creating forms that does not require advanced computer skills, offers rich features, and is quickly implemented, tested and deployed. It was implemented for 15 projects and supported thousands of daily users for a complex interactive study at the national level.
Introduction Despite the increasing computerization of health facilities, health data are still being collected manually to supplement the computerized databases when conducted observational retrospective or prospective studies. There are three main types of tools for manually capturing and storing data: spreadsheets, Electronic Data Capture (EDC) systems and handmade applications. Spreadsheets are part of office suite software. In the same manner as text editors or presentation programs, spreadsheets are prevalent because they are automatically installed on the workstation and easy to handle  [1]. However, they can be modified by a faulty manipulation, they do not support the generation of standardized and interoperable data, and it is particularly difficult to manually handle for large datasets with high number of rows and columns.
EDCs collect data in an electronic Clinical Report Form (eCRF) and are generally composed of a graphical user interface component for data entry, a validation component and a database  [2]. There are two main ways to design a form with an EDC: a metadatadriven approach and a GUI approach. In the first case, a table with the list of required variables, types, labels and constraints are loaded into the EDC to automatically structure the visual interface and the database. In the second case, graphical widgets are manually positioned on the interface by drag and drop. The widgets are then configured to take into account the characteristics of each variable. Although they have been reported as a mean of improving research efficiency  [3] , both approaches often offer limited functionality, and proprietary license is a barrier for scientific projects without sufficient funding  [4].
The last alternative consists in developing handmade applications from scratch. For this, a web technology (such as PHP, Node. js, etc. ) is generally associated with a database management system, which control the user interface and data storage, respectively. This approach allows to develop forms with unlimited functionalities, but requires advanced programming skills and is time consuming. The objective of this project is to provide a free and open-source tool facilitating the developer and end-user work during the eCRF design stage. The tool has to allow creating forms on the fly, immediately ready to be tested and used, with a minimum of code and coding skills.
Methods We performed extensive interviews among our peers in medical, pharmaceutical and environmental sciences to assess their needs in term of data capture tools. To bridge the gap between spreadsheets, EDCs and handmade applications, our solution had to answer the following design goals: (i) enable the creation of the project, the design of the interface, and the formatting of the data structure and storage with a minimum of code; (ii) facilitate the development and interactions between developer and end-user, (iii) support standard widgets but also rich functionalities, (iv) view and export the data easily, (v) provide training, (vi) generate shareable forms, (vii) support an offline mode and mobile/tablet format, and (viii) be free and open-source. To bridge the gap between existing approaches, we have developed a framework which supports the generation the interface, data structure, and storage in one line using a common programming language rather than a meta-language or a graphical library.
We have developed Goupile project using free and open-source technologies. The front-end is built using HTML/CSS/Javascript, while the back-end is implemented in C++. Data is stored on the server as JSON (JavaScript Object Notation) objects in a SQLite database. The choice of SQLite was made for three reasons: (i) we wanted to compile and use Goupile as an autonomous single-file binary, (ii) we use relational tables for other functionality, such as user management, and (iii) SQLite is a solid battle-tested database engine with an extensive test suite. Each record does not necessarily have the same variables, which allows the form to evolve and store new variables. Some data can be stored client-side with IndexedDB (offline mode). The standard widgets are implemented with predefined Javascripts functions: number, text, dropdown list, radio button, checkbox, date.
Projects At this time, Goupile has been used for 15 projects, including 3 multi-center projects. The design of Goupile was flexible enough for us to implement the 17 modules of the MINI for DSM-V assessment online, despite the complex flow of the questions. We were also able to conduct a complex interactive study at the national level with Goupile, with several neuropsychological assessments, where the users had to watch multiple videos and photos and answer to associated questions. Several studies were developed on Goupile by non-programmers, who where able to do create most of the form each time. We typically intervened only at the end to fix minor mistakes and help to code the more complex conditions needed for some questions.
Discussion We offer a tool that allows to quickly design and edit a form using ready-to-use Javascript functions, immediately visualize and test the form, and visualize/export collected data. The use of ready-made functions directly modifies the appearance of the form and the structure of the database, thus saving time compared to an application developed from scratch. These functions are developed in a common language (Javascript), rather than a meta-language or a GUI, which offers a number of customization possibilities for the eCRF. Finally, the hosting on a server, and the data quality rules are advantages compared to the spreadsheets. Goupile is provided free of charge, and the code is free and open-source (AGPL 3. 0). The tool was well received by the users, both for the form design and data collection phases.
Experimental setup First, the control plane is deployed as a cluster of a set of dockerized OpenFlow ONOS  [22]  controllers. Then, the DQN agent is implemented based on Python  [15]  and dockerized on Docker Containers  [16]. The latter interacts with the control plane based on the ONOS Northbound API. The control plane consists of 12 controllers. We used the network emulation tool OpenvSwitch  [23]  to implement the experimental topology, which consists of 32 nodes. To generate traffic among hosts, we used Iperf  [24]. The control plane Network Measurement modules collect statistics (latency, throughput, and per-flow size) from the devices and report those time-series statistics to the InfluxDb database  [20]. Note that, to show the importance of using DQN on a large space of states and actions, we augmented the collected statistics by using data generated with Python.
Indeed, we evaluated connectivity shifts, defined as connectivity differences between pairs of consecutive sliding windows, separately for cortical and subcortical regions. We found that subcortical shifts were positively correlated with network cortical shifts (all correlations >0:5). Both cortical and subcortical connectivity shifts showed (in absolute value) a heavy tail distribution, with more frequent low differences (Fig. 4b left ). Thus, we defined a jump when a large connectivity difference occurred (0:29, corresponding to the top 5% values). Then, we tested the simultaneity of cortical and subcortical reorganization by comparing the probability that cortical and subcortical jumps occur simultaneously (estimated as P subcortical changes|cortical changes ð Þ ) under the null hypothesis of independent processes, and in the observed data (see "Methods" for details on this analysis).
a At first behavioral scores of sub-acute patients were considered. When dynamical PCs were used as dynamic regressors, only the global measure of behavioral impairment (NIHSS-total) resulted to be better estimated by the combination of static and dynamic regressors, than only static ones. The bar plot shows the R 2 for the reduced (only static, ST) model, for the unreduced (static and dynamic, ST+DYN) model, and for the reduced model (only dynamic, DYN). b As a second step, we used static and dynamic measures at sub-acute stage, to explain the difference in behavioral scores from 2 weeks to 1 year. When dynamical PCs were used as dynamic regressors, the explanation of score changes in Language and Visual Memory task was significantly better. Source data are provided as a Source data file.
Imaging The data used in these analyses are part of the Washington Stroke Cohort  33, 38, 89. The database contains patients with first-time stroke, studied 1-2 weeks (mean = 13. 4 days, SD = 4. 8 days), 3 months, and 12 months after stroke onset. A group of 30 age-matched control subjects was studied twice at an interval of 3 months. All imaging was performed using a Siemens 3T Tim-Trio scanner at the Washington University School of Medicine (WUSM) and a standard 12-channel head coil. The imaging protocol included structural MRI, resting-state blood oxygen dependent level (BOLD) MRI, diffusion MRI, and arterial spin labeling. Structural scans included: (1) a sagittal T1-weighted MP-RAGE (TR = 1950 ms, TE = 2. 26 ms, flip angle = 90°, voxel size = 1. 0 × 1. 0 × 1. 0 mm); (2) a transverse T2-weighted turbo spin-echo (TR = 2500 ms, TE = 435 ms, voxel-size = 1.
5 mm. Finally, the resulting residual time series were projected on the cortical surface of each subject divided into the 324 ROIs developed by Gordon et al. 90  , plus 19 subcortical ROIs derived from the FreeSurfer subcortical atlas  91, 92. The original parcellation includes 333 regions, but all regions with <20 vertices (~50 mm 2 ) were excluded, as in previous works  33, 43. We used this parcellation to relate our results with previous works analyzing the same dataset  33, 34, 38, 39, 43, 88. Only subjects with at least 180 good frames were considered for the analyses. As a result of the pre-processing, 114 subjects were available at 2 weeks (sub-acute), 80 at 3 months, and 65 at 12 months, 24 and 20 controls at the first and second acquisition, respectively.
Several choices of K (from 2 to 10) were used for supplementary analyses and comparisons (SI-Fig. 2 ). For each DFS, we evaluated three indexes that represent the most common static FC biomarkers observed in stroke, namely: (1) the average homotopic inter-hemispheric connectivity within each network; (2) the average intra-hemispheric connectivity between DAN and DMN regions, as a measure of network integration; (3) the overall Newman's modularity among cortical networks. Similar to ref. 33, we used the code from the Brain Connectivity Toolbox  94  , publicly available at sites https://sites. google. com/site/bctnet/, with modules' assignments chosen a priori based on Info-Map community detection in ref. 90. Modularity was calculated at edge densities ranging from 4 to 20%, as suggested in ref.
Relationship with anatomical lesions and structural disconnections Structural MRI data acquired from 47 sub-acute stroke patients used for the DFSs analyses were used to measure each lesion's anatomical impact at the voxel level. Lesions were manually segmented on each patient's structural MRI scans using the Analyze software package  96. The T1-weighted, T2-weighted, and T2-FLAIR scans were used in conjunction to ensure complete lesion delineation. If present, surrounding vasogenic edema was included in the lesion definition for all patients. All segmentations were reviewed by two board-certified neurologists (Maurizio Corbetta-MC-and Alexandre Carter) and were reviewed a second time by MC. The final segmentations were used as binary lesion masks for subsequent processing and analysis steps.
Lesion masks were transformed into MNI atlas space using a combination of linear transformations and non-linear warps and were resampled to have isotropic voxel resolution. The structural disconnectome matrices used in this work were derived in ref. 34 for the same patients' dataset, using the same 324 cortical parcels used in this work and 35 subcortical and cerebellar regions (34 parcels from the automatic anatomical labeling (AAL) atlas  97  that corresponded to different portions of the thalamus, basal ganglia, and cerebellum, and also included 1 region from the Harvard-Oxford Subcortical Atlas that corresponded to the brainstem). Specifically, for each patient, the disconnectome matrix was defined as a square matrix of dimensions 359 × 359, where each entry in position ij represented the percentage of streamlines connected regions i and j that were disconnected by the As described in ref.
34, the template structural connectome was derived from a publicly available diffusion MRI streamline tractography atlas, constructed using data from 842 Human Connectome Project participants  98. The atlas data were accessed under the WU-Minn HCP open access data use term. For the computation of structural disconnection matrices, the following software was used: DSI studio 2019 (http://dsi-studio. labsolver. org), FreeSurfer V6 (https://surfer. nmr. mgh. harvard. edu), Connectome workbench v1. 5. 0 (https://www. humanconnectome. org/ software/get-connectome-workbench), GRETNA 22. 0 (https://www. nitrc. org/projects/gretna/), Analyze v. 12 (https://analyzedirect. com/), Surf Ice v2 (https://www. nitrc. org/projects/surfice), MRIcroGL v1. 2. 2021 (https://www. nitrc. org/projects/mricrogl).
The optimal λ (λ opt ) value was the one that minimized the prediction error over the training set, and the predictions obtained with λ opt were considered as the model regressors. Model accuracy was assessed through the coefficient of determination R 2 : R 2 = 1 À ∑ N s i = 1 y i À ŷi À Á 2 ∑ N s i = 1 y i À y 0 À Á 2 , where y 0 = 1 N s ∑ N s i = 1 y i ð7Þ and ŷi is the estimated value of y i. The statistical significance of each model was estimated through a permutation test, with N = 10,000 iterations. For each iteration, the behavioral scores were randomly permuted across subjects, and the LOOCV with λ optimization was used to fit the RR model to the randomized scores. The p-value for the observed R 2 was defined as the probability of the R 2 of the randomized dataset to be larger than the observed R 2.
Relationship with behavior Neuropsychological evaluation. The same subjects (controls and patients) were also examined at each time point with a battery of neuropsychological tests covering different cognitive domains, such as motor, attention, language, visual, and memory functions as described in refs. 38 Visual: computerized perimetry. Imaging and behavioral testing sessions were usually performed on the same day. Scores were only recorded for tasks that subjects were able to complete. Dimensionality reduction was performed on the performance data using principal component analysis as described in detail in ref. 38. Briefly, tasks were first categorized as language, motor, attention, memory, and visual. Next, a PCA was run on each category and the first component was used as a domain score.
Finally, patients' behavioral scores were z-scored w. r. t. controls' scores, to highlights behavioral impairments. Of the 47 patients analyzed, the behavioral scores were available for 45 patients (language), 43 patients (motor left and right), 40 patients (attention), 38 patients (memory), and 24 patients (visual) at the subacute stage. Similarly, at three months (one year) the following data were available: 45 (41) patients (language), 46 (42) patients (motor), 42 (40) patients (attention), 41 (42) patients (memory), and 28 (28)  patients (visual). In addition to these domain-specific scores, the patients' clinical severity was assessed through the National Institutes of Health Stroke Scale (NIHSS)  100  , which includes 15 subtests addressing: level of consciousness (LOC), gaze and visual field deficits, facial palsy, upper and lower motor deficits, limb ataxia, sensory impairment, inattention, dysarthria and language deficits.
Statistics and reproducibility The sample size was determined based on previous works relating to the same dataset  34, 38, 43. After pre-processing, 114 subjects were available at 2 weeks (sub-acute), 80 at 3 months, and 65 at 12 months, 24 and 20 controls at the first and second acquisition, respectively. For the implementation of the dynamical functional analysis, only subjects with a sufficient number of good frames (300) after motion scrubbing were considered. Furthermore, we selected only patients who participated to all the three recordings (2 weeks, 3 months, 12 months after stroke). Therefore, 47 patients were considered. The number of control subjects with sufficient frames were 20 during the first visit and 20 during the second visit. Our statistical analyses are based on common parametric tests (Wilcoxon rank, T-test, F-test for Generalized Linear Mixed Effect Model, likelihood ratio test) or simple non-parametric permutationbased tests which we implemented with customized code.
Acknowledgements C. F. , M. A. , A. B. , and M. C. are supported by  FLAG-ERA JTC 2017  (grant  ANR-17-HBPR-0001 ). C. F. and M. C. are supported by  Fondazione Cassa di Risparmio di Padova e Rovigo (CARIPARO) -Ricerca Scientifica di Eccellenza 2018  -(Grant Agreement number  55403 ). A. B. has received funding from the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  under the  Specific Grant  Agreement No. 945539  ( Human Brain  Project  SGA3 ). M. C. is also supported by  MIUR -Departments of Excellence Italian Ministry of Research  ( MAR-T_ECCELLENZA18_01 );  Ministry of Health Italy Brain connectivity  measured with high-density electroencephalography: a novel neurodiagnostic tool for stroke -NEUROCONN ( RF-2008 -12366899 );  H2020 European School of Network Neuroscience -euSNN ,  H2020-SC5-2019-2  (Grant Agreement number  869505 ).
Widespread genetic changes in transcription initiation Having validated our experimental dataset and examined the broad patterns of allelic differences across organs, we next set out to dissect the genetic basis for each stage of the Pol II life cycle. We first focused on defining allele specific patterns of transcription initiation. The 5' end of nascent RNA, denoted by the 5' end of paired-end ChRO-seq reads, marks the transcription start site (TSS) of that nascent RNA  (Kwak et al. , 2013; Tome et al. , 2018). We identified TSSs in which at least 5 unique reads share the same 5' end inside of regions enriched for transcription initiation identified using dREG after merging all samples from the same tissue  (Wang et al. , 2018). Using a recently described hierarchical strategy  (Tome et al.
Paused Pol II is positioned in part through physical constraints with TFIID, a core component of the pre-initiation complex  (Fant et al. , 2020). As a result of such connections with the pre-initiation complex, short insertions or deletions affecting the distance between the TSS and the maximal pause site altered the frequency of pausing at a model gene (D. melanogaster HSP70;  (Kwak et al. , 2013) ). In our dataset, changes in pausing were highly enriched for small insertions and deletions between the maxTSS and pause site (n = 56 (21%); expected = 22 (8%); p < 1e-5, Fisher's exact test). Changes in pause position were correlated with changes in the size of the insertion or deletion, such that the number of bases between the max TSS and the pause was typically less than 5 bp (Figure  5C ).
Next we examined how allelic termination windows were associated with changes in nearby transcription. Allelic termination windows were more likely to occur in highly expressed transcripts, which may partially reflect increased statistical power for detecting changes supported by larger numbers of reads. Additionally, allelic termination windows were more likely to have allelic changes in the transcription level of an adjacent transcript compared with matched transcripts without an allelic termination window (liver: odds ratio = 1. 65, p = 1. 83e-15; brain: odds ratio = 1. 23, p = 0. 018; Fisher's exact test). We tested whether the association between allelic termination and adjacent transcript expression was also found when allelic termination and the adjacent transcript were encoded on opposite strands, hence avoiding the interpretation that AlleleHMM was more likely to detect allelic termination windows near an allelic difference with a large magnitude.
This procedure generated a tagged SAM file (vW tag) providing the coordinates of allele specific alignments and their mapping position. Scripts can be found here: https://github. com/Danko-Lab/F1_8Organs/blob/main/termination/F1_RNAseq_forManuscript. sh dREG: For each organ, we merged all reads from each replicate and cross to increase the power of dREG. BigWig files representing mapping coordinates to the mm10 reference genome were uploaded to the dREG web server at http://dreg. dnasequence. org  (Wang et al. , 2018). All of the output files were downloaded and used in subsequent data analysis. Scripts used to generate the BigWig files can be found at : https://github. com/Danko-Lab/F1_8Organs/blob/main/F1_TSN_Generate_BigWig. sh Transcript unit prediction using tunits: We used the tunit software to predict the boundaries of transcription units de novo  (Danko et al.