dataesr/scientific-paragraphs-categorization huggingface dataset:
be/8ajESET2mqI. Figure 5 Individual-level module heatmap. Changes in transcript abundance were determined at the individual level across all modules constituting the repertoire. These changes are represented on a heatmap, where an increase in abundance of a given module is represented in red, and a decrease in abundance is represented in blue. The subjects are organized as columns and the modules as rows. The ordering on the heat map was determined by hierarchical clustering. Figure 6 Web application to visualize multi-tiered module ngerprinting. An application was developed to explore the changes in transcript abundance at the module level across the 16 reference datasets used to construct the repertoire. Three types of plot can be displayed and exported: 1) ngerprint grids; 2) module heatmaps displaying changes in abundance in modules comprising a given aggregate across the 16 reference datasets; and 3) module heatmaps displaying changes in abundance in modules comprising a given aggregate across individuals constituting a given dataset.
For accessing the application, please visit: https://drinchai. shinyapps. io/dc_gen3_module_analysis/#. For a demonstration video, please visit: https://youtu. be/y__7xKJo5e4. Figure 7 Module aggregate abundance patterns across the 16 disease or physiological states. A. Patterns of changes in transcript abundance at the aggregate and cohort levels. Each column on the heat map corresponds to a "module aggregate", numbered A1 to A38. Modules A9-A14 and A19-A24 were excluded as they each only included one module. Each row on the heatmap corresponds to one of the 16 datasets used to construct the module framework. A red spot on the heatmap indicates an increase in abundance of transcripts comprising a given module cluster for a given disease or physiologic state. Figure 9 Abundance patterns across individuals.
A. Changes in abundance for A28 modules. The heatmaps display the changes in abundance for the same six modules (rows) across individuals (columns) in four reference cohorts. The rows and columns on the heatmap are arranged based on similarities in abundance patterns. B. Changes in abundance for A28 and A35 modules. The heatmaps display the changes in abundance of six modules constituting aggregate A28 and 21 modules constituting aggregate A35 (rows) across individuals (columns) in four reference datasets. Functional annotations associated with different modules is indicated by a color code and corresponding legend. Such heatmaps can be accessed and exported for all 16 datasets and 38 module aggregates using the web application: https://drinchai. shinyapps. io/dc_gen3_module_analysis/# (under the "MODULES x INDIVIDUALS" tab).
Figure6: Web application to visualize multi-tiered module fingerprinting. An application was developed to explore the changes in transcript abundance at the module level across the 16 reference datasets used to construct the repertoire. Three types of plot can be displayed and exported: 1) fingerprint grids; 2) module heatmaps displaying changes in abundance in modules comprising a given aggregate across the 16 reference datasets; and 3) module heatmaps displaying changes in abundance in modules comprising a given aggregate across individuals constituting a given dataset. The application is accessible via this link: https://drinchai. shinyapps. io/dc_gen3_module_analysis/#. Demonstration video: https://youtu. be/y__7xKJo5e4.
B. Representation of the modules and genes constituting aggregate A28. The circle plot represents the six modules constituting aggregate 28, and the transcripts constituting each of the modules. Some genes on the Illumina BeadArrays can map to multiple probes, which explains the few instances where the same gene can be found in different modules. C. Patterns of changes in transcript abundance at the module-level and gene-level for aggregate A28. The circle plots illustrate the changes at the gene-level for this aggregate for 6/16 datasets. The position of the genes on each of these plots is the same as shown in panel B. Genes for which transcript abundance is changed are shown in red (increase) or in blue (decrease). D. Patterns of changes in transcript abundance at the module and gene level for aggregate A28 in subjects treated with IFNα or IFNβ treatment The circle plots show changes in abundance for A28 transcripts in patients with hepatitis C infection treated with IFNα [GSE11342 (16)] or patients with multiple sclerosis treated with IFNβ [GSE26104 (17)].
co-expression analyses carried out across a wide range of diseases and physiological states. It constitutes the third iteration released by our group since 2008. Another blood transcriptome repertoire was developed and made available by our collaborators from Emory University [Li et al. : (9)]. While methodological differences exist for the construction of such frameworks the principle remains the same: i. e. utilization of reference datasets for constitution of reusable sets of transcriptional modules. Collections of gene sets are commonly used for the interpretation of transcriptome profiling data [Gene Set Enrichment Analyses (GSEA): (25) (26)]. Such reference collections are very large,.
A blue spot indicates a decrease in abundance of transcripts. No color indicates no change. Disease or physiological states were arranged based on the level of similarity in the patterns of aggregate activity, determined via hierarchical clustering. B. Representation of the modules and genes constituting aggregate A28. The circle plot represents the six modules constituting aggregate 28, and the transcripts constituting each of the modules. Some genes on the Illumina BeadArrays can map to multiple probes, which explains the few instances where the same gene can be found in different modules. C. Patterns of changes in hepatitis C infection treated with IFNα [GSE11342 (16)] or patients with multiple sclerosis treated with IFNβ [GSE26104 (17)]. transcript abundance at the module-level and gene-level for aggregate A28.
The circle plots illustrate the changes at the gene-level for this aggregate for 6/16 datasets. The position of the genes on each of these plots is the same as shown in panel B. Genes for which transcript abundance is changed are shown in red (increase) or in blue (decrease). D. Patterns of changes in transcript abundance at the module and gene level for aggregate A28 in subjects treated with IFNα or IFNβ treatment The circle plots show changes in abundance for A28 transcripts in patients with.
Availability of data and material Transcriptome profiling data are deposited in the Gene Expression Omnibus, https://www. ncbi. nlm. nih. gov/geo/, under the accession GSE100150. lib294 lib301 lib315 lib224 lib225 lib328 lib335 lib308 lib322 lib355 lib222 lib279 lib341 lib223 lib286 lib348 lib265 lib259 lib272 lib226 lib288 lib295 lib309 lib242 lib248 lib323 lib329 lib302 lib316 lib349 lib232 lib273 lib336 lib237 lib280 lib342 lib253 lib260 lib266 lib227 lib310 lib289 lib296 lib243 lib249 lib324 lib330 lib303 lib317 lib350 lib233 lib274 lib337 lib238 lib281 lib343 lib254 lib261 lib267 lib228 lib290 lib297 lib311 lib244 lib250 lib325 lib331 lib304 lib318 lib351 lib234 lib275 lib338 lib239 lib344 lib282 lib255 lib262 lib268 lib229 lib291 lib298 lib312 lib245 lib251 lib326 lib332 lib305 lib319 lib352 lib235 lib276 lib339 lib240 lib283 lib345 lib256 lib263 lib269 lib230 lib292 lib299 lib313 lib246 lib252 lib327 lib333 lib306 lib320 lib353 lib277 lib236 lib340 lib241 lib284 lib346 lib257 lib264 lib270 lib231 lib293 lib300 lib314 lib247 lib334 lib307 lib321 lib354 lib278 lib285.
Then, the genes in each module were submitted as a list to LitLab Editor and manually calculate the "Microsoft Excel (2010) with Visual Basic for Applications (VBA), Linux-based command line validated using LitLab's built-in validation tool and/or NCBI Gene ). The tool is available online: https://gsan. labri. fr/. A page listing analysis results for all 382 generation 3 blood transcriptome modules can be accessed here: https://ayllonbe. github. io/modulesV3/index. html. (https://www. ncbi. nlm. nih. gov/gene) prior to submission for analysis using all domains available.
Study species Pyrgus armoricanus is a bivoltine butterfly with occurrences throughout most of central and southern Europe, the northern range limit being in southern Sweden and Denmark  (Kudrna et al. , 2011). In our study region, Filipendula vulgaris and Helianthemum nummularium are the most important larval host plants  (Eilers et al. , 2013) , which occur in dry, unimproved calcareous meadows. P. armoricanus is relatively sedentary with an average lifetime movement distance of 295 m (maximum = 7447 m)  (Fourcade et al. , 2017). Due to its small area of occurrence and the large variation in abundance between years, P. armoricanus is listed as endangered on the red list of Sweden  (Artdatabanken, 2015).
In order to test for effects of propagule size, we translocated two adult female butterflies to six of the sites (three in the north and three in the south), and four adult female butterflies to rest of the sites (Table  1 ). We deliberately kept the number of translocated individuals low, both because we wanted to simulate natural colonisation events, and also because P. armoricanus is a rare species and most source populations are relatively small, so the number of individuals available for translocation was limited. We identified potential target sites for the translocation based on (i) their spatial location and (ii) the presence of at least one of the two larval host plants, F. vulgaris and H. nummularium. The information on the presence of these plants was extracted from a nation-wide inventory of valuable semi-natural grasslands taken from the TUVA database  (Jordbruksverket, 2015).
In this inventory, F. vulgaris and H. nummularium were two of the indicator plant species that should be recorded if present in a certain grassland. We selected the final set of translocation sites based on their habitat quality for P. armoricanus, which we assessed in the field as having (i) a high abundance of at least one of the host plants, and (ii) management in the form of grazing. These factors were previously identified as key aspects of P. armoricanus habitat quality  (Fourcade & Öckinger, 2017). Pyrgus armoricanus is not legally protected in Sweden. Nevertheless, we contacted the Swedish Environment Protection Agency (Naturvårdsverket) to make sure no special permits were required, and informed the County Administration (Länsstyrelsen) in Skåne as well as the Swedish Species Information Centre (Artdatabanken) about the project and the location of the translocation patches.
Mapping of potential habitats After 2 years (i. e. in 2012), it became evident that P. armoricanus had spread to patches adjacent to some of the translocation patches, and from 2013 onwards we initiated a monitoring programme for these areas. Potential grasslands with P. armoricanus habitat were first identified from the TUVA database (Jordbruksverket, 2015) based on the observed presence of the larval host plants F. vulgaris or H. nummularium followed by field visits to confirm this. In addition, we visited semi-natural grassland sites in the vicinity of the translocation patches that were missing from the database and identified potential suitable habitat patches for P. armoricanus by occurrence of F. vulgaris or H. nummularium occurred in these sites. If host plants were not present in the entire grassland site, we mapped their distribution and defined discrete habitat patches as either separated from other patches by non-grassland habitat (e.
Land cover. To analyse the influence of land cover surrounding each patch, we created areas surrounding the respective patch (buffers) with a radius of 100, 250 and 500 m, though only the data from the 100 m buffers were included in the final analysis. This was because the larger buffers had a very large overlap between patches, and yielded similar results to the 100 m buffer in a preliminary analysis. We used the Svenska Marktäckedata database, which is the Swedish version of the CORINE database and is administrated by the Swedish Environmental Protection Agency, as a source for land cover types. We extracted the area of two land cover categories in the buffers. First, we merged forested areas and water bodies as a 'barrier' category since these two land cover types do not constitute much of the land cover by themselves (no patch was surrounded by more than 30% forest cover in the buffer).
Second, we extracted the area of agricultural land because it is the main land use around the translocation patch in Edenryd. Patch connectivity and distance to translocation site. We measured connectivity of each habitat patch according to the Si index of  Hanski (1999). S i = X j6 ¼i e -αdij N j where Si describes the connectivity of patch i, d ij is the Euclidian distance between patches i and j, N j the population size of patch j, and α a constant describing the decrease in immigration probability from patch j with increasing distance. In this case, we used α = 0. 0034, derived from a previous analysis of mark-recapture data of P. armoricanus  (Fourcade et al. , 2017). In our models, we used the average connectivity across all years for each patch. Since translocation is a special situation where there is just one initial point (i.
Temperature. We obtained daily temperature data from the Swedish Meteorological and Hydrological Institute (SMHI) from the weather station in Kristianstad. We extracted for each year the mean temperature during the larval (August 1 of previous year until May 15 of current year) and adult periods (May 15 until June 15). In a preliminary analysis we tested for any correlations with precipitation, but since this did not explain any of the observed patterns in abundance or patch occupancy, we chose to include temperature as the only climatic variable.
Secondary spread. The translocation patch in Edenryd was occupied by P. armoricanus in every year since the translocation event. In addition, the newly established population has also expanded to neighbouring patches (Fig. 2 ). The questions addressed in the analysis were aimed at discovering factors associated with the expansion dynamics. Prior to analyses, we checked for correlation between all explanatory variables with a correlation matrix as well as with the variance inflation factor (vif) from the HH package  (Heiberger, 2018). We found no correlation above 29% for the explanatory factors in the matrix, and variance inflation factors were below 1. 23. All statistical analyses were performed using R (Version 3. 1. 1). Model diagnostics in the form of residual plots were performed using the DHARMa R package  (Hartig, 2019)  that creates scaled residuals from a simulation-based approach for easier interpretation.
We used binomial generalised linear models (GLMs) with occupancy as response variable and all possible combinations of local habitat and landscape variables as explanatory variables. Occupancy was modelled as the ratio of the number of times a patch had been occupied out of the number of years it had been surveyed, with the latter included as frequency weights in the model to meet the assumptions of a binomial regression. The resulting models were ranked by Akaike's Information Criterion corrected for small sample sizes (AICc) using the dredge function from the MuMIn package  (Barton, 2018). The explanatory variables found in all models with ΔAICc <2 were retained and we interpreted results based on a reduced model containing these remaining variables. Since we did not detect strong evidence of overdispersion in the model (observed-to-simulated residuals ratio = 1.
Acknowledgements Cecilia Ronnås  assisted with the translocation experiments, and  Harriet Arnberg  and  Mikael Molander  with the population monitoring. Three anonymous reviewers provided helpful comments. This study has been funded by the  Swedish Research Council  (contract  621-2010-5589 ), the  Royal Swedish Academy of Agriculture and Forestry (KSLA) , a strategic grant from the  Department of Ecology, Sveriges Lantbruksuniversitent (SLU) , to EÖ, and by the  Swedish Research Council FORMAS  (grant  2016-00667 ) to YF. Open access funding provided by  University of Natural Resources and Life Sciences Vienna (BOKU).
An evaluation module introduced in  [4]  was also implemented, and allows generating dose constraints to the organs-at-risk (OAR) based on the literature and the fractionation scheme selected for a specific patient. The user evaluates the plan using visual warnings with a color code that highlights dose constraints violations to the organs, either in the TPS-planned dose or in Monte Carlo calculated distribution of Moderato. The Tomotherapy (Accuray) and the VSI Cyberknife (Accuray, earlier model released before the M6) installed at our institution were modelled and integrated in the platform. The Monte Carlo code selected for these devices was BEAMnrc/DOSXYZnrc  [5, 6]  (although other codes can be used in Moderato). The use of phase-space files and approximation techniques allowed to speed up the simulations substantially, down to a re-calculation time between 15 and 45 minutes.
Acknowledgements The authors wish to thank the colleagues who kindly agreed to share their data in order to test the prediction model:  Sebastian Klüter ,  Raphaël Moeckli ,  Christoph Fürweger ,  Mahmoud Touleimat ,  Pascal François ,  Sonia Sobczak  and  Vincent Marchesi. Second author is funded by  Interreg   2 Seas 2014-2020 program  co-financed by the  European Development Fund  under grant contract n°  2S04-022.
The interobserver variability of the eight scales was assessed. A weighted Cohen kappa coe cient (κ) was calculated for each pair per scale used with κ < 0. 2, κ = 0. 21 to 0. 4, κ = 0. 41 to 0. 6, κ = 0. 61 to 0. 8, and κ > 0. 8 corresponding to poor, fair, moderate, strong, and near-complete agreement, respectively  [28]. We performed adjusted analyses via ordinal logistic or linear regression modelling, as appropriate. All statistics were carried out using JMP (version 14. 0, SAS, Cary, NC, USA) except for the ROC analyses and kappa coe cient calculation, which were performed using MedCalc (version 9. 2, MedCalc Software, Ostend, Belgium). Statistical signi cance was assumed at p values of 0. 05 and below.
Consequently, some degree of bias is inevitable. To avoid some of this inherent bias, the two observers analysing the CT scans were blinded to clinical outcome and DCI occurrence. Our cohort included neurological ICU patients. Consequently, the less severely ill patients, who probably had the lowest amounts of blood and lowest risk for DCI, were likely not included. Finally, the exclusion of patients with non-aneurysmal haemorrhage (peri-mesencephalic, traumatic, or arteriovenous malformation) biased the data toward a relatively high overall risk of DCI and poor outcome. Nevertheless, DCI occurred in 39% of patients, which is comparable to proportions in other recent studies  [31].
mass-balance, thickness variations, ice-flow velocities and length fluctuations over 50 years) come from the French glacier monitoring program called GLACIOCLIM (Les GLACIers, un Observatoire du CLIMat; https://glacioclim. osug. fr/). For the present study, additional detailed observations were carried out in the framework of the SAUSSURE program (Sliding of glAciers and sUbglacial water pressure (https://saussure. osug. fr). The main part of our study focuses on a small area of Argentière Glacier (∼0. 2 km 2 ) located at 2,350 m a. s. l. in the ablation zone (Fig. 1  and 2 ). In this area, the glacier is ∼600 m wide, the horizontal ice flow velocity is ∼55 m a -1  (Vincent and others, 2009)  and the maximum ice thickness is 250 m  (Rabatel et al. , 2018). Experiments conducted in boreholes  (Hantz and Lliboutry, 1983)  indicate that the bed is composed of hard rock with no thick and deforming sediment layer.
Data In the selected area, point annual surface mass balances and ice flow velocities were monitored accurately at the end of each ablation season between 2016 and 2019 from 19 ablation stakes (Fig. 2 ). Our study also used surface mass balance and ice flow velocity observations from a small part of the ablation zone of the Mer de Glace glacier, at the location named "Tacul glacier" (Fig. 1 ). The ablation stakes are 10 meters long and made of five 2-m long sticks tied together with metallic chains. Errors in ablation measurements mainly come from the mechanical play of the jointed sticks. The uncertainties of the annual surface mass balance measurements performed in this ablation zone have been assessed at 0. 14 m w. e. a -1  (Thibert et al. , 2008). Topographic measurements were performed to obtain the 3D coordinates of the ablation stakes.
Prior to the survey flights, we collected GNSS measurements of ground control points (GCPs) that consist in rectangular pieces of red fabric (100x60 cm) with white painted circles (40 cm diameter) on the glacier  (10 in 2018, 20 in 2019)  and ten 40 cm diameter white circles painted on rocks on the sides of the glacier. The original horizontal resolutions of the ortho-photo mosaics and digital elevation models (DEMs) are 10 cm and 1. 00 m, respectively. The photos from the survey were processed using the Structure for Motion (SfM) algorithm that is implemented in the Agisoft Metashape Professional version 1. 5. 2 software package  (Agisoft, 2019). The SfM stereo technique was then used to generate a dense point cloud of the glacier surface. This dense point cloud was used to construct the DEMs using the GCPs surveyed during the field campaigns.
Spatial and temporal variability of the vertical velocities Our dataset shows that the spatial variability of vertical velocities can be large and strongly varies depending on the considered area at the glacier surface. For instance, we found strong spatial variability in the vertical velocity pattern at 2,350 m a. s. l. on Argentière Glacier. Our data suggest that the vertical velocity spatial gradient can reach 1. 5 m a -1 /100 m in this region. As a consequence, a horizontal deviation of 10 m could lead to a vertical velocity change exceeding the measurement uncertainty (0. 15 m a -1 ). Despite this strong spatial variability, we found small changes in vertical velocities with time. Below, we discuss the results of a numerical experiment conducted on Argentière Glacier to understand why.
Note that the measurement uncertainty related to the in situ measurements of point surface mass balance is 0. 14 m w. e. a -1 in the ablation zone  (Thibert et al. , 2008). Further tests performed on dataset in other regions of the Argentière and Mer de Glace glaciers show standard deviations of ±0. 2 to ±0. 4 m w. e. a -1 between reconstructed and observed point surface mass balances, despite the fact that these measurements were not designed for this purpose. For these tests, we used the averaged vertical velocities obtained over the last decade. Given the recent improvements in satellite sensors, it is conceivable to apply our method using high spatial resolution satellite images like Pléiades or WorldView (0. 5 m resolution). For these point surface mass balance reconstructions, note that, given the strong spatial variability of vertical velocity, it is crucial to determine the altitudes of the surface at each end of the horizontal displacement vector at the exact sites on which the vertical velocities are known.
Acknowledgments This study was funded by  Observatoire des Sciences de l'Univers de Grenoble (OSUG)  and  Institut des Sciences de l'Univers (INSU-CNRS)  in the framework of the  French GLACIOCLIM (Les GLACIers, un Observatoire du CLIMat) program. This study was also funded by l 'Agence Nationale de la Recherche  in the framework of the  SAUSSURE program  (Sliding of glAciers and sUbglacial water pressure (https://saussure. osug. fr)( ANR 18 CE1 0015 01 ). IGE and ETGR are part of  LabEx OSUG@2020 (Investissements d'avenir - ANR10 LABX56 ). We thank all those who conducted the field measurements. We are grateful to  H. Harder  for reviewing the English.