dataesr/scientific-paragraphs-categorization huggingface dataset:
The treatment of patients who die or live outside the Basque Country cannot be retrieved in the system. Adherence was determined primarily for patients who were prescribed urate-lowering medications, but also antihypertension treatments, oral hypoglycemic medications for diabetes, and lipidlowering agents as a comparison. Analysis of adherence data was determined at the time of scheduled follow-up visits and also at the following time periods: 1st, 2nd, 3rd year of follow-up and beyond 3 years of followup. Multivariable models were constructed to determine factors associated (p \ 0. 2) with an MPR \ 0. 8. Statistical analysis was performed using the statistical package SPSS23.
*p \ 0. 05 compared to other medications Fig. 2  Adherence for urate-lowering medication (Gout) as MPR (%) and percentage of patients showing MPR [ 0. 8 during sequential strata of follow-up time gout mostly coincide in reporting low adherence as MPR or proportion of days covered, with an average of 42%, ranging from 17% to 83. 5%, 47% of patients being adherent  [13] , and MPR 44%, ranging from 17% to 78%, 46% of patients being reported as adherent  [14]. Therefore, adherence to treatment is to be considered poor in patients with gout. Adherence rates differ depending on populations included: single centers and prospective cohorts show higher rates of adherence than those retrieved from administrative databases  [13, 14]. Although some authors have found no difference in adherence among countries  [14] , others have concluded that data from Oceania and Asia show higher adherence rates  [13].
In fact, mean adherence to ULT measured with MPR and using a health insurance database in Japan has been shown to be over 80% for allopurinol and benzbromarone  [15] , twofold that reported for Europe or the USA using similar databases  [13]. Our data suggest that patients who are prospectively followed up show high adherence rates and close to 90% patients reach sUA therapeutic target. Poor adherence for ULT is associated with poorer clinical outcomes as sUA levels are a surrogate for gout outcomes  [16, 17]. Different factors may influence adherence. Our patients showed a similarly high MPR for other oral medications compared with ULT. Our patients are older and commonly show multiple comorbid conditions, variables that have been described to be associated with increased   [18]. Other factors influencing adherence include cultural  [15]  and behavioral ones  [19].
CONCLUSION Adherence rates are high in clinical cohorts empowered with modest educational support and routine follow-up. Adequate adherence is associated with continuing follow-up, the presence of comorbid conditions, and with increased rates of sUA within therapeutic target. This can be easily interpreted: empower and support patients, do not blame them, in order to achieve high rates of adherence and better patient outcomes. Helsinki Declaration. Data were retrieved from a gout clinical database that is approved the local ethics committee (Cruces University Hospital Ethics and Investigation Committee), with informed-signed permission from patients or relatives if appropriate. Data Availability. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
ACKNOWLEDGEMENTS We heartfully thank the participants (patients and relatives) of the study. Funding. This work has been partially supported by a grant ( FPR)  from  Asociacion de Reumatologos del Hospital de Cruces ; no other sources of funding have been used. No Rapid Service Fee was received by the journal for the publication of this article. Authorship. All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.
Design basis and model simulation The above model equations for all domains with appropriate boundary conditions and process parameters were solved using COMSOL Multiphysics ® (version 5. 3, 2018) which uses finite element method (FEM), and MATLAB R2017a using LiveLink™ for MATLAB. PARDISO solver is used for the steady state model equations. For pseudo-steady state studies a time dependent solver was used to solve non-steady state equations, keeping the same mapping and solver configurations. Geometry is meshed after defining all the parameters and model equations for respective domains. More refined meshes were applied on mass transfer boundaries, especially at interface. Parametric studies were performed by post processing in COMSOL. Fresh feed gas mixture input at inner side of fibers has a constant concentration of CO 2.
However, current limitations in drilling technology prevent their direct exploration. If debris on VFF surfaces contains a component of englacial and/or subglacial debris, those materials could be sampled without access to the subsurface. This could reduce the potential cost and complexity of future missions that aim to explore englacial and subglacial environments on Mars. Methods: We use a 1 m/pixel digital elevation model (DEM) derived from 25 cm/pixel High Resolution Imaging Science Experiment (HiRISE) stereo-pair images, and a false-colour (merged IRB) HiRISE image. We measured the dip and strike of the VFF-internal structures using ArcGIS 10. 7 and QGIS software. We also input the DEM (and an inferred glacier bed topography derived from it) into ice flow simulations using the Ice Sheet System Model, assuming no basal sliding and present-day mean annual surface temperature (210K).
Continuous geodetic measurements in landslide prone regions are necessary to avoid disasters and better understand the spatiotemporal and kinematic evolution of landslides. The detection and characterization of landslides in high alpine environments remains a challenge associated with difficult accessibility, extensive coverage, limitations of available techniques, and the complex nature of landslide process. Recent studies using space-based observations and especially Persistent Scatterer Interferometry (PSI) techniques with the integration of in-situ monitoring instrumentation are providing vital information for an actual landslide monitoring. In the present study, the Stanford Method for Persistent Scatterers InSAR package (StaMPS) is employed to process the series of Sentinel 1-A and 1-B Synthetic Aperture Radar (SAR) images acquired between 2015 and 2019 along ascending and descending orbits for the selected area in the French Alps.
We applied the proposed approach, based on extraction of Active Deformation Areas (ADA), to automatically detect and assess the state of activity and the intensity of the suspected slow-moving landslides in the study area. We illustrated the potential of Sentinel-1 data with the aim of detecting regions of relatively low motion rates that be can attributed to activate landslide and updated pre-existing national landslide inventory maps on a regional scale in terms of slow moving landslides. Our results are compared to pre-existing landslide inventories. More than 100 unknown slow-moving landslides, their spatial pattern, deformation rate, state of activity, as well as orientation are successfully identified over an area of 4000 km 2 located in the French Alps. We also address the current limitations due the nature of PSI and geometric characteristic of InSAR data for measuring slope movements in mountainous environments like Alps.
The terms "landslide", "mass movement", "slope movement", "slope instability", and "slope failure" are used as synonyms in this study. A high quality surface displacement field of landslides is important to provide insights into their geometries and mechanical properties  [2]. Therefore, monitoring its surface motion over a wide area of space is a key challenge in order to quantify any potential landslide risk  [3]. Landslide inventory databases and maps are documenting the identification of landslide processes including the spatial distribution and characteristics such as geometry, volume, total length of area, causal factors, temporal frequencies, and sliding rates of different types of past landslide activities  [4, 5]. This information might also provide a clue to the locations of future mass movements that are very helpful in predicting the landslide susceptibility and vulnerability in order to support local authorities for hazard mitigation  [6] [7] [8].
In addition, it can also provide input for other applications including land-use planning; developing and refining emergency response plans; as well as prioritizing mitigation measures  [9]. However, landslide maps may have different resolutions and details depending on the purpose of the inventory, scales, data collection method, and characteristics of the available imagery  [10, 11]. In France, for example, national landslide databases provided by French Geological Survey (BRGM) are mostly based on historical records, field inventory and detailed geomorphological formation surveys and aerial orthophotos. Landslide inventories can be prepared using a variety of conventional and innovative techniques depending on the extent of the study area. Visual interpretation of landslides, geomorphological field mapping as well as stereoscopic imagery techniques are considered as traditional methods based on landform analysis and are only useful over small areas.
InSAR Datasets The Copernicus Sentinel-1 C-band SAR mission is a joint initiative of the European Commission (EC) and the European Space Agency (ESA), who is operating the Sentinel-1A and -1B twin spaceborne segments. The Sentinel-1 Terrain Observation with Progressive Scan (TOPS) mode represents an important advantage when compared to other sensors' modes as it provides wide area coverage and short revisit time (6 days over Europe and 12 days globally). Sentinel-1 data are free and openly accessible via various sources, in our case the Copernicus Open Access Hub. In the present study, we processed two independent sets of Sentinel-1A/B imagery, acquired along descending and ascending orbits (Track 88 [Asc] and 66  [Dsc] ) to map the surface displacement field of slow-moving landslides in the French Alpine slopes.
Methodology of Landslide Mapping For investigating the landslide distribution and geohazard activity mapping as well as displacement monitoring using Sentinel-1 data over the selected study area, we followed the procedure based on the estimation of the mean velocity and the time-series of deformation using the PSI approach followed by the extraction of active deformation areas (ADAs) implementing certain basic statistical criteria and a density-based clustering algorithm. Here, we integrated morphological and satellite PSI data with the pre-existing landslide inventory. After estimating the PSI velocities, within a PSI post-processing stage, density and the rate of the local ground deformations are assessed and the PS displacements in LOS direction are projected along the direction of the steepest terrain slope.
PSI Processing Methodology All interferograms were generated using the open source (GNU General Public License) InSAR processing system called "Generic Mapping Tools Synthetic Aperture Radar (GMTSAR)"  [55]. To correct the topographic contribution to the radar phase, we used the Shuttle Radar Topography Mission (SRTM) 3-arcsecond digital elevation model  [56]. All interferograms were computed based on a single master network for PSI analysis. The choice of the master images minimizes the spatial and temporal baselines. The single master stacks of interferograms were processed using the StaMPS software package  [57, 58]. StaMPS allows the identification of PS points, using both amplitude and phase information. In the first step, the initial selection of PS points is performed based on their noise characteristics, using amplitude analysis.
Eps (ε) defines the neighborhood around a data point. If the Cartesian distance between two points is lower or equal to 'eps' then they are considered neighbors and they will belong to the same cluster. In our case, eps parameter defines the radius of influence to consider the active PSs as members of an individual ADA cluster. Through visual inspection, we experimented with different 'eps' values in the selected areas and a value of 30 m has been adopted. MinPtS defines the minimum number of active contiguous data points (PS) within the eps radius to be an ADA. In this work, a value of 5 is adopted. Thus, if the grouped PSs are less than five, they are considered as noise or outliers and cannot represent an ADA cluster. Finally, for each active deformation area (ADA), we estimated the number of aggregated active PSs; their minimum, maximum and average velocities; as well as the local steepest slope and morphological aspect angle (i.
e. , local directional mean). Following the detection of the active deformation areas, we performed local terrain slope and local terrain aspect analysis using the 30 m resolution ALOS Global Digital Surface Model data for the study area. Local terrain slope, which can be regarded as the most basic and important topographic parameter, not only describes the relief and morphology of the earth's surface but is also widely used for many types of environmental applications including landslide monitoring and analysis of mass movements  [72]. Local terrain aspect identifies the compass direction of the place with respect to north. It can also be thought of as the slope direction. In this study, we used local terrain slope to classify landslides and distinguish them from other subsidence related signals.
T-fill sequencing and data analysis Two biological replicates were used and the RNA from the total extracts (input) and immunoprecipitated samples (RIP) was sequenced. Constructions of sequencing libraries were carried out as previously described  [45]  from 1 μg RNA for inputs and 150 ng RNA for the RIP samples. Data were aligned to yeast reference genome version R64 for strain S288c using a custom computational pipeline, followed by the calculation of RIP enrichment using the DESeq2 R Bioconductor package as previously described  [44, 46].
Chromatin immunoprecipitation and sequencing (ChIP-Seq) Chromatin immunoprecipitation was performed using three biological replicates for strains Puf3-TAP WT and Puf3-TAP rpb4Δ, and two for the WT strain. For each sample, 100 ml of cells were grown in YPD medium to OD 600 ~ 0. 7. The SNF21-TAP-natR S. pombe strain (spike-in control, SP641, h90 ura-r TetR-tup11 KanR-tetO-snf21-TAP-natR ura4-D18) was grown in YES medium to OD 600 ~ 0. 7. Cells were crosslinked by adding 1% formaldehyde (Thermo Fisher) directly to the culture and shaking for 15 minutes. Formaldehyde was quenched for 5 min by adding 0. 125 M glycine. Cells were pelleted, washed 4 times with cold TBS and flash-frozen. Pellets were thawed on ice, resuspended in 500 µl of zymolyase solution (1. 2 M sorbitol, 10 mM Tris-HCl, pH 8, 10 mM CaCl 2 , 1% v/v beta-mercaptoethanol, 0.
S1 ). By using the 'DEseq2' software  [52] , the normalized fold change in the IP samples was calculated (Fig. 1A ). To define the Rpb4-bound transcripts, a false discovery rate (FDR) of < 10% was applied, as previously described  [44] , along with a specific cut-off consisting of Log 2 fold-change of RIP vs. input higher than 0 and padj<0. 1. We identified 1458 mRNAs differentially enriched in the RIP samples over the total extract (Table  S3 ). Notably, when binding stringency was increased to a 1. 5 fold change (padj<0. 1), enrichment persisted and then 777 Rpb4-bound mRNAs were identified. However, as Rpb4 was expected to bind a significant fraction of the transcriptome (unlike other RPB), we decided to adopt the more permissive criteria. Furthermore, the RIP-Seq experiment results were corroborated by analysing the enrichment of some of the identified Rpb4-bound mRNAs, by RT-qPCR, in relation to both a no TAP-tag control strain and to some Rpb4 non-bound mRNAs, in the input and immunoprecipitated samples.
Rpb4 and Puf3 associate with a common group of target mRNAs As shown above, the Rpb4-bound mRNAs are enriched for the mitochondrion-related GO categories. In addition, the mRNA encoding mitochondrial proteins have been described to be bound by the member of the Pumilio and FBF family RBPs Puf3 at their 3ʹUTR region  [40] [41] [42] 44]. These results suggest a potential relation between Rpb4 and Puf3. Accordingly, we speculate that Rpb4-bound mRNAs could also be imprinted by Puf3. To explore it more in detail we compared our RIP data to different available datasets of Puf3 mRNA interactors identified by distinct methodologies. We firstly compared our data by the RIP-Seq procedure to data from two different Puf3 RIP-Seq analyses  [44, 54]. As shown in Fig. 2A  and Figure  S2 , the Rpb4-bound transcripts significantly matched the Puf3-bound mRNAs compared to the dataset defined by Kershaw et al  [54] , and to a lesser extent compared to the more stringent dataset defined by Gupta et al  [44] , probably due to few identified Puf3-bound mRNAs as a consequence of the high fold-change used.
To gain more insight, we also compared our data to other datasets of Puf3-bound transcripts identified by other methodologies, HITS-CLIP  [55] , RIP-ChIP  [56]  and PAR-CLIP  [57]. As shown in Fig. 2B  and Figure  S2 , again, a significant overlap was detected. To be more stringent, we also performed the same kind of analysis by selecting a 1. 5 fold-change for our RIP data (RIP vs. input). In this case, the data from our analysis significantly matched the other used datasets, with similar or even higher percentage of overlapping (Figure  S2 ). To gain more insight into these analyses, we then defined as Puf3-bound mRNAs all the Puf3 targets that were common in at least three of the five datasets used, which are presented in Fig. 2   [44, [54] [55] [56] [57]. Accordingly, we defined 223 mRNAs as Puf3-bound mRNAs (Table  S5 ).
Rpb4-bound mRNAs decay faster The cells lacking Rpb4 or rpb1 mutants (rpo21-4 and rpb1-84) that affect the Rpb4 association with RNA pol II reduce Rpb4-mRNA imprinting and generally lead to increased mRNA stability. This   [52]. A specific cut-off was applied to define the Rpb4-bounded transcripts consisting of Log 2 Fold-Change of RIP vs. input higher than 0 (dashed grey line) and padj<0. 1 (dashed red line). (B) RT-qPCR to analyse several Rpb4-bound and unbound mRNAs from the RIP experiments corresponding to the Rpb4-TAP and the no-TAP tag control strains. Data are shown as the average and standard deviation (SD) of at least three independent experiments (RIP vs. input). rRNA 18S was used as a normalizer. demonstrates the role of Rpb4 in regulating mRNA stability  [7, 13, 14] , which probably depends on the association between Rpb4 and general mRNA decay machinery Pat1/Lsm1-7  [14].
To test this hypothesis, we used the published wild-type and rpb4∆ mRNA half-life dataset, from the cells grown in YPD at the permissive temperature of 30 ºC  [13] , obtained by the GRO method  [59]. As seen in Fig. 3B  (left panel), corroborating the data in Fig. 3A , the Rpb4bound mRNAs showed lower half-lives than the overall mRNA in the wild-type strain at the permissive temperature of 30 ºC. When we analysed the mRNA half-lives in the rpb4Δ mutant strain, a significant general increase in mRNA stability was observed (Fig. 3B , middle panel) in agreement with previous reported data  [13]. Strikingly, this increase was significantly less marked for the Rpb4-bound mRNAs that were also bound by Puf3 (Fig. 3B , middle panel). The less marked stability of the Rpb4-bound mRNAs versus the whole population was, however, identical when comparing the cells with (wt) or without (rpb4∆) this protein, except for the Rpb4/Puf3 commonly-bound mRNAs (Fig.
3B , right panel). These results indicate that Rpb4 had no specific effect on the stability of its mRNA targets, but a global effect on the whole transcriptome,   [55] , RIP-CHIP  [56]  and PAR-CLIP  [57]. (C) Venn diagrams depicting the common mRNAs bound by Rpb4 and Puf3 (for Puf3, they were selected as those in common in at least 3 of the 5 datasets indicated in B). (D) Boxplots showing the distribution of significant Rpb4 enrichment (Log2) over the two different categories of polyadenylation isoforms belonging to the genes with a particular predicted RBP binding motif (with motif or without motif). In the boxplots, the polyadenylation isoforms with no predicted motif are depicted in blue, and the isoforms with a predicted motif are denoted in red. The RBP motifs were taken from  [58]. p-value <0.
1 was considered significant. except for the targets shared with Puf3. It is worth noting that Puf3 mRNA targets were destabilized in the cells grown exponentially in YPD medium  [37, 38]. Thus we conclude that the stability of Puf3 targets depends less on Rpb4 than the transcriptome average. As a set of the Puf3 targets (as previously define) were not bound by Rpb4, we also explored the half-lives of the Puf3-bound mRNAs from the rpb4Δ mutant and its isogenic wild-type strain, using the same datasets. Unlike the Rpb4-bound mRNAs, the Puf3-bound mRNAs showed no significant differences in the half-lives in relation to the overall mRNAs in the wild-type strain (Fig. 3C , left panel). In addition, all the Puf3 targets (both those targeted and not by Rpb4) displayed a similar less marked increase in their stability than the overall mRNA population when Rpb4 lacks (Fig.
Puf3 integrity depends on Rpb4 associated with chromatin-bound RNA pol II and not on Rpb4-mRNA imprinting The above data suggest that Rpb4 is necessary for Puf3 integrity. Then, in order to investigate whether the alteration in Puf3 migration could depend on either the Rpb4 subunit bound to chromatin-associated RNA pol II or free Rpb4, we took advantage of the rpb1-84 mutant strain, which affects RNA pol II assembly and partially dissociates Rpb4 from the rest of the enzyme  [48]. This mutation leads to different chromatin-associated RNA pol II subcomplexes, some of which lack Rpb4, without altering Rpb4 cellular content  [48]. Then the Puf3-TAP protein was analysed by western blot in crude extracts of the rpb1-84 mutant strain and its isogenic wild-type strain containing this TAP-tagged protein.
Funding This work has been supported by grants from the  Spanish Ministry of Economy and Competitiveness (MINECO)  and  ERDF  ( BFU2016-77728-C3-2-P  to F. N. and  BFU2016-77728-C3-3-P  to J. E. P-O),  Spanish Ministry of Science and Innovation (MICINN)  and  ERDF  ( RED2018-102467-T  to F. N. and J. E. P-O), the  Junta de Andalucía  ( BIO258  to F. N. ),  Junta de Andalucía-Universidad de Jaén (FEDER-UJA   1260360  to F. N. ), the  Generalitat Valenciana  ( AICO/2019/088  to J. E. P-O) and  DFG grant  ( STE 1422/4-1 ) to L. M. S. VP's laboratory is funded by the  Swedish Research Council  ( VR 2016-01842 ), a  Wallenberg Academy Fellowship  ( KAW 2016. 0123 ), the  Swedish Foundations' Starting Grant (Ragnar Söderberg Foundation) ,  Karolinska Institutet  (SciLifeLab Fellowship, SFO, KID and KI funds).
Data collection The following data were extracted from each patient's electronic medical chart: age, gender, clinical and biological variables upon admission, and time between symptoms' onset and ICU admission. Simplified Acute Physiology Score (SAPS) 2 and Sequential Organ Failure Assessment (SOFA) were calculated upon ICU admission. Advanced life support measures taken during the ICU stay such as invasive mechanical ventilation, ECMO, vasopressor support, and renal replacement therapy were also collected. Finally, we recorded the length of ICU stay, time spent under invasive mechanical ventilation, and biological variables. Mortality was assessed at day 14 and day 28 after ICU admission. SARS-CoV-2 load in respiratory tracts (nasopharyngeal swab or tracheal aspiration) and plasma SARS-CoV-2 load were collected within 24 h of ICU admission and at day 7.