dataesr/scientific-paragraphs-categorization huggingface dataset:
5) 172 (98. 9) 150 (98. 7) Pathology databank 235 (51. 6) 184 (53. 0) 146 (52. 0) 102 (52. 3) 89 (51. 1) 82 (53. 9) Questionnaire 128 (28. 1) 96 (27. 7) 84 (29. 9) 57 (29. 2) 44 (25. 3) 39 (25. 7) Age at genetic diagnosis of PHTS No. (%) 437 (96. 0) 329 (94. 8) 274 (97. 5) 188 (96. 4) 163 (93. 7) 141 (92. 8) Age, median (IQR) 38 (28-47) 36 (25-44) 37 (29-49) 35 (24-43) 39 (28-46) 38 (26-44) Coding effect, No. (%) 443 (97. 4) 335 (96. 5) 276 (98. 2) 190 (97. 4) 167 (96. 0) 145 (95. 4) Missense 146 (33. 0) 116 (34. 6) 86 (31. 2) 63 (33. 2) 60 (35. 9) 53 (36. 6) Truncating 296 (66. 8) 219 (65. 4) 189 (68. 5) 127 (66. 8) 107 (64. 1) 92 (63. 4) Other 1 (0. 2) 0 (0. 0) 1 (0. 4) 0 (0. 0) 0 (0. 0) 0 (0. 0) Domain location, No. (%) 443 (97. 4) 335 (96. 5) 276 (98. 2) 190 (97. 4) 167 (96. 0) 145 (95.
Acknowledgements This work is supported by the  EC  through the  ERC  project  SUPRA2DMAT  ( GA-833707 ) and the  Graphene Flagship Core 3 project  ( GA-881603 ) as well as the  Labex  projects  CSC  ( ANR-10LABX-0026 CSC ) and  NIE  ( ANR-11-LABX-0058 NIE ) within the  Investissement d'Avenir program   ANR-10-IDEX-0002-02  the  International Center for Frontier Research in Chemistry , the  Institut Universitaire de France (IUF)  and the  Chinese Scholarship Council. Received: ((will be filled in by the editorial staff)) Revised: ((will be filled in by the editorial staff)) Published online: ((will be filled in by the editorial staff)).
Introduction Compressed sensing (CS) has already attracted great interest in various fields. Examples include medical imaging  [1, 2] , communication systems  [3] [4] [5] [6] , remote sensing  [7] , reconstruction algorithm design  [8] , image storage in databases  [9] , etc. Compressed sensing provides an alternative approach to Shannon's vision to reduce the number of samples and/or reduce transmission/storage costs. Other approaches also address this issue, such as random sampling  [10]. Compressed sensing recovery is a linear optimization problem. The most common CS retrieval algorithms explore the prior knowledge that a natural image is sparse in certain domains, such as the wavelet domain, where simple and efficient noise reduction is possible  [11] [12] [13] [14] [15] , or in the discrete-gradient domain, which we will develop in this work.
To overcome these intrinsic drawbacks of the total variation model, we introduce a nonlocal self-similarity constraint as a complementary regularization. Nonlocal self-similarity can restore high-quality images. To make our algorithm robust, an augmented Lagrangian method was used efficiently to solve the above inverse problem. The remainder of this paper is organized as follows. In Section 2, the basics of the methods used to image restoration are presented. The tools of denoising image considered in the proposed framework are described in Section 3. Section 4 presents our proposed algorithm, called DCSR. The experimental results from the processing of Lena, Barbara and Cameraman images, available in the public and royalty-free database https://ccia. ugr. es/cvg/dbimagenes/index. php (accessed on 1 June 2021), and the comparison with competing methods are shown in Section 5.
Recent studies also showed that sea level is accelerating at 0. 12 ± 0. 07 mm yr -2 at the global scale  (Ablain et al. , 2019)  and ranges between -1 mm yr -2 and +1 mm yr -2 at the regional scale  (Prandi et al. , 2021). The Sentinel-6 Michael Freilich (S6-MF) mission, recently launched in November 2020 on the same historical T/P-Jason orbit, will allow the GSML time series to be extended once the current validation phase is completed (early 2022). Several other altimetry missions (e. g. ERS-1, ERS-2, Envisat, Cryosat-2, SARAL-AltiKa) have also been launched from 1991 onwards, all in different orbits at lower altitudes and with lower revisit rate (e. g. 35 days for Envisat). Although these missions were not designed to be as stable as T/P, Jasons and S6-MF, their data is nevertheless very relevant to improve and verify the long-term stability of the climate altimeter record.
Altimeter Data Since the S3A launch in February 2016, Jason-3 and SARAL/AltiKa have continuously provided high-quality sea level measurements, as reported in the validation reports of both missions (see  (Roinard and Michaud, 2020)  and  (Jettou and Rousseau, 2020) ). Furthermore, Jason-3 has also been the reference mission since October 2016 for computing the GMSL indicator on AVISO (https://www. aviso. altimetry. fr/msl). These two missions are therefore selected in this study to perform cross-comparisons with S3A from July 2016 onwards (the first months after the S3A mission launch between February and July 2016 were used for calibration purposes and are therefore not suited for GMSL measurement). For the same reasons, Jason-3 and SARAL/AltiKa are selected to perform cross-comparison with S3B from December 2018 onwards, as well as S3A which also covers the entire S3B period.
Other altimetry missions partially cover the S3A or S3B periods like Jason-2, HY-2A, HY-2B, and Cryosat-2. Among these missions, only Jason-2 could be chosen because of its very good stability, however the end of life of the mission in October 2019 reduces the interest to use these data for cross-comparisons with S3A, Jason-3 and SARAL/AltiKa. The altimeter products used are the non-time critical (NTC) along-track Level-2+ (L2P) products from the Copernicus Altimetry Marine service under the CNES responsibility for Jason-3 and SARAL/AltiKa, and Eumetsat responsibility for S3A https://doi. org/10. 5194/os-2021-106 Preprint. Discussion started: 16 November 2021 c Author(s) 2021. CC BY 4. 0 License. and S3B. These products contain the along-track sea level anomaly (SLA, see Eq. (  1 )) calculated after applying a validation process fully described in the product handbook of each altimeter mission.
2 where Fig. 3  exhibits lower  trends of 0. 66 ± 0. 62 mm yr -1 between S3A and Jason-3, and 1. 38 ± 0. 90 mm yr -1 between S3A and SARAL/AltiKa. The very likely drift in the S3A GMSL is also observed through independent comparisons with tide gauges, using the method provided by  Valladeau et al. , 2012 and Ablain, 2018 , and data from the GLOSS/CLIVAR tide gauge network. Over the July 2016 to December 2020 period, a significant relative GMSL drift of 1. 18 ± 0. 63 mm yr -1 (1-σ) is also detected between S3A and the GC tide gauges network. All of these consistent analyses reveal that a drift in the S3A GMSL between 1. 0 and 1. 3 mm yr -1 ± 0. 3 mm yr -1 is most likely detected. However, the S3A GMSL drift is much larger than the 0. 3 mm yr -1 GMSL PTR-induced drift anticipated by  Poisson et al.
Acknowledgement This study has been founded in the frame of the  Sentinel-3 Mission Performance Centre (MPC)  project supported by  ESA/ESRIN. We first acknowledge  Sylvie Labroue (CLS) and Benoit Meyssignac (LEGOS)  who provided support to this study at its beginning. We also acknowledge  Matthias Raynal  from  CNES  for providing some input data and advice. https://doi. org/10. 5194/os-2021-106Preprint. Discussion started: 16 November 2021 c Author(s)  2021. CC BY 4. 0 License.
Temperature and salinity from the TSG were calibrated with the nearest CTD measurements. Wind measurements, air temperature, and humidity were also collected underway at about 10 m above the sea surface with a Geonica Meteodata automatic weather station. A hull-mounted Teledyne RD Ocean Surveyor Acoustic Doppler Current Profiler (ADCP) operating at a frequency of 75 and 150 kHz recorded underway ocean currents with 16 m and 8 m depth bins, starting at 10 m and reaching up to 700 and 400 m depth, respectively. The ADCP was operated at 75 kHz over the deep ocean and at 150 kHz over the shelf, recording one profile approximately every 1 min. Turbidity from water samples was determined on board with a Multiparameter Senso Direct 150 Lovibond. In situ chlorophylla was also determined on board by acetone extraction and fluorometer reading following  Yentsch and Menzel (1963).
Fluorescence values from the TSG were calibrated using 32 measurements of chlorophyll-a from water samples collected at 5 m depth by empirically computing a scale factor to minimize the root square mean error. Fluorescence values from the CTD were previously adjusted with a "dark offset" calculation for each profile, by subtracting the mean value of the aphotic zone in values deeper than 200 m to each profile. The adjusted chlorophyll-a data compare well and are significantly correlated (Supplementary Figure  1 ), although absolute values of chlorophyll-a are out of the scope of this study, which focuses on identifying low and high-productivity regions. Water samples for nutrients were fixed on board and measured at the Institute of Marine Sciences (ICM-CSIC) in Barcelona, Spain. At the laboratory nitrate, nitrite, and phosphate concentration were determined using a Seal Analytical MT19 Auto Analyzer with the colorimetric technique and an automated system by continuous flow analysis applying the methodology of  Grasshoff et al.
, 2019)  within the period of the cruise; (IV) L4 Sea Surface Salinity (SSS) v03. 21 from the European Space Agency Sea Surface Salinity Climate Change Initiative consortium at 50 km and a weekly resolution, spatially sampled on a 25 km EASE (Equal-Area Scalable Earth) grid and 1 day of time sampling. This dataset combines the satellite measurements from SMOS, SMAP, and Aquarius covering the period January 2010-September 2020 (see  Boutin et al. , 2021) ; (V) Ssalto/Duacs Multimission Altimeter derived absolute dynamic topography (ADT) and derived surface geostrophic velocity fields at a 0. 25 • horizontal grid for the period 1993-2019 distributed by CMEMS  (Capet et al. , 2014; Pujol et al. , 2016). This latter dataset is also used as input for the Tracked Ocean Eddies (TOEddies) automatic detection algorithm developed by  Chaigneau et al.
MATERIALS AND METHODS CTD profiles were generated in 1 dbar bin average. All the gridded fields were linearly interpolated and previously smoothed with a 5 dbar centered moving average to remove spiciness from the plots. ADCP data was processed with Cascade V7. 2 (Le  Bot et al. , 2011)  with tide correction and a 16 m vertical resolution. Along and across slope velocities were computed by assuming a 40 • inclination of the slope with respect to the zonal axis. Conservative temperature ( , • C) and absolute salinity (S A , g kg -1 ) were calculated from observations from the Thermodynamic Equation of SeaWater 2010  (McDougall and Barker, 2011)  and used for the water mass characterization. Water masses in the deep ocean were classified based on neutral density (γn) ranges and thermohaline properties following  Valla et al.
Other datasets ERA 5 reanalysis Understand the mean and the anomalous conditions of the winds during the period of the cruise in the entire basin. Spatio-temporal resolution: Hourly, 25 km. ETOPO 1 bathymetry Bottom depth. Horizontal resolution: 2 km profiles and retained only those with good quality (flagged as 1 or 2), and the co-localized values between 1,000 and 2,500 m. To study the vertical distribution of the shelf waters with Argo floats, we interpolated the irregular sampling to a regular 10 m interval, which is the coarser resolution of the Argo profiles, in order to get the same number of observations per profile. For all the observations, we colocalized each measurement to obtain the closest satellite measurement in space and time to compare. Using satellite altimetry, we followed the criteria proposed by  Lumpkin and Garzoli (2011)  to detect the location of the confluence over the slope "as the latitude where the surface current vectors interpolated to the 1,000 m isobath change direction from southward (Brazil Current) to northward (Malvinas Current) in 77 day low-passed velocity fields.
SUMMARY AND CONCLUSION The hydrographic cruise that took place in April-May 2016 in the Uruguayan EEZ provided an unprecedented dataset to investigate local water masses, ocean dynamics, and cross-shelf exchanges at the Brazil Malvinas Confluence. Despite the survey covering a relatively small area, our study shows that it is characterized by a highly variable and dynamic environment. Water properties can fluctuate from below 0 • C to over 25 • C in conservative temperature and from almost freshwater to over 37 g kg -1 in absolute salinity. The region hosts intense western boundary currents and mesoscale activity developing offshore, displaying large oceanic space-time variability at all scales (from synoptic, to interannual and interdecadal) driving cross-shore exchanges. The first half of 2016 was an anomalous period in the southwestern Atlantic in terms of atmospheric and oceanic circulation.
ACKNOWLEDGMENTS We deeply acknowledge all the people behind the cruise planning and sampling processing, especially those on board  RV Sarmiento de Gamboa , as well as all the open access datasets. GM acknowledges Uruguayan law No. 18. 381 (exp No. 2018/14000/020893 ) and  Phillip Miller  for the very useful discussions. We also acknowledges  William Sadvige ,  Tamaryn Morris , and  Anna Rubio  for their constructive comments and suggestions that greatly improved the manuscript.
FUNDING This work was supported by the  European Union 's  Horizon 2020 research and innovation program  under grant agreements no. 817578  (TRIATLAS), the  TOEddies CNES-TOSCA  research grant. We also acknowledge the mesoscale calculation server CICLAD (http://ciclad-web. ipsl. jussieu. fr)dedicated to  Institut Pierre Simon Laplace  modeling effort for technical and computational support. GM received funding from bourse  ANII-Campus France  ( POS_CFRA_2017_1_146868 ). AP acknowledges the support from the  Inter-American Institute for Global Change Research/CONICET (Argentina)  grant  RD3347.
DATA AVAILABILITY STATEMENT The full processed data necessary to reproduce the reported finding is available at https://doi. org/10. 17882/86206. Raw TSG and meteorological underway measurements are available at http://data. utm. csic. es/viewer/?vessel=sdg&cruise=20160408& dataset=tss. Raw CTD and ADCP data is available for research under the previous authorization of the Uruguayan national oil company (https://www. ancap. com. uy). ADT and chlorophyll-a data were downloaded from http://marine. copernicus. eu/, SST from https://psl. noaa. gov/, SSS from https://catalogue. ceda. ac. uk/uuid/fad2e982a59d44788eda09e3c67ed7d5 and eddy tracking from https://vesg. ipsl. upmc. fr/thredds/catalog/IPSLFS/ rlaxe/catalog. html?dataset=DatasetScanIPSLFS/rlaxe/Database_ South_Atl. zip.
Descriptive and Univariate Analyses To assess the representativeness of the studied cohort, we compared the characteristics of the respondents to the Nonrespondents and to the whole CARTaGENE cohort (age, gender, education level, country birth, and province birth). To describe the characteristics of the populations, means with standard deviation (SD) and frequencies were reported for continuous and qualitative variables, respectively. The number of missing data per variables were also reported. For comparing means between groups, we used t-test. For categorical variables, we used chi-squared test or exact Fisher test if needed. To take into account the multiple comparisons, pvalues were corrected with the Benjamini and Hochberg method  (18). We reported odds ratios (OR) with the 95% confidence interval (95% CI) only for variables with a p-value lower than 5%.
In this study, since age was the main factor for vaccine priority, we adjusted for age effect in the linear part of the model. Using a penalized maximum likelihood method with the Bayesian information criterion (BIC), we selected the optimal hybrid treebased model  (20). As regression-trees are known to be prone to instability which means that changes in the dataset may modify the structure of the tree, we also performed an ensemble analysis by fitting a series of hybrid tree-based models obtained from different bootstrap samples of the original dataset and reported the variable importance scores  (21). Larger scores highlight which variables were most relevant for the vaccine hesitancy. In this work, this ensemble analysis was not intended to provide individual predictions for vaccine hesitancy but to give arguments regarding the reliability of the selected optimal treebased model.
ACKNOWLEDGMENTS We would like to thank all the CARTaGENE participants for their generous investments in health research. We also would like to thank the  CARTaGENE employees  without whom this study would not have been possible:  Philippe Malric ,  Thibault de Malliard ,  Caroline Amzallag ,  Mathilde Duplaix ,  Gabrielle Thauvette ,  Catherine Labbé , and  Alexandra Obadia.
DATA AVAILABILITY STATEMENT The datasets for this article are not publicly available because the data were collected in the context of the CARTaGENE cohort. In accordance with the consent signed by the participants, data are accessible for research projects evaluated by Ethics Committees and by a committee on data and sample access. Requests to access the datasets should be directed to CARTaGENE (http://cartagene. qc. ca; access@cartagene. qc. ca; +1 514-345-2156).
This colony front spread outward at a continuous rate followed by fruiting waves until the entire plate was covered. The uneven colony edge was visualized under the microscope, which revealed that the amoebae were organized into striking elongated, motile collectives that extended outward from the colony center (Figure  1D ; Video S1, each black dot corresponds to a contractile vacuole, and approximates one cell). These collectives were dynamic and formed branches that fused and separated with great flexibility as they flowed in an outward direction into the virgin bacterial lawn (Figures  1D  and S2 ; Video S1). Cell collectives migrated in close proximity to one another. Occasionally, collective units crossed over one another and maintained their direction of migration, suggesting a distinct identity and collective cohesion (Figure  S2 ; Video S1).
Plates were then scored for an outwardly directed movement by the appearance of (1) individual polarized cells with distinct contractile vacuoles (lobose amoebae) and an orientation outward from the colony center; (2) small one-cell wide, head-to-tail collectives (under 20 cells); (3) complex collectives of over 20 cells; and (4) fruiting bodies. The invasive and collective properties all appeared over time in a density-dependent manner, with the cells at the highest concentration immediately progressing to collective invasion after germination (Figure  5B ). These data suggest that collective invasion is an emergent property. 26 he role of the bacteria in collective invasion was next analyzed. KpGe plates were spread unevenly to create bare patches in the bacterial lawn. Invasive collectives were visualized as they encountered these food deserts.
The retraction or merger of cells appeared to occur via cytokinetic tethers (Figure  6D ). Mitotic events were scored for occurrence of binary versus multiple daughter cells, with 10 percent of mitotic events being non-binary, and rarely did events give rise to more than cells (Figure  6E ). To image the nuclear states, cells were imaged with Hoescht DNA dye. Hoescht dye labeled both rod-shaped internalized bacteria and round nuclei (Figure  6F ). Most cells contained one nucleus; however, larger cells often had two nuclei (Figure  6F ). Together, these results are consistent with the initial account that a low percentage of F. alba cells appeared bi-and tri-nuclear. 20 These data suggest that F. alba cells predominantly contain one nucleus but are tolerant of multinuclear states. Retraction via cytokinetic tethers is a plausible explanation for these multinuclear states, but it is necessary to develop nuclear imaging tools to visualize individual mitotic events in cells to be certain.
ACKNOWLEDGMENTS We thank  W. James Nelson  for critical reading of the manuscript. We thank  Nabil Hanna  and  Thierry Soldati's lab for Dictyostelium advice. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 638376/Segregactin ), from the  Swiss National Science Foundation  (grant  310030B_182825 ), and from  NCCR Chemical Biology  funded by the  SNSF.
ACKNOWLEDGMENTS The authors thank  Simon Spies ,  Jan-Hendrik Otto  and  Joachim Stroth  from the HADES collaboration for fruitful discussion at the "Transport meets HADES, symposium during the HADES Collaboration Meeting XL" symposium and  Szymon Harabasz  from the HADES collaboration at the " CPOD2021 -International Conference on Critical Point and Onset of Deconfinement " conference. This article is part of a project that has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement  STRONG -2020 -No 824093. Furthermore, we acknowledge support by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ): grant  BR 4000/7-1  and by the  GSI-IN2P3  agreement under contract number  13-70. JS acknowledges support from the  BMBF  through the ErUM-data-pilot project as well as the  Samson AG.
The final course  (Williams & Penman, 2023 ) is outlined in Table  1. Senior mindfulness teachers in New Zealand (E-LO), South Africa (BG, MJ, LK, JK, MW, and the UK (AP) recruited participants (with prior experience of mindfulness) and asked if they would be willing to take part in evaluation of the new program. It was explained that the course had been designed for those who are already familiar with mindfulness and offered an opportunity to deepen and extend their meditation practice. All participants completed a consent form. The study was approved by the Oxford University Ethics Committee (ER67367/RE001) and data analysis was conducted in Oxford (KW, LR, EM, LT) independently of the mindfulness teachers. A total of nine classes were run. Self-report data were collected at two time points, precourse (T0) and post-course (T1).
Data Analyses Means and standard deviations were computed for symptom measures (measuring anxiety, depression, and perceived stress; PHQ-9, GAD-7, and PSS, respectively), well-being (WEMWBS), and mindfulness (FFMQ-15). Mirroring the  Baer et al. (2021)  paper, reliable change was calculated using the algorithm developed by  Evans et al. (1998) , who define "reliable change" as a function of the baseline SD of the measure and its reliability (using Cronbach's α scores). Using this algorithm, reliable change for the PHQ-9 (depression) was calculated as a change in raw score of 3. 45 or more (in either direction); reliable change for the GAD-7 (anxiety) was calculated as a change in raw score of 2. 71 or more (in either direction). In accordance with the Baer et al. (  2021 ) paper, we used the same 5-item categorization, using reliable change and the original measures' cutoffs for clinical significance: (a) "reliable and clinical improvement," (b) "reliable improvement," (c) "no reliable change," (d) "reliable deterioration," and (e) "reliable and clinical deterioration.
52 AE 0. 87). Omalizumab may also be considered in patients with neoplasia, which is a contraindication to most immunosuppressive drugs (4. 56 AE 0. 82). 64 Dupilumab. Dupilumab is an anti-IL-4 Ra mAb, which is approved in atopic dermatitis. It is off-label in BP (4. 36 AE 0. 86). An open retrospective series on 13 patients showed that 54% of patients achieved disease clearance, suggesting the potential efficacy of the drug. The drug was well tolerated. 65, 66 New therapeutic approaches, including blockade of IL-17, IL- 12/IL-23, IL-5Ra, eotaxin, neonatal Fc receptor and LTB4/ C5aR, are currently being tested in BP (see update in: https:// clinicaltrials. gov/ct2/results?cond=bullous+pemphigoid& term=&cntry=&state=&city=&dist=).