dataesr/scientific-paragraphs-categorization huggingface dataset:
Patients This was a retrospective radiological study of prospectively collected data in a high-volume arthroplasty referral institution. Inclusion criteria were rTKA using tricompartimentale prosthesis, regardless of the etiology and constraint required at revision. Three groups were identified based on the use and type of stem. In group NS (no stem), no stem was used for either the femoral or tibial implants. In group OS (offset stem), a stem with offset was used for at least one component. In group SS (straight stem), a straight stem was used for at least one component. Between January 2010 and May 2022, 862 patients underwent rTKA. Of these patients, 789 patients (305 males and 484 females), with a mean age of 72. 7 ± 10. 2 years old [39; 96] matched the eligibility criteria and were included in the analysis.
Data assessment All surgeries were performed by four senior orthopedic surgeons specializing in adult reconstructive knee surgery. To determine the appropriate component size and positioning, all patients underwent pre-operative radiographic evaluation with templating. Patient information was abstracted from a prospective registry and is summarized in Table  1. Tibial tubercle osteotomy was performed for difficult exposure, component extraction, or patellar maltracking. A tibial offset was used when a straight stem did not allow optimal tibial coverage with a stem longer than the previously used stem. A femoral offset was used when a straight stem did not restore the posterior or anterior offset or led to a medial or lateral overhang. Anteroposterior and lateral radiographs were performed post-operatively in all the patients.
Two orthopedic surgeons independently analyzed the post-operative radiographs for the necessity of offset stems. Using Traumacad Ò software (Traumacad Ò , Petach-Tikva, Israel) femoral and tibial components were templated for size and positioning, and stems were templated for length, diameter, and need for offset. An offset stem was deemed necessary retrospectively when it was not possible to use a straight stem while respecting the following criteria: (1) adequate coverage of the epiphysis (the largest possible implant without overhang); (2) the shortest stem possible (75 or 100 mm for standard cases, or a stem with sufficient length to bridge a weak zone); (3) an absence of malalignment due to the stem positioning (Figure  2 ). In case of discrepancy between the surgeons, a third experienced surgeon assessed the case and took the final decision.
Statistical analysis Collected data were analyzed with SPSS (Version 24. 0). Continues variables were expressed as mean ± sd [range]; categorical variables as percentages. The Kolmogorov-Smirnov test was utilized for normality analysis. Mann-Whitney U-test was utilized for the comparison of the continuous variables in the independent samples, for non-normal distribution, in a population divided into two categories. The Kruskal-Wallis test was utilized for the comparison of continuous variables in the independent samples, for non-normal distribution, in a population divided into more than 2 categories. Pearson-v 2 (crosstabulation) was utilized for the comparison of categorical variables. The level of significance was set at p < 0. 05.
The southwestern part of Bangladesh is a highly disaster-prone area with a mixed ecosystem, which is directly affected by climate change and undergoing remarkable changes in agricultural activities that are exacerbating the dynamics of soil organic carbon (SOC) and soil total nitrogen (STN). To study the spatial distribution of SOC and STN dynamics in such ecosystem, we chose Dacope Upazila and applied different interpolation methods, and compared them to nd out the best approach to demonstrate the spatial pattern of soil carbon and nitrogen status in 2000 and 2020 and correlated them with the terrain and environmental variables. We used the SRDI soil database to obtain the SOC and STN data of the study area in 2000 and 2020. To identify the elevation, aspect, slope, land surface temperature (LST), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI), we downloaded the digital elevation model (DEM) and satellite images of 2000 and 2020.
Data collection Data was collected from the database of Soil Resource Development Institute (SRDI), Bangladesh. SRDI is a government organization of Bangladesh under the Ministry of Agriculture and carries out research and survey to assess the soil quality to improve the agricultural production in Bangladesh. The organization publishes a report every year on different Upazila regarding soil fertility status. We collected SRDI reports published in 2000 and 2020 on Dacope Upazila, and the sampling locations (Fig. 1 ) along with other soil variables were obtained from those reports. At the sampling locations, soil was sampled at the depth of 0-15 cm. SOC and STN was determined from the samples by following Walkley-Black wet oxidation (Walkley and Black, 1934) and Kjeldahl  (Bremner, 1960 ) method, respectively.
Data sources of environmental variables A suite of 3 topographic variables, including elevation, aspect, and slope, and 3 spectral indices, including land surface temperature (LST), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI), were chosen to represent the effects of these factors on the distribution of SOC and STN. The topographic variables were determined using the ArcGIS 10. 4. 1 software. A digital elevation model (DEM), which uses the terrain elevation data to produce a digital simulation of a landscape surface, was downloaded from earthexplorer. usgs. gov website with a resolution of 30m. For the spectral indices, Landsat-5 TM imageries of 2000 and Landsat-8 OLI-TIRS imageries of 2020 were downloaded from the same website. Landsat-5 TM comprises of 7 spectral bands with 8-bit radiometric resolution.
Model validation To compare the e ciency of aforementioned interpolation techniques, we cross validated the datasets. We considered mean error (ME), root mean square standardized error (RMSSE), and coe cient of determination (R 2 ) for validating each method. The equations used for cross validation were: ME = (6) RMSSE = (7) R 2 = (8) Here, are the predicted value, standard error of the predicted value, measured value, and average values of the i sample at x location, respectively, and n indicates the number of samples. The geostatistical analyses were done in ArcMap software.
Descriptive statistics The basic statistics of pH, EC, SOC, STN content, C:N ratio, and other environmental variables, including LST, NDVI, NDWI, elevation (m), slope ( o ), and aspect of the study area in the year 2000 and 2020 is outlines in Table  1. In 2000, the SOC varied from 7. 37 g/kg to 28. 65 g/kg with a standard deviation of 4. 63 g/kg, and STN varied from 0. 07 g/kg to 0. 25 g/kg with a standard deviation of 0. 05 g/kg. On the other hand, in 2020, the SOC and STN content varied from 7. 59 g/kg to 24. 71 g/kg and 0. 65 to 2. 13 g/kg, respectively. Coe cient of variation (CV), the ratio of standard deviation (SD) to the mean, is a statistical measurement of the relative dispersion of a dataset around the mean value. Larger CV value indicates a higher variability present in the datasets.
Specifically, we followed 4 main steps. First, we mapped relevant EP indicators by compiling all available data combined with statistical modelling tools. In our framework, each ES is assessed through several EP indicators. Second, we investigated the spatial aggregation of EP by delineating hotspots (i. e. areas with significantly higher values of a given EP) and coldspots (i. e. areas with significantly lower values of a given EP). Third, we identified positive and negative spatial associations between EP to inform on non-additive effects of EP in the assessment of ES and identify links between EP values and activities. Documenting EP associations and hotspots and coldspots are preparatory steps to cost-effective strategies for ES conservation. Finally, we assessed total EP hotspots and coldspots exposure to anthropogenic activities, as well as their spatial overlaps with existing MPAs.
The Grande Vasière and its benthodemersal biodiversity This study specifically focused on the bentho-demersal ecosystem of the GV located in the Northern part of the Bay of Biscay over a 21,500 km² mid-shelf mud belt  (Bourillet et al. , 2006;  green dashed line on Figure  1 ). We compiled the data on bentho-demersal communities from two annual scientific surveys monitoring the GV. LANGOLF-TV is an underwater video survey which has been conducted each spring since 2014 in the GV for Norway lobster stock assessment  (Vacherot et al. , 2019). Available video footages from 2014 to 2018 were reanalyzed to identify all organisms visible. Video of the seabed are watched during 7 minutes and each species between two lasers (spaced 0. 75 meters apart) is counted and identified at the lowest taxonomic level by visual inspection  (Meŕillet et al.
, 2018b). EVHOE, part of the International Bottom Trawl Survey, targets the demersal species between October and November in the Bay of Biscay and the Celtic Sea since 1997  (Laffargue et al. , 2021). To fit the LANGOLF-TV temporal window, we selected the 2013-2019 period (in 2017, the survey was cancelled due to mechanical issue and no data was collected). During this survey, a GOV (Grande Ouverture Verticale) demersal trawl with a codend of 20 mm stretched mesh is towed during 30 minutes at a speed of ~3. 5 knots. The total catch is weighted and sorted by species for counting and a subsample is put aside for length measurements, maturity stage identification and sexing. The species observed in the surveys are listed in Supplementary Material A. Sampling protocols, selection of species and predictions of their spatial distribution are detailed in Supplementary Material B.
Regarding the GLM, we tested environmental (depth, current speed, temperature, sediment type, roughness and depth) and fishing (gillnets and bottom trawl fi shing time ) predic tors (Supplementary Material C). Then, we compared the ability of both technics to predict taxon's abundance by performing a Monte Carlo cross-validation with 200 iterations and selecting the model with the lower root mean square error. The full process is detailed in Supplementary Material B. Each taxon abundance was then predicted on the same spatial grid of 2. 5 x 2. 5 km (18 taxa by GLM or GLMM and 45 by kriging). To that end, we allocated values from the original grid of each predictor in each new pixel ("grid cell" hereafter) proportionally to their intersecting area. To approximate the total biomass, we used mean wet weights per individual by species from EVHOE and from the literature for two taxa from LANGOLF-TV absent in the EVHOE database (20 g for Pennatulacea from  Murillo et al.
, 2013; Liquete et al. , 2013; Hattam et al. , 2015). We identified 4 ES that were relevant to the GV according to 6 experts involved in the project, and that could be quantified, in other words, ES for which we had enough data on underpinning EP to design mappable indicators: sea food provisioning, biological control related to food web functioning, regulation of environmental conditions related to nutrient cycling and life cycle maintenance (Figure  2 ). The scarcity of data, sedimentary models and resolved phylogenetic tree for all taxa in our dataset was too limiting to make a robust assessment of regulation of seabed conditions and gene pool protection. Coastline protection and recreation were also disregarded in the absence of clear assumptions how the Grande Vasière supply services to beneficiaries located outside the studied area (i.
Sea food provisioning Sea food provisioning directly relies on the amount and diversity of commercial taxa  (Hattam et al. , 2015). We measured the stock of commercial taxa with the estimated total biomass of 20 commercial taxa identified in the biodiversity datasets (P. 1. 1 in Table  1 ; see Supplementary Material A for the species' list). Considering people's preference for variety in their consumption behavior  (Thiele and Weiss, 2003) , we also estimated the diversity of commercial species as a component of this ES. We assumed that a high diversity of fished species would better satisfy the different societal demands for sea food. To that end, we calculated the Shannon's index on the biomasses of commercial taxa (P. 1. 2 in Table  1 ). Shannon's index is higher for higher levels of species richness and an even distribution of biomass across species (or taxa in our case).
Life cycle maintenance Life cycle maintenance mainly relies on seed dispersal and nurseries. According to the literature, we identified two wellestablished functional areas in the GV: hake (Merluccius merluccius) nursery areas located in the North of the area  (Alvarez et al. , 2001; Alvarez et al. , 2004; Mahéet al. , 2007; Woillez et al. , 2007)  and sole (Solea solea) spawning grounds in the South and West  (Arbault et al. , 1986; Koutsikopoulos and Lacroix, 1992; Petitgas, 1997; Mahéet al. , 2007). We estimated juvenile hake abundance from EVHOE by filtering individuals with a body length inferior to 42. 85 cm (L50 from ICES, 2016). Then, juvenile hakes were treated as a separate taxon when modelling the spatial distribution of hake's biomass. In the absence of data on juvenile soles, we used data available on adult soles in the area and during the reproduction period.
Spatialized monthly sole biomass data were estimated through Bayesian models applied to fishing and biodiversity datasets following  (Alglave et al. , 2022). We performed a Net Persistence Index (NPI), adapted from  Colloca et al. (2009)  and  Milisenda et al. (2021) , to identify the persistent hotspots and coldspots of juvenile hake's annual abundance and adult sole's biomass during February and March. The definition of a hotspot and coldspot is given below. NPI is the proportion of years the grid cell has been selected as a hotspot minus the proportion of years as a coldspot. The output maps show the areas with repeatedly high values over years of juvenile hakes' abundance and adult soles' biomass.
FDR is preferred to family-wise error rate corrections (e. g. Bonferroni correction) which are too conservative when applied to multiple comparisons (Caldas de  Castro and Singer, 2006). We used the 'spdep' R package  (Bivand and Wong, 2018; version 1. 1-12)  to perform this procedure on each of the 9 EP indicators. Then, we combined maps of EP hotspots (and coldspots) and existing MPAs of the GV to visually inspect overlaps. Finally, we approximated the global ecosystem service supply by calculating, then mapping, the total ecosystem process value as the sum of EP in each grid cell, following the Total Ecosystem Service Value (or TESV) index developed by  Maes et al. (2012). To that purpose, ecosystem process values were standardized between 0 and 1 by dividing each value by the maximum observed value for the given EP.
Then, we further identified significantly higher and lower values of the total ecosystem process value using the Getis-Ord local G* statistic and FDR, as proxies of hotspots and coldspots of the overall ecosystem services supply. One step further, we identified spatial associations (also called bundles; see  Mouchet et al. , 2014  for methodological details) of EP using a Principal Component Analysis (PCA). We used the 'PCA' function from the 'FactoMineR' R package  (Lêet al. , 2008; version 2. 4 ). We deduced spatial covariations among EP from correlations between EP and PCA axes (i. e. we assumed that two or more EP highly correlated to the same axis, spatially covary). Then, we extracted the coordinates of grid cells of the four first PCA axes (i. e. axes with an eigenvalue higher or equal to 1) to build heatmaps revealing the spatial covariation among EP.
g. marine sand extraction). A third managed area, the "hake box" area  (European Commission, 2002) , exclude all gears with a mesh size inferior to 100 mm, except for vessels targeting Norway lobster if they have a 100 mm square mesh panel  (European Parliament and Council, 2019). Maps of N2000 areas and the Marine Nature Park have been retrieved from the INPN website (https://inpn. mnhn. fr). Geolocated fishing effort between 2016 and 2020 registered with the Vessel Monitoring System (VMS) were provided by the French Directorate of Marine Fisheries and Aquaculture (DGAMPA) and IFREMER. This dataset contains position, trajectory and speed of fishing vessels as well as sales notes and fishing logbooks. The collection of these data is mandatory for vessels superior to 12 meters in the European Union.
These data were processed by the SACROIS algorithm (Système d'Information Halieutique (SIH), 2017) that deduces VMS fishing time from vessel speed (< 4. 5 knots) and the distance to the nearest harbors, aggregated at a 0. 05°x 0. 05°resolution. After resampling raw data to fit our 2. 5 x 2. 5 grid, we calculated mean annual fishing time (in hours) by gear and grid cell. We split fishing time between active bottom gears (bottom trawl and Danish seine) and gillnets, which are the main fishing gears used on the area. We used the same method as EP to identify hotspots and coldspots of fishing (active bottom gears and gillnets). Prospection for marine sand extraction is already occurring and should remain restricted to this area. A floating offshore windfarm project in the North-West of the GV, however, is also ongoing but its size and exact location are unknown at this early stage.
Discussion ES and their underpinning EP are rarely integrated into management plans as operational objectives due to methodological issues, e. g. difficulty to define relevant ES indicators, unavailability of data and, therefore of ES maps (but see  Egoh et al. , 2010; Farella et al. , 2020 for instance). In this study, we contributed to overcome this gap by building spatial indicators based on several datasets and species distribution modelling. Hereafter, we will discuss the spatial congruence and mismatch among EP, how they may affect ES supply assessment, and with conservation scheme and uses.
Acknowledgments This study was part of the  PACMAN  project supported by the  European Maritime and Fisheries Fund (EMFF)  and  France Filière Peĉhe. The authors are deeply indebted to the  French Directorate of Marine Fisheries and Aquaculture (DGAMPA) , to the team from the  IFREMER French Fisheries Information System (HISSEO)  for providing access to the VMS data. We also thank  Jean-Francois Le Roux  ( IFREMER-LOPS-OC ) for the data extraction from the MARS3D model.
Flow cytometry TGF-b-treated or -untreated human Vg9Vd2 T lymphocytes were stained with FITC-labeled anti-human TCR Vd2 mAb (#IMMU389, Beckman Coulter) and APC-labeled anti-human CD3ϵ mAb (#UCHT1, Beckman Coulter), to check the purity of the populations. T lymphocytes were phenotyped using PElabeled anti-NKG2A (#Z199, Beckman Coulter), PE-labeled anti-NKG2D (#ON72, Beckman Coulter), PE-labeled anti-CD85j/ILT2 (#HP-F1, Beckman Coulter), APC-labeled anti-ICAM1 (#HA58, Biolegend). Acquisition was performed using an Accuri C6 PLUS flow cytometer (BD Biosciences) and the collected events were analyzed using FlowJo software (Treestar). Examples of raw cytometry data and gating strategies are shown in Supplemental Figure  S1.
The acquisition was performed using a FACSCalibur flow cytometer (BD Biosciences) and the events were analyzed using the FlowJo software (Treestar, Ashland, OR). To perform cytotoxic assays, tumor cells were previously treated with zoledronate overnight at indicated concentrations before loading with 51 Cr (75 µCi/10 6 cells) for 1 h. After washes, loaded tumor cells were cocultured with TGF-b-treated or -untreated human Vg9Vd2 T lymphocytes (effector to target ratio 10:1) for 4 h. The activity of 51 Cr released in supernatants was measured using a MicroBeta counter (Perkin Elmer). Specific target cell lysis was calculated as follows: % Lysis=[(Experimental release-Spontaneous release)/(Maximum release-Spontaneous release)] x 100. Spontaneous and maximum release values were obtained by adding culture medium or 1% Triton X-100, respectively, to 51 Cr-loaded target cells in the absence of human Vg9Vd2 T lymphocytes.
Transcriptional analyses Briefly, RNA was prepared from dry pellets of resting Vg9Vd2 T lymphocytes (10 6 cells/>95% purity) which have been collected following treatment (3 days), or not, with TGFb. RNA was extracted and purified using the NucleoSpin RNA + isolation kit from Macherey-Nagel. Both the quality and the amount of extracted RNA were checked using Nanodrop OneC. DGE-sequencing was performed by the BIRD core facility (SFR Bonamy, Nantes University); data were analyzed using R programming tools. All sequencing data and protocol used are available under GEO (deposit number #GSE213696).
Results The levels of TGF-b and gd TCR transcripts are both linked to the prognosis of human glioblastoma (GBM) cancer patients The abundance of TGF-b transcripts was analyzed in silico within several human tumors using GEPIA (Gene Expression Profiling Interactive Analysis, http://gepia. cancer-pku. cn/) which is an interactive web-based tool developed to deliver fast and customizable functionalities based on TCGA (The Cancer Genome Atlas) and GTEx (Genotype-Tissue Expression) data  (14). These transcriptomic analyses evidenced elevated quantities of TGF-b transcripts in some human tumors (red), such as glioblastoma (GBM), lower grade glioma (LGG) or head and neck squamous cell carcinoma (HNSC), as compared to associated normal tissues (green) (Figure  1A ). Interestingly, these analyses did not only evidence increased levels of TGF-b1 transcripts (TGFB1) in GBM, they also showed the elevated presence of Vd TCR chain transcripts (TRDC) in these brain tumors (Figure  1B ).
After establishing the LTO improvements, thermalhydraulic (TH) calculations are performed, including uncertainty quantification relevant to the PTS assessment (WP2). The impact of both LTO improvements and human factors on the results of TH analysis will be quantified and later assessed by subsequent structural mechanics and fracture-mechanics benchmarks within WP3 and WP4. The third work package (WP3) consists of performing deterministic structural and fracture-mechanics analyses to quantify the safety margins related to both LTO improvements and uncertainties in TH analyses. The analyses to be used for deterministic margin assessments by the APAL partners will first be tested on a common deterministic benchmark. In the fourth work package (WP4), probabilistic margin assessments based on probabilistic fracture-mechanics analyses will be performed.
The optimized design specifications from WP4 were integrated into WP5 for updated technical specifications of the components of the system. Prior to this, WP5 delivered the system's overall architecture when integrated into a reference NPP. With the system architecture and the improved component specification, the models in ATHLET, ATHLET/Dymola, and CATHARE were updated and simulation of sCO2-4-NPP modules integrated into a real NPP will be carried out. The Dymola results will be used to build the real-time simulator of the sCO2 heat removal system for coupling to the existing KONVOI simulator. WP6's objective is to validate the sCO2-4-NPP system in the virtual KONVOI NPP. In parallel with WPs 2-5, WP3 will document regulatory requirements and provide input to those WPs regarding the specific requirements to be considered in the design of components and the system as a whole.