dataesr/scientific-paragraphs-categorization huggingface dataset:
T he massive dissemination of carbapenemase-pro- ducing Enterobacterales poses a global threat to public health. Carbapenem antibiotics remain the last line of defense against highly resistant Enterobacterales. Carbapenemases have been identified in 3 of the 4 classes of the Ambler classification: class A carbapenemases (mostly Klebsiella pneumoniae carbapenemase types)  (1) , class B carbapenemases or metalloβ-lactamases (mostly New Delhi metallo-β-lactamase [NDM], Verona integron-mediated metallo-βlactamase [VIM], or imipenemase types)  (2) , and class D carbapenemases (mostly oxacillinases  [OXAs]  of OXA-48 types)  (3). In France, the most prevalent carbapenemases are of OXA-48 type  (4). According to the Beta-Lactamase Database (http://www. bldb. eu), >50 OXA-48-like carbapenemase variants have been identified.
05) (Figure  1 , panel A; Table  2 ). The prevalence of NDM and OXA-232coproducing isolates also slightly increased (0. 15% among NDM and 0. 27% among OXA-48-like from 2013-2018 to 2019-2021) (Figure  1 , panel A; Appendix Table  2 ). We performed short-read next-generation sequencing on all K. pneumoniae strains producing OXA-232 during 2015-2021 (n = 95) using a HiSeq system (Illumina, https://www. illumina. com) and submitted them to GenBank (Appendix Table  1 ). We assembled Illumina reads using shovill 1. 1. 0 (https:// github. com/tseemann/shovill) and SPAdes 3. 14. 0 (http://bioinf. spbau. ru/spades) multilocus sequence typing programs, and we performed resistome analysis using pubMLST (https://pubmlst. org) and Resfinder (https://cge. cbs. dtu. dk/services/ResFinder). For phylogenetic analysis, we mapped next-generation sequencing reads to the reference genome (K.
pneumoniae HS11286 [GenBank accession no. NC_016845. 1]) using SNIppy 4. 6. 0 (https://software. cqls. oregonstate. edu/updates/snippy-4. 6. 0). We visualized metadata and phylogenetic trees using iTOL 6. 5. 2 (https://itol. embl. de). Among the 95 patients colonized or infected with OXA-232-producing K. pneumoniae, 19 had recently returned from Asia (including 15 from India) and 12 from the Middle East. Among K. pneumoniae isolates, we identified 14 different STs, 5 of which were  We observed a diversification in OXA-232-producing K. pneumoniae STs over the last 2 years of the study period. In addition, the number of K. pneumoniae ST-231 isolates decreased, whereas the number of K. pneumoniae ST-2096 isolates increased (Figure  1 , panel B). We built single nucleotide polymorphism (SNP) matrices and phylogenetic trees for the 2 main STs (ST-231 and ST-2096) and compared them to epidemiologic data.
Statistical analyses All graphs and statistics were performed using the GraphPad Prism software version 5. 02 (San Diego, USA). Data are presented as means and standard error of the mean (±SEM) from at least 3 independent experiments. The Kruskal-Wallis test was used to compare differences in multiple groups and the Mann-Whitney (two-tailed) test was used to compare non-parametric variables between two groups. p values < 0. 05 were considered statistically significant as indicated in the figure legends.
0I , P QQ ∝ |a 1,16 | 2 = B 2 1 B 2 2 B 2 3 ω 2 1S 256ω 6 0I. (6) Hence, the results show that the 4SSE DNP transfer efficiency has a ω -6 0I dependence compared to the ε ∝ ω -2 0I relation exhibited by the conventional 2SSE. Besides that, Eq. (  5 ) reveals that 4SSE depends on the product of three e-1 H dipolar couplings, i. e. , its DNP efficiency is exceptionally sensitive to the number of 1 H spin close to the unpaired electrons. To confirm our hypothesis that the observed DNP peaks at |Ω/2π| ∼ 45 MHz are indeed due to the 4SSE, we performed numerical simulations on a four-spin e-1 H-1 H-1 H system using a customized DNP simulation package in MATLAB. 24, 30, 32 Indeed, the simulated DNP field profile (Fig. 3 ) shows that two peaks are observed at |Ω/2π| ∼ 45 MHz. We note that the two peaks are not necessarily symmetric, a feature that was also observed in experimental results.
Collectively, these data indicate that the chronic absence of VASH-SVBP detyrosinating enzymes in SVBP KO neurons leads to a reduction in the proportion of acetylated MTs. To determine whether short-term inhibition of the VASH-SVBP enzyme likewise lowers the degree of tubulin acetylation, we treated wild-type neurons with the membrane-permeant protease inhibitor alkyne-epoY, which specifically inhibits the tubulin carboxypeptidase activity of VASH-SVBP  (Aillaud et al. , 2017). Immunofluorescence microscopy showed that a 12 h incubation with the inhibitor caused a noticeable weakening of both acetylated and detyrosinated MT fluorescence (Figure  4A ). We then performed quantitative immunoblotting analysis of alkyne-epoY-treated cortical cultured neurons and FIGURE 2 hippocampal neurons at DIV21 infected with shTTL and their control at DIV17.
Western blot analysis and quantification RIPA supernatants (10 µl) were subjected to electrophoresis on stain free 4%-15% gels (Bio Rad) and then quickly transferred to Nitrocellulose using  Trans-Blot Turbo Transfer System (Bio Rad). Proteins on the membrane were revealed using specific antibodies against acetylated and α tubulin. Anti Acetylated-Tub (1/10,000) and anti α-tubulin (1/10,000) antibodies were used with the appropriate peroxidase-/labeled secondary antibodies. Secondary antibody signal was revealed using Pierce ECL Western blotting substrate (Thermo scientific) and analyzed with ChemiDoc ™ MP Imaging System (Bio Rad) using Image Lab software (stain free gel and chemoluminescence protocol) for quantification. For each lane of the blot, the software measures the integrated volume of the band corresponding to the antigen of interest.
Statistical analysis Data analyses, statistical comparisons, and graphs were generated using GraphPad prism or the R programming language. Statistical analysis of differences between two groups was performed using Student's t tests for populations with Gaussian distribution or else with Mann Whitney's test. For Figure  1B  and Supplementary Figure  S1B , as regular two-way ANOVA was not suitable we used a linear mixed model and calculation of model coefficients by restricted maximum likelihood estimation (using the R lmer package). The significance of fixed effects (Braak stage and brain region) was then evaluated by Wald type II F tests (with Kenward-Roger correction) of the null hypothesis for each of the model coefficients. Post hoc comparisons were performed between non-weighted marginal means, using Sidak tests.
Acknowledgments We thank  I. Jacquier  and  C. Corrao  for technical support;  C. Magallon ,  S. Andrieu ,  L. Romian ,  F. Mehr ,  F. Rimet , and  S. Bama-Toupet  for animal care and the engineers of the  Photonic Imaging Center of Grenoble Institute Neuroscience (Univ Grenoble Alpes-Inserm   U1216 ) which is part of the  ISdV core facility  and certified by the  IBiSA label  and in CEA-IRIG animal facility (GRAL,  ANR-17-EURE-0003 ). We are grateful to  B. Di Cara  and P.
Funding This work was supported by: INSERM; CEA,  CNRS, University Grenoble Alpes , by  France Alzheimer  ( CAPAlz-AAP SM 2018 ),  ANR  ( SPEED-Y ,  ANR-20-CE16-0021 ) and  NeuroCAP-Servier  grants to M-JM; by  NIH/NIA   RO3 AG060025 ,  NIH/NIA   R56 AG050658 ,  NIH/NIA   R01 AG050658  and  NIH/NINDS   R21 NS120076-01  grants to FB; by  NIH/NIA   RO3 AG060025  (Co-I),  NIH/NIA   R56 AG050658  (Co-I), the  Henry and Marilyn Taub Foundation , and the  Thompson Foundation  (TAME-AD) grants to AnS; by the  Alzheimer's Association  Grant  AARF-20-685875  to JP. Salary of JM-H came from  SBPLY/19/180501/ 000190PRT program  of  JCCM (Junta de Castilla La Mancha ) co-founded by  FEDER.
Dynamics of global wetlands are closely linked to biodiversity conservation, hydrology, and greenhouse gas emissions. However, long-term time series of global wetland products are still lacking. Using a diagnostic model based on the TOPography-based hydrological MODEL (TOPMODEL), this study produced an ensemble of 28 gridded maps of monthly global/regional wetland extents (with more reliable estimates at mid-low latitudes) for 1980-2020 at 0. 25° × 0. 25° spatial resolution, calibrated with a combination of four observation-based wetland data and seven gridded soil moisture reanalysis datasets. The gridded dynamic maps of wetlands capture the spatial distributions, seasonal cycles, and interannual variabilities of observed wetland extent well, and also show a good agreement with independent satellite-based terrestrial water storage estimates over wetland areas.
4 ). To understand the role of wetlands on global hydrological and biogeochemical cycles, an accurate estimation of wetland extent and its temporal variations is required  4, 5. Global wetland inventories are usually static and only document long-term (usually maximum) wetland extent, thereby lacking information on temporal dynamics  [6] [7] [8] [9]. Besides, given the lack of accurate definition, the small spatial scale of many wetland systems, and inventory sampling limitations, estimates of global wetland extent have considerable discrepancies with a range of 2. 1-29. 8 Mkm 2 (ref. 10 ). Satellite-based datasets from the visible, infrared, or microwave bands have been developed to characterize the spatiotemporal dynamics of wetlands  11, 12  , but with different degrees of success depending on the environments (Table  1 ).
The optical satellite data such as the global surface water from the European Commission's Joint Research Centre (hereafter JRC)  13  provides a high spatial resolution (30 m) but only detects open water bodies without dense vegetation for 1984-2015. Microwaves, including active and passive can penetrate dense vegetation and clouds but are limited by small spatial coverage or low spatial resolution, respectively  12. Multi-sensors datasets such as the Global Inundation Extent from Multi-satellites version 2 (GIEMS-2)  14  and the Surface WAter Microwave Product Series (SWAMPS)  15  capitalize the strengths of different satellites and provide maximum information of surface inundated extent since the 1990s, but miss some small wetlands due to their coarse global 0. 25° × 0. 25° resolution  14.
Hence, mapping the long-term dynamics of wetland extent is still a challenge. For modeling wetland areas, the simple hydrological model TOPMODEL (TOPography-based hydrological MODEL) developed 40 years ago  16  has been widely used with different settings to delineate wetlands  [16] [17] [18] [19] [20]. Owing to topography being the main factor determining water pathways, TOPMODEL uses a compound topographic index (CTI) to redistribute soil water in sub-grid elements of a larger land surface grid-cell or a catchment  21. The always saturated or regularly saturated sub-grids can be regarded as wetlands. Global-scale CTI datasets usually have a high spatial resolution, for example, 30 arcsec for the HYDRO1k dataset developed by the U. S. Geological Survey and 15 arcsec for the CTI dataset of Marthews et al.
(ref. 20 ) and Xi et al. (ref. 25 ), this study produced an ensemble of 28 sets of monthly global/regional wetland maps for 1980-2020 at 0. 25° × 0. 25° spatial resolution based on seven reanalysis soil moisture (SM) datasets and four observation-based wetland data. Different SM datasets provide the uncertainties of inputs of the model while different wetland data cover different definitions of wetlands for the convenience of different research purposes. Evaluation against the corresponding wetland calibration data, independent wetland datasets, and satellite-based terrestrial water storage estimates suggests that the well-calibrated diagnostic model can capture the spatial distributions, seasonal cycles, and interannual variabilities of observed wetland extent well. Our intent is for this dataset to provide opportunities for long-term wetland-related studies beyond the era of satellite-based datasets.
Methods The conceptual flow chart of the process is provided in Fig. 1. We used seven reanalysis SM data (Table  2 ) masked with soil temperature (ST) and soil freeze/thaw status to calculate water table depth, i. e. the input of TOPMODEL, given the obvious disagreements between the input datasets. The diagnostic algorithms based on TOPMODEL were used following Stocker et al. (ref. 20 ) and Xi et al. (ref. 25 ), where the optimized parameters were calibrated with long-term maximum wetland areas from four observation-based wetland datasets (Table  1 ). Details about these datasets and computational processing are shown as follows. reanalysis soil moisture datasets. Seven long-term reanalysis SM datasets used in this study include NCEP-DOE (National Centers for Environmental Prediction-the Department of Energy)  26  , MERRA-Land (the Modern-Era Retrospective Analysis for Research and Applications)  27  , MERRA-2 (ref.
RFW (long-term maximum) Observation-based wetland data Agency)  30, 31  , and ERA5-Land  30, 31. Key characteristics of the seven SM data are listed in Table  2. The datasets differ by their spatial and temporal resolutions, the time-period they cover, as well as the definition of the soil layers. More details are provided for each dataset below. • NCEP-DOE NCEP-DOE is an updated version of the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis 1 project, which uses a state-of-the-art analysis/forecast system to perform data assimilation with past data from 1948 to the present  32. NCEP-DOE features the newer physics and observed SM forcing and also eliminates several previous errors, such as oceanic albedo and snowmelt term during the entire period, and snow cover analysis error from 1974 to 1994 (ref.
26 ). With a spatial resolution of about 210 km, there are two vertical soil layers in NCEP-DOE for both SM and ST: 0-0. Observation-based wetland/flooded area data. In terms of large uncertainties in current wetland datasets (Table  1 ) we selected four widely used and available satellite/satellite-based wetland/flooded area data including GIEMS-2 (ref. 14 ), RFW (the Regularly Flooded Wetland map)  10  , WAD2M (a global dataset of Wetland Area and Dynamics for Methane Modeling)  33  , and G2017 (the pantropical wetland extent from an expert system model)  9  for parameter calibration. Among them, GIEMS-2 and WAD2M include monthly wetland dynamics, while RFW and G2017 are static. The comparison of the four wetland datasets is shown in Supplementary Fig. 1 ; details on each data are provided below.
Globally, the mean annual maximum and long-term maximum inundated extent after removing the rice paddies using the Monthly Irrigated and Rainfed Crop Areas dataset (MIRCA2000)  35  for the period 1992-2015 are 6. 7 and 10. 9 million km 2 (hereafter Mkm 2 ; sum of mean annual maximum or long-term maximum inundated extent for each grid cell) respectively. The rice paddies are removed here as they are not natural wetlands and cannot be simulated with TOPMODEL. • RFW RFW is a static, high-resolution map (15 arc-sec) of regularly flooded wetlands, developed by overlapping flooded areas (permanent wetlands and flooded vegetation classes) for 2008-2012 from the ESA-CCI land cover map  36  , mean annual maximum inundated areas (including wetlands, rivers, small lakes, and irrigated rice) for 1993-2004 from GIEMS-D15 global inundation extent (downscaled using GIEMS-1)  37  , and longterm maximum surface water areas for 1984-2015 from JRC global surface water bodies product  13.
The large permanent lakes and reservoirs are distinguished using the HydroLAKES database  38. Globally, RFW covers 9. 7% of the land surface area (~13. 0 Mkm 2 ) including wetlands, river channels, deltas, and flooded lake margins, but excluding large lakes  10. Due to the mean annual maximum or long-term maximum inundation/surface water extent for 1984-2016 from the three wetland data is used, we treated RFW as the longterm maximum wetland extent in this study. Besides, given that GIEMS-D15 includes artificial rice paddies, we removed them with MIRCA2000 from RFW (~11. 9 Mkm 2 after removing rice paddies).
• WAD2M WAD2M dataset used in this study is an improved version of the SWAMPS v3. 2 from Jensen et al. (ref. 15 ), covering the years 2000 to 2018. With a spatial resolution of 25 km × 25 km, this data was used as input wetland area data of phase 2 of the Global Methane Budget  33. Given that the initial SWAMPS failed to detect wetlands lacking surface inundation and to differentiate between lakes, wetlands, and other surface water bodies, Zhang et al. (ref. 33 ) modified it using a series of independent static wetland distribution data  7, 9, [39] [40] [41]  in an attempt to include missing wetlands under dense canopies. Besides, they removed inland waters (lakes, rivers, and ponds) and rice agriculture with JRC and MIRCA2000, respectively. Globally, the mean annual maximum and long-term maximum wetland extent for the period 2000-2018 estimated by WAD2M are 8.
At grid-scale, the analytical relationship can be represented as: Γ Γ - =- - CTI CTI M( ) (1) i x i x where CTI indicates the topographic index, defined as the log of the ratio of contributing area to the local slope. We used the CTI data at 500 m × 500 m resolution from Marthews et al. (ref. 22 ), where lakes, reservoirs, mountain glaciers, and ice caps are removed using the Global Lakes and Wetlands Database 7. The CTI x indicates the average of CTI i of all sub-grids (index i) within the grid cell x. M indicates a tunable parameter that describes the exponential decrease of soil transmissivity with depth  21. Γ i is the water table of the pixel i and Γ x is the mean water table of the grid x. When Γ i is at the soil surface (i. e. Γ i = 0), the threshold CTI * x above which all pixels are flooded for the grid x is derived: CTI CTI M (2) * x x x Γ = + ⋅ The wetlands area is defined as the flooded areas (i.
n represents the time-series length for wetland extent. For simulations calibrated with RFW and G2017, the RMSE was computed with the long-term maximum (hereafter called MAX) monthly wetland area because the two data sets are static and only record the MAX wetland extent. While for simulations calibrated with GIEMS-2 and WAD2M which include temporal variations of wetland area, we calibrated the parameters with all months, mean seasonal cycle, yearly maximum, and MAX wetland area, but only showed the optimal simulations calibrated with MAX wetland area in this work to keep consistency with RFW and G2017. Besides, to provide more choices for users, we combined all of the four wetland datasets (i. e. the union of long-term maximum wetland extent) to generate a new wetland map (hereafter called MAX_all), and then used the MAX_all to calibrate the parameters to produce seven sets of global wetland extent products with seven soil moisture datasets.
For spatial patterns, we calculated the RMSE of wetland area between our simulations and corresponding wetland calibration data following Eq. (  5 ), and evaluated the spatial patterns of simulated wetland extent in two wetland hotspots including Amazon basin and Western Siberia lowlands with three independent global/regional water products. For Amazon basin, we used the global surface water dataset from JRC  13  (optical satellite images) and the wetland map produced using mosaics of Japanese Earth Resources Satellite (JERS-1) L-band SAR imagery from Hess et al. (ref. 44 , hereafter H2015). For West Siberian lowlands, we used JRC and the Boreal-Arctic Wetland and Lake Dataset (BAWLD, only covers the north of ~55°N) produced using an expert assessment and extrapolated using random forest modelling from climate, topography, soils, permafrost conditions, vegetation, wetlands, and surface water extents and dynamics  45.
Data records The global wetland dynamics dataset (GWDD)  48  produced in this study consists of 28 sets of monthly global/ regional wetland extent products derived from seven reanalysis soil moisture data and calibrated with four observation-based wetland products, and can be available at https://doi. org/10. 5281/zenodo. 4571667. The spatial resolution for the dataset is 0. 25° × 0. 25°. The temporal coverage of each product is determined by the input soil moisture data in Table  2. These data are stored in NetCDF format with one file per year, defined by three dimensions (lon, lat, and time, indicating longitude, latitude, and month respectively) and a variable (fwet, i. e. wetland fraction).
3  and Supplementary Fig. 2 ), suggesting the effectiveness of the global implementation of TOPMODEL to simulate wetland dynamics. In some parts of wetland hotspots such as Hudson Bay lowlands and West Siberian lowlands, however, the RMSE is found to be more than 9%, which could be related to the limitation of the SM-based hydrological model in representing wetlands with lateral flow and overland flow contributing water (Supplementary Text 2) and wetlands with frozen soils in the high latitudes (Methods). Regionally, the comparison of simulated wetland extent with the corresponding wetland calibration data and independent global/regional wetland datasets (JRC, H2015, and BAWLD) suggests that our simulations can reproduce the spatial patterns of long-term maximum wetland fraction from the corresponding wetland calibration data in the two wetland hot spots (Amazon basin and West Siberian lowlands), and also show a good consistency with the independent observation-based wetland products (Figs.
4  and 5 ). However, there are substantial disagreements among simulations calibrated with different observation-based wetland data. By comparing the spatial distributions of wetland extent from simulations using different SM data and different wetland calibration data, we found that the differences in grid-cell wetland fraction among our ensemble of simulations mainly result from the different calibration wetland data (Supplementary Figs. 3  and 4 ). For example, the simulations based on the same SM data (MERRA-2) but calibrated with different wetland products show a >10% standard deviation (SD) of wetland fraction in most wetland hotspots, against a <3% SD for simulations with the same wetland data (RFW) but different SM data (Supplementary Fig. 5 ). Hence, hereafter we only show the results from one out of the same reanalysis family (NCEP, MERRA, GLDAS-Noah, or ECWMF) for different calibration wetland observations (simulations calibrated with GIEMS-2, RFW, WAD2M, and G2017 are denoted by S GIEMS-2 , S RFW , S WAD2M , and S G2017 , respectively).
Seasonal cycle of wetland extent. Regarding temporal dynamics of simulated wetland extent, we first present the mean seasonal cycle of wetland area at global scale and for three latitudinal zones (60°S-30°N, 30°N-50°N, and 50°N-90°N) from 1980 to 2020 using four SM data (Fig. 6 ). Following the sigmoid function established by Stocker et al. (ref. 20 ), with a given M, the seasonal and interannual variabilities in wetland fraction within a grid are determined by the input SM. Hence, the wetland area at global scale and for the three latitudinal Fig. 4  Evaluation of the simulated wetland extent against the corresponding wetland calibration data and independent regional wetland maps for Amazon basin. All simulations showed here (a-d) are produced based on soil moisture from MERRA-2. The wetland map from JRC (i) represents the maximum surface water extent for 1984-2015 from the global surface water dataset from JRC.
The wetland map from JRC (i) represents the maximum surface water extent for 1984-2015 from the global surface water dataset from JRC. The wetland map from BAWLD represents wetlands including bog, fen, marsh, and tundra wetland from BAWLD (only covers the north of 55°N). (Supplementary Figs. 8 9 ). To satisfy the need for the more accurate absolute value of monthly wetland extent for S GIEMS-2 and S WAD2M , we provided additional simulations calibrated with yearly maximum wetland area in our resulting products. Moreover, we also compared the simulated and observed month of annual maximum wetland extent at grid scale. Throughout the globe, our ensemble of simulations can reproduce spatial patterns of the observed month of annual maximum wetland extent basically (Supplementary Fig. 10 11 ). interannual variability of wetland extent.