dataesr/scientific-paragraphs-categorization huggingface dataset:
Wind speed data for wave height (Hs) calculations were extracted from the coupled climate models involved in the ISMIP3b project (https:// www. isimip. org/). The obtained CVI values are divided into four classes, with percentile ranges of 0-25%, 25-50%, 50-75%, and 75-100%. The upper quartile is taken as 'very high vulnerability, ' the lower quartile corresponds to 'low vulnerability, ' and the remaining two classes represent in ascending order 'moderate' and 'high vulnerability. ' Additional results of these investigations are exposed in Supplementary Note 2 and Supplementary Fig. S10.
Acknowledgements This work was funded by the  German Federal Ministry of Education and Research (BMBF)  through the  West African Science Center for Climate Change and Adapted Land Use (WASCAL). It also benefited from funding from the  Economic Community of West African States (ECOWAS) , based on the funding agreement with Sweden and the existing Memorandum of Understanding between ECOWAS and WASCAL. The authors S. S, S. S,  E. K. D. ,  S. A. K. S. B. S , and M. D. B. D, were partially supported by the  UPSCALERS  project (" Upscaling Site-Specific Climate-Smart Agriculture and Land-use Practices to Enhance Regional Production Systems in West-Africa , " N o  AURGII-1-074-2016 ), which is part of the  African Union Research Grants  financed through the Financing Agreement between the  European Commission  and the  African Union Commission  ( DCI-PANAF/2015/307-078 ).
The content of this document is the sole responsibility of the authors. They can under no circumstances be regarded as reflecting the position of the  African Union Commission  or the  European Union Commission. A. G. acknowledges the support of the  Programme d'Investissements d'Avenir de l ' Agence Nationale de la Recherche (France) , through Contract  ANR-17-MPGA-0015  (PRODUCT). We are all grateful to the national hydrological and meteorological services of WASCAL member countries for contributing to the observed in-situ hydrometeorological data. We thank the modeling groups and the  ISI-MIP coordination team  for their roles in producing, coordinating, and making the ISI-MIP model output freely accessible (https:// www. isimip. org/). Fruitful discussions were held with  Mamadou Sadio.
Data availability ISIMIP3b data are available for download at https:// www. isimip. org/. Bias-corrected WASCAL high-resolution climate simulation datasets can be downloaded free from the WASCAL Data Infrastructure (WADI) at https:// wascal-datap ortal. org/2. 0/. The raw, uncorrected WASCAL high-resolution simulation outputs can be downloaded at https:// cera-www. dkrz. de/. Crop, hydrological, coastal vulnerability index simulation outputs, and economic estimation data are available upon request through the corresponding. Inflation rates for the 15 countries of the Economic Commission of West African States (ECOWAS) are available at https:// www. imf. org/ exter nal/ datam apper/ PCPIP CH@ WEO/ OEMDC/ ADVEC/ WEOWO RLD.
We report the first results of FEL singleshot scattering images collected from a prototype magnetic multilayer sample widely studied in recent years  (Stamps et al. , 1997; Pfau et al. , 2012; Vodungbo et al. , 2012; Wang et al. , 2012; Willems et al. , 2017). Sorting the collected data on the basis of pulse-deposited energy, we unequivocally observed a loss of the magnetic scattering efficiency before the sample damage threshold, in agreement with previously reported data gathered on the same material both in the EUV and in the soft X-ray regime  (Schneider et al. , 2020; Philippi-Kobs et al. , 2021; Wang et al. , 2012; Wu et al. , 2016). Finally, as a potential application of the CMOS-BSI camera in FEL-based timeresolved experiments, a proof-of-principle experiment with the goal of measuring the gain in experimental time with respect to commercially available CCD detectors is given.
Since the IR laser pulse is derived directly Figure 2 Dark corrected image of a diffraction peak of striped domains in a Co/Pt multilayer recorded using the AXIS-SRW camera with an integration time of 14. 5 ms in HDR mode. by the same master oscillator seeding the FERMI FEL, the intrinsic arrival time jitter at the sample plane between the two pulses is below 10 fs  (Danailov et al. , 2014). Hence, it is possible to perform time-resolved experiments without the need to sort the data in postprocessing using quite complicated additional information coming from timing tools along the beam path  (Harmand et al. , 2013; Bionta et al. , 2014). This peculiar feature of the FERMI seeded FEL source allows pump and probe experiments to be performed on the fly, where the arrival time between the FEL and the IR laser is varied continuously during the acquisition, moving the mechanical optical delay line of the beamline and collecting the data at the source repetition rate.
During our data analysis, we followed the same procedure described elsewhere (Leveilleet al. , 2021). Each delay scan contains approximately 1000 data points and has been recorded in a total acquisition time of 20 s. By comparison, in Fig. 5 (b), we display a similar delay scan recorded during a previous experiment on the same sample with the standard CCD-BSI camera that needs a total acquisition time of around 600 s (600 shots/point). As can be seen and highlighted by a smoothing of the raw CMOS data (50 neighbouring points), both curves display similar trends, despite the fact that for the CMOS we have one shot per point, while each point corresponds to 600 shots for the CCD-BSI. The small difference after 0. 5 ps comes from the slightly different IR laser fluence used to pump the sample in both experiments (5 mJ cm À2 for the CMOS and 6.
9 mJ cm À2 for the CCD-BSI). This discrepancy could be due to a different energy distribution inside the IR pumping laser spot size  between the two measurements that were performed with different experimental setups; however, the remarkably identical dynamic on a short time scale (<0. 5 ps) compared to the short acquisition time shows unequivocally the potential of the CMOS detector in time-resolved scattering experiments. In order to better highlight the data quality, we display as a red line in Fig. 5 (b) a moving smooth average, illustrating that we have a similar signal-to-noise between the CMOS and the CCD-BSI. Moreover, it is noteworthy that, as a result of the fastest reading time, the higher data density (recorded points/ time interval) recorded in the ultrafast decrease during the first few hundred femtoseconds enables a more accurate fit of this part of the curve, which is of tremendous interest in the framework of ultrafast demagnetization dynamics.
Acknowledgements The authors thank the staff of SOLEIL for their support, in particular,  Arafat Noureddine  for his help with the Tango control implementation and help with the FERMI control integration. Christian-Yves Coteand his teams from  AXIS Photonique  are acknowledged for their technical input and fruitful discussions. Financial support from the  Agence Nationale de la Recherche (ANR) ,  France , with  MEDYNA and FLAG-ERA  SographMEM are acknowledged.
More information can be found in Table  1. Key results have been reproduced using only ensemble members which are available for both the historical and SSP5-85 scenario simulations and are shown in the Supplement (Fig. S7  and Table  S1 ). The 6-hourly wind components are utilized at 850, 500, and 250 hPa for calculation of the vorticity fields necessary for TC identification (more information in Sect. 2. 2). The 6-hourly mean sea level pressure and 10 m wind data are also used to investigate the intensity of the cyclones. Monthly mean temperature and specific humidity profiles are utilized to calculate potential intensity (PI), monthly mean relative humidity, wind, and SSTs to construct the genesis potential index  (Emanuel and Nolan, 2004). Using the same tracking and TC identification scheme as CMIP6 models, the European Centre for Medium-Range Weather Forecasts fifth reanalysis (ERA5;  Hersbach et al.
, 2020)  is used for verification of model TC climatologies from 1984-2014. The 6-hourly relative vorticity fields from ERA5 are used for cyclone tracking and TC identification, and 6-hourly sea level pressure and 10 m winds from ERA5 are used to investigate cyclone intensity. The 6-hourly position, 10 m wind speed, and sea level pressure data from the Hurricane Database version 2 (HUR-DAT2;  Landsea and Franklin, 2013)  are used between 1984 and 2014 in Sect. 3. 1 to compare TC intensity and spatial distribution with those simulated in ERA5 and CMIP6.
Environmental field analysis Changes in large-scale environment fields known to be associated with TC genesis and intensification are investigated in CMIP6 using the genesis potential index (GPI;  Emanuel and Nolan, 2004). GPI = 10 5 η 3/2 H 50 3 V pot 70 (1 + 0. 1 V shear ) -2 , ( 1 ) where η is the 850 hPa absolute vorticity, H is the relative humidity at 600 hPa, V shear is the magnitude of the 250-850 hPa wind shear, and V pot is the potential intensity (PI;  Emanuel, 1986) , implemented using the tcpyPI python package  (Gilford, 2021). Fields are first re-gridded to a common resolution (1 × 1 • ) to ensure comparability. Deep layer steering flow is also used and is defined as in  Colbert and Soden (2012). However, due to data availability, we use the 250 hPa field instead of the 200 hPa field.
( A4 ) Code and data availability. HURDAT2 data can be downloaded from the NOAA's Hurricane Research Division (https://www. aoml. noaa. gov/hrd/hurdat/, last access: 30 September 2019;  Landsea and Franklin, 2013). ERA5 data can be obtained from the Copernicus C3S Climate Data Store (https://www. ecmwf. int/en/forecasts/ datasets/reanalysis-datasets/era5, last access: 1 September 2022;  Hersbach et al. , 2020). CMIP6 data can be obtained from the Earth System Grid Federation (https://esgf. llnl. gov/, last access: 1 September 2022;  Eyring et al. , 2016). Potential intensity is calculated using the tcpyPI Python package, available at https://github. com/dgilford/tcpyPI (last access: 1 August 2022;  Gilford, 2021). TRACK can be downloaded from https://gitlab. act. reading. ac. uk/ track/track (last access: 1 August 2022;  Hodges, 1994 Hodges, , 1995 Hodges, , 1999)) , and version 1.
E. M. Sainsbury et al. : Future European post-tropical-cyclone risk in CMIP6 models the clarity of this paper. Elliott Michael Sainsbury  was funded by the  Natural Environment Research Council (NERC)  via the  SCE-NARIO Doctoral Training Partnership  with additional CASE funding from  BP. Reinhard K. H. Schiemann ,  Kevin I. Hodges ,  Alexander J. Baker , and  Len C. Shaffrey  are supported by the  UK National Centre for Atmospheric Science (NCAS)  at the  University of Reading. Alexander J. Baker  acknowledges funding from the  PRIMAVERA  project received from the  European Commission  and  NERC  funding through the  North Atlantic Climate System Integrated Study (ACSIS)  grant. We thank  Olivier Boucher  and  Thibaut Lurton  ( IPSL ) for re-running the IPSL-CM6A-LR model to provide us with the SSP5-85 scenario data needed to include the model in this study.
The IPSL-CM6 experiments were performed using the HPC resources of TGCC (project gencmip6), provided by  GENCI  ( Grand Équipement National de Calcul Intensif). This work benefited from  French  state aid, managed by the  ANR  under the " Investissements d'avenir" programme. Stella Bourdin  is supported by public funding from the  CEA  and the  EUR IPSL-Climate. Len C. Shaffrey  and  Reinhard K. H. Schiemann  acknowledge funding from the  NERC   CANARI  project. Financial support. This research has been supported by the  Natural Environment Research Council  (grant nos. NE/S0077261/1 ,  NE/N018044/1 , and  NE/W004984/1 ), the  European Commission   Horizon 2020 Framework Programme (PRIMAVERA , grant no. 641727 ), the  Grand Équipement National De Calcul Intensif  (grant no. 2021-A0100107732 ), and the  Commissariat Général à l'Investissement  (grant no.
For more examples, see Fig. 6 , 7, 8, and Table  1. The Python code for calculating D LR can be found online (https:// github. com/ YueWa ngMat hbio/ TreeM etric, DOI: https:// doi. org/ 10. 5281/ zenodo. 64002 67). Consider an m-level tree. For each vertex in level l, to compare the label strings of its subtrees, we need at most 2 m-l steps. Therefore, the left-regularization on each level needs at most 2 m steps, and the total number of steps is no more than (m + 1)2 m. Thus the worst-case time com- plexity of computing the left-regular metric is O(n log n) , where n is the vertex number. The space complexity of computing the left-regular metric is trivially O(n). If the.
To elucidate these questions, we used an experiment, during which adult German participants were first tested on understanding thematic role assignments in complex German sentences, and then trained to improve their performance. We explored and compared structural connectivity matrices obtained by tractography from diffusion MRI data acquired before and after training and related these to behavioral performance at both time points. In the analyses, we focused on the aspect of long-range connectivity in the language network, rather than metrics that might be more sensitive to local microstructural properties, such as fractional anisotropy or mean diffusivity.
A different content question was then asked and again acknowledged by feedback. Irrespective of the correctness of the second answer, the experiment was continued with the next trial. During the measurements, MEG was recorded with a Neuromag Vectorview device (results reported in  Wang et al. , 2021). The performance of the participants was measured as the percentage of correct answers to the first content questions (#correct / (#incorrect + #missed)). Finally, T1, diffusion, and resting state functional MRI data were acquired three times: before the experiment (scan a), immediately after it (scan b), and 3 weeks later (scan c). See below for technical details.
In addition, in order to avoid spurious inter-hemispheric connections, we added to the analysis a 5-region-parcellation of the corpus callosum from a white matter parcellation. This step was executed using the Freesurfer software. After visual inspection for large artifacts, diffusion data were corrected from susceptibility-induced distortion, subject motion, and artifacts due to eddy currents. To estimate diffusion parameters at each voxel, Bayesian inference was performed through the tool bedpostx, which also resolves voxels with crossing fibers. Subsequently, probabilistic tractography was applied to reconstruct sample streamlines using the probtrackx2 command with default parameters (5000 samples per seed voxel, maximum of 2000 steps per streamline, curvature threshold of 0. 2, step length of 0.
5 mm). In order to identify experimental effects related to language training, tractography was performed separately for each hemisphere. This was done in order to avoid potential misidentifications of connections when tracking through the highly convergent corpus callosum region. This way, we generated a seed-to-seed connectivity matrix per subject and hemisphere. The matrix entries C ij correspond to the number of streamlines generated from region i (source) and entering region j (target). Both tools, bedpostx and probtrackx2, are part of the FSL software.
For the pairwise comparison in Q4, we performed Mann-Whitney U-tests, also followed by FDR-BH correction. The network based analysis (iii) is motivated by the notion that in the brain network nodes and connections do not act in isolation, but as part of larger (sub-)networks. Consequently, any kind of specialization, either occurring during a live-time development Connectivity matrices, and relevant Matlab's code used for SVD analysis and plotting are available on a GitHub repository at https://github. com/hschmidt82/Yerevan_public.
Bioinformatics Analysis Open reading frame (ORF) and amino acids sequences were predicted by using ORF Finder (https://www. ncbi. nlm. nih. gov/ orffinder/). Structural domains of Hl-CASP9 were predicted by using the SMART (http://smart. embl-heidelberg. de/), ScanProsite (http://prosite. expasy. org/) and BLAST (http:// blast. ncbi. nlm. nih. gov) programs. Molecular weight (M. W. ) and theoretical isoelectric point were calculated according to deduced amino acids by ProtParam (http://web. expasy. org/ protparam/). Alignment for amino acids sequences among various species was performed with the Clustal Omega program (http://www. ebi. ac. uk/Tools/msa/clustalo/) and presented by using the Jalview program (http://www. jalview. org/). Phylogenetic trees were constructed based on amino acid differences (p-distance) according to the neighbor-joining method (pairwise deletion) with 1,000 bootstrap replicates by using MEGA X.
Data Transformation and Statistical Analysis All data were analyzed by using one-way ANOVA and presented as mean ± SEM (n = 3), followed by Fisher's least significant difference (LSD) test with SPSS 22. 0 (IBM Software, USA). Statistical significance was determined at p > 0. 05 (i. e. , n. s. ), *p < 0. 05, **p < 0. 01 or ***p < 0. 001.
Molecular Cloning and Sequence Analysis of Hl-CASP9 In this study, full-length cDNA sequence of caspase-9 was obtained from H. leucospilota by using 3'-/5'-RACE approaches, which has been deposited in GenBank under the accession number MW759843. The Hl-CASP9 cDNA is 1,841 bp in length, encompassing a 5'-untranslated region (UTR) of 180 bp, a 3'-UTR of 398 bp and an ORF of 1,263 bp encoding a protein of 420 a. a. (Figure  S1A ) with a deduced molecular weight of 47. 25 kDa and a predicted isoelectric point of 6. 44. Based on analysis with the SMART program, a caspase recruitment domain (CARD, residues 1-90) at the Nterminus, and a CASc domain (residues 146-415) at the Cterminus were predicted in the amino acid sequence of Hl-CASP9 (Figure  1A ). Within its CASc domain, two active sites (His228 and Cys271), a substrate pocket (containing 12 conserved amino acids), a proteolytic cleavage site (containing 2 conserved amino acids) and a dimer interface (containing 15 conserved amino acids) were determined with the Blast program in NCBI (Figure  S1B ).
Analysis of CD19 expression confirmed that the CD11c neg B220 + populations were indeed B cells; CD19 was also expressed by IgA + plasma cells, which were also increased by day 14 (Figure  S1F ). Histological analysis of colon sections showed elevated B cell numbers were mainly found in enlarged TLSs by day 14 compared with day 0 (Figure  S1G ). Further analysis of areas of tissue damage at day 14 revealed that the increased number of B cells could be found close to and even infiltrating the ulcerated epithelium (Figure  1G ). To translate these findings into humans, we analyzed a dataset from UC patients  (Smillie et al. , 2019) , which confirmed that B cells were increased during active disease, specifically during the inflammatory phase (Figure  1H ). Altogether, these results showed that B cells dramatically expanded to become the most dominant immune cell type within the colonic tissue during the recovery phase in an experimental model of colitis.
, 2010). All B cells analyzed were increased by day 14 compared with day 0 IgM + IgD + naive B cells and plasma cells, being the most dominant populations in the colonic tissue (Figure  S1K ). However, B1 cells showed the highest expansion on day 14 compared with day 0, followed by IgM + IgD + naive B cells, memory cells, and GC B cells (Figure  S1L ). To check whether particular BCR clones were enriched or repressed during DSS colitis, we performed clonotype reconstruction using VDJ gene reads extracted with TRUST4  (Song et al. , 2021)  on the bulk RNA-seq dataset during the course of DSS kinetics (GEO: GSE131032)  (Czarnewski et al. , 2019). We observed that VDJ repertoires were dominated by Ig light chains followed by IgA's heavy chain, which is expected in mucosal sites. Surprisingly, no shift in BCR chain proportions was observed during acute inflammation (days 6-8) nor during tissue repair (days 10-14).
B cell depletion does not alter the intrinsic epithelial and stromal cell programs during mucosal healing Interactions between stromal cells and epithelial cells are critical for promoting MH  (Owens and Simmons, 2013). To explore potential changes in stromal cells and epithelial cell composition between control and BCD animals we selected IECs (Epcam + ) and stromal cell (Vim + ) populations from our scRNA-seq dataset  (Figures 4B and S4F). Re-clustering of the epithelial cell cluster resulted in 6 epithelial and 8 stromal cell subsets (Figures  4D  and 4E ). Epithelial cell clusters were defined as stem cells (Lgr5, Olfm4, Ascl2, and Axin2), goblet cells (GC; Muc2, Agr2, Klf4, and Tff3), EEC (Chga, Chgb, Tac1, and Tph1), epithelialmesenchymal transition (EMT) cells (Vim, Sparc, Foxf1, and Tgfbr2), as well as enterocytes 1 and 2 (E1 and E2; Alpi and Mep1a)  (Gr€ un et al.
B cells decrease interactions between stromal and epithelial cells To gain insights into which cells might share space within the tissue and therefore have increased chances of interactions, we took advantage of our recently generated ST (Visium, 10X genomics) dataset of the entire colon during MH (day 14)  (Parigi et al. , 2022)  and integrated our scRNA-seq datasets from IEC and stromal cells (Figure  5A ). First, we deconvolved the ST dataset using non-negative matrix factorization (NNMF)  (Lin and Boutros, 2020)  to infer activity maps associated with lymphoid structures, as well as areas of injury and remodeling. NNMF analysis of the colon day 14 ST dataset inferred activity maps associated with B cells and TLSs, as seen by high expression of B cells (e. g. , Ighm, Ighd, Cd79a/b, and Cd19) and TLS markers (e.
Using the inbuilt ligand-receptor database in SingleCellSignalR (Cabello-Aguilar et al. , 2020), and after filtering intracellular genes out, we observed an overall increase in potential interactions between stromal and IEC single cell dataset in BCD compared with control mice, in which 146 interactions were specific to BCD tissue and only 26 interactions were specific to B cell-sufficient tissue (Figure  5D ). KEGG signaling pathway analysis showed increased potential interactions involved in ECM receptors, as well as focal adhesion, in BCD compared with control animals (Figure  5D ). The highest BCD/ control ratio for potential ligands was observed in Bmp5 + SCs, whereas the BCD/control ratio for receptor expression was enriched in E1 and EMT (Figure  S5C ), in agreement with the observed increased potential interactions between Bmp5 + SCs and either E1 or EMT in BCD compared with control animals (Figure  5E ).
B cell depletion decreases the distance between stromal fibroblasts and epithelial cells To gain further mechanistic insights at the localization and abundance of all major cell types during tissue damage, we performed robust spatial deconvolution on our day 14 ST datasets  (Parigi et al. , 2022)  using SDCD  (Dong et al. , 2021). Spatial deconvolution of cell types revealed that Bmp5 + SCs and EMT transcriptomic signatures are predominantly present in the injury/remodeling area (Figure  6A ). To empirically demonstrate that B cells impact the spatial co-localization between Bmp5 + SCs and EMT, we performed ST analysis on the colon of mice at day 14 after DSS administration, both with or without BCD (Figures  6B, 6C,  S5D,  and S5E ). Cluster analysis confirmed decreased B cellassociated transcripts and increased transcripts associated with tissue remodeling in BCD colons compared with controls (Figures  S5E  and S5F ).