dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions and Perspectives MALDI-TOF MS has clearly become one of the most essential tools in medical mycology laboratories. Its benefits on patient management and public health have been recently evaluated  [76]. Its flexibility and ease of use have made it possible to adapt or develop improved methods to better deal with the specificities of Fungi. During recent years, we have experienced several of these protocols, commercial or in-house, in order to tailor MALDI-TOF MS to our everyday needs (Table  1 ). Figure  2  summarizes the different applications and possible strategies for an optimal use of this technology in our mycology laboratory. Like others, we created in-house libraries for specific projects or rare species identification. We also combine the use of commercial databases and the MSI database for our everyday mold identification.
In addition, we use the specific IDFP medium for the identification of non-Aspergillus molds. Finally, in some particular cases, we accept lower identification scores than recommended. These strategies have significantly enhanced our turnaround time and our level of accuracy for the identification of yeasts and molds. Other free databases exist such as MicrobeNet developed by the CDC and it would be interesting to evaluate its added value in our everyday identification process. More exciting applications of MALDI-TOF MS such as typing strains methods have been successfully applied by other laboratories and would be very useful for local or national epidemiological studies  [77, 78]. Innovative and new applications based on MALDI-TOF will probably continue to appear in the future, as illustrated by the recent demonstration of its usefulness to identify also macrofungi species and its potential applications in food safety  [79].
Acknowledgments: We would like to thank: the MSI database developers:  Anne-Cécile Normand  and  Renaud Piarroux  (from  La Pitité Salpétrière Hospital, Paris, France )  Pierre Becker ,  Ann Packeu  and  Dirk Stubbe  (from the  Belgian coordinated collections of Micro-organisms, Belgium );  Thomas Girard  our application engineer in the  Infectious Agents department, CHU Grenoble Alpes ;  Lucie Graham  for English proofreading.
6k ). Cavity morphogenesis occurred through the apparent coalescence of multiple fluid-filled cavities  23  (Extended Data Fig. 6l ). Aquaporin 3 (AQP3), the water transporter most highly expressed in human blastocysts  11  , was initially visible in all cells (36 h) and was then restricted to TE analogues (96 h) (Extended  c, Expression level of markers of each blastocyst lineage. d, Unsupervised distance map generated using the top 30 genes that are enriched in clusters 0, 1 and 3 (defined in the UMAP in b). Note that this list includes epiblast markers specific to the blastocyst stage (for example, SUSD2, KLF17 and PRDM14). e, f, UMAP of single-cell transcriptome of cells from blastocyst-like structures, naive PSCs and primed PSCs integrated with published datasets from human embryos at pre-implantation, peri-implantation (in vitro cultured blastocysts) and gastrulation (Carnegie stage 7, that is, between embryonic days 16 and 19) stages.
For example, >90% of morphologically adequate structures generated from the lines analysed formed >97% of analogues of three blastocyst-stage lineages (see Fig. 1h  and Extended Data Fig. 3i ). An exception is the line Shef6, which efficiently formed morphologically correct structures but appeared less efficient at forming PrE analogues. See also Supplementary Table  2. Blastoids reproducibly formed at high efficiency and we did not observe differences based on the number of passages after resetting in PXGL culture conditions. The effect of LPA, NAEPA (Sigma-Aldrich, N0912) and Verteporfin (Selleck Chemicals Llc, S1786) on the yield of blastoid formation was assessed by culturing naive PSC aggregates in PALY medium (without LPA) complemented with molecules added every day from 0 to 96 h. The Verteporfin treatment was executed without exposure to the light.
Single-cell RNA-seq data analysis Smart-Seq transcriptome sequencing experiments were analysed using genome sequence and gene annotation from Ensembl GRCh38 release 103 as reference. For gene-expression quantification RNA-seq reads were first trimmed using trim-galore v0. 6. 6 and thereafter aligned to the human genome using hisat2 v2. 2. 1. Uniquely mapping reads in genes were quantified using htseq-count v0. 13. 5 with parameter -s no. TPM estimates were obtained using RSEM v1. 3. 3 with parameter-single-cell-prior. Further analysis was performed in R v4. 0. 3 with Seurat v4. 0. 1. Based on initial evaluation of per-cell quality control metrics and outlier identification using the median absolute deviation algorithm, cells with ≤2,000 detected genes or ≥12. 5% mitochondrial gene percentage were filtered out.
05. UMAP was used for visualization. For integration of Smart-seq experiments from multiple sources we followed the previously described procedure  10. Published data from E-MTAB-3929 (human preimplantation embryos  37  ranging from embryonic day 3 to 7), GSE109555 (in vitro cultured blastocysts  38  ) were downloaded, and data from Carnegie stage 7 embryo were kindly provided by the authors of the study  39. All the data was preprocessed to obtain per gene read counts using the same protocol as described for blastoid cells, in the case of GSE109555 including adaptations to Article accommodate UMI and CB information following the authors' instructions (https://github. com/WRui/Post_Implantation). For GSE109555 we used 1,000 cells randomly subsampled from the 3,184 high-quality single cells described in the original publication.
Similar to ref. 10  , we excluded cells belonging to haemogenic endothelial progenitors and erythroblasts. After evaluation of per-cell quality control metrics, and as in ref. 10  , cells with >2,000 detected genes and <12. 5% mitochondrial gene percentage were retained. Genes detected in at least five cells in any dataset were retained. log-normalization was performed using computeSumFactors in scran package v1. 18. 7, per-batch scaling normalization using multiBatchNorm in batchelor v1. 6. 3. Datasets were aligned using the fastMNN approach via SeuratWrappers v0. 3. 0 using the log-normalized batch-adjusted expression values. MNN low-dimensional coordinates were then used for clustering and visualization by UMAP. The data processing and analysis pipelines are publicly available at https://github.
Bulk RNA-seq library preparation and sequencing Bulk RNA-seq libraries were prepared using Smart-Seq2 protocol as previously described  36. For each sample, 50 cells were pooled together and prepared for sequencing. The libraries were then sequenced using an Illumina Novaseq 6000 with 50-bp paired end mode. For each sample, approximately 10 million reads were obtained. Bulk RNA-seq data analysis RNA-seq reads were first trimmed using trimgalore v0. 5. 0 and reads mapping to abundant sequences included in the iGenomes Ensembl GRCh38 bundle (rDNA, mitochondrial chromosome, phiX174 genome, adapter) were removed using bowtie2 v2. 3. 4. 1 alignment. Remaining reads were analyzed using genome and gene annotation for the GRCh38/hg38 assembly obtained from Ensembl release 94. Reads were aligned to the genome using star v2.
Microscopy and image analysis The phase-contrast images were acquired using Thermo Fisher scientific EVOS cell imaging system and inverted wide-field microscope Axio VertA1. The number of blastoids or cavitated structures were counted manually for each well. After 96 h, a blastoid is defined based on the morphological parameters as described in previous sections. The fluorescent images and time-lapse images were acquired using Olympus IX83 microscope with Yokogawa W1 spinning disk (Software: CellSense 2. 3; camera: Hamamatsu Orca Flash 4. 0) or Nikon Eclipse Ti E inverted microscope, equipped with a Yokogawa W1 spinning disc (Software: Visiview 4. 5. 0. 7 ; camera: Andor Ixon Ultra 888 EMCCD). The confocal images were analysed and display images were exported using FIJI 1. 53k or Bitplane Imaris 9.
7. 0 softwares. For cell counting, Bitplane Imaris software was used. Cell count parameters were set for size and fluorescence strength of voxels and then overall cell count data was obtained for each image using the Imaris spot function. Note that large cavities in blastoids increase the depth of the imaging field and cause poor signal from deeply located cells. Therefore, our counting data in Figs. 1h, 3g could be underrepresented values, particularly in the case of trophectoderm cells. The quantification of the percentage of blastoids forming the NR2F2 axis was done manually. To do so, blastoids stained to detect NR2F2 expression were imaged using a confocal spinning disk microscope. The images were projected using a 3D-project function in FIJI. The blastoid was classified to have an axis when NR2F2 expression was restricted to its polar half with no expression or lower level of expression in the mural half.
The inverted pattern of NR2F2 expression was classified as an invert axis. The blastoids with NR2F2 expression on their both polar and mural halves were classified to have no axis. Confocal immunofluorescence images of human blastocysts were acquired with a Nikon confocal microscope and a 20× mim or 25× silicon objective. Optical sections of 1 μm-thick were collected. The images were processed using Fiji (http://fiji. sc) and Volocity 6. 3 visualization softwares. Volocity software was used to detect and count nuclei.
Acknowledgements This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme (ERC-Co  grant agreement no. 101002317  ' BLASTOID: a discovery platform for early human embryogenesis '). H. H. K. is supported by the  Austrian Science Fund (FWF) ,  Lise Meitner Programme M3131-B. This  project has also received funding from the  ANR  ' BOOSTIVF '. L. D. thanks the iPSCDTC and MicroPICell core facilities. We thank  Y. Takashima  for sharing the H9 and H9-GFP cell lines;  A. Smith ,  P. Andrews  and  G. Guo  for sharing the  HNES1 ,  Shef6 , niPSC 16. 2b and cR-NCRM2 cell lines;  H. Baharvand  for sharing the endometrial organoids;  K. Woltjen  for sharing the PB-TAC-ERP2 and pCAG-PBase plasmids;  K. Guan  for sharing pQCXIH-Myc-YAP, pQCXIH-Myc-YAP-5SA, pQCXIH-Myc-YAP-S94A plasmids;  J.
Data analysis The confocal images were analyzed using FIJI 1. 53k, Bitplane Imaris 9. 7. 0 or Volocity 6. 5 softwares. The cell-cell interactions were infered using the Cellinker Webpage (no version number available)FACS data were analysed using FACS DiVa 9. 0. 1All the statistical analysis were performed using Graphpad Prism 8. 1. 1 (330)Data was analyzed using trim-galore v0. 6. 6, hisat2 v2. 2. 1, htseq-count v0. 13. 5, RSEM v1. 3. 3, R v4. 0. 3, Seurat v4. 0. 1, computeSumFactors in scran package v1. 18. 7, multiBatchNorm in batchelor v1. 6. 3, SeuratWrappers v0. 3. 0. For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e.
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A description of any restrictions on data availability -For clinical datasets or third party data, please ensure that the statement adheres to our policy Raw data for single cell and bulk RNA sequencing data of blastoids were deposited at the GEO repository under the accession number GSE177689. Published human embryo data are available in the GSE109555, E-MTAB-3929.
During the experiment, the pressure inside the DAC was measured, before and after each energy scan, using the ruby fluorescence method  [36] , following the calibration of Dorogokupets and Oganov  [37]. High pressure measurements (HP) were carried out at room temperature at seven pressure values in the range between 0. 3 GPa and 26 GPa. In order to get a good statistics, the data at each pressure point was obtained by collecting at least four energy spectra, which were checked for energy scale alignment and then averaged. For each pressure point (AP and HP data), the experimental absorption signals α(E) = log(I o /I t ) were treated along the standard procedures for pre-edge background subtraction, edge jump normalization and extraction of the structural extended X-ray absorption fine structure (EXAFS) signal χ(k) using the ESTRA software suite  [38].
Standard EXAFS Data Analysis The quantitative EXAFS data analysis was firstly carried out following a standard multi-shell  [24, 39]  data analysis procedure using the program FitEXA  [38]. The k 2 -weighted raw spectra were fitted in the 3-14. 5 Å -1 k-range to the model function k 2 χ th. The nonlinear least-square data refinement and error analysis has been implemented using the MINUIT routine package  [40]. The theoretical EXAFS function is given by a sum of partial contributions (coordination shells) χ th = ∑ i χ i , each χ i being calculated from the standard EXAFS formula [38]: χ i (k) = S 2 0 N i A i sin(2kR i + φ i ) kR 2 i e -2k 2 /σ 2 i e -2R i /λ i. ( 1 ) This model is valid in the case of small and harmonic (Gaussian) disorder  [41]  and assumes each contribution originating from a Gaussian shaped coordination shell defined by three structural parameters: the multiplicity number N i , the average coordination distance R i and the mean square relative displacement (MSRD) factor σ 2 i.
In the present study, EvAX simulations of the Nb K edge EXAFS data were performed using the Linux Cluster of the Department of Matematica e Fisica of the University Roma Tre. The minimization procedure has been performed in the k 2 -weighted WT  [48] , considering k between 3-16 Å -1 and R between 0-6 Å. The Nb 3 Sn crystallographic structure from Ref. [43]  was used as the starting configuration for a population of 32 supercells, each one consisting of 3 × 3 × 3 cells with periodic boundary conditions, containing a total of 216 atoms (162 Nb + 54 Sn) per supercell. The theoretical χ CA (k) EXAFS spectra were calculated considering MS paths with up to 4 scattering legs with 6 Å maximum length. At each RMC iteration, new supercell configurations were generated by randomly displacing all the atoms of the simulation box by a maximum displacement of 0.
For example, the three-body configuration (g (3) ) is defined by two distances and the angle between them. The structural parameters defining the g (n) (interatomic distances and bond angles) and the corresponding statistical parameters (mean, variance, and anharmonic terms), are calculated directly from the 3D atomic models using a specifically written Python code with the NumPy  [50]  library package. In order to evaluate the simulation's uncertainties, four independent RMC runs were performed using different seeds for the pseudo-random number generator  [25] ). The parameters and their uncertainties reported in Figure  5  correspond to the average values and standard deviations of those obtained from the different seeds.
At least one charged particle must have a p T > 1. 6 GeV and be inconsistent with originating from a PV. Consistency with a PV is defined based on the χ 2 difference between vertex fits including and excluding the particle under consideration. We use data samples collected from 2011 to 2018, at centre-of-mass energies of 7, 8, and 13 TeV, corresponding to an integrated luminosity of 9 fb -1. We simulate both B * 0 s2 decays and decays of a B * * 0 s meson with a mass of 300 MeV above the B + K -mass threshold. In the simulation, pp collisions are generated using Pythia  [20]  with a specific LHCb configuration  [21]. Decays of hadronic particles are described by EvtGen  [22]. The interaction of the generated particles with the detector and its response are implemented using the Geant4 toolkit  [23]  as described in Ref.
Conclusions An excess is observed in the B + K -mass spectrum at a mass approximately 300 MeV above the B + K -threshold, which we interpret as resulting from the decays of multiple excited B * * 0 s mesons. The structure is not well described by a single resonance; the significance for a two-peak structure relative to a one-peak hypothesis is greater than 7σ. A single resonance which can decay through both the B + K -and B * + K -channels is disfavoured but cannot be excluded. Future investigation with a larger data sample and with the B * + K -final state fully reconstructed could potentially better determine the exact structure in this mass range. Under the two-peak hypothesis, we have measured the masses and widths of the two states. Based on predictions for the masses of excited B * 0 s states, the new observation is likely to result from L = 2 orbitally excited mesons.
Thus, an high-Z impurity, e. g. Xe  [22] , is seeded in the EU-DEMO core (on top of the SOL seeded radiator, which is Ar), with the purpose of enhancing the overall fraction of power exhausted via radiation, which uniformly distributes on the very large first wall surface and does not concentrate on the small target wetted area. The energy flows for the two devices are depicted in Fig. 1. The differences between P heat and P sep , and between P sep and P target , correspond to the core and SOL radiation amount, respectively. In the EU-DEMO group, kinetic profiles for the core region are typically evaluated with the 1. 5D code ASTRA  [24, 25, 26] , and this encompasses also the radiation source profiles for the core (where "radiation" includes all sources, namely synchrotron, bremsstrahlung and line radiation).
In fact, the ITER FW concept has been shown in the past years to be inadequate for EU-DEMO. This is due to the higher foreseen irradiation damage level, which requires structural materials (like Eurofer) able to withstand more than 5 dpa of neutron damage. This solution, however, limits the tolerable steady-state heat flux to ~ 1 MW/m 2 , i. e. a factor 3-4 below the ITER specifications There is in fact only a ~ 3 mm metal layer between the plasma chamber and the high pressure coolant  [4, 5]. The heat flux on FW corresponding to (core and SOL) radiation is evaluated with the code CHERAB, which is a Monte Carlo ray tracing code  [29, 30]  able to determine the power deposition profile on 3D surfaces (although the plasma source is always assumed to be toroidally symmetric). Results corresponding to the source above are shown in Figs.
Biskra, the former and still active focus of ZCL, is only 190 km from M'Sila city (Figure  1A ). Starting from a longitudinal survey on the incidence of CL cases from 1982 to 2020 in the M'Sila focus, we gathered data on the diversity of the phlebotomine sandfly and rodent fauna and ecological factors in this region. We put them in perspective with the overall CL epidemiological situation in Algeria. These analyses are helpful to improve future public health strategies aimed at controlling ZCL.
Study Area and Study Design The province of M'Sila locates between 35 • 18 and 35 • 32 North and 4 • 15 and 5 • 06 East, 240 km south-east of Algiers, in the country's highlands. Rainfall varies from 69 mm to 200 mm/year, and the average annual temperatures between 13 • C and 23 • C, with extremes of -5 • C in winter to 46 • C in summer  [20]. The province encompasses 15 Daïras (districts), and the most populated one is M'Sila, a city followed by Boussaâda, called "the gateway to the Sahara" (Figure  2 ). In the endemic foci of M'Sila, we collected data on ZCL incidence from 1982 to 2020 and other associated information, including clinical aspects of lesions, the dynamic and diversity of the phlebotomine, and the diversity of the wild rodent's population. In parallel, data on CL incidence in Algeria were computed for the 48 provinces of the country from 1982 to 2017  [8].
Sequencing and Phylogeny of Chosen Isolates ITS1 amplicons were purified using ExoSAP (ThermoScientific, EU). Leishmania species were identified by DNA sequencing using an ABI Prism1 Big Dye™ Terminator, Cycle Sequencing Ready Reaction Kit, and AB1 3130 sequencing system (ABI, PE Applied Biosystems), with the same primers used for PCR. DNA sequences from both strands were aligned and edited using the Staden software package  [32]. The MEGA version 7 software  [33]  was used to conduct multiple sequence alignments (ClustalW option) and construct a phylogenic tree. Homology searches were performed using BLAST (Basic Local Alignment Search Tool) using the NCBI (National Center for Biotechnology Information) server  [34]. The generated sequences were aligned using SeaView4  [35]  to study the targeted genes' genetic variability.
Data Management and Statistical Analysis Data management and analysis were performed using Excel 365 (Microsoft office, USA); Graph-prism (8. 02, GraphPad Software Inc. , San Diego, CA, USA); XL STAT (v2020, Addinosoft) and Stata 11 (v16. 1, StataCorp, TX, USA). Maps that relate the spatial distribution of CL cases and the cumulative incidence within the 48 provinces and the cumulative number of patients in the 15 districts of M'Sila were constructed using Photoshop (v2015, Adobe). A one-factor ANOVA analysis used to test CL per province's incidences per year was performed using the Graph-prism (v8. 02, GraphPad Software Inc). and Stata 11 software. Dunnett's post-test was used when the ANOVA p-value was less than 0. 05. A principal component analysis (PCA) was performed using XL STAT.
Metagenomics analysis The paired-end reads were assembled into contigs using Pandaseq. 35 The high-quality sequences containing both primers (forward and reverse) were then selected for the next step. At the filtering stage, all sequences containing N and sequences which were shorter than 200nt were removed. Sequences longer than 500nt were trimmed. In addition, the forward and reverse primers were also removed from each of the sequences. To remove the chimeric sequences we applied an additional filtering step using UCHIME  36  by USEARCH. 37 The filtering steps were carried out using the QIIME pipeline. 38 ] [41]  The clustering of OTUs was performed with 97% identity for each metagenome. The Silva SSU and LSU database and release 132 from the Silva website were downloaded and integrated.
From this, a local database of predicted amplicon sequences was built by extracting the sequences containing both primers. 16S sequences from 375 species isolated in our laboratory from diagnosis or culturomics were added to the database (Supplementary Table  6 ). A reference database of 14,459 sequences was thus generated. All the putative species of previous analysis were also added. Finally, the generated database contained 76,368 sequences ready to be used. Regarding taxonomic assignments, we applied at least five reads per OTU because our samples were diluted at 1/1,000, and after sorting we had 1,000,000 cells per tube for each population: live, dead, and injured bacteria. The OTUs were then searched against each database using BLASTN. 42 The sequences were assigned a taxonomic classification using the criteria of ≥ 77% identity for domain, ≥ 75% identity for phylum, ≥ 80% identity for class, ≥ 85% identify for order, ≥ 90% identity for family, ≥ 94% identity for genus and ≥ 97% sequence identity for species.
The quantitative variable for microbial abundance was the total reads (sum of reads in the eight fecal samples for each species). A comparison of read numbers between live, injured, and dead bacterial group was carried out using the non-parametric Kruskal-Wallis test. Statistical analyses were performed using XLSTAT 2019. 3. 1 (Addinsoft, Paris, France) and Graph Pad (Prism v8. 2. 1) (GraphPad Software, San Diego, California USA).