dataesr/scientific-paragraphs-categorization huggingface dataset:
The photon energy was 10. 91 keV, and the beam size was 200 μm x 20 μm (H x V). At beamline 11-BM, samples were placed on a robotic stage inside a vacuum chamber (10 -2 torr) with the sample 267mm away from a Pilatus 800K (Dectris) area detector. The photon energy was 13. 5 keV, and the beam size was 200 μm x 50 μm (H x V). GIWAXS analysis: The GIWAXS data were processed using the GIXSGUI package (version 1. 7. 3) running on Matlab 2018b (Mathworks). All patterns were corrected with conditions such as detector sensitivity, X-ray polarization, and geometrical solid-angle parameters. Sector-cut integration was taken from the GIWAXS map and used for general analysis. The diffraction patterns were analyzed using the structural characteristics of 2D perovskite crystals reported in our previous work.
33  The software SingleCrystal by CrystalMaker was used to simulate the x-ray diffraction of 2D perovskite crystals and assign Miller indices to diffraction peaks in the GIWAXS patterns (see example in Fig. 1b-d ). The details of the procedure is described in Supplementary Fig. 2  and 3  and involves simulating the diffraction of crystal with different orientation [uvw] with respect to the incident x-ray beam and ponder the weight of each pattern in the experimental data. Extraction of the size of the particles in solution: Detailed multi-angle DLS analysis of precursor solutions is provided below. For each solution, the angular dependence of the autocorrelation function was measured. Curves were fitted using a single-or bi-exponential decay model and the resulting correlation fitting residuals were plotted, followed by a linear regression of Γ vs q 2 between 30° and 150°.
Acknowledgments: The work at  Rice University  was supported by the  DOE-EERE 2022-1652 program. J. E. acknowledges the financial support from the  Institut Universitaire de France. This work was supported by the  Office of Naval Research (ONR)  under grant  N00014-20-1-2725. This research used facilities of the  Advanced Photon Source , a  U. S Department of Energy (DOE) Office of Science User Facility  operated for the  DOE Office of Science by Argonne National Laboratory  under Contrast No. DE-AC02-06CH11357. This research used the beamline 11-BM of the National Synchrotron Light Source II, a  U. S. Department of Energy (DOE) Office of Science user facility  operated for the  DOE Office of Science by Brookhaven National Laboratory  under Contract No. DE-SC0012704.
These approaches are unable to simultaneously describe ALICE and LHCb data with the same set of parameters, suggesting that the independent parton fragmentation scheme is insufficient to fully describe the results. An alternative explanation has been offered by a statistical hadronisation model, taking into account an augmented list of charm-baryon states based on guidance from the Relativistic Quark Model (RQM)  [18]  and lattice QCD  [19] , which is able to reproduce the Λ + c /D 0 ratio measured by ALICE. The magnitude of the relative yields of Λ 0 b baryons and beauty mesons in pp collisions measured by LHCb  [20] [21] [22]  and CMS  [23]  offers further evidence that the fragmentation fractions in the beauty sector also vary between collision systems. The measurement of baryon production has also been important in heavy-ion collisions, where the high energy density and temperature create a colour-deconfined state of matter  [24].
This adds to the evidence that small systems also exhibit collective behaviour, which may have similar physical origins in pp, p-A, and A-A collisions  [33]. It has been suggested that hadronisation of charm quarks via coalescence may also occur in pp and p-Pb collisions  [34] [35] [36]. In this letter, the measurements of the prompt production of the charm baryon Λ + c in pp collisions at √ s = 5. 02 TeV in |y| < 0. 5 and in p-Pb collisions at √ s NN = 5. 02 TeV in -0. 96 < y < 0. 04 A description of the ALICE detector and its performance are reported in  [39, 40]. The pp data sample was collected in 2017 and the p-Pb data sample was collected in 2016 during the LHC Run 2. Both pp and p-Pb collisions were recorded using a Minimum Bias (MB) trigger, which required coincident signals in the two V0 scintillator detectors located on either side of the interaction vertex.
Intracellular SARS-CoV-2 nucleocapsid staining Cells were resuspended 48 h post-infection and fixed in 5% formaldehyde in PBS (Gibco) for 30 minutes at room temperature. Cells were permeabilised with 0. 1% Triton X100 (Sigma) in PBS for 10 min. Cells were blocked with 5% FCS in PBS (blocking buffer) for 30 min, and incubated with sheep anti-SARS-CoV-2 nucleocapsid antibody (1:10000)  [97]  in blocking buffer for 1 h. Samples were incubated with anti-sheep AlexaFluor 488 IgG (1:500, Invitrogen) in blocking buffer for 1 h. Cells were washed twice with PBS, resuspended in PBS, and nucleocapsid positive cells were enumerated by flow cytometry using a FACSCanto II (BD Biosciences). Data were analysed with Graphpad Prism 9.
This work was funded by  King's Together Rapid  COVID-19 Call awards to MHM, SJDN and KJD; the  Wellcome Trust  ( 106223/Z/14/Z  to MHM); a  Huo Family Foundation  Award to MHM, SJDN, and KJD; and  NIAID  Award  U54 AI150472  to MHM. We acknowledge the Genotype-to-Phenotype UK National Virology Consortium funded by  MRC/UKRI  ( MR/W005611/1 ) and  Public Health England  for providing viral isolates. HW and CG were supported by the  MRC-KCL Doctoral Training Partnership in Biomedical Sciences  ( MR/ N013700/1 ). This work was supported by the  Department of Health  via a  National Institute for Health Research comprehensive Biomedical Research Centre award to Guy's and St. Thomas' NHS Foundation Trust  in partnership with  King's College London and King's College Hospital NHS Foundation Trust (MHM).
9% of residual heterozygosity per line. All genotyping CEL files from the GeneTitan were analyzed with the APT suite. The wheat core-collection of 372 accessions was provided by the Biological Resource Center on small grains cereals (INRA, France)  28. The Ae. tauschii collection and the synthetic wheat panel were obtained from the National Small Grains Collection (USDA-ARS, Aberdeen, Idaho, USA) and the Wheat Genetics Resource Center (KSU, Manhattan, Kansas, USA), respectively. All these accessions and the wheat cultivars collection originated worldwide are further described in Supplementary Data 5 and 6. Segregating populations and EMS-derived families were evaluated at PRI under the protocol previously described  21. Evaluation of the SHW collection was performed at INRA. Three plants per one liter pots filled with ½ blond and ½ brown peat mosses were paintbrush inoculated with a 10 6 solution of spores per ml on the second leaves 14 days after sowing.
The plants were kept under 100% humidity for 48 h and then grown under 85% humidity, 16 h daylight period, and 21/18 °C (day/night). Symptoms were recorded visually around 21 dpi. Genetic mapping. A complete list of primers used is supplied as Supplementary Data 7. In the first phase of mapping and analysis, 30 polymorphic simple sequence repeat (SSR) markers and six previously developed sequence-tagged site (STS) markers  41  (Supplementary Fig. 1 ) were used to genotype the population of 170 DH lines, and a genetic linkage map of chromosome 3D was assembled as previously described  21. Over 300 bin-mapped expressed sequence tagged (EST) sequences that were known to map to the most distal deletion bin of 3DL (containing the Stb16q locus) were then downloaded and used to develop PCR primers, which were tested on TA4152-19 and ND495 for polymorphism and used to generate a genetic linkage map.
Regression mapping was used to identify markers significantly associated with STB resistance (Supplementary Fig. 1 ). In a second phase, we used the ITMI (175 DH, 4,209 markers + 416,856 GBS markers) reference genetic maps  22, 42  to increase further the number of markers at the Stb16q locus. Polymorphic SSR markers barc323 and cfd9 flanking Stb16q on the DH TA4152-19 × ND495 population were used to genotype the ITMI population and analyzed using the GenMapper software. Using the RQTL package and Carthagene  43  , we constructed draft 3D linkage maps that included the Stb16q flanking markers. In parallel, based on the reference map containing 4209 markers, we extracted individuals with recombination events located closely to the Stb16q region. Based on these individuals, we selected all markers with the same genotype as markers barc323 and cfd9 as well as markers that might be in between according to the distribution of recombination events.
A total of 42 genotyping-by-sequencing (GBS)-derived SNP markers originating from 33 short GBS tags were identified. These 33 GBS tags were subjected to BLAST searches against D genome sequences  23, 44. A total of 11/33 GBS sequences matched to 10 scaffolds anchored to the linkage map produced by Jia et al. 23. Comparisons between the 3D linkage map and the physical map indicated that the Stb16q region (between barc323 and cfd9) corresponds to a physical region containing 26 scaffolds (from scaffold51510 to scaffold39406, Supplementary Fig. 2 ). No match was identified with the second dataset  44  that contained mostly short genic sequences. In order to use the high number of SNP markers developed in this second dataset, we aligned the short sequences to the 26 large scaffolds surrounding the Stb16q locus.
We identified six short sequences that matched one of the large scaffolds. We developed 18 SNPs KASP assays for each of the SNPs from the 10 K assay present in the region and tested them against the parents of the mapping populations. Only three of them (cfn80007, cfn80011, and cfn80016) were genetically mapped using the bi-parental DH mapping population (Supplementary Fig. 2 ). SNPs genotyping was achieved with the KASP™ genotyping chemistry according to manufacturer instruction (LGC group) in an 8. 11 µl final volume and were analyzed on the LightCycler ® 480 Real-Time PCR System (Roche Life Science). In addition, using SSR Locator  45  , we searched for all SSRs present on the 26 large scaffolds. Using the Primer3Plus software, primers were designed for 15 SSRs containing dinucleotide and mononucleotide motifs with a high number of repetitions and tested on individuals TA4152-19, ND495, ditelosomic 3DS/L, deletion line 3DL-3  46  , and the DH population.
This library was sequenced in one PacBio RS II SMRT Cell using the P6 polymerase in combination with the C4 chemistry (sequencing service following the standard operating procedures was provided by IGM Genomic Center). Assembly of the PacBio RS II reads was performed following the HGAP workflow. The SMRT ® Analysis (v2. 2. 0) software suite was used for HGAP implementation Reads were first aligned using BLASR  50, 51  against "Escherichia coli str. K12 substr. DH10B, complete genome". Identified E. coli reads and low-quality reads (read quality <0. 80 and read length <500 bp) were removed from data used for the BAC clone sequences assembly. Vector sequences were trimmed along the assembly process. Each BAC assembly was individualized by matching their BES on the ends of assembled sequences with BLAST.
Two plants from family 236 were identified as susceptible. Crk6 and Unk1 CDS were sequenced from both plants using Sanger sequencing. A point mutation at position 1523 (C to T) was identified in Crk6. The functional impact of this mutation was analyzed using PROVEAN  25  (score = -5. 617) and the consensus classifier PredictSNP 54 (87% confidence). Heterozygous M 2 and M 3 plants at this position were allowed to selfpollination. A total of 54 M 4 plants (M4_TA4152-19_236_C1) derived from heterozygous M 3 plants were phenotyped with the Stb16q avirulent isolate IPO88018. Mutation genotyping was performed using the KASP TM genotyping chemistry (LGC Genomics) and primers cfn80052 (Supplementary Data 3) and analyzed on the Light Cycler 480 Real-Time PCR system (Roche Applied Science). Complementation.
This collection was composed of 73 SHWs, 45 accessions originated from the wheat core-collection  28  , 23 recent cultivars, 12 accessions from the wheat Stb differential  16  , ND495 and two landraces (Supplementary Data 4). Stb16q CDS was entirely sequenced only for accessions carrying the same exon1 as TA4152-19. PCR products were generated using the AmpliTaq Gold ® Master Mix (Applied Biosystems), purified using Agencourt AMPure XP (Beckman Coulter), and Sanger sequenced at GATC Biotech SARL (Konstanz, Germany). The MEGA software was used to align the different sequences and to identify haplotypes. Development of STARP and KASP diagnostic markers. The KASP markers cfn80044 and cfn80045 developed from SNPs at position 755 (counting from the translation start site in TA4152-19) (G/A) and 704 (G/A), respectively were used to screen the wheat worldwide collection and the Ae.
Tan spot evaluations were conducted using Py. tritici-repentis isolates Pti2 (race 1) and DW5 (race 5). Each isolate was inoculated on to 25 BC 5 F 2 plants following the methods and procedures described in Liu et al. 68  and scored using the lesion type scale developed by Lamari and Bernier  69. Statistical analysis. To evaluate significant differences between Stb16q expression data and Z. tritici biomass during a time course infection on wheat carrying the resistant (TA4152-19) or the susceptible (ND495) Stb16q haplotype, we applied the non-parametric Van der Waerden test using the RStudio environment. Group containing the same letter do not differ significantly at P < 0. 05 according to the khi2 test. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
ARTICLE NATURE COMMUNICATIONS | https://doi. org/10. 1038/s41467-020-20685-0 6 NATURE COMMUNICATIONS | (2021) 12:433 | https://doi. org/10. 1038/s41467-020-20685-0 | www. nature. com/naturecommunications (https://github. com/PacificBiosciences/Bioinformatics-Training/wiki/HGAP).
Acknowledgements We dedicate this work to the memory of  Olivier Robert  who passed away while we just cloned the gene. We gratefully acknowledge his dedication to initiate and coordinate this project. We thank  Quaid-i-Azam University (Dr. Umar Masood Quraishi, Pakistan) , the  CIMMYT  ( Dr. Velu Govidan  and  Dr. Suchismita Mondal , Mexico), the Dnipro state agrarian and economic university ( Dr. Mykola Nazarenko , Ukraine), the  National Agricultural Research Institute  ( Dr. Martín C. Quincke , Uruguay), the  National Plant Germplasm System (USA) , the  Australian Grains Genebank (Australia)  and  Dr. Richard Oliver , the  Wheat Genetics Resource Center (USA) , the  Biological Resource Centre on Small Grains Cereals (INRAE GDEC, France) ,  KWS  ( Viktor Korzun ) and the  Breedwheat consortium  for providing seeds of the different wheat accessions.
Data availability The source data for Supplementary Figs. 5  and 7 , Supplementary Data 1, 2 and Supplementary Tables  1  and 2  are provided as a source data file. Stb16q haplotypes and BAC clones sequences are available as GenBank accession numbers MT231554, to 231565, MT932490, MT942596, and MT942597. BAC clones raw sequencing data are available as SRA number PRJNA680685. Source data are provided with this paper.
Gaussian Process Factor Analysis Simulations As a first test-bed, we consider a simulation framework based on the Gaussian process factor analysis model  [17] , for which posterior predictive densities are tractable (fully-specified in Appendix B, with derivations for the ground truth). This model is obtained by using a linear Gaussian conditional distribution in place of the negative binomial decoder in (3): x l z l ∼ Normal W z l + β, σ 2. ( 13 ) We adapt the observation model of the VAE and the TreeVAE accordingly. Each simulated dataset is based on a tree with 100 leaves (cells) and 100 genes. All results are averaged across ten simulations. In this setting, we compare the posterior predictive on the latent variables p θ (z i x 1∶L ) as well as the one on the features p θ (x i x 1∶L ) to their respective ground truth.
Gaussian Process Poisson Log-normal Simulations Although the linear Gaussian system described previously helps diagnose the effectiveness of our inference procedure, it remains an unrealistic model for describing scRNA-seq data. Therefore, we instead use a Poisson Log-normal observation model as a more realistic simulation framework. More precisely, in place of the observation model described in (3), we generate x l as: y l z l ∼ Poisson (exp{W z l + β}) (14) x l y l ∼ Binomial (y l , p) , (15) in which p was adjusted to bring the frequency of zeros to 80% in the dataset. Each simulated dataset is based on a tree with 500 leaves (cells) and 1000 genes. All results are averaged across ten simulations. Because the ground truth distribution for both posterior predictive densities are not accessible, we slightly modify the evaluation metrics presented previously.
Finally, we investigate the relationship between certainty and accuracy in our TreeVAE model. As expected, our estimates become more uncertain closer to the root and this in turn affects model accuracy (Figure  2 ). We next assess the performance of the TreeVAE model on real CRISPR/Cas9 single-cell lineage tracing data. As a proof of concept, we analyzed a single clone of 603 cells from a recent dataset that traced the lineages of lung cancer tumors as they metastasized throughout a mouse  [3].
As a first metric, we report the cross-entropy score on the latent space (CE, as in previous experiments). Then, we propose a comparison of the evidence lower bound (ELBO) on observed samples. Although in this experiment we do not have  held-out data because we may only observe one cell at each leaf of the tree, we expect these numbers to be comparable because the same neural architecture, noise models, and hyperparameters were used for fitting both models. Our results, reported in Table  3 , suggest that TreeVAE better fits the data and proposes an approximate posterior that is more reflective of the tree structure. Finally, we investigate the behavior of TreeVAE's predictions of ancestral gene expression. As expected, TreeVAE's imputations are more certain closer to the observations at the leaves and becomes more uncertain for nodes closer to the root (Figure  3a ).
First, we expect that integrating accurate representations of unobserved cellular intermediates with accurate uncertainty quantification will enable a more sensitive differential expression analyses on the phylogeny. Second, the representations of ancestral states will also enable the identification of putative causal genes leading to particular phylodynamic patterns like subclonal expansions or differential metastatic behavior (as in  [3] ). Finally, these robust representations throughout a phylogenetic model will aid in the development of quantitative models of cell-state transitions throughout dynamic processes. There is currently an unmet need to develop datasets that would aid in the construction and validation of models like TreeVAE. Ideally, these datasets would track at least a subset of marker genes dynamically over the course of development and could be used to evaluate predictive accuracy on real scRNA-seq data.
Utilizing time-series datasets from model organisms for which cell lineages are stereotypical, like that of C. elegans, could be of great use towards this aim (e. g. ,  [22] ). Beyond this, we envision that maturing light-sheet microscopy technologies  [23, 24]  could be paired with unbiased readout of cellular transcriptomic states to create comprehensive molecular atlases of dynamic processes. Since the first report of CRISPR/Cas9-based single-cell lineage tracing  [25] , the promise of these technologies has been to build complete, probabilistic fate maps of complex, dynamic biological processes. Yet, this promise has yet to be fully realized. Towards this, we foresee the TreeVAE model setting a foundation for this aim and beyond. With efforts around improving the engineering of the software, formulating sound statistical questions from these inferences, and developing sufficient ground truth datasets for evaluation we believe that more sophisticated TreeVAE models will be a key tool in the single-cell lineage tracing toolbox.
Seeing that the objective of the simulation is to control the relative position of the handle surfaces considering manufacturing and contact deviation on the mating parts, the functional requirement is built through the Minkowski sum of the operands that define the handle surfaces, as shown in Eq. (  10 ), with t f = 0. 4 and k 1 = k 2 = 1/2. According to the enumeration of the parts and the surfaces presented in 2,0/1,12 = Γ 2,0/1,12-a ∩ Γ 2,0/1,12-b Γ 2,0/1,12-a = (Γ 2,0/2,1 ⊕ Γ 2,1/1,12 ) ∩ (Γ 2,0/2,2 ⊕ Γ 2,2/1,12 ) Γ 2,0/1,12-b = Γ 2,0/2,3 ⊕ Γ 2,3/1,3 ⊕ Γ 1,3/1,0 ⊕ Γ 1,0/1,12 (15) All the operands involved in the former relations were created with the open-source software PolitoCAT  [31]  and calculated at the point M (35, 0, 20). The tolerance and dimensional limits are taken into account based on the application of the spectrometer and the manufacturing processes that are usually used on the parts (see Figs.
So far, this method has been used in the cases where the deformation involved only one type of dislocation slip. For deformation that involves more types of dislocations or even boundary sliding, the method needs to be extended. Based on such a situation, we conducted a thoroughly investigation on the DRV and DRX mechanisms of the high-temperature α phase in a TiAl alloy, Ti-44. 81Al-3. 96Nb-0. 98Mo-0. 15B (at. %). A simple uniaxial compression was applied to the alloy at 1280 °C where the alloy was composed of single α phase  [42]. The hot-compressed microstructures were analyzed based on the electron backscatter diffraction (EBSD) measurement data. The high-temperature α phase was reconstructed using the home-made software, Merengue 2 (as detailed in  [35] ), based on the measured EBSD data and the Blackburn OR.
05. The temperature was controlled by means of type S thermocouples spot-welded directly on the surfaces of the specimens. At the end of the compression, they were water-quenched to preserve the deformed microstructure. To obtain the initial microstructural information before the deformation, one specimen was held isothermally at 1280 ºC for 10 min in a resistance furnace and then water quenched. The mesoscale microstructures were characterized using a JEOL 6500F scanning electron microscope (SEM) equipped with a field emission gun. The crystallographic features of the microstructures were characterized by EBSD using an Oxford EBSD camera and the Aztec online acquisition software package (Oxford Instruments). To achieve the surface quality for EBSD measurements, the specimens were first mechanically polished then electrolytically polished with a solution of 5% perchloric acid, 35% butan-l-ol and 60% of methanol at 35 V for 10 s at temperatures lower than 5 °C.
The EBSD patterns were acquired at the accelerating voltage of 20 kV with a step size of 1 μm for coarse microstructure and a step size of 0. 1 µm for fine microstructure. The γ phase was indexed using the tetragonal structure (P4/mmm) parameters. The online orientation determination was assisted with the pseudo-symmetry function  [43]  incorporated in the Aztec online acquisition software package to secure the reliability and accuracy. In order to analyze the microstructure characteristics of the high-temperature α phase during compression, the transformed part of the α phase was reconstructed from the low temperature γ phase  [35]. After reconstruction, the obtained orientation data of the α phase were analyzed with the Channel 5 (Oxford Instruments) to reveal the features of grain boundaries, substructures and intragranular disorientation.