dataesr/scientific-paragraphs-categorization huggingface dataset:
Models were trained with a cohort of 1233 GIST and validated on an independent cohort of 286 GIST. All WSIs from center 1 are available under controlled access at the European Genome-phenome Archive via accession number X(process is ongoing, will be completed at publication). Results. DL models outperformed the Miettinen classi cation for relapse-free-survival prediction in localized GIST without adjuvant Imatinib (C-index=0. 83 in testing and 0. 72 for independent validation). DL splitted Miettinen intermediate risk GIST into high/low risk groups(p-value=2. 1e-03). DL models achieved an area under the receiver operating characteristic curve (AUC) of 0. 81, 0. 91 and 0. 71 for predicting mutations in KIT, PDGFRA and wild type respectively in testing and 0. 84, 0. 93 and 0. 56 in independent validation.
8e-17 and 1. 3e-10 for Deep Miettinen and Miettinen respectively for localized untreated patients Fig. 2c ). Furthermore, the Deep Miettinen model was capable of stratifying classical Miettinen intermediate and high risk groups into sub groups of different prognosis (log-rank test p-value = 2. 1e-03 for both intermediate and high risk groups, Fig. 2c ). Same trend was observed in center 2 (log-rank test p-value = 2. 9e-01 and 3. 1e-e01 for intermediate and high risk groups respectively, Supplementary Fig. 2a ) using a dataset adapted threshold for high and low risk groups (a cutoff that led to a comparable number of high risk patients as de ned by the Miettinen). When examining the predictive tiles that were related to different risks, we noticed that mitoses, marked nuclear atypia, high cellular density, epithelioid cell component, necrosis and hemorrhage were associated with high risk while cytoplasmic vacuolization, low cellular density, collagenous stroma, mild nuclear atypia and spindle cell component were associated with low risk (Fig.
Declarations Contributors JMC, JYB and YF contributed to the design of the study. YF and AC performed the image preprocessing, deep learning models training and conception of gures and tables. YF performed the statistical testing. AC performed the tissue classi cation. EB was involved in data collection. MK, RP, FLL and JMC reviewed the histological images and performed the mitotic counting. JODT, BS and CM gave advice on algorithmic design and statistical analysis. Deep learning work ow. Our DL pipeline consists of 3 main steps. Data preprocessing. For one raw WSI, we apply a matter detection algorithm on it in order to extract the tissue area and to remove the artifacts (blur area, etc). On the extracted tissue area, we apply a tiling which consists of dividing the whole-slide images into tiles of 112 × 112 μm (224 × 224 pixels) at a zoom level of 0.
5 microns per pixel. Feature extraction. We trained from scratch a 50layer ResNet using an inhouse dataset of sarcoma of 1287 WSI (942,626 tiles) and Momentum Contrast v2 (MoCo v2) algorithm, a self-supervised learning algorithm based on contrastive loss. Using this model, we extracted 2048 features from each of the tiles, such that a slide could be represented as a N × 2048 matrix with N equals to the total number of tiles. Predictive models. MLP were trained using the 2048 features to predict mutation types or survival risk.
Comparing AapF with ArlJ showed the greatest variability in the N-terminal region, suggesting a potential role in determining the proteins' substrate selectivity (pilin for ArlF vs. archaellin for ArlJ) (Supplementary figure  14 e, f ). AapE shows sequence similarity with the archaellum assembly ATPase ArlI (Supplementary Figure  16 a )  58. This similarity is reflected by the Alphafold2 prediction for AapE, which is consistent with the structure of the AAA ATPase (Supplementary Figure  16 b ). Indeed, it has previously been shown that the homolog from S. solfataricus functions as an ATPase 58. As is common for AAA ATPases, ArlI is a hexameric protein  59  , and so we modelled AapE as a hexamer (Supplementary Figure  16 c, d ). Homologues of AapE and AapF were found in all six archaeal strains that also encode for an Aap-like pilin.
Cryo-EM image processing The Full-frame motion correction package from cryoSPARC  45  , was used to align the frames of 6272 movies. Defocus variation was then estimated using the patch CTF estimation program. The e2helixboxer program from EMAN2  70  , was used to manually pick Aap filaments to generate basic 2D classes in Relion. These classes were then imported to cryoSPARC, so the Filament tracer program could pick out fragments comprising of Aap filaments. Originally only the best 2D classes were picked, the quality of the unbiased 3D helix refine job remained poor, likely due to a limited range of projections. To overcome this, a larger number of 2D classes were picked, aiming to include more views compromising for resolution. Using these 2D classes a total of 947 729 particles was subjected to non-biased 3D refinement, resulting in map with 3.
The subsequently generated maps and models were then used to create a morph in ChimeraX 71 , showing the flexibility of the filaments. Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy (CIBSS -EXC-2189 -Project ID 390939984). CH was supported by the Agence Nationale de la Recherche (grants #ANR-16-CE16-0009-01 and #ANR-21-CE16-0021-01).
Acknowledgements This work was financially supported by project No  KK2312-20  (energysaving process and technology of chemical production for response to climate change) by the  Korea Research Institute of Chemical Technology (KRICT)  and by the  Ministry of Trade, Industry and Energy  (project No  20015736 ). We also thank to the  Pohang Acceleration Laboratory (PAL)  for the synchrotron diffraction beam time. D. D. B. thanked the  Center for Computational Engineering and Sciences (FAPESP/CEPID  Grant #  2013/08293-7 ) for the computational resources. This work was also granted access to the HPC resources of CINES under the allocation A0120907613 made by  GENCI.
The X-ray beam had a total flux of 2 × 10 12 photons/s at 20 KeV, and the beam was vertically de-magnified by a 1D focusing compound refractive lenses. An X-ray beam profile of 50 µm (V) × 500 µm (H) was used in the measurement. The scattering signals were captured by a Pilatus 100K 2D area detector, and the 2D images were used to perform the data processing. Geometric corrections were applied for all 3D RSM and half-order super-reflection data. RSM data were processed by the RSMap3D software developed by APS. Nanomechanical Measurements via Atomic Force Microscopy: The AFM measurements were taken with a Cypher AFM (Asylum Research) using BudgetSensors Multi75DLC carbon-coated tips, which had a force constant of ≈3 N m -1 , a tip radius of ≈15 nm, and a free-space resonance frequency of ≈75 kHz.
Acknowledgements R. X. , K. J. C. , and V. H. contributed equally to this work. The authors acknowledge assistance in the Raman measurement from  Dr. Jennifer Fowlie. This work was supported by the  Division of Materials Sciences and Engineering, Office of Basic Energy Sciences (DOE-BES) ,  U. S. Department of Energy , under contract no. DE-AC02-76SF00515. This work was granted access to the HPC resources of CALMIP (Allocation No. 2022/P19004 ). This research used resources 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 Contract No. DE-AC02-06CH11357. Secondharmonic generation at the Molecular Foundry was supported by the  Office of Science, Office of Basic Energy Sciences , of the  U.
FA9550-18-1-0480. M. A. acknowledges support from the  Materials Sciences and Engineering Division, Office of Basic Energy Sciences, Office of Science, U. S. Department of Energy , under Contract No. DE-AC02-05-CH11231  ( Materials Project  program  KC23MP ) for the development of novel antiferroelectric materials. L. W. M. acknowledges additional support from the  Army Research Office  under grant  W911NF-21-1-0118. R. X. acknowledges partial support by the start-up funding from  North Carolina State University. L. C. acknowledges financial support from the  Ford Foundation  and the  University of California President 's Postdoctoral Fellowship Program. M. M. W. is supported by the  National Science Foundation Graduate Research Fellowship  under Grant  DGE-1656518. X. W. G. and M. M. W. acknowledge support from the  U.
05. Volcano plots were generated using EnhancedVolcano R package  61  and default cutoffs were used: FC| > |2 and p value 10e -6. D Differentially expressed genes in REH GCR cells in dexamethasone versus vehicle. The log 2 FC, p values and adjusted p values (padj) were calculated using DESeq2 default settings (alpha = 0. 05, lfcThreshold = 1, pAdjustMethod = "BH") and significant genes were considered the ones with padj <0. 05. Volcano plots were generated using EnhancedVolcano R package  61  and default cutoffs were used: FC| > |2 and p value 10e -6. E NES (normalized enrichment score) after GSEA analysis using the Reactome database. Blue bars indicate cell cycle-related pathways downregulated in dexamethasone-treated cells, while red bars highlight BCR downstream signaling pathways upregulated in dexamethasone-treated cells.
Glucocorticoids modulate MRD cells towards mature B-cell states To understand if the phenotype and signaling modulation induced by dexamethasone occur in patients receiving GCs, we analyzed minimal residual disease (MRD) from nine patients enrolled on the AIEOP/I-BFM clinical trial (NCT01117441) at diagnosis and day 8 by CyTOF  7, 20. Patients on this protocol received only prednisone during the first week of induction therapy; thus, these samples represent pre and post-GC timepoints (Fig. 5E ). Developmental classification of the leukemic cells demonstrated a reduction of pro-BI and pre-BI cells with a significant increase of pre-BII cells and mature B-cells at day 8 (Fig. 5F ). Differential expression analysis performed on the pre-BII and immature populations revealed significantly increased expression of CD20, CD19, and CD24 (p = 0.
RNA sequencing analysis RNA sequencing was performed on healthy bone marrow and BCP-ALL cell lines. For healthy bone marrow, RNA samples were submitted for library preparation and sequencing to SFGF (Stanford Functional Genomics Facility). cDNA libraries were generated using the SMARTer Ultra Low kit (Takara Bio/Clonotech Laboratories) and a low-input library kit was used for library generation before being sequenced using Illumina HiSeq. Normalized counts were analyzed by using Ingenuity Pathway Analysis (Qiagen IPA) software. Comparison by trend was performed based on canonical pathways and upstream regulators to identify pathways upregulated from pre-pro-B to pro-B-cells and from pro-B to pre-B-cells using an insignificance threshold of 0. 05. Linear regression was performed to identify pathways highly correlated with the most significant canonical pathway (BCR pathway) and upstream regulator (BCR complex) by using GraphPad Prism and R 2 was calculated.
All the pathways with R 2 > 0. 70 are listed in Supplementary Data 1. Human BCP-ALL cell lines, NALM6, REHwt, and REH GCR, were treated with vehicle (ethanol) or dexamethasone (1 μM) for 48 h. Cells were collected for each condition, spun at 1500 rpm for 5 min, and washed twice with PBS. Afterward, RNA was extracted by using RNeasy Plus Micro Kit and RNA was sent to MedGenome Inc. (Foster City, CA, USA), where samples were tested for their purity using Agilent Bioanalyzer (RIN >8). All the samples passed QC standards before and after library preparation with the Illumina TruSeq mRNA stranded kit. Samples were sequenced on the NovaSeq instrument, 30 M paired reads (60 M total reads). FASTQ files were first pre-processed using fastp to perform quality profiling, read filtering, and adapter trimming.
Afterward, clean reads were aligned to the human reference genome (hg38) and salmon was used to quantify transcript abundance  58. Then, the transcript level expression data were summarized to the gene level using tximport R package; 59 low-expressed genes (less than 10) were removed before proceeding with differential expression analysis. DESeq2 package in R  60  was used to calculate the differentially expressed genes between dexamethasone-treated cells and vehicletreated ones. The log2FoldChange (log 2 FC), p values and adjusted Fig. 6  | Combination of dexamethasone with dasatinib reduces engraftment and increases survival in vivo. A Experimental workflow B Tumor growth by bioluminescence. Dot plots represent the measured radiance for each mouse, while the bar indicates the mean radiance for each group.
001. Source data are provided as a Source Data file. The experimental workflow was created with Biorender. com. p values (padj) were calculated using default settings (alpha = 0. 05, lfcThreshold = 1, pAdjustMethod = "BH"), and significant genes were considered the ones with padj <0. 05. All the differentially expressed genes in NALM6 and REH GCR, comparing dexamethasone-treated vs vehicle-treated cells, are listed in Supplementary Data 2. Volcano plots were generated using EnhancedVolcano R package  61. Gene Set Enrichment Analysis (GSEA) was performed by GSEA software v4. 1. 0 (BROAD Institute-UC San Diego joint project) using default settings. The reactome database was used for the analysis and the normalized enrichment score (NES) was plotted by using GraphPad Prism. In vitro and ex-vivo cell perturbations Healthy cells and primary leukemic cells were cultured in StemSpan SFEM serum-free media (Stemcell Technology) with vehicle (ethanol plus DMSO), dexamethasone (Sigma-Aldrich, #D9184) (1 µM), dasatinib (LC Laboratories, D-3307)(100 nM) or their combination for 48 h before being processed by CyTOF.
Cells were then washed with CSM and stained with 1:5000 191 Ir/ 193 Ir DNA intercalator (Fluidigm) in PBS with 1. 6% PFA overnight at 4 °C. The following day cells were washed with CSM and ddH 2 O, filtered, and resuspended in 139 La/ 142 Pr/  59  Tb/ 160 Tm/ 175 Lu normalization beads before being analyzed using a Helios mass cytometer (Fluidigm) at 200 events/sec rate. IMD files were normalized with publicly available Matlab Normalizer v0. 3  66  (https://github. com/nolanlab/bead-normalization) and debarcoded using publicly available Matlab Single-cell Debarcoder v0. 2  67  (https:// github. com/nolanlab/single-cell-debarcoder). Raw count values were transformed using the hyperbolic arcsine function with a cofactor of 5, before proceeding to analysis.
Single-cell developmental classification Primary leukemic cells were developmentally classified to the closest healthy B-cell population, as previously described in ref. 7. Briefly, live lineage negative (Lin -) cells (cPARP -/CD3 -/CD33 -) from healthy human bone marrow (hBM#7), included in each barcoding plate, were gated in ten developmental populations of normal B lymphopoiesis with Stanford Cytobank software (https://stanford. cytobank. org) using the gating strategy shown in Supplementary Fig. 4A. The distribution of each population was based on the expression of 9 B-cell developmental proteins that were used for the manual gating: CD45, CD20, CD34, CD38, CD179b, IgMi, TdT, and CD19. Live Lin -(cPARP -/CD3 -/CD33 -) B + leukemic cells were then assigned to the most similar healthy fraction based on the shortest Mahalanobis distance among distances to all healthy developmental populations in these nine dimensions.
A cell was designated as "unclassified" if none of the distances were below the classification threshold (Mahalanobis distance = 9). To control for batch effect, leukemic cells were always classified using the healthy BM control included in the same barcoding plate. The R script used for the B-cell developmental classification is publicly available (https://github. com/kara-davis-lab/DEVclassifier). In vivo experiments NOD/SCID/IL2Rγ -/-(NSG) mice were purchased from the Jackson Laboratory, housed, and treated under Stanford University Committee on Animal Welfare-approved protocol. Six-to-eight weeks old female mice were engrafted with 2. 0 × 10 5 NALM6-Luc + cells via intravenous (I. V. ) injection. When engraftment was detectable by bioluminescence imaging (BLI) (BLI > 0. 5 × 10 6 ), mice were randomized in four different experimental groups: vehicle, dexamethasone (American Reagent, Inc.
Whensoever suspected leukemiaunrelated deaths occurred, FACS analysis of bone marrow and spleen were performed to confirm that death was not related to a high burden of leukemia (Supplementary Fig. 6C ). In vivo imaging was performed using a Spectrum IVIS instrument. Mice were anesthetized by isoflurane (2-3% in oxygen), were injected via I. P. with 100 mg/kg D-luciferin (Caliper Life Sciences, Hopkinton, MA), and 4 min later images were acquired with medium binning, beginning at 30 s exposure, or less if images were saturated. Images were analyzed with Living Image 4. 7. 3 software (PerkinElmer) and total flux measurement (photons/second) was quantified over the whole animal.
Statistical analysis Data analysis was performed using GraphPad Prism v9. 3. 1 software and R for RNA-Seq data. To test the statistical significance between the two groups, we applied a two-tailed unpaired or paired Student's t-test. Comparisons between more than two groups was performed using one-way ANOVA when groups had equal variance or Krustal-Wallis test, nonparametric, when no equal variance could be assumed. For multiple comparison corrections we used Fisher LSD's test to compare samples to a control group or Bonferroni/Tukey tests to compare all the experimental groups. Single-cell phosphoprotein expression was performed by using the Wilcoxon rank-sum test followed by the Bonferroni test for correction and -log of the adjusted p value has been plotted. Survival in animal experiments was represented by Kaplan-Meier curves, and significance was estimated by using a longrank test.
Acknowledgements We thank  Kenneth Weinberg ,  Julien Sage ,  Matthew Porteus ,  Lucille Stuani ,  Timothy Keyes ,  Sean Green ,  Yu-Chen Lo , and  Patrick Demoss , for productive discussions. In addition, we thank  Cristina Bugarin  and  Chiara Buracchi  for their technical support. This work was supported by  Hyundai Hope on Wheels  (K. L. D. ),  St. Baldrick's Foundation Scholar  (K. L. D. ),  Oxnard Foundation  (K. L. D. ),  Fondazione Tettamanti  (G. G. ), Stanford Maternal and  Child Health Research Institute (postdoctoral fellowship  to J. S. and endowed fellowship to K. L. D. ), and  American Society of Hematology (ASH)  ( Scholar Award  to P. D. ).
Data availability Gene expression data generated for this study are publicly available in the GEO repository, accession number GSE214319. Mass cytometry data (annotated fcs. files) are available on FlowRepository, ID number FR-FCM-Z64E. Source data are provided as a Source Data file. The remaining data are available within the article, supplementary information, or source data file. Further correspondence and material requests should be addressed to Jolanda Sarno: jolanda@stanford. edu. Source data are provided with this paper.
Subject Water Science and Technology Specific subject area High-resolution DTM-based derivatives for hydrogeomorphological analysis across France Type of data Image How the data were acquired The dataset was derived from the national airborne DTM (RGE ALTI® DTM, version 2. 0) at 5 m spatial resolution and the vector layer at 1:5,0 0 0 scale of the channel network of the national hydrological database (BD TOPAGE®, version 1). Geospatial analyses were performed using R software  [1]  and its terra  [2]  , Rsagacmd  [3]  , and whitebox  [4]  packages. Data format Analyzed (GIS raster layers in GeoTIFF format) Description of data collection The 5 m dataset includes: -a topographic wetness index (TWI), calculated using the FD8 algorithm with a 7 × 7 median filter; -a multi-scale topographic position color composite (MTPCC), calculated using the maximum deviation from mean elevation at micro-(5-300 m), meso-(300-1,000 m), and macro-(1,0 0 0-5,0 0 0 m) scales; -a vertical distance to channel network index (VDCNI), generated by subtracting the channel network base level from the DTM.
Value of the Data • These three DTM derivatives have a very high spatial resolution (5 m) across France. • These raster layers are ready to use in GIS and avoid time-consuming processing. • The dataset could be useful for scientists in many fields of research (e. g. environmental sciences, hydrology, geomorphology, pedology, archeology, risk assessment), as well as for stakeholders and engineering agencies working on land-use planning, conservation, or water-resource management. • The three layers can be used as predictive variables in spatial modeling and as a visual aid for field collection.
Data Description The dataset consists of three DTM derivatives, produced at 5 m spatial resolution in GIS raster format across mainland France (upper left corner: 51 °05'20''N and 5 °09'00''W // lower right corner: 41 °20'02''N and 9 °15'43''E), which are illustrated in Fig. 1  and described in Table  1. They were derived from the national airborne DTM at 5 m spatial resolution (RGE ALTI®, version 2. 0) and the vector layer of the channel network of the national hydrological database at 1:5,0 0 0 scale (BD TOPAGE®, version 1). The first DTM derivative is the topographic wetness index (TWI), which characterizes potential soil wetness as a function of the contributing area and local slope  [9]. The index ranges from 0-30, with a larger value indicating a higher probability of wet soil. The second DTM derivative is the multi-scale topographic position color composite (MTPCC), which uses 3 bands to describe the position of a pixel relative to its neighborhood at micro-(5-300 m, band 3), meso-(300-1,000 m, band 2), and macro-( > 1,0 0 0 m, band 1) scales.
As a result, default viewing parameters for bands 1-3 in the widely used freeware QGIS (red, green,  and blue, respectively)  [10]  generate a 3-color composite ( Fig. 1  ). The largest values for a given scale indicate the highest topographic positions. The third DTM derivative is the vertical distance to channel network index (VDCNI), which expresses the vertical height (in m) between the elevation of a pixel and that of the nearest channel. The elevation of the channel represents that of the water's surface on the date of airborne acquisition. The largest values indicate the largest difference in elevation from the channel. Negative and positive values indicate that a pixel is located below or above the nearest channel, respectively. The three DTM derivatives are publicity released in 280 tiles of 50 × 50 km in GeoTIFF raster format and projected in the French Lambert-93 system (EPSG code 2154), which is the original coordinate system of the DTM data.
Each of the three DTM derivatives is available in a specific Zenodo repository that includes: • 280 GeoTIFF raster files ( < XXX_0 0 0 >. tif), with each file corresponding to a 50 × 50 km tile. The letters "XXX" correspond to the acronym of the DTM derivative, and the number indicates the tile of interest • 1 vector tile index in Google Earth format (tile_index. kmz) showing the location of each tile. This file was created to facilitate downloading a layer only for an area of interest. All three DTM derivatives have the same tiling. To decrease storage space and download time, each raster file was packed in 7-Zip freeware format (. 7z or. tar. gz)  [11].