dataesr/scientific-paragraphs-categorization huggingface dataset:
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 The pan-Amazonian hydraulic traits data set (ψ50, ψdry, HSM50) and branch wood density per species per site, as well as forest dynamic and climate data per plot presented in this study will be available, before the publication, as a ForestPlots. net data package at https://forestplots. net/data-packages/Tavares-et-al-2023. Basal-area-weighted mean leaf mass per area is displayed in SM.
, 68,66nce explained by individual predictors when using SMA models to predict plot-level relative ΔAGB, with ΔAGB calculated as (AGB end - AGB start )/ period of monitoring length/standing woody biomass. Climatic data (MAT, MAP and MCWD), HTs (Ψ 50 , Ψ dry and HSM 50 , defined as the difference between Ψ dry and Ψ 50 ) and other plant traits (stem and branch wood density and LMA) are indicated as red, blue, brown and green bars, respectively. Asterisk denotes statistically significant bivariate relationships after correcting for multiple hypothesis testing, using Bonferroni-corrected P < 0. 05. Stem wood density values were extracted from the Global Wood Density database65,66. Bivariate plots and statistics for all predictor variables considered are shown in Extended Data Fig. 6and Supplementary Table3.
3 However, key studies have shown that the accuracy of any gene-expression based computational deconvolution tool is mostly derived from the signature matrix, or marker-genes, underpinning it, which must be derived from the tissue of interest. 5, 10, 11 e, thus, decided to create a tool that can specifically quantify cancer cell types, as delineated by Neftel et al,  2  and immune cell types from bulk IDHwt GBM tumor sequencing data. We developed this tool by amalgamating four independent single-cell GBM datasets to derive signature matrices for use with CIBERSORTx and marker-genes for use with MCPcounter. The latter was chosen as it has been benchmarked as one of the most accurate marker-genebased tools available, giving consistently high correlation with ground truths across cell types. 12 We then compared results from these GBM-specific programmes to those from orthogonal cell quantification, using single-cell resolution imaging mass cytometry, on the same IDHwt GBM samples.
Copy-number Variant Analysis to Remove Neoplastic Cells Single cell datasets were amalgamated. Neoplastic cells were filtered, as has been done previously, by inferring and removing those with large-scale copy-number variations such as Chr. 7 amplification and Chr. 10 deletion using the inferCNV R package (version 1. 3. 3). 18, 19 The inferCNV object was created using "CreateInfercnvObject()" taking the raw counts (stored in the "RNA" assay of the Seurat object) for each dataset. Annotations were not provided, instead each dataset was grouped according to sample (ie, patient). The gene ordering file used was derived using the annotations from Ensembl Genes 91 for Human build 38 (GRCh38), taking the gene name, chromosome, and gene span. The "ref_group_names" argument was set to NULL, to average.
Quality Control Filtering Each dataset underwent individual quality control (QC) in which metrics were used to filter out poor quality cells according to dataset-determined thresholds (Supplementary Table  S1 ): the number of reads, or unique molecular identifiers (nUMI_min); the number of non-zero count genes (nGene); the percentage of mitochondrial genes (mitochondial_ratio_min); the percentage of ribosomal genes; and the cell complexity (gene_complexity_min), which is a composite measure derived as log 10 (nGene)/ log 10 (nUMI_min).
Dataset Normalization Post-filtering, each dataset was normalized individually using SCTransform, whilst regressing out dataset-specific confounding sources of variation such as ribosomal/mitochondrial ratio using the vars. to. regress function argument. Moreover, due to the disparity in the total number of cells in each dataset, a different number of variable features were passed to the variable. features. n function argument. The specific normalization criteria for each dataset are in Supplementary Table  S2.
Clustering and Cell-type Assignment Dimensionally reduction was performed on the integrated datasets using principal component analysis (PCA) using RunPCA with default settings. This was followed by uniform manifold approximation and projection (UMAP) which was implemented using RunUMAP with custom parameters a = 0. 6 and b = 0. 75. Shared nearest-neighbor graphs were constructed based on Euclidean distance using FindNeighbours; taking the default k (k = 20), the first 30 principal components and using the rann method for finding nearest neighbors. Clusters were identified using FindClusters, with the "smart local moving" (SLM) algorithm used for cluster optimization. 20.
Cell-type Annotation Cell counts per cluster, for each clustering resolution parameter (0. 1-0. 8 in 0. 1 increments) were cross tabulated with immune cell-type labels transferred from dataset GSE163120. The 0. 7 resolution cross-tabulation (Supplementary Table  S3 ) was used, based on cluster robustness and stability,  21  to assign cell-type annotation labels to clusters where the majority of cells had labels for either one distinct cell type or and/or where the cells were labeled were unknown. The T-cell, NK-cell and TAM labeled clusters could not be assigned and were sub-clustered to further resolve them. This constituting isolation of these cells and repeat of the above methodology, from the point of having normalized data, to separate cell types.
CIBERSORTx Reference Expression Profile The single cell data used to derive the neoplastic expression profiles used with CIBERSORTx was obtained from the Gene Expression Omnibus (GEO: GSE131928). These data comprised ~23 000 cells which were filtered to include only adult GBM samples. Each cell came with a score corresponding to 6 neoplastic cell states: these were converted to four states and then each cell was assigned to a neoplastic cell state or as a hybrid as described in Neftel et al. 2  The neoplastic single cell data was combined with the labeled immune single cells and then randomly down-sampled such that the total number of cells in the resulting reference matrix was 5075 and of roughly equal class type (Table  2 ). 25.
Sample Preparation and Mass Cytometry 5 µm sections, taken consecutively from the same blocks that underwent bulk RNA sequencing (see above), were stained with a cocktail of all 33 conjugated antibodies after dewaxing (Xylene) and HIER antigen retrieval in Tris-EDTA (pH9) with 0. 5% Tween 20. Sections were incubated for 30 min in 0. 3 µM irridum to counterstain the nuclei prior to air drying. A minimum of three 2 mm 2 regions of interest (ROI) were annotated per sample within the area corresponding to that from which RNA was extracted from the adjacent sections. Images were generated on the Hyperion Tissue Imaging cytometer by ablation of the ROI at a 200 Hz frequency with a 1-µm diameter laser. Raw MCD files were created and exported as ome-tiff from MCD Viewer software (Fluidigm).
Image Pre-processing Following export, the raw data were converted from to ome-tiff format and segmented into single cells using the steinbock pipeline comprised of the following steps  28  Pixel classification was done using Ilastik (version 1. 3. 3): Tiff stacks were generated for each of the proteins in the panel and pixels classified into two channels as either nuclear, or background. These were used to train a random forest classifier, which returned probability masks for each image. The generated probability maps were processed to create single-cell masks using the image analysis software CellProfiler (version 4. 1. 3). First, probabilities were histogram-equalized (256 bins and kernel size of 17), and then a Gaussian filter was applied to enhance contrast and smooth the probabilities.
Neuro-Oncology CellProfiler single cell masks were ultimately overlaid onto the single-cell segmentation masks and single-channel tiff images of all measured channels to extract single-cell marker expression means. The single-cell data was read into R using read_steinbock from the imcRtools R package (version 1. 2. 3) and the expression counts were transformed using an inverse hyperbolic sine function (cofactor = 5). The expression counts were corrected for channel spillover using a non-negative least squares method as previously described. 29 Briefly, each metal-conjugated antibody was spotted on an agarose-coated slide, and this was ablated to generate a background signal which could be used for compensation using the R Bioconductor package CATALYST (version1. 20. 1).
Image Analysis All downstream data visualizations, including Image and cell segmentation QC were completed using the cytomapper (version 1. 8. 0) and dittoseq (version 1. 8. 1) R packages. 30 Batch effect correction of segmented cells was completed using harmony (version 0. 1. 0). 31 Cells were clustered based on their similarity in marker expression using the PhenoGraph clustering algorithm (k =45) implemented in Rphenograph (version 0. 99. 1). 32 Cluster IDs were mapped on top of UMAP embeddings (n_neighbors = 40) derived using the uwot R package (version 0. 1. 11). Cell-type classification was completed using marker enrichment modeling, implemented in the MEM R packages (version 2. 0. 0), selecting for markers with enrichment scores equal to or greater than 3 (display. thresh = 3)  33  for the first clustering, which defined immune cells.
Creating and Comparing the Cell Deconvolution Approaches MCPcounter was run via the R Package (version 1. 2. 0) in two modes: default mode (MCP default ) used the universal set of 110 immune cell-type marker genes that come provided as standard, meaning no neoplastic cell populations were included; GBM mode (MCP GBM ) used the GBMspecific neoplastic and immune cell marker genes derived as outlined above. The "Create Signature Matrix" module of CIBERSORTx was run with default parameters and quantile normalization disabled, to create a signature matrix using the singlecell-derived immune and neoplastic expression profiles detailed above. This signature matrix was then used to infer cell fractions of bulk RNA-Seq sample mixtures using the CIBERSORTx High-Resolution docker container (https:// hub.
docker. com/r/cibersortx/hires). For all runs, the bulk RNAseq dataset was input as the "mixture" file and the respective signature matrix was input as the "sigmatrix" file. For all runs, the Batch correction was done in "S-mode" by setting the "rmbatchSmode" parameter to TRUE and the input signature matrix's respective CIBERSORTx-created "source gene-expression profile" was input. Finally, absolute mode was set to FALSE for all runs. Cell population quantities inferred from the GBM sample RNAseq for all expression based deconvolution approaches were compared with those from the IMC using the Pearson Correlation Coefficient.
Developing GBMdeconvoluteR GBMdeconvoluteR was developed as an interactive web application using the Shiny R package (version 1. 7. 1) and packaged as a portable container image using the rocker/ shiny:latest base Docker image. The custom image was stored in the Azure Container Registry and deployed using the Azure App Service. All code can be found at https:// github. com/GliomaGenomics/GBMDeconvoluteR.
Results Identifying GBM-specific Cell-type Profiles Four independent single cell GBM datasets (Table  1 ) were used to derive marker genes, or signature gene-expression matrices, for GBM tumor-infiltrating immune cells: B cells, T-cells, natural killer (NK) cells, microglia, tumor associated macrophages (TAM), monocytes, mast and dendritic cells (DC). Figure  1A  outlines the process. Datasets underwent pre-processing independently to filter out poor quality cells and copy-number analysis to remove neoplastic cells, before being amalgamated. There were significant batch effects owing to different sequencing platforms and originating centres but these were effectively removed using regularized negative binomial regression  34  (Figure  1B  and Supplementary Figure  S1A ). One dataset (GSE163120) included the immune cell annotations determined by the original study.
Marker genes for GBM cancer cell subtypes were adopted from Neftel et al. 2  In that study, four neoplastic GBM cell types were delineated from single cell data. We extracted the marker genes that Neftel et al  23  showed to delineate the four subtypes, but then removed those that are also expressed in the GBM tumor microenvironment, and would therefore confound the results of application to bulk tissue profiles (Supplementary Table  S7 ). Single cell expression profiles for annotated GBMassociated immune cells, from our combined datasets, or for annotated GBM cancer cell subtypes, from Neftel et al, were amalgamated into a full gene-expression matrix. This was then sub-sampled to produce a total of 5075 single cell gene-expression profiles with roughly equal representation of each cell type (Table  2 ).
The process adopted to amalgamate several independent single cell GBM datasets and create a GBM-specific immune cell reference signature gene-expression matrix (for input to CIBERSORTx) or marker gene set (for input to MCPcounter). (B) The inherent batch effects in the amalgamated data are evident in dimensionality reduction plots where clusters initially separated by originating datasets (far left), but were removed by normalization (middle left and Supplementary Figure  S1A ). Initial clustering and cell type assignment of the normalized data was unable to resolve TAM and microglia, and T-and NK-cells (middle right) but further sub-clustering enabled these cell types to be further delineated (far right and Supplementary Figure  S1B ). (C) A dot plot showing the expression of chosen GBM-specific immune cell type markers (y-axis) in each cell type in the amalgamated single cell data (x-axis).
Immune Cell Quantification MCPcounter can be used in default mode in which in-built canonical immune cells markers are employed. When running the programme in this mode it can only be used for immune cell estimation and we refer to it as MCP default. In contrast, the mode using the GBM-tissue specific immune and neoplastic cell markers listed in Supplementary Tables  S6  and S7  is denoted MCP GBM. In addition, at the time of preparing this manuscript a larger GBM-specific single cell resource, GBmap, was made available that amalgamated 26 single cell brain and GBM datasets. 24 We, thus, also ran MCPcounter using the GBmap marker genes, denoting this as MCP GBmap. We inspected the concordance between the absolute cell proportions predicted by CIBERSORTx, or the relative cell type prevalence scores that resulted from each version of MCPcounter, and the quantification by IMC.
Application to TCGA Data Our results show that MCP GBM is able to accurately quantify immune and neoplastic cells in GBM-tissue bulk sequencing data. To show how this can be useful in gaining biological and clinical insights from large-scale studies, we applied MCP GBM to bulk RNAseq data from 93 GBM samples from The Cancer Genomic Atlas (TCGA). This gave a score per cell type per sample, allowing us to quantify the correlation of cell type prevalence across patients (Figure  3A ). Recent spatial, multi-omics studies have suggested that different neoplastic GBM cell types associate with, and are programmed by, different environmental niches of GBM tumors. 4 A key finding was that mesenchymal (MES) cancer cells associate with both myeloid and lymphoid compartments, whereas the remaining neoplastic cell types (AC-, NPC-and OPC-like cells) are significantly depleted in immune-rich regions.
Discussion We have developed the first publicly available GBM-specific deconvolution tool that can infer both neoplastic and nonneoplastic cell population prevalence from bulk GBM tumor RNA sequencing data. This tool was developed by amalgamating four independent, human, single cell sequencing datasets to create tissue specific cell type gene-expression reference profiles. The single cell data was from de novo IDHwt GBM either at initial diagnosis (primary) or upon recurrence. Recurrent GBMs have been shown to have altered transcriptional profiles which may impact on the accuracy of the deconvolution results, 3 so we included these samples in the tool development and validation. We found that our approach is suitable for deconvoluting recurrent GBM tumors but, in keeping with the aforementioned studies, neoplastic cell deconvolution is not as accurate at the longitudinal time point.
Our study confirms, as shown elsewhere, that tissue specific reference datasets are necessary to achieve maximal accuracy in expression based deconvolution. 5, 10, 11 e used IMC to ascertain the ground truth of cell type characterization and quantification. We then compared this with the results from the gene-expression based approaches to determine which should underpin our tool, and to establish its accuracy. However, it must be noted that the regions that underwent IMC, whilst encompassed within, were substantially smaller than regions that underwent RNAseq (Figure  2A  and Supplementary Figure  S2 ), and that the GBM microenvironment is notoriously heterogeneous. 4  That, and the fact that IMC was performed on different, albeit, adjacent tissue sections, means that a deviation from perfect correlation is not just a result of gene-expression deconvolution tool performance, but also in bona fide differences in cell proportions.
We applied GBMdeconvoluteR to data from TCGA and were able to confirm recent findings from single cell resolution multi-omics studies, regarding the specific enrichment of MES neoplastic cells in immune compartments, and depletion of other GBM cancer cell types. However, because our approach is easily applicable to large-scale sequencing dataset, we could expand upon this further to show that this association is stronger in samples from patients with worst prognosis. This leads to the hypothesis that quantifying immune:neoplastic cell interactions could be prognostic, or that targeting them could be therapeutically beneficial, exemplifying the value in applying GBMdeconvoluteR to gain biological and clinical insights. In summary, GBMdeconvoluteR can be used to assess associations between cell type quantities and phenotypic, molecular or clinical characteristics with applications for target identification, gaining mechanistic insight or stratifying samples for retrospective therapeutic evaluation or prospective precision medicine approaches.
HIV, human immunodeficiency virus; HBV, hepatitis B virus; HCV, hepatitis C virus; IGF-1R, insulin-like growth factor 1 receptor; KPS, Karnofsky Performance Scale; RT, radiotherapy; SOC, standard of care; TMZ, temozolomide. © Copyright 2023 Imvax, Inc. All rights reserved. NOW ENROLLING Phase 2b study of IGV-001 in patients with newly diagnosed glioblastoma (NCT04485949) OBJECTIVES Patients who take part in the trial* must: • Have newly diagnosed glioblastoma • Be 18 to 70 years of age • Have a KPS score ≥70 (unable to work but able to care for themselves overall) Patients are not allowed to participate* in the trial if they have: • A tumor that is on both sides of the brain • Had previous surgery or anticancer treatment for glioblastoma • Glioblastoma that came back • Another cancer † while having glioblastoma or within the last 3 years that is not cured • A weakened immune system (example: HIV, HBV, HCV) or an autoimmune disorder (example: Crohn's disease) • Heart disease or history of heart issues.
The ever-increasing volume of radar data and the sometimes very wide extent of floods justify this need to bound processing, and radar images could take a long time to download depending on available data rates. Cloud computing services offer a way around these issues. Since Google Earth Engine (GEE) has come on stream  [30] , a growing number of applications have been developed to use this service for remote sensing and geographic information systems. GEE is a cloud-based geospatial analysis platform that allows users to visualise and analyse Earth observation (EO) data. GEE's database includes all freely accessible satellite imagery and is refreshed daily. Imagery can be processed directly in the cloud without the need to download it  [31] , which is very useful in countries like Madagascar, where bandwidth can be low and downloading heavy data complicated.
Google Colab, an online Python interpreter, is the tool used with GEE's Python application programming interface (API). In this paper, we report on the development of an image-processing chain for identifying floods from several Sentinel-1 scenes. The approach is designed to address the challenges of monitoring extensive areas using synthetic aperture radar (SAR) and its automatisation. This chain is implemented in Google Colab using the Python API. It was first tested and calibrated on the immediate outskirts of Antananarivo (Madagascar) using optical and radar images acquired before and after a series of floods in 2018. It is then applied to the whole of Madagascar after the passage of Cyclone Batsirai on 5 and 6 February 2022. The next section of the paper covers the data we used, where we calibrated the method and how we carried out the testing.
HAND Dataset Flood-proneness can be measured by the GIS-based flood index FSI (flood susceptibility index)  [36]  or a topographic index, among them GFI (geomorphic flood index)  [27]  or HAND (height above nearest drainage)  [28]. The advantage of the HAND index is that it can be calculated from a DEM without hydrology data. A set of HAND values covering the entire globe is already available in GEE  [37]. DEMs used are SRTM (30 m) and Viewfinder Panoramas (90 m)  [38]  for latitudes above 60 degrees, where SRTM data are lacking. To obtain the HAND, it is assumed that water in a given pixel flows towards the next downstream pixel following the steepest slope. Any given pixel may receive water from zero, one or several pixels upstream of it. Water can thus accumulate in a pixel, since water can only flow into a single pixel downstream.
The direction of water flow in each pixel is first deduced from the DEM. The water accumulation rate is then calculated for each pixel (the number of upstream pixels feeding water into it). All pixels with a water accumulation rate above a given threshold, called the flow accumulation threshold (FAT), are considered part of the thalweg network. These are valley lines along which water flows and accumulates when it rains. Once all points in the thalweg network are identified, a matrix connecting each pixel in the study zone to the first point in the network receiving water from this specific pixel is established. This connection matrix, combined with the DEM, enables the value of the HAND parameter to be deduced by calculating elevation difference between each of the connected two points. Two values of FAT were considered for the HAND dataset available in GEE: 100 for local drainage basins and 1000 for large drainage basins.
Data Used for Application To simulate a calculation covering the entire territory of Madagascar, flood detection is extended over a 12-day period. This approach aims to ensure complete coverage of the territory using Sentinel images, as the revisit frequency of Sentinel-1 satellites is every 12 days. To ensure a significant presence of flooding, the period following the passage of Cyclone Batsirai on 5 and 6 February 2022, is chosen. A total of 30 Sentinel-1 scenes were required. They were acquired between 5 and 17 February 2022. Radar images acquired at the same period as these post-flood images for each previous year were used as reference images.