dataesr/scientific-paragraphs-categorization huggingface dataset:
CMap/LINCS analysis To identify potential drug targets for S6 and S7 subtypes, and to compare PURE01 subtypes S1-5 and ABACUS predicted subtypes S1-5, we utilized the Connectivity Map (CMap) LINCS gene expression resource  21, 69  to identify perturbed gene expression signatures that were similar (vs dissimilar) to the gene expression signatures of each of these subtypes. For each subtype, we ranked coding genes by the signal-to-noise ratio (SNR) and submitted gene symbols for the top 150 high-SNR and 150 low-SNR genes to the expression Query tool (https:// clue. io) using the Touchstone v1 L1000 dataset  70. Results from each query assigned a connectivity score between -100 to 100 to each CMap perturbagen. A positive score indicated that the query signature was similar to a perturbagen signature (i.
Acknowledgements JJM is supported by grants from  VHA BX003692-01 ,  DoD  ( W81XWH-18-0257  and  W81XWH-19-1-0477 ),  AACR-Bayer Innovation  and  Discovery Grant , the  Polsky Urologic Cancer Institute   Research Award. AN is supported by grants/research support by  Merck ,  Ipsen ,  Immunomedics , and  Astra Zeneca. The authors are grateful to  Sanjeev Mariathasan  and  Genentech  for facilitating access to the IMvigor010 cohort.
Data availability The raw sequencing data have been deposited in the EGA database (https://ega-archive. org/studies) under the accession code EGAS00001005549: (a) for the PURE01 cohort, bulk RNA-Seq data derived from human samples; (b) scRNA-Seq data for human muscleinvasive tumor samples; (c) bulk RNA-Seq data for the human RT4 cell line treated with either the KDM5Bi C70, an FGFRi, or a DMSO control; and (d) bulk ATAC-Seq data for RT4 cells treated with the KDM5B inhibitor C70, or with a DMSO control. Access can be granted by contacting the corresponding author (joshua. meeks@northwestern. edu) with responses addressed within 14 working days. Data will be available for at least 24 months from publication. Human subject data are available under restricted access to protect patient information.
For the ABACUS cohort, clinical and bulk RNA-Seq data can be requested from the EGAS00001004445 Data Access Committee (DAC, bci-cecmqa@qmul. ac. uk); data for the IMvigor010 cohort (EGAS00001004997) can be requested at devsci-dac-d@gene. com. Source data to generate most figure panels are publicly available at Zenodo (https://doi. org/10. 5281/zenodo. 7750550).
We collected 16S rRNA gene amplicon sequence data from 11 studies sampling the oral and nasopharyngeal, or gut microbiome of COVID-19-infected and uninfected subjects. Our synthesis included 1,159 respiratory (oral and nasopharyngeal) microbiome samples and 267 gut microbiome samples and spanned four countries. We hypothesized that (1) because of stronger immune responses, infected patients would have a lower microbiome richness across microbiome compartments; (2) the microbiomes of infected patients would be more variable from host to host than that of healthy individuals, in line with the AKP; (3) as SARS-COV-2 is primarily a respiratory disease, the oral and nasopharyngeal microbiomes would be more strongly affected than the gut microbiomes; and (4) COVID-19 infection would result in consistent shifts in microbiome composition in both compartments.
Literature search, selection criteria, and data extraction This study was conducted through a robust literature search and selection using the RepOrting standards for Systematic Evidence Syntheses (ROSES) guidelines  (Haddaway et al. , 2018)  (Figure  1 ). In January 2022, we performed a keyword search on the Web of Science database (www. webofscience. com) to identify and select relevant published articles. We used the terms "COVID-19" OR "SARS-CoV-2" OR "severe acute respiratory syndrome coronavirus 2" OR "coronavirus disease 2019" OR "nCoV" OR "novel coronavirus" AND "microbiome" OR "microbiota" OR "microflora" or "flora'' OR "biome". Furthermore, additional studies were included from other sources including PubMed and Google Scholar. We included published articles that performed 16S rRNA gene or transcript amplicon sequencing from gut/stool, nasopharyngeal, and oral samples collected from COVID-19+ individuals.
We only included articles that were published in the English language. The authors independently reviewed the titles and abstracts of all the selected studies. Other COVID-19-related publications outside the scope of this study, as well as related commentaries, editorials, reviews, systematic reviews, and metaanalyses, were excluded. For each selected study, we extracted information about the NCBI accession numbers to the 16S rRNA gene/transcript sequences, author names, patient characteristics, sample types, sample size, the 16S rRNA gene region sequenced, and the sequencing platform used. Studies were grouped based on the systems examined: gut microbiome, or oral, nasopharyngeal, and upper respiratory tract (URT) microbiomes (heretofore oral/URT).
Bioinformatics and statistical analyses All sequences were downloaded from NCBI as. fastq files and sequence data were processed using R (v 4. 1) software  (Bunn, 2008)  and the dada2  (Callahan et al. , 2016)  package. Following preliminary assessments of quality, we downloaded data for 1,588 samples from 11 studies. First, forward reads from each study were inspected to determine the optimal processing parameters using the plotQualityProfile function (detailed for each study in Supplementary Table  1 ), and trimmed to 100 base pairs with the filterAndTrim function, with maxEE = 2 and truncQ = 2. Reads were assigned a taxonomy using SILVA V. 132  (Quast et al. , 2013). The proportion of reads lost at each processing step for each study is shown in Supplementary Figure  S1. According to available metadata, technical controls (e.
To determine the contribution of COVID-19 infection to oral/URT and gut microbiomes, we performed a distance-based variance partitioning analysis using the varpart function of vegan, with Bray-Curtis dissimilarities. The Bray-Curtis dissimilarities between samples taken from the same region among COVID-19-infected and uninfected patients were used to measure microbiome variance. Unless otherwise noted, diversity measures are presented as mean ± standard deviation. To test the effect of COVID-19 infection on microbiome diversity (i. e. , Hill numbers, H1) and variability (i. e. , Bray-Curtis dissimilarities, H2), we used linear mixed effect models, with the study as a random effect, and COVID-19 infection as a fixed effect using the lmer function from the lmerTest package  (Kuznetsova et al.
, 2017). To compare the effect of SARS-CoV-2 infection on gut and oral/URT microbiomes (H3), we used linear mixed effect models, with the study as a random effect, and the interaction between COVID-19 status and the sampled region (i. e. , gut vs. oral/URT) as a fixed effect. In addition, a contrast analysis was performed using the emmeans function from the emmeans package  (Lenth et al. , 2023)  to quantify the effect of COVID-19 infection within sampled regions. Model assumptions and performances were tested using the performance package  (Lüdecke et al. , 2021) ; all model outputs and performances are found in the Supplementary Materials. To identify bacterial genera that were consistently under-or overrepresented in SARS-CoV-2-infected patients, we used microbiome-oriented linear models (MaAsLin2 package;  Mallick et al.
001; ** p < 0. 01) and with "n. s. " otherwise. FIGURE  3  COVID-19 increases host-to-host variability across microbiomes. Community distances were measured as the Bray-Curtis distance between individuals in the same study, from the same body site, and with the same infection status. Points are colored by studies, and the average across studies is shown with a black point. Significant differences between infected and non-infected patients across studies are indicated with asterisks where significant (***p < 0. 001). the host's environment, such as diet and exposure to pollutants, which is population-specific. By simultaneously reanalyzing microbiome data from various microbiome compartments in infected and uninfected patients across the world, we sought to identify consistent, COVID-19 infection-specific changes in the human microbiome.
Acknowledgments RR was funded by the  Alexander von Humboldt Foundation, Germany. RB acknowledges support by the  Saxon State Ministry for Science, Culture and Tourism (SMWK) ,  Germany -[3-7304/35/6-2021/48880 ]. SJ acknowledges support of sDiv, the  Synthesis Center of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig , funded by the  German Research Foundation  ( DFG-FZT 118 ,  202548816 ). We acknowledge support by the  iDiv Open Science Publication Fund  as well as the  Open Access Publishing Fund of Leipzig University , supported by the  German Research Foundation  within the program  Open Access Publication Funding.
We used 10 Ground Control Points (GCPs) in order to correctly georeference the drone images (Supplementary Table T2), acquired by differential GPS and marked by 0. 8x0. 8 m square orange targets easily visible in the images. We obtained the latitude and longitude coordinates (WGS84) and the height above the ellipsoid of 10 locations, of which 8 on the landslide deposit and 2 on the slope adjacent the distal, frontal edge of the deposit (Figure  4 ), using a Leica dGPS GS09. We occupied the points for >5 minutes and post-processed the data using the permanent GPS station in Akureyri (AKUR run by 100 LMI (Landmaelingar Íslands)) 38 km away from the site. The resulting positions had errors much less than the error produced by inherent uncertainty in identifying the control points in the drone images (the worst position had a standard deviation of 2.
4 cm in vertical position, yet this was typically 3 mm). We calculated the elevation above the geoid (i. e. , elevation above sea level) using an online geoid calculator (GeoidEval). We built a digital elevation model (DEM; 4. 65 cm/px resolution) and an orthoimage (2. 38 cm/px resolution) of the Dalvík landslide using Agisoft Metashape (dataset 105 provided in  Magnarini et al. (2023) ).
, 2018). CTX images were used to 115 identify the top of the headscarp and toe of the deposit so their elevation values could be extracted from the DEM. We generated digital elevation models (DEMs) and orthoimages of four landslides (M-Landslide ID 31; M-Landslide ID 42; M-Landslide ID 46; M-Landslide ID 47) using CTX  (Malin, 2007; Malin et al. , 2007)  image pairs (Supplementary Table  T4 ). CTX-derived DEMs were generated using the USGS Integrated Software for Imagers and Spectrometers (ISIS) software and commercial image analysis software SOCET SET  (Kirk et al. , 2008). DEMs were vertically and horizontally 120 controlled to Mars Orbital Laser Altimeter topographic data  (Smith et al. , 1999; Zuber et al. , 1992). The resolution of DEMs and orthoimages are 20 m/pixel and 6 m/pixel, respectively.
Acknowledgements. GM gratefully acknowledges  UKRI Science Technology Facilities Council (STFC)  funding  ST/V000799/1. 350 SJC  is funded for her HiRISE work by the  French space agency CNES. SJC, MP and CB are funded by the  Agence Nationale de la Recherche  in the framework of the project  ANR-19-CE01-0010 PERMOLARDS. We acknowledge the financial support from the  Observatoire Sciences de l'Univers Nantes Atlantique (OSUNA).
Mismatch Repair Deficiency (dMMR)/Microsatellite Instability (MSI) is a key biomarker in colorectal cancer (CRC). Universal screening of CRC patients for MSI status is now recommended, but contributes to increased workload for pathologists and delayed therapeutic decisions. Deep learning has the potential to ease dMMR/MSI testing and accelerate oncologist decision making in clinical practice, yet no comprehensive validation of a clinically approved tool has been conducted. We developed MSIntuit, a clinically approved artificial intelligence (AI) based pre-screening tool for MSI detection from haematoxylin-eosin (H&E) stained slides. After training on samples from The Cancer Genome Atlas (TCGA), a blind validation is performed on an independent dataset of 600 consecutive CRC patients. Inter-scanner reliability is studied by digitising each slide using two different scanners.
As a result of this preprocessing, we obtained n = 537 (MSI: 83) and n = 554 (MSI: 86) slides for MPATH-DP200 and MPATH-UFS cohorts respectively. Any deep learning system in pathology requires a threshold to convert continuous prediction values into actionable categories. To address the issue of variations in data acquisition protocols that may impact deep learning model prediction distributions, we used a calibration strategy to ensure a sensitivity between 0. 93 and 0. 97 was obtained (see methods section"Calibration step", Fig. 1b ). This step enabled MSIntuit to reach high sensitivity, which is critical in a clinical setting, without sacrificing MSIntuit' specificity, which is important to guarantee the tool's clinical utility. This process led us to choose a threshold of 0. 20045 on the MPATH-UFS dataset and 0.
20202 on the MPATH-DP200 dataset. The similarity of the two thresholds suggests that the variations between UFS and DP200 scanners did not meaningfully impact MSIntuit predictions, despite the model having been trained on data collected using another scanner (Leica Aperio). MSIntuit performance was boosted using self-supervised learning, allowing it to rule out almost half of the non-MSI population with high sensitivity During model development, we found that using a feature extractor pre-trained with SSL on millions of histology tiles yielded a performance improvement. To illustrate this, we compared this approach against two other feature extraction approaches, keeping the other steps of MSIntuit pipeline unchanged (matter detection, QC, …, see Methods section). The first approach consisted of using an extractor pre-trained on ImageNet dataset, while the second consisted of using an extractor pre-trained on 100,000 colorectal cancer images to identify nine tissue classes  13.
Although ImageNet only contains natural images, the first method has been used widely in the computational pathology community because there is no dataset of histology images equivalent to ImageNet in terms of size (1. 2 million images) and annotation diversity (1000 classes). The second approach has the advantage of leveraging a feature extractor directly trained on colorectal cancer images. However, this feature extractor has seen a lower number of distinct images during training, which may impact its representation capacity. We also compared MSIntuit against iDaRS, a recently published method which finetunes an extractor pre-trained on ImageNet to predict MSI using an innovative weakly supervised approach 8. MSIntuit's approach largely outperformed the other methods by more than eight AUROC points on PAIP, MPATH-DP200 and MPATH-UFS (Supplementary Tables  1, 2 ).
Altogether, we believe this demonstrates the strength of our validation, as well as the robustness of MSIntuit. A key technical strength of the approach is the use of SSL to extract features from the histology images. Using this method, we were able to train a feature extractor tailored for histology on four million CRC histology images without the need for any labels. As already pointed out by previous studies, we observed that such methods were more robust to scanner variations and largely outperformed feature extractors pretrained on ImageNet dataset for MSI prediction task, an approach still widely used in medical imaging  [21] [22] [23]. We also showed that MSIntuit outperformed MSPath, a scoring system which uses clinical and pathological variables known to be associated with MSI. It is worthy to note that this system is subject to interobserver variability and requires a time-consuming assessment of several histological features by pathologists, which explains why it is not used in clinical practice given their high workload.
Preprocessing of whole-slide images A preprocessing pipeline was applied to reduce dimensionality and clean the data before training any model (Fig. 6a ). The first step of our pipeline consisted of detecting the tissue on the WSI: a U-Net neural network was used to segment part of the image that contains relevant matter, and discard artefacts such as blur, pen marker etc. , as well as the background  28. U-Net is a fully convolutional neural network architecture widely used for biomedical image segmentation tasks. This U-Net network was previously trained on 460 H&E and IHC slides from an internal dataset where tissue was manually annotated, and validated on 115 slides with a Dice score of 0. 96. This network was applied on images of size 2048 × 2048 μm (512 × 512 px, at a resolution of 4 MPP) extracted from the WSI.
The second step consisted of splitting the slide into smaller images, called "tiles", of 112 × 112 μm (224 × 224 px, at a resolution of 0. 5 MPP). At least 50% of the tile had to be detected as foreground by the U-Net model to be kept. For training, a maximum of 8,000 tiles were extracted from each slide while all tiles were extracted for inference. The final step consisted of extracting features from each tile: 2048 relevant features were extracted using a wide 50-layer residual net (ResNet50) network (the bottleneck number of channels is twice as large in every block) trained in a self-supervised fashion with Momentum Contrast (MoCo) v2  12, 29. This network was trained on four million tiles from the TCGA-COAD dataset, with heavy data augmentation (random cropping, random flips, colour jitter, random grayscale, gaussian blur), and without using any labels.
Calibration step To address the issue of variations in data acquisition protocols (e. g. stainers or scanners) that may impact deep learning model prediction distributions, we used a calibration step. This step ensured that MSIntuit yielded a clinically relevant sensitivity without sacrificing the tool's specificity. For both MPATH-DP200 and MPATH-UFS, 30 slides from the same randomly selected dMMR/MSI patients were used to define the operating threshold leading to 1/30 misclassification (meaning, one slide was classified as "MSS-AI", and 29 were classified as "Undetermined"). The impact of the slide selection during the calibration step is reported in Supplementary Table  11. The number of slides used in this calibration step was chosen after a sensitivity analysis on several internal datasets showed that 30 slides were sufficient to ensure with a high likelihood that the sensitivity of MSIntuit on the validation set was between 0.
Tool's consistency across slides from different blocks of the same tumour For a subset of 200 patients of MPATH-DP200 dataset, one to four other tumour slides coming from different blocks of the same surgical resection were digitised, resulting in a total of 398 additional slides. We characterised the tumour morphology of these slides using a ResNet18 model from the TIAToolbox library trained to classify each tile into one of the following categories: adipose tissue, debris, lymphocytes, mucus, smooth muscle, normal colon mucosa, cancer-associated stroma, colorectal adenocarcinoma  31. We then assessed the variations in MSIntuit predictions according to the slide chosen to be processed by the tool. We also determined how each tissue type category impacted MSIntuit prediction, and which kind of slide was preferable to be selected for MSIntuit processing.
Acknowledgements The results published here are in whole or part based upon data generated by the TCGA Research Network: https://www. cancer. gov/tcga. Regarding the PAIP dataset: De-identified pathology images and annotations used in this research were prepared and provided by the  Seoul National University Hospital  by a grant of the  Korea Health Technology R&D  Project through the  Korea Health Industry Development Institute (KHIDI) , funded by the  Ministry of Health & Welfare, Republic of Korea  (grant number:  HI18C0316 ). This work was granted access to the HPC resources of IDRIS under the allocation  AD011012519  made by  GENCI. We thank  Pierre Courtiol ,  Simon Jégou ,  Benoit Schmauch , and  Olivier Moindrot  for their contribution in the early development of the model. We thank  Sanjana Vasudevan  for her corrections of the manuscript.
Data availability All images and the associated MSI status for the TCGA cohort used in this study are publicly available at https://portal. gdc. cancer. gov/ and cBioPortal (https://www. cbioportal. org/). Deidentified pathology images and annotations from the PAIP cohort can be obtained via appropriate data access requests at http://www. wisepaip. org/paip. Datasets MPATH-DP200 and MPATH-UFS are the property of Owkin, France and are available upon request for academic use only. Source data are provided with this paper.
Code availability An implementation of the U-Net is available at https://github. com/ milesial/Pytorch-UNet. An implementation of MoCov2 is available at https://github. com/facebookresearch/moco. An implementation of Chowder algorithm is made available at https://github. com/ CharlieCheckpt/msintuit (https://zenodo. org/badge/latestdoi/ 670039349)  32.
Data sources The bulk of seabird census and CRI data used in this study was acquired over the 2017-2023 period through dedicated survey campaigns (2. 2. 2-4. ). We gathered additional information on species abundance estimates and invasive rodents' presence from accounts of post-2000 surveys conducted in all four sub-regions. This includes published bibliography (e. g. ,  Borsa et al. , 2010; Philippe-Lesaffre et al. , 2023) , grey literature reports and survey accounts from provincial rangers (Table  A4 ). Maps and GIS metadata from the New Caledonia-based website Géorep. nc (https://georep. nc/) were used as reference for GIS analysis (2. 2. 5-6).
Geographical extent of study sites & habitat characterization The geographical extent of each surveyed CRI was delimited by aerial imagery. For 53 islets (Table  A2 ), we produced original, highresolution aerial maps using unmanned aircraft systems (DJI Phantom 4). For the remaining 47 islets, aerial imagery was obtained from freely available satellite data, with a minimum resolution of 0. 6 m/px. We retained the high-tide mark (=shoreline) as the external boundary of each CRI's emerged area and set it as a reference for calculating CRI surface and perimeter. We then estimated the roundness of each site (Polsby-Popper score of shape compactness). We characterized the composition and spatial distribution of seabird breeding habitat in 5x5m grids drawn upon each CRI. The use of gridded data was preferred to continuous habitat characterization (i.
Invasive rodents We assessed the presence or absence of invasive rodents (black rat Rattus rattus, Polynesian rat R. exulans, house mouse Mus musculus) on 59 sites by deploying snap-trap transects during surveys. Among the 41 other CRIs, we assumed that no permanent rodent populations were hosted on 18 small cays (<0. 2 ha) with low or no plant cover, and derived information on rodent presence in the 23 remaining sites from existing bibliography and data sources. Since only 14 of the 100 study CRIs currently host rodents, all species (black rat, Polynesian rat, house mouse) were pooled together in a single presence/absence category for further analysis.
Data curation and variable treatment 2. 3. 1. Descriptors for the seabird community We evaluated species richness for each CRI as the total number of breeding species recorded during the considered surveys (2. 2. 1). This includes species that exhibit high site fidelity (e. g. , WTS, black noddy) and species with occasional breeding (e. g. , black-naped tern Sterna sumatrana, greater crested terns Thalasseus bergii). We then averaged seabird counts to produce representative estimates of site-wise abundances, in breeding pairs. For species with high site fidelity and yearly breeding, abundance was calculated as the mean of available estimates (one value per breeding season during which the species was surveyed). For species with occasional presence, abundance was averaged across all surveys, including years without breeding activity.