dataesr/scientific-paragraphs-categorization huggingface dataset:
12 of the Binary masks describing the 21 ocean regions for the regional modelling activities in the fisheries and marine ecosystems available at 0. 25°and 1°resolution. These masks have been used for the ocean forcing data extractions (see CRF part of this table). HWSD data at high resolution (30 arcsec). Information about the fraction of agricultural land within each ISIMIP 0. 5°×0. 5°g rid cell was taken from MIRCA2000 (Portmann et al. , 2010). If there is no soil information for an ISIMIP grid cell, e. g. due to differing land-sea-masks, the information from neighbouring cells is used. For further details please see GGCMI-HWSD (2023). We provide the binary land-sea mask of the W5E5 dataset. It is a conservative land mask where grid cells that in reality cover both land and ocean are counted as ocean.
Thus, climate conditions over the land grid cells of this land-sea mask can be safely assumed to represent climate conditions over land rather than a mix of climate conditions over land 2020). the main mask described above (Lange and Büchner, simulations in ISIMIP3. It marks more grid cells as land than mask from ISIMIP2b is provided to be used for global water version without Antarctica. In addition, the generic land-sea adjusted using W5E5. The mask is also provided in a and the ISIMIP3b climate forcing that has been bias-W5E5, i. e. GSWP3-W5E5 and 20CRv3-W5E5 of ISIMIP3a and ocean. This refers to all climate datasets based on allow for the quantification of the effect of historical changes in direct human forcings, ISIMIP3a also contains an experiment where all Large Marine Ecosystem masks mandatory Regional Marine Ecosystem masks optional Land-sea mask optional direct human forcings are held constant at year 2015 levels.
5 Land transformation 985Table 13 : Land transformation and wood harvest provided as part of the ISIMIP3a direct human forcing. 13 Variable Variable specifier Unit Resolution Datasets Wood harvest primf-harv (wood harvest area from primary forest land) primn-harv (wood harvest area from primary non-forest land) Fraction of the national land area, kg in case of biomass Annual, national sum Based on LUH2 v2h (Hurtt et al. , 2011, 2020; del Valle et al. , 2022) secmf-harv (wood harvest area from secondary mature forest land) secyf-harv (wood harvest area from secondary young forest land) secnf-harv (wood harvest area from secondary non-forest land) primf-bioh (wood harvest biomass carbon from primary forest land) primn-bioh (wood harvest biomass carbon from primary non-forest land) 980 are based on a compilation by Zhang et al.
, 2011a, b) with a subset of the Georeferenced global Dams And Reservoirs (GeoDAR v1. 2) database (Wang et al. , 2022), developed at Kansas State University (KSU), and provided by Jida Wang ahead of publication. These additional dams have 1045 construction or projected finalisation dates between 2016 and 2025, while GRanD v1. 3 includes dams constructed up until 2017. In total, the combined database now includes 7331 dams whose construction was finished by 2025. It includes dams that were constructed before, but still in existence during, the simulation period (the first reported dam was finished in the year 286). In total the reported dams have a global cumulative storage capacity of approximately 6932 km 3 (Figure.
Large colonisation rates are especially selected for at higher trophic levels, so the latter would need to have less stringent bounds. One direct way to test our predictions is experimental, with appropriate systems such as ciliates or other microorganisms  (Altermatt et al. , 2015; Fox & Morin, 2001; Pennekamp et al. , 2014). More generally, we might use observational data from the field to look for some of the qualitative patterns we have identified -these patterns could indeed be diagnostic of whether eco-evolutionary dynamics are at play or not. Of course, natural communities and food chains experience processes and contingencies left out in models (e. g. autocorrelation and/or clustering of perturbations, diffuse trophic levels). This would often complicate the task, but datasets such as the one used in  Wang et al.
Such qualitative differences between ecological and eco-evo models are usually the most susceptible to be detected, and might even leave their mark in real-world patterns (e. g. Dwyer et al. , 2022). Datasets or experiments documenting the impact of habitat destruction on maximum food-chain length and overall occupancy should thus offer the best potential to evaluate the importance of eco-evolutionary dynamics for food-web dynamics. Our results derive from a spatially homogeneous and mean-field patch-occupancy model. Interestingly, such a simple model faithfully reproduces the results presented in  Wang et al. (2021) , who used stochastic simulations of a spatially realistic model. This gives additional credence to our predictions. Our results could be extended in different ways, including more complex food-web topologies (omnivory), introducing several species per trophic level (apparent predation at the metacommunity scale) or using trophic level-specific patch definitions  (McCann et al.
Each P. fijiensis individual corresponded to a fungal culture strain derived from a single ascospore (i. e. , sexually produced spore) isolated from necrotic infectious banana leaf tissue as described in  Zapater et al. (2008). All populations except for the population from Congo (COG) had previously been genotyped using 21 microsatellites in a global-scale study of P. fijiensis history  (Robert et al. , 2012). Strain culture, DNA extraction, and microsatellite genotyping with the 21 microsatellite markers were carried out for the population COG as described in  Robert et al. (2012). One microsatellite marker (MfSSR355) was monomorphic at the scale of Africa and was discarded; all the analyses in this study were therefore performed using 20 markers. The microsatellite dataset are deposited in the dryad repository: doi:10.
| Genetic diversity and population differentiation Population and diversity indices were estimated from a diploidised dataset, after re-coding haploid genotypes into diploid ones. Gametic linkage equilibrium between all pairs of loci was tested in all populations, by Fisher's exact tests implemented in Genepop 4. 0  (Raymond & Rousset, 1995; Rousset, 2008). We used the false discovery rate (FDR) procedure implemented in the R package Qvalue  (Storey, 2002)  to control for multiple testing. Gene diversity H s was estimated following  Nei (1987)  with Fstat 2. 9. 3  (Goudet, 1995).
6. 94 (Earl & vonHoldt, 2012). Clustering results of independent replicate runs were checked for congruence and optimally aligned using Clumpp version 1. 1. 2 (Jakobsson & Rosenberg, 2007)  and final results were represented with distRuct version 1. 1  (Rosenberg, 2004). To corroborate those results using a different clustering approach, we also performed a Discriminant Analysis of Principal Components analysis (DAPC,  Jombart et al. , 2010)  implemented in the adegenet package  (Jombart, 2008; Jombart & Ahmed, 2011). Because the population COG from Congo has been sampled 10 years after the other African populations, which can introduce bias in the analysis, we performed the two analyses, one including all the African populations and one without the population from Congo.
| ABC inference of the pathways of dissemination across Africa ABC scenario choice was carried out, to compare the historical hypothesis of a single western Gabonese introduction (Figure  1b ) with a hypothesis of several introductions (as suggested by the high F ST values between countries, see Results; Figure  1d ), and with that of a single introduction elsewhere (Figure  1c ). To do so, we used the ABC methodology relying on the Random Forest statistical tool, named ABC-RF and implemented in the R package abcrf  (Pudlo et al. , 2016).
An example of the template and estimation files required for Fastsimcoal is given for each scenario in the Dryad repository (repository available upon acceptance). ABC methods involve the comparison of observed and simulated datasets on the basis of summary statistics. In this study, we computed the following summary statistics using aRlsumstat v. 3. 5. 2, a command line version of aRleQuin  (Excoffier & Lischer, 2010) : the mean and standard deviation over loci of population-specific number of alleles, allelic range, heterozygosity, and Garza-Williamson's M index  (Excoffier et al. , 2005)  which was computed as in  Garza and Williamson (2001)  and also in a modified version to deal with monomorphic populations; we further used the mean and standard deviation over loci of averages over populations of the number of alleles, the allelic range, the heterozygosity and the M index.
Considering three genetic clusters, we observed a similar structuration when considering the COG population or not (Appendix S5). The population CIV, GA1, and NGA were relatively homogeneous with most of the individuals from CIV being preferentially assigned to the blue cluster and most of the individuals from GA1 and NGA being preferentially assigned to the yellow cluster. The individuals from the other populations showed mixed ancestries in the three clusters with different proportions. 3. 3 | ABC scenario choice: general trendsGraphic representations of the summary statistics (PCA and their distribution) revealed that the simulations performed under the eight scenarios using the parameter values drawn from prior distributions were relatively close to the observed dataset. First, the representation of the PCA in the space of the summary statistics showed that the observed dataset fell well within the simulated ones (Appendix S6).
Second, most of the observed summary statistics did not appear to deviate from the distributions of the summary statistics computed from the simulated dataset (see Appendix S7 as an illustration using the trio of populations PHL, CIV, and CA6). Three summary statistics were consistently found to be at the right extremity of the distribution: the standard deviation over loci of the number of alleles and of heterozygosity in the PHL population (Ksd_3 and Hsd_3) and the standard deviation over loci of the Garza-Williamson's M index of some African populations (GWsd_2). Three other summary statistics were often found at the right extremity of the distribution: the standard deviation over loci and populations of the heterozygosity (sd_H) and of the Garza-Williamson's M index (sd_NGW), and the standard deviation over loci of some African populations (GWsd_1; see Appendix S8).
Barrès, J. -M. Cornuet, A. Estoup, and T. Guillemaud for their advices, and to the Associate Editor and the reviewers for their constructive remarks. Data were produced through the molecular genetic analysis technical facilities of the IFR119 with the help of E. Desmarais and F. Cerqueira. Bioinformatics analyses were performed using the Core Cluster of the Institut Français de Bioinformatique, the MESO@LR-Platform at the University of Montpellier and the INRAE MIGALE bioinformatics facility (MIGALE, INRAE, 2020. Migale bioinformatics Facility, doi: 10. 15454/1. 55723 90655343293E12). FU N D I N G I N FO R M ATI O N This work was supported by the Région Languedoc-Roussillon, the Agence Nationale de la Recherche (project EMERFUNDIS ANR 07-BDIV-003 and EMILE ANR 09-BLAN-0145-01), and French Agropolis Fondation (Labex Agro -Montpellier, BIOFIS project number 1001-001 and Labex Agro -Montpellier, E-SPACE project number 1504-004).
(Pudlo et al. , 2016) the number of simulations for which we obtained a summary statistic higher than the observed one. Then, we used the R package abcrf v1. 7. 1(Pudlo et al. , 2016)to provide a classification vote for the eight scenarios representing the number of times a scenario is selected as the model best fitting the dataset among a forest of 3000 trees, and in a second step the posterior probability of the best-selected model. The number of simulated datasets per scenario and the number of trees in the random forests were chosen to ensure the stability of the prior error rate estimates. We evaluated the influence of both the size of the reference table and the number of trees in the RF being evaluated for three trios of populations (Appendix S2) and used the same values for the 25 analyses.
Design and Procedure In a within-subject experimental design, participants were exposed to one AI-generated video per condition. Each participant saw one video per condition, where targets and conditions were presented in a random order (98. 42% of the sample saw different targets across conditions). Because the targets were recruited from two universities (one in India and one in Switzerland) and given the quantity of data (around 650 AI-generated videos to be combined in the Qualtrics online survey system), data collection was split into three waves. Hence, the video presented for each condition was randomly selected from a pool of 70-75 targets, rather than 200 targets. This also means that participants could randomly watch female or male targets, but only saw targets from the same culture (i.
The detection of tumour gene mutations by DNA or RNA sequencing is crucial for the prescription of effective targeted therapies. Recent developments showed promising results for tumoral mutational status prediction using new deep learning based methods on histopathological images. However, it is still unknown whether these methods can be useful aside from sequencing methods for efficient population diagnosis. In this retrospective study, we use a standard prediction pipeline based on a convolutional neural network for the detection of cancer driver genomic alterations in The Cancer Genome Atlas (TCGA) breast (BRCA, n = 719), lung (LUAD, n = 541) and colon (COAD, n = 459) cancer datasets. We propose 3 diagnostic strategies using deep learning methods as first-line diagnostic tools. Focusing on cancer driver genes such as KRAS, EGFR or TP53, we show that these methods help reduce DNA sequencing by up to 49.
9% with a high sensitivity (95%). In a context of limited resources, these methods increase sensitivity up to 69. 8% at a 30% capacity of DNA sequencing tests, up to 85. 1% at a 50% capacity, and up to 91. 8% at a 70% capacity. These methods can also be used to prioritize patients with a positive predictive value up to 90. 6% in the 10% patient most at risk of being mutated. Limitations of this study include the lack of external validation on non-TCGA data, dependence on prevalence of mutations in datasets, and use of a standard DL method on a limited dataset. Future studies using state-of-the-art methods and larger datasets are needed for better evaluation and clinical implementation.
Material and methods Study design. This study is retrospective and uses anonymized scanned WSIs retrieved from the TCGA project through the Genomic Data Commons Portal (https:// portal. gdc. cancer. gov/). We applied our method to the following tumor types: breast (BRCA), lung (LUAD), colon (COAD). The clinical data is registered with dbGaP study accession number: phs000178. v11. p8. For patient information, please refer to the original TCGA publications  [32] [33] [34]. All experiments were conducted in accordance with the Declaration of Helsinki and the International Ethical Guidelines for Biomedical Research Involving Human Subjects. Ethics oversight of the TCGA study is described at https:// www. cancer. gov/ about-nci/ organ izati on/ ccg/ resea rch/ struc tural-genom ics/ tcga/ histo ry/ polic ies.
Datasets. All data, including histological images and information about the participants from the TCGA database are available at https:// portal. gdc. cancer. gov/. Genetic data for patients in the TCGA cohorts are available at https:// portal. gdc. cancer. gov/. The corresponding authors of this study are not involved in data sharing decisions of the TCGA database. The TCGA-BRCA dataset included 719 slides from 34 different centers, the TCGA-LUAD dataset included 541 slides from 33 different centers and the TCGA-COAD dataset included 459 slides from 24 different centers. All available data were used for subsequent analyses. Molecular labels. Molecular labels were determined from the masked somatic mutations maf file of somatic mutation using the MuTect2 36 algorithm corresponding to the dataset.
Image preprocessing. Aperio SVS files of formalin-fixed paraffin-embedded (FFPE) diagnostic slides (labeled by a "DX" in their name) from the 3 datasets were first selected. We did not exclude any diagnostic slide from the analysis. We then extracted the foreground using an in-house trained U-net, and tiled the images in non-overlapping patches of 600 × 600 pixels at a 5 × resolution, thus leading to 331,263 patches for TCGA-BRCA dataset, 302,544 patches for TCGA-LUAD dataset and 178,224 patches for TCGA-COAD dataset. These patches are associated with the label 1 if the gene is mutated, 0 if the gene is non mutated according to the "Molecular labels" section above. We then used an EfficientNetB7  38  neural network truncated at its last layer on which we added a global average pooling layer in order to output an embedding vector of size 2560.
Gene prediction from histopathology. For each of the 3 datasets, we selected all genes having at least one mutation in more than 10% of the slides in the dataset and we divided the dataset in a train set (70%) and a test set (30%) without any overlap between cancer site between train set and test set following Howard et al. recommendations for train and test splitting  39  and stratified for positive and negative slides. Tissue source site was provided by the barcode of the TCGA slides  40. For each of these genes, we created an ensemble of 3 multilayer perceptrons (MLP) with 2 intermediate layers of 64 and 16 neurons with ReLU activation function and one final layer with 1 output with a sigmoid activation function. These 3 MLP were trained for 5 epochs on the train set using the Adam optimizer with a learning rate of 1e-4 and the binary cross entropy loss, then applied on the test set and their prediction values were averaged between the 3 MLP for each tile.
The slide-level predictions were calculated using the 99th percentile of its tile prediction values. Each gene prediction was run 5 times with random initialization and AUC were tested against the theoretical random value of 0. 5 with a one-sided Student Cancer driver genes metrics estimation. We used the gene set described in the "Gene prediction from histopathology" section and restricted the diagnostic strategies analysis to cancer driver genes. We defined the cancer driver genes as being annotated as "Oncogene" or "Tumor suppressor" in the OncoKB database  42. We divided the dataset in 5 folds without any overlap between cancer sites between folds for cross-validation following Howard et al. recommendations for train and test splitting  39. Tissue source site was provided by the barcode of the TCGA slides  40.
Results A significant proportion of mutations are detectable with deep learning methods on whole-slide imaging. We first set up a deep learning pipeline predicting solid tumor gene mutations from WSI using an EfficientNetB7 38 pre-trained on ImageNet dataset. In this study, 3 datasets from GDC Portal were analyzed, a lung adenocarcinomas dataset, a colorectal cancer dataset and a breast carcinoma and adenocarcinoma dataset, respectively TCGA-LUAD with 522 patients (541 slides), TCGA-COAD with 459 patients (459 slides), TCGA-BRCA with 687 patients (719 slides). Tables  1  and 2  provide a comprehensive overview of the patient population, including their demographic and clinical characteristics, as well as disease features. In order to validate the ability of our deep learning pipeline to predict gene mutations, we systematically trained the deep learning pipeline on every mutation having a prevalence greater than 10% in the dataset to focus on mutations that can show statistical significance, these correspond to 205 tested genes.
For each gene we split the dataset in a train set (70%) and a test set (30%) from distinct cancer sites (see Methods). We found that 50% of the gene had PPV (DL)where max P(DL)≤T P(DL)  a statistically significant predictable mutational status with our baseline DL pipeline (one-sided Student t-test corrected using Benjamini-Hochberg procedure with a FDR of 0. 05, Fig. 1 ). However, given the low number of samples, especially for mutation with low prevalence, one can expect that more of these effectively have a predictable mutational status. Therefore, this validates the ability of our model to predict gene mutations from WSI.
Mutations on cancer driver genes are detectable. We next focused our analysis on cancer driver genes, we defined the cancer driver genes as being annotated "Oncogene" or "Tumor suppressor" in the OncoKB database  42. Selected genes are CDH1 in TCGA-BRCA, TP53 in TCGA-BRCA, APC in TCGA-COAD, KRAS in TCGA-COAD, TP53 in TCGA-COAD, EGFR in TCGA-LUAD, KEAP1 in TCGA-LUAD, KRAS in TCGA-LUAD, PTPRD in TCGA-LUAD, STK11 in TCGA-LUAD and TP53 in TCGA-LUAD. We found that mutations in TP53 were detectable in TCGA-COAD, TCGA-LUAD, TCGA-BRCA with an AUC of 0. 64 ± 0. 044, 0. 68 ± 0. 003 and 0. 79 ± 0. 003 respectively. TP53 was found detectable in the 3 datasets which suggest that TP53 is ubiquitously detectable and could have a pan-tumoral morphological signature; however TP53 was significantly easier to find in the TCGA-BRCA dataset.
8% to 69. 8% at a 30% capacity, from 43. 8% to 85. 1% at a 50% capacity, and from 65. 2% to 91. 9% at a 70% capacity. These values have to be compared with the value of a random test equaling the capacity (e. g. 30% for a 30% capacity). For example, with the APC gene from the TCGA-COAD dataset, the strategy is performing lower than the theoretical random test; this effect is probably due to stochastic variability on an uninformative DL test. At a 30% capacity, CDH1 gene in TCGA-BRCA showed 69. 8% ± 8. 1% sensitivity, TP53 gene in TCGA-BRCA showed 56. 5% ± 4. 5% sensitivity and EGFR in TCGA-LUAD gene showed 43. 5% ± % sensitivity. At a 50% capacity, CDH1 gene in TCGA-BRCA showed 85. 1% ± 6. 0% sensitivity, TP53 gene in TCGA-BRCA showed 73. 5% ± 3. 0% sensitivity and TP53 gene in TCGA-LUAD showed 65.
This study is a preliminary study and has clinical limitations. First the lack of a true external dataset to validate the results on non-TCGA data. Results are dependent on the prevalence of the mutation, which is here approximated by the prevalence in the datasets but might not be the same in real application. Finally, we used a standard baseline DL method on a limited dataset as a proof of concept, but it does not show the state-of-the-art of the technology. A similar analysis with state-of-the-art methods and a larger dataset should be made to better evaluate the potential of the technology and refine the potential of the strategies. Clinical implementation would require better and clearer performances validated at least in an external dataset in order to reach regulatory compliance, or in a prospective trial in order to have robust conclusions about the true clinical value as stated in van der Laak et al.
Data availability The data analyzed in this study were obtained from The Cancer Genome Atlas (TCGA), which is publicly available through the Genomic Data Commons (GDC) Data Portal (https:// portal. gdc. cancer. gov/). The GDC Data Portal provides access to a variety of TCGA data types, including genomic, transcriptomic, proteomic, and clinical data from thousands of tumor and normal tissue samples across multiple cancer types. Data can be downloaded through the GDC Data Transfer Tool or accessed through the GDC Data Portal API. Detailed information on the available data and instructions for accessing and downloading the data are provided on the GDC Data Portal website. All data used in this study were used in accordance with the GDC Data Use Policy.