dataesr/scientific-paragraphs-categorization huggingface dataset:
org/, accessed on 2 November 2022). To validate the results on independent cohorts, we have obtained three additional datasets. We obtained the transcriptome profiling data for the E-MTAB-1980 dataset from Array Express, while we obtained the clinical data from supplementary data in the original publication  [27]. We obtained the GSE167093 dataset from Genome Expression Omnibus (GEO)  [28]  and the "Braun et al. " and JAVELIN renal 101 datasets from the supplementary data of corresponding publications  [29, 30]. The original authors have already normalized all datasets of independent cohorts, and details about normalization are available in their respective publications. An overview of the clinicopathological characteristics of patients in datasets for which clinical data were publicly available is given in Table  1.
Univariable and Multivariable Cox Model We applied two thresholds on gene expression: (a) filtering out genes that are not expressed in at least half of patients (median expression = 0, in log2(TPM + 1)), and (b) filtering out genes that are not variable enough to be precisely measured in a clinical environment (quantile 80-quantile 20 = 0, in log2(TPM + 1)). After the filtering process, we used each remaining gene to train a univariable Cox model. For this purpose, we used the R "survival" package (v3. 3. 1) [31, 32]  and we used the obtained p-values to judge the goodness of fit of the Cox model. Next, we performed a univariable Cox model on clinical covariates:the age of a patient, gender, clinical stage, and histological grade, as well as COL7A1 expression. We then used all the mentioned covariates to learn a multivariable Cox model and compared changes in the p-value of the COL7A1 covariate between the univariate and multivariate Cox models.
Kaplan-Meier Curves We divided patients into groups of high and low COL7A1 expression using a cut-point determined by the "surv_cutpoint" function of the "survminer" package (v0. 4. 9)  [33]. We applied the same process to the other three independent datasets. The division of patients according to COL7A1 expression into four groups was performed by dividing the outlierfree range of expression into five equal sections using the "cut" function. We computed and drew all Kaplan-Meier curves using the packages "survival" and "survminer".
GSEA To better understand the biological implications of COL7A1's aberrant expression, we investigated genes that are co-expressed with COL7A1. Gene set enrichment analysis (GSEA) allows us to investigate how the expression of a set of genes that belong to a common biological pathway varies in expression together with COL7A1  [34]. We performed GSEA using the "ClusterProfiler" (v4. 6. 0) package  [35]. For this purpose, we investigated coexpression through Pearson's correlation between COL7A1 expression and the expression of other genes available in each investigated dataset. Gene sets (pathways) on which we have performed GSEA were obtained from the Molecular Signature database (MSigDB, v2022. 1), with a focus on the Hallmark dataset  [34, 36].
Proliferation and Migration Assay Proliferation and migration were assessed using an Incucyte ZOOM (Sartorius, Göttingen, Germany) video microscope. For proliferation, cells were plated in a 96-well plate at a density of 5000 cells per well, and cell confluence was measured by taking pictures every 2 h for 72 h. The cell number was also quantified by counting the cells at T-0 h and T-48 h in four different images for each condition (between 200 and 900 cells per image). The migration assay was performed by plating 30,000 cells per well in a 96-well plate. After overnight cell adhesion, a wound was made using the wound maker (Sartorius). Wound confluence over time was measured by taking pictures every 2 h during 24 h. An analysis of confluence was performed thanks to the Incucyte zoom software.
COL7A1 Expression Is Prognostic of Overall Survival With the aim of discovering novel prognostic biomarkers for ccRCC patients, we have applied a systematic approach to identify genes whose expression levels in tumors are related to overall patient survival. After the filtering processes that we described in the Material and Methods section, around 17,000 genes remained. We learned a univariable Cox model on all genes that passed filtering, and the resulting table was ordered according to the model's p-value. The top ten genes with the lowest p-value are presented in Table  2 , with COL7A1 being the one whose expression level is the most significantly linked to survival. To confirm the correlation of COL7A1 expression with survival, we trained a univariable Cox proportional hazards model on the independent E-MTAB-1980 dataset.
The resulting model was significant, with a p-value of 0. 0023. To visualize the prognostic capabilities of COL7A1, we have divided TCGA:KIRC patients into groups of high and low expression (Figure  1a ). The resulting Kaplan-Meier curve shows that patients with high COL7A1 expression in tumors have a significantly worse prognosis (log-likelihood p-value < 0. 0001) exhibiting a median survival of 4. 3 years (3. 3-5. 25, 95% confidence interval (CI)) with a 5-year survival probability of 43. 7% (34. 75-52. 5%). For patients with low COL7A1 expression, it was not possible to determine median survival as the survival curve does not cross the 50% line; however, this group had a 5-year survival probability of 73. 1% (67. 8-78. 9%). While the number of patients in the E-MTAB-1980 dataset is much lower compared to the TCGA:KIRC dataset, and patients are predominantly in the early stages, we can still see a similar stratification of patients according to risk (Figure  1b ).
Patients in the high COL7A1 expression group have a significantly worse prognosis than those in the low expression group (p-value = 0. 0014), confirming the results obtained on the TCGA:KIRC dataset. The 5-year survival probability of the high expression group was 58. 3% (39. 8-85. 5%), in contrast to 85. 2% (77. 4-93. 7%) in the low expression group. To further investigate the prognostic power of COL7A1, we divided the range of COL7A1 expression in the TCGA:KIRC dataset into four equal levels in terms of expression range. The lowest level of expression is almost identical to the low expression group in Figure  1a. The other three levels of expression showed a gradually increased risk and shorter survival time for patients in relation to COL7A1 expression levels (Figure  1c, d ). The 5-year survival probability of each group from the lowest the highest COL7A1 expression is 72.
Additionally, all 12 mitochondrial genes expressed in the TCGA dataset and Braun et al. showed a clear anti-correlation pattern with COL7A1 (Figure  3b ,d and Figure  S7 ). Since the E-TMAB-1980 and GSE167093 datasets originate from expression arrays, they do not contain mitochondrial genes. To investigate the expression of genes for which the encoded protein is a part of mitochondria, we performed GSEA analysis on the cellular components (CC) of the Gene Ontology database. Most of the genes were found to be anti-correlated with COL7A1 (Figure  3c, e ).
Future predictive models may be improved by the incorporation of new biomarkers. By stratifying the Kaplan-Meier curve according to the stage and grade of tumors, we showed that COL7A1 can improve the prediction of patient survival based on these two variables. This feature is particularly important for patients with stage II or III cancers due to their high uncertainty of outcome. The TCGA and E-MTAB-1980 cohorts were completed before the use of recent treatment options, including mTOR and immune checkpoint inhibitors, which are given to patients with advanced stages. The prognostic potential of COL7A1 remains with these new therapeutic options, as we demonstrated with the Braun et al. and JAVELIN renal 101 cohorts. Results of univariable and multivariable Cox models additionally support the claim that COL7A1 expression can improve prediction based on clinical variables only.
Patterns of COL7A1 expression; Figure S2: Kaplan-Meier curves and corresponding histograms showing differences in overall survival in the E-MTAB-1980 and the Braun et al. cohorts; Figure S3: Kaplan-Meier curves and corresponding histograms showing differences in overall survival of the TCGA:KIRC divided by grade; Figure S4: Kaplan-Meier curves showing differences in progression free survival of patients with advanced ccRCC in JAVELIN Renal 101 clinical trial cohort based on COL7A1 expression; Figure S5: Graphical representation of correlation between COL7A1 and four genes that belong to proliferation-related pathways; Figure S6: Graphical representation of correlation between COL7A1 and four genes that belong to metabolism-related pathways; Figure S7: Graphical representation of correlation between COL7A1 and mitochondrial genes; Figure S8: Proliferation and Migration assay kinetics of 786-O shCOL7A1 cell lines.
Author Contributions: Conceptualization, L. G. and O. F. ; data analysis, D. K. , L. G. and R. J. ; experiments, I. S. and O. F. ; original draft preparation, D. K. , L. G. , C. C. , I. S. and O. F. All authors have read and agreed to the published version of the manuscript. Funding: This research was supported in part by the University of Grenoble Alpes, the Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), and the Institut National de la Santé et de la Recherche Médicale (Inserm). This article was developed in the framework of the Grenoble Graduate School in Chemistry, Biology, and Health, CBH-EUR-GS (ANR-17-EURE-0003), and Labex GRAL (ANR-10-LABX-49-01).
Data Availability Statement: Publicly available datasets were analyzed in this study. These data can be found here: https://www. cancer. gov/tcga; https://www. ebi. ac. uk/arrayexpress/experiments/ E-MTAB-1980/; supplementary data of  [29] ; https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi? acc=GSE167093; The R script to reproduce the results presented in this article is available at https: //github. com/IRIG-BCI-IMAC/COL7A1-project (all accessed on 19 December 2022).
: Patterns of COL7A1 expression; Figure S2: Kaplan-Meier curves and corresponding histograms showing differences in overall survival in the E-MTAB-1980 and the Braun et al. cohorts; Figure S3: Kaplan-Meier curves and corresponding histograms showing differences in overall survival of the TCGA:KIRC divided by grade; Figure S4: Kaplan-Meier curves showing differences in progression free survival of patients with advanced ccRCC in JAVELIN Renal 101 clinical trial cohort based on COL7A1 expression; Figure S5: Graphical representation of correlation between COL7A1 and four genes that belong to proliferation-related pathways; Figure S6: Graphical representation of correlation between COL7A1 and four genes that belong to metabolism-related pathways; Figure S7: Graphical representation of correlation between COL7A1 and mitochondrial genes; Figure S8: Proliferation and Migration assay kinetics of 786-O shCOL7A1 cell lines.
Finally, opaque models can conceal dataset bias, and lead to socially unfair models  [36]. In this work, we are particularly interested in explaining models in supervised learning scenarios in order to gain further insights about the concept related to a specific response. For example, assume one has a model that predicts the presence of brain tumor in MRI scans with very high accuracy. What are the most relevant morphological features that indicate the presence of tumor, and where are they located? Can we discover new features of the disease from what the model has learned? Many important problems of this kind exist, but the necessary tools to answer these questions effectively and efficiently are still lacking. The foundational work by Ribeiro et al. [33]  spurred exciting advances in local feature attribution methods, such as Grad-CAM  [34] , Integrated Gradients  [41] , and DeepLIFT  [37].
This quickly becomes intractable in image classification problems when f is a convolutional neural network and n ≈ 10 6 , or larger. As a result, all state-of-the-art image explanation methods based on Shapley coefficients rely on some approximation strategy to work around this computational limitation. For instance, GradientExplainer  [27]  extends Integrated Gradients  [41]  by sampling multiple references from the background dataset to integrate on. Similarly, DeepExplainer  [27, 7]  builds upon DeepLIFT  [37]  by choosing a per-node attribution rule that can approximate Shapley coefficients when integrated over many background samples. Finally, PartitionExplainer employs a hierarchical clustering approach to compute Owen's coefficients  [31, 26] , which can improve runtime compared to the naive computation of Shapley coefficients.
Experiments We now move to demonstrate the performance of h-Shap and of other state-of-the-art methods for image attributions. Our objective is mainly to compare with other Shapley-based methods, such as GradientExplainer  [27] , DeepExplainer  [27, 7] , and PartitionExplainer 5. We also include LIME 6   [33]  given its relation to Shapley coefficients, and Grad-CAM  7  [34] because of its popularity. We study three complementary binary classification problems of different complexity and input dimension: a simple synthetic benchmark, a medical imaging dataset, and a general computer vision task. We focus on scenarios where the ground truth of the image attributions (i. e. what defines the label) is well defined and available for evaluation. All experiments were conducted on a workstation with NVIDIA Quadro RTX 5000.
Our code is made available for the purpose of reproducibility 8. When possible, each method was set to use as much GPU memory as possible, so as to minimize their runtime. DeepExplainer, GradientExplainer, and PartitionExplainer were constrained the most by memory, reflecting their limitation in analyzing large images. We use h-Shap with both an absolute threshold τ = 0, and a relative threshold τ equal to the 70 th percentile, which we refer to as τ = 70% with abuse of notation. Finally, we perform full randomization sanity checks  [2]  on the network used in the synthetic dataset for all explanation methods. We refer the reader to Supplementary Material B for these results. Figure  3 : Ablation examples for all explanation methods removing all important pixels from the original image 3a.
Synthetic dataset We created a controlled setting where the joint data distribution is completely known, giving us maximal flexibility for sampling. We generate images of size 100 × 120 pixels with a random number of non-overlapping geometric shapes of size 10 × 10 and of different colors, uniformly distributed across the image. Each image that contains at least one cross receives a positive label, and each image without any crosses receives a negative label. Alongside with the images, we generate the ground truth saliency maps by setting all pixels that precisely lie on a cross to 1, and every other pixel to 0. We generate 8000 positive and negative images, and we randomly sample train, validation, and test splits, with size 5000, 1000 and 2000 images, respectively. We train a simple ConvNet architecture, optimizing for 50 epochs with Adam  [24] , learning rate of 0.
P. vivax (malaria) dataset Moving on to a real and high-dimensional problem, we explore the BBBC041v1 dataset, available from the Broad Bioimage Benchmark Collection 9   [25]. The dataset consists of 1328, 1200×1600 pixels blood smears with uninfected (i. e. red blood cells and leukocytes) and infected (i. e. gametocytes, rings, trophozoites, and schizonts) blood cells. The dataset also comprises bounding-box annotations of both healthy and sick cells. We consider the binary problem of detecting images that contain at least one trophozoite, yielding 655 positive and 673 negative samples. Given the small amount of data available, we augment the training dataset with random horizontal flips, and we randomly choose 120 positive, and equally many negative images as the testing set. We apply transfer learning to a ResNet18  [18]  network pretrained on ImageNet.
LISA traffic light dataset. We finally look at a general computer vision dataset consisting of driving sequences collected in San Diego, CA, available from 10 [21, 32]. The complete dataset counts 43 007 frames of size 960 × 1280 pixels, and 113 888 annotated traffic lights. From this set, we take daytime traffic images, and train a model to predict the presence of a green light in a sample image. We respect the original train/test  As before, we use data augmentation and apply transfer learning on a pretrained ResNet18. We optimize all parameters of the pretrained ResNet18 network for 25 epochs with stochastic gradient descent -learning rate 0. 001, momentum 0. 9. We use cross-entropy loss and learning rate decay of 0. 2 every 10 epochs. After training, we achieve a test accuracy of ≈ 93 %.
Ablation tests As commonly done in literature  [27, 39, 17]  we remove the top k scoring features of all methods by setting them to their expected value, and plot the logit of the prediction as a function of k. For these experiments, we use τ = 0 so as to find all the features that are relevant for the model. Fig. 3  shows ablation results on one example image from the synthetic dataset for all explanation methods. We expect a perfect method to remove all crosses from the image -and only those. We can appreciate how h-Shap removes mostly only the crosses, while other methods also erase other shapes which should not be identified as important. Furthermore, removing more relevant features should produce a steeper drop of the prediction logit. We include the respective curves in Fig. D. 2, depicting that h-Shap's logit curves either drop the fastest towards 0 or provide the lowest logit at complete ablation.
Discussion and Limitations Before concluding, we want to delineate the limitations of h-Shap, the most important of which is its MIL assumption on the data distribution. The methodology proposed in this work is designed to identify local findings that produce a positive global response, accurately and efficiently. These are precisely the important features C analyzed in Sec. 3. This setting is controlled by the ratio of the size of the actual object that defines the label, and the minimal feature size of the algorithm. As an example, Fig. 5  depicts a zoomed-in version of the map produced by h-Shap for one of the samples from the P. vivax dataset, for different values of s. We see that even when s is somewhat smaller than the object, h-Shap still recognizes the important features in the image.
(10) As can be seen, if the model f is linear, and the features are independent, then this expression simplifies to E[ f (X C∪{i} ) -f (X C )] = f ([ XC∪{i} , E[R]]) -f ([ XC , E[R]]), (11) where E[R] is an unconditional expectation which can be easily computed over the training data, and is precisely the fixed baseline we employed in this work. How realistic are these assumptions in our case? First, the cases that we study here approximately satisfy feature independence in a local sense, and it is therefore reasonable to consider the input features as independent when s -the minimal feature size-is greater or similar to the size of the concept we are interested in detecting. Indeed, this is precisely true in the synthetic dataset, where each 10 × 10 pixels shape is sampled independently from the others.
B Sanity checks Some interpretability methods have been shown  [2]  to be unreliable in that they do not truly rely on what the model has learned, i. e. the precise parametrization of f. For this reason,  [2]  advocates for some sanity checks. Following this observation, we perform full model randomization tests on all methods compared in this work. The intuition behind model randomization tests is that if the explanation method actually depends on features learned by the model, the explanations should degrade as model weights are randomized. We perform full randomization tests in the sense that we randomly initialize all the parameters in the simple network used in the synthetic dataset.
e. trade becomes exogenous in the national and regional models and updated pathways are reviewed by local researchers. As in steps 4 and 5, reporting templates and the global database are updated after the trade adjustment. Step 8 Results on each global target are displayed on an online dashboard (Scenathon. org) and depending on the gap between the computed target indicators and the global targets after trade adjustment, the FABLE Consortium members decide if another cycle of nationalglobal interactions should be launched.
Acknowledgments This work was supported by  Norway's International Climate and Forest Initiative , the  MAVA Foundation , and the  Gordon and Betty Moore Foundation. The authors would like to thank the  Food and Land Use Coalition (FOLU)  and the  World Resources Institute  for their support,  Lucie Adenäuer  and  Marluce Scarabello  for their comments on the manuscript, and two anonymous referees for their thorough review of the paper and their constructive feedback.
This ensures the com- parability of the results across countries and allows for aggregating results at the global level. The online platform and com- putational environment of the Scenathon is called the Linker tool. Step 5 The global database is compiled after the backend applications of the online portal automatically compute a set of review quality processes, e. g. on tables structure and num- ber format. Step 6 Export quantities from each exporter are proportionally adjusted to match global imports for each product and each time step, i. e. if total imports are 30% lower than total exports, assumed exports from all coun- tries and regions are decreased by 30% (cf 1) zero net forest loss from 2030 onwards SI). This means that the evolution of total (New York Declaration on Forests, 2014); trade volume is driven by countries' and (2) an increase of at least 15% in the regions' assumptions about the evolution land where natural processes predominate of the internal demand for each product, Figure 1.
Then, this complete skeleton was used as a target for the child morphing. Once the full skeleton and skin shape were defined for the target person in a seated posture, we used the batch Kriging option of the PIPER software to facilitate the morphing. When morphing the child model into our target male adult, at first, only the node coordinates were changed and the mesh was kept the same (number of elements, connectivity). For the bones as well as for the regions away from the regions of interest, a same mesh was kept. For the regions of interest for comfort application such as the buttocks, we refined the mesh size of the soft tissue under the sitting bones with an element size of around 10 mm (Figure  1H ). Then, the mesh was simplified in regions of limited interest (e. g. , internal organs) and made symmetrical.
Here, we use a new global database of hydraulic traits  32  and edaphoclimatic and phylogenetic information coupled with random-forest modelling  33  to estimate drought exposure (P min ) and xylem drought resistance (P 50 and P 88 ) and hence hydraulic risk (HSM), for 44,901 woody plant species. We georeferenced these predictions using species distribution data  34  and mapped aggregated hydraulic metrics for species assemblages at a 5 km resolution, globally. Then, we used linear models to test which metrics of hydraulic risk characterization (species-assemblage mean and minimum hydraulic risk, its variability and the number of species with high hydraulic risk) can predict observed DIM, using precisely georeferenced records of DIM occurrence  3. Finally, we use maximum entropy models  35  to project DIM occurrence probability worldwide using different edaphoclimatic predictors and the newly derived hydraulic metrics.