dataesr/scientific-paragraphs-categorization huggingface dataset:
Thick multi-year ice usually shows high uncertainty. The existence of sea ice is marked by the OSI-SAF ice-type product in CS2SMOS. We use the reprocessing mode CS2SMOS version 203 product, which uses the OSI-SAF OSI-430-b ice concentration product to identify grid cells filled with more than 15% of ice and separate the first-year ice from multi-year ice. The dataset is available for the Arctic winter from October to April, whereas it is unavailable for the melting summer season due to wet surface interference in both Cryosat-2 and SMOS. As a merged dataset, the CS2SMOS is subject to different interpretations because Cryosat-2 provides the absolute thickness over the ice cover, defined as the sea ice volume divided by the ice-covered area fraction in the grid cell. In contrast, SMOS provides the "effective" thickness, similar to the state variable used in neXtSIM for SIT.
OSI-SAF sea ice drift for validation As an independent dataset, we use the OSI-SAF OSI-405 SID product to validate the SID forecast. The product is a daily product obtained from the measurements of passive and active microwave instruments that have a horizontal resolution varying from 10 to 15 km in the Arctic. The SID is retrieved by the continuous maximum cross-correlation method  (Lavergne et al. , 2010)  and is provided on a polar stereographic regular grid of 62. 5 km resolution every 48 hours. We only validate the model ice drift against this product where uncertainties on the observation-based estimates are lower than 2. 5 km/2days in the winter period. The discarded high-uncertainty observations are primarily located near the north pole due to fewer observations and near the ice edge where ice drifts so fast that it reaches the limit of which the continuous maximum cross-correlation method can handle.
Perturbations of atmospheric and ocean forcing As a crucial component of the ensemble-DA system, the forecast ensemble is generated by neXtSIM through randomly perturbed atmospheric forcing and model parameters (ice cohesion in the present case), plus randomly perturbed oceanic forcing. The former two perturbations have been investigated in  Cheng et al. (2020) , which shows that although the sea ice model is nonlinear, the modeled SID is less sensitive to the initial model state than to the atmospheric forcing applied throughout the run. In addition, uncertainties from the ocean processes (the TOPAZ4 data) are also considered in this study. In the atmospheric forcing dataset, we perturb the horizontal 10 m wind velocities, the downward longwave radiation, and the snowfall rate that are considered to be dominant factors for a winter period.
In the ocean forcing dataset, the SSS and SST are perturbed since they are known to impact SIC and SIT through thermodynamic processes. Using the same perturbation system as TOPAZ4  (Sakov et al. , 2012) , the perturbations are spatio-temporally correlated with a decorrelation time scale of 2 days and a horizontal decorrelation length scale of 250 km, with the random field generator introduced by  Evensen (2003). The random perturbation fields have standard deviations of √ 3 m/s for wind speed, √ 50 W/m 2 for the longwave radiation, 100% for snowfall rate (using an unbiased lognormal distribution), 0. 1 • C for SST and 1 PSU (Practical Salinity Unit) for the SSS. To limit the impact of SST on the ice formation around the ice edge, we multiply the SST perturbations by the open water fraction in a grid cell.
Following  Sakov et al. (2012) , the perturbations of horizontal wind velocities are derived from a surface-level pressure perturbation with a non-divergent constraint. The non-divergent constraint prevents the sea ice cover from breaking up excessively. In addition to perturbing the upstream forcing dataset, the ice cohesion field is initialized as a heterogeneous random field following a uniform distribution between 20 and 40 kPa without spatial correlation  (Cheng et al. , 2020) , which is different for each ensemble member. The cohesion field is kept constant unless a remeshing occurs, in which case the cohesion on the newly created model grid cells is given as the average of their nearest neighbors.
Instead of using the CS2SMOS built-in uncertainty, we define the observation error variance σ 2 obs,SIT , referring to the empirical formula of  Xie et al. (2018) , as an increasing function of ice thickness h ice , σ 2 obs,SIT =      min(0. 2, 0. 02e 1. 8(hice-3) ), h ice > 3m, max(0. 02, 0. 1e 1. 5hice ), otherwise. (2) The coefficients in Eq. (  2 ) are fine-tuned based on a 3-year-long dataset of observations.
The Deterministic Ensemble Kalman filter (DEnKF) We use the DEnKF  (Sakov and Oke, 2008)  to assimilate the SIT and SIC observations. Unlike the stochastic EnKF, the DEnKF does not require the observations to be perturbed to avoid filter divergence. DEnKF is also conceptually more straightforward compared to other transform-based deterministic EnKFs (see  Carrassi et al. , 2018 , for the differences between various EnKF flavors). The open-source DA software, EnKF-C version 2. 8. 0  (Sakov, 2014 ) is adopted to perform the DA. The EnKF-C provides the functionality for automated quality control and ensemble inflation and localization, which are briefly recalled below.
Moreover, we observe a large reduction of the (both positive and negative) model SIT biases by assimilating the CS2SMOS SIT data (Section 6. 3). Specifically, the experiment SIT7 cuts the model SIT bias almost to zero, and its corresponding RMSD is the lowest among all experiments. The SIT errors are also reduced in the joint SIC-SIT assimilation experiments, although to a lesser extent, which implies that the apparent benefit of multivariate DA becomes detrimental when assimilating both SIT and SIC, which is further discussed latter. Furthermore, small improvements on SIT are visible when assimilating SIC only, SIT7, which agree with the small effects from multivariate DA found in the data assimilation framework of the coupled ocean-ice model, ROMS-CICE  (Fritzner et al. , 2019). Although the improvements on the SID are less apparent, the model performs on par with the earlier results with direct insertion  (Williams et al.
It indicates a promising direction for the future development of the neXtSIM-F forecast system distributed through the EU Copernicus Marine Environmental Monitoring Service for the Arctic, using an ensemble-DA framework instead of the current deterministic data assimilation approach. We recommend that it is sufficient to assimilate the CS2SMOS SIT product weekly for the Arctic winter sea ice forecasts to significantly enhance the SIT forecast and slightly improve the SID forecast. However, the assimilation of SIC in a stand-alone model is limited by the accuracy of the ocean boundary conditions, and our attempt to include the slab ocean SST and SSS in the state vector has not been successful. In future work, in implementing the presented method in the operational system, an evaluation will be carried out on multi-season and multi-year forecasts.
Indeed, this first study focused on the winter season due to the limited availability of CS2SMOS products. The situation would be more complicated in the summer scenario since the sea ice dynamics are much more active. In particular, we do not expect the free run to be as skillful in predicting SIE as it is in the winter, see  Williams et al. (2021) , leaving more room for improvements thanks to DA, and the assimilation of SIC could be more effective. In agreement with the findings in  Williams et al. (2021) , the overall increase in performance on predicting SID when using DA The stand-alone sea-ice data assimilation system inherits warm biases from the ocean forcing, which limits the efficiency of SIC assimilation. This should be improved in a coupled ice-ocean model to be pursued in further work.
Acknowledgements. We thank  Dr. Pavel Sakov  for helpful discussions and improvement regarding the EnKF-C code and  Dr. Jiping Xie  for contributing the TOPAZ interface to sea ice observations. We thank the  EUMETSAT OSI-SAF centre  for providing the sea ice concentration and SID data, https://osi-saf. eumetsat. int/. The production of the merged CryoSat2-SMOS sea ice thickness data was funded by the  ESA  project  SMOS & CryoSat-2 Sea Ice Data Product Processing and Dissemination Service , and data from 9/2019 to 4/2020 were obtained from  AWI. The work is funded by the  DASIM-II grant  from  ONR  (grant no. N00014-18-1-2493  and  N00014-18-1-2204 ). AC, CJ, AA, and PR acknowledge the support of the project  SASIP  funded by  Schmidt Futures - a philanthropic initiative that seeks to improve  societal outcomes through the development of emerging science and technologies.
Data availability. The ECMWF atmospheric data is available on the ECMWF website (https://www. ecmwf. int/en/forecasts/datasets/set-i# I-i-a_fc). The TOPAZ4 data is available from Copernicus Marine Environment Monitoring Service -The Arctic ocean physics analysis and forecast data (https://resources. marine. copernicus. eu/product-detail/ARCTIC_ANALYSIS_FORECAST_PHYS_002_001_a/INFORMATION). The OSI-SAF SIC (SSMIS) data is available at https://osi-saf. eumetsat. int/products/osi-401-b. The OSI-SAF SID data is available at https://osi-saf. eumetsat. int/products/osi-405-c.
Depletion of T cells in Tau(P301S) mice via intraperitoneal administration of anti-CD4 and anti-CD8 antibodies is su cient to ameliorate brain atrophy and improves behavior, suggesting that in ltration of T cells in the brain parenchyma is a key step in neurodegeneration. However, it remains to be demonstrated that a similar association between neurodegeneration and adaptative immune response occurs in the brain of patients with AD. In this work, I took advantage of the largest single-nucleus RNA-sequencing (snRNA-seq) dataset available from patients with AD and age-matched controls  [7]  to characterize the innate and adaptative immune responses in the brain of patients with AD at different pathological stages. I show that the number of T cells increase in the brain of patients with AD at late compared to early Braak stages.
This phenomenon coincides with changes in microglial subpopulations and activation of signaling mechanisms between microglia and T cells, including the CXCL16 pathway. Transcriptomic data from 40,000 cells originally annotated as microglia/peri-vascular macrophages (Micro/PVM) was downloaded from the Chan Zuckerberg CELL by GENEhttps://cellxgene. cziscience. com/collections/1ca90a2d-2943-483d-b678-b809bf464c30.
Progressive increase in the proportion of T cell populations in the brain of patients with AD To further characterize the adaptative immune response in the human brain with different degrees of tauopathy, I separated cells of this response from the complete dataset and clustered them again using unsupervised clustering (Fig. 2A ). I detected 11 clusters that could be annotated into 8 major cell types/subtypes based on the expression of top markers (Supplementary Table  3 ) and cell-type speci c markers such as CD3D, CD3G, CD8A and CD8B (CD4 + and CD8 + T cells), NKG7 and GNLY (natural killer -NK cells), CD19, JCHAIN (B cells), MRC1, CD68 and CD14 (macrophages), S100A8, CD68 (neutrophils), FCGR3A and NR4A1 (monocytes) (Fig. 2A-B ). Using GLM, I found that cells from cluster 0 (CD8 + T cells) were 1.
Shift of T cell states correlates with the increase of DAM populations in the brain of patients with AD To characterize microglial states associated with Tau pathology in the brain of patients with AD, I performed the unsupervised clustering of microglial cells from the original dataset and identi ed 14 microglial clusters (Fig. 3A ). The relative proportion of cells in all these clusters signi cantly changed according to Braak stages (Fig. 3B ), indicating a high dynamic of microglial states during Tau pathology progression. The odds ratio of cells belonging to clusters 2, 4, 5, 6, 9, 10, 12 and 13 showed the higher increments (up to 2-to 38-folds) between Braak stages (Supplementary Table  6 ). Next, I took advantage of CellID  [9]  to extract microglia gene signatures at single-cell resolution using a large set of genes previously identi ed in well-de ned conditions  [1, 3, 12].
In addition, the aberrant expression of the mitochondrial protein FASTKD1 in aggressive forms of acute lymphoblastic leukaemia, impacts mitochondrial activity and drives an oncogenic epigenome reprogramming  [15] [16] [17]. Finally, the out-of-context activity of ATAD2, a gene predominately expressed germline/ embryonic stem cell, affects histone chaperone-bound chromatin dynamics and could promote oncogenic genome reprogramming  [18] [19] [20]. Aberrant activation of a placenta-specific gene, ADAM12, favouring tumour invasion, activated following an oncogenic signalling pathway, is another example  [21]. Additionally, there are accumulating data in the literature showing that some of the known "official" Cancer Testis genes (CTdatabase, http:// www. cta. lncc. br)  [22] , also significantly contribute to malignant cell transformation  [23] [24] [25].
Many tissue-specific genes are frequently activated in breast cancer Using the publicly available RNA-seq data of several normal tissues provided by GTEX and NCBI Sequence Read Archive, we computed the patterns of expression for all annotated genes. To identify genes with a tissue-predominant expression, we used an outlier detection technique based on the Z-score as presented in the Methods section. In total, 1882 tissue-specific genes encoding for testis, placenta or ESC were found. The patterns of expression of the 1882 selected genes in normal tissues are shown in Supp. Figure  S1A. The tissues where each of these genes are predominantly expressed (testis, placenta, ESC) are listed in Supp. Table  S1. A Venn diagram in Supp. Figure  S1B  presents the number of genes that show a shared tissue-specific expression pattern.
For each of the 1882 tissue-specific genes, we used the transcriptomic data from breast non-tumour tissues (where these genes are silent) of the dataset TCGA-BRCA to establish a threshold of signal below which the gene was considered as not expressed. The threshold of expression was defined as the mean signal + two standard deviations calculated in the nontumour breast samples. Based on this threshold value, for each gene, we measured the proportion of breast tumour samples where the gene was expressed (signal over the threshold, considered as ON). The results are shown as a heatmap in Fig. 1. These data confirm that, as expected, many tissue-specific genes are aberrantly expressed in a number of breast tumours, with variable frequencies, depending on the gene and the breast tumour molecular subtype.
In this case, the expression level of the genes expressed in epithelial cells may be underestimated in the non-tumour samples due to their low representation. To accurately address this point, one would need to analyse single cell data which are unfortunately unavailable in the TCGA-BRCA cohort. For this reason, we selected the genes potentially activated in tumours in this preliminary step and then we relied on a thorough full machine learning approach described in next section to accurately identify the thresholds of gene activations during the survival analysis.
A combination of five ectopically expressed genes robustly predicts patients' prognosis in four independent test datasets For each of the selected 626 tissue-specific genes, we performed a survival analysis as described in the Methods section. Briefly, we identified the genes for which it was possible to establish a stable activation threshold (defining the "OFF/ON" status of the corresponding gene), associated with patients' survival prognosis, based on criteria explained below. This approach separated patients in two groups with significantly different survival probabilities. For this purpose, we used a specific dedicated approach based on machine learning principles to reduce possible artefacts and overfitting issues in survival analysis. The main steps of the method are summarized in Supp.
These constraints were implemented through a formal machine learning pipeline separating all breast cancer cohorts into the training, validation and test datasets. In addition, we used a technique of random threefold cross-validations to introduce perturbations of the subsets of samples during the training step. The details are provided in the Methods section. The calculations were performed in several steps and described below. First, in the training dataset TCGA-BRCA we identified 28 genes for which a stable threshold was detected and the activated status of the gene (ON) was significantly associated with a shorter disease-free survival probability (p-value < 0. 05, FDR < 0. 2). In the second step, we used three validation datasets GSE25066, GSE21653 and GSE42568 to include a possible heterogeneity across different datasets in the learning process and to evaluate the robustness of the selected genes.
These 28 genes were then ordered according to the p-values obtained by the logrank tests in each validation dataset (Supp. Table  S3 ). These results showed that the gene DNMT3B was found significantly associated with disease-free survival in all three validation datasets (p-value < 0. 05). Another four genes EXO1, MCM10, CENPF and CENPE were found significantly associated with disease-free survival in 2 of 3 validation datasets and in the third dataset the obtained p-value was also relatively low (p-value < 0. 1). We selected these five genes as candidate biomarkers. For all other genes, we obtained high non-significant p-values in at least one dataset. These genes were considered not sufficiently robust; therefore, they were not validated. All five genes have normal predominant expression profile in embryonic stem cells; they are also expressed in testis at lower levels (Supp.
The results of the GEC performance in the test cohorts are shown in Fig. 2E-H. Our new GEC tool accurately predicted patients' disease-free survival prognosis in all test datasets, providing significant p-values < 0. 05 for the logrank test between two groups GEC 0-1 (favourable prognosis) and GEC 2-5 (unfavourable prognosis), as well as for Cox proportional hazard model considering the number of activated genes in the GEC as an explanatory variable. For information, we also presented the results of the GEC tool in the training dataset (Fig. 2A ) and in the validation datasets (Fig. 2B-D ) that were also significant, as expected.
Multivariate analysis shows that the GEC tool provides complementary information to known risk factors To evaluate the impact of the new GEC prognosis tool compared to other known risk factors in breast cancer, we performed a multivariate survival analysis using Cox proportional hazard model with the following explanatory variables: GEC, age, molecular subtype and tumour stage. For this analysis we selected the datasets with available annotations of the molecular subtype. Five datasets were eligible: TCGA-BRCA, GSE25066, GSE21653, E-MTAB-365 and Yau-2010. When the information about age or tumour stage was missing in some datasets, we used the available covariates. The results of the multivariate analysis are shown in Table  1. In all considered datasets, the GEC tool provided significant and stable prediction of disease-free survival probability while corrected for other risk factors.
The GEC tool identifies patients with a high risk of relapse inside the luminal-A and luminal-B subtypes In five datasets TCGA-BRCA, GSE25066, GSE21653, E-MTAB-365 and Yau-2010 we identified the GEC status for all samples and then performed survival analysis separately in each molecular subtype of breast cancer. Figure  3A -B show the distribution of all samples (five datasets pooled, n = 2681) in luminal-A, luminal B, HER2-enriched and basal-like subtypes according to the number of activated genes in the GEC panel. We found that the majority of tumours in luminal-A molecular subtype, which is generally considered of a good prognosis, does not express any of the five genes of the GEC panel (68. 1%) or only one gene of five (18. 5%) as shown in Fig. 3A. However, 13. 3% of tumours in this subtype still express between 2 and 5 GEC genes, corresponding to aggressive forms of breast cancer (Fig.
3B ). Survival curves in Fig. 3C  shows that this particular group of patients have a significantly higher risk of relapse compared to other patients with luminal-A subtype of breast cancer. Similarly, in luminal-B molecular subtype, the GEC tool is able to identify a subset of patients with a significantly higher risk of relapse (Fig. 3D ). These results indicate that the GEC tool can be particularly interesting to use in clinical practice to efficiently identify the fraction of patients with a high risk of relapse inside the group of a priori favourable disease-free prognosis. In HER2-enriched and basal-like subtypes, respectively, we didn't find significant association between the GEC status and disease-free survival (Fig. 3E-F ). This result is probably due to an uneven distribution of the molecular subtypes in the training dataset, containing a majority of luminal A subtype (53.
Fig. 3  Results of the GEC tool in molecular subtypes of breast cancer. A: Distribution of breast cancer samples for five pooled datasets (TCGA-BRCA, GSE25066, GSE21653, E-MTAB-365 and Yau-2010) in luminal-A, luminal B, HER2-enriched and basal-like subtypes according to the number of activated genes in the GEC panel. The bar plots show the percentage of samples for each GEC group (from GEC 0 to GEC 5) in each molecular subtype. B: Same for the groups GEC 0-1 and GEC 2-5. C-F: Kaplan-Meier survival curves showing disease-free survival probability in luminal-A, luminal B, HER2-enriched and basal-like subtypes, respectively, according to the number of expressed genes in the GEC panel, presented in two groups: GEC 0-1 and GEC 2-5. The p-values obtained from the logrank test and Cox proportional hazard model as well as the hazard ratios are displayed on the top of each plot.
Gene Set Enrichment Analysis (GSEA) shows shared molecular signatures of the aggressive GEC + tumours in several breast cancer datasets Differential expression analysis and the corresponding GSEA were performed in ten independent breast cancer datasets (see Supp. Table  S4 ) between the group of tumours without GEC ectopic expressions (GEC-) and those with major GEC ectopic expressions of 4 or 5 genes (GEC +). The intermediate group of tumours classified in GEC 1-3 was excluded from the differential analysis. We considered here only the extreme GEC groups in order to identify the most striking differences in molecular signatures between the aggressive tumours GEC + and the tumours of favourable prognosis GEC-. The survival curves of the groups GEC-and GEC + in the dataset TCGA-BRCA are shown in Fig.
4A  as an example. The heatmap in Fig. 4B  illustrates the expression of the genes down-and up-regulated in GEC + versus GEC-breast cancer samples with an absolute fold change of expression values above 1. 5 and an adjusted Mann-Whitney p-value < 0. 05. Respectively, 1593 and 1301 genes were down-and up-regulated in the dataset TCGA-BRCA. In order to characterize the molecular profile of GEC + aggressive breast tumours, we performed Gene Set Enrichment Analysis (GSEA) for all 10 datasets to highlight biological pathways correlating with GEC + compared to GEC-samples (Figs. 4C  and 5 ). The GSEA profiles of the aggressive GEC + form of breast cancer revealed a significant and consistent up-regulation in gene sets involved in cell proliferation and cell cycle progression. In addition, the GEC + tumours were found significantly enriched in the signatures of metastatic breast cancers prone to develop distant metastases in brain and lung.