dataesr/scientific-paragraphs-categorization huggingface dataset:
5 MPP). At least 50% of the tile must have been 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 : 2,048 relevant features were extracted using a wide Resnet50 network  [27]  (the bottleneck number of channels is twice larger in every block) trained in a self-supervised fashion with MoCo v2  [28]. This network was trained on 4 million tiles from the TCGA-COAD dataset, with massive data augmentation (random cropping, random flips, colour jitter, random grayscale, gaussian blur), and without using any labels. Feature extractor weights were frozen both for inference and training.
This registration process was done using the Elastix and Transformix softwares  [31, 32]. Non-rigid registration parameters were first computed on sub-sampled WSIs (8μm per pixel), optimising the Mattes Advanced Mutual Information on ten consecutive levels of resolution. Those parameters were finally applied to the high resolution UFS WSI in order to obtain aligned WSIs at identical resolutions. Even though known MSI-related features are found only within tumour regions, we found that applying our model on the whole slide yielded slightly better results. In the table below, we compare the performance of two models : one model trained and validated using only tumour regions of the slide, and MSIntuit which keeps the whole slide for training and validation. tumour regions were defined using a tumour detection model (see section Quality Checks of Material and Methods).
Comparison of Self-Supervised Learning and ImageNet-pretrained approaches. A) ROC curves of our tool's method which uses a feature extractor trained with MoCo v2 on millions of colorectal cancer images (MSIntuit) and a method which uses a feature extractor trained on ImageNet dataset in a supervised fashion (ImageNet), on TCGA, PAIP, MPATH-DP200 and MPATH-UFS cohorts. Apart from the feature extraction part, the same pipeline was used for the two methods (QC, calibration, downstream model etc. ). B) Correlation of the predictions on the same slides on the UFS/DP200 scanners for ImageNet and MSIntuit methods resulted in a Pearson's correlation of 0. 82 (p<0. 001) and 0. 98 (p<0. 001) for ImageNet and MSIntuit methods, respectively.
compared the results of two methods : the first one being the one of MSIntuit which uses a feature extractor trained with MoCo v2 on millions of colorectal cancer images, while the second one uses a feature extractor trained on ImageNet dataset in a supervised fashion. Apart from the feature extraction, the same pipeline was used for the two methods (QC, calibration, downstream model etc. ). Results obtained on TCGA (cross-validation), PAIP and MPATH-DP200 cohorts are reported. Supplemental table 3: Training the model on FFPE slides only versus FFPE and frozen slides of TCGA-COAD. Both FFPE and snap-frozen slides are available for most patients of the TCGA-COAD dataset, the dataset we used for training. Although MSIntuit is intended to be used on FFPE slides, we found that using frozen slides in addition to FFPE ones during Chowder training slightly improved performance when validating the tool on FFPE samples, likely because the Chowder model gained robustness with this augmentation strategy all the while doubling our sample size.
Acknowledgments 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 and  Sarah Ulstrup  for her help in designing the figures.
Supplementary figures 14 Supplemental figure 3. 143 Comparison of Self-Supervised Learning and ImageNet-pretrained approaches. A) ROC curves of our tool's method which uses a feature extractor trained with MoCo v2 on millions of colorectal cancer images (MSIntuit) and a method which uses a feature extractor trained on ImageNet dataset in a supervised fashion (ImageNet), on TCGA, PAIP, MPATH-DP200 and MPATH-UFS cohorts. Apart from the feature extraction part, the same pipeline was used for the two methods (QC, calibration, downstream model etc. ). B) Correlation of the predictions on the same slides on the UFS/DP200 scanners for ImageNet and MSIntuit methods resulted in a Pearson's correlation of 0. 82 (p<0. 001) and 0. 98 (p<0. 001) for ImageNet and MSIntuit methods, respectively.
47 [0. 43-0. 51] Supplementaltable 4 : 4 compared the results of two methods : the first one being the one of MSIntuit which uses a feature extractor trained with MoCo v2 on millions of colorectal cancer images, while the second one uses a feature extractor trained on ImageNet dataset in a supervised fashion. Apart from the feature extraction, the same pipeline was used for the two methods (QC, calibration, downstream model etc. ). Results obtained on TCGA (cross-validation), PAIP and MPATH-DP200 cohorts are reported. Supplemental table 3: Training the model on FFPE slides only versus FFPE and frozen slides of TCGA-COAD. Both FFPE and snap-frozen slides are available for most patients of the TCGA-COAD dataset, the dataset we used for training. Although MSIntuit is intended to be used on FFPE slides, we found that using frozen slides in addition to FFPE ones during Chowder training slightly improved performance when validating the tool on FFPE samples, likely because the Chowder model gained robustness with this augmentation strategy all the while doubling our sample size.
In the table below, we compared the performance of two models : one model trained using only FFPE slides, and another model which uses both FFPE and frozen slides for training (MSIntuit). In the table below, we display the results obtained when validating on FFPE slides of TCGA-COAD (cross-validation), PAIP and MPATH-DP200 datasets (external validation). Training/Testing on tumour regions only. MSI patients, n (%) Specificity 78 (18%) 0. 28 [0. 24-0. 31] 12 (26%) 0. 36 [0. 33-0. 4] 123 (21%) 0. 47 [0. 43-0. 51] dMMR/MSI diagnosis MSI-PCR MSI-PCR MMR-IHC 4-plex, followed by MSI-PCR for indeterminate cases Scanner Aperio Aperio AT2 Ventana DP200 & Phillips Ultra Fast Intellisite Age at diagnosis, IQR 68 (58-77) - 74 (64-82) Grade 1, n (%) - - 219 (39%) Grade 2, n (%) - - 296 (53%) Grade 3, n (%) - - 46 (8%) Stage I, n (%) 70 (17%) - 46 (8%) Stage II, n (%) 157 (39%) - 91 (15%) Stage III, n (%) Cohort 117 (29%) Metric -FFPE Only FFPE & Frozen 281 (47%) (MSIntuit) Stage IV, n (%) TCGA-COAD 60 (15%) AUROC -0.
Moreover, most contaminants are singly charged as well, making identifications of immunopeptides much harder  (10). The nontryptic nature of immunopeptides hampers not only the acquisition but also the identification of immunopeptide spectra. To match each acquired spectrum with the peptide from which the spectrum originated, proteomics database search engines such as SEQUEST  (11) , X! Tandem  (12) , Andromeda  (13) , or PEAKS DB  (14)  generate in silico spectra for all potentially matching peptides. The complete list of peptides that could be in the sample is called the search space. It is important to note that spectra from peptides that are not included in this search space cannot be identified, even though they were acquired. In standard shotgun proteomics experiments, in silico tryptic digestion of relevant proteins yields a broad yet representable search space.
Even though PEAKS DB is not specifically designed for immunopeptides, it is highly interesting due to its de novo-assisted database searches, which tend to work well for large search spaces. Even though these tools can help with immunopeptide identification, they do not use all available information, such as retention time and fragment ion intensity patterns. Previously, it has been proven that integrating retention time predictions in standard proteomics workflows can improve identification rates  (20). Similarly, adding peak intensity predictions to postprocessing tools such as Percolator can also improve identification rates drastically  (21) , which has already been proven to work for immunopeptides as well by efforts such as Prosit  (22, 23). Similarly, tools such as DeepLC  (24)  and MS 2 PIP (25-27) can provide accurate retention time predictions and peak intensity predictions, respectively, to aid in postprocessing.
Training and Evaluation of New MS 2 PIP Spectrum Prediction Models for Immunopeptides To train and test new MS 2 PIP models, five publicly available immunopeptide data sets and one publicly available chymotrypsindigestion data set were downloaded from PRIDE Archive  (29, 30). Similarly, for evaluation on representative unseen data, four distinct data sets were downloaded: (i) a data set containing HLA-I immunopeptides, (ii) a data set containing HLA-II immunopeptides, (iii) the data set with tryptic peptides that was previously used to evaluate the existing MS 2 PIP higher-energy collision-induced dissociation (HCD) models, and (iv) a data set containing chymotrypsin-digested peptide data. The corresponding ProteomeXchange project identifiers as well as the number of unique peptides and HLA patterns for each data set are listed in supplemental Table  S1.
As tandem mass spectrometry (MS2) fragmentation patterns are highly dependent on the instrument, instrument settings, fragmentation method, and any applied labeling methods  (25) , all MS 2 PIP train, test, and evaluation data must originate from experiments with the same experimental parameters. Unlabeled HCD data from Quadrupole-Orbitrap instruments was used, as this makes the newly trained models widely applicable and plenty of training data is readily available on public repositories. For each PRIDE Archive project, the raw mass spectrometry files were converted to Mascot Generic Format (MGF) files using ThermoRawFileParser (v1. 3. 4)  (31). The corresponding identification files were converted to MS 2 PIP input files using custom Python scripts and were further filtered to retain unique combinations of peptide sequence, modifications, and precursor charge at 1% FDR.
Next, all spectra were combined into one MGF file. The universal spectrum identifier (32) was used as unique identifier for each PSM to ensure reproducibility and a one-on-one mapping between peptide identifications and spectra. Data from each PRIDE Archive project was either used as train/test data or as evaluation data to ensure fully independent data sets, except for the chymotrypsin data, where the same project was used to provide both training/ testing data (70%) and evaluation data (30%). This split was made after selecting unique peptide-modification-charge combinations, to ensure no overlap in samples between both splits. Similarly to the 2019 MS 2 PIP models, new models were trained with the XGBoost machine learning algorithm  (33). The Hyperopt (34) , package (v0. 2. 5) was used in combination with four-fold cross-validation for hyperparameter optimization, allowing 400 boosting rounds and early stopping fixed at 10 rounds.
Hyperparameters were optimized for each training data set separately, as well as for b-and y-ion models. All selected hyperparameters are shown in supplemental Table  S2. To evaluate each model, the Pearson correlation coefficient (PCC) was calculated between observed and predicted b-and yion peak intensities for each spectrum. The model performances were further analyzed by peptide length and precursor charge. Ultimately, three models were trained: the immunopeptide model solely trained on immunopeptides, the immunopeptide-chymotrypsin model trained on immunopeptides supplemented with chymotrypsin-digested peptides, and the nontryptic immunopeptide model solely trained on nontryptic immunopeptides. Ultimately, two models were integrated into MS 2 PIP: (i) the immunopeptide model and (ii) the immuno-chymotrypsin model.
The former can be used for immunopeptide peak intensity predictions and the latter for tryptic and more general nontryptic peptide predictions. For further analysis into rescoring immunopeptide PSMs, only the immunopeptide model was used, as it showed the best performance for both HLA-I and HLA-II immunopeptides. To compare MS 2 PIP predictions with Prosit predictions, the same evaluation data sets were used as mentioned above. Prosit (v1. 1. 2) was downloaded from GitHub (https://github. com/kusterlab/prosit) and the hcd_hla and irt_prediction models were downloaded from Figshare (https://figshare. com/projects/prosit/35582). MS 2 PIP predictions were acquired for the general proteomics and chymotrypsin evaluation data with the immuno-chymotrypsin model and for the HLA-I and HLA-II evaluation data with the immunopeptide model.
MS 2 Rescore was validated on a large-scale HLA class I data set  (37) , which was also used to validate the recently published Prositrescoring effort for immunopeptides (PXD021398)  (23). This allows both an evaluation of the improved identification rates due to the new MS 2 PIP models and a straight-forward comparison with Prosit rescoring. First, the msms. txt identification files for the projects' two MaxQuant searches (alkylated and nonalkylated samples), the Prositrescored Percolator output files, and the raw mass spectrometry files were downloaded from PRIDE Archive. The mass spectrometry files were then further processed with ThermoRawFileParser (v1. 3. 4)  (31)  and the PSMs for each of the two MaxQuant searches were rescored separately. Two rescoring methods were evaluated: (i) using only search engine features, replicating a normal Percolator run, and (ii) using the full MS 2 Rescore feature set, including search engine-, MS 2 PIP-, and DeepLC-features.
Evaluation of MS 2 Rescore Across Collision Energy Settings and Peptide Abundances To further analyze MS 2 Rescore performance for various experimental collision energy settings, replicate LC-MS/MS runs were performed on HL60 cells at collision energy values of 25, 27, 30, 32, and 35 NCE (supplemental Methods). The resulting spectra were searched with the Andromeda search engine (MaxQuant v1. 6. 14. 0) against the human UniProtKB-SwissProt (14-09-2020; 20, 388 sequences, Taxonomy ID 9606) database without any enzyme specificity. A minimal peptide length of seven amino acids was required. Oxidation (M) was set as variable modification with a maximum of three modifications per peptide. Mass tolerances were set at 5 ppm and 20 ppm for MS1 and MS2 spectra, respectively. FDR was kept at 100% with the use of a decoy strategy for downstream rescoring with (i) only search engine features and (ii) the full MS 2 Rescore feature set.
Evaluation of MS 2 Rescore on HLA Class II Peptides To validate MS 2 Rescore for HLA class II peptides, another set of raw mass spectrometry files were downloaded from PRIDE Archive (PXD015408). As the uploaded search engine results were already filtered at 5% FDR, the spectra were reanalyzed with PEAKS DB (v10. 5) (14)  with the same search parameters that were used in the original publication, that is, no enzyme specificity, precursor error tolerance of 10 ppm, fragment ion tolerance of 0. 01 Da, with oxidation (M), deamidation (NQ), and trioxidation (C) as variable modifications, searched against the UniProtKB-SwissProt database (01-2021, 22, 235 sequences, Taxonomy ID 10090). The mzIdentML identification file as well as the corresponding MGF files were exported from PEAKS DB and were rescored with MS 2 Rescore with (i) only search engine features and (ii) the full MS 2 Rescore feature set, as described above for the evaluation on HLA class I peptides.
General Data Processing and Data Visualization All plots, unless specified, were generated in Jupyter notebooks (v6. 4. 0) using Python (v3. 8. 3) with the Matplotlib (v3. 4. 2) (39), Seaborn (v0. 11. 0)  (40) , UpSetPlot (v0. 6. 0), and spectrum_utils (v0. 3. 5) (41) libraries.
MS 2 Rescore is Unbiased to Different HLA Classes To evaluate the performance of MS 2 Rescore on HLA class II peptides, another publicly available data set was reanalyzed. However, while for the HLA class I data set human immunopeptides and MaxQuant search engine (13) results were used, for this HLA class II data set, mouse data was searched with PEAKS DB  (14). As was the case for HLA class I peptides, MS 2 Rescore significantly increases the identification rate for HLA class II peptides with 15% and 57% for the 1% and 0. 1% FDR threshold, respectively (supplemental Fig. S11 ). These increases are, however, slightly lower than for the HLA class I data set. Moreover, where previously conventional rescoring showed a significant increase in comparison to search engine rescoring, here, the gain in comparison with no rescoring is lower for both identification rate as well as number of unique identified peptides.
This is likely due to (i) the less extensive search engine features that are calculated for the PEAKS DB pipeline in MS 2 Rescore (supplemental Table  S3 ) and (ii) to the fact PEAKS DB is likely better equipped to identify immunopeptides than MaxQuant due to its de novo-assisted database search  (14). Nevertheless, the full MS 2 Rescore feature set, including peak intensity and retention time predictions, still results in a significantly higher identification rate. Altogether, these results show that MS 2 Rescore generalizes well across HLA class I and class II immunopeptides, across different species, and can boost the performance from different search engines.
DISCUSSION By training new peak intensity prediction models, we were able to greatly enhance immunopeptide identification rate through PSM rescoring. While all newly trained MS 2 PIP models greatly enhance peak intensity predictions for immunopeptides, the model trained solely on immunopeptides performed best. Even though the immuno-chymotrypsin model contained the same immunopeptide train set, the addition of the chymotrypsin-digested peptides did lower the performance slightly. Similarly, not including chymotrypsin-digested peptides in the training data resulted in lower accuracies for the chymotrypsin-digested peptides. Indeed, immunopeptides are generally much smaller and consequently carry a lower charge state; as a consequence, these immunopeptide-specific MS 2 PIP models are not able to predict the behavior of longer and higher charged peptides in the mass spectrometer.
While both immuno-and chymotrypsin-digested peptides are considered nontryptic, their properties can be very different, leading to reduced accuracy of peak intensity of MS 2 PIP when applied on a different type of nontryptic peptides. Surprisingly, while immuno-and chymotrypsin-digested peptides are antagonistic, immunopeptides and tryptic peptides seem synergistic in terms of training data. This comes as no surprise as almost 50% of the immunopeptide training data consists of tryptic peptides. Immunopeptides are thus not necessarily nontryptic. However, the actual occurrence of tryptic peptides in immunopeptidomics samples is most likely much lower. This unrepresentatively sized tryptic portion most likely originated from the tryptic bias in current immunopeptidomics workflows. Indeed, in previous studies, tryptic MHC peptide coverage could rise to 70%  (42).
Acknowledgments -NanoLC-MS/MS instruments were supported by the  French Proteomic Infrastructure  ( ProFI FR2048 ;  ANR-10-INBS-08-03 ). Funding and additional information -R. B. acknowledges funding from the  Vlaams Agentschap Innoveren en Ondernemen  under project number  HBC. 2020. 2205. ; R. G. received funding from the  Research Foundation Flanders (FWO)  [ 1S50918N ]. S. D. and L. M. acknowledge funding from the  European Union 's  Horizon 2020 Programme  ( H2020-INFRAIA-2018-1 ) [823839]; L. M. acknowledges funding from the  Research Foundation Flanders (FWO)  [ G028821N ] and from  Ghent University Concerted Research Action  [ BOF21/GOA/ 033 ]. A. D. received funding from the  Research Foundation Flanders (FWO)  [ 1SE3722 ].
DATA AVAILABILITY MS 2 Rescore is available at https://github. com/compomics/ ms2rescore. All additional code used in this work is available at compomics/ms2rescore-immunopeptidomics-manuscript (github. com). The data used for training and evaluation of the newly trained models, the models themselves as well as the MS 2 Rescore output is available on Zenodo at https://doi. org/10. 5281/zenodo. 6532013. Additional mass spectrometry proteomics data have been deposited to the Proteo-meXchange Consortium via the PRIDE (30) partner repository with the data set identifier PXD033868. Supplemental data -This article contains supplemental data  (6, 37, (43) (44) (45) (46) (47) (48) (49) (50). Conflict of interest -The authors declare that they have no conflict of interest with the contents of the article.
191 Â 10 11 ;  Braucher et al. , 2015). Full process blank 10 Be/ 9 Be ratios of 5. 4 ± 0. 6 Â 10 À15 and 5. 5 ± 1. 0 Â 10 À15 were used to correct 10 Be concentrations of VALP and VSAV/COGNE samples, respectively. The online CREp program  (Martin et al. , 2017 ; https://crep. otelo. univ-lorraine. fr/#/init) was used in order to calculate the 10 Be surface-exposure ages of our samples (Table  1 ), and to re-calculate, for consistency, previously-published 10 Be surface-exposure ages  (Baroni et al. , 2021; see Figs. 4  and 5 for locations and Table  S2  for details). A 10 Be production rate by neutron spallation at sea-level and high-latitude (SLHL) of 4. 16 ± 0. 10 at g À1 yr 1  (Claude et al. , 2014)  was used and scaled at the sampling location with the LSDn scaling scheme  (Lifton et al.
, 2014). Corrections for atmospheric pressure according to the ERA-40 reanalysis data set  (Uppala et al. , 2005)  and for geomagnetic field fluctuations according to the Lifton-VDM2016 geomagnetic database  (Lifton, 2016)  were integrated in the scaling scheme. Field measurements were used to calculate topographic shielding correction at each sampling location based on  Dunne et al. (1999). 10 Be surfaceexposure ages were corrected for an erosion rate of 0. 1 mm ka À1 on the basis of field observations of surface striations and low intergranular relief, suggesting low erosion rates and consistent with previous works conducted in the Dora Baltea catchment  (Wirsig et al. , 2016; Serra et al. , 2022). No snow correction was applied due to the uncertainties in estimating temporal and spatial variability of snow accumulation in mountainous settings.
, 2014) , annual temperature variation amplitudes (dTa) of 8 C and atmospheric temperature lapse rate of 0. 0065 C/m (as in  Protin et al. , 2019). We then performed a trial-and-error process to best fit the observed modern mass-balance data (Fig. S2 ). By simultaneously fitting the two mass-balance datasets, we obtained a melting positive degree day factor (mPDD) of 5. 6 mm w. e. C À1 d À1 that was adopted in the subsequent iSOSIA simulations. Simulations were run using the ice-corrected bedrock DEM of the Dora Baltea catchment with a resolution of 60 m  (Viani et al. , 2020) , and a critical snow accumulation slope (i. e. minimum slope for avalanching) of 0. 7, which was found to best reproduce ice accumulation in cirque glaciers during preliminary tests. We ran simulations with fixed climate forcing until steady-state conditions (i.