dataesr/scientific-paragraphs-categorization huggingface dataset:
Optimality of the dataset We have found that our original dataset made of arbitrarily chosen glacier/valley topographies to train IGM was far from optimal (e. g. , containing redundant data). Criteria for the optimality of the dataset (in terms of model fidelity to data size) were investigated. First, we attempted (not shown) to use principal component analysis to preprocess and reduce the dimensionality of data while preserving the original structure and relevant relationships [e. g. Brinkerhoff and others, 2021]. However, our iterative selection scheme was found to be more efficient to filter nonrelevant data: the glacier dataset was reduced by about 75% without affecting the emulator accuracy.
Varying basal sliding conditions Real-world glacier modelling requires the tuning of ice flow parameters to observational data. For that purpose, our Stokes emulator can take the basal sliding coefficient as input thanks to an exploration of this parameter in the training dataset. Our results have shown that the interpolation errors between the training states remain fairly low (Tab. 2). Note that while we used spatially constant sliding coefficients for training, our emulator can take a spatially variable sliding coefficient (e. g. , resulting from surface data assimilation) as input field. In such a case, it would be desirable to assess the emulator accuracy against reference solutions based on spatially sliding coefficients to evaluate whether the training data should be augmented with such solutions.
On the other hand, IGM has the following limitations, which call for further development: One must keep in mind that the applicability of IGM is dictated by the dataset used for training the emulator. For instance, the CfsFlow-trained emulator presented here will not be able to model the ice flow of glaciers, whose dimensions exceed the hull defined by the set of training glaciers (Fig. 6 ). The current emulator assumes isothermal ice for simplicity, i. e. , it ignores the effect of ice temperature on ice deformation and basal sliding. In our approach, we have natural inflow and outflow conditions at the border of the computational domain, and no specific other boundary conditions can be prescribed. IGM's ice flow emulator works only with regular gridded data, and using unstructured meshes is incompatible with this strategy.
Acknowledgements We acknowledge Ed Bueler, Fabien Maussion and an anonymous referee for their valuable comments on the original manuscript. We are thankful to Constantine Khroulev and Olivier Gagliardini for support with PISM and Elmer/Ice, respectively. The Python code PyPDD by J. Seguinot has greatly helped the integration of the PDD model in IGM. The emulator was originally motivated to speed-up an ice flow model within the framework of project 200021-162444 supported by the Swiss National Science Foundation (SNSF). Michael Imhof is acknowledged for providing the script that served to produce Fig. 5.
Accumulation is equal to solid precipitation when the temperature is below 0 • C, and decreases to zero linearly between 0 • C and 2 • C. Ablation is computed proportionally to the number of positive degree days  [Hock, 2003]  with factors f i = 8 mm K -1 day -1 w. e. for ice and f s = 3 mm K -1 day -1 w. e. for snow, which are taken from the EISMINT intercomparison experiments for Greenland  [MacAyeal, 1997]  As today's climate forcing, we took the monthly temperature and precipitation from the WorldClim dataset  [Fick and Hijmans, 2017]. Starting from icefree conditions, we linearly decreased temperature at a rate of 1 • C per century until a large icefield covering the tile was built. Then, the cooling was reversed symmetrically into warming at the same rate until the ice has completely disappeared.
) by replicating Icefield A, Aletsch and Rhone glacier transient simulations with IGM. It must be stressed that none of original Icefield A, Aletsch and Rhone glacier simulations were Simulation Data (CPU) Emulator (GPU) generation training Icefield Glacier ∼ 150 h ∼ 625 h ∼ 8 h ∼ 24 h Table 3: Computational costs required for the gener- ation of all datasets on the CPU and for the training of the emulator on the GPU. used for training IGM.
Background Intron retention (IR) occurs when an intron is transcribed into pre-mRNA and remains in the final mRNA. It is a type of alternative splicing that is gaining increased interest in human health and disease research. Originally described in plants and viruses, IR has now been shown to be a common form of alternative splicing in mammalian systems with a major impact on normal biology and disease  [1] [2] [3] [4] [5] [6] [7]. However, detecting IR events poses several specific difficulties. Introns are highly heterogeneous genomic regions, both in length and sequence features. In mammals, IR levels are generally low and thereby subject to incomplete coverage and higher count overdispersion. As a result, software that is not specifically tuned for IR detection generally performs poorly and databases that provide transcript isoform sequences fail to list many IR events  [4, 8].
We previously published a method called IRFinder, an algorithm for detecting and quantifying IR events, that is frequently used as a benchmark for IR detection and quantification  [8] [9] [10] [11] [12]. This software and its associated database have been critical in the detection and interpretation of IR events in numerous studies  [13] [14] [15] [16] [17] [18] [19]. However, building on 4 years of user feedback, it is apparent that IRFinder is lacking features that would enable bench scientists to more reliably identify actionable IR events, share IR data, and dynamically analyze changes in IR levels between multiple samples. We have implemented a suite of features in a new version of our software called IRFinder-S. Specifically, we have (1) created a dynamic database that allows users to perform a meta-analysis, contrast IR from multiple samples, and view IR in an internal browser; (2) created an infrastructure allowing users to share IR detection results from their own samples; (3) implemented a convolutional neural network that analyzes genomic coordinates, as a genome browser would display, and pinpoints IR events that are most likely candidates for further wet-lab analysis; (4) implemented IR detection from thirdgeneration long sequencing technologies; and (5) implemented and tested differential analysis of IR levels between samples.
Results and discussion IRBase enables the visualization and contrast of IR events as well as data sharing It is essential to visualize and contrast specific intron retention events detected by computational approaches before spending resources on their experimental validation. This allows users to understand the transcriptional context of a predicted IR event but also to assess whether the event is common to other cell types or specific to their experiment of interest. We therefore created a web application that allows users to upload their own data, decide whether to keep them private, or share them with other users and visualize the results in a javascript version of the IGV genome browser. We propose two types of tracks to visualize the IR events: a bar mode, showing the ratio values like a BedGraph and an IRFinder track to visualize the abundances of the flanking regions, the number of reads spliced and intron read depth (Fig.
1A  and Additional file 1: Fig. S1 ). These views can integrate results from publicly available datasets and shared data from other users (Fig. 1B ). Currently, IRbase accepts results from hg38 and ENSEMBL annotation and contains 935 cell lines (downloaded from https://portals. broadinstitute. org/ccle). This database is fully integrated within the IRFinder detection tool; users who have predicted IR events using our software are prompted to upload and share their results. By facilitating the upload process and allowing easy integration using flexible labelling of experiments using user-defined tags, we ensure that the database can grow steadily. The database is accessible for meta-analyses across tissue types and conditions and allows users to contrast multiple experiments in one interface.
IRFinder-S integrates long read detection of IR Third-generation sequencing technologies, especially direct RNA sequencing, represent a unique opportunity for the detection, characterization, and validation of IR. Because these technologies are capable of sequencing individual RNA molecules from start to end, they can elucidate the full structure of transcripts with retained introns. As a consequence, long reads can be considered as a means of validating IR predictions obtained from SR data. The increased availability of long reads facilitates the study of splicing structure, including a more reliable identification of IR events. IRFinder-S proposes a dedicated version of the algorithm for long-read sequencing (Fig. 2  and Materials and Methods). In this long-read mode, we make multiple adjustments to the algorithm to account for the specificities of long-read data but also to account for the fact that these reads will often serve as the validation of IR and thus the default parameters are more stringent.
However, this process is time-consuming and variable from user to user. Thus, we tried to reproduce this expert viewing by using a deep-learning approach that would detect these patterns from a dataset of high-quality IR events. To this end, we trained a convolutional neural network (CNN) using high confidence retained introns confirmed by long reads as ground truth. This CNN filter is directly integrated into IRFinder, and it works by transforming coverage data into visual arrays that are submitted to the CNN (Fig. 3A ). To test this approach, we used an inducible cell reprogramming system based on human MCF10A cells that recapitulates the epithelial-mesenchymal transition (EMT, Materials and Methods) for which we had access to both short-and long-read RNA-seq data (Fig. 3B ). In this system, MCF10a cells stably express the EMT-inducing transcription factor Snail fused to the estrogen receptor.
Upon treatment with tamoxifen, the first changes in alternative splicing can be observed as soon as 24h, while a complete cell reprogramming is reached upon 7 days of treatment. We thus used as a training set three biological replicates of untreated epithelial cells and three replicates treated for 1 day with tamoxifen, which corresponds to the first day of the EMT transition. As a first external validation set, we used three biological replicates of cells treated for 7 days with tamoxifen, corresponding to the fully induced mesenchymal-like state. This division aims to validate the model on new IR events that are likely to emerge in the mesenchymal-like state and therefore never seen by the model in the training dataset. As a second external validation set, we used long-read data of GM12878 B-Lymphocite cell lines, provided by the nanopore consortium  [21].
Because there was no short read (SR) dataset provided with this experiment, we used the GM12878 Illumina data from an earlier ENCODE study, processing the data as described in our previous study  [22]. We considered IR events detected in both short reads and long-reads as bonafide IR events to measure true positives (Material and Methods). We trained the model to recognize the true positive introns from the false positive ones in a 10-fold cross-validation procedure. We then evaluated our model on a biologically distinct dataset where the cells had fully transitioned to their mesenchymal-like state. On this independent test set, it achieved a sensitivity of 0. 90 and a specificity of 0. 88, with a balanced accuracy of 0. 89 (Fig. 3B, right ). We then evaluated our model on a different cell line, GM12878, where the model achieved a sensitivity of 0.
81, specificity of 0. 83, and a balanced accuracy of 0. 82. We then benchmarked IRFinder-S against iREAD  [12] , a recent software dedicated to the analysis of intron retention, MAJIQ  [23] , a software designed for the analysis of alternative splicing events that adjust the PSI value of retained intron, and Whippet  [24] , another software that uses fastq files to compute PSI values. These software were selected based on their popularity but also on whether they could output a measure of retained versus spliced out introns. The results are shown in Table  1  and Additional file 1: Fig. S2. It is worth noting that Whippet excludes a high number of introns prior to their quantification steps since it builds its reference based only on known retained introns and would thus be unable to detect rare or unannotated IR events.
Implementation and validation of differential IR analysis In our first version of IRFinder, we suggested methods to analyze differential IR (DIR) using either standalone scripts written in a different coding language or a procedure requiring the user to have extensive knowledge of data transformation and statistical languages such as R. In IRFinder-S, we include IRFinder Diff, an integrated method that allows end to end analysis using either the density-based approach, DESeq2  [25] , or the PSI-based approach, SUPPA2  [26]  adapted for IR ratios (Material and methods). The output can be used in SUPPA2 downstream analysis for clustering analysis for example. Our choice of algorithms was based on the popularity of these two approaches for the analysis of transcriptomic data. We now wanted to test if they were suitable for the detection of differential IR.
Conclusion Until recently, IR detection ran parallel with the analysis of other splicing events without taking into account inherent difficulties in measuring intronic expression. As a result, IR has been systematically underestimated. Despite the recent development of specialized software for detecting IR, the measurement of IR levels has been problematic. Here, we introduce IRFinder-S to overcome major obstacles in IR detection and exploration. These include a database to explore IR in numerous tissue types and share IRFinder results, the addition of a CNN filter to drastically reduce the false-positive rate of IR detection, the inclusion of an experimentally validated approach to detect differential IR, and the ability to analyze long-read sequencing data. In addition, IRFinder-S overcomes many issues unveiled in the last 4 years thanks to community feedback, such as the possibility to give pre-computed low mappability areas, whose creation step takes most of the time during the reference creation, the possibility to link pre-existing STAR reference folders, and a detailed help divided by run modes.
Methods IRbase 2. 0 The new version of IRbase consists in a frontend, implemented with Angular 10, a mySQL database containing the basic information about each sample submitted and the introns having IRratio higher than 0. 05, warning different than "LowCover" and a tag-based aggregation system that allows fast queries to obtain statistics on large number of samples. The backend is implemented in node express version 4. 17. 1. We generated two novel tracks to show IRFinder results (IRFinder-IR-[non]dir. txt files) directly on igv. js, one displaying the IRratio as bedgraph and one that combines the additional information included in the file allowing the representation in detail of the flanking exons, the spliced reads, and the intron depths, as shown in Fig. 1. The user authentication is managed by Google's service firebase and is necessary in order to upload new samples.
Convolutional neural networks The network was trained on the epithelial datasets labeled T0 and T1 (days 0 and 1 of treatment) and validated on the mesenchymal dataset T7, described in our previous work  [22]  and having biological samples sequenced with both unstranded short and stranded long-read technologies. We use IRFinder to analyze the raw data, and for each pair of data belonging to a sample, we selected the introns with IRratio above 0. 05 and no warnings in short reads, as putative IR candidates. We then used the long reads as ground truth of the corresponding intron: we labeled as true positive IR, the introns with no warning, depth (intron abundance + exon abundance) of 25, and IRratio above 0. 1 and as false positive IR, the introns with 50 depth and IRratio of 0. Our rationale is that it is easier to assert the existence of IR events than to assert their absence; thus, we pushed the required depth for negative events to 50 to increase their likelihood of being true negatives.
Benchmark To compare IRFinder's results with the output of iREAD  [12] , Whippet  [24]  (v1. 6. 1) and MAJIQ  [23]  (Build v2. 1-c3da3ce), we used the reference genome hg38 and ENSEMBL v100 annotation, generating the required reference files for each software. We paired the results of each method with the introns of the ground truth determined from the long reads in the test datasets as described in the previous chapter. We used two arbitrary thresholds, 0. 05 and 0. 10, for the PSI values of Whippet and IRFinder's IRratio to classify the introns in IR and non-IR. For what concerns MAJIQ, we considered as no IR the introns without a PSI value adjusted for intron retention and the introns having an adjusted PSI value lower than the two arbitrary thresholds.
Differential intron retention The DESeq2 constructor is used to fit a GLM based on the intronic abundance (intron depth column) and the exonic abundance (the maximum between LeftSplice and RightSplice) to test the fold change of IR between two conditions. The SUPPA2 wrapper uses IRratio values instead of percent splice in (PSI) values, both spanning from 0 to 1, and the exon abundance instead of transcript per million (TPM) values, considering so far the expression of the exons surrounding each intron rather than the average transcript expression. In both cases, the user can decide to remove introns with warnings (by default, introns with LowCover in at least one sample are removed) and to set a threshold on the minimum IRratio that at least one sample has to meet (by default 0. 05). The command line interface offers a simple tool to use DESeq2 or SUPPA2 on two or more sets of samples, requiring only the location of the IRFinder result files IRFinder-IR-[non]dir.
Availability of data and materials Cell line data used to help populate the database was taken from: https://portal. gdc. cancer. gov. Direct RNA Nanopore and Illumina RNA-seq MCF10A samples have been deposited on GEO under accession number GSE126638  [27]. GM12878 cell line, the long read data, was available from the Nanopore consortium at https://github. com/nanoporewgs-consortium/NA12878. We made use of the Run1 (MinION ONT direct-RNA, kit SQK-RNA001, pore R9. 4) generated by the UCSC laboratory. These long reads were corrected using short read data from the same cell line sequenced by a separate consortium. These data were available from the GEO website (https://www. ncbi. nlm. nih. gov/sra/SRX159827). After the quality control using FastQC, we kept and pooled together runs SRR521447, SRR521448, SRR521453, SRR521454, and SRR521455.
Hence, the current methodologies do not capture the potential overlap between brain signatures and clinical manifestations nor the distributed nature of their neural substrate, now familiar from network analyses of functional imaging data  14. Therefore, a comprehensive framework that would systematically associate brain disconnections with cognitive-behavioural assessments is needed for accurate precision medicine  [15] [16] [17] [18] [19]. Modelling distributed relations is computationally expensive and requires large scale data. With advances in data modelling and the availability of databases, tackling the high complexity of clinical-anatomical relationships is now conceivable. Beneath the surface complexity there may lie a simpler order that can be described within a compacted representational space.
The next challenge will be making AI patient-centric for a more effective deployment into the clinical routine and to e ciently bene t patients' quality of life  26. To drive the realisation of this challenge forward, we propose a modelling approach that employs a morphospace to predict neuropsychological assessments of one of the most common neurological disorders: stroke  27. We rst mapped the distribution of 1333 brain disconnection patterns in stroke -the disconnectome morphospace. A second dataset (training set) with rich neuropsychological measures 1year after stroke was imported into this disconnectome morphospace. This second dataset enriched the morphospace with clinical symptoms obtained from 86 neuropsychological assessments. An out-ofsample "validation set" with the same neuropsychological data served to assess prediction accuracy.
This procedure, hereafter referred to as disconnectome symptoms discoverer (DSD), reliably predicted the performance of patients with an average accuracy > 80%. To make the DSD tool readily available to the clinical-academic community and facilitate its incorporation into the clinic, we provide an open-access web application (http://disconnectomestudio. bcblab. com), in which individual disconnection patterns can be uploaded to predict the expected 1-year neuropsychological scores. The web application will be interactively updated, thanks to future crowdsourcing, informing the DSD model with any newly available datasets.
Results The disconnectome morphospace The rst dataset (N=1333 stroke lesions  28  ; see Supplementary Table  1 ) was processed to obtain disconnectome maps. Disconnectome maps quantify the pattern of connections interrupted by each lesion based on the high-resolution tractography of a healthy population  12, 29, 30. Subsequently, the Uniform Manifold Approximation and Projection (UMAP)  31  method was used to embed disconnection complexity. A latent 2 dimensions con guration of the disconnectome maps was obtained. Figure  1  indicates that patients' disconnectome pro les distribute based on lesion location and commonly disconnected tracts. For instance, patients with major left or right hemisphere disconnections were embedded in the right and left half of the morphospace, respectively. Similarly, patients with posterior or anterior disconnections were localised at the top or the bottom of the embedded space.
The composite morphospace The extent to which the disconnectome morphospace can predict different neuropsychological performances is currently unknown. To answer this question, we took advantage of the second independent dataset of stroke patients (N=119 stroke lesions  14  ; see Supplementary Table  1 ) that was extensively explored with standard neuropsychological assessments (N=86, see Supplementary Table  2 ). For each patient of the second dataset, disconnectome maps were calculated and imported into the disconnectome morphospace using the UMAP de ned transformation. To tackle uncertainty, patient coordinates in the morphospace were spatially smoothed (see methods). In so doing, each patient's coordinates in the disconnectome morphospace were converted into probabilities of localisation.
For each patient, the rst three-component of the spatial PCA were entered into a multiple regression analysis to predict single-patient neuropsychological scores 1 year after symptom onset. The multiple regressions created equations, modelling the relationship between each patient's potential localisation in the disconnectome morphospace (i. e. , as de ned by the three rst components of the spatial PCA) and their neuropsychological scores. In so doing, we obtained a composite morphospace that takes advantage of the joint strengths of the two datasets. The composite morphospace accurately and reliably predicted 83 out of 86 neuropsychological scores with a small to large effect size (see Supplementary Table  3 ).