dataesr/scientific-paragraphs-categorization huggingface dataset:
Model evaluation. OoBNet was evaluated on the test dataset, which was used neither for model training nor validation. Furthermore, external evaluation was done on two independent and multicentric datasets as described above. The predictions of OoBNet were compared to human ground truth annotations. The performance of OoBNet was measured as precision, recall, F1-score, average precision, and receiver operating characteristic area under the curve (ROC AUC). Precision is the proportion of true positives among all positive predictions (true and false positives), also referred to as positive predictive value. Recall is the proportion of true positives among all relevant predictions (true positives and false negatives), also referred to as sensitivity. F1-score is the harmonic mean of precision and recall.
Results OoBNet was trained, validated, and tested on an internal dataset of 48 videos with a mean duration ± standard deviation (SD) of 123 ± 79 min. containing a total of 356,267 frames. Thereof, 112,254 (31. 51%) were out-ofbody frames. External validation of OoBNet was performed on a gastric bypass dataset of 10 videos with a mean duration ± SD of 90 ± 27 min. containing a total of 54,385 frames (4. 15% out-of-body frames) and on a cholecystectomy dataset of 20 videos with a mean duration ± SD of 48 ± 22 min. containing a total of 58,349 frames (8. 65% out-of-body frames). The full dataset statistics and the distribution of frames across training, validation and test set are displayed in Table  1. The ROC AUC of OoBNet evaluated on the test set was 99. 97%. Mean ROC AUC ± SD of OoBNet evaluated on the multicentric gastric bypass dataset was 99.
94 ± 0. 07%. Mean ROC AUC ± SD of OoBNet evaluated on the multicentric cholecystectomy dataset was 99. 71 ± 0. 40%. The full quantitative results are shown in Table  2. Confusion matrices on the test set, the multicentric gastric bypass dataset, and the multicentric cholecystectomy dataset are displayed in Fig. 3A-G. OoBNet was evaluated on a total of 111,974 frames, whereof 557 frames (0. 50%) were falsely classified as inside the body even though they were out-of-body frames (false negative predictions). Qualitative results illustrating false positive and false negative predictions of OoBNet are displayed in Fig. 4. A video with qualitative results of OoBNet is provided in the Supplementary (Supplementary Video S1, which illustrates how endoscopic videos can be anonymized using OoBNet).
Discussion This study reports the development and multicentric validation of a deep learning-based image classifier to detect out-of-body frames in endoscopic videos. OoBNet showed a performance of 99% ROC AUC in validation on three independent datasets. Using the provided trained model or the executable application, OoBNet can easily be deployed to anonymize endoscopic videos in a retrospective fashion. This allows to create video databases while preserving the patient's and OR staff 's privacy and furthermore facilitates the use of endoscopic videos for educational or research purposes without revealing any sensitive information. To our knowledge, OoBNet is the first out-of-body image classifier trained on videos of multiple interventions and validated on two external datasets. Previous work by our group used an unsupervised computer vision approach to identify out-of-body frames.
Based on the redness-and brightness-levels of images, they were classified at an empirically set threshold as inside the body or out-of-body  24. Zohar et al. used a semi-supervised machine learning approach to detect out-of-body scenes in a large dataset of laparoscopic cholecystectomy videos yielding a 97% accuracy  25. However, this previous study has two major limitations. On the one hand, the main performance metric reported is accuracy. Accuracy is sensitive to the data distribution, or the prevalence of a given observation. On the other hand, it was trained on a dataset of a single intervention type only. This does not ensure that the model generalizes to other intervention types. Typically, image classifiers are trained to distinguish visually distinct classes. Classifying images of an endoscopic video as inside or out-of-body seems analogous.
However, between inside and out-of-body there is a transition where the camera is moved inside or outside the body which might appear ambiguous. Therefore, the definition when an image is inside or out-of-body is crucial. We defined that the valve of the optic trocar is the visual cue for the transition from inside to out-of-body and vice versa. To err on the side of privacy protection as soon as the valve is visible the frame is considered as out-of-body even if the camera is still inside the optic trocar. Using a LSTM module in the model architecture we took in to account the temporal context of inside and out-of-body frames and avoided misclassification in the phase transition from inside to out-of-body and vice versa due to phase flickering. Despite OoBNet's excellent performance, even in external validation, the model has its limitations.
S1 ). As qualitative results show (Fig. 4 ), the performance of OoBNet was limited if the endoscopic vision was impaired by surgical smoke, fog, or blood. Furthermore, OoBNet predicted false positive results when objects (mesh, swabs, tissue) were so close to the camera, that the vision was blurred, and even for a human annotator it was difficult to distinguish whether a given frame is out-of-body or not. Further work to improve OoBNet's performance would include a model training on a larger set of those edge cases with impaired endoscopic vision. Moreover, OoBNet predicted false negative results if an out-of-body frame visually resembled an inside scene. Manual inspection of all false negative predictions (n = 557) on all test datasets revealed three privacy sensitive frames, where OR staff potentially could have been identified.
However, out of 111,974 frames OoBNet was evaluated on not a single frame revealed the identity of the patient, the time, or the date of the intervention. Nevertheless, videos anonymized with OoBNet need manual revision to ensure medical confidentiality before they are stored, shared, or published. Though, OoBNet reduces the time needed for manual revision as false negative predictions are often situated in temporal proximity to true positive predictions. In external validation OoBNet showed a drop of up to 6. 7% points F1-score. This is in line with results from multicentric validation of other AI models in the surgical domain. For example, state of the art surgical phase recognition models have demonstrated variable performance in multicentric validation  26, 27. Furthermore, EndoDigest, a computer vision platform for video documentation of CVS in laparoscopic cholecystectomy, showed a 64-79% successful CVS documentation when validated on a multicentric external dataset compared to 91% successful CVS documentation on the internal dataset  14, 20.
Therefore, the performance of AI models trained and evaluated on a single dataset should be regarded cautiously, and these results further highlight the need for external validation of AI models. Our model, however, has shown to generalize well on videos from several external centers. The significance of OoBNet lies in its high reliability to identify of out-of-body frames in endoscopic videos. OoBNet is trained on a set of highly diverse endoscopic surgeries, including robotic surgeries, to account for the different visual appearances of anatomy, instruments and ORs. Furthermore, OoBNet is evaluated on two independent datasets to show its ability to generalize across centers. OoBNet is publicly shared as a tool to facilitate privacy preserving storing, handling, and publishing of endoscopic videos.
In conclusion, OoBNet can identify out-of-body frames in endoscopic videos of our datasets with a 99% ROC AUC. It has been extensively validated on internal and external multicentric datasets. OoBNet can be used with high reliability to anonymize endoscopic videos for archiving, research, and education. False positive model predictions (OoBNet predicts the frame to be outof-body even though it is not). Bottom row: False negative model predictions (OoBNet predicts the frame to be inside the body even though it is out-of-body). Below each image the binary human ground truth annotations and the probability like model predictions are provided. In (A), surgical smoke is impairing the vision. In (B-D), a mesh, a swab, and tissue are so close, that-lacking the temporal context-it is difficult to distinguish even for a human annotator whether it is out-of-body or not.
Acknowledgements Joël L. Lavanchy  was funded by the  Swiss National Science Foundation  ( P500PM_206724 ). This work was supported by  French  state funds managed by the  ANR  within the  National AI Chair program  under Grant  ANR-20-CHIA-0029-01 (Chair AI4ORSafety ) and within the  Investments for the future program  under Grant  ANR-10-IAHU-02  ( IHU Strasbourg ). A prior version of this manuscript was deposited on arXiv: https:// doi. org/ 10. 48550/ arxiv. 2301. 07053.
, 2008). The JC classification is an objective scheme based on 9 daily grid-point mean sea-level pressure data. It is an extension of the Lamb classification from 10 to 27 circulation weather types (CWTs). The method was adapted to the Iberian Peninsula in accordance with the proposal by  Spellman (2000)  and moved 5º to the east  (Gilabert and Llasat, 2017). The CWTs are calculated using the surface pressure of the NCEP/NCAR reanalysis dataset. CWTs: Cyclonic (C), Anticyclonic (A), pure advectives (N, S, E, W, NE, SE, NW, SW), hybrid cyclone-advectives (CN, CNE, CE, CSE, CS, CSW, CW, CNW), hybrid anticycloneadvectives (AN, ANE, AE, ASE, AS, ASW, AW, ANW). 𝑅𝑀𝑆𝐸 = & 1 𝑁 )(𝑀 ! -𝑂 ! ) " # !$%. % " ⁄ 𝑀𝐵 = 1 𝑁 )(𝑀 ! -𝑂 ! ) # !$% 𝑅 = ∑ 1𝑀 ! -𝑀 ´31𝑂 ! -𝑂 ´3 # !$% 4∑ 1𝑀 ! -𝑀 ´3" # !$% 5 % " ⁄ 4∑ 1𝑂 ! -𝑂 ´3" # !$%.
Acknowledgments This work has been made possible thanks to the financial support of the  European Research Council (ERC)   Consolidator  project:  Integrated System Analysis of Urban Vegetation and Agriculture  ( 818002-URBAG ), the  Spanish Ministry of Science, Innovation and Universities , through the " Maria de Maeztu" programme for Units of Excellence  ( CEX2019-000940-M ), and the funding and recognition awarded to the research group Sostenipra ( 2021 SGR 00734 ) by the  Department of Research and Universities of the Generalitat de Catalunya. This work also has been granted by the  Spanish Ministry of Science and Innovation MCIN   AEI/10. 13039/501100011033  under contract  PID2020-113614RB-C21 , by the  Spanish government  under grant  PRE2018-085425  and by the  Catalan government  under contract  2021 SGR 00574.
To avoid the potentially confounding effects of autosomal inversion polymorphisms, our initial inference by PCA relied on the inversion-free X chromosome. Given the pan-African distribution of An. funestus, previously published genomic variation data from Ghana and Uganda  (23)  were included to provide broader context (SI Appendix, Table  S1 ). The analysis revealed two main clusters (Fig. 1A ). One contains all 70 of the Burkina Faso An. funestus that would have been classified as Folonzo by inversion karyotyping using the deterministic algorithm of Guelbeogo et al. (26) , as well as another 22 Burkina Faso An. funestus that would have been Burkina Faso-F Burkina Faso-K Burkina Faso-Un Ghana Uganda ! 3R 3L Admixture proportion 0 1 0 1 X 2 R 2 L K F A C B Fig. 1. Genetic structure of An. funestus mosquitoes sampled from Burkina Faso.
We estimated demographic parameters under the chosen IIM model using two independent methodologies, ABC and an alternative approach based on Generative Adversarial Networks (GAN) implemented in the software pg-gan  (40). The latter uses real data to adaptively learn parameters capable of generating simulated data indistinguishable from real data by machine learning  (40). It operates directly on genotype matrices and avoids reducing genotypes to summary statistics, unlike ABC. Importantly, genetic data simulated under the IIM model by pg-gan and ABC produced similar summary statistics (SI Appendix, Text, and Table  S8 ). If the chosen IIM model closely approximates the true demographic history, we expect that the observed data and data simulated under the IIM model should have comparable summary statistics.
To verify this expectation, posterior predictive checking was performed by simulating 10,000 new datasets with population parameter values drawn from the posterior distributions of the population parameters estimated from the retained pg-gan runs. Summary statistics were calculated using noncoding regions only, under the assumption that noncoding regions are less affected by selection. The median of each observed summary statistic was compared with the distribution of the 10,000 simulated test statistics. We calculated the percentile for each median value within the distribution of the simulated data statistic. All observed medians fell within the 17 to 84% percentiles for K, and the 20 to 59% percentiles for F, providing confidence in the IIM model. Under the IIM model, we estimated that the ancestral K-F lineage split from an outgroup population of An.
Prospects for a Molecular Diagnostic Tool for Ecotype Identification. To our knowledge, formal morphometric analyses have not been conducted on the K and F ecotypes in Burkina Faso. However, for all practical purposes, K and F are morphologically indistinguishable, and we have shown here that chromosome inversion-based identification tools are imprecise at best. Both future evolutionary ecology studies, and vector surveillance for malaria epidemiology and control, will critically depend upon molecular diagnostics, which are lacking. Motivated by this goal, we began by assembling a complete ribosomal DNA (rDNA) gene unit by leveraging the long single-molecule (PacBio) database developed during construction of the An. funestus FUMOZ AfunF3 reference genome  (35)  (SI Appendix, Text). Noncoding spacer regions of rDNA have historically proven fruitful targets for identifying fixed SNP differences between closely related anopheline sibling species  (57) , owing to the uniquely rapid evolutionary dynamics of the tandemly arrayed rDNA genes.
Despite near-complete success including a substantial amount of intergenic spacer upstream and downstream of the transcription unit (SI Appendix, Table  S12 ; GenBank accession OP870452), alignment of genomic sequences from K and F to the rDNA assembly yielded no evident fixed differences (SI Appendix, Fig. S14 ), consistent with their early stage of divergence. As an alternative, we screened the genomic sequence data for fixed differences between K and F, using variation (VCF) files compiled separately for K and F samples. This resulted in a total of seven fixed SNP differences, all of which mapped to intronic positions of a single gene (the GPCR AFUN019981) on chromosome 2R, spanning ~15 kb (SI Appendix, Fig. S15 ; for alignment, see Data, Materials, and Software Availability). The 5′-end of the predicted mRNA lies only ~2.
Role of Inversions in Ecotypic Role of Standing Genetic Variation in Ecotypic Differentiation. Our demographic inference suggests a remarkably recent split between K and F ecotypes, approximately 1,300 y ago. Such a rapid time-frame is by no means unprecedented, as the well-studied case of host shifting in Rhagoletis pomonella from downy hawthorn to domesticated apple is as recent as 150 y ago  (62). However, it begs the question of how ecotypic differentiation, which is expected to involve allelic variants at multiple loci underlying polygenic traits, could arise so quickly on the basis of new mutation alone. The pattern that is emerging from a growing empirical database is that many of the adaptive alleles as well as adaptive genomic architectures (e. g. , chromosomal inversions) implicated in ecotype formation are considerably older than the ecotypes themselves  (4) (5) (6).
Data analysis A data analysis strategy was adopted from  (18)  and was used in our previous studies  (5) (6) (7). RT was calculated as the time between the appearance of a number on the screen and the first button press. The median of RT (mRT) was calculated for each of the five blocks. In order to normalize participants' baseline performance, mRT from blocks 2-4 was divided by the mRT from block 1 and multiplied by 100% (mRT as a percent of the mRT of block number 1, mRT%). This conversion allowed us to compare differences in the learning dynamic despite the discrepancies in RT between subject groups. Two major indices were chosen to reflect implicit motor learning tasks. The first is the decrease of mRT% across the first and the last block containing the sequence (difference between mRT% of blocks no.
Two-way ANOVA was used to evaluate the effect of the group and the hand on the indices of implicit motor learning (the difference between mRT% of blocks no. 2 and 4 and the rebound) and mRT% of blocks 1-5 during the limited response time SRTT variant. The daily dosage of antipsychotics was converted to the equivalent of olanzapine according to  Leucht et al. (20). Associations between demographic and clinical variables (age, years, education, equivalent of the daily dose of olanzapine, and the duration of treatment) and the indices of implicit motor learning were calculated by a series of simple linear regression analyses. ANOVA, effect size, and posthoc comparison were performed using the rstatix package. For other analyses, functions from the stats package were used. For the visualization of the results, ggplot2 and ggpubr packages were used.
Sample molecular classification Mouse samples were classified using corresponding human homolog genes, obtained using the biomaRt R package (v2. 40. 5, genome assemblies GRCm38. p6 and GRCh38. p13) [26]. Lund taxonomy (LundTax) and BASE47 classes were obtained using BLCAsubtyping (v2. 1. 1) [7]. Consensus subtypes were determined with the ConsensusMIBC (v1. 1. 0) R package  [7]. The UROMOL study NMIBC classes were obtained using the online application  [27].
Gene expression changes during progression To identify significant expression changes during progression, we used an R package designed for RNA-seq time-course data, Next maSigPro (v1. 56. 0) (see Supplementary materials and methods). Genes with significant temporal changes were clustered into k = 2 to k = 8 solutions. Functional enrichment was evaluated in each k solution using the clusterProfiler (v3. 12. 0)  [28]  and ReactomePA (v1. 28. 0)  [29]  R packages. The lowest k solution capturing the most patterns of co-expression changes and grouping biologically related genes was selected as the final clustering solution (k = 5).
A gene set with increasing expression during Ba/Sq carcinogenesis identifies NMBIC patients at risk of progression We then explored the clinical relevance of cluster 4, a set of genes with a coordinated upregulation starting in early carcinogenic stages. We first investigated the expression of cluster 4 genes in different stages of human BLCA, using a publicly available dataset combining both NMIBC (n = 213) and MIBC (n = 95) samples  [35]. Unsupervised hierarchical clustering using the expression of all human orthologs of cluster 4 genes distinguished two groups with distinct cluster 4 metagene scores (mean-centered expression of all cluster 4 genes), which were associated with stage (Figure  5A ). Further investigation confirmed that cluster 4 metagene score significantly increased in a stepwise manner with stage in human tumors (Figure  5B ).
Comparison of functional enrichment associated with the observed dysregulated genes revealed common biological processes (Figure  6C ). Both datasets pointed to terms related to EMT as well as to downregulation of EGFR signaling in sarcomatoid samples. To compare the changes in EMT in mouse and human tumors, we calculated a previously published EMT score  [32]  in both our BBN and Ba/Sq human samples. Sarcomatoid cases had a significantly higher EMT score than non-sarcomatoid samples in both datasets (supplementary material, Figure  S6A ), further demonstrating the EMT profile of sarcomatoid tumors in our model and its relevance to the human sarcomatoid variant.
82, p = 0. 001 in our BBN cohort). In contrast, EGFR regulon activity was negatively correlated with EMT in both mouse and Ba/Sq human cohorts, but not in non-Ba/Sq human samples (supplementary material, Figure  S6E, F ). FGFR1 expression alone was also highly correlated with EMT score in a larger MIBC dataset (TCGA, n = 408, R = 0. 82, p < 2. 2 Â 10 À16 ) as well as in BLCA cell lines (CCLE, n = 36, R = 0. 85, p = 6. 9 Â 10 À11 ), suggesting a stroma-independent FGFR1 expression (supplementary material, Figure  S6G, H ). Sensitivity to FGFR1 knockout was significantly correlated with both FGFR1 expression and EMT score in BLCA cell lines (supplementary material, Figure  S6I, J ) in a genome-wide CRISPR screen (n = 29, Achilles Project, Broad Institute). As previously suggested  [39, 40] , EGFR knockout had no effect on viability in cell lines with the highest EMT scores, including in cells with the highest sensitivity to FGFR1 inhibition (Figure  6E ).
Acknowledgements We would like to thank  Elodie Chapeaublanc  for her assistance in data management and GEO submission, and  Clémentine Krucker  for digital histology slide data management. This work was supported by a grant from  Ligue Nationale Contre le Cancer (JF, LC, YA, FR IBP)  as an associated team (Equipe labellisée); the ' Carte d'Identité des Tumeurs' program  initiated, developed, and funded by  Ligue Nationale Contre le Cancer ; and by the  French Institut National du Cancer INCa  ( PRT-K 2013 ,  Programme de Recherche Translationnelle en Cancérologie , ' DIATRIBBE '). JF was supported by the  Fondation ARC  pour la recherche sur le cancer and by  Groupe Pasteur Mutualité. LC was supported by the  Fondation Médicale pour la Recherche. High-throughput sequencing was performed by the  ICGex NGS platform of the Institut Curie  supported by the grants  ANR-10-EQPX-03  ( Equipex ) and  ANR-10-INBS-09-08  ( France Génomique Consortium ) from the  Agence Nationale de la Recherche  (' Investissements d'Avenir' program ), by the  ITMO-Cancer Aviesan (Plan Cancer III) , and by the  SiRIC-Curie program  ( SiRIC Grants INCa-DGOS-465  and  INCa-DGOS-Inserm_12554 ).
Applied to the magnetometer data of the Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft from November 2014 to February 2020 (MY32-MY35), we detect structures closely resembling MMs lasting in total more than 170, 000 s, corresponding to about 0. 1% of MAVEN's total time spent in the Martian plasma environment. We calculate MM-like occurrences normalised to the spacecraft's residence time during the course of the mission. Detection probabilities are about 1% at most for any given controlling parameter. In general, MM-like structures appear in two main regions, one behind the shock, the other close to the induced magnetospheric boundary, as expected from theory. Detection probabilities are higher on average in low solar EUV conditions, whereas high solar EUV conditions see an increase in detections within the magnetospheric tail.