dataesr/scientific-paragraphs-categorization huggingface dataset:
Driver regulons Results from SCENIC and scvelo were combined to identify regulons that could be responsible for the transcriptomic induction of driver genes. Similar to the steps mentioned above, SCENIC lists of regulons were used to infer connections between transcription factors and driver gene. Networks were generated as explained above, and annotated with "Active regulon" or "driver gene". The lists of individual driver regulons of each dataset were then combined and the most recurring driver regulons were identified. The code is available at this address: https://github. com/TajbakhshLab/DriverRegulators.
Gene set enrichment analysis Gene set enrichment analyses were performed on either the top markers (obtained from Seurat function FindAllMarkers) or from driver genes (obtained from scvelo), using Cluego  (Bindea et al. , 2009). "GO Molecular Pathway", "GO Biological Process" and "Reactome pathways" were used independently to identify common and unique pathways involved in each dataset. In all analyses, an enrichment/depletion two-sided hypergeometric test was performed and p-values were corrected using the Bonferroni step down method.
The BV421 and Alexa Fluor 405 were used interchangeably to identify the Calcein Blue+ live cells. The PE-Texas Red channel was used to discard mTomato+ cells and Propidium Iodide + cells. The percentage of cells captured by each gate is displayed on each plot. C, E, G, I) Violin plots of gene count, UMI count and mitochondrial fraction for each dataset. D, F, H, J) Gene count and UMI count per cell type for each dataset. A "High count" cluster was found in the E11. 5 dataset, and was removed in downstream analysis. (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Acknowledgements We acknowledge funding support from the  Institut Pasteur ,  Association Française contre le Myopathies ,  Agence Nationale de la Recherche  ( Laboratoire d'Excellence Revive, Investissement d'Avenir ;  ANR-10-LABX-73 ) and  MyoHead ,  Association Française contre les Myopathies  (Grant # 20510 ),  Fondation pour la Recherche Médicale  (Grant #  FDT201904008277 ), and the  Centre National de la Recherche Scientifique. We gratefully acknowledge the  UtechS Photonic BioImaging ,  C2RT ,  Institut Pasteur , supported by the  French National Research Agency (France BioImaging ;  ANR-10-INSB-04 ; Investments for the Future).
( Figure 7. A shared program including Wnt/b-cat activity supports non-myogenic fate transition at various stages and anatomical locations A-D) Velocity UMAP highlighting the non-myogenic cluster at each stage (dotted box), from which the underlying network was inferred. Driver genes and regulatory networks (regulons) were produced for each stage independently, and a stage-specific network of active transcription factor and associated driver gene targets was built. Size of nodes corresponds to the number of edges (connections) they have, i. e. the number of driver genes the transcription factor regulates. E) Histogram displaying frequency of appearance of most predominant transcription factors as driver regulators (4= present in all 4 datasets as driver regulon, 1= present in a single dataset).
Introduction: Rheport is an online rheumatology referral system allowing automatic appointment triaging of new rheumatology patient referrals according to the respective probability of an inflammatory rheumatic disease (IRD). Previous research reported that Rheport was well accepted among IRD patients. Its accuracy was, however, limited, currently being based on an expert-based weighted sum score. This study aimed to evaluate whether machine learning (ML) models could improve this limited accuracy. Materials and methods: Data from a national rheumatology registry (RHADAR) was used to train and test nine different ML models to correctly classify IRD patients. Diagnostic performance was compared of ML models and the current algorithm was compared using the area under the receiver operating curve (AUROC).
Rheport dataset and data pre-processing Prior to completing the questionnaire, users need to register and actively consent that their data is uploaded pseudonymized to the RheumaDatenRhePort (RHADAR) registry  (7). RHADAR is a German real-world rheumatology registry including adult patients. Participating rheumatologists are encouraged to add their final diagnosis to the RHADAR registry. We only included fully completed questionnaires with matched added final diagnosis by the treating rheumatologist. Patients having consented until August 17th, 2020, were included in this analysis. Descriptive statistical analyses were carried out using to describe gender, age, inflammatory marker-, and family history-status, based on patient questionnaires and final diagnosis according to treating rheumatologist (Table  3 ).
Most ML-models work with numerical data only  (13). All the features in the dataset were non-numerical and had to be transcribed. For ordinal features (CRP, ESR, duration of joint swelling and joint complaints) a value was assigned for 10. 3389/fmed. 2022. 954056 every category. For the remaining nominal features one-hotencoding was used. For every category of the feature, a new dummy column was created. The entries in these columns were either 0 or 1. To address the unequal IRD distribution in the dataset [and general population referred to rheumatologists (3, 4)], we used the Python library imbalanced-learn  (14). This library provides different methods for over-and undersampling during training and testing of the ML models, such as SMOTE  (15). The dataset was used for training nine different MLmodels, applying the Python library scikit-learn (16) including: K-nearest neighbor, decision tree, support vector machine, neural network, logistic regression, random forest, bagging classifier, gradient boosting, and AdaBoost.
Model training, testing, and feature importance analysis Using a validation dataset and setting it aside to test the model after training, results in less training data. Crossvalidation avoids this problem and we therefore used a threefold cross-validation approach, where 2 parts acted as the training data while 1 part was used for testing. This was done 3 times, so that every part of the data once acted as the validation data. The prediction error is the average of the 3 computed errors. Cross-validation is also used for hyperparameter tuning. A combination of both is called nested cross-validation. Without nested cross-validation, the same data that would be used for tuning the hyper-parameters and to estimate the model performance, which might result in overfitting. To avoid this, nested cross-validation uses an inner loop for tuning the hyper-parameters and an outer loop for generating the prediction error.
Patient dataset A dataset of 4,897 Rheport questionnaires from seven German rheumatology centers with verified diagnosis was available. After removing invalid data (test vignettes, untitled cases) and duplicate data, another 1,158 patients with missing  final diagnosis and 146 incomplete questionnaires had to be removed. The final dataset consisted of 2,265 patients (see Figure  1 ). Table  3  displays the final diagnostic categories according to the treating rheumatologists. 690/2,265 (30. 5%) of the patients were diagnosed with an IRD, the most common IRD being rheumatoid arthritis 339/690 (49. 1%). Self-reported patient characteristics according to the Rheport questionnaire are listed in Supplementary Material 2. The majority of patients were female (69. 3%) and younger than 60 years of age (76.
4%  (26). In a previous single-center study (4), we compared Rheport to an artificial intelligence-based symptom checker Ada, resulting in a similarly limited sensitivity and specificity of 53. 7, 51. 8, and 53. 7 and 63. 6%, respectively. A strength of the study is the real-world and multicenter nature and size of the dataset used. A strength of Rheport is that only rheumatologists can add their final diagnosis. This represents extra work for the participating rheumatologists but adds high quality data, enabling a continuous improvement of the system  (7). This study has some limitations. The majority of patients did not report any laboratory results. This might have influenced the results, especially regarding the high feature importance. Schneider et al. recently reported the added value of including laboratory parameters for machine learning based classification of inflammatory bowel disease in children  (9).
The high feature importance of inflammatory markers suggests that adding additional laboratory data such as antibody status could improve the accuracy of Rheport. As patients seem eager to use self-sampling  (28, 29) , future research should investigate the integration of self-sampling into online symptom checkers/self-referral tools. In a next study we will analyze whether it makes a difference if the information is entered by patients themselves or referring physicians. Despite the multicenter nature, the results are limited to one country and further validation studies are needed. Especially for more rare IRDs, also only scarcely included in the dataset, the accuracy is likely to be worse. New patient data and matching final diagnoses enable perpetual improvement of the algorithm. Implementing machine learning predictive models into clinical routine raises additional questions such as trust and liability.
Acknowledgments We thank all the patients, and especially  Nils Körber  (by  Nils Körber  and  Joachim Elgas GbR ,  Erlangen ) who developed RheumaDok and the RHADAR database. We would also like to thank all the rheumatological specialist assistants supporting the clinical documentation in the participating centers. The present work is part of the PhD thesis of the first author JK (AGEIS,  Université Grenoble Alpes, Grenoble, France ).
Funding This study was funded by the  RHADAR GbR (A Network of Rheumatologists) ,  Bahnhofstr. 32, 82152 Planegg,  Germany. RHADAR GbR  received honoraria from  UCB Pharma GmbH ,  Sandoz Deutschland/Hexal AG ,  Lilly GmbH , and  Galapagos Biopharma Germany GmbH , and research support from  Novartis Pharma GmbH. The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication. This work was supported by the  Deutsche Forschungsgemeinschaft  ( DFG-FOR 2886 PANDORA-Z01/Z01/C1/A03  to JK, AK, DS, and  GS  and  DFG-Heisenberg ES 434/8-1  to BE).
By crossing our transcriptomic data with those of Drosophila gene expression profile database, we found that Med19-dependent genes are highly enriched with spatially-regulated genes while the expression of most constitutively expressed genes is not affected upon Med19 loss. Whereas globally downregulation does not exceed upregulation, we identified a functional class of genes encoding spatially-regulated transcription factors, and more generally developmental regulators, responding unidirectionally to Med19 loss with an expression collapse. Moreover, we show in vivo that the Notch-responsive wingless and the E(spl)-C genes require Med19 for their expression. Combined with experimental evidences suggesting that Med19 could function as a direct transcriptional effector of Notch signaling, our data support a model in which Med19 plays a critical role in the transcriptional activation of developmental genes in response to cell signaling pathways.
Gene expression profile analyses Expression scores in 58 different Drosophila tissues and developmental stages (detailed S2 Table ) of each genes found significantly expressed in the wing imaginal disc were extracted from the modENCODE Drosophila RNA expression profiling project using DGET tool  [20]  https://www. flyrnai. org/tools/dget/web/. The HK � and SR � gene lists (S4 Table ) analyzed in the Fig 3C-3F were generated as followed. To select for constitutively expressed genes-the HK � genes-using the expression scores matrix, those containing any of 0, 1, 2, 3 score (null or very low expression) in their expression profile were excluded. For SR � genes, the selection criteria were 0 score occurrence � 7 and sum of occurrence of 0, 1, and 2 scores � 52. Heat maps, box plots, violin plots, and statistical analysis were made using Prism9 (Graphpad).
Med19 depletion affects the expression of spatially restricted but not constitutively expressed genes We looked for a link between the expression change upon Med19 AID loss and the expression mode (constitutive versus highly regulated). We extracted from the modENCODE Drosophila gene expression database the expression profiles of each gene we found expressed in the wing imaginal disc, compiling expression scores in 58 different developmental stages and tissues (Fig 3A left panel and S2 Table for details)  [27]. We next used the resulting expression scores matrix to generate heat maps in which the expression profile of each gene (columns in the heat maps shown in Fig  3 ) was sorted as a function of the value of the expression fold change (horizontal axis Fig  3A ). Strikingly, comparing the expression profile heat maps of the genes whose expression is the most affected (Log2FC>1 and <-1) with that of those the less impacted (-0.
Acknowledgments We are grateful to the  genotoul bioinformatics platform Toulouse Midi-Pyrenees and Sigenae group  for providing help and storage resources on the local Galaxy instance http://sigenaeworkbench. toulouse. inra. fr. We thank  M. Aguirrebengoa  from the big-A facility of CBI and  Alexandra Manchego  for their help in bioinformatics and statistical analyses,  J. Favier  for the  CBI Drosophila genome edition facility ,  F. Payre , and  A. Vincent  for critical reading of the manuscript. We acknowledge the  Bloomington Drosophila Stock Center  for providing transgenic flies. Lastly, we thank the Toulouse RIO Imaging platform.
Funding: This work was supported by grants from the  Association pour la Recherche sur le Cancer  (ARC PJA 20141201932-to MB ), the  Agence Nationale de Recherche  ( ANR-16 CE12-0021-01  to MB and HMB), and institutional basic support from the  Centre National de Recherche Scientifique (CNRS)  and  Toulouse III University. AP obtained a PhD fellowship from the  French « Ligue Nationale contre le cancer. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Predatory bug A wild population of the predatory bug N. volucer was collected in 2010 from weeds in Saint Paul, La Réunion and further maintained on a T. vaporariorum colony in the La Coccinelle© facility. Since then, the N. volucer population has been maintained on T. vaporariorum on tobacco in a greenhouse at the company. Specimens of this N. volucer population were examined by a mirid specialist (JC Streito, INRA, CBGP, France) who confirmed their identity. In addition, DNA was extracted from four specimens, and a portion of the gene that encodes COI was used for DNA barcoding of the N. volucer population. Paratypes are available in the insect collection of CIRAD, UMR PVBMT in Saint-Pierre, La Réunion, vouchers no. MATI00012_0101 and MATI00035_0101; GenBank accession numbers KT201360 and KT201350.
Data analysis All statistical analyses were performed with the statistical program R 4. 1. 1 (R Development Core Team, 2021). Different statistical approaches were used, as detailed below in each experimental section: Fisher's exact tests or χ 2 tests, pairwise Wilcoxon rank-sum tests, and the generalized linear mixed model [GLMM, package lme4  (Bates et al. , 2014) ], with deviance tests on fixed effects (based on a χ 2 test) and, if necessary, Tukey's all pair comparison tests [package multcomp  (Hothorn et al. , 2008) ]. A Bonferroni-like correction was applied for pairwise tests. The P-values of significance level are indicated for all tests.
Acknowledgements. We thank  Martial Grondin  (CIRAD),  Elenie Minatchy  and  Mickaël Tenailleau  (ARMEFLHOR),  Louis Dijoux ,  Stéphane Robert ,  Rosaire Tangama  and  Tristan Schmitt  ( La Coccinelle© ) for technical assistance. We thank  Agathe Allibert  ( CIRAD ) for statistical assistance. We thank  B. Jaffee  for his English editing that has contributed to the improvement of this manuscript. The authors acknowledge the Plant protection Plateform (3P, IBISA) where all experiments were conducted. This research is a collaborative research between the three institutions that are part of the  UMT BAT: CIRAD, ARMEFLHOR  and the biofabric Coccinelle©. Financial support. This work was funded by  CIRAD , the  French Ministry of Agriculture (MAAF) ,  Région Réunion  and the  European Union :  European Agricultural Funds for Rural Development (EAFRD).
Wild Fruit Availability Between January 2016 and January 2019, temporal wild fruit availability was evaluated by monthly phenology surveys on 10 transects, each 500 m long, distributed through the Sebitoli chimpanzee community home range  [70]. As many as 445 trees from 46 species known to be eaten by chimpanzees according to long-term SCP data were monitored. For each tree, we attributed a score from 0 (no item) to 4 (maximum) to describe the abundance of fruits, leaves, and flowers. We calculated a monthly food availability index (FAI) for wild fruits only adapted from Hockings et al. [30] : FAI = ∑ G i × F i ∑ G i × 4 × 100 (1) where G i is the basal area of the tree, i, and F i is its abundance score for an item. Some favorite species for chimpanzees were absent from plots or presented a clumpy distribution, such as Mimusops bagshawei  [55].
Energy Gains We focused our analysis on 13 fruits known to be consumed by the Sebitoli chimpanzee community with nutritional data available for the study site (Table  2 ). Fruit collection and the drying process were realized in 2015 by S. Bortolamiol following Rothman et al. [83] , and dried samples were analyzed by S. Ortmann (see Supplementary Materials). We calculated the caloric gains ingested per FNN as below: Gains = Σ(ingestion f requency) j × (time spent) j × DM f ruit j × kcal gDM j (kcal) ( 10 ) where j is the fruit species, and DM is the dry matter.
Due to the lack of nutrition data, we only selected FNNs of 6 h or more when the 13 food items studied covered at least 80% of feeding time to analyze the ingestion rate, i. e. , kcal per minute of feeding, and energy balance. For each response variable, we built a linear mixed-effect (LMM) model including maize availability (maize, non-maize) and wild fruit availability (HFA, LFA) as main effects and their interaction and individuals as a random effects. All assumptions were validated, except residuals normality for DLT, TDEE, ingestion rate, and energy balance, even after basic transformations of our data (log and Box-Cox). By plotting the model residuals per individual, we found extreme intraindividual variations. We therefore tested the significance of the random effect with the anova() function between the mixed model and the null model (without random effect).
Focal Nest-To-Nest Distribution Representing over 3407 h of observation, 640 FNNs were collected (mean duration per FNN = 5 h 17 min; range = 4 min-12 h 27 min) for 27 individuals (12 females and 15 males). The 206 FNNs with a duration superior or equal to 6 h included the monitoring of 20 individuals (three non-maternal adult females (AF), five mothers (MO), nine adult males (AM), and three subadult males (YO); 1 to 35 FNNs per individual, median = 8. 5). The distribution of FNNs according to domestic and wild resources is detailed in Table  3. Index of wild fruit availability. HFA: high fruit availability; LFA: low fruit availability for wild fruits; na: months with missing phenology data (n = 3); * only FNNs with at least 80% of the total feeding time covered by the 13 fruits used in nutritional analysis.
Acknowledgments: We are grateful to  Uganda Wildlife Authority  and the  Uganda National Council for Science and Technology  for giving us the authorization to conduct research at  Sebitoli in Kibale National Park, Uganda. Great Ape Conservation Project ,  Fondation Ensemble ,  Fondation pour la Nature et l'Homme, Fond Français pour l'Environnement Mondial , and  Fondation Prince Albert II  provided financial support for this research. We deeply thank  Jean-Michel Krief , co-director of SCP;  John Paul Okimat , coordinator of SCP; and field assistants for their precious work, particularly  Joseph Alinaitwe ,  Emmanuel Balinda ,  Edward Kalyegira ,  Deogracius Kyomuhangi ,  Wilson Muzahura ,  Abraham Nyakana ,  John Koojo ,  Nelson Tugume , and  John Kakonge  for chimpanzee monitoring and the phonological survey, as well as  Clovis Alikonyera Charles, Phillip , and  Musinguzi Twesige  for crop census.
g. ,  Fisher et al. , 1993; Cas et al. , 2011; Shimizu et al. , 2019). Consequently, there are large uncertainties that require the use of techniques to consider them in probabilistic terms (e. g. ,  Neri et al. , 2015; Rutarindwa et al. , 2019; Tadini et al. , 2022a; Bevilacqua et al. , 2021 Bevilacqua et al. , , 2022)). Several volcanic complexes in the world have produced catastrophic caldera-forming eruptions in the past and may do so again in the future (e. g. ,  Lowenstern et al. , 2006; Larsen et al. , 2007; Aoki, 2008; Williams, 2012; Newhall et al. , 2018; Suñe-Puchol et al. , 2019). The expected recurrence Published by Copernicus Publications on behalf of the European Geosciences Union. rate of M7-8 eruptions at local and global scales has been evaluated from different datasets and through various statistical approaches  (Decker, 1990; Simkin, 1993; Mason et al.
Note that our study is not dependent on accurately knowing the volumes of the Aso-4 or its individual flow units. Indeed the volumes have large uncertainties so we can use such estimates to constrain the range of volumes of interest in the modeling and then back check that the volumes with a high likelihood of reaching the observed distances are consistent with the known facts. This work is organized in six sections. In Sect. 2 we describe the main characteristics of the volcanism of the Aso caldera. In Sect. 3 we briefly outline the first-order integral PDC models used in this analysis and the simplified strategy adopted to account for the shielding effects of topography. In Sect. 4, we present the main results derived from this study, including data elicited by expert judgment (Sect. 4. 1), the minimum PDC volume and mass that would be able to reach the marker sites (Sect.
g. , thermodynamics modeling of cooling effects and air entrainment effects (e. g. ,  Bursik and Woods, 1996; Fauria et al. , 2016) , two layer systems  (Burgisser and Bergantz, 2002; Doyle et al. , 2007; Kelfoun, 2017; Valentine, 2020) , the development of dense and dilute regimes within the same flow (Esposti  Ongaro et al. , 2011 Ongaro et al. , , 2020;; Kelfoun and Gueugneau, 2022; Neri et al. , 2022) , the effects of pore pressure on basal friction  (Roche et al. , 2021; Aravena et al. , 2021) , and the buildup of coherent turbulent structures and gravity waves  (Lube et al. , 2020; Brosch et al. , 2021). However, since more advanced models require more detailed data to fit additional input parameters, simplified models are suitable for probabilistic modeling in uncertain frameworks, like the present Aso-4 case study, especially for capturing extreme probabilities.
In particular, model 1 is single-phase and the calculated volume represents the bulk solid material at the elicited flow density. On the other hand, ,models 2a-c are two-phase and include solid particles and interstitial gas, but the volume calculated only represents the solid phase, with a density that is also elicited and generally greater than the flow density of model 1. Nevertheless, because of the possible topographic effects in the propagation of PDCs, the simulation of 115-145 km runout in the absence of topographic effects is not a sufficient condition to assess the likelihood of marker site incursions for those distances from the source. Therefore, in a second step, we tested the inclusion of the topography in numerical simulations, using the program BoxMapProb (Aravena et al. , 2020), which can be found in https://github.