dataesr/scientific-paragraphs-categorization huggingface dataset:
Transcriptomic correlation analysis The mRNA expression profile of TCGA-COAD, retrieved from cBioPortal, was normalized using the RSEM method  [31]. Gene co-expression network analysis (WGCNA) is a bioinformatics method based on expression data and is typically used to identify gene modules with highly synergistic changes  [32]. We first constructed a gene co-expression network for the mRNA expression profile using the R package WGCNA, during which the soft threshold for the network was set to the recommended value selected by the function pickSoftThreshold (Figure  S1 ). Setting the minimum module size to 100 and other parameters to default, we identified 24 transcriptomic modules (Figure  S2 ). The biological functions of the modules were annotated by the Gene Ontology (GO) over-representation test using the R package clusterProfiler  [33] , during which the Benjamini-Hochberg method was used to adjust P value for controlling false discover rate.
Statistical analysis The ROC curves were drawn using pROC and ggplot2 in R (version 3. 6. 1). The area under the ROC curve and confidence intervals were calculated in pROC. The significance of AUC differences was tested using the Wald test statistic  [36]. The optimal cutoff points of the ROC curves were estimated using the Youden Index  [37]. The Wilcoxon Rank Sum test was used to compare two paired groups and visualized as a boxplot using R package ggpubr. Spearman's rank correlation coefficients were used for correlation analysis.
External validation of EPLA in an Asian-CRC cohort We further measured the generalizability of our model in an Asian-CRC cohort. It was noteworthy that there existed great differences between the Asian-CRC cohort and the TCGA-COAD cohort, not only in patient race but also in the slide preparation techniques (Table  S1 ). As a consequence, the EPLA model trained on TCGA-COAD only achieved an AUC of 0. 6497 (95% CI: 0. 6061-0. 6933) on the external validation data set Asian-CRC (Figure  3A ). Considering the wide variations in medical practice, we therefore applied transfer learning to generalize the EPLA model by fine-tuning our model using only 10% of cases from Asian-CRC, and thus achieved an AUC of 0. 8504 (95% CI: 0. 8158-0. 885) in the remaining data set (Figure  3A-B ). Moreover, we analyzed the performance of the EPLA model for MS status prediction across tumor stages; the EPLA model achieved high prediction performance in both non-metastatic and metastatic CRC cases, with an AUC of 0.
8768 (95% CI: 0. 8427-0. 9110) in the stage I-III subgroup and an AUC of 0. 8242 (95% CI: 0. 7460-0. 9023) in the stage IV subgroup, indicating the robustness of the model in predicting MS status of CRC (Figure  S4 ). We subsequently evaluated the amount of data needed for transfer learning by increasing the proportion of cases from Asian-CRC for model fine tuning. The performance of the fine-tuned model steadily improved, resulting in 0. 8627 (95% CI: 0. 8208-0. 9045), 0. 8967 (95% CI: 0. 8596-0. 9338), 0. 9028 (95% CI: 0. 8534-0. 9522) and 0. 9264 (95% CI: 0. 8806-0. 9722) AUCs in the ratios of 30%, 40%, 60% and 70%, respectively, implying that transfer learning was an effective measure to overcome the heterogeneity between different cohorts (Figure  3C ).
For example, in Kather's study, an MSI classifier trained on TCGA, which was mainly made up of Western populations, and only achieved an AUC less than 0. 70 in the KCCH cohort, a Japanese cohort  [20]. Furthermore, the histology slides in TCGA-COAD were flash-frozen slides that utilize water crystallization during the freezing process, often resulting in an altered appearance of the tissue structure as compared to the FFPE slides used in Asian-CRC which provided more tissue structure clarity. This data difference could not be effectively eliminated by only color normalization (data not shown), indicating that more advanced techniques, such as transfer learning, are necessary. As expected, EPLA showed performance degradation in Asian-CRC by simply applying the model trained on TCGA-COAD, but the results improved significantly after transfer learning.
Although IFN-γ at the same time induces feedback of up-regulation of PD-L1 on both tumor and immune cells, anti-PD-L1 therapy pertinently blocks the suppressive mechanisms, and thus inclines the balance of immune microenvironment to the inflamed phenotype  [45]. Furthermore, we provided evidence of a tight connection of the pathological signatures with antitumor activity from the perspective of transcriptomic profiles, consistent with the relationship between MS status and immunity. The nature of AI models has limitations in our model. The performance of deep learning models largely depends on the size and quality of the training set. We still need to expand the training data to improve the accuracy and generalizability of the model. Although the model has been verified in TCGA and an Asian cohort respectively, a large prospective clinical trial is necessary before we can deploy it as a routine MSI testing method in clinical practice.
Acknowledgments This study was supported by the  National Natural Science Foundation for Young Scientists of China  (No. 81802863 ), the  Natural Science Foundation of Guangdong Province  (No. 2018030310285 ), the  Outstanding Youths Development Scheme of Nanfang Hospital, Southern Medical University  (No. 2017J003 ), the  Key Area Research and Development Program of Guangdong Province, China  (No. 2018B010111001 ) and  Science and Technology Program of Shenzhen, China  (No. ZDSYS201802021814180 ). We would like to thank the  Shanghai Tongshu Biotechnology Co. , Ltd. for MSI detection and we also thank  Dr. Yu-Fa Li  in the department of pathology and laboratory medicine,  Guangdong General Hospital  for helping with pathological phenotype analysis of colorectal cancer.
Hepatitis E virus is a zoonotic pathogen for which pigs are recognized as the major reservoir in industrialised countries. A multiscale model was developed to assess the HEV transmission and persistence pattern in the pig production sector through an integrative approach taking into account within-farm dynamics and animal movements based on actual data. Within-farm dynamics included both demographic and epidemiological processes. Direct contact and environmental transmission routes were considered along with the possible coinfection with immunomodulating viruses (IMVs) known to modify HEV infection dynamics. Movements were limited to 3,017 herds forming the largest community on the swine commercial network in France and data from the national pig movement database were used to build the contact matrix.
Recently, we have also shown, by combining French network indicators with epidemiological data, that the in-degree and ingoing closeness of farms were associated with high HEV withinfarm seroprevalence  [26]. To represent infection spread at a regional or national scale, multi-scale models can be designed by coupling infection dynamics within herds together with interactions between interconnected herds. Such approaches have already been developed, particularly to explore the transmission of bacterial diseases between cattle farms  [27] [28] [29] [30]  or pig herds  [31]. Several approaches have been recently used to implement such models that may be computationally challenging  [32] [33] [34]. In particular, the SimInf package developed in R software is recognized as an efficient and flexible modelling framework for fast event-based epidemiological simulations of infectious disease spread  [32].
The three first types of events (corresponding to the sow reproductive cycle: ges-fa, birth, fa-ges) are always internal (i. e. the animals remain in the same farm), when the three others (corresponding to movements of growing pigs: fa-pw, pw-fi, fi) can be either internal or external (i. e. the animals are shipped to another site). Selecting the pens of destination is a two-step process detailed in Fig 2. First, the type of movement (internal or external) is selected with probability pExt that the animals are shipped to another farm, derived from real movement data (section 2. 1. 3). In case of external movement, the destination site is sampled among the set of possible destination farm from the movement database (see below). When leaving the finishing sector (fi event), two possible pathways were considered for growing pigs: (i) animals leaving FF, PWF and FI farms are sent to the slaughterhouse; (ii) a fraction of females is used for the renewal of the sow population either on the same farm (i.
Data on animal movements between farms. Dataset. French pig movement data recorded during the period 1 st June 2012 to 31 st December 2014 were used to drive the population demographics in the model. The data originated from the National Swine Identification Database (BDporc). The dataset, described in detail in Salines et al. [35] , contained 21,446 farms and 2,382,510 between-farm movement records. Briefly, the main features of all swine holdings in mainland France (continental France and Corsica) were included in the database: identification number (ID), type of holding, type of farming activity, farm size and location. Movements of pigs were reported at the batch level with the following information: farm IDs where animals were loaded or unloaded, round number and chronological sequence of the operations forming the round, batch size and animal category.
In this community, around 78,000 movements occurred over the study period. Data derived from this community were used to feed SimInf population dynamics sub-model. To achieve this task, we first Each time animals have to be shipped from a sector, as defined by the production cycle, the type of event (i. e. internal versus external) is determined according to the probability pExt that is the probability that animals are shipped externally, as defined by the population data. In cases of no free pens found internally (resp. externally), external (resp. internal) movement is considered. If all pens (internally and in contact farms) are full, animals are sent to slaughterhouse. If animals are shipped externally, the destination site is sampled in the contact neighbours of the farm of origin, the probability pCont of a destination farm to be sampled being defined in the population data.
For FA farms, fa-pw movements are always external, so that: pExt faÀ pw i ¼ 1 Similarly, pw-fi movements are always external for FPW and PW farms, leading to: pExt pwÀ fi i ¼ 1 for these two farm types. For the other farm types, one may assume that, for an average-sized farm as designed in the population model, the total number of animals shipped over the study period from a sector a to a sector b is: n a;b average ¼ n days BBI � n pigs batch where n days is the total number of days over the study period, BBI the number of days between two successive batches (i. e. between-batch interval) and n pigs batch the average number of pigs per batch. Denoting R i average the ratio between the actual size of a farm i as recorded in the population data and the average size of the farm i as designed in the population model, the expected number of animals shipped by the farm i from a sector a to a sector b over the study period can be expressed as: nExp a;b i ¼ n a;b average � R i average Let nObs a;b i denote the observed number of animals shipped externally by a farm i from a sector a to the sector b of another farm (as recorded in the population data).
Again, the batch-management system was the same for all farms (seven batches, i. e. three weeks interval) which could be upgraded in the future to make it possible to explore the effect of the batch-management system, which was shown to affect HEV on-farm persistence  [24]. The between-farm demographics was derived from real data recorded in the national pig movement database from 2012 to 2015. These data were incorporated in the model in the form of a contact matrix with probabilities (i) for internal or external transfer (ii) and, in the latter case, for transfer to a given neighbour. By doing so, possible temporal evolutions of the pig movement network were not taken into account, but the descriptive analysis we had previously performed showed a stable structure of the network over the study period  [35].
Museo ToolBox in details Museo ToolBox is organized into several modules (Figure  1 ): • processing: raster and vector processing. • cross-validation: stratified cross-validation compatible with scikit-learn. • ai: artificial intelligence module built upon scikit-learn  (Pedregosa et al. , 2011). • charts: plot confusion matrix with F1 score or producer/user's accuracy. • stats: compute statistics (such as Moran's Index  (Moran, 1950) , confusion matrix, commision/omission) or extracting truth and predicted label from a confusion matrix. The main usages of Museo ToolBox are: 1. Reading and writing a raster block per block using your own function. 2. Generating cross-validation, including spatial cross-validation. 3. Fitting models with scikit-learn, extracting accuracy from each cross-validation fold, and predicting raster.
Artificial Intelligence The artificial intelligence (ai) module is natively built to implement scikit-learn algorithms and uses state of the art methods (such as standardizing the input data). SuperLearner class optimizes the fit process using a grid search to fix the parameters of the classifier. There is also a Sequential Feature Selection protocol which supports a number of components (e. g. a single-date image is composed of four bands, i. e. four features, so a user may select four features at once). See the SuperLearner documentation and examples.
Spatial cross-validation • Spatial Leave-One-Out  (Karasiak et al. , 2019). Karasiak, N. , (2020). Museo ToolBox: A Python library for remote sensing including a new way to handle rasters. Journal of Open Source Software, 5(48), 1978. https://doi. org/10. 21105/joss. 01978 • Spatial Leave-Aside-Out. • Spatial Leave-One-SubGroup-Out (using centroids to select one subgroup and remove other subgroups for the same class inside a specified distance buffer). See the cross-validation documentation and examples.
describes how RasterMath reads a raster, performs the function, and writes it to a new raster. The objective is to allow the user to focus solely on the array-compatible function while RasterMath manages the raster part. See RasterMath documentation and examples. Karasiak, N. , (2020). Museo ToolBox: A Python library for remote sensing including a new way to handle rasters. Journal of Open Source Software, 5(48), 1978. https://doi. org/10. 21105/joss. 01978.
Acknowledgements I acknowledge contributions from  Mathieu Fauvel , beta-testers (hey  Yousra Hamrouni ), and my thesis advisors:  Jean-François Dejoux ,  Claude Monteil  and  David Sheeren. Many thanks to Marie for proofreading. Many thanks to Sigma students:  Hélène Ternisien de Boiville ,  Arthur Duflos ,  Sam Antonetti  and  Anne-Sophie Tronc  for their involvement in RasterMath improvements in early 2020.
2019 ). However, it is important to get a more precise idea of the mass loss rate of such objects to understand the role of the stellar irradiation and its processes of interaction with the planet atmosphere  (Fossati et al. 2018; Allan & Vidotto 2019; García Muñoz & Schneider 2019). This paper reports on the results of the Sensing Planetary Atmospheres with Differential Echelle Spectroscopy (SPADES) program  (Bourrier et al. 2017b (Bourrier et al. , 2018;; Hoeijmakers et al. 2018 Hoeijmakers et al. , 2019)). This program, performed with the HARPS-N spectrograph, is a twin of the Hot Exoplanetary Atmospheres Resolved with Transit Spectroscopy (HEARTS) program based on the use of the HARPS spectrograph  (Wyttenbach et al. 2017; Seidel et al. 2019; Bourrier et al. 2020). Throughout this work, we introduce the python CHESS code (the CHaracterization of Exoplanetary and Stellar Spectra code) and its sub-modules, which allow us to measure, separate, and model the stellar and exoplanet spectra.
The HARPS-N transit observations 1 are described in Sect. 2. In Sect. 3, we propose a comprehensive analysis of spectroscopic transit data and present an improved method to extract transmission spectra. Furthermore, we show a stellar pulsation analysis and a Rossiter-McLaughlin analysis, and we present the Balmer lines signatures we found in the transmission spectrum of KELT-9 b. In Sect. 4, we present a new model and retrieve fundamental atmospheric properties from the high-resolution data observations. In Sec 5, we discuss the constraints we find on the KELT-9b atmospheric temperature, mass loss rate, and local thermodynamic properties. We present our conclusions in Sect. 6.
The HARPS-N dataset We observed two transits of the ultra-hot Jupiter KELT-9 b around its bright A0V host star with the HARPS-N spectrograph  (Cosentino et al. 2012 ) mounted on the 3. 58 m Telescopio Nazionale Galileo (TNG) telescope in the Roque de los Muchachos observatory (La Palma, Spain) 2. We dedicated a full night of continuous observations to each transit of KELT-9 b on 31 July 2017 and 20 July 2018 (see Table  2 ). One fiber recorded the target flux (fiber A) and the other monitored the sky (fiber B). In total, we registered 95 spectra, each with an exposure time of 600 s. The time series cover each transit in full and cover baselines of ∼2-3 h before and ∼1. 5 h after each transit, used to build accurate out-of-transit master spectra. The typical signal-to-noise ratio (S/N) calculated in the continuum around 400 and 600 nm is between 27-131 and 70-190, respectively (no spectra were discarded in the analysis; however, the spectra collected on the second night are affected by a failure in the ADC, making their blue part much noisier than in the first night).
Finally, the CHESS. KING module (KINematic and Geometric properties of the system) handles all the system parameters (see Table  1 ) needed during the data analysis. We identified 44 spectra (22 in each night) fully in transit (i. e. , entirely exposed between the first and fourth contacts) and we considered the 51 remaining spectra out of transit. The HARPS-N raw frames are automatically reduced with the HARPS-N Data Reduction Software (DRS version 3. 5). The DRS pipeline consists of an order-by-order optimal extraction of the two-dimensional echelle spectra. Then, for each exposure, the daily calibration set is used to flat-field, blaze correct, and wavelength-calibrate all the 69 spectral orders. Finally, gen lines, making it a complementary analysis. We note that no Helium (λ10830Å) has been found in  Nortmann et al.
The CCFs are constructed by correlating each spectral order with a weighted stellar mask (a line list containing the main lines of the spectral type of interest,  Pepe et al. 2002). Then, the data are further prepared with the CHESS. ROOK module (spectRoscopic data tOOl Kit). For the KELT-9 observations, we use a customized A0 mask (as in  Anderson et al. 2018)  containing about 600 lines of typically Fe i, Fe ii, and other metal species. The A0 mask is sensitive to both the stellar and planetary spectra. Since the stellar spectrum is rotationally broadened, we computed the CCFs over a 700 km s -1 window around the systemic velocity and with an oversampling step of 0. 082 km s -1 (a tenth of the pixel size). Then, we conducted the CCF analysis on the sum of the CCF of all the 69 orders 3 (Fig.
2 ). The use of a custom A0 mask boost the stellar CCFs contrast by a factor of ∼10 in comparison to the standard DRS reduction, allowing us to have a precision similar to the independent reduction presented in  Borsa et al. (2019). The correction of the telluric absorption lines in each spectrum is the purpose of the CHESS. KNIGHT module (Knowledge of the NIGHtly Telluric lines). In this study, we used the molecfit software, version 1. 5. 7, provided by ESO  (Smette et al. 2015; Kausch et al. 2015). Following the method presented in  Allart et al. (2017)  and applied in  Hoeijmakers et al. (2018 Hoeijmakers et al. ( , 2019)) ;  Seidel et al. (2019) , synthetic telluric spectra were computed with the real-time atmospheric profiles measured above the TNG/HARPS-N location and then fitted to each observed spectrum prior to dividing them.
1 and 2-3 R P for hot Jupiters) is a good approximation of aeronomy models (e. g. , Lammer et al. 2003; Lecavelier des Etangs et al. 2004;   10 Eq. 13. 14 of Heng (2017, Chapter 13) should be corrected as follows: M 2 -ln(M 2 ) -1 = 4 ln(x) + 4 (1/x -1), where the Mach number M = v/c s , and x = r/r s , with c s , r s being the sound speed, and the sonic point radius, respectively. A&A proofs: manuscript no. CHESS  Yelle 2004; García Muñoz 2007; Murray-Clay et al. 2009; Koskinen et al. 2013; Salz et al. 2016 ). Indeed, above the thermobase where the temperature changes dramatically, the temperature encounters a maximum and is varying much slower through several orders of magnitude in pressure than in the lower regions. For the atmospheric constituent structure, we use a precomputed chemical equilibrium table obtained with the chemical equilibrium code of  Mollière et al.
The kinetic broadening in the exosphere would change the shape of the lines, making them asymmetric  (Ehrenreich et al. 2015; Bourrier et al. 2015 Bourrier et al. , 2016;; Allart et al. 2019). This effect has not yet been seen for the KELT-9 b Balmer lines. Thus, additional data gathering, and modeling will be necessary to get a coherent image of the KELT-9 b atmosphere consistent with all the observations.
Acknowledgements. A. W. acknowledges the financial support of the  SNSF  by grant number  P2GEP2_178191  and  P400P2_186765. This work has been carried out within the frame of the  National Centre for Competence in Research "Plan-etS " supported by the  Swiss National Science Foundation (SNSF). This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement number  724427 ; project  Four Aces ). I. S. and P. M acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No. 694513  and  832428 ; ExoplanetBio). L. P. acknowledges that the research leading to these results has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No.
These complexes harbour enzymes responsible for protein ubiquitination and dephosphorylation, respectively  22, 23. Using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database of physical and functional interactions  24  , we found that the targets identified from the screen show significantly higher network connectivity than expected by random chance (p-value < 1. 0e-16) (Supplementary Fig. 2 ). Moreover, we identified categories such as hippo signalling, hedgehog signalling, spliceosome and ubiquitin mediated proteolysis as overrepresented. One of the highest scoring candidates in our screen was dBRD4/fs(1)h (Fig. 1b, e, f ), the only Bromodomain and Extra Terminal Domain (BET) family protein in Drosophila 25. dBRD4, like BRD4 in mammals, produces two distinct isoforms (long isoform dBRD4-L and short isoform dBRD4-S), that both associate with chromatin  25.
Effect of RNAi of a candidate (X) on reporter signal is assayed. Renilla signal serves a control for transfection efficiency. b Data distribution of primary RNAi screen. Scatterplot of Z-scores and luciferase signal (average of two replicates) are plotted for each knockdown (within the Z-score range of -70 to +6). Data points from grey shaded areas were used for secondary screen. Grey datapoints: candidates excluded due to strong effect on Renilla signal (see Methods for more details on filtering and analysis of RNAi screen), orange datapoints: positive control knockdowns, blue datapoints: negative control knockdowns (GST, GFP and Diap1), green triangles: other candidates (dBRD4, Nurf complex and PAF complex). c Scheme of secondary screen reporter assay. Upper part as in a, lower part: Gal4-DBD fused to Gal4 activation domain (Gal4-AD) represents the canonical full length Gal4.