dataesr/scientific-paragraphs-categorization huggingface dataset:
Purification and identification of readthrough proteins by mass spectrometry For readthrough GST purification, the yeast strain was transformed with pYX24-GST vectors and GST was purified as previously described  [16]  unless the yeast was grown in the presence of 50 μg•mL -1 of paromomycin (Sigma Aldrich, St Louis, MO, USA). Mass spectrometry analysis was performed on Coomassie-stained gel bands corresponding to the readthrough GST proteins. Briefly, bands were subjected to in-gel LysN digestion and readthrough peptides were further identified and quantified with a Triple-TOF 4600 mass spectrometer (AB Sciex, Warrington, UK) coupled to the nanoRSLC system (Thermo Fisher Scientific). Proteins were identified with the Mascot algorithm and an in-house database constructed by merging SwissProt with user-generated GST sequences, each harbouring one of the 20 amino acids in the readthrough site.
Gly542*/p. Gly542* patient were obtained by nasal brushing as previously published  [28]  (ClinicalTrials. gov identifier NCT02965326; the study was approved by the Ile de France 2 Ethics Committee, and written informed consent was obtained (AFSSAPS (ANSM) B1005423-40, no. Eudract 2010-A00392-37; CPP IDF2: 2010-05-03-3)). Cells were amplified and conditionally reprogrammed before being seeded onto microporous filters (0. 33 cm 2 Transwell; Corning, New York, NY, USA). Once seeded on filters, cells were cultured on an air-liquid interface and re-differentiated  [29]. When the transepithelial resistance of reconstituted human nasal epithelial cell culture was >600 Ohm•cm -2 , cell cultures were incubated for 48 h with drugs added to the basal side. Cells were treated for 48 h with dimethyl sulfoxide (DMSO) vehicle, or gentamicin (1 mg•mL -1 ) in the presence or absence of VX809 (3 µM).
Drug-induced readthrough levels Basal and drug-induced readthrough levels associated with 15 CFTR PTCs (listed in table  S1 ) were measured in vitro. PTCs were selected based on their position and frequency in the CF population, as referred to in the CFTR2 database (www. cftr2. org). These 15 PTCs represent 86% of the CF alleles containing a stop codon (8368 occurrences out of 9745). Basal readthrough was variable, ranging from 0. 03% to 0. 52% (figure  1a  and table  S2 ). Gentamicin significantly increased readthrough to levels ranging from 0. 14% to 2. 70%. Results were similar in a larger set of 29 PTCs, where four PTCs were found not to respond to treatment (figure  S1a  and table  S2 ). This response was compared to that obtained with negamycin, a structurally different molecule known to promote readthrough  [30].
According to the amino acid incorporated, recoding of the 15 selected PTCs would lead to the production of either WT proteins or protein variants (figure  4  and table  S5 ; topology based on  [34] ). For most of the protein variants, we only considered the major recoded amino acid. For the frequent p. Tyr122*, p. Gly542* and p. Trp1282* mutations, both major and minor recoded amino acids were considered. The p. Trp1282* mutation was not studied as the major recoded protein corresponded to the WT protein, while the minor recoded proteins ( p. Trp1282Arg and p. Trp1282Cys) were known CF-causing mutations leading to nonfunctional channels (CFTR1 Cystic Fibrosis Mutation Database; www. genet. sickkids. on. ca/PicturePage. html). Therefore, 17 protein variants were selected to be characterised, as five are predicted to be recoded to WT (figure  4  and table  S5 ).
Acknowledgements L. J. Galietta  ( Telethon Institute of Genetics and Medicine (TIGEM), Naples, Italy ) is acknowledged for kindly providing the halide-sensitive YFP construct, and  R. Matsuda  ( Dept of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan ) for providing the negamycin. This work benefited from the facilities and expertise of the SICaPS proteomic platform of  I2BC (Institute for Integrative Biology  of the Cell (I2BC),  CEA ,  CNRS ,  Université Paris-Sud ,  Université Paris-Saclay, Gif-sur-Yvette, France ) for the mass spectrometry analysis.
This study was support by the  Agence Nationale de la Recherche  (grant number  ANR 13 BSV8 0012 02 ),  Association Vaincre la Mucoviscidose  (grant numbers  RF20130500893  and  RF20150501332 ) and  Cystic Fibrosis Foundation Therapeutics  (grant number  2014CFF000R-PF000100282 ). Funding information for this article has been deposited with the Crossref Funder Registry. Conflict of interest: None declared.
2001; Wong et al. 2012). In this paper we report a dedicated XMM-Newton observation of AX J0043-737. With the precise position localisation capability of XMM-Newton and its good sensitivity, we classify AX J0043-737 as an AGN behind the SMC. This breaks the decade long mystery on the nature of the source. Using other archival observations we also study its long-term variability. Sect. 2 describes the observations and data reduction, Sect. 3 the results and discussion and Sect. 4 the conclusions and summary.
Observations and data reduction AX J0043-737 was observed with XMM-Newton on 16 October, 2015 (OBSID:0764780201) for 40 ks. In addition we found an archival XMM observation where the source was in the field of view, and also analysed the two ASCA observations of the source. The details of the observations are given in Table  1. XMM-Newton/EPIC  (Strüder et al. 2001; Turner et al. 2001 ) observations were processed with the latest XMM-Newton data analysis software SAS version 16. 1. 0 1. Examination for periods of high background flaring activity was performed by extracting light curves in the energy range of 7. 0-15. 0 keV and removing time intervals with background rates ≥ 8 and 2. 5 cts ks -1 arcmin -2 for EPIC-PN and EPIC-MOS respec- tively  (Sturm et al. 2013). Event extraction was performed using the SAS task evselect applying the standard filtering criteria (#XMMEA EP && PATTERN<=4 for EPIC-pn and #XMMEA EM && PATTERN<=12 for EPIC-MOS).
ASCA observations  (Tanaka et al. 1994)  were analysed following standard procedures 2. Only data from the gasscintillation imaging proportional counters (GIS2 and GIS3) were used for this work, as the source fell in a CCD gap for the X-ray CCD detectors (SIS). Spectra were extracted from the screened events using the FTOOLS task xselect after applying standard filtering criteria.
Timing analysis The PN data of XMM2 were taken in small window mode, providing a time resolution of 5. 7 ms. We searched the PN data in the 0. 3-10 keV energy range for possible periodic signals using a Fourier transform in the frequency range spanning ∼ 0. 00002 -88. 2 Hz. No significant signal was found, with a 3σ upper limit on the pulsed fraction of ∼40% (assuming a sinusoidal profile). We also performed a search with the Z 2 test in a small interval bracketing the ASCA tentative detection (85-90 ms). The most prominent peak occurs at 87. 53022(2) ms, with a 8 × 10 - 7  single-trial probability. When the ∼6 × 10 4  independent period searches are taken into account, its statistical significance is rather low ( 2σ). Since the significance of this signal entirely relies on the marginal ASCA1 detection, which was not confirmed by the ASCA2 data set, we conclude that there is no evidence in the PN data for any periodic signal, not even restricting the search around the ASCA value.
Spectral analysis For the XMM-Newton observations circular regions with radii of 20 and annular regions with inner and outer radii of 40 and 60 were used for the source and background extraction respectively. The SAS tasks rmfgen and arfgen were used to create the redistribution matrices and ancillary files for the spectral analysis. The spectra were binned to achieve 25 counts in each bin and not to oversample the intrinsic energy resolution by a factor larger than 3. For the spectral extraction of the 2 ASCA observations, a different prescription was required owing to the poor angular resolution of the ASCA X-ray telescopes (XRT). The PSF of XRT has a relatively sharp core (FWHM of ∼ 50 ) but broad wings (with half-power diameter of 3 ). The PSF is dependent on the energy, the angle from the optical axis as well as the azimuthal angle of the source position.
This was obtained by fitting the spectrum extracted from XMM1 with an absorbed power-law model (Table  2 ). Comparing the PSFs resulted in a total spillover fraction of 11. 06% from XMMU J004207. 7-734503 into AX J0043-737 and 9. 76% from AX J0043-737 into XMMU J004207. 7-734503 in the energy range of 0. 8-7 keV within the respective source extraction regions. The redistribution matrices for the GIS data were obtained from the HEASARC calibration database 6  7. The ancillary files were generated using the command, arf. The spectra were binned to achieve 25 counts in each bin. X-ray spectral analysis was performed using the XSPEC fitting package, Version 12. 8. 1  (Arnaud 1996). In this section, we report the spectral parameters obtained by fitting XMM2. The total count rates in the source and background regions for XMM2 were 0.
Acknowledgements. The  XMM-Newton  project is supported by the  Bundesministerium für Wirtschaft und Technologie/Deutsches Zentrum für Luft-und Raumfahrt (BMWi/DLR ,  FKZ 50 OX 0001 ) and the  Max-Planck Society. PE acknowledges funding in the framework of the  NWO Vidi  award  A. 2320. 0076. We thank the referee for useful suggestions which significantly helped to improve the paper.
Sample Collection Wild boars and red deer are collected by hunters, under the supervision of the local hunting federations during the hunting season (generally from August to March each year), and badgers are collected by trappers (accreditation required), under the supervision of pest control officers. In infected areas, where one of the control measures is to reduce badger populations, badger can be trapped, mostly from March to August. Field stakeholders (hunters, trappers, pest control officers) submit animals, organs, or tissues (from a standardized list of samples described in Tables  2, 3 ) directly to the local laboratory or store them in cooling rooms or freezers for later analysis. Data is collected for each animal on the species, estimated age (juvenile or adult), sex, date, location of collection and body condition (degradation, presence of lesions in the carcass, etc.
Data Analysis An infected animal is defined as an animal with an analytical result demonstrating M. bovis (or if it had been found M. tuberculosis or M. caprae) by molecular diagnosis or by bacterial culture. All results presented in this article come from the Sylvatub national database. Results are presented by calendar year for badgers and by 12 month period starting with the beginning of the wild boar hunt (August to the end of July). The Sylvatub system was set up in September 2011; as a result surveillance findings for wild ungulates are presented from the 2011-2012 hunting season to the 2016-2017 hunting season and from 2012 to 2017 for badgers. To simplify, at-risk areas are renamed with numbers as follows, by chronological order of detection of TB in wildlife: 1: Brotonne-Mauny forest (Seine-Maritime); 2: Côte-d'Or; 3: Dordogne/Charente/Charente-Maritime/Haute-Vienne/Corrèze/Gironde; 4: Dordogne/Lot; 5: Béarn (Pyrénées-Atlantiques/Landes/Gers); 6: Ardennes/Marne; 7: Marne (Reims mountain); 8: Loir-et-Cher (Sologne); 9: Lotet-Garonne; 10: Pays Basque; and 11: Ariège/Haute-Garonne (Figure  2 ).
Regarding the number of analyzed animals, we counted only those with an interpretable analysis result (positive or negative). Apparent prevalence and 95% confidence intervals were calculated using exact binomial tests and p-values using the Fisher's exact test. Data analysis was performed using LibreOffice Calc (version 5. 2) and R Studio software (version 3. 3. 1). Maps were generated using QGIS software (version 2. 16. 3).
However, it would be also interesting to follow the relationship between the trends observed in some areas in cattle associated with disease control measures and the trend in the prevalence of these wild animal populations. Although the present dataset does not allow precise time comparison between P1 and P2 due to variations of sampling patterns and method for analyses, the ongoing surveillance may contribute to constitute consistent time series that may be analyzed for that concern. Nevertheless, in Côte-d'Or (area 2), if we considered that the apparent prevalence in P1 is underestimated because of the average sensitivity of the culture compared to that of the PCR, one can hypothesize that the prevalence has decreased in badgers from P1 to P2. In Dordogne/Charente (area 3), where both event-based and targeted surveillance in badgers have been effective, we observe that badgers collected by event-based surveillance (mainly roadkilled badgers) were more infected than those trapped.
ACKNOWLEDGMENTS The authors would like to thank the local state veterinary services, the local hunting federations, the local services of the  National Game Hunting and Wildlife Agency , the local laboratories for analysis, the local Animal Health Defense Associations, and  Julie Rivière , the first coordinator of the Sylvatub system from 2011 to 2012. We are grateful to all the trappers and their associations, Pest control officers and their associations and hunters of wild ungulates without whom the surveillance could not have taken place. Thank to  Craig Stevens  for his very useful comments on this article and his careful reading.
Image Processing and Agglutination Indicator Computation An image processing algorithm was developed using Matlab R2013a (MathWorks, Natick, MA, USA) to automatically and quantitatively measure the agglutination  [22]. This agglutination quantification relied on a correlation indicator between different pixel lines. Kinetic results were achieved by computing the indicators for every image. The image processing was performed on the whole picture length within a main region of interest (ROI) of 50 pixels in height for 1:5 diluted bloods and 800 pixels in height for undiluted bloods starting at the line 101 from the V-groove. The first hundred lines have been removed to avoid artifacts due to the edges. The correlation indicator was determined as the correlation between lines distant from five pixels in height for 1:5 diluted bloods and 12 pixels for undiluted bloods.
Acknowledgments: The authors thank Avalun for funding,  Patrick Pouteau (Avalun) ,  Vincent Poher  (Avalun) and  Jean Berthier  ( CEA ) for the microfluidic chip design and all the helpful discussions. We are also grateful to  Gwenola Sabatte ,  Johanna Spiaczka  and  Claire Danjean  ( Avalun ) for the formulation of the reagents and  Guillaume Costa  ( CEA ) for hydrophilic treatment of the microfluidic chip. LETI/DTBS and INAC/SyMMES are parts of the  Arcane Labex program , funded by the  French National Research Agency  ( ARCANE  project no. ANR-12-LABX-003 ).
Broadband SED modeling 3. 1. The CIGALE code CIGALE 1  (Code Investigating GALaxy Emission) is a SED modeling software package that has two functions: a SED modeling function and a SED fitting function  (Roehlly et al. 2014, Boquien et al. , in prep). Even though the philosophy of CIGALE, originally presented in  Noll et al. (2009) , remains, the code has been rewritten in Python and additions have been made in order to optimize its performance and broaden its scientific applications. The SED modeling function of CIGALE allows the building of galaxy SEDs from UV to radio by assuming a combination of modules which model the star formation history (SFH) of the galaxy, the stellar emission from stellar population models  (Bruzual & Charlot 2003; Maraston 2005) , the nebular lines, the attenuation by dust (e.
Conclusions To probe and study phenomena that occur in a very short amount of time, we need a large statistic to be able to catch them in the act. The goal of this work is to identify galaxies that just underwent a rapid (<500 Myr) and drastic downfall of their starformation rate (more than 80%) from broadband SED modeling since large photometric sample can provide the statistic needed to pinpoint these objects. In this purpose, we choose to apply our method on the ZFOURGE sample to provide both statistics and large photometric coverage of the SED from UV to NIR. -We fit the whole ZFOURGE sample using the CIGALE code with two different SFHs: one normal delayed-τ SFH and one truncated SFH, and using  Bruzual & Charlot (2003)  models. We show that a handful of sources are better fit using the truncated SFH, that assume a recent instantaneous break in the SFH, compared to the more commonly used delayed-τ SFH.
Cohort Description A total of 849 women were included in this prospective triple cohort study. Newly diagnosed RA patients (n = 283) were associated with their FDR, and with age-matched healthy controls (Figure  1A ). During the 20 years cohort follow-up, the number of FDR who developed RA was 26 corresponding to 9. 1 cases/1,000/year if considering a median duration followup of 9. 0 years. For the FDR developing RA subgroup (FDR-RA), time from enrollment to RA diagnosis was 38. 4 months (range: 12-123 months) and available data during their followup is depicted in Figure  1B. With the objective to study episodes of infection associated with RA development, a questionnaire was administered at baseline in order to evaluate episodes of infection in the previous year. The questionnaire evaluation was made with the subject by a rheumatologist, and if necessary information were retrospectively completed by exploring the outpatient medical records (see Supplementary Table  1 ).
Next, to delve into the episodes of infection within the previous year, the 26 FDR developing RA, the 257 non-RA FDR, the 283 controls, and the 283 newly diagnosed RA patients were compared. For each specific type of infection, collected data included (i) annual infection prevalence (%); (ii) the total number of infection events per year (incidence), and (iii) the episode duration per year for each specific type of infection in the subject's history (Table  1 ). In addition, the prevalence for viral hepatitis, tuberculosis, and Mycoplasma sp. contamination during pregnancy were also collected.
We have improved and adapted caspo-ts to deal with a midscale Prior Knowledge Network (PKN) with 64 nodes and 178 edges in order to learn the BNs of four breast cancer cell lines (BT20, BT549, MCF7, UACC812) from their time series phosphoproteomic datasets. Importantly, the PKN did not contain any information about the temporal changes or dynamic properties of the proteins. This information was learned from a dataset describing the dynamics of signaling processes for those breast cancer cell lines as part of the HPN-DREAM challenge. In comparison to the current methods that learn signaling networks as Boolean models using static measurements  [9, 10] , and one-time point measurements across multiple perturbations  [11] [12] [13] [14] , our method allows us to handle time series data. A further advantage is the guarantee of discovering optimal BNs, where the distance between original and over-approximated time series is minimal.
This is achieved by using computational solvers such as Answer Set Programming (ASP)  [15]. Our results show that the ASP component of our method allows us to filter the explosion of possible dynamical states inherent to this type of problem, and thanks to that filtering, the model-checking step allows us to provide BNs exactly reproducing the binarized time series data. These BNs are referred to as true positive (TP) BNs. Our results point to measurements in the time series HPN-DREAM dataset that contradict the experimental setting and to perturbations that show contradictory dynamics. We observed that given the same PKN, the solving time was different for each cell line dataset. For cell lines BT20, BT549, MCF7 our method found TP BNs, while for the UACC812 cell line dataset it was impossible to find a TP BN within a time-frame of 7 days.
This computation time difference is due to the different structure of the solution space among cell lines. This could point to the situation where the dataset is not explainable by the prior knowledge network, which may give valuable insights to experimentalists. For example, that the number of consistent experimental perturbations is not sufficient, and that the knowledge of the PKN is incomplete given this dataset. We also show that this method is capable of recovering time series measurements with a Root Mean Square Error (RMSE) of 0. 31, the minimum achieved so far as compared to other participants of the HPN-DREAM challenge. Our method focuses on learning optimal BNs' structures. It does not predict time-series traces of the proteins from the learned BNs. However it detects the minimum distance that is possible to obtain from the proteins of the learned BNs in comparison to the time-series traces in the testing data.
This is the main conceptual difference of our method compared to those proposed by the HPN-DREAM challenge. This difference needs to be considered when comparing the RMSE score. Based on a comparison with the canonical mTOR pathway, we show that the discovered context specific BNs have an average AUROC score of 0. 77. We found 38% of the cell line specific interactions explaining the heterogeneity among these four cancer cell lines, which can be observed in different cell line specific networks, shown in S1, S2, S3 and S4 Figs. All in all, our results show that caspo-ts handles real (HPN-DREAM) datasets, where data points are incomplete and subject to experimental error. Our method is applicable to any kind (gene or protein expressions) of time series datasets measured upon different perturbations.
Related work Regarding the training of BNs with respect to multiple perturbation datasets, CellNOpT (CNO)  [16]  assembles BNs from a Prior Knowledge Network (PKN) and phosphoproteomic datasets. Their tool has been implemented using stochastic search algorithms (more precisely, a genetic algorithm), to suggest multiple BNs explaining the data  [17]. However, stochastic search methods cannot generate a complete set of solutions, hence they cannot guarantee a global optimal solution. In  [11, 12] , the authors overcome this problem by proposing caspo, an approach based on ASP to infer BNs explaining the underlying protein signaling network. This approach can generate all possible optimal Boolean models as compared to the CellNOpt approach. The authors in  [14] , presented a framework based on integer linear programming (ILP) to learn the subset of interactions best fitted to the experimental data.
This data will be discretized in order to link it with further BNs' discovery (ASP solving and model checking steps). Caspo-ts workflow. Caspo-ts receives as input data a prior knowledge network (PKN) and a discretized phosphoproteomic dataset. In this example the phosphoproteomic data consists of two perturbations involving akt (inhibitor) and hgf (stimulus): 1) akt = 0, hgf = 1 and 2) akt = 1, hgf = 0. A black colored perturbation means the inhibitor or stimulus was perturbed (1) while white represents the opposite (0). Readouts are specified in blue and describe the time series under given perturbations. Using this input data, caspo-ts, performs two steps: ASP solving and model checking. In the ASP solving step: (i) a set of BNs, compatible with the PKN, is generated, (ii) afterwards an over-approximation constraint is imposed upon each candidate BN to filter out invalid BNs, that do not result in an over-approximation of the reachability between the Boolean states given by the phosphoproteomic dataset, and finally (iii) BNs are optimized using an objective function minimizing the distance to the experimental measures.