dataesr/scientific-paragraphs-categorization huggingface dataset:
The temporal patterning gene regulatory network is a privileged target of the COMPASS and PRC2 complexes in tumors To further elucidate the mechanisms causing the enrichment of Imp + CSC-like tNBs under the perturbed tumor conditions, we focused on the genes that were differentially expressed in the population of Imp + tNBs. Using Seurat R package (62), we computationally isolated for each condition the populations of Imp + tNBs by selecting the clusters encompassing most Imp + E93 -tNBs (clusters highlighted in red in fig. S5 ). Then, the selected population of Imp + tNBs for the three perturbed conditions was respectively compared to the population of Imp + tNBs of the control condition to identify differentially expressed genes (data S2). Reactome pathway analysis of the differentially expressed genes indicated that the respiratory electron transport genes of mitochondria are up-regulated in Imp + tNBs of the trx RNAi and E(z) RNAi trx RNAi conditions compared to control Imp + tNBs (Fig.
Image acquisition and processing Images were acquired on the Zeiss LSM 780 confocal microscope with Zen software. Cell counting and tumor volumes were estimated using, respectively, FIJI (Fiji is just ImageJ) Multi-point tool and its 3D Object Counter plugin  (78). For figures, the scale is indicated in the figure legends, and all images are single confocal sections unless otherwise stated in the legend. To compare the mitotic rate of control and trx RNAi tumors, the average area of tNBs under the two conditions was measured using the segmentation algorithm Cellpose (79) based on the cortical staining of Mira. Segmentation showed that tNBs in trx RNAi tumors are, on average, about 20% larger than tNBs under the control condition (32. 26 m 2 versus 27. 05 m 2 , respectively). Extrapolation of the cell area to cell volume, considering tNBs as spheres, leads to an approximate 30% difference (137.
Statistical analysis Quantifications for tumor volume, percentage of Imp + tNBs, the number of PH3 + tNBs, and the number of Dcp1 + tNBs were repeated identically and independently at least two times and given as dot plots and boxes and whiskers. As in most experiments, less than 30 samples were collected, and each result was statistically analyzed with an appropriate nonparametric statistical test using GraphPad Prism version 8. 1. 1 for Windows (GraphPad software, La Jolla, CA, USA; www. graphpad. com). A P value lower than 0. 05 was considered as statistically significant (*P ≤ 0. 05, **P ≤ 0. 01, ***P ≤ 0. 001, and ****P ≤ 0. 0001).
Single-cell data processing Single-cell mRNA sequencing data were analyzed using the 10x Genomics suite Cell Ranger 2. 0. 1 with default settings for demultiplexing, aligning reads to the Drosophila genome (10x Genomics prebuilt dm6 reference genome) with STAR, and counting unique molecular identifiers to build transcriptomic profiles of individual cells. This first level of analysis generates quality metrics (Q30, number of reads by sample…), FASTQ files, and filtered gene matrices. Cell Ranger preprocessing retained 5796 cells with a median number of 1806 genes per cell for the control condition  (20) , 2977 cells with a median number of 1959 genes per cell for the E(z) RNAi condition, 2410 cells with a median number of 700 genes per cell for the trx RNAi condition, and 3942 cells with a median number of 1190 genes per cells for the E(z) RNAi , trx RNAi condition.
Filtered gene matrices generated via Cell Ranger 2. 0. 1 were then processed with the R package Seurat v4. 0. 3, using the online tutorials as guide (https://satijalab. org/seurat/vignettes. html) (80). Cell cycle genes were regressed out using the CellCycleScoring and ScaleData functions. Integration of Imp + tNBs of all conditions was performed following the FindIntegrationAnchors function  (62). Trajectories and pseudotimes in Figs. 4  and 6  were generated using the SeuratWrappers and Monocle 3 packages  (64). Codes for Seurat and Monocle analyses are found in data S4.
Acknowledgments: We thank  C. Desplan ,  A. Gould ,  W. McGinnis ,  P. Macdonald ,  W. Odenwald ,  S. Russell ,  A. Saurin , and  N. Sokol  for flies and antibodies. We are grateful to  P. Outters  and  R. Perbost  at  HalioDx (Marseille)  for generating the single-cell transcriptomic data,  P. Grenot at Ciphe  for help with FACS,  L. Spinelli  for help with bioinformatic analysis, and  E. Legait  for tNB segmentation. We also acknowledge the  Bloomington Drosophila Stock Center  ( NIH P40OD018537 ), the  Vienna Drosophila RNAi Center (VDRC) ,  TRiP at Harvard Medical School  ( NIH/NIGMS   R01-GM084947 ),  Kyoto DGRC  and  NIG-Fly Stock Centers  for flies, and the  Developmental Studies Hybridoma Bank (DSHB)  for monoclonal antibodies. We thank  France-BioImaging / PICsL infrastructure  ( ANR-10-INSB-04-01 ).
Aims. Three-dimensional (3D) maps of Galactic interstellar dust are a tool for a wide range of uses. We aim to construct 3D maps of dust extinction in the Local Arm and surrounding regions. Methods. To do this, Gaia EDR3 photometric data were combined with 2MASS measurements to derive extinction towards stars with accurate photometry and relative uncertainties on EDR3 parallaxes of less than 20%. We applied our hierarchical inversion algorithm adapted to inhomogeneous spatial distributions of target stars to this catalogue of individual extinctions. Results. We present the updated 3D dust extinction distribution and provide an estimate of the error on integrated extinctions from the Sun to each area in the 3D map. The full computational area is similar to the one of the previous DR2 map, that is to say with a 6 × 6 × 0.
8 kpc 3 volume around the Sun. Due to the addition of fainter target stars, the volume in which the clouds can be reconstructed has increased. Due to the improved accuracy of the parallaxes and photometric data in EDR3, extinctions among neighbouring targets are more consistent, allowing one to reach an increased contrast in the dense areas, while cavity contours are more regular. We show several comparisons with recent results on dust and star distributions. The wavy pattern around the Plane of the dust concentrations is better seen and exists over large regions. Its mean vertical peak-to-peak amplitude is of the order of 300 pc; interestingly, it is similar to the vertical period of the spectacular snail-shaped stellar kinematical pattern discovered in Gaia data. Conclusions. The Gaia EDR3 catalogue allows for a significant improvement of the extinction maps to be made, both in extent and quality.
Their development is considerably boosted by the Gaia mission data releases. Three-dimensional dust extinction mapping is based on the tomographic inversion of measured extinction-distance pairs or extinction-distance probability distributions for large numbers of targets distributed in 3D space. What differs among the various 3D reconstructions is the choice of the photo-3D maps are only available at the CDS via anonymous ftp to cdsarc. u-strasbg. fr (130. 79. 128. 5)  or via http://cdsarc. u-strasbg. fr/viz-bin/cat/J/A+A/661/A147 metric and(or) spectro-photometric catalogues used to estimate individual extinctions, the source of distances, that is parallax, photometry, or combined parallax and photometry, and finally the inversion technique. Such choices result in various extents of the mapped volumes.
The corresponding, detailed maps were computed based on Gaussian random processes or techniques derived from information field theory  (Sale & Magorrian 2018; Rezaei et al. 2020; Leike et al. 2020). The present work uses a different technique based on iterations of inversions at increasing resolutions. This technique has been previously used to compute a dust map based on Gaia Data Release (DR2) and 2MASS photometric data combined with Gaia DR2 parallaxes. The basic principles of the 3D inversion have been detailed in  Vergely et al. (2001)  and the new hierarchical technique has been described in  Lallement et al. (2019). Along this iterative process, the inversion is restricted to regions of 3D space with a target density high enough to achieve the current resolution, that is their total volume is gradually decreasing and there is a simultaneous increase in the volumes where the retained solution is the solution of the previous inversion.
This allowed us to adapt the spatial resolution to the concentration of the targets at each location. The advantage of this hierarchical technique is the limited cost in computational time and the possibility to increase the physical dimensions of the maps while avoiding creating unreal structures due to the scarcity of targets. The Gaia Early Data Release 3 (EDR3, Gaia Collaboration 2021) contains a new catalogue of parallaxes and luminosity measurements, with significant improvements with respect to Gaia DR2, in particular the accuracy of parallaxes has significantly increased and the homogeneity of photometric measurements has thoroughly improved. We made use of the new measurements to produce a larger and higher quality catalogue of individual extinctions to enter the inversion process.
For the top of the HR diagram, we selected only stars within 500 pc of the Galactic Plane to avoid being dominated by metal-poor giants. A 7 degree monotonic polynomial 1  was adjusted to each colour-colour relation G -X as a function of G -K, removing strong outliers (ten times the median absolute deviation (MAD) of the residuals) one by one. The colour range of our calibration is -1. 0 < (G -K) 0 < 3. 7 for the top of the HR diagram and -1. 0 < (G-K) 0 < 3. 0 for the dwarfs. It is important to note that the colour range is smaller than in  Lallement et al. (2019)  due to the selection of stars near the Galactic Plane. The extinction coefficients used are provided in the Gaia auxiliary data pages 2. They have been derived similarly to those used in  Lallement et al. (2019)  with the update of the Gaia passbands from  Riello et al.
As we have warned in previous presentations of maps, our use of a correlation kernel prevents us from reaching precision on the latter quantity because the extinction is forced to be distributed in volumes that can be wider than the actual clouds. On the other hand, it is important to note that errors on integrated extinctions, the measured quantities, do not suffer from the imposed finite resolution as much and they are expected to be much smaller. Here the error budget could not be computed by means of a classical approach, that is Gaussian error propagation. The main contribution to the error comes from uncertainties on both extinction and distance; however, the target distribution is an important factor too, and the correlation length has an impact on extinction density errors. We have preferred a statistical approach and used errors on target extinctions and distances to build randomly distributed datasets of extinctions and distances around their observed values.
Mapping improvements The second main evolution is the increased contrast, that is the larger number of peak densities and, conversely, the increased vacuity of the large cavities. To illustrate this, isocontours for the same relatively high value of differential opacity, namely 10 -2 mag pc -1 , are drawn on both the previous and new map in Fig. 5  in a smaller region around the Sun, and they help to visualise the increased number of opaque areas in the new map. Because our minimum kernel is similar in the two maps (25 pc), this trend is a sign of better consistency within the dataset, and it reflects the improved accuracy of parallax distances and extinction estimates. Conversely, a careful look at the differences between the two images shows that most of the cavities appear to be emptier, and elongated structures within them that were due to a lack of consistency of the data or distances that were too uncertain have disappeared in the new map.
Comparisons with recent results on dust and stars It is important to compare dust cloud mapping based on distinct photometric data in order to detect potential differences. To do so, we have used the recent catalogue of molecular cloud (MC) distance assignments of  Zucker et al. (2020). In this catalogue, molecular cloud locations are based on the PanSTARRS photometric data and Gaia DR2 parallaxes. For clouds within 2 kpc, DR2 and EDR3 parallaxes slightly differ individually; however, the MC distance assignments are based on large numbers of targets, and errors are expected to be smoothed out in a large extent. Figure  10  displays the projection onto the midplane of the ≥300 MCs from the catalogue, superimposed on the dust extinction in this plane. The figure shows a remarkable agreement, that is to say clouds are located within or are extremely close 4 https://explore-platform.
We suggest a common origin for these two phenomena. If this is the case, the grain and gas distributions could be associated with the kinetic 3D tomography of the stars in order to help clarify their origin. With the low-resolution blue and red spectrographs on board the satellite, future Gaia data releases will provide a much larger number of reliable extinction measurements. The dual hierarchical method has proven to be well suited to the inversion of large input data sets due to its low computational time and will be well adapted to these data. In the future, one may expect much improved dust distributions, ideally accompanied by co-spatial gas kinematics. Such results may enter self-consistent models of stars and gas to help decipher the origin of the present-day Galactic structure and the epochs and impacts of disk traversals and bar and arm disruptions.
Acknowledgements. We thank our anonymous referee for useful comments. J. L. V. and N. L. J. C. acknowledge support from the  EXPLORE  project. EXPLORE has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  101004214. J. L. V. acknowledges support from the  THETA Observatoire des Sciences de l'Univers in Besançon. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https: //www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This work also makes use of data products from the  2MASS , which is a joint project of the  University of Massachusetts  and the  Infrared Processing and Analysis Center/California Institute of Technology , funded by the  National Aeronautics and Space Administration  and the  National Science Foundation.
Acknowledgements The authors acknowledge the financial support by the  Australian Research Council  ( CE200100010 ,  DP190101559 ,  DP200101353 ), the use of the ACT Node of the  Australian National Fabrication Facility (ANFF-ACT) , the  German Academic Exchange Service DAAD  ( 57559284 ), the  German Ministry of Education and Research  ( 13N14877 ), and the  UA-DAAD  exchange scheme. M. A. W. , T. P. , F. S. , D. A. , and I. S. gratefully acknowledge the financial support by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation , Project Numbers:  CRC 1375 NOA 398816777 , subprojects  B2  and  C2 ,  STA 1426/2-1 ,  PE 1524/13-1 ,  SE 2749/1-1 ,  407070005 ), the  European Union  by project  METAFAST-899673-FETOPEN-H2020  and the  Erasmus+ Programme  ( KA107 ) and by the  Thuringian State Government within its ProExcellence initiative  ( ACP 2020  ) and within  Quantum Hub Thuringia  ( 2021 FGI 0043 ).
M. R. and L. X. appreciate funding by the  UK Research and Innovation Future Leaders Fellowship  ( MR/T040513/1 ). A. F. acknowledges financial support by the  German Academic Exchange Service  ( GSSP-2017 ). M. A. W. acknowledges support by the  Max Planck School of Photonics. [Correction added after initial publication: In Figure 2b, the representation of the (110) GaAs crystal was shown rotated around the y-axis, when it should be rotated around the x-axis, in accordance with the equations and calculations derived in the work presented here. This has now been corrected. ] Open access funding enabled and organized by Projekt DEAL.
Empirical evidence for these effects is lacking: our second contribution is to provide a test for the constraining impact of institutional, operational and political hurdles. If confirmed, they may shed light on the variations observed in mandate responsiveness. We opt for a within-case comparison focused on Germany as a way to explore different dimensions of governing parties' capacity to set their campaign priorities on the legislative agenda, while holding the general context constant. Germany is both representative of typical parliamentary democracies and a fertile ground for testing our hypotheses, given considerable variance in all factors of interest. We create a dataset on electoral and legislative priorities across all policy sectors, merging data collected by the Comparative Agendas Project (CAP) 1 with national-, issue-and party-level data.
Case selection and empirical strategy This study measures the reflection of German governing parties' mandate priorities in public policy, and how several capacity conditions constrain this relationship. This requires data on issue attention in both governing parties' manifestos and policymaking, measured on the same scale. We use data collected by the German CAP team on issue emphasis in party manifestos and enacted laws. 4 We use fixed-effects Poisson regression models to model the monthly number of laws enacted on a given topic and analyse how the government's electoral priorities (percentage of manifestos on a given topic) translate into legislative attention (monthly number of laws adopted on the same topic). The data are available over a period of three decades  and make it possible to use interaction terms to analyse how the relationship between manifesto and legislative priorities varies depending on all conditions of interest.
Independent variable: Priorities in governing parties' platforms Our main independent variable, which is based on content analysis of governing parties' manifestos, captures the proportion of sentences devoted to each issue in each manifesto. 7 Party manifestos are a core source for research on the issue content of party competition. They are most relevant for our specific research question, as an authoritative document produced by parties as unitary actors, in contrast to individual politicians' claims or parties' routine press releases, whose binding character may be disputed. German governments consistently comprised of two parties over the period of study. 8 For each administration, each coalition partner's manifesto was coded by assigning each sentence to a CAP issue category. These data were then used to measure the attention devoted to each topic (percentage of manifesto).
Acknowledgements Thanks to a fellowship of the  Humboldt Foundation ,  Isabelle Guinaudeau  benefited from ideal working conditions at the  University of Stuttgart. We would like to address our warmest thanks to  Christian Breunig  for giving us access to the German CAP data as well as for excellent comments. We also benefited from  Michael Herrman 's very helpful advice as to our modelling strategy. We are grateful for further feedback and comments received from  André Bächtiger ,  Patrick Bernhagen ,  Elisa Deiss-Helbig ,  Angelika Vetter , as well as from the participants of the panel on 'Political Parties: Strategies and Outcomes' at the online ECPR General Conference in 2020. Last but not least, this project importantly benefited from our exchange with Emiliano Grossman with whom we have been working in parallel on related matters.
On those samples, culture was performed irrespective of RDT result) (D) and with a certain combination of diagnostic outcomes (E), presenting outcomes that were performed on all samples (RDT, qPCR on pla and caf1 genes). Model fits to these proportions are provided with black dots and lines indicating model predictions and 95% credible intervals, respectively. The underlying data and code to reproduce this figure are available on Open Science Framework (https://osf. io/nbc4t/). BP, bubonic plague; MB, molecular biology; PP, pneumonic plague; qPCR, quantitative polymerase chain reaction; RDT, rapid diagnostic test. https://doi. org/10. 1371/journal. pbio. 3001736. g001 specificity was around 80% for both PP (82%, 80% to 84%) and BP (85%, 81% to 89%) (Fig 3A  and Table  A in S1 Text). Additional analyses including an initially implemented classical polymerase chain reaction (cPCR) protocol confirm that these lacked specificity (PP: 55%, 52% to 58%, BP: 62%, 56% to 69%) (Table B in S1 Text) and justifies its timely replacement by MB.
Here, conf denotes confirmed and prob denotes probable. Classifications �1 qpcr and 2 qpcr represent results based on qPCR solely, i. e. , in the absence of confirmatory cPCR, with �1 qpcr denoting "at least 1 gene positive" and 2 qpcr "both genes positive. " The underlying data and code to reproduce this figure are available on Open Science Framework (https://osf. io/nbc4t/). BP, bubonic plague; cPCR, classical polymerase chain reaction; MB, molecular biology; PP, pneumonic plague; qPCR, quantitative polymerase chain reaction; ROC, Receiver operating characteristic. https://doi. org/10. 1371/journal. pbio. 3001736. g004 almost all Y. pestis infected (PP: 98%, 91% to 100%; BP: 100%, 99% to 100%), deriving from perfect specificity of culture and the strict criterium requiring both RDT and MB to be positive.
Data Samples from clinically suspected cases (2,414) were sent to the CLP at the IPM for diagnostic testing. Treatment of suspected cases was not contingent on biological results. Biological samples were taken from cases presenting at health care settings with symptoms consistent with plague (i. e. , for BP: presence of an isolated, painful adenopathy; for PP: cough (<5 days), bloody sputum, chest pain with fever)  [3]. There was no formal clinical case definition that needed to be satisfied for patients to be tested. Samples included bubo aspirates from BP and secondary PP, sputum samples for PP, and liver and/or lung aspirates from deceased cases. All samples were tested for fraction 1 (F1) capsular antigen using an RDT  [11]. Initially, MB was performed using cPCR targeting the pla gene on all samples.
MB is considered here due to its potential for being considered as a classifier by itself. Here, conf denotes confirmed and prob denotes probable. Classifications �1 qpcr and 2 qpcr represent results based on qPCR solely, i. e. , in the absence of confirmatory cPCR, with �1 qpcr denoting "at least 1 gene positive" and 2 qpcr "both genes positive. " The underlying data and code to reproduce this figure are available on Open Science Framework (https://osf. io/nbc4t/). BP, bubonic plague; cPCR, classical polymerase chain reaction; MB, molecular biology; PP, pneumonic plague; qPCR, quantitative polymerase chain reaction; ROC, Receiver operating characteristic.
Acknowledgments The authors would like to thank for their continuous support during the epidemic the other team members of the  IPM laboratories (from the Virology, Experimental Bacteriology, Infectious Diseases Immunology, Malaria Research Units , the  Clinical Biology Laboratory , and the  Hygiene Food and Environment Laboratory ), members of supporting research units at  IP Paris (CIBU, Epidemiology of Emerging Infectious Diseases Unit, Molecular Genetics of RNA Viruses Unit) , experts deployed through the  Global Outbreak Alert Response Network (GOARN) , and all the colleagues who were instrumental in improving and implementing epidemiological and laboratory surveillance and supported the epidemic response efforts.