dataesr/scientific-paragraphs-categorization huggingface dataset:
1% SDS, 0. 5% DOC, 1% NP-40, Protease inhibitor (Roche; 1187358001)). Lysis was performed on ice for 30 minutes. Lysates were cleared by centrifugation at 8000 g for 8 minutes at 4°C, and 20 μl of Laemmli 6X (12% SDS, 30% Glycerol, 0. 375M Tris-HCl pH6. 8, 30% 2-mercaptoehtanol, 1% bromophenol blue) was added and samples were boiled at 95°C for 15min. Lysates were resolved on Criterion or 4%-20% Biorad precast SDS-PAGE gels and transferred on PVDF membrane. Membranes were saturated and proteins were blotted with antibodies (listed in Key Resources Table) in 5% non-fat dry milk, PBS, 0. 1% Tween buffer. ECL signal was recorded on the ChemiDoc-XRS or ChemiDoc Touch Biorad Imager. Data was analyzed and quantified with the Image Lab software (Biorad). Quantitative PCR-50,000 to 200,000 MDDCs were lysed in TRIzol reagent (Thermo Fisher) and then RNA was isolated following the manufacturer's instructions with minor modifications.
IFNL1 Protein Quantification-IFNL1 protein concentrations were measured on supernatants from infected or treated MDDCs using a LEGENDplex Human Anti-Virus Response assay (BioLegend) according to the manufacturer's protocol. Data were acquired on a BD FACSVerse (BD) and analyzed with LEGENDplex Software (BioLegend). Bioassays for type I IFN-To quantify IFN activity from infected or stimulated cells we assayed supernatants with HL116 reported cells that contain firefly luciferase gene under control of the IFN-inducible 6-16 promoter  (Uzé et al. , 1994). Supernatants from treated or untreated MDDCs were transferred to 20,000 HL116 cells in 96 well plates. After 7 h, HL116 cells were lysed in passive lysis buffer and subsequently scored for luciferase activity using a luciferin-based method (Promega).
Heatmap is color coded according to the -log p value of TF enrichment. (E) Differential gene expression contrast between CD86-high and CD86-low populations as visualized by Gephi software, highlighting edges in clusters 2 and 8. Ranking is by Enrichr combined score (-log (p) * Z score). (F) Enrichment of differentially expressed genes separated by network cluster for the CD86high versus CD86-low contrast as in (E). (G-I) Potential protein-protein interactions (G), Reactome pathway enrichment (H), and single gene perturbations (I) predicted from differentially expressed genes in the CD86-high versus CD86-low contrast. (J) Network connections for RARA and STAT2 visualized by Gephi software, color coded by contrast, and superimposed onto a gray network backdrop. See also Figure  S6.
ACKNOWLEDGMENTS This project was supported by the  Howard Hughes Medical Institute  (to D. R. L. ) and the  Helen and Martin Kimmel Center for Biology and Medicine  (to D. R. L. ), grants from the  National Institutes of Health  ( F32AI093231  to  J. S. J. ,  R21AI084633  to D. R. L. , and  R01AI025032 ,  R01AI032972 , and  U19AI100627  to A. A. ),  Sidaction  ( VIH2016126002  to N. M. and  17-1-AAE-11097-2  to N. M. ),  ANRS  ( ECTZ36691 ,  ECTZ25472 , and  ECTZ71745  to N. M. ), state funding from the  Agence Nationale de la Recherche  under the  Investissements d'avenir program  ( ANR-10-IAHU-01  to M. M. M. ), and the  ATIP-Avenir INSERM program  (to M. M. M. ).
We determined, using a tailor-made MATLAB program, that this results from an increase in the concentration of Cdc42•GTP at the tip, rather than an alteration in the position of the maximum signal or spread of active Cdc42 further down the filament (Additional file 1: Figure  S5A-D ). These results suggest that, in response to a resistive force, there is an increase in cell polarization, perhaps reflecting a direct response to such external forces. We speculate that this higher level of active Cdc42 during invasive growth is due to the increased recruitment of the Cdc42 activator, Cdc24  [38]. We next examined active Rho1, as cell wall stress mediated by the cell surface mechanosensors Wsc1/Mid2 results in Rho1 depolarization in S. cerevisiae  [39, 40]. Figure  11c  and d  show that, in contrast to the increase in tip localized active Cdc42, active Rho1 is depolarized during invasive growth.
Micro-fabrication Microchambers were fabricated using standard softlithography methods  [56, 57] ). Chambers were 5 μm deep and 10 μm in diameter with 15 μm spacing between them. The overall thickness of microchamber preparations was approximately 150 μm. PDMS microchambers of varying stiffness were generated by varying the ratios of polymer and cross-linker (Sylgard 184; Dow Corning). Polymer-cross-linker mixtures were spin coated (Laurell Technologies corporation) on molds (5 s 100 rpm followed by 50 s 500 rpm) to achieve ~150 μm thickness and then cured for 1 h at 60 °C PDMS (10:1). Thick PDMS frames were then placed on top of the partially cured chambers, which were baked for an additional 2 h at 90 °C, in order to peel the thin chambers off the mold. For viscoanalyzer measurements, 20 × 20 × 5 mm PDMS pieces were similarly cured and samples subjected to an amplitude of oscillation of 5-150 μm at 10 Hz frequency using a DMA3007000 Metravib 52 (Areva) with Dyna-set20 software.
Microscopy and image analysis Cells were imaged as described  [33]  using either spinning disk confocal microscopy or wide-field fluorescence microscopy with a UPLANAPO 1. 2 NA × 60 or Plan-Neofluar 0. 75 NA × 40 objectives, respectively. Images were acquired at indicated times, with 0. 4 or 0. 5 μm zsections (13 to 24) to capture the entire filament. For growth depth experiments, 100 × 0. 2 μm z-sections were acquired. For the analyses of extension rate, compartment morphology, tip morphology, and cell depth images were deconvolved with the Huygens Professional software version 18. 04 (Scientific-Volume Imaging) with recommended settings and a signal to noise ratio of 10, and sum projections were used. Reflection images in XZ were acquired on an upright Leica DM5500 TCS SPE laser-scanning microscope (Leica Microsystems, Mannheim, Germany) equipped with a galvanometric stage, using an APO 0.
Only invasive compartments that were entirely within the PDMS were used for analyses. Active Cdc42 and Rho1 polarization was determined using a fixed size ROI to quantitate the signal intensity at the tip and in a subapical region. The ratio of the tip to subapical signal was averaged over the time-lapse, and the mean for invasive cells was normalized to the mean ratio for noninvasive cells. The percentage of cells undergoing subapical bending within a chamber and filament buckling within PDMS was assessed using DIC images. For the determination of errors from values derived from multiple measurements, classical error propagation was carried out  [59]. Statistical significance was determined with Student's t test. For the representation of 3D DIC images, an algorithm was applied to image z-stacks using a custom-developed MATLAB program called InFocus inspired from Extended Depth of Field plugin  [60].
The program extracts from any 3D multi-channel acquisition, the local contrast per channel. From these maps, it determines the Z of highest contrast per pixel from one or a combination of channels and then extracts a smoothened plane to get a 2D multichannel image. Parameters can be adapted through the graphical user interface. For quantitation of filament tip curvature over time, we developed another MATLAB program called TipCurve with an intuitive interface dedicated for morphological analyses along the major axis of filamentous cells. For such analyses, 3D images were converted into 2D images by sum projection, and similar to the HyphalPolarity program  [33] , a backbone was extracted from images over time, but also the cell contour, specifically extracting the tip first, we estimate the extremity of the backbone subtracted by the tip radius.
Funding This work was supported by the  CNRS, INSERM, Université Nice-Sophia Antipolis ,  Université Côte d'Azur  and  ANR  ( ANR-15-IDEX-01 ,  ANR-11-LABX-0028-01 , and  ANR-16-CE13-0010-01 ), and  EU H2020 (MSCA-ITN-2015-675407 ) grants and the  Platforms Resources in Imaging and Scientific Microscopy facility (PRISM) and Microscopy Imaging Côte d'Azur (MICA).
Statistical analysis has become increasingly important in the field of OSL dating since it has become possible to measure signals at the single grain scale. The accuracy of large chronological datasets can benefit from the inclusion, in chronological modelling, of stratigraphic constraints and shared systematic errors. Recently, a number of Bayesian models have been developed for OSL age calculation; the R package 'BayLum' allows implementing different such models, in particular for samples in stratigraphic order which share systematic errors. We first show how to introduce stratigraphic constraints in 'BayLum'; then, we focus on the construction, based on measurement uncertainties, of dose covariance matrices to account for systematic errors specific to OSL dating. The nature (systematic versus random) of errors affecting OSL ages is discussed, based -as an exampleon the dose rate determination procedure at the IRAMAT-CRP2A laboratory (Bordeaux).
The effects of the stratigraphic constraints and dose covariance matrices are illustrated on example datasets. In particular, the interest of combining the modelling of systematic errors with independent ages, unaffected by these errors, is demonstrated. Finally, we discuss other common ways of estimating dose rates and how they may be taken into account in the covariance matrix by other potential users and laboratories. Test datasets are provided as supplementary material to the reader, together with an R Markdown tutorial allowing to reproduce all calculations and figures presented in this study.
Introduction Optically stimulated luminescence (OSL;  Huntley et al. , 1985)  allows dating the last exposure of quartz grains to sunlight. The Single Aliquot Regenerative (SAR) dose protocol consists of comparing the natural luminescence signal to laboratory-generated signals induced by artificial irradiations  (Murray and Wintle, 2000; Wintle and Murray, 2006). The corresponding measurements, in particular at the single-grain scale, result in large datasets characterised by important scatter, owing to a number of dispersion factors (see, e. g. Thomsen et al. , 2005). An OSL age is then obtained by dividing the equivalent dose (i. e. in the case of coarse quartz grains, the dose absorbed by the mineral) by the dose rate to which quartz grains were exposed since the last exposure to light.
, 2015; Combès and Philippe, 2017, and references therein). To implement the Bayesian models of  Combès et al. (2015)  and  Combès and Philippe (2017)  in practice, and provide easy access to the community, an R package (R Core Team, 2020) named 'BayLum'  (Christophe et al. , 2020; version 0. 2. 0)  has been developed and released on the Comprehensive R Archive Network (CRAN; see also  Mercier et al. , 2017 , for a first implementation of the central dose model from  Combès et al. , 2015). First features of this 'BayLum' package were presented by  Philippe et al. (2019)  and its performances, when one is confronted either with large dose values or with dose variability issues, were tested in laboratory-controlled experiments  (Heydari and Guérin, 2018)  and later applied to various case studies  (Lahaye et al.
Herein we present a Bayesian modelling case study. (1) We start with how data should be pretreated prior to using the 'BayLum' package; a simple example of chronological modelling (samples considered independent, i. e. without stratigraphic constraints and shared errors) is first presented, yielding an output from the 'BayLum' package to serve as a reference for the following, more elaborate models. (2) In the next step, we detail how the user can integrate stratigraphic constraints and the effect on the chronological inference. (3) Then, most importantly we explain how to build a dose covariance matrix in practice to take into account systematic errors (for the definition of this matrix, the reader is referred to  Combès and Philippe, 2017)  and what effect it has on a series of ages. (4) For this purpose, we base our approach on dose rate measurements as performed by  Guérin et al.
(2015b)  at the IRAMAT-CRP2A laboratory. The effect of integrating independent data such as radiocarbon ages, which do not share the systematic errors affecting OSL data, is then illustrated. (  5 ) Finally, we discuss different ways to measure dose rates and various assumptions that can be made regarding the nature (systematic or random) of additional sources of errors in OSL dating. To help the reader, we provide as supplementary information an R markdown document with commented lines of code and example datasets, so that everything presented here may be reproduced.
Case study To illustrate how to model OSL ages, both in stratigraphic constraints and sharing systematic errors, using the R 'BayLum' package, we use the data from two sediment samples (FER 1 and FER 3) already dated by quartz OSL  (Guérin et al. , 2015b). These samples were taken from the archaeological site of La Ferrassie (France) and prepared following standard chemical preparation procedures applied to luminescence-dating samples. While modelling with 'BayLum' may be applied to both multi-grain and single-grain OSL datasets, in the following we only focus on single-grain data, as this is probably where the need for appropriate statistical models is most acute (the reliability of multi-grain OSL has been demonstrated when using a plain average (mean) for palaeodose estimation; see, e. g.
While  Combès et al. (2015)  argue that this results in a better statistical inference about the age (or palaeodose), it also comes with a downside: the user cannot visualise the data during the statistical analysis. In particular, the fact that the user must specify the list of grains to be included in the analysis implies that one should always pre-treat the samples in a standard way, by using, e. g. Analyst  (Duller, 2015)  or the R 'Luminescence' package  (Kreutzer et al. , 2012; Kreutzer et al. , 2020)  to visually check the data but also investigate the effect of various selection criteria on the datasets (see for example  Thomsen et al. , 2016 , on the effect of applying various selection criteria when with frequentist statistical models; see  Heydari and Guérin, 2018 , for a similar study in a Bayesian framework).
In practice, the data is contained in two folders named after the samples and provided as Supplementary Material. Each folder contains one BIN/BINX-file (i. e. OSL measurements; note that only a small fraction of the measured grains is included here Supplementary Material) and four CSVfiles: -'DiscPos. csv' lists all selected grains; -'Rule. csv' gives the rules for generating Lx/Tx data (integration channels for both the natural or regenerated and test dose signals, uncertainty arising from the reproducibility of the OSL measurements, and number of SAR cycles to remove for curve fitting, if any -it may, for example, be desirable to remove recycled points and/or IR depletion points); -'DoseSource. csv' gives the laboratory source dose rate and its variance; -'DoseEnv. csv' gives the dose rate to which the sample was exposed during burial.
Stratigraphic constraints Samples FER 1 and 3 belong to two different stratigraphic layers: sample FER 1 (Layer 7) lies above sample FER 3 (Layer 5B), so we know that the age of sample FER 1 must be less than that of sample FER 3. To encode this information, the function AgeS_Computation() takes as argument the object StratiConstraints, which is a matrix whose size depends on the number of analysed samples. First, the data in the DATA object (which is the output of the function https://doi. org/10. 5194/gchron-2020-40 Preprint. Discussion started: 3 December 2020 c Author(s) 2020. CC BY 4. 0 License. Generate_DataFile()) must be ordered in stratigraphic order from top to bottom: thus, in our case the list of names used by the function Generate_DataFile()is FER 1, FER 3 (rather than FER 3, FER 1).
Note: in the markdown document provided as Supplementary Material, the corresponding code lines are commented and perhaps make this description easier to follow. Running the function AgeS_Computation() with this matrix of stratigraphic constraints only marginally affects the ages, in this case, the 95% C. I. become  [34. 3; 42. 9] ka and [38. 1; 48. 5]  for samples FER-1 and FER-3, respectively (Table  1 ). One can also look at the bivariate scatter plot of observations from the joint posterior distribution (Fig. 4 ): one can see that this scatter plot is truncated in the upper left-hand corner -illustrating the fact that the age of sample FER 1 can never be greater than that of sample FER 3 (see Fig. 2  for comparison). By contrast, the KDE estimate (Fig. 5 ) also shows a positive correlation but does not reveal the truncation (whereas the stratigraphic constraint imposes a null probability for all pairs of ages above the 1:1 line).
General considerations In the previous calculations, all the variance is treated as random, whereas common, systematic errors should not allow solving stratigraphic inversions (they affect all ages in the same direction, although to varying degrees). One of the main advantages of applying the models implemented in the 'BayLum' package -contrary to other chronological modelling tools such as OxCal  (Bronk Ramsey and Lee, 2013)  or Chronomodel  (Lanos and Philippe, 2018)  -lies in the possibility to include the structure of uncertainties specific to OSL dating. For instance, a radiocarbon age is derived only from the ratio of 14 C to 12 C (on top of which comes the more complex problem of calibration), whereas an OSL age involves a large number of measurements, each with its uncertainty  (Aitken, 1985; 1998).
The samples FER 1 and FER 3 are directly above and below, respectively, the Châtelperronian layer at the site (layer 6). Sample FER 2 from this layer being poorly bleached, it is at present impossible to model with 'BayLum'. However, an alternative to estimate the age of FER 2 consists of supposing that it has a uniform prior probability density between the ages of samples FER 1 and FER 3: 𝑃(𝐴 2 |𝑑𝑎𝑡𝑎)~∬ 𝕀 [𝐴 1 ;𝐴 3 ] 𝐴 3 -𝐴 1 𝜋(𝐴 1 , 𝐴 3 |𝑑𝑎𝑡𝑎)𝑑𝐴 1 𝑑𝐴 3 where   is the age of sample i,  [ 1 ; 3 ] is the indicator function between  1 and  3 , and ( 1 ,  3 |) is the posterior joint density of  1 and  3 knowing the data (i. e. the density estimated with 'BayLum'). Doing so (see the markdown file for the corresponding code lines), working from the output of 'BayLum' one obtains a 95% C. I. of  [36; 46]  ka, which can be compared with the confidence interval of [36; 48] ka obtained by  Guérin et al.
Integration of independent chronological data (radiocarbon) The 'BayLum' package also offers the possibility to include radiocarbon ages in the chronological models  (Philippe et al. , 2018) ; more specifically, radiocarbon ages are calibrated within 'BayLum', using the function AgeC14_Computation() or Age_OSLC14() (in the latter case the function necessitates at least one OSL age calculation). Introducing covariance matrices to account for systematic errors on OSL data does not reduce the OSL age uncertainties; however, it becomes particularly useful to correct for estimation biases when more precise ages, unaffected by these systematic errors, are integrated into the models. To illustrate this, we decided to construct two models constraining the age of FER 3; for illustration purposes, in this section, we used the simplistic  matrix described above in section 5.
g. radiocarbon and OSL ages, first to thoroughly examine the corresponding datasets independently: how were the data produced (with which experimental procedure)? Are the provided uncertainties reliable (or is there an unrecognised source of error that should be included in the https://doi. org/10. 5194/gchron-2020-40 Preprint. Discussion started: 3 December 2020 c Author(s) 2020. CC BY 4. 0 License. evaluation of uncertainty)? Users are also encouraged to examine the consistency of results produced by each method, in light of the stratigraphy. In a second stage, before modelling of independent ages, we would recommend assessing the consistency of these datasets -do they (at least broadly) agree? And if not, can a parsimonious explanation be found? For example, it is rather common, when performing Bayesian modelling with tools such as OxCal, to observe a large fraction of ages considered as outliers; such observations should urge users to examine their data again and come up with likely explanations (note: to this date, no outlier model has been developed for the OSL ages in 'BayLum').
Conclusion New models for building chronologies based on OSL, with the possibility to incorporate radiocarbon, have been proposed in the literature  (Combès et al. , 2015; Combès and Philippe, 2017). These models have been demonstrated to improve the chronological inference based on OSL data and in particular, the accuracy of OSL ages  (Guérin et al. , 2015; Heydari and Guérin, 2018). The R package 'BayLum' was developed to implement these models;  Lahaye et al. (2018 ), Carter et al. (2019 ), Heydari et al. (2020)  and  Heydari et al. (in review)  have used some of them to establish the chronologies of sedimentary sequences dated by OSL, resulting in generally more precise chronologies. In this article, we have presented a case study on how to build simple models and observe output data, in particular through bivariate plots of age probability densities.
Acknowledgements The authors thank  Andrew Millard ,  Rex Galbraith  and an anonymous referee for helpful comments on a previous version of this article. This study received financial supports of the Région Aquitaine (in https://doi. org/10. 5194/gchron-2020-40Preprint. Discussion started: 3 December 2020 c Author(s)  2020. CC BY 4. 0 License.
We measured a Curie temperature of Tc=124 K and a saturation magnetisation of 0. 91 µB/Co at 9 T (which becomes linear at 0. 2 T and 0. 89 µB), in good agreement with the literature values  27  (see Fig. 1F  for magnetisation curves). When cleaving CoS2 for ARPES experiments, we obtained two distinct cleavage planes with the surface normal pointing along the (111) and (100) directions. We performed photon energydependent ARPES measurements with soft X-ray photons (hv=350-800 eV) to locate the highsymmetry planes along the kz direction normal to the sample surface (see supplementary materials). The band structure in the kz=π plane containing the R-point is displayed in Fig. 2. For the data measured on the (111) cleavage plane, we can clearly identify circular Fermi-surface pockets at the Rpoint in the corner of the Brillouin zone.
Ab-initio calculations We employed density functional theory (DFT) as implemented in the Vienna Ab Initio Simulation Package (VASP). For the Generalized Gradient Approximation (GGA) calculations, the exchange correlation term is described according to the Perdew-Burke-Ernzerhof (PBE) prescription together with projected augmented-wave (PAW) pseudopotentials, while of the Local Spin Density Approximation (LDA), the exchange correlation term is described according to the Dudarev simplified rotationally invariant approach, together with PAW pseudopotentials. The kinetic energy cut off was set to 400 eV. For the self-consistent calculation, a grid of 7x7x7 k-points was used. For DOS calculation, a grid of 11x11x11 k-points was used, with 1000 energy points. The spectral function of the Fermi-surfaces shown in Figs.