dataesr/scientific-paragraphs-categorization huggingface dataset:
In general, priors are set as flat priors so that the posterior distribution is mostly influenced by the data  (Sorensen et al. , 2016). In the current study, minimally informative priors for most parameters were used, thus posterior distribution was allowed to be determined mainly by the data used in this analysis. Estimation of between study heterogeneity is also crucial for the meta-analysis which might affect inferences. In traditional meta-analysis approach, choosing a suitable heterogeneity estimator is challenging and might add arbitrariness to the inferences  (Veroniki et al. , 2016). However, in Bayesian framework, accounting for uncertainty is straightforward and even estimates are valid for small dataset  (Pappalardo et al. , 2020). Therefore, compared to traditional frequentist approach, Bayesian meta-analysis approach used in the current study is better, more powerful and advantageous to deal with between study heterogeneity with a careful choice of priors even with limited number of studies and missing values  (R€ over, 2020; Pappalardo et al.
In contrast, in this study we found a better fit of the model using digestible Met or SAA intake instead of dose as explanatory variable. Growth response of birds fed different Met sources depends on the Cys levels in the diet for starter phase, but it was not the case for grower or finisher phase of broiler  (Pillai et al. , 2006). This interaction between Met source and Cys level had been reported by  Dilger and Baker (2008). These authors observed that excess Cys in Met deficient diet led to decrease in feed intake and adverse effect on OH-Met growth response. Therefore, we also used the digestible SAA intake as the explanatory variable instead of Met to determine if it makes any difference between Met sources. In the case of SAA requirement, the grower dataset contained fewer data points for broilers fed SAA at or below requirements.
Therefore, all data points were included in the subset of grower dataset for further analysis. Using digestible SAA as the response variable also did not make the difference in ADG of broilers at any phases fed 2 different sources of Met. It is important to note that we also observed a very large variability of ADG for birds fed control diets because Met concentration in control diets varied widely across studies. Further analysis revealed that variation in Met concentration in basal Table  5. Linear model parameters 1 and leave-one-out information criterion (LOOIC) that were fitted to the new subset of data using two different forms of synthetic methionine or sulfur amino acid intake (excluding basal methionine) as independent variable and ADG as dependent variable. 2 k QP,Met : Sub setting was done using quadratic-plateau model using methionine as independent variable.
Our study included data from studies published between 1985 and 2019 where greater than 50% (20 of 39) of the studies included were conducted or published between 2007 and 2019. In contrast, the meta-analysis by  Sauer et al. (2008)  included studies published between 1983 and 2006  Sauer et al. (2008). also modeled the growth as response to the dose in percent of active supplemental Met whereas in the current study, the models were applied using the supplemental Met intake as explanatory variable. This latter variable takes into account the feed intake which has been shown by several authors to vary according to the dietary SAA level  (Vazquez-Anon et a. , 2017; Zhao et al. , 2018). Additionally, the analyzed active supplemental Met dose (%) is also rarely given in most of the studies; thus, using supplemental Met dose (%) does not capture the variability during feed manufacture and of feed consumption.
An image zero-point is determined by comparing PSF photometry of the stars to updated stellar catalogs of PS1 observations  (Chambers et al. 2017). The PS1 templates are convolved with a three-Gaussian kernel to match the PSF of the nightly images, and the convolved templates are subtracted from the nightly images with HOTPANTS  (Becker 2015). Finally, a flux-weighted centroid is found for each SN position and PSF photometry is performed using "forced photometry": the centroid of the PSF is forced to be at the SN position. The nightly zero-point is applied to the photometry to determine the brightness of the SN for that epoch. SN 2020tlf was observed with ATLAS ( = -9. 40 -157. 8 days since first light), a twin 0. 5m telescope system We used the calibration database (CALDB) version 20201008.
(2018b). The photometry presented here are weighted averages of the nightly individual 30 sec exposures, carried out with forced photometry at the position of SN 2020tlf. We observed SN 2020tlf with the Las Cumbres Observatory Global Telescope Network 1-m telescopes and Las Cumbres Observatory imagers from 21 Sept 2020 to 29 March 2021 ( = 14. 34 -203. 5 days since first light) in -bands. We downloaded the calibrated BANZAI  (McCully et al. 2018)  frames from the Las Cumbres archive and re-aligned them using the command-line blind astrometry tool solve-field  (Lang et al. 2010). Using the photpipe imaging and photometry package  (Rest et al. 2005; Kilpatrick et al. 2018) , we regridded each Las Cumbres Observatory frame with SWarp (Bertin 2010) to a common pixel scale of 0. 389 centered on the location of SN 2020tlf.
Optical/NIR Spectroscopy In Figure  5 , we present the complete series of optical spectroscopic observations of SN 2020tlf from -9 to +257 days relative to the -band maximum ( = 10 -270 days relative to first light). A full log of spectroscopic observations is presented in Appendix Table  A1. SN 2020tlf was observed with Shane/Kast (Miller & Stone 1993) and Keck/LRIS  (Oke et al. 1995)  between -9 and +257 days relative to the -band maximum. For all these spectroscopic observations, standard CCD processing and spectrum extraction were accomplished with IRAF. The data were extracted using the optimal algorithm of  Horne (1986). Low-order polynomial fits to calibration-lamp spectra were used to establish the wavelength scale and small adjustments derived from night-sky lines in the object frames were applied.
We employed custom IDL routines to flux calibrate the data and remove telluric lines using the well-exposed continua of the spectrophotometric standard stars  (Wade & Horne 1988; Foley et al. 2003). Details of these spectroscopic reduction techniques are described in  Silverman et al. (2012). Spectra of SN 2020tlf were also obtained with Keck NIRES and DEIMOS, as well as Binospec on MMT and the Dual Imaging Spectrograph (DIS) on the Astrophysical Research Consortium (ARC) 3. 5-m telescope at Apache Point Observatory (APO). All of the spectra were reduced using standard techniques, which included correction for bias, overscan, and flat-field. Spectra of comparison lamps and standard stars acquired during the same night and with the same instrumental setting have been used for the wavelength and flux calibrations, respectively.
Bolometric Light Curve We construct a bolometric light curve by fitting the ZTF, PS1, Las Cumbres Observatory, ATLAS and Swift photometry with a blackbody model that is dependent on radius and temperature. The extremely blue UV colors and early-time color evolution of SN 2020tlf near maximum light impose non-negligible deviations from the standard Swift-UVOT count-to-flux conversion factors. We account for this effect following the prescriptions by  Brown et al. (2010). Each spectral energy distribution (SED) was generated from the combination of multi-color UV/optical/NIR photometry in the 2, 2, 1, , /, /, , , , , , and  bands (1500-10000 Å). In regions without complete color information, we extrapolated between light curve data points using a low-order polynomial spline. We present SN 2020tlf's preand post-explosion bolometric light curve in addition to its blackbody radius and temperature evolution in Figure  6.
LIGHT CURVE AND SPECTRAL MODELING We performed non-LTE, radiative transfer modeling of the complete light curve and spectral evolution of SN 2020tlf in order to derive properties of the progenitor and its CSM. Our modeling approach was similar to that presented in  Dessart et al. (2017) , both in terms of initial conditions for the ejecta and CSM, the simulations of the interaction with the radiationhydrodynamics code HERACLES  (González et al. 2007; Vaytet et al. 2011; Dessart et al. 2015) , and the post-processing with the non-LTE radiative-transfer code CMFGEN. For the progenitor star, we considered three models of RSGs produced by three different choices of mixing length parameter  MLT. A greater  MLT boosts the convective energy transport in the H-rich envelope and produces a more "compact" progenitor.
The interaction configurations described above are used as initial conditions for the multi-group radiationhydrodynamics simulations with the code HERACLES. For simplicity, we assume spherical symmetry and perform all simulations in 1-D; an asymmetric explosion could cause variations in the observed light curve and/or spectral evolution such as an extended SBO or slower evolving early-time light curve evolution. We use eight groups that cover from the ultraviolet to the far infrared: one group for the entire Lyman continuum, two groups for the Balmer continuum, two for the Paschen continuum, and three groups for the Brackett continuum and beyond. We also compute gray variants for some of the calculations: these tend to yield a shorter and brighter initial luminosity peak because the gray opacity underestimates the true opacity of a cold CSM crossed by high-energy radiation (see  Dessart et al.
2015  for discussion). The difference between multi-group and gray transport is, however, modest because of the relative small CSM mass and extent. We adopt a simple equation of state that treats the gas as ideal with adiabatic index of  = 5/3. From the HERACLES simulations, we extract the total luminosity crossing the outer grid radius as a function of time (the time origin for our light curves is usually set when the total lu-minosity recorded first exceeds 10 41 erg s -1 ). We also extract the hydrodynamical quantities (radius, velocity, density, and temperature) at selected epochs to post-process with the nonmonotonic velocity solver in the non-LTE code CMFGEN (e. g. , see  Dessart et al. 2015)  and compute the emergent spectrum from the ultraviolet to the infrared. This approach captures the relative contributions from the fast ejecta, the dense shell at the interface between the ejecta and the CSM, the unshocked ionized CSM, as well as the outer cooler unshocked CSM.
One limitation with this version of CMFGEN is the use of the Sobolev approximation (line transfer is therefore simplistic and line blanketing is underestimated) and the necessity to fix the temperature, which results from the hydrodynamics solution and the influence of the shock. This temperature from HERACLES is not very accurate since the radiation hydrodynamics code treats the gas in a simplistic manner (the kinetic equations are not solved for). The composition adopted in our CMFGEN calculations at early times is homogeneous and corresponds to mass fractions of  He = 0. 34,  C = 1. 28 × 10 -3 ,  N = 3. 29 × 10 -3 ,  O = 4. 67 × 10 -3 (and other metals at their solar metallicity value;  H = 1- all ), which are the values predicted for a 15 M star  (Davies & Dessart 2019). The model atoms used in CMFGEN differ for early and late post-explosion times.
Shane 3-m observations were conducted on the stolen land of the Ohlone (Costanoans), Tamyen and Muwekma Ohlone tribes. VLA observations were conducted on the stolen land of the Chiricahua and Mescalero Apache tribes, and the Pueblo people. The Young Supernova Experiment and its research infrastructure is supported by the European Research Council under the European Union's Horizon 2020 research and innovation programme (ERC Grant Agreement No. 101002652, K. Mandel), the Heising-Simons Foundation (2018-0913, PI R. Foley; 2018-0911, PI R. Margutti), NASA (NNG17PX03C, PI R. Foley), NSF (AST-1720756, AST-1815935, PI R. Foley; AST-1909796, AST-1944985, PI R. Margutti), the David & Lucille Packard Foundation (PI R. Foley), VILLUM FONDEN (project number 16599, PI J. Hjorth), and the Center for AstroPhysical Surveys (CAPS) at the National Center for Supercomputing Applications (NCSA) and the University of Illinois Urbana-Champaign.
We thank  Jim Fuller  and  Samantha Wu  for stimulating discussion and RSG models. W. J-G is supported by the  National Science Foundation Graduate Research Fellowship Program  under Grant No. DGE-1842165  and the  IDEAS   Fellowship Program  at  Northwestern University. W. J-G acknowledges support through  NASA  grants in support of  Hubble Space Telescope programs   GO-16075  and  GO-16500. This research was supported in part by the  National Science Foundation  under Grant No. NSF   PHY-1748958. The Margutti team at  UC Berkeley  and  Northwestern  is supported in part by the  National Science Foundation  under Grant No. AST-1909796  and  AST-1944985 , by  NASA  through Award Number  80NSSC20K1575  and by the  Heising-Simons Foundation  under grant #  2018-0911  (PI:  Margutti). Raffaella Margutti  is a  CIFAR Azrieli Global Scholar  in the Gravity & the  Extreme Universe Program 2019 , and a Sloan Fellow in Physics, 2019.
This work was granted access to the HPC resources of CINES under the allocation  2019 -A0070410554  and  2020 -A0090410554  made by  GENCI ,  France. MRD  acknowledges support from the  NSERC  through grant  RGPIN-2019-06186 , the  Canada Research Chairs Program , the  Canadian Institute for Advanced Research (CIFAR) , and the  Dunlap Institute at the University of Toronto. D. A. Coulter  acknowledges support from the  National Science Foundation Graduate Research Fellowship  under Grant  DGE1339067. Q. W. acknowledges financial support provided by the  STScI Director's Discretionary Fund. M. R. S. is supported by the  National Science Foundation Graduate Research Fellowship Program  Under Grant No. 1842400. A. G. is supported by the  National Science Foundation Graduate Research Fellowship Program  under Grant No.
The ZTF forced-photometry service was funded under the  Heising-Simons Foundation  grant # 12540303  (PI: Graham). IRAF is distributed by NOAO, which is operated by  AURA, Inc. , under cooperative agreement with the  National Science Foundation (NSF ). The UCSC team is supported in part by  NASA  grant  80NSSC20K0953 ,  NSF  grant  AST-1815935 , the  Gordon & Betty Moore Foundation , the  Heising-Simons Foundation , and by a fellowship from the  David and Lucile Packard Foundation  to  R. J. F. Some  of the data presented herein were obtained at the  W. M. Keck Observatory , which is operated as a scientific partnership among the  California Institute of Technology , the  University of California , and  NASA. The Observatory was made possible by the generous financial support of the  W. M.
This work makes use of observations from the  Las Cumbres Observatory  global telescope network following the approved NOIRLab programs  2020B-0250  and  2021A-0239. Las Cumbres Observatory telescope time was granted by  NOIR-Lab through the Mid-Scale Innovations Program (MSIP). MSIP is funded by  NSF. W. M. Keck Observatory access was supported by  Northwestern University  and the  Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA). Based in part on observations obtained with the  Samuel Oschin 48-inch Telescope at the Palomar Observatory  as part of the  Zwicky Transient Facility  project. ZTF is supported by the  NSF  under grant  AST-1440341  and a collaboration including  Caltech , IPAC, the  Weizmann Institute for Science , the  Oskar Klein Center at Stockholm University , the  University of Maryland , the  University of Washington ,  Deutsches Elektronen-Synchrotron and Humboldt University, Los Alamos National Laboratories , the  TANGO Consortium of Taiwan , the  University of Wisconsin at Milwaukee , and the  Lawrence Berkeley National Laboratory.
Operations are conducted by the  Caltech Optical Observatories (COO) , the  Infrared Processing and Analysis Center (IPAC) , and the  University of Washington (UW). This work has made use of data from the  Asteroid Terrestrial-impact Last Alert System (ATLAS)  project. The  Asteroid Terrestrial-impact Last Alert System (ATLAS)  project is primarily funded to search for near earth asteroids through  NASA  grants  NN12AR55G ,  80NSSC18K0284 , and  80NSSC18K1575 ; byproducts of the NEO search include images and catalogs from the survey area. This work was partially funded by  Kepler/K2  grant  J1944/80NSSC19K0112  and  HST GO-15889 , and  STFC  grants  ST/T000198/1  and  ST/S006109/1. The ATLAS science products have been made possible through the contributions of the  University of Hawaii Institute for Astronomy , the  Queen's University Belfast , the  Space Telescope Science Institute , the  South African Astronomical Observatory , and  The Millennium Institute of Astrophysics (MAS), Chile.
The  Pan-STARRS1 Surveys  ( PS1 ) and the PS1 public science archive have been made possible through contributions by the  Institute for Astronomy , the  University of Hawaii , the Pan- STARRS  Project Office, the  Max-Planck Society  and its participating institutes, the  Max Planck Institute for Astronomy, Heidelberg  and the  Max Planck Institute for Extraterrestrial Physics, Garching , The  Johns Hopkins University, Durham University , the  University of Edinburgh , the  Queen's University Belfast , the  Harvard-Smithsonian Center for Astrophysics , the  Las Cumbres Observatory Global Telescope Network Incorporated , the  National Central University of Taiwan, STScI ,  NASA  under grant  NNX08AR22G  issued through the  Planetary Science Division of the NASA Science Mission Directorate, NSF  grant  AST-1238877 , the  University of Maryland, Eotvos Lorand University (ELTE) , the  Los Alamos National Laboratory , and the  Gordon and Betty Moore Foundation.
2-fold and of 1 µM poly-GA fibrils by ~8-fold  (A) Quantitative analysis of the %cells displaying large dipeptide repeat aggregates (1 µM) overtime (1 h, 2 h, 4 h). Different cell cultures were used in this experiment such as human fibroblasts (hFibro, red), iAstrocytes (iAstro, green), HeLa cells (orange), and HEK293T cells (blue). ~8,000 cells/culture were analysed. The data were collected from three independent biological replicates. Bar graphs of mean ± SEM. (B) Orthogonal views from AiryScan microscopy show the uptake of 1 µM poly-GA oligomers and 1 µM poly-GA fibrils in vimentin-stained healthy iAstrocytes after 24 h exposure. Scale bar = 10 μm. (C) 3D-rendered lateral view of a single vimentinstained healthy iAstrocyte shows the uptake of 1 µM poly-GA fibrils through the xz dimension.
Our results showed that poly-GA DPRs undergo astrocyte-to-neuron propagation, with the fibrils being six-times more efficient in transferring to motor neurons compared with oligomers. Thus, DPRs at later stages of aggregation might be more prone to transfer from affected to naïve cells, which is in agreement with previous findings related to other amyloid proteins  (Ray et al, 2020). Somewhat surprisingly, we were unable to ***P ≤ 0. 001, and ****P ≤ 0. 0001. The data were collected from three independent biological replicates. (C) Healthy iAstrocytes were imaged by dual-colour STORM after 24 h incubation with 0. 5 µM ATTO647N-PolyGA oligomers or fibrils (magenta) and anti-LAMP1 staining (green). Clus-DoC colocalization analysis for Poly-GA relative to LAMP1 (magenta graph), and LAMP1 relative to Poly-GA (green graph) shows the respective %colocalized molecules (among total molecules detected).
Bar graphs of mean ± SEM; graphs are indicative of 30 regions-of-interest (4 × 4 μm) per condition chosen only in artefact-free regions. Unpaired two-tailed t test with Welch's correction. ***P ≤ 0. 001. Scale bar = 5 μm. The data were collected from three independent biological replicates. detect any cytotoxicity in co-cultured motor neurons associated with poly-GA DPRs, nor any increase in APOJ lipoprotein in iAstrocyte CM, which has previously been linked to astrocyte-induced toxicity  (Guttenplan et al, 2021). Cytotoxicity might perhaps require the synergistic presence of other DPR species (such as poly-GR and poly-PR), which were not included in our experimental settings. Nonetheless, our data suggest an important role for astrocytes in the transmission of C9ORF72-derived DPRs to neighbouring motor neurons.
Coalescence measurements For phase-separation experiments, soluble GA and PA DPRs (stock concentration = 100 μM) were diluted in ddH 2 O to either 20, 10 or 1 μM. After vortexing, the mixture was pipetted onto glass-bottom slides (Ibidi), and assembly formation was monitored over time. Images were acquired immediately, as well as after 2 and 24 h using a Leica SP5 confocal microscope with a 63× 1. 4 NA oil immersion objective, 561 channel. To capture fine details for 3D rendering, assemblies were imaged with a z-stack following Nyquist sampling for optimized pixel density. Huygens Professional version 19. 10 (Scientific Volume Imaging, http://svi. nl) was used to deconvolve z-stack data using the CMLE algorithm (with SNR:10 and 40 iterations) and subsequently for 3D-volume and surface rendering thus generating Videos 1 and 2.
The 3D rendering for Videos 3 and 4 was performed using the software Imaris v7. 7. 2 (Bitplane); no deconvolution was applied here. Number, circularity and size of DPR assemblies were quantified with FIJI  (Schindelin et al, 2012)  and plotted using GraphPad Prism 8. The FIJI plug-in Trainable Weka Segmentation (Arganda-Carreras et al, 2017) was used to finely measure the circularity of liquid droplets in heterogeneous PA assemblies (20 μM, 24 h) (Fig  S3D  and E ).
MSD(Δt) analysis Human iAstrocytes were exposed to 1 µM ATTO550-labelled Poly-GA DPR fibrils for 24 h. Cells were then washed several times with PBS and, while keeping them in 5% CO 2 and 37°C, z-stack live-imaging was performed by taking 60 frames at ~1 frame/s rate (1 frame = 1 full z-stack) at ~190 nm lateral resolution (Zeiss LSM880, airyscan mode). Single DPR aggregates were then detected as single particles and analysed by the open-source FIJI-plug-in TrackMate  (Tinevez et al, 2017)  using difference of Gaussians (DoG) detection (estimated blob diameter = 0. 8 µm; threshold = 100) and Simple LAP tracker (linking max distance = 1 µm; gap-closing = 1 µm; max frame gap = 5). Video 5 was produced in 3D-volume rendering mode using Arivis Vision 4D software (Arivis AG) after complete image stack deconvolution was performed for each frame with Huygens Professional version 19.
For this analysis, the time range fitted was 20 s, and only trajectories greater than 30 s in length were analysed (380 separate trajectories met this criterion). For the MSD(Δt) analysis on DPR trajectories in primary mouse cortical neurons (Fig  S6A  and B ), we first used TrackMate for producing DPR tracks and then we implemented the MATLAB class @msdanalyzer written by Jean-Yves Tinevez (https://github. com/ tinevez/msdanalyzer GitHub), already used in a previous study  (Tarantino et al, 2014)  and explained in its details  (Miura & Sladoje, 2020). MSD plots and Log-Log fit plots were produced with MATLAB R2018b using the aforementioned class. A detailed explanation of how the analysis was performed (from the generation of TrackMate DPR tracks to the production of graphs after MSD analysis) can be found on GitHub at the following link: https://github.
Flow cytometry 1321N1 human astrocytoma cells (1. 2 × 10 6 /sample) were washed six times in PBS (to eliminate any remaining DPRs in the media), trypsinized, and then resuspended in 500 μl of PBS. Suspended cells were then analysed by using an LSRII flow cytometer (BD Bioscience) with excitation at 488 nm and BD FACSDiva software (version 8. 0. 1; BD Bioscience) to excite the ATTO550 fluorophore. Detection of ATTO550+ signal was set at 610/20 voltage. Cells not treated with DPRs acted as control producing the gating to discriminate between the ATTO550+ and the ATTO550-cells.