dataesr/scientific-paragraphs-categorization huggingface dataset:
R E S E A R C H A R T I C L E were then incubated in 15 mL/L hydrogen peroxide in PBS for 15 minutes at room temperature and washed with PBS once. The standard PLA protocol was then performed starting from the blocking step. Microscopy. Immunofluorescent images were captured using either a Zeiss Axio Imager Z2 microscope or an LSM 780 Meta confocal microscope (Carl Zeiss). Electron microscopy images were captured using a JEOL 1200 EX II Electron Microscope (University of Texas [UT] Southwestern Electron Microscopy Core). SYTOX Green assay was analyzed using a BD Pathway 855 automated microscope. Statistics. Experimental data were analyzed using GraphPad Prism 8 software. P values were calculated using 2-tailed Student's t test and 1-or 2-way ANOVA (for multiple-group analyses) followed by Tukey's post hoc test.
Acknowledgments The authors thank  L. Nguyen  for technical assistance and  H. Smith  for assistance with manuscript preparation. This work was supported by  NIH  grants  R01 CA109618  (BL),  U19 AI109725  (BL),  U19 AI142784  (BL),  R01 DK091392  (MCH),  R01 DK092461  (MCH),  5P30 CA124543  (AZ and BAP), and  R01 HL138720  (JS);  Cancer Prevention Research Institute of Texas  grant  RP120718  (BL);  Fondation Leducq  grant  15CBD04  (JS and BL); the  Pak Foundation for Endowed Professors Collaborative Research Support (MCH) ; the  "Institut National de la Santé et de la Recherche Médicale" (ATIP AVENIR) (PER) ; the  "Agence Nationale pour la Recherche"  ( ANR-14-CE12-0011 ,  ANR-14-CE35-0022 ,  ANR-18-CE14-0006-01 ) (PER); the  Swiss National Foundation  ( 310030 ,  163064 ,  310030 ,  182332 ) (JP); and a  Fondation Motrice  grant (JP).
Study design. We conducted an observational cohort study, with follow-up visits at week 12, 20, 32 and 64 (NCT02573948) in accordance with local and international guidelines. The implementation of this cohort and its follow-up were done with the direct contribution of local peer support groups of drug users. The study was reviewed and approved by the Institutional Review Boards of the Hai Phong University of Medicine and Pharmacy (Vietnam) and the Icahn School of Medicine at Mount Sinai (USA).
Data collection and data analyses. The data were collected through electronic case report forms on tablet computers. Baseline participant characteristics were presented using number and percentage for categorical data; and median and interquartile range (IQR) for quantitative variables. For HIV and HCV incidence estimation, we assumed that the infection occurred at midpoint between the last negative test and the first positive test. The incidence was then defined by the total number of new infections that occurred between the RDS and the final visit, divided by the quantity of follow-up (in person-years) during the at-risk period. Only participants with two test results were included in these analysis. They could have a follow-up time quantity of 32-weeks if lost to follow-up after the second visit or a time of 64-weeks if they completed the 64-week follow-up, whether they attended or did not the 32-week visit.
Acknowledgements This work was supported by the  French "Agence Nationale de Recherche sur le SIDA  et les hépatites virales" [# 12299 & #12353 ] and  US National Institute of Drug Abuse  [# P30DA011041  & # R01DA041978 ]. Expertise France funded the HCV viral loads. The funding agencies did not interfere neither with the design of the study nor with the interpretation of the results. We are grateful to Provincial Health authorities and Viet Tiep hospital direction for their continuous support during the study. We want to specifically acknowledge  Dr. Vu Van Cong ,  Pr. Truong Thi Xuan Lien  and  Pr. F. Barré-Sinoussi  ( ANRS Vietnam site coordinators ) for their help at the early stage of the project and their inestimable advices. Composition of DRIVE study team:  Vu Van Cong ,  Nguyen Van Dinh ,  Cao Thi Kim Giang ,  Nguyen Thanh Hai ,  Ted Hammet ,  Hoang Thi Hoa Le ,  Marie Jauffret Roustide ,  Le Thi Loan ,  Nguyen Hoang Long ,  Dang Thi Ly ,  Nguyen Huu Nhan ,  Dao Nhat Quyen ,  Vu Minh Son ,  Nguyen Thi Thanh ,  Doan Thi Thu ,  Nguyen Thi Thu.
The "generalized crescent" (GC) geometric models considered in EHTC VI yielded fit qualities comparable to those of image reconstructions for the 2017 data, and in this paper, we apply two variants of the GC model to the pre-2017 data sets. Owing to data sparsity, we restrict the parameter space of the models to a subset of that considered in EHTC VI containing only a handful of parameters of interest. Throughout this paper, we use perceptually uniform color maps from the ehtplot library 4  to display the images. In some of the figures we present models blurred to a resolution of 15 µas, adopted in this paper as the effective resolution of the EHT. The EHT instrument resolution measured as the maximum fringe spacing in 2017 is about 25 µas, however, for the image reconstruction methods employed in EHTC IV a moderate effect of superresolution can be expected  (Honma et al.
Model specification Given the image morphology inferred from the 2017 data, the primary parameters of interest we would like to constrain using the earlier data sets are the size of the source, the orientation of any asymmetry, and the presence or absence of a central flux depression. The analyses presented in this paper use two simple ringlike models -similar to those presented in  Kamruddin & Dexter (2013)  and  Benkevitch et al. (2016)  -both of which are subsets of the GC models from EHTC VI. The first model we consider is a concentric "slashed" ring, where the ring intensity is modulated by a linear gradient, hereafter denoted as RT. In this model, the flux is contained within a circular annulus with the inner and outer radii R in and R out , respectively. The model is described by five parameters: 1.
The equivalent five parameters for this model are: 1. the mean diameter of the ring d = 2R in = 2R out , 2. the position angle of the bright side of the ring 0 ≤ φ B < 2π, 3. the width of the Gaussian blurring kernel 0 < σ < 40 µas, 4. the total intensity 0 < I 0 < 2 Jy, and 5. the slash 0 < β < 1. This second model, hereafter referred to as RG, reduces to a circular Gaussian for d σ. Both the RT and RG models provide a measure of the source diameter (d), the orientation of the brightness asymmetry (φ B ), and the presence of a central flux depression. We quantify the latter property using the following general measure of relative ring thickness (from EHTC VI) f w = R out -R in + 2σ √ 2 ln 2 d , (5) where R out = R in for the RG model and σ = 0 for the RT model. All data sets except 2009 contain observations from intrasite baselines ("zero baselines"), see Table  1.
For M87 * , these baselines are sensitive to the flux from the extended jet emission on ∼ arcsecond scales (EHTC IV, see also the bottom panel of Figure  3 ) and do not directly inform us about the compact source structure on scales of ∼ tens of microarcseconds. However, the intrasite baselines still provide useful constraints on station gain parameters (see Section 3. 2), and hence, we do not flag them. Rather, we parameterize this large-scale flux using a large symmetric Gaussian component consisting of two parameters, flux and size. This component is entirely resolved out on intersite baselines and thus has no direct impact on the compact source geometry. Ultimately, the models that we use have 5 geometric parameters for the 2009 data set and 5+2=7 geometric parameters in all other cases.
Fitting procedure and priors We perform the parameter estimation for this paper using Themis, an analysis framework developed by  Broderick et al. (2020)  for the specific requirements of EHT data analysis. Themis operates within a Bayesian formalism, employing a differential evolution Markov chain Monte Carlo (MCMC) sampler to explore the posterior space. Prior to model fitting, the data products are prepared in a manner similar to that described in EHTC VI. Descriptions of the likelihood constructions for different classes of data products are given in  Broderick et al. (2020). One important difference between the 2017 and pre-2017 data sets is that the latter contain almost exclusively visibility amplitude information, rather than having access to the robust closure quantities in both phase and amplitude  (Thompson et al.
By explicitly modeling station-based gains, we correctly account for the otherwise covariant algebraic structure of the visibility calibration errors  (Blackburn et al. 2020). At each MCMC step, Themis marginalizes over the gain amplitude parameters (subject to Gaussian priors) using a quadratic expansion of the log-likelihood around its maximum given the current parameter vector; see  Broderick et al. (2020)  for details. For the analysis of the 2009-2013 data sets presented in this paper, we have adopted rather conservative 15% amplitude gain uncertainties for each station, represented by symmetric Gaussian priors with a mean value of 0. 0 and standard deviation of 0. 15. The width of these priors reflects our confidence in the flux density calibration rather than the statistical variation in the visibility data.
This is motivated by the theoretical interpretation of the asymmetric ring feature (EHTC V). In the case of the 2012 data set, for which a very limited number of closure phases is available, we report the orientation φ B of the maximum likelihood (ML) estimators, noting the bimodal character of the posterior distributions and the aforementioned 180 • degeneracy. These caveats do not apply to the 2017 data set, for which substantial closure phase information is available and breaks the degeneracy. It is important to recognize that the parameters of a geometric model have no direct relation to the physical parameters of the source, unlike direct fitting using GRMHD simulation snapshots  (Dexter et al. 2010; Kim et al. 2016; Fromm et al. 2019 , EHTC V) or raytraced geometric source models  (Broderick & Loeb 2009; Broderick et al.
MODELING SYNTHETIC DATA In order to verify whether the (u, v)-coverage and signal-to-noise ratio of the 2009-2013 observations are sufficient to constrain source geometric properties with simple asymmetric ring models, we have designed tests using synthetic VLBI observations. The synthetic observations are generated with the eht-imaging software  (Chael et al. 2016 (Chael et al. , 2018b ) by sampling four emission models (GRMHD1, GRMHD2, MODEL1, MODEL2) with the (u, v)-coverage and thermal error budget reported for past observations. Additionally, corruption from time-dependent station-based gain errors has been folded into the synthetic observations. The groundtruth images that we use correspond to ray-traced snapshots of a GRMHD simulation and published images of M87 * (EHTC IV), reconstructed based on the 2017 observations.
M87 * images 5 Hereafter, we use the natural units in which G = c = 1. For additional synthetic data tests, we consider images of M87 * generated based on the 2017 EHT observations, published in EHTC IV. We consider two days of the 2017 observations with good coverage and reported structural source differences (EHTC III; EHTC IV, Arras et al. 2020) -2017 April 6th (MODEL1) and 2017 April 11th (MODEL2), see the first row of Figure  7. MODEL2 was also shown in the first panel of Figure  1. While these models are constructed based on the observational data, we resample them numerically to obtain synthetic data sets considered in this section. Synthetic closure phases on the SMT-SMA-CARMA triangle computed from these models were shown in Figure  4. Note that there is a subtle difference between resampling a model constructed based on the 2017 data with a numerical model of the 2017 array and direct modeling of the actual 2017 data, considered in Section 5.
Results for the synthetic data sets Figure  7  shows a summary of the maximum likelihood (ML) RT model fits to the synthetic data sets; each column shows the fits for a single ground-truth image, and each row shows the fits for a single array configuration. Though our simple ring models cannot fully reproduce the properties of the abundant and high signal-to-noise data sampled with the 2017 array (i. e. , fits to these data sets are characterized by poor reducedχ 2 values of χ 2 n ∼ 5), they nevertheless recover diameter and orientation values that are reasonably consistent with those reported in EHTC VI for the 2017 observations, including the counterclockwise shift of the brightness position angle between 2017 April 6th (MODEL1) and 11th (MODEL2). We note that the underfitting of the data set sampled with the 2017 array results in an artificial narrowing of the parameter posteriors (the full posterior is captured in EHTC VI by considering a more complicated GC model).
ML estimators for 2009-2013 data sets, on the other hand, typically fit the data much closer than the thermal error budget. Because we model time-dependent station gains in a small array (often only two to three telescopes observing at the same time in 2009-2013 with missing detections on some baselines), the number of model parameters may be formally larger than the number of data points, and this complicates our estimation of the number of effective degrees of freedom (see also Section 5. 3). As a consequence, we cannot gen-erally utilize a χ 2 n goodness-of-fit statistic as was done in EHTC IV and EHTC VI. 6 The relevant parameter estimates and uncertainties from the RT model fits are listed in Table  2. In Figure  6  we show the marginalized posteriors for the diameter and position angle parameters.
For each synthetic data set we also indicate the image domain position angle η, defined as η = Arg k I k e iφ k k I k , ( 11 ) where I k is the intensity and φ k is the position angle of the kth pixel in the image. A similar image domain position angle estimator was considered in EHTC IV. We notice that the image-and model-based estimators may occasionally display significant differences (e. g. , GRMHD1). However, they are both sensitive to global properties of the brightness distribution, unlike some other estimators that could be considered, such as, e. g. , the location of the brightest pixel. For the diameter d estimates reported in Table  2  we list both the median and ML values, with 68% and 95% confidence intervals, respectively. For the orientation angle φ B we list the ML values with 68% confidence intervals, and for the fractional thickness f w we list the 95th distribution percentile.
We note that in 3 out of 4 synthetic data sets, the limited number of closure phases provided by the simulated data sets with the 2012 coverage is enough to correctly break the degeneracy in the position angle φ B , discussed in Section 3. 3. For the synthetic GRMHD data sets, the preference for the correct brightness position angle is very strong (see Figure  6 ). For the MODEL data sets, the effect of closure phases is much less prominent, the distributions remain bimodal, and in the case of the MODEL1 data  set, the ML estimator points at the wrong orientation, suggesting brightness located in the north. We also consider the fractional thickness f w of the ring, as defined in Equation  5. The fractional thickness provides a measure of whether the data support the presence of a central flux depression, a signature feature of the black hole shadow, or if it is consistent with a disklike morphology (i.
e. , f w ≈ 1 for the RT model). We find that f w is less well constrained than the diameter d, consistently with the conclusions of EHTC VI. In some cases the ML estimator corresponds to a limit of a disk-like source morphology without a central depression (see Figure  7 ). Only the 2013 and 2017 synthetic data sets allow us to confidently establish the presence of a central flux depression, with posterior distributions excluding f w > 0. 7 for all synthetic data sets (see Table  2 ). For the 2009-2012 coverage synthetic data sets, f w is not constrained sufficiently well to permit similar statements. We find that the RG model produces re-sults that are typically consistent with those of the RT model (see Appendix A, Figure  16 ).
Closure phases predicted by the ML estimators for the RT and RG models are indicated in Figure  4. The 2017 data sets are consistent with the orientation imposed by the jet position prior. The second rows of Figures  8  and 9  show the ML estimators blurred to a resolution of 15 µas. In the third rows of Figures  8  and 9  we present "mean images" for each data set, obtained by sampling 2 × 10 4 sets of model parameters from the MCMC chains and averaging the corresponding images. The mean images highlight structure that is "typical" of a random draw from the posterior distribution, though we note that a mean image itself does not necessarily provide a good fit to the data. Because of the rotational degeneracy, the orientation is always assumed to be the one closer to the orientation given by the ML estimate of φ B for the construction of these images.
The 2009 and 2011 data sets do not constrain the orientation well. All ML estimators and mean images from the RT model fits show a clear shadow feature, indicating that a disk-like, filled-in structure is disfavored by all of the observations (however, for 2009-2012 it cannot be excluded with high confidence based on the relative thickness parameter f w distribution, Table  3  and Appendix B). This is contrary to the synthetic data results shown in Figure  7 , where some of the ML estimators correspond to a disk-like morphology. On the other hand, the mean images for the 2009-2012 RG model fits show a significant flux density interior to the ring, indicating that these data sets are consistent with a symmetric Gaussian source model, having no central flux depression. This is a consequence of the resolution being limited by the lack of long baselines prior to 2013.
( 13 ) In Equations 12-13 we follow the notation of Equation  6 , that is, Vi represents the visibilities of the geometric model while Vi corresponds to the model modified by applying the estimated gains, representing the final fit to observations V i. Uncertainties σ i correspond to the thermal error budget. We only account for nonzero baselines, which describe the compact source properties. In the bottom rows of Figures  11 12 we indicate two error bars. Black error bars correspond to the thermal uncertainties σ i , while the red ones correspond to inflated uncertainties s i = σ 2 i + 2 • (0. 15V i ) 2 , (14) approximately capturing the uncertainty related to the amplitude gains. For all 2009-2013 data sets, the flexibility of the full model is sufficient to fit the sparse data to within the thermal uncertainty level with a best-fit ML estimator.
Consistency with the prior analysis In order to assess model-related biases and verify to what extent our simplified models recover geometric parameters consistent with the ones reported by EHT, i. e. , image domain results given in EHTC IV Tables  5  and 7 , and geometric modeling results given in EHTC VI Tables 2 and 3, we gather these results in Table  4. For the details of the methods and algorithms, see explanations and references in EHTC IV and EHTC VI. We notice that (1) differences between methods may be as large as 30 • for the same data set, (2) models considered in this work measure diameter and position angle consistently with more complex crescent models (GC) and with the image domain methods within the expected intermodel variation, (3) RT and RG models are too simplistic to fully capture the properties of the 2017 data sets, resulting in underfitting, indicated by higher values of χ 2 n , and (4) posteriors of the RT and RG models are narrower for the 2017 data sets than the GC posteriors as an effect of the underfitting.
source morphology and stability. While the constraining power of the 2009-2013 data sets is rather weak in comparison to the 2017 observations, we find evidence for a persistent ring structure that shows modest struc-tural variability. Both the persistence and variability offer important constraints for models of M87 *. We will now discuss our evidence and theoretical implications for the presence of the shadow feature in 2009-2017 (Sec- tion 6. 1), the persistence of the source geometry as an argument for its theoretical interpretation (Section 6. 2), and the variability of the source geometry (Section 6. 3). We also summarize the limitations and caveats of the theoretical interpretation within the GRMHD framework (Section 6. 4).
Presence of the shadow feature In Section 5 we have discussed the fits of the asymmetric ring models to the M87 * observations, indicating that all data sets are consistent with such a geometry. Within the framework of a ring model, a key question for the archival observations is whether we unambiguously detect the inner flux depression seen in the 2017 results, the expected feature of a black hole shadow. While maximum likelihood estimators clearly indicate this feature in all cases (Figure  8 ), a detailed inspection of the posterior distributions shown in Appendix B allows us to conclude that only the 2013 archival data set provides a robust detection of the central flux depression, constraining the relative thickness of the ring f w to less than 0. 5 (see Table  3 ). For the 2009-2012 data sets, the RT model posteriors indicate some preference toward the presence of a flux depression; however, a disk-like filledin geometry cannot be excluded with a high degree of confidence (Appendix B).
Black hole shadow or a transient feature As discussed in Section 3, the 2009-2013 data sets can be successfully modeled with both an asymmetric Gaussian and a ring model, with each giving a similar fit quality. However, the maximum likelihood Gaussian models are very inconsistent in size, shape, and orientation across different years, see Figure  14. In contrast, the best-fitting ring models, as seen in Figure  8 , are similar in size over all epochs under the priors described in Sections 3. 2 and 3. 3. Even when considered separately from the 2017 results, this consistency supports our choice to interpret the 2009-2013 morphology as a ring and to draw conclusions from the results of fitting asymmetric ring models to the data. The 95% confidence intervals of all 2009-2017 diameter posteriors lie within 40±12 µas, while ML estimators of the diameter from all years lie within 43 ±5 µas, see Table  3.
These values are also consistent with the M87 * diameter measurement of 42±3 µas reported by EHTC VI and with the expected size of an observed black hole of mass/distance corresponding to the stellar dynamics measurement  (Blakeslee et al. 2009; Gebhardt et al. 2011). All 2009-2017 diameter measurements are inconsistent with the gas dynamics mass measurement  (Walsh et al. 2013) , which predicts a diameter roughly half as large. The consistency in the diameter across multiple observational epochs supports the interpretation of the ring-like feature as emission from the immediate surroundings of the supermassive black hole. The four EHT observations of M87 * in 2017 spanned about a week, corresponding to a timescale of ∼ 15 M. With such a short time span, we cannot exclude a transient origin for the source morphology using 2017 data alone.
Time variability of the source geometry In addition to conclusions from the persistence of the ring structure, we can also draw inferences from the variability observed in the ring structure across the 2009-2017 data sets. In particular, the spread of the diameter and brightness position angle estimates (Table  3 ) are significantly larger than the spread for corresponding static synthetic data sets (Table  2 ). As a specific example, the circular standard deviation of the ML position angle estimators given in  , 4 • for GRMHD1, GRMHD2, MODEL1, and MODEL2, respectively. For the observational data (Table 3) the circular standard deviation is equal to 48 •. This larger spread suggests that we are detecting intrinsic structural variability despite the large uncertainties in the parameters estimated with pre-2017 observations.