dataesr/scientific-paragraphs-categorization huggingface dataset:
For the comparisons, we selected four datasets from the SPHERE-IFS instrument obtained in various conditions of observation leading to different degrees of difficulty for the detection and estimation tasks. The four datasets are obtained from the SPHERE-IFS raw data using the pre-reduction and handling pipeline of the SPHERE consortium  (Pavlov et al. 2008). Background, flat-field, bad pixels, registration, true-North, wavelength and astrometric calibrations are also performed during this step, see  Pavlov et al. (2008) ,  Zurlo et al. (2014) ,  Maire et al. (2016)  for the detailed procedures. These processings are followed by additional steps implemented at the SPHERE Data Center  (Delorme et al. 2017)  to refine the wavelength calibration, reduce the cross-talk and somewhat cope with bad pixels.
We did not apply other post-processings such as highpass filtering. Three of these calibrated datasets are dedicated to the evaluation of the detection performance on fields reported in the literature to contain point sources. The fourth one is used for the evaluation of the spectrum recovery performance. To do so, we numerically injected fake point sources at low fluxes to perform this evaluation. The four datasets considered were recorded around the following reference targets: HR 8799 (HIP 114189) which is a A5V type star located in the Pegasus constellation. It hosts four confirmed exoplanets, only three of them (HR 8799 c, d, and e) are within the SPHERE-IFS field of view (the last one, HR 8799 b, is visible with the larger field of SPHERE-IRDIS). All of them were discovered  (Marois et al.
2008) , confirmed  (Marois et al. 2010) , and widely studied  (Bowler et al. 2010; Currie et al. 2011; Marley et al. 2012 ) by direct imaging. In the following, this dataset is used as a baseline to compare the overall performance of the detection algorithms on sources at a standard level of contrast (between 10 -6 and 10 -5 ) in the considered spectral band. β Pictoris (HIP 27321) which is a A6V type star located in the Pictor constellation. It hosts two known exoplanets (β Pictoris b and c) as well as a protoplanetary disk made of gas and dust. β Pictoris b was discovered  (Lagrange et al. 2009 ) and confirmed  (Lagrange et al. 2010 ) by direct imaging. β Pictoris c was discovered more recently by the radial velocities method  (Lagrange et al. 2019b ). In the following, we do not consider the presence of β Pictoris c 8.
New datasets around this star allowed to constrain better the orbit of the β Pictoris b and to refine its photometry  (Lagrange et al. 2019a ). In the following, we use a dataset at a challenging epoch (before the conjonction reported The recorded dataset around HD 172555 was made of 62 temporal frames but we selected T = 31 frames by discarding one of them over two, preserving the amount of the parallactic rotation ∆ par to slightly increase the difficulty of the spectrum estimation task. in  Lagrange et al. 2019a ) since the angular separation of the exoplanet is smaller than 0. 15 arcsec, which corresponds to nine pixels away from the coronagraph mask. HD 131399 (HIP 72940) which is a A1V type star located in the Centaurus constellation. It forms a triple system with two other stars (HD 131399 B and C) located about 349 au from the brightest star HD 131399 A  (De Zeeuw et al.
1999; Dommanget & Nys 2002; Pecaut & Mamajek 2013). This system also hosts a faint point source (HD 131399 Ab) discovered by direct imaging  (Wagner et al. 2016) , at first supposed to be a bounded exoplanet. However, further joint analysis of GEM-INI/GPI and VLT/SPHERE datasets to refine the astrometry and the spectrum estimation of the candidate companion led to the conclusion that HD 131399 Ab is more likely to be a background brown dwarf  (Nielsen et al. 2017 ). In the following, this dataset is used to compare the behavior of detection algorithms in the case of a faint point source falling close to the limit of the instrument's field of view. HD 172555 (HIP 92024) which is a A5V type star located in the Pavo constellation  (Schütz et al. 2005; Lisse et al. 2009 ). The analysis of directly imaged (A)(S)DI series was conducted in several surveys but, to the best of our knowledge, no point source was ever detected  (Nielsen et al.
2008; Nielsen & Close 2010). We use this dataset to conduct the spectrum estimation of numerically injected fake point sources. Table  3  summarizes the main observation parameters of these datasets. It can noted that the conditions of observations were not particularly good for three of these datasets (seeing values between 1. 20 and 1. 43). Besides, a Matlab TM implementation of the PACO ASDI routines for computing spectrally combined wwS/N maps is available online 9.
Detection results In the following, the detection criteria is based on wwS/N map with PACO ASDI since it offers interesting properties in terms of detection sensitivity and controlled false alarms rate when thresholding at 5σ (see Sect. 3. 2. 3). In our comparison to the state-of-the-art algorithms, we use the final signal-to-noise map (denoted "combined S/N") provided by the different pipelines. The "combined S/N" map is generally obtained by a weighted mean of the signal-to-noise ratio computed in each channel. This combination is generally followed by a post-processing step via 9 http://doi. org/10. 5281/zenodo. 3679426 so-called "unsharp filtering" (high pass filtering) to improve the visual quality of the combined S/N map by attenuating some spurious background artifacts. In PACO ASDI, the spatial whitening and spectral whitening operations can be seen as data-driven and locally-adaptive filters.
It reaches more than 20% for TLOCI-ASDI on the β Pictoris and HR 8799 datasets. PACO ASDI provides stationary detection maps on the whole field of view (including at the vicinity of the host star and close to the borders of the field of view) so that a unique detection threshold can be set. The stationarity is explained both by the local statistical modeling of the spatio-spectral correlations of the background and the explicit consideration of the missing or aberrant data which are flagged as outliers. Regarding the detection accuracy of the algorithms, only PACO ASDI ensures a statistically-grounded control of the PFA in the sense that no false alarm is generally observed at the 5σ threshold. Unlike PACO ASDI, all other algorithms considered lead to several false detections in the field of view (many more than expected at 5σ).
In practice, astronomers are familiar with the "false alarms issue" and generally differentiate candidate companions from the false alarms by visual inspection of the multi-spectral data and of the reduction results. Regarding the detection sensitivity of the tested algorithms, only PACO ASDI leads to detection peaks significantly higher than the conventional detection threshold (τ = 5) for all the known point sources, even when they are close to the host star (as β Pictoris b) or close to the borders of the field of view (as HD 131399 Ab). For example, for HR 8799, only one source can be unambiguously detected at 5σ with state-of-the-art algorithms on the dataset we have reduced. The other sources can be distinguished by a visual inspection of the detection maps but require to lower the detection confidence at about 3.
5 for the exoplanet HR 8799 e and at 4. 3 for the exoplanet HR 8799 d. The best results with existing methods are obtained with KLIP-ADI, leading to the detection of HR 8799 c (top-left corner of tend to show that in certain cases it is not so easy to distinguish true detections from false alarms via visual inspection: on the considered β Pictoris dataset processed with TLOCI-A(S)DI and KLIP-A(S)DI algorithms, it seems difficult to visually discriminate β Pictoris b (combined S/N ∈ [1. 9; 3. 3]) and HD 131399 Ab (combined S/N ∈ [0. 2; 2. 0]) from false alarms since the shape of the detection peaks (blobs spatially correlated on a few pixels) are sometimes quite similar. The case of HD 131399 is interesting since the dataset we consider in this paper was already processed by other authors. For example,  Nielsen et al.
Spectrum estimation performance In this section, we evaluate the performance for the spectrum extraction of point sources. As mentionned in Sect. 1, the Fig. 15. Estimated spectrum of the detectable fake faint point sources (#1 to #6 plus #8, #10, and #12) obtained with TLOCI-ADI (blue), TLOCI-ASDI (orange), and PACO ASDI (green). The spectrum ground truths of the different fake faint point sources are marked by black lines. The given 1σ confidence intervals are those predicted by the considered algorithms. A zoom around the ground truth is added for sources #10 and #12. recovered spectra are not corrected for the stellar spectrum in this paper. We first use numerical injections of fake point sources for the quantitative characterization of PACO ASDI. For this purpose, we use the dataset around HD 172555 (see Sect.
5. 1) with no detectable source. Figure  12  gives the wwS/N map obtained with PACO ASDI and the combined signal-to-noise ratio maps obtained with other algorithms on this dataset, before injecting the fake point sources. While state-of-the-art algorithms reveal several areas above the detection threshold at τ = 5, experts did not identify consistent detection peaks by closer inspection. In addition, PACO ASDI does not identify any significant detection peak at 5σ. We inject 12 fake point sources in the field of view with a variety of mean contrast and true spectra. Table  4  gives astrometric and photometric information about the fake point sources. Sources #1 to #6 have a mean flux lower or equal to 5 × 10 -5 while sources #7 to #12 have a mean flux lower or equal to 8. 5 × 10 -6. Figure  13  presents the wwS/N maps obtained with PACO ASDI around HD 172555 with fake point sources #1 to #6 (left) and #7 to #12 (right) simultaneously injected.
This observation could illustrate why experts tend to apply also ADI detection and/or characterization algorithms on ASDI datasets by processing each spectral channel independently  (Nielsen et al. 2017; Perrot et al. 2019; Gibbs et al. 2019; Maire et al. 2020 , see also  Maire et al. 2014; Rameau et al. 2015  for the challenge of ASDI processing). The empirical confidence intervals are also smaller with PACO ASDI. The RMSE of the estimated spectra is reduced by a factor at least two by PACO ASDI with respect to other algorithms (on average, by a factor 3. 6 for the three sources analyzed in Table  5 ). Moreover, the confidence intervals provided by the method are in good match with the observed standard deviations of the Monte-Carlo simulation: the ratio between the predicted standard deviation and the empirical standard deviation is between 0.
94 and 1. 21 for PACO ASDI, which is closer to one than for other methods (i. e. , PACO ASDI confidence intervals are more reliable). We also illustrate astrometric and spectrum estimations on the real point sources in the first three datasets. Table  6  presents the estimated astrometry and Fig. 17  gives the estimated spectra with PACO ASDI of the exoplanets HR 8799 c-de, β Pictoris b, and the background source HD 131399 Ab. The datasets are processed in a totally automatic fashion as described in  Flasseur et al. (2018c)  for ADI datasets. A joint refinement of the astrometry and photometry estimations of the source with A9, page 22 of 29.
9 and 10), and the observing conditions of the considered dataset were not particularly good (seeing about 1. 30, see Table  3 ). Nielsen et al. (2017)  use photometric and astrometric estimations from VLT/SPHERE and GEMINI/GPI to show that HD 131399 Ab is more likely a background brown dwarf. This result was a revision of the previous status of HD 131399 Ab firstly considered as an exoplanet just after its discovery and confirmation on the basis of (noisy) extracted astrometry and photometry  (Wagner et al. 2016). The difficulty to ascertain that HD 131399 Ab was an exoplanet based on the first observations illustrates the importance of algorithms that can provide reliable astrometric and spectrum estimations. We expect PACO ASDI to help refining the estimated orbit and the spectral characterization of future candidate companions.
Conclusion ASDI observations provide very rich data for the detection and characterization of point sources such as exoplanets. Data processing algorithms form an important component in high contrast imaging. Despite extensive efforts in the design of coronagraphic instruments, the separation of the signal of interest (off-axis sources) from the background signal of the on-axis star has to be performed under adverse conditions: large temporal and spectral fluctuations of the intensity of the background, strong correlations and contamination by outliers. Data processing algorithms must be designed with the aim to be robust to all these characteristics of the noise. We have shown in this paper that data-driven statistical modeling paved the way to reliable source detection and characterization methods.
PACO ASDI, the data processing algorithm introduced in this paper, produces detection maps with improved sensibility compared to existing methods. In particular, the detection maps are obtained through a whitening and weighting strategy accounting for the spectral correlations. We show that this general principle is also beneficial to combine detection maps from other existing detection methods. An important additional feature of PACO ASDI is the control of the probability of false alarms: detection maps can be reliably thresholded. Using the conventional 5σ threshold generally produces no false alarm in an IFS field. This contrasts with detection maps obtained with other methods for which many false alarms are observed, in particular at very small angular separations and close to the borders of the field of view.
Full exploitation of the field of view seems to be a feature of PACO ASDI that is shared by few other methods. The elaborate statistical model of PACO ASDI, accounting for spatial, spectral, and temporal fluctuations, is also used to characterize the astrometry and photometry of the detected point sources. By refining the model of the background jointly with the estimation of the source spectrum, the bias due to source self-subtraction is prevented. The spectrum is estimated using a parameter-free spectral regularization. Our numerical A9, page 23 of 29 A&A 637, A9 (2020) experiments show reduced estimation errors compared to standard methods. Beyond the direct analysis of ASDI datasets, PACO ASDI also provides information on the achievable contrast, ric astrometric accuracies that are reached for given instrumental and observational conditions.
Estimated astrometry (separation and true-north aligned angle) of the real faint point sources known in the used datasets with PACO ASDI. Pictoris b 0. 1423 ± 0. 0063 216. 25 ± 1. 70 HD 131399 Ab 0. 8412 ± 0. 0065 194. 74 ± 0. 59 Notes. The confidence intervals are given at 1σ.
Acknowledgements. We thank  A. Boccaletti  ( LESIA, Paris, France ) who provided the transmission of the SPHERE coronagraphs. We also thank the anonymous  Referee  for his careful reading of the manuscript as well as his insightful comments and suggestions. This work has made use of the SPHERE Data Centre, jointly operated by  OSUG/IPAG (Grenoble, France) ,  PYTH-EAS/LAM/CESAM  ( Marseille, France ),  OCA/Lagrange (Nice, France) ,  Observatoire de Paris/LESIA (Paris, France) , and  Observatoire de Lyon/CRAL (Lyon, France).
Data Data from a repository for the 2019 Novel Coronavirus at John's Hopkins University Center for Systems Science and Engineering (JHU CSSE) were used  (Dong, Du et al. 2020). Using these date-stamped entries of daily reported cases and reported deaths, we extracted seven timeseries pairs for the countries (including all territories) of Ireland, the United Kingdom, Italy, Spain, Germany, France and Switzerland. Data records from January 22 nd to April 5 th , 2020 were modelled. For the ordinary differential equation (ODE) model, daily cases and daily accumulated deaths were fitted while for the HMM model, daily cases and daily deaths were fitted, corresponding to the state equations (see  (Friston, Parr et al. 2020) ).
To link these ODEs to the observed data we employed an observer function which assumed a variable rate of case reporting for symptomatic (without requiring hospitalization) and asymptomatic individuals. A priori we assumed that only 1% of individuals infected who were asymptomatic received tests. We assumed that 20% of symptomatic cases who do not require hospitalization receive tests. And that 100% of infected individuals who are hospitalised receive tests. The levels of 1% and 20% testing were free parameters in our model. The 100% for hospitalised tests was fixed. Finally, 100% of deaths were assumed to be recorded. Finally, we placed a prior on the initial number of individuals in each state. A priori, we assume 100 individuals in infected states. We tested two alternatives for S0: In the ODE model (Model: ODE) we initialised S0 to 1 Million x _0 individuals, where _0 = 1.
This parameter would be optimised for each individual dataset and so could accommodate total sizes; e. g. if _0 = 4. 9 a posteriori then the total population of Ireland would be considered initially susceptible. We also tested a 'cities'-based version of the ODE model (Model: ODE_City) that might recapitulate the death and case rates for each country. For this, we altered the observer function and imposed a prior of 1 Million x _0 individuals, where _0 = 1. Then we scaled the case and death rates by the population in millions (See Appendix A for equations). For this if we obtained _0 = 1 a posteriori then the total initial susceptible population would also correspond to the total population of Ireland, but the epidemic dynamic would comprise 4. 9 distinct outbreaks. See  (Friston, Parr et al.
Results The key aim of our analysis was to estimate the likely immunity after the current set of cases and deaths. To ascertain the initial susceptibility S0, we examined the posterior estimate from both model types and its Bayesian credible intervals. However, first we examined the evidence for each model, relative to the worst performing model. We used two ODE models, with different constructs for epidemic sizes/metapopulations. The first ODE model (ODE) assumed a prior of 1 million susceptible individuals (S0), the. The second ODE model accounted for several effective populations of size 1 million (ODE_City). The third model was the HMM model, which also assumed a prior of 1 million initial susceptible individuals. Of all three models, ODE_City was the worst performing model for all countries data (Figure  2A ).
The goal of the committee's project was the identification and characterization in urine of tissue-or pathology-specific miRNA patterns (ie, changes in individual miRNAs and/or a panel of differentially expressed miRNAs) for DIKI in rats. The overall strategy to identify nephron segment-specific urinary miRNAs is depicted in Figure  1. In 11 studies, rats were dosed with nephrotoxicants known to result in injury to distinct nephron segments, ie, the glomerulus, proximal tubule, thick ascending limb (TAL) of the loop of Henle, and CD. A metaanalysis of the miRNA data from these 11 studies was performed to identify significantly modulated urinary miRNAs which were specific to a histopathology finding in 1 nephronal segment or common to more than 1. In parallel, sRNAsequencing (sRNA-seq) experiments were conducted to identify enriched expression of miRNAs in nephron segments of the kidney isolated by laser capture microdissection (LCM).
Sections were cut from these blocks, stained by hematoxylin and eosin and examined by a qualified veterinary pathologist from the respective study sites. Observations were recorded using the histopathology lexicon of the Predictive Safety Testing Consortium  (Sistare et al. , 2010)  in order to compare the histopathological findings across studies. The microscopic lesion severities were defined and graded as follows; minimal-grade 1, mild (slight)-grade 2, moderategrade 3, marked-grade 4, or severe-grade 5. Slides and histopathology data from all studies used in this HESI-based project were reviewed by a resident pathologist at Sanofi, Pfizer, Bayer, and Janssen, depending on where the study was generated, to assess morphological diagnoses and consistency in grading and to identify key treatment-related findings for each toxicant.
Urinary miRNA Profiling Using TaqMan Low-Density Arrays Using previously described methods  (Pavkovic et al. , 2014) , total RNA including sRNA species (eg, miRNAs) was isolated from 200 ml urine using the miRNeasy Mini Kit (Qiagen, Hilden, Germany), which also included 2. 5 fmol synthetic ath-miR-159a as a spike-in within each urine sample. Urinary miRNA profiles were subsequently analyzed using 3 ml of the RNA eluate with TaqMan low-density array (TLDA) cards A (all studies) and B (all   Statistical analysis workflow. Urine samples corresponding to marked or severe grades of histopathological kidney lesions in studies with puromycin on day 6 and cisplatin on days 8 and 26 were excluded from the analysis to reinforce the probability to detect normalizers. A consistent analysis workflow was used for each study using Array Studio software (Omicsoft, USA).
Library construction was performed according to Illumina's TruSeq sRNA sample prep protocol (Illumina, San Diego, California). Each of the twelve samples was barcoded. Library quality was assessed using the Agilent Bioanalyzer DNA chip and quantified using Ribo Green dye and standard methods. Sample libraries were pooled and divided across 2 lanes and 4 flow cells to reach the targeted read count. Illumina HiSeq2000 analysis was performed using 50 bp single-end reads, with a targeted read depth per sample of 40 million reads. FASTQ files are available for download at the Gene Expression Omnibus (GSE156590). Subsequent analysis was performed using Partek Flow (Partek Inc. , St. Louis, Missouri). Briefly, raw sequence was filtered for rRNA, tRNA, and mitochondrial DNA sequence using Bowtie 2 (v 2.
Identification of Nephron Segment-Enriched miRNAs by sRNA-Seq and FirePlex in Nephron Segments Isolated by LCM A limitation of the current dataset and meta-analysis is that few nephrotoxicants produce highly discrete histopathological lesions, there is often some degree of secondary lesions at other sites, and elevated urinary protein biomarkers can be associated with injury to multiple nephron sites. To increase the chance of finding miRNA candidates that may be associated with specific nephron segment expression but not necessarily with urine enrichment due to secondary effects of chemical exposure, miRNAome-wide measurements were performed with sRNAseq using independent LCM-isolated SD rat nephron in control samples and validated with medium-scaled candidate measurements using the Abcam FirePlex platform.
To determine nephron segment specificity, we utilized 3 different methods of detection: ddPCR, the Abcam FirePlex assay, and sRNA-seq for analysis of LCM samples. These technologies represent targeted, panel, and global approaches for miRNA measurements and we highlighted those miRNA measurements that agreed among the data obtained by these methods. Indeed, when determining nephron specificity or enrichment of miRNAs, there was general consensus-33 of the 56 (59%) exhibited moderate to strong correlation (>0. 50) when comparing average expression between nephron segments for a miRNA measured on the FirePlex and sRNA-seq platforms. In addition, a strong average correlation coefficient (0. 84) for segment miRNA enrichment was observed among the 4 miRNAs detected by all 3 methods (Figure  9 ).