dataesr/scientific-paragraphs-categorization huggingface dataset:
Radio properties of the clusters are studied in  Pasini et al. (2021). ICM morphology and X-ray scaling relations based on eROSITA data are studied in  Ghirardini et al. (2021a)  and  Bahar et al. (in prep. ) , respectively. Spectroscopic follow-up results will be provided in Ider  Chitham et al. (in prep. ). The catalog of cluster candidates that are misidentified as point sources will be presented in  Bulbul et al. (in prep. ). Moreover, the main eFEDS X-ray source catalog is provided in  Brunner et al. (2021). The eFEDS simulation results are introduced in detail in  Liu et al. (2021). The paper is organized as follows. In Sect. 2 we describe the eROSITA observations of the eFEDS field and the source detection in detail and discuss the potential contamination in the sample. We also summarize in this section the optical confirmation and redshift determination of the clusters on the basis of optical photometric and spectroscopic survey data.
In Sect. 3 we examine the selection function of the cluster sample. In Sect. 4 we provide the details of the X-ray data analysis we performed in this work and of the X-ray observables. In Sect. 5 we compute the X-ray luminosity function of the cluster sample. In Sect. 6 we perform a search for superclusters in the eFEDS field on the basis of the spatial distribution and redshifts of the clusters. Our conclusions are summarized in Sect. 7. Throughout this paper, we adopt the concordance ΛCDM cosmology with Ω Λ = 0. 7, Ω m = 0. 3, and H 0 = 70 km s - 1  Mpc -1. Quoted error bars correspond to a 1σ confidence level unless noted otherwise.
eROSITA observation and data calibration The data are processed with the eROSITA Standard Analysis Software System (eSASS,  Brunner et al. 2021)   1. The details of the data reduction and calibration are described in  Brunner et al. (2021)  and  Dennerl et al. (2020). We here provide a short summary of the analysis steps. Pattern recognition and energy calibration are applied for all the seven eROSITA telescope modules (TMs) to produce calibrated event lists. The event lists are then filtered after the determination of good time intervals, dead times, corrupted events and frames, and bad pixels. Using startracker and gyro data, we assign celestial coordinates to the reconstructed X-ray photons, which can then be projected on the sky so that images and exposure maps can be produced. We select all valid pixel patterns here, that is, single, double, triple, and quadruple events, but use only photons that are detected at off-axis angles ≤ 30.
Optical confirmation and redshift determination The photometric redshifts of the clusters are determined based on the multicomponent matched filter (MCMF) cluster confirmation tool (see  Klein et al. 2018 Klein et al. , 2019 , for more details). The MCMF tool takes optical photometric datasets and searches for galaxy overdensities along the line of sight at the position of a cluster candidate. To do this, the galaxy cluster richness is measured as a function of redshift using a red-sequence technique and within an aperture defined from the X-ray count rate. Peaks in richness versus redshift space are fit by specific peak profiles, and redshift and richnesses are recorded for multiple possible counterparts along the line of sight. MCMF is then run on random lines of sight excluding regions around X-ray candidates.
MCMF on eFEDS was run on two photometric datasets with different optical filters and depth: DECaLS g, r, z and unWISE W1 bands, and HSC g, r, i, z bands. DECaLS is free from strong calibration issues and provides full coverage of the eFEDS field, but is ∼ 2 magnitudes shallower than HSC-SSP data. The deep HSC-SSP data in the g, r, i, z bands provide good photometric redshifts out to high redshifts (z ∼ 1. 3), where DECaLS has shallower data and misses the i band. The results of both MCMF runs were then combined to the final catalog. Spectroscopic redshifts, on the other hand, were then derived by cross-matching the clusters with public spectroscopic redshifts including SDSS up to DR16  (Blanton et al. 2017) , GAMA  (Driver et al. 2009) , and 2MRS  (Huchra et al. 2012 ) and requiring either three or more redshifts consistent with the photo-z estimate and within R 500 (estimated using the relation between X-ray count rate and mass, see  Klein et al.
For halos and subhalos with masses M 500c above 5 × 10 13 M , cluster images were drawn from a library of emission measure profiles inferred from actual datasets, leading to a reproduction of the observed scaling relations between mass, luminosity, and temperature  (Comparat et al. 2020). The assignment of halos and profiles depends on their mass, redshift, and dynamical state; a large positional offset between the dark matter center of mass and its highest density peak will preferentially lead to a low central value of the emission measure profile. An ellipsoidal shape is given to cluster images that follows the halo triaxial shape. Halos of lower masses are simulated with the same method, but we decreased their flux to obtain realistic fluxes for groups. The method suffers from the existing Malmquist bias in the library of profiles we used and creates groups that are too bright.
The source spectra are absorbed by an amount depending on the local Galactic absorption column density. Stellar sources as well as cosmic X-ray background and emission from the Galaxy are accounted for using measurements acquired during the calibration and performance verification phases. The SIXTE simulation software  (Dauser et al. 2019 ) is fed with the actual spacecraft attitude file during the eFEDS mapping, hence the simulations faithfully reproduce the exposure variations in the field. The resulting event lists are processed similarly to the true eFEDS event lists, see Sect. 2. 1. In particular, extended sources are identified using identical thresholds in detection and extent likelihood as in the real catalog.
Finding source counterparts Matching the input galaxy cluster catalogs and the extended source lists poses a challenge because the sky density of the input galaxy cluster catalogs is higher than that of the extended source lists. Fixing a matching radius or performing a nearest neighbor search might lead to unrealistic matches because of projection effects. In order to mitigate this issue, we applied a Bayesian matching procedure that is based on the NWAY software and formalism  (Salvato et al. 2018). Cross-correlation between the twocatalogs avoids specifying an explicit scale length, except for a maximum radial search of 3 around each source, which is set to save computational cost. We accounted for angular extents by artificially increasing the positional uncertainties of input and detected sources.
The faint point sources within the images were excised, while the bright ones were modeled as delta functions convolved with the PSF to eliminate residual emission due to the wings of the PSF. The resultant model image was fit with the soft-band image of eFEDS observations using the Monte Carlo Markov chain (MCMC) code emcee (Foreman-Mackey et al. 2013) to find the best-fit parameters, consisting of the parameters of the number density model. We integrated the surface brightness profile and converted it into luminosity and flux by constructing an apec model in Xspec, adopting the ICM temperature measured from the spectral analysis. Clearly, the energy conversion factor (ECF) depends on the temperature, which itself varies with radial distance from the cluster center. Taking advantage of the weak dependence of this conversion on temperature  (Pacaud et al.
Spectral analysis We extracted spectra and computed ancillary response files (ARFs) and redistribution matrix files (RMFs) from the seven TMs using the eSASS algorithm srctool with the latest version of the calibration database. For the extraction radius that we used Fig. 7. Luminosity redshift distribution of the clusters with significant luminosity measurements (> 2σ) for 300 kpc (upper panel) and 500 kpc (lower panel). The black curve shows the flux limit: 10 -14 erg s -1 cm -2 for 300 kpc and 1. 5 × 10 -14 erg s -1 cm -2 for 500 kpc (without a K correction). for the spectral analysis, we opted for two fixed physical apertures, 300 kpc and 500 kpc. The first was chosen for a fair comparison with similar flux-limited surveys, for instance, the XXL survey  (Pacaud et al. 2016 ). The second is a compromise between the aim to include more photons and the rapidly decreasing signal-to-noise ratio (S/N) in the cluster outskirts.
Conclusions We have presented the first catalog of eROSITA-selected galaxy cluster and group candidates and their physical properties as detected in the sky area covered by the eROSITA Final Equatorial-Depth Survey (eFEDS). The optical follow-up for redshift and confirmation was performed using the MCMF tool on the basis of data from the HSC-SSP survey and the DECaLS survey. Our conclusions are summarized below. In the area of 140 square degrees that is covered by eFEDS, we detected 542 candidate clusters and groups of galaxies down to the flux limit of ∼ 10 -14 erg s -1 cm -2 in the soft band (0. 5-2 keV) within 1. The clusters are distributed in the redshift range z =[0. 01, 1. 3], and the median redshift is z median = 0. 35. The contamination fraction of noncluster sources in the sample, dominated by AGN misidentified as extended sources, is ∼ 1/5, as obtained from simulations.
Acknowledgements. This work is based on data from eROSITA, the soft X-ray instrument aboard SRG, a joint  Russian-German  science mission supported by the  Russian Space Agency (Roskosmos) , in the interests of the Russian Academy Article number, page 16 of 25 Liu et al. : eFEDS cluster and group catalog of Sciences represented by its  Space Research Institute (IKI) , and the  Deutsches Zentrum für Luft-und Raumfahrt (DLR). The SRG spacecraft was built by  Lavochkin Association (NPOL)  and its subcontractors, and is operated by NPOL with support from the  Max Planck Institute for Extraterrestrial Physics (MPE). The development and construction of the eROSITA X-ray instrument was led by  MPE , with contributions from the  Dr. Karl Remeis Observatory Bamberg & ECAP (FAU Erlangen-Nuernberg) , the  University of Hamburg Observatory , the  Leibniz Institute for Astrophysics Potsdam (AIP) , and the  Institute for Astronomy and Astrophysics of the University of Tübingen , with the support of  DLR  and the  Max Planck Society.
We propose a strategy to identify classes of signals in continuous single-station seismograms in an unsupervised fashion. Our strategy relies on extracting meaningful waveform features based on a deep scattering network combined with an independent component analysis. Based on the extracted features, agglomerative clustering then groups these waveforms in a hierarchical fashion and reveals the process of clustering in a dendrogram. We use the dendrogram to explore the seismic data and identify different classes of signals. To test our strategy, we investigate a two-day-long seismogram collected in the vicinity of the North Anatolian Fault, Turkey. We analyze the automatically inferred clusters' occurrence rate, spectral characteristics, cluster size, and waveform and envelope characteristics.
Hierarchical Clustering In general, cluster analysis groups objects based on their similarity to each other (e. g. ,  Xu & Wunsch, 2008). Objects in the same cluster are more similar to each other than objects in separated clusters. The similarity between objects is measured on a set of certain characteristics called features. Finding the most relevant features for this task will be discussed later. Various algorithms exist to find groups of objects in a data set. This study utilizes hierarchical clustering with a bottom up approach, namely agglomerative clustering. Hierarchical clustering relies on a similarity matrix, which defines the similarity (e. g. , a specific distance in the feature space) between all objects in a data set (S. STEINMANN ET AL.
Data We test our proposed workflow on continuous three-component seismic data from the station DC06 of the DANA experiment in Turkey (see for instance  Poyraz et al. , 2015 , and the map shown in Figure  3a ). Originally, the experiment was conducted to investigate the crustal structure beneath the western segment of the North Anatolian Fault. We choose the data set for mainly two reasons. First of all, the data set contains both seismic and anthropogenic activity, which is a typical situation in most seismological studies. Second of all, an existing template matching catalog provides labels for the seismicity in this area. The catalog was built following the methodology in  Beaucé et al. (2019). We choose to analyze the seismic data from the 25th to the 27th of July 2012. During the period of these two days, a high rate of localized seismicity with 148 cataloged events occurred on and around the northern strand of the North Anatolian fault (see Figures  3a  and 3b ).
For the temporal pooling operation, we apply maximum pooling, since we are interested in detecting and classifying non-stationary events such as the seismic burst. If the focus of classification is the background noise, average pooling might be the better choice (as suggested in  Seydoux et al. , 2020). The moving pooling window is 20. 48 s large and does not overlap. Hence, the time resolution of the deep scattering spectrogram is also 20. 48 s. For dimensionality reduction, we apply an independent component analysis using the FastICA algorithm from the scikit-learn Python library. In this study, we select the appropriate number of independent components according to the reconstruction loss between the original data and the reconstructed data after compression with an ICA (detailed in Appendix B).
The regional catalog is downloaded from IRIS. For calculating the seismic phase arrivals at the station, we use the TauP module of ObsPy with the velocity model of  Kennett and Engdahl (1991). We consider a sample related to an event of the catalog if the 20 s window of the sample overlaps with the window between the P wave arrival and the decaying coda. The waveform data of D. 2 and D. 3 are presented in Figure  8. Figure  8a  indicates the samples which can be explained by arrivals of a regional or local event, and Figure  8b  shows the samples which can not be explained by arrivals of a regional or local event. Note that one sample in the feature space represents ca. 20 s of waveform data and each horizontal waveform displayed in Figure  8  contains multiple consecutive 20 s windows. Subcluster D.
Acknowledgments The authors acknowledge support from the  European Research Council  under the  European Union   Horizon 2020 research and innovation program  (grant agreement no. 742335 , F-IMAGE). This work has also been supported by  MIAI@Grenoble Alpes  ( ANR-19-P3IA-0003 ). E. B. was also supported by funds associated with  Robert D. van der Hilst 's Schlumberger chair. IRIS Data Services are funded through the  Seismological Facilities for the Advancement of Geoscience (SAGE)  Award of the  National Science Foundation  under Cooperative Support Agreement EAR-1851048. Moreover, the authors want to thank the editors,  Andrew Valentine  and an anonymous reviewer for their time and effort to improve the study in many ways.
Data Availability Statement The facilities of IRIS Data Services, and specifically the IRIS Data Management Center, were used for access to waveforms, related metadata, and/or derived products used in this study. The data of the DANA array can be found at DANA (2012). The scattering network which was used in this study can be found at https://doi. org/10. 5281/zenodo. 5518136. The python packages ObsPy, SciPy, and Scikit-learn were heavily used for processing the data  (Beyreuther et al. , 2010; Pedregosa et al. , 2011; Virtanen et al. , 2020). Maps were created with the python package Cartopy (Met Office, 2010 -2015). We used map tiles by Stamen Design, under CC BY 3. 0. Data by OpenStreetMap, under ODbL.
In this study, we addressed the effects of monotherapy and combination therapy using anti-PD-1 and anti-CTLA-4 therapeutic antibodies in graft mouse tumor models. We demonstrated that the two antibodies affect the tumor immune microenvironment in different ways, even if they both displayed efficient anticancer activity. Anti-PD-1 and anti-CTLA-4 alone or in combination displayed different anti-cancer efficacy between the CT26 colorectal and the 4T1 triple negative breast cancer models. Interestingly, analysis of publically available patient databases with breast cancer, and in particular with triple negative patients, tends to suggest that high expression of interactors of PD-1, e. g. PD-L1 and PD-L2 and of CTLA-4, e. g. CD80 and CD86, generally expressed in the tumor microenvironment  [24, 25] , are strongly correlated with the prognosis for patients.
Clinical survival analysis The clinical data from breast cancer cohorts shown here are based on mixed publicly available data: E-MTAB-365*, GSE11121, GSE12093, GSE12276, GSE1456, GSE16391, GSE16446, GSE16716, GSE17705, GSE17907, GSE19615*, GSE20271, GSE2034, GSE20685, GSE20711, GSE21653*, GSE2603*, GSE26971, GSE2990, GSE31519*, GSE3494, GSE37946, GSE42568, GSE45255*, GSE4611, GSE5327, GSE6532, GSE7390, GSE9195. Overall survival (OS) was constructed using the Kaplan-Meier method with the online software 'Kaplan-Meier Plotter' (https://kmplot. com/analysis/) [26]. Probeset 1554519_at (CD80), 205685_at (CD86), 220049_s_at (PD-L2 or PDCD1LG2), 227458_at (PD-L1 or CD274) have been used. The log-rank test was used for comparison between low and high expressing groups. *Cohort including ER-, PR-, HER2-patients.
Statistics Statistical analyses and graphical representations were done using GraphPad Prism (version 8. 4. 3). p values < 0. 05 were considered as statistically significant (* p < 0. 05; ** p < 0. 01; *** p < 0. 001; **** p < 0. 0001). All data per group have been checked for normality using the D'Agostino-Pearson test. In case of normal distribution, a parametric test has been used, and in case of non-normal distribution, a non-parametric test has been used. For lung metastasis and T cell tumor infiltration, data were analyzed using a one-way ANOVA or Kruskal-Wallis (group as factor). In case of a significant group effect, post-hoc Tukey's or Dunn's multiple comparison tests were done. For tumor volume and body weight, data were analyzed using a mixed-effects model (REML) or a two-way ANOVA (group and day as factors) with repeated measures at each day.
Analysis of public clinical data: High expression of interactors of CTLA-4 and PD-1 are correlated with better prognostic value in breast cancer We analyzed a publically available dataset of patients with breast cancer and also with exclusive triple negative subtype. Patients were stratified between high and low expression using best cut-off value for dedicated interactors of CTLA-4, e. g. CD80 and CD86, but also interactors of PD-1, e. g. PD-L1 and PD-L2  [22]. We observed that high expression of the four cellular receptors expressed in the tumor microenvironment  [22, 24] , correlated with longer relapse-free survival (RFS) of patients and interestingly, combined expression correlated better for better RFS, except when compared to PD-L1 alone (Fig. S3 ). These data suggest that multiple targeting for immunotherapy might be of particular therapeutic interest, in particular when associated with our preclinical results showing enhanced anti-tumoral effect of combined therapy in the 4T1 graft model (Fig.
Nevertheless, a recent study from the group of Shiaw-Yih Lin demonstrated that high tumor mutation burden, that has been proposed as a predictive biomarker, did not correlate with clinical benefits for patients treated with immune checkpoint blockade across solid cancer types using a large dataset from The Cancer Genome Atlas  [55]. Conversely, both mismatch repair deficiency and microsatellite instability have been identified as effective predictors of immunotherapy response  [56, 57]. Colorectal adenocarcinoma demonstrated relatively higher mismatch repair deficiency as compared to breast cancer patients  [56]. Moreover, high mismatch repair deficiency is associated with better therapeutic benefit at least with anti-PD-1 inhibition in phase II clinical studies  [58, 59]. High microsatellite instability is also observed in colorectal cancer patients with good response for immunotherapies  [60].
However,  Baron et al. (2015a)  confirmed weak correlations between dust extinction and many DIBs, which weakens this argument. Furthermore, a position correlation with dust extinction could be seen around 865 nm in Fig. 2  in  Baron et al. (2015a) , strengthening the DIB hypothesis for this feature. In this work, we use the unique set of Gaia-RVS spectra of cool stars observed over the whole sky which has just been published as part of the third Gaia data release (DR3; Gaia Collaboration 2016, 2022e). We aim to confirm λ864. 8 as a broad DIB rather than a stellar component by the use of stacked Gaia-RVS spectra to detect it and then analysis of its correlations with dust extinction and DIB λ862.
Data The Gaia-RVS spectra have a wavelength coverage of  nm and a medium resolution of R ∼ 11 500  (Cropper et al. 2018). The stellar parameters and chemical abundances, including effective temperature (T eff ), surface gravity (log g), mean metallicity ([M/H]), and individual abundances of up to 13 chemical species, are derived by the General Stellar Parametrizer from spectroscopy (GSP-Spec) mod-ule of the Astrophysical parameters inference system (Apsis, Gaia Collaboration 2022b,c). We refer to Gaia Collaboration (2022c) for detailed descriptions of the parametrization and a validation of the measurement of λ862 in individual RVS spectra. About one million Gaia-RVS spectra have been published in Gaia DR3. We use a sample of 648 944 spectra that meet the following criteria (given as an ADQL query example in Appendix A): 1.
Acknowledgements. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https: //www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the L12, page 5 of 7 A&A  666 ,  L12 (2022 ) institutions participating in the Gaia Multilateral Agreement. This work was supported in part by: the  German Aerospace Agency (DLR)  grant  50 QG 2102 ;  SFB 881  " The Milky Way System " of the  German Research Foundation (DFG).
In their experiments, they also implemented two other learning processes called Q-learning softmax regression (Q-SR) and true online state action reward state action Q biased softmax regression (TOSL-QBIASSR), to compare the computational cost, to show that TOSL-iBASSR takes the first most effective learning process with less number steps to accomplish the task, then Q-SR, and at last it comes TOSL-QBIASSR which demands more learning steps to complete the given task. The future work will deal with dynamic obstacles. An improved path planning algorithm based on Q-Learning is proposed in  [51]. Deep reinforcement learning (DRL) for autonomous self-learning robot navigation in unknown and indoor environments without using a map or planner is designed  [52]. The data used in this work is a fusion of recovered 2D laser scanner data, RGB-D camera data, and the orientation of the robot to the goal.
Acknowledgments We thank the patients, the nurses and the  Department of Internal Medicine 2  staff who participated in this study,  Laura Wakselman ,  Naima Zemirli  and  Juliette Blondy  from clinical research unit (URC) of  Pitié-Salpêtrière hospital  for helping with regulatory and ethical issues. We warmly thank the members of both branches of the  Laboratory of Human Genetics of Infectious Diseases  for discussions and  Y. Nemirovskaya ,  M. Woollet ,  D. Liu ,  S. Boucherit ,  C. Rivalain ,  M. Chrabieh  and  L. Lorenzo  for administrative assistance.