dataesr/scientific-paragraphs-categorization huggingface dataset:
Individual attributes (such as racial groups) partly explain the emergence of distinct clusters, beyond income levels. Our findings also highlight the notion that inequality is multidimensional in nature. A comprehensive policy design to address this issue should entail the wider possibility of individual movement across the urban landscape to accommodate larger socioeconomic heterophily and further interaction between socioeconomic classes. Table  1 : Summary statistics. It summarises size of dataset used in this study, including population information (eg: number and income) extracted from census as well as Foursquare users and POIs in the 20 Urban Areas. Number of population is the summation of real population in the census tract that appears in the Foursquare dataset. Number of users is counted from pool of users with identified home locations after running home location algorithm explained in SM A.
Specifically, the SST was negative and the lake was frozen for the whole summer all 6 years, whereas other data sets showed that the lake usually starts to melt in May. This can be explained by the way NU-WRF calculates the temperature of a far inland lake. If a body of water is identified as a lake in the land use classification (Table  1 ) but the lake is not present in the SST data set, NU-WRF extrapolates the SST from externally resolved water bodies (NASA, 2020). The closest external water body is the Arctic Ocean in the North, which is mostly frozen through summer, unlike the Great Slave Lake in our study area. To cope with this problem, the default SST data are replaced with data from the Group for High-Resolution SST (GHRSST) level 4 SST daily analysis (Danish Meteorological Institute, Center for  Ocean and Ice, 2007; Hoyer et al.
CAPE × P The last lightning index used in this study is the product of CAPE (in J kg -1 ) and the precipitation rate (P, in kg (m 2 s) -1 ), expressed in flashes (m 2 s) -1 , as developed by  Romps et al. (2014). This product is a good proxy for lightning distribution over land when multiplied with a constant of proportionality (η/E) to convert it to a flash density CAPE × 𝑃𝑃 = 𝜂𝜂∕𝐸𝐸 ⋅ CAPE ⋅ 𝑃𝑃 (7) where η/E consists of the dimensionless conversion efficiency η and the energy discharge per flash E (in J). Romps et al. (2014)  found that CAPE × P correlates best with the observed lightning with η/E equal to 1. 3 × 10 -11 J -1  (Romps et al. , 2014). In this study, we used the maximum CAPE for each grid cell calculated with the wrf-python package  (Ladwig, 2017).
Lightning Observations The observational data used for evaluation are provided by the Canadian Lightning Detection Network (CLDN). The data cover the area between 58-66°N and 108-125°W for the years 2015-2020. Earlier data are available but subject to large biases due to the use of older sensor technology and therefore not used. The data set consists of individual flashes measured with a spatial and temporal precision of 0. 0001° (∼5 m) and 10 -3 s, respectively. A classification into cloud-cloud and cloud-ground lightning is also provided. For further evaluation, the observational data are converted to an hourly flash density at the two different resolutions. The flashes are summed regardless of the type (cloud-cloud or cloud-ground) since the different lightning indices used in this study do not differentiate between types of flashes.
Precipitation and Surface Energy Fluxes The ability of the model to accurately simulate surface energy fluxes is key to the quality of lightning predictions. Therefore, the modeled LH and SH as well as total precipitation patterns of the different model configurations are evaluated in a first step. This is done by comparing the spatial patterns of the 6-year summer averages of the precipitation, LH, and SH modeled by the different model configurations against MERRA-2 reanalysis data. The MERRA-2 data have a spatial and temporal resolution of 0. 5° × 0. 625° and 6 hr, respectively  (Gelaro et al. , 2017). This is both much coarser than what is used in this study, so the MERRA-2 data are regridded to the exact grid of the NU-WRF simulations.
Data Availability Statement Lightning data from the CLDN can be obtained from Environment and Climate Change Canada (https://www. canada. ca/en/environment-climate-change/corporate/contact. html) through a data usage agreement. Sea surface temperature (SST) data can be downloaded from Danish Meteorological Institute, Center for Ocean and Ice (  2007 ). The LIS input data sets are freely available online from: https://lis. gsfc. nasa. gov/ldt-data. The MERRA-2 data  (Gelaro et al. , 2017)  were obtained from: https://goldsmr4. gesdisc. eosdis. nasa. gov/data/MERRA2/. In order to access the data, an account at https://urs. earthdata. nasa. gov/home is needed. NU-WRF software (version 10) can be requested from: https://nuwrf. gsfc. nasa. gov/software. The simulation output volume of the NU-WRF experiments was too large (several TB) to be shared on a repository.
funding this research (FWO. G095720N). The computer resources and services used in this work were provided by the High-Performance Computing system of the Vlaams Supercomputer Center, funded by the Research Foundation-Flanders (FWO) and the Flemish Government. We thank Environment and Climate Change Canada for their generous permission to use Canadian Lightning Data Network data as observational reference for the model output. We thank Carlos Cruz from the NU-WRF team for technical support, and Alexander Gruber and Nicole Van Lipzig for valuable feedback on the conducted research and the manuscript. The authors appreciate the constructive reviews from the three anonymous reviewers and the editor.
Conclusions cIAP1 mainly exerts its activity by controlling cell fate of its protein partners. Thanks to their ability to promote the conjugation of ubiquitin chains of different types, they can modulate the stability, localization and/or the activity of intracellular proteins and they can change the composition of signalling platforms by modifying the intermolecular binding affinities. Thus, IAPs have the ability to control the implementation of signalling pathways and their regulations in time and space. Up to date, more than 30 cIAP substrates have been identified (recently reviewed in  [15] ). A database search for proteins containing IBM-like sequence found many proteins with different cellular functions  [115] , greatly expanding the number of potential IAP-binding partners. The identified IAP substrates are involved in various cellular processes essential for maintaining cell homeostasis (innate immune response, DNA damage response, cell cycle regulation…).
The last observations suggest that cIAP1 and TRAF2 require each other and form an E3-ubiquitin ligase complex. cIAP1 E3-ligase activity is stimulated by K63-linked ubiquitination that can be mediated by TRAF2 or TRAF6  [34, 95]. The stability of cIAP1 can be controlled by phosphorylation  [116]  and its regulation by S-nitrosylation, oxidation processes have also been reported  [117, 118]. Smac mimetics designed to block XIAP anti-apoptotic activity are also potent inhibitors of cIAP1. They have been developed as anticancer agents. However, because of the ability of cIAP1 to regulate RIP1 activities, numerous preclinical studies are exploring their potential in the treatment of inflammatory and infectious diseases. Funding: Our work is supported by grants from "La Ligue contre le cancer" Comité de la Côte d'Or », the "Conseil Regional de Bourgogne-Franche-Comté", the French National research Agency, ("Investissements d'Avenir" program ANR-11-LABX-0021), the European Union program FEDER, and fellowships from the "Ministere de l'Enseignement Supérieur et de la Recherche" of France (AZ).
Transcriptomic analyses in reconstituted HAE We hybridized 200 ng total RNA from HAE cells with a customized 96-gene panel, with counting on an nCounter FLEX platform according to the manufacturer's instructions. Table  S1  provides more information about the panel and the genes analyzed. Data processing and normalization were performed with nSolver analysis software (v4. 0; NanoString Technologies), and the results are expressed as a fold-change induction relative to mock (untreated/uninfected) conditions. A heatmap of gene expression profiles from unsupervised hierarchical clustering (Euclidean distance matrix with Ward's method) was generated with Genomics Suite 7 (Partek).
Statistical analysis OR and P values for the effect of auto-Abs neutralizing each type I IFN on critical influenza relative to healthy individuals from the general population, adjusted for age in four categories (<16, 16 to <50, 50 to <70, and ≥70 yr) and sex, were estimated by means of Firth's bias-corrected logistic regression  (Firth, 1993; Heinze and Schemper, 2002) , as implemented in the logistf package of R software. We tested for an interaction between the effect of auto-Abs and age, by adding an age × auto-Abs interaction term to the logistic regression model, with age classified into two categories (<70 yr vs. ≥70 yr).
Acknowledgments We thank  Dr. Cato Jacobs  for her contribution to the sampling of UZLeuven patients in Belgium. The  Laboratory of Human Genetics of Infectious Diseases  is supported by the  Howard Hughes Medical Institute , the  Rockefeller University , the  St. Giles Foundation , the  National Institutes of Health (NIH ;  R01AI088364  and  R01AI163029 ), the  National Center for Advancing Translational Sciences, NIH   Clinical and Translational Science Award program  ( UL1 TR001866 ), the  Fisher Center for Alzheimer's Research Foundation , the  Meyer Foundation , the  JPB Foundation , the  French National Research Agency (ANR)  under the  "Investments for the Future" program  ( ANR-10-IAHU-01 ), the  Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence  ( ANR-10-LABX-62-IBEID ), the  French Foundation for Medical Research  ( EQU201903007798 ), the  ANRS -COV05, ANR-RHU  program  ANR-21-RHUS-08 ,  ANR GENVIR  ( ANR-20-CE93-003 ),  ANR GenMISC  ( ANR-21-COVR-0039 ), and  ANR AABIFNCOV  ( ANR-20-CO11-0001 ) projects, the  European Union 's  Horizon 2020 research and innovation program  under grant agreement  824110  (EASI-genomics), the  HORIZON-HLTH-2021-DISEASE-04 program  under grant agreement  01057100  (UNDINE), the  Square Foundation, Grandir-Fonds de solidarité pour l'enfance , the  Fondation du Souffle , the  SCOR Corporate Foundation for Science , the  French Ministry of Higher Education, Research, and Innovation  ( MESRI-COVID-19 ),  Institut National de la Santé et de la Recherche Médicale (INSERM) ,  REACTing-INSERM , and the  Université Paris Cité.
This work was partly supported by the  Center for Research on Influenza Pathogenesis and Transmission , a  National Institute of Allergy and Infectious Diseases (NIAID)-funded Center of Excellence for Influenza Research and Response  (contract no. 75N93021C00014 ), and the  FLUOMICS Consortium (NIH-NIAID  grant  U19AI135972 ) to both  A. García-Sastre  and  R. A. Medina , and by  NIAID  grant  U19AI142733  and  U19AI168631  to  A. García-Sastre. Work in the Medina laboratory was also supported by the  PIA ACT 1408 ,  FONDECYT 1161971  and 1212023 grants from  Agencia Nacional de Investigación y Desarrollo of Chile. The VirPath team is supported by  INSERM REACTing  (Research & Action Emerging Infectious Diseases),  CNRS , and Mérieux Research grants. B. Padey is supported by an  ANRT   CIFRE PhD scholarship.
I. Meyts is a Senior Clinical Investigator at the  Research Foundation-Flanders  and is supported by the  CSL Behring Chair of Primary Immunodeficiencies , a  CSL-Behring Research Grant ,  KU Leuven   C1  grant  C16/18/007 , a  VIB GC PID Grant ,  Fonds Wetenschappelijk Onderzoek  grants  G0C8517N ,  G0B5120N , and  G0E8420N , and the  Jeffrey Modell Foundation. Open Access  funding provided by  Rockefeller University. Author contributions:  Q. Zhang ,  A. Pizzorno ,  L. Miorin ,  P. Bastard ,  A. Gervais ,  T. Le Voyer ,  L. Bizien ,  J. Manry ,  J. Rosain ,  Q. Philippot ,  K. Goavec ,  B. Padey ,  A. Cupic ,  E. Laurent ,  K. Saker ,  M. Vanker , and K. Särekannu performed experiments and collected and analyzed data. T. García-Salum ,  M. Ferres ,  N. Le Corre ,  J. Sánchez-Céspedes ,  M.
Primary antibodies were incubated overnight at 4 o C. List of primary antibodies used is provided in Supplemental Table  3. Membranes were washed and incubated with horseradish peroxidase (HRP)-conjugated secondary antibodies for 1 hour at RT. Bands were detected using enhanced chemiluminescence (ECL) (Thermo Scientific, PI32106). Quantifications were obtained using ImageJ software (NIH).
Bone Immunofluoresence Microscopy and Histologic Staining Femurs and humeri bones were dissected and fixed in 4% paraformaldehyde (PFA) at 4ºC overnight. After washing in water, samples were then decalcified for 10 days using 14% (w/v) EDTA (pH 7. 4), embedded in paraffin, and sectioned onto slides at 5μm. Hematoxylin and Eosin (H&E), Masson's Trichrome,) staining were performed according to manufacturer's instructions. The sections were then counterstained with 0. 1% Fast Green. Bright field imaging was performed on an EVOS M5000 Imaging System. Data are representative of at least three independent experiments.
The matrix condition number of the PM, i. e. the ratio of the highest and lowest eigenvalues, is close to 4, assuring that the PM is not ill-conditioned; therefore no additional regularisation is applied. The whole procedure is performed with the TUNFOLD package  [49] , version 17. 9. Finally, the residual inefficiencies are obtained from the rate of particle-level jets that cannot be matched to any detector-level jets passing the event selection, and corrected bin-by-bin on the particle-level distribution x.
Results In Fig. 6 , the inclusive jet cross sections are presented as functions of the jet p T and |y| for R = 0. 4 and 0. 7. The cross sections are shown for four absolute rapidity intervals: |y| < 0. 5, 0. 5 < |y| < 1. 0, 1. 0 < |y| < 1. 5, and 1. 5 < |y| < 2. 0 with jet p T > 97 GeV. The data are compared with fixed-order NNLO QCD predictions using CT14 PDF, corrected for NP and EW effects. The data cover a wide range of the jet p T from 97 GeV up to 3. 1 TeV. In Fig. 7  (Fig. 8 ), the ratios of the measured cross sections to the NNLO QCD predictions using different scale choices and to the NLO+NLL predictions with various PDFs sets are shown for jets with R = 0. 4 (0. 7). In general at high p T , smaller (higher) cross sections are predicted than experimentally measured for the central (forward) rapidities.
The theoretical uncertainties are larger at high p T and are dominated by the PDF uncertainties. The scale uncertainties are significantly smaller at NNLO as compared to NLO+NLL. The prediction using jet p T as renormalisation and factorisation scale results in a harder p T spectrum than in case of scale set to H T parton. The NLO+NLL calculations predict harder p T spectrum than the NNLO calculations. All predictions describe the data well within the experimental and theory uncertainties. The CT14, NNPDF 3. 1, and MMHT 2014 PDF sets include CMS and ATLAS measurements of inclusive jet cross sections at 7 TeV, whereas ABMP 16 does not include LHC jet measurements and HERAPDF 2. 0 is based only on the HERA DIS data. Predictions obtained with these PDFs are similar at low p T , although significant differences at high p T are observed.
Data sets used In this QCD analysis, the double-differential inclusive jet production cross section with R = 0. 7 is used together with the charged-and neutral-current DIS cross sections of HERA  [21]. In addition, the normalised triple-differential tt cross section  [22]  from CMS is used. As demonstrated in Ref. [22] , the top quark pole mass m For the QCD analysis, the open-source framework XFITTER  [67] [68] [69]  version 2. 2. 1, extended to SMEFT prediction, is used. The DGLAP  [70] [71] [72] [73] [74] [75]  evolution is implemented using QCDNUM  [76]  version 17-01/14. The analysis is performed at NLO or NNLO, depending on the physics case, as described in the following. The correlations of the experimental statistics and systematic uncertainties in each individual data set are included.
Theoretical calculations used in QCD analysis The SM theoretical predictions for the inclusive jet production cross section at NLO and NNLO are obtained as described in Section 4. 1 and are corrected for NP and EW effects. The NNLO calculation is approximated by k-factors, obtained as a ratio of fixed-order NNLO to NLO calculations using CT14nnlo PDF in each bin in p T and |y|. These are applied to the NLOJet++ prediction interfaced to XFITTER using fast-grid techniques of FASTNLO  [53]. In a similar way, the NLO prediction is improved to NLO+NLL as explained in Section 4. The QCD prediction for the normalised triple-differential cross section of the tt production is available only at NLO and is described in detail in Ref. [22]. The renormalisation and factorisation scales are set to the four-momentum transfer Q for the DIS data and to the individual jet p T for inclusive jet cross section measurements.
Vector-like and axial vector-like exchanges allow interactions also between right-handed quarks, giving c 5 = c 1 in both cases. For interactions between quark lines of different handedness, the vector-like exchange implies c 3 = 2c 1 , whereas the axial vector-like model has c 3 = -2c 1. Further details of the theoretical model are given in Ref. [23]. In the QCD analysis, the SMEFT prediction for the double-differential cross section of the inclusive jet production reads σ SMEFT = σ NLO FASTNLO k NLO+NLL EW NP + CI, (9) where k NLO+NLL is given in Eq. (  3 ) and EW and NP are explained in Sections 4. 2-4. 3; the CI term is computed at NLO using the CIJET software  [24]  interfaced to XFITTER.
Fit uncertainties originate from the uncertainties in the used measurements and are obtained by using the Hessian method  [83]  implying the tolerance criterion ∆χ 2 = 1, which corresponds to the 68% confidence level (CL). Alternatively, the fit uncertainties are estimated by using the MC method  [84, 85] , where MC replicas are created by randomly fluctuating the cross section values in the data within their statistical and systematic uncertainties. For each fluctuation, the fit is performed and the central values for the fitted parameters and their uncertainties are estimated using the mean and the root mean square values over the replicas. Parameterisation uncertainty is estimated by extending the functional forms of all PDFs with additional parameters D and E, which are added, independently, one at a time.
HERA-only HERA+CMS xg(x) = A g x B g (1 -x) C g (1 + E g x 2 ), (17) xu v (x) = A u v x B u v (1 -x) C u v (1 + D u v x + E u v x 2 ), (18) xd v (x) = A d v x B d v (1 -x) C d v (1 + D d v x), (19) xU(x) = A U x B U (1 -x) C U , (20) xD (x) = A D x B D (1 -x) C D. (21) First, the analysis is performed in the standard model. Then, alternatively, the SMEFT fit is done. Both SM and SMEFT fits are performed at NLO to be consistent with the order of the theoretical prediction for the tt data and for the CI corrections to the SM Lagrangian, although the SM prediction for the inclusive jet cross section is available at NNLO. The partial and global χ 2 values for the SM and SMEFT fits are listed in Table  6. The fits with all CI models and various Λ values resulted in very similar χ 2 values. The PDFs resulting from SM NLO fit are presented in Fig.
A comparison of the measurement with predictions using various PDFs is shown in Fig. B. 1. Although the PDF parametrisation remains identical, higher precision in PDF and QCD parameters is expected by using NNLO grids consistently in the QCD analysis. These new results supersede those obtained by using the k-factor technique. The value of the strong coupling constant α S (m Z ) is extracted simultaneously with the PDFs and corresponds to α S (m Z ) = 0. 1166 ± 0. 0014 (fit) ± 0. 0007 (model) ± 0. 0004 (scale) ± 0. 0001 (param. ), showing improved precision with respect to the NNLO result obtained using the k-factor technique. The global and partial χ 2 values for each data set in the NNLO fits using the interpolation grids are listed in Table  B. 1, where the χ 2 values illustrate a general agreement among all the data sets.
The impact of the CMS jet data in the QCD analysis (HERA+CMS fit) at NNLO is illustrated in Fig. B. 3, where the result is compared with the alternative fit using only the HERA DIS data (HERA-only fit). Table  B. 1: Partial χ 2 per number of data points, N dp , and the global χ 2 per degree of freedom, N dof , as obtained in the QCD analysis at NNLO of HERA+CMS jet data, using NNLO interpolation grids for the 13 TeV inclusive jet cross section. In the DIS data, the proton beam energy is given as E p and the electron energy is 27. 5 GeV. band represents the results of the NNLO fit using HERA DIS and the CMS inclusive jet cross section at √ s = 13 TeV (using the HERA DIS data only). The PDFs are shown with their total uncertainty. The prediction for the inclusive jet cross section is obtained using NNLO interpolation grids.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid and other centres for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the  CMS detector , and the supporting computing infrastructure provided by the following funding agencies:  BMBWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ ,  FAPERGS , and  FAPESP (Brazil) ;  MES  and  BNSF (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  MINCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RIF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC PUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRI (Greece) ;  NK-FIA (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  MES (Latvia) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CIN-VESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MOS (Montenegro) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom ,  RAS ,  RFBR , and  NRC KI (Russia) ;  MESTD (Serbia) ;  SEIDI, CPAN, PCTI , and  FEDER (Spain) ;  MOSTR (Sri Lanka) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ; ThEPCenter, IPST, STAR, and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
Clinicopathologic features of patients from whom PDXOs were derived are summarized in Supplemental Table  1  (supplemental material available online with this article; https://doi. org/10. 1172/JCI151604DS1). The total number of cells comprising tumor and stromal compartments within PDXOs and PDOs was measured after segmentation of organoids into cellular elements. Using multicolor confocal IF microscopy and Imaris software, thick sections of organoids  (40-120 μm)  were captured in 3D space complete with corresponding fluorescence signals (Figure  1A  and Supplemental Video 1). Cell segmentation algorithms were used to quantify cellular elements on the basis of (a) the presence of a nucleus, using DAPI for nuclear segmentation and quantitation of nuclei, and (b) cytoplasmic staining, as detected by α-SMA, CK-19, and annexin A5.