dataesr/scientific-paragraphs-categorization huggingface dataset:
Galaxy profile fitting with GALFIT We fit the cluster member galaxies using the IRAF interface to GALFIT  (Peng et al. 2002 (Peng et al. , 2010)). We use our interpolated Moffat profile PSF described in detail in Appendix C for the GeMS Ks imaging. For the HST data we choose not to adopt the common methodology of creating a PSF using the Tiny Tim  (Krist et al. 2011 ) software, which is ideal when the PSF is under-sampled, but does not model the PSF of multi-drizzled data. In our case, the PSF is wellsampled due to the multi-drizzling process, and consistent across the field of view, so we use an isolated star PSF for profile fitting. Considering the modest signal-to-noise ratio and angular extent of the sources of interest in the SPT-CL J0546-5345 cluster we adopt a single Sérsic profile fit to each source.
Sérsic indices In Figure  8  we give the histogram of Sérsic indices for cluster members and field galaxies. The median for the cluster is n = 3. 8 ± 0. 5, while the median for the entire sample is 3. 7 ± 0. 5. The large median Sérsic index for the cluster members indicates early-type galaxies, so in this section we compare with other authors' early-type samples. These are mostly selected to be quiescent, with the exceptions of  Delaye et al. (2014)  and some datasets in the compilation by  Damjanov et al. (2011)  which have a morphological selection. We see no trend of Sérsic index with cluster-centric radius, indicating no effect of environment on morphology. This is consistent with Huertas-Company et al. (  2013 ) who did not find a significant difference in morphology between field and central galaxies within a population of quiescent early-type galaxies in COSMOS.
SUMMARY AND CONCLUSIONS We present the first high angular-resolution measurement of the rest-frame Y -band stellar mass -size relation for the galaxy cluster SPT-CL J0546-5345 at z = 1. 067. We demonstrate our strategies for addressing the data processing challenges associated with these complex imaging data in order to achieve a final stacked image with a mean PSF FWHM ∼ 110 mas (corresponding to a radius of 450 pc at the cluster redshift), with a uniform sky subtraction free from strong residual images that echo faint field sources. The spatial variation of the PSF is modelled as a two-dimensional Moffat profile fit to known stars across the field and interpolated at the location of each measured galaxy. Forty-nine cluster member galaxies are detected, with median Sérsic index n = 3. 8 ± 0.
ACKNOWLEDGEMENTS Based in part on observations obtained at the  Gemini Observatory , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under a cooperative agreement with the NSF on behalf of the Gemini partnership: the  National Science Foundation  ( United States ), the  National Research Council (Canada) ,  CONICYT (Chile) , the  Australian Research Council (Australia) ,  Ministério da Ciência, Tecnologia e Inoval ¸ão (Brazil)  and  Ministerio de Ciencia, Tecnología e Innovación Productiva (Argentina). Elements of the data presented in this paper were obtained from the  Mikulski Archive for Space Telescopes (MAST). STScI is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS5-26555. Support for MAST for non-HST data is provided by the  NASA Office of Space Science via  grant  NNX09AF08G  and by other grants and contracts.
ERaBLE uses as input data a potentially very large collection of distance matrices, where each matrix is obtained from a different genomic region -either directly from its sequence alignment, or indirectly from a gene tree inferred from the alignment. Our experiments show that ERaBLE is very fast and fairly accurate when compared to other possible approaches for the same tasks. Specifically, it efficiently and accurately deals with large data sets, such as the OrthoMaM v8 database, composed of 6,953 exons from up to 40 mammals. Conclusions: ERaBLE may be used as a complement to supertree methods -or it may provide an efficient alternative to maximum likelihood analysis of concatenated alignments -to estimate branch lengths from phylogenomic data sets.
Methods In this paper, we assume that the analysis focuses on a collection of orthologous genomic regions, or genes, G 1 , G 2 ,. , G m , whose evolution mostly differs because of rate heterogeneity. In other words, the trees describing their evolution are topologically compatible  [31]. This is an optimal scenario for the methods we describe here, but it does not preclude their application to real-world datasets where this assumption will be necessarily violated to some degree. Gene tree topological incompatibilities may in fact arise due to incomplete lineage sorting  [32, 33] , gene duplication and loss  [34] , or even lateral gene transfer (see  [35]  for an excellent review of these phenomena). An even stronger assumption, which is useful to clarify the meaning of branch lengths and rates at a genomic level, is that of the proportional model  [4, 36] , which we describe further below.
• Model rates definition. Similarly to the model tree, the "model gene rates" must be the same as t 1 ,. , t m up to their scale. The absolute values of t 1 ,. , t m are in fact unrecoverable from the data. By imposing Eq. (  5 ) to the rescaled rates, we must have: r k = t k F , where F = 1 N m k=1 N k t k. OrthoMaM data set. OrthoMaM (v8)  [44]  consists of a collection of single-copy orthologous phylogenetic markers, selected among the genomes of the 40 mammals in the Ensembl v73 database  [48]. We downloaded the entire set of the.
Acknowledgements We wish to thank  Alexis Criscuolo ,  Pascal Giorgi ,  Vincent Lefort ,  Vincent Ranwez  and  Bastien Vialla  for useful discussions and assistance. The PhD of MB is funded by the  Labex NUMEV. MB, OG and FP are also funded by the  EC H2020  project  VIROGENESIS  (grant number  634650 ). This publication is contribution No  2015-173  of the  Institut des Sciences de l'Evolution de Montpellier  ( UMR5544 -UM + CNRS + IRD ).
After incubation with 1600 different compounds from the Pharmakon 1600 library (all drugs added at 10 micromolar in DMSO), the percentage of ciliated cells was assessed in single wells by fluorescence microscopybased visualization of the primary cilium using antibodies targeting cilium-associated acetylated tubulin. Nuclei were stained with Hoechst-33258. Images were analyzed using IN Cell Developer software according to the workflow illustrated in Figure  2. A cell was considered ciliated when the primary cilium signal (fluorescent dot) was enclosed within the nuclear border of the segmented nuclei. If more than one cilium-like dot was detected within the nuclear border, the bigger dot was selected as the match. The number of ciliated cells was then determined by counting the total number of nuclei and linked cilia in 20 fields in a single well.
, which form bottlenecks in the therapeutic development process. This can be overcome by drug repurposing, a strategy that reduces the time frame, decreases the costs and improves the success rate by redirecting existing drugs for a new indication. Most of the drugs from the Pharmakon 1600 Library have already been tested for safety in humans and data is available on their pharmacology, formulation and toxicity, paving the way for an accelerated development of cilia-based therapeutics. The success of our screening approach also sets the stage to screen other libraries of new compounds and to establish these as potential novel antineoplastic agents and/or agents exploitable in other cilium-related diseases, including classical ciliopathies.
T6793-. 5ML), which stains the ciliary axoneme. A fluorescent secondary antibody (Life Technologies, AlexaFluor 488, Cat No. A21145) was used against the primary antibody. The nuclei were counterstained with Hoechst-33258 (Cat No: 382061, Calbiochem). 20 randomized fields per well were imaged at a single plane of focus at 20X magnification in DPBS (Sigma) to reduce auto-fluorescence from the medium and to minimize signal-to-noise ratio. Images acquired by the IN Cell Analyzer 2000 (GE Healthcare) were analyzed using IN Cell Developer software (GE Healthcare).
Proliferation assays Spheroid assay -Tumor spheroids were formed on agarose-coated (1%) 96-well plates by seeding L3. 6 pancreatic cancer cells in 200 μl of culture medium. The plates were kept undisturbed in a humidified incubator at 37°C and 5% CO 2 for 4 days to facilitate spheroid formation. Treatment with compounds was started when the cells aggregated to form spheroids. Compounds and 50% of the medium were refreshed every 4th day. Images were captured at the beginning of treatment with 5X objective mounted on an inverted light microscope. Imaging was done every 4 days for 20 days from the start of treatment. The images were analyzed by ImageJ software (rsb. info. nih. gov/ij) to calculate the differences in the volume of spheroids at different time points. BrdU incorporation assay -BrdU incorporation assay was performed using 5-Bromo-2-deoxy-uridine Labeling and Detection Kit III (Cat.
Cell cycle analysis CFPAC-1 cells were plated at a density of 300, 000 cells/well of a 6-well plate and allowed to grow for two days. This was followed by compound treatment for 48 h and harvesting of cells by trypsinization. Cells were fixed with 4% formaldehyde and permeabilized in 0. 1% Triton X100 solution. The permeabilized cells were washed with PBS and stained with Vybrant DyeCycle Green Stain (Cat. No. V35004, ThermoFisher Scientific). Cell cycle data was acquired by measuring at least 10, 000 events per sample with BD FACSCanto Flow Cytometer (Becton Dickinson). Data was analyzed using FlowJo software (Becton Dickinson) and the cell cycle distribution was calculated by ImageJ software.
Statistical analysis GraphPad Prism version 6 for Mac OS X (GraphPad Software, San Diego, California, USA) was used for statistical analysis. All data are expressed as mean ± SEM. Differences between two groups were assessed using the t-test. To determine differences between more than two groups, two-way ANOVA was used followed by Dunnett's multiple comparisons test. Differences with p values of < 0. 05 were considered to be statistically significant.
Microarray Peripheral blood obtained from 5 healthy controls was processed and sorted on a BD FACSAria cell sorter into CD3+CD4+CD8-CD57+PD1-and CD3+CD4+CD8-CD57-PD1-populations. Antibodies were used against CD3 (BD #560366), CD4 (BD #557852), CD8 (eBioscience #47-0088-42), CD57 (BD #555619) and PD1 (BioLegend #329906). Total RNA was extracted from the sorted populations and quality assessed. cDNA synthesis and amplification was performed, and fragmented and biotinylated samples were hybridized to the Affymetrix Human U133 plus 2. 0 probe array. The arrays were scanned and probe intensity measurements were normalized across the samples using the robust multichip average (RMA) algorithm. GEO accession GSE64805.
After 7 days of co-culture, VPD450 dilution and phenotype were analyzed by FACS. Antibodies were purchased from BD Biosciences (V450-labeled anti-CD14, APC-labeled anti-CD209, APC-Cy7-labeled anti-CD3, BUV395labeled anti-CD4, PerCP-Cy5. 5-labeled anti-CD8, BV605-labeled anti-CCR7, PE-labeled anti-CD57, APC-labeled anti-27, PE-Cy7-labeled anti-CD38) or from Miltenyi Biotec (FITC-labeled anti-CD45RO). Flow cytometry data were recorded with an LSR Fortessa cell analyser (BD Biosciences) and analyzed with Diva software (BD Biosciences).
Legendre, et al described a higher percentage of CD57+ (then, Leu7+) CD4 T cells in patients that experienced rejection compared to stable patients  (36). This study, predating the tacrolimus era, combined with studies reported herein, strongly implicates this cell type as worthy of attention. Collectively, these data support the finding that CD57+ CD4 T cells play a role in allograft rejection, particularly in the absence of CNIs, and that prospective assessment of this population could be used to identify patients at particular risk for ACR, especially in costimulation blockade-based regimens. Graft survival rates have improved in the past 20 years largely due to the development of targeted immunosuppressant drugs. Traditional immunosuppression management consists of universalized algorithms of serum drug levels and dosing regimens that are generally applied to transplant populations.
3), distributed microstructure model (Section 2. 4) and the new extended distributed microstructure model (Section 3). Section 4 describes the numerical scheme for carrying out two-scale computations in full detail including mesh configuration, spatial discretization, coupling between scales, time stepping and code implementation. In Section 5, the two-scale computational code is applied to two challenging test cases: heat conduction in a composite material and unsaturated water flow in a two-soil system. The accuracy and computational efficiency of the two-scale numerical solutions are assessed against solutions to the fine-scale model and the macroscopic model. Future research directions and a summary of the work is given in Section 6.
Code implementation We have implemented the two-scale numerical scheme discussed in the previous sections in a computational code developed in C++. Linear algebra operations, such as those required when generating an orthonormal basis for the Krylov subspace (Arnoldi's iteration), are carried out using BLAS and LAPACK subroutines. The computational cost of the code is dominated by evaluations of the vector-valued function g(Ψ) that appears in the ODE system  (26). These evaluations are themselves dominated by a loop over the number of macroscopic elements (i = 1,. , m). Iteration i of this loop involves the following sub-tasks: (i) Compute the macroscopic numerical fluxes F k,σ for edges σ located in element i and assemble these according to the macroscopic discrete equations (  17 ). (ii) Compute the source S i (23) for element i and distribute its value according to the macroscopic discrete equations (  17 ) via equation (  25 ).
The error is computed using N uniformly distributed points along the line x 1 = 0. 0125, which passes through the centre of the the first column of micro-cells in Figure  7. The error is computed at a small chosen value of time at which the fine-scale field possess a large gradient near x 2 = 0. 5 and rapid variation along the line x 1 = 0. 0125 due to the inclusions. The wall time, mean time step size and mean Krylov subspace dimension (to compute the matrix-vector product given in equation 27) over the entire length of the simulation are recorded to assess the computational cost of each model. The calculations required to compute the effective conductivity K eff for the macroscopic model and DMM were carried out separately in MATLAB and hence were not included in the aforementioned wall time.
Furthermore, the accuracy of the two-scale models (DMM and EDMM) is almost the same across the different conductivity ratios while the accuracy of the macroscopic model declines sharply as the conductivity ratio is decreased. For the simulations tested, on average, DMM is 72. 2 times faster than the fine-scale model while an average speedup of 2. 93 is recorded for EDMM. Due to its longer simulation times, Case B provides a good test problem to benchmark the performance of the parallel implementation of the code discussed in Section 4. 7. Table  3  records the wall time and speedup factor of the two-scale model (EDMM) for K b /K a = 10 -3. These results demonstrate that the speedup is less than ideal (linear) with a factor of 6. 69 achieved using 10 OpenMP threads. Nevertheless, parallelisation is helpful, reducing the wall time from over 2 hours and 20 minutes down to less than 22 minutes.
This research forms part of an Australian Research Council (ARC) funded project on two-scale modeling and provides an intermediate step towards our long term goal of developing a two-scale model for multi-phase transport/flow capable of handling completely general heterogeneous media. Future research avenues will investigate problems involving micro-cells that do not completely tesselate the domain, extend the code to more sophisticated model equations (e. g. , two-phase flow) and initiate an investigation into two-scale modeling for general non-binary heterogeneous media.
Acknowledgements This work was supported by  Australian Research Council (ARC)  grant  DE150101137. The first author also thanks the  Laboratory of Chemical Engineering & Materials (LGPM)  and the  Centre of Excellence for White Biotechnology (CEBB) at École Centrale Paris (now CentraleSupelec)  for postdoctoral funding during 2013-2014. Additionally, the second author acknowledges the  School of Mathematical Sciences  at  QUT  for travel support during his visit to QUT in 2015. Finally, we gratefully acknowledge the authors of the open source software packages and libraries that made it possible to develop the code in this project and thank the two anonymous reviewers for their detailed analysis and suggestions that improved the quality of the final manuscript.
Overview of Nunchaku Nunchaku is the spiritual successor to Nitpick but is designed as a stand-alone OCaml program, with its own input language. Whereas Nitpick generates a succession of finite problems for increasing cardinalities, Nunchaku translates its input to one first-order logic program that targets the finite model finding fragment of CVC4  [2] , a state-of-the-art SMT (satisfiability modulo theories) solver. Using CVC4 as a backend allows Nunchaku to reason efficiently about arithmetic constraints and (co)algebraic datatypes  [36]  and to detect unsatisfiability in addition to satisfiability. Support for other backends, including Kodkod  [43]  (used by Nitpick) and Paradox  [16] , is in the works. We also plan to integrate backends based on code execution and narrowing, as provided by Quickcheck for Isabelle/HOL  [10] , to further increase the likelihood of finding counterexamples.
Related Work There are many competing approaches to refuting logical formulas. The main ones are finite model finding and code execution. Alternatives include infinite model generation  [11] , counterexample-producing decision procedures  [13] , model checking  [17] , and saturations  [1]. Finite model finding consists of enumerating all potential finite models, starting with a cardinality of one for the domains. Some model finders explore the search space directly; FINDER  [40] , SEM  [46] , Alloy's precursor  [22] , and Mace versions 3 and 4  [30]  are of this type. Other tools reduce the problem to propositional satisfiability and invoke a SAT solver; these include early versions of Mace (or MACE)  [31] , Paradox  [16] , Kodkod  [43]  and its frontend Alloy  [23] , and FM-Darwin  [3]. Finally, some theorem provers implement finite model finding on top of their proof calculus; this is the case for KIV  [35] , iProver  [25] , and CVC4  [38].
Agsy for Agda  [27]  employs narrowing. Isabelle's Quickcheck combines random testing, bounded exhaustive testing, and narrowing in one tool  [10]. Finally, ACL2  [29]  combines random testing and theorem proving. Our experience with Isabelle is that Nitpick and Quickcheck have complementary strengths and weaknesses  [5, Section 3. 6]  and that it would be a mistake to rely on a single strategy. For example, debugging the axiomatic specification of the C++ memory model  [9]  was a heavy combinatorial task where Nitpick's SAT solving excelled, whereas for the formalization of a Java-like language  [28]  it made more sense to develop an executable specification and invoke Quickcheck. Nunchaku currently stands firmly in the finite model finding world, but we plan to develop an alternative translation pipeline to generate Haskell code and invoke QuickCheck, SmallCheck, Lazy SmallCheck, and HBMC.
Acknowledgment We are grateful to the anonymous reviewers for making many useful comments and suggestions and for pointing to related work. We also thank  Mark Summerfield , who suggested many textual improvements. Cruanes is supported by the  Inria technological development action "Contreexemples utilisables par Isabelle et Coq" (CUIC). Nunchaku would not exist today had it not been for the foresight and support of  Stephan Merz ,  Andrew Reynolds , and  Cesare Tinelli.
Electronic decision-support tools are now an essential component of government strategies to battle non-native species, with taxon-specific, paper-based risk analysis schemes often being replaced by taxon-generic tools. This study reports on the development of a decision-support tool for aquatic species, the Aquatic Species Invasiveness Screening Kit (AS-ISK), which replaces five taxon-specific toolkits for amphibians, freshwater and marine fish and invertebrates (FISK, MFISK, FI-ISK, MI-ISK, Amph-ISK). Adapted from Pheloung et al. 's Weed Risk Assessment (WRA), the "-ISK" toolkits were also "self-automated workbooks" in Excel ® VisualBasic ® architecture of the 'WRA. In creating AS-ISK, we incorporated the conceptual approach (questions and guidance) of the generic risk screening module of the European Non-native Species in Aquaculture Risk Analysis Scheme (ENSARS) into a single "turnkey application" that was based on the Excel ® VisualBasic ® architecture used by the WRA and its "-ISK" derivatives.
Protocols Questions and guidance in the ENSARS generic screening module were critically reviewed concurrent with the questions and guidance in FISK v2 during face-to-face and Skype ™ meetings between authors GHC, HT and PDS. In the revision process, particular attention was directed towards characteristics that are considered to facilitate the invasions of aquatic environments of all types. As such, the review and revision process involved the criteria described for FISK v2  (Lawson et al. 2013)  to achieve: 1) improved clarity, where changes reduced ambiguous terminology or uncertainty in interpretation of questions; 2) enhanced ecological applicability, where modifications addressed a wider range of ecological characteristics; and 3) increased climatic suitability, where changes allowed for increased flexibility of climate-match source information or direct incorporation of physiological tolerances.
Electronic toolkit The original WRA toolkit  (Pheloung et al. 1999)  was available as a "self-automated workbook" (sensu  Bovey et al. 2009)  written in Microsoft ™ Excel ® for Visual Basic for Applications (VBA ® ) code. The WRA template was later adapted for use with FISK v1 to accommodate all necessary changes for application to freshwater fish and replicated into its "sister" versions, and eventually upgraded to FISK v2  (Lawson et al. 2013 ). However, the software architecture of the "-ISK" programs, albeit improved relative to the original WRA template by virtue of its dialog-driven interface, was still somewhat primitive. This is unlike a "turnkey application" (sensu Walkenbach 2007), also known as "dictator application"  (Bovey et al. 2009) , which represents the most advanced level of Excel VBA ® software development.
Specifically, a turnkey application allows complete distinction between user interface, business logic and data access/storage tiers; this is ensured by separating the data/user interface from the underlying code. This turnkey software architecture was employed for development of AS-ISK, whose graphical user interface (GUI) consists of a collection of tightly controlled dialogs that separate the user interface from the data storage layer (and with the code-based business logic tier located in between). The end result of this upgrade is a turnkey application that allows complete control over the end user's interaction (Supplementary material Figure  S1 , which like all other supplementary figures mentioned here below may be found in the Appendix 1; whereas the cited tables are found in a separate, supplementary Excel ® file).