dataesr/scientific-paragraphs-categorization huggingface dataset:
Several MRs, which we identified in our dataset as up-regulated, are widely acknowledged as IEGs in general, like FOS, JUN, JUND, ATF3, CREM, CEBPA, and CEBPB (previous section and Fig. 3a ), thus serving as validation of our approach. Several of these MRs are known to be involved in WAT conversion to the brite phenotype, such as CREM, CEBPA, and CEBPB  [46, 47]. Moreover, in our TRN we also identified TFs which are not yet recognized as IEGs, but were acutely responsive to NE, such as the heat shock transcription factor 1 (HSF1) and the nuclear factor, interleukin 3-regulated (NFIL3). Interestingly, the highest ranked MR (NES = 5. 5) was HSF1, a TF involved in the transcriptional activation of the heat shock response (HSR)  [48]  and known to regulate energy expenditure through activation of a PGC1α-dependent metabolic program  [7].
Functional enrichment analysis elucidates a complex response to NE in metabolic and signaling pathways Our results revealed that NE stimulation up-regulated several IEGs that we also identified as MRs and LRs with pleiotropic function. Thus, we speculated whether they participate in the crosstalk between pathways and performed a functional gene enrichment analysis of the up-regulated DEGs using the R/Bioconductor package FGNet  [50] , reconstructing the functional gene network from up-regulated genes. FGNet which also organized the network into metagroups that condense multiple KEGG pathways, Reactome pathways, and GO terms (Fig. 4 ). The genes clustered in four metagroups. These metagroups contain genes classified according to their functions and seven clusters of common genes that are part of two or more metagroups.
Moreover, after sequencing, we searched for the expression of several macrophage markers, but their expression levels were magnitudes lower than those of adipocyte markers (Additional file 2: Figure  S1 ) so that we can assume that the contribution of macrophages to the expression signature in our dataset is negligible in comparison to adipocytes. Over the last decades, several studies focused on lipid and glucose mobilization triggered by NE in WAT through the ADRβ/cAMP/PKA/PI3K axis  [51] , and it was assumed that these functions were performed mostly without the need for "de novo" transcription. However, our results show that several TFs are acutely transcribed upon NE treatment, together with more than 2,000 DEGs, indicating an acute shift in gene regulation and cell fate after NE stimulation.
RNA-seq alignment and differential expression analysis Raw RNA sequencing reads were aligned against the human hg38 genome using STAR aligner with default parameters  [99]. The mapped reads were assigned to genes using featureCount from the bioconductor package Rsubread  [100]. All annotated genes were quantified across each condition, corresponding to the University of California at Santa Cruz (UCSC) GRCh38. 84 annotation. Normalization and differential expression analysis were performed using the R/Bioconductor package DESeq2  [101]. The effect size / differential expression threshold was situated in an absolute log 2 fold change of at least 0. 19 (1. 17 FC) and an adjusted p-value (padj) < 0. 01.
Network reconstruction Network reconstruction was performed in Cytoscape with the BisoGenet plugin  [36, 103] , using DE genes as bait nodes. Edges from experimentally validated interactions were added using the following parameters: Organism > Homo sapiens, gene identifiers only; Data Settings > protein-protein interactions; DIP, BIOGRID, HPRD, INTACT, MINT and BIND databases and all experimental methods; Protein-DNA interaction from experimentally validated interactions; BIND and ENCODE databases, all experimental methods; Method > Criteria to build the network, connecting input nodes with the "By adding edges" option, and as Output > Genes.
Transcription factor and regulatory network analysis We performed a gene based motif enrichment analysis of our network by using the Cytoscape iRegulon plugin  [45]. The following parameters were used: Species> Homo sapiens; Search space> gene-based; Motif collection> 10 k; Track collection> 1120 ChIP-Seq tracks; Putative regulatory region> 20 kb centered around TSS; Motif ranking database> 20 kb centered around TSS (10 species); Track ranking database> 20 kb centered around TSS (ChIP-Seq derived); Region-based parameters>default; Recovery parameters: Enrichment score threshold> 3. 0; ROC threshold for AUC calculation > 0. 03; Rank threshold > 5000; Transcription factor prediction: Minimum identity between orthologous genes > 0. 0 and Maximum FDR on motif similarity > 0. 001. Circos plots of predicted transcriptional networks were created using Circa software (OMGGenomics, 2017).
Acknowledgements We thank  Florian Hildner  and  Heinz Redl ,  Austrian Cluster  for  Tissue Regeneration, Linz, Austria , for providing primary preadipocytes. We are grateful to  Ilse A. Valtierra-Gutierrez ,  Rocío Enríquez-Gasca ,  Rocío Dominguez-Vidaña , for proofreading, observations and suggestions; to  Alejandra Medina-Rivera  and  Daniela Ledezma-Tejeida  for proofreading; and to  Anne Loft ,  Anastasia Georgiadi , and  Frits Mattijssen  for helpful discussions.
Photometric redshift catalogue The photo-z catalogue is obtained from the CFHTLS data release T0007 2. CFHTLS T0007 photo-zs were computed in the 154 deg 2 sky coverage of CFHTLS using multicolour images in the u * g r i z filters of MegaCam at CFHT. We note that the i filter had to be changed during the course of the survey. The photo-zs were obtained using the LePhare software  (Arnouts et al. 1999; Ilbert et al. 2006 ). Details about the method are given in  Coupon et al. (2009). Briefly, the photo-zs were computed using 62 templates obtained after having optimized four templates from  Coleman et al. (1980)  and two starburst templates from  Kinney et al. (1996) , and linearly interpolated between them to better sample the colour-redshift space using the VVDS spectroscopic sample (e. g.
5 Mpc. In this way, the kernel size (diameter) of the adaptive-KDE in the densest region (corresponding to galaxy clusters) is ∼ 1 Mpc, which is the right smoothing scale for detecting clusters as it is the typical size of cluster cores. In fact, our previous way of choosing the smoothing scale in AMACFI was oversmoothing the underlying distribution and was thus more suited for supercluster than cluster detection. The SExtractor software is then applied to the density maps to detect structures. The major modification in this step of the process is the way the detection threshold is set. This threshold is now set to a number of galaxies per Mpc 2. For this, we iteratively compute the background level (field) in galaxies per Mpc 2 in each slice using our density map, and set the detection threshold to the 95% upper confidence limit on this number density following  Gehrels (1986).
Mock catalogue modification We converted the SDSS magnitudes of the simulated mocks to CFHTLS Megacam i -band magnitudes following the relation from the Megacam pages 3  : i Megacam = i SDSS -0. 085 × (r SDSS -i SDSS ). (5) To make the mock galaxy catalogues from  Henriques et al. (2012)  comparable to our data, we need to add realistic noise to the redshift of each galaxy in the mock. Since the error on the photo-zs depends on redshift and on magnitude, we compute the mean 1σ uncertainty on individual photo-zs (as given by the Le-Phare software) in bins of 0. 1 in redshift and 0. 25 in magnitude for the W1 field. On average, this 1σ uncertainty closely follows the statistical error computed using spectroscopic redshifts, justifying the use of this quantity for our purpose. We decided not to add noise on the lightcone magnitudes themselves as the errors for the CFHTLS T0007 data release are below 0.
01 for 95% of the sample at i < 23, and thus negligible compared to photo-z errors. We then apply a Gaussian error with a zero mean and a standard deviation corresponding to the mean LePhare 1σ uncertainty in the corresponding bin of the lightcones. As in  Adami et al. (2010) , we did not account for catastrophic errors on the photo-zs in this simplified model. The effect on cluster detection is expected to be small since the outlier rate stays well below 10% for the chosen magnitudes and redshift cuts. The final step consists in applying a masking procedure representative of the one used by TERAPIX on the CFHTLS T0007. To this end, we modify the sky coordinates of the galaxies in the lightcones to match a subarea of the CFHTLS representative of the observed masks and apply the VENICE program 4  to remove galaxies from the masked regions.
7. The fit was done using the John Meyers python implementation of the linmix_err IDL routine  (Kelly 2007 ) with a default superposition of three Gaussians. This Bayesian approach considers that the errors follow a Gaussian distribution, while the error on the richness is Poissonian by nature. However, we verified that every cluster has a high enough richness (R > 10) for the Poisson law to be closely approximated by a Gaussian. We note that four clusters only reach R > 5; we kept them in the analysis as they should not significantly affect the errors on the fit and improve the representation of low-mass clusters. We obtain β = 14. 68 ± 0. 04, α = 1. 34 ± 0. 15, and the intrinsic dispersion σ int = 0. 13 ± 0. 03. Comparing our scaling relation with the literature is not straightforward since different richness estimators will yield different scaling relations.
Completeness limit One key point when computing GLFs is to properly define the 90% completeness limit of the sample. The CFHTLS data have the advantage of being homogeneous across the whole field and the 80% completeness limits were computed by TERAPIX. However, these depth limits show substantial variations from tile to tile. Indeed, the deepest tile has an 80% completeness limit of c i ,80% = 24. 07, while the shallowest has c i,80% = 23. 30 (the mean being c i ,80% = 23. 72). To obtain the 90% completeness limit, we used data provided in T0007 of the CFHTLS, where the completeness limit is assessed using simulations. We decided to be conservative and to take the same 90% completeness limit in all the fields: c i ,90% = 23. 0. This has two main advantages: the first is that we were able to study homogeneously our entire sample, and the second is that photo-zs become noisier for magnitudes fainter than i = 23 (σ ∆z phot /(1 + z s ) ∼ 0.
For example, MicNeSs software  (Suez et al. , 2016)  automatically estimates SSR genotypes from NGS data (originally optimized for Roche 454 sequencing). MicNeSs estimates the true alleles for each individual and locus from the observed distribution of SSR lengths aiming to correct the artifactual insertions or deletions produced by PCR amplification. Regarding the detection of distinct alleles that could be taken as identical by standard genotyping approaches, the main improvement of MicNeSs is the inclusion of substitutions during genotype scoring, allowing point mutations to occur within the SSR motif. However, it does not consider the potential singlenucleotide polymorphisms (SNPs) and insertion/deletion polymorphisms (indels) in the flanking regions. Vartia et al. (2016)  found a high infraspecific incidence of SNPs and indels in flanking regions of SSR loci.
However, these authors did not explore the influence of this information in the estimation of genetic diversity and structure. The potential of NGS to significantly improve SSR genotyping stems from increasing data quality by correctly identifying alleles, facilitating data comparisons among laboratories and studies  (Guichoux et al. , 2011) , and allowing a better understanding of the molecular evolution of SSR loci by discerning variation due to the number of units of the SSRs, indels, and SNPs  (Putman and Carbone, 2014). In the context of a project dealing with the evolutionary history of carob (Ceratonia siliqua L. ), a Mediterranean fruit tree, we aimed at improving SSR genotyping using NGS and comparing different scoring methods. Sequence variation within newly characterized microsatellite markers was investigated in carob populations.
SSR characterization Total DNA of one individual was extracted from leaves stored with silica gel using the NucleoSpin Plant II Kit (Macherey-Nagel Sarl, Hoerdt, France). Size-selected fragments from genomic DNA were enriched for SSR content using magnetic streptavidin beads and biotin-labeled GATA and GTAT repeat oligonucleotides (Dynabeads M-280; Thermo Fisher Scientific, Waltham, Massachusetts, USA). The SSR-enriched library was sequenced in a paired-end MiSeq 250 × 250 Nano V2 (Illumina, San Diego, California, USA), and the paired-end reads were merged with FLASH 1. 2. 9  (Magoč and Salzberg, 2011). A de novo assembly was then performed with the merged paired-end reads using MIRA 4. 0. 1 software  (Chevreux et al. , 2004). Primers were designed with MSATCOMMANDER 0. 8. 2. 0  (Faircloth, 2008) ; duplicated reads and those containing more than one SSR array were removed.
Illumina universal adapter sequences 5′-AAGACTCGGCA-GCATCTCCA-3′ and 5′-GCGATCGTCACTGTTCTCCA-3′ were then added to the 5′ or 3′ end of the locus-specific forward and reverse primers, respectively. We also included five pairs of primers for expressed sequence tag (EST)-SSR regions previously described for C. siliqua (La  Malfa et al. , 2014)   spacer). The primers for rpl32-trnL were specifically designed for C. siliqua for this study using the plastome sequence available in GenBank (KJ468096). The set of primers with the Illumina adapters (Appendix S1) was amplified in a total volume of 15 μL containing 0. 3 μL of dNTPs (10 mM), 0. 6 μL of each primer (10 μM), 0. 15 μL of Q5 High-Fidelity DNA Polymerase (5 U/μL; New England Biolabs, Ipswich, Massachusetts, USA), and approximately 5 ng of DNA.
However, in contrast to our approach, the final genotype is based solely on variation in the number of repeat units, whereas the existence of other polymorphisms within the SSR or flanking regions is not investigated. Suez et al. (2016)  produced an innovative software (MicNeSs) capable of recognizing repeat motifs within NGS-produced sequences and, additionally, following up the scoring of an SSR pattern stopped by single mutations. We compared our results with those obtained by MicNeSs and found that this software significantly overestimates heterozygosity indices, as its inner algorithm for detecting the true alleles in the data set is exclusively based on the frequency of the observed distributions of the SSR patterns (see  Suez et al. , 2016  and the software manual for details). In addition to improving the accuracy of genotyping, our approach offers more information on molecular variation.
Moreover, IM also purged the excess of heterozygotes that other scoring methods artificially generated. An increased deviation toward heterozygotes was observed in F IS values in both the MN and AS scoring methods (Table  1 ). Such deviation could be due to AS and MN being sensitive to paralog copies that   2005 ) is shown for each scoring method, as well as the F ST values calculated by STRUCTURE  (Pritchard et al. , 2000)  for each predefined population and K. generate false heterozygote genotypes or to the method used in MicNeSs (MN) software to select true alleles  (Suez et al. , 2016).
ACKNOWLEDGMENTS This work benefited from equipment and services from the molecular biology lab facility at the  Institut Méditerranéen de Biodiversité et d'Ecologie marine et continentale (IMBE)  and from the genotyping and sequencing core facility (iGenSeq) at  ICM (Hôpital Pitié Salpêtrière, Paris, France ). This research is part of the  DYNAMIC  project funded by the  French National Research Agency  ( ANR-14-CE02-0016 ). J. V. benefited from a postdoctoral fellowship funded by  DYNAMIC  ( ANR-14-CE02-0016 ) and a  Marie Skłodowska-Curie Individual Fellowship (704464-YAMNO MICS-MSCA-IF-EF-ST ).
SKUAD SKUAD stands for "Software Kit for Ubiquitous Agent Development". This free multi-platform toolkit is still being developed. It allows us to create multi-agent systems using Java language. It has been developed since 2013 by the Collective Adaptive Systems Research Group in the Laboratory of Mathematics and Computer Science (LIM) at the University of Reunion Island. The idea comes from the observation of the outburst of the Internet of Things, and the necessity to have a software that can make this mass of objects more consistent. For that purpose, SKUAD can operate in an ambient (agents can operate in our real environment) and in a simulated mode.
The SimSKUAD Simulation Platform The SimSKUAD is the simulated operating mode of SKUAD. In this mode, the agents evolve following a simulated time. Devices constituting the agent's environment are virtual. The SimSKUAD uses the temporality model as a default time scheduling approach. However, its architecture is flexible enough to allow us to easily replace the scheduler. Thus, we were also able to implement the time-stepped approach. Different optional modules have already been developed for SimSKUAD. Examples are Mod2D allowing agents simulations in a continuous space environment or ModGrid which allowing agents simulations in a discrete space environment. More details can be found on the SKUAD website  (Payet, 2018). In this paper, we focus in a module called Mod-GIS that allows agents to evolve in Geographical Information System (GIS) kind of environment.
As a consequence, images of background galaxies appear slightly stretched and distorted, the phenomenon known as weak gravitational lensing. Considering that by definition the weak-lensing effect is small, it is necessary to average over a large number of background sources in order to quantify it. Future wide-field experiments like LSST (LSST Science Collaboration 2009) and the ESA space mission Euclid 1  (Laureijs et al. 2011)  will both increase the number and redshift range of the background source population used to measure the weak-lensing signal. Given vastly larger datasets and substantially improved image quality, next-generation surveys will thus permit the most stringent tests yet on a variety of different theoretical scenarios  (Amendola et al. 2018). In these tests, weak lensing will be a primary tool in our effort to understand the dark universe, and in particular the nature of the late-time acceleration phase.
Weak lensing Weak gravitational lensing describes small distortions in the observed shapes of distant galaxies induced by the gravitational fields of massive structures in the universe along the line of sight. Significant information about the distribution and masses of cosmic structures is imprinted on galaxy images as the statistical alignment of their observed ellipticities. This signal has been used successfully in the data analysis of numerous galaxy surveys to constrain cosmological parameters (e. g. Heymans et al. 2013; Hildebrandt et al. 2017; DES Collaboration 2017)  and is potentially a key probe to test gravity and models beyond ΛCDM in future weak lensing surveys such as Euclid  (Amendola et al. 2018). For this purpose, the combination of weak lensing with other probes, such as galaxy clustering (Planck Collaboration XIV 2016), helps to break degeneracies in measuring the gravitational potentials.
2013) , which is a modified version of the GAD-GET code  (Springel 2005)  that implements the extra force and the chameleon screening mechanism (see  Khoury & Weltman 2004)  characterising f (R) gravity theories. As was already done in  Baldi et al. (2014) , such an MG solver was combined with the particle-based implementation of massive neutrinos developed for GADGET by  Viel et al. (2010). Therefore, massive neutrinos were included in the simulations as a separate family of particles with its specific transfer function and velocity distribution, so that both CDM and neutrino particles contributed to the density source term that enters the calculation of the f R extra degree of freedom. Initial conditions were produced by generating two separate but fully correlated random realisations of the linear density power spectrum for CDM and massive neutrino particles as computed by the linear Boltzmann code CAMB  (Lewis et al.
However, it is not a priori obvious that the statistics of such maps will be significantly different enough to distinguish the models from each other. Whether this is possible, for which statistics and for which filtering scales, are the questions we seek to answer in the following sections. We compute all aperture mass maps in this paper using the mr_transform binary of the publicly available Interactive Sparse Astronomical Data Analysis Packages (iSAP 2 ). This is a C++ code with many optional multi-resolution wavelet transforms, which, given the large size of our mass maps, affords a significant speed increase over direct-space 2D convolution. Various statistics of the maps, which are discussed in the next section, are computed using an independent Python code that we have validated on maps from the CoDECS simulations used in  Giocoli et al.
Acknowledgements. AP acknowledges support by an Enhanced Eurotalents Fellowship, a Marie Skłodowska-Curie Actions Programme co-funded by the European Commission and Commissariat à l'énergie atomique et aux énergies alternatives (CEA). AP wishes to thank M. Kilbinger, S. Pires, D. Elbaz, and S. Casas for many useful discussions while preparing this paper. VP and MB thank L. Lombriser and K. Koyama for useful discussions on f (R) models. CG and MB acknowledge support from the Italian Ministry for Education, University and Research (MIUR) through the SIR individual grant SIMCODE, project number RBSI14P4IH, from the grant MIUR PRIN 2015 "Cosmology and Fundamental Physics: illuminating the Dark Universe with Euclid" and from the agreement ASI n. I/023/12/0 "Attività relative alla fase B2/C per la missione Euclid".
A significant literature can be found on saliency-driven exploration: eye saccades modelization  (Itti et al. , 1998; Oliva et al. , 2003; Le Meur and Liu, 2015) , detection of auditory salient events  (Kayser et al. , 2005; Duangudom and Anderson, 2007) , or audiovisual objects exploration  (Ruesch et al. , 2008; Tsiami et al. , 2016). However, most of these models propose either a solely off-line solution requiring prior training from large databases, or an immutable saliency characterization of events. Moreover, the fact that these models only deal with the low-level characteristics of the perceived data leads often to an absence of wider context inclusion, be it through a form of memory, or through the semantics of the events. In addition, saliency can somehow differ from importance, depending on the task to accomplish: attention can be driven by behaviorally important but not salient stimuli while, on the other hand, very salient stimuli but showing no behavioral importance can be disregarded by the attentional networks  (Corbetta and Shulman, 2002; Indovina and Macaluso, 2007).
It is the taking possession by the mind, in clear and vivid form, of one out of what seem several simultaneously possible objects or trains of thought". More particularly, the proposed HTM system is dedicated to the implementation of an overt and endogenous  (Driver and Spence, 1998; Le Meur et al. , 2006)  attentional reaction: the head turning. This reaction, known to be one of the attentional behavior involved in the mechanism of attention reorientation to unpredictable stimuli  (Thompson and Masterton, 1978; Corbetta et al. , 2008; Corneil et al. , 2008) , aims at bringing the visual sensors in front of the sources of interest hence enabling the robot to gather and analyze additional data. In addition, the HTM system provides the robot with an adaptive enough online learning behavior so that it can endlessly integrates new useful information to its self-created audiovisual database.