dataesr/scientific-paragraphs-categorization huggingface dataset:
We limit our clusters at the analysis stage to those above 10 43 ergs s -1. This was done primarily because such objects constitute poor clusters and low mass groups, often with less well-constrained X-ray measurements in the context of RASS data (e. g. Böhringer et al. 2001). If included, however, we observe very little change to our results (less than 5% in most cases). In this work, we rely on velocity dispersion as a proxy for dynamical cluster mass. The velocity dispersion of a cluster is known to be an effective tracer of the overall mass of the cluster halo (e. g. Caldwell et al. 2016) , however, this technique is prone to certain biases. For example, in the case of colliding clusters, atypically high galaxy peculiar velocities can also arise due to the cluster being out of dynamical relaxation (as an example, the Bullet cluster has a velocity dispersion of ∼ 1500 kms -1 , see  Markevitch et al.
On closer inspection of the light profiles for most of our BCGs, we found no significant evidence for the addition of a secondary component, so therefore adopt a single-Sérsic as our model type to fit to each BCG. Numerous profile-fitting codes exist in the literature, such as: GIM2D (Simard 1998), ProFit  (Robotham et al. 2017) , Imfit  (Erwin 2015) , PyMorph  (Vikram et al. 2010)  and GALFIT (GALFIT3,  Peng et al. 2010). For this work, we opt to use GALFIT, which uses a Levenberg-Marquardt ('downhill-rolling') based χ 2 minimisation technique for fitting light profiles (see  Kennedy et al. 2016  for a recent use of the software). It is also capable of generating light profiles based on fixed input parameters, which we use to create mock galaxies as described in section 3. 4. We run GALFIT using the Structural Investigation of Galaxies via Model Analysis code, or SIGMA  (Kelvin et al.
2012) , which is an R-based pipeline software used with great success in the GAMA survey  (Driver et al. 2011 ) to provide Sérsic model fits to 167,600 galaxies in five optical (SDSS-ugriz,  Fukugita et al. 1996 ) and 4 near-infrared (UKIRT-Y JHK) passbands (see  Hill et al. 2011). SIGMA is capable of performing a full fit, including: object extraction through Souce Extractor (SExtractor;  Bertin & Arnouts 1996) , creating a model of the field PSF, estimating the local sky about an object, masking external objects and, finally, fitting a profile through GALFIT. Details of our implementation of SIGMA are provided in the upcoming section; more specific information can be found in  Kelvin et al. (2012).
SIGMA Pipeline and Implementation SIGMA is designed to fit objects in multiple bands in tandem, requiring only the coordinates at which an object is located as an input file alongside any image data corresponding to the desired fitting bands. SIGMA produces an output file containing any relevant SExtractor parameters used in fitting, the background estimate (if requested), and the profile-fitting results from GALFIT. A description of the pipeline is given below, alongside any parameter settings used here when running SIGMA. i. Image Cutout: To begin, SIGMA accesses the WCS information in the header of an image file containing an object. It then converts the celestial RA/DEC coordinates of an object into x/y cartesian pixel coordinates using the sky2xy routine in the WCSTools package  (Mink 1998).
The number of secondaries to be used during fitting is specified by the user prior to running SIGMA; any other objects are designated as 'tertiaries' and masked out prior to fitting (see vi. ). iii. PSF Extraction: The output catalogue from SExtractoris used to find objects in order to estimate the PSF of the field. The PSF Extractor software package (PSFEx; Bertin 2013) is applied to create the empirical PSFs used in SIGMA, which are smoothed by fitting the end result with a Moffat profile  (Moffat 1969). For an object to qualify for computing the PSF, SIGMA requires a S/N > 10 and an eccentricity, , of > 0. 05. PSFex then estimates the FWHM of the object, requiring that 2 < FWHM < 10 pixels and that the object lies within the central 50% of the distribution. A minimum of 10 objects are required to compute a PSF; if this criterion is not met, SIGMA will loop back and attempt to run PSFEx on a larger cutout area.
The Influence of Stellar Mass on BCG Structure To determine which parameters are the primary drivers behind the correlations in our data, we have performed a Spearman rank analysis on our main sample (table  3 ). We have also provided a partial Spearman analysis as well to test the robustness of our results against selection effects, which is summarised in the Appendix for the parameters of interest (tables 4-10; computed using MATLAB's 'partialcorr' routine), following a similar practice to  Collins & Mann (1998). We hold our significance level at the standard value of p ≤ 0. 05 throughout (log 10 [p s ] ≤ -1. 301). Figure  12  suggests a strong correlation between the effective radius and stellar mass (luminosity) of our objects (Spearman rank coefficient, r s = 0. 65, log 10 [p s ] < -45), which remains largely unchanged when any dependence on the environmental parameters are removed (see tables 5, 6 and 9).
g. Bautz & Morgan 1970 provided a basic classification scheme for galaxy clusters with this issue in mind). However, the richness values can be used to provide a robust measure of the total stellar mass of a cluster when coupled with abundance-matching, as demonstrated by  Old et al. (2015). Arguably, a more physically-motivated measure of environment for our purposes than richness would take overall luminosities of galaxies within a cluster into account; this point was also raised by  Zhao et al. (2015a). Such a measure is shown in figure  15  using the luminosity-weighted environmental density maps of  Tempel et al. (2012) , smoothed to scales of 1 Mpc h -1. The maps are computed from SDSS DR8 data and constitute the largest contiguous region of the SDSS footprint; we matched the SPIDERS BCGs with the catalogues within 3", finding 102 common objects which had a corresponding measurement of environmental density.
CONCLUSIONS We created a sample of 329 BCGs from the X-ray selected SPIDERS clusters survey, and investigated three cluster properties of interest: X-ray luminosity, richness, and halo mass, the last property of which we estimated through the cluster velocity dispersion. We modelled our BCGs with single-Sérsic profiles using the SIGMA pipeline. We tested the ability of our pipeline to recover parameters from SDSS data using 10 4 model profiles, outlining a final science sample of 198 BCGs. Finding the results of our best-fitting parameters to be generally consistent across bands, we derived stellar masses for our BCGs based on  Taylor et al. (2011). We conclude the following: • Our simulations suggest a strong codependency between n, R e and apparent magnitude. • We detected a negative bias in Sérsic index with effective radius, as a result of the degeneracy between the background level and profile wings.
ACKNOWLEDGEMENTS We thank the anonymous referee for their insightful comments that greatly improved the clarity of the paper. K. E. Furnell is supported by a  Science and Technologies Funding Council (STFC)  award. C. A. Collins,  I. K. Baldry  and L. S. Kelvin are supported by an  STFC  research grant ( ST/M000966/1 ). K. E. Furnell would also like to personally thank the SPIDERS and CODEX teams for their cooperation, collaboration and support throughout this work. K. E. Furnell  would also like to thank  L. S. Kelvin  for his consistent advice, support and enabling the use of the SIGMA software throughout this project. This paper makes use of data from the  Sloan Digital Sky Survey. Funding for the  Sloan Digital Sky Survey IV  has been provided by the  Alfred P. Sloan Foundation , the  U.
In the neocortex, studies integrating these complementary features have leveraged the potential of "multiscale" neuroimaging to characterize localized properties of individual regions with unprecedented detail  (21)  and to identify overarching topographic principles that govern the interplay between structure and function  (22). The current work used high-resolution structural and functional neuroimaging to identify microstructural features of hippocampal organization in healthy participants. Taking advantage of the openly accessible HCP dataset, we identified common and.
1A ). We selected one group of unrelated healthy young adults [n = 217 (122 women), mean ± SD age = 28. 5 ± 3. 7 y] as a discovery dataset and used another group [n = 134 (77 women), age = 28. 7 ± 3. 8 y] for validation. Details on the subject selection can be found in SI Appendix, Supplementary Materials and Methods and Fig. S1. The First Gradient of Functional Connectivity the Hippocampal Long Axis. In line with recent between-subfield functional connectivity analyses  (25) , there were differences in overall subfield connectivity profiles (SI Appendix, Fig. S2 ); however, all were closely integrated within default mode and temporolimbic networks (Fig. 1B ). To study spatial transitions of hippocampal connectivity to neocortical regions across individual subfield surface points, we used a nonlinear diffusion embedding technique that was previously applied to map spatial gradients in neocortical connectivity  (26).
The first three gradients (G1-G3) were analyzed, as these explained 52% of variance and corresponded to the clearest elbow in the scree plot (SI Appendix, Fig. S3 ). G1 described an anterior/ posterior axis across the hippocampus as a whole (Fig. 1C ) and explained 31% of variance (SI Appendix, Fig. S3 ). Anterior regions had greater connectivity to default mode, limbic, and somatomotor areas, while posterior regions showed greater connectivity with posterior areas, especially visual and dorsal/ventral attention networks. To confirm that G1 related to long-axis specialization, we verified its correspondence with landmarks of anterior/posterior anatomy using manually drawn segmentations of hippocampal head, body, and tail from an independent dataset (27) (Fig. 2 ). Applying k-means (k = 3) clustering on G1 achieved the highest overlap with the anatomy-based tripartite subdivision of the hippocampus (Dice = 0.
Despite some variability across subfields, mean hippocampal T1w/T2w correlated significantly to qT1 (CA1-3: left/right, r = -0. 85/-0. 82; subiculum, r = -0. 55/0. 38; CA4-DG, r = -0. 17/-0. 47; see SI Appendix, Fig. S7 ). The third principal component, G3, explained a further 10% in connectome variance (SI Appendix, Fig. S3 ), but it did not follow as clear a pattern as G1 and G2, likely representing a mixture of both. Indeed, it described both anterior/posterior (specifically in subiculum and CA4-DG) and medial/lateral gradients (in CA1-3 and anterior subiculum). Reliability and Reproducibility. Overall subfield-to-cortex connectivity and within-subfield gradient findings were consistent across the four different scans in the HCP dataset, demonstrating excellent test/retest stability (Fig.
Increased availability of high-resolution MRI has highlighted the possibility for segmenting subfields in vivo using both manual and automatic techniques  (17, 33, 34) , permitting detailed investigations of hippocampal organization. Specifically, submillimeter resolution provided by 3T and beyond can resolve strata rich in white matter as well as the hippocampal sulcus, landmarks reliably separating CA subfields and subiculum from DG. Based on subfields that were automatically segmented in the HCP dataset  (17, 18) , we employed advanced postprocessing techniques to generate medial surface representations running through the core of each  (24). This approach allowed for a spatially specific multiparameter sampling throughout the entire hippocampus, while retaining its interlocked anatomical organization.
The dense and continuous representation of hippocampal subfield surface connectivity offered a substrate for diffusion map embedding, a nonlinear dimension reduction algorithm operating on connectomes that was recently applied to describe the topography of neocortical rs-fMRI connections  (23). In the hippocampus, this data-driven approach revealed a primary axis of connectivity variation along the anterior/posterior axis in all subfields, converging with a large body of animal work showing gradual changes in anatomical connectivity, gene expression, and electrophysiological properties along the hippocampal long axis  (15). Coregistering MRI landmarks (i. e. , hippocampal head, body, and tail) from an independent dataset  (27) , we demonstrated a robust correspondence to macroscopic segments of long-axis anatomy.
Materials and Methods A detailed description of the subjects inclusions, image processing, and analysis methodology can be found in SI Appendix, Supplemental Materials and Methods. In brief, we selected healthy adults from the HCP S900 release for whom all four rs-fMRI and structural scans were available. We selected two cohorts without family relationships, both within and between cohorts, and acceptable image quality: discovery [n = 217 (122 women), mean ± SD age = 28. 5 ± 3. 7 y] and validation [n = 134 (77 women), age = 28. 7 ± 3. 8 y]. All MRI data used in this study were publicly available and anonymized. Participant recruitment procedures and informed consent forms, including consent to share deidentified data, were previously approved by the Washington University Institutional Review Board as part of the HCP.
We also sampled cortical time series using the surfaces provided by HCP and subcortical time series using segmentations from FSL FIRST  (50). We correlated hippocampal and cortical time series, and used Fisher z transformations to render correlation coefficients more normally distributed. Subfield connectivity in Fig. 1B  was mapped using linear and mixed-effects models in SurfStat [www. math. mcgill. ca/keith/surfstat/ (51)]. Diffusion embedding (ref. 26 ; Matlab code: https://github. com/MICA-MNI/micaopen/) identified principal gradients in rs-fMRI connectivity along subfield surfaces, with the anterior/posterior gradient shown in Fig. 1C  and the medial/lateral gradient shown in Fig. 3B. We repeated diffusion embedding based on metaanalytical coactivation maps derived from Neurosynth in Fig.
2   (28). To assess the relation between functional organization, hippocampal anatomy, and microstructure, we related rs-fMRI gradients to manual segmentations of hippocampal head, body, and tail in Fig. 2   (27)  and to surfacesampled T1w/T2w intensity in Fig. 3B , a proxy for myelin content  (20)  (see also comparison between HCP-derived T1w/T2w intensities and quantitative T1 relaxation times from ref. 27) (SI Appendix, Fig. S7 ). Findings were consistent in the left and right hippocampus (SI Appendix, Figs. S2-S6, for right hemisphere findings). We demonstrated test/retest stability in all individuals the discovery cohort in Fig. 4A , by correlating connectivity and gradients maps between two scans within each subject to the other two. Furthermore, we assessed reproducibility, by correlating subfield connectivity and gradient maps between the discovery and validation dataset in Fig.
ACKNOWLEDGMENTS. R. V. d. W. was supported by the  McGill Faculty of Medicine ; S. L. was supported by a Jeanne Timmins Costello Fellowship; S. -J. H. was supported by the  Canadian League Against Epilepsy ; N. B. and A. B. were supported by  Canadian Institutes of Health Research (CIHR)  and received salary support from the  Fonds de la Recherche du Quebec-Santé (FRQS). B. C. B. acknowledges research support from the  National Science and Engineering Research Council of Canada  ( NSERC Grant Discovery 1304413 ), the  Canadian Institutes of Health Research (CIHR  Grant  FDN-154298 ),  SickKids Foundation  ( NI17-039 ), and  Azrieli Center for Autism Research of the Montreal Neurological Institute , as well as salary support from  FRQS  ( Chercheur Boursier ). J. S. was supported by the  European Research Council  (Grant  WANDERINGMINDS -ERC646927 ).
The hypothalamic silencing of miR-200a increased the expression level of LepR and Irs-2 mRNA and reduced body weight gain in ob/ob mice model  [29]. In this context, we have recently found that leptin modulated for the expression of miRNAs, i. e. miR-383, miR-384-3p and miR-488, that target POMC mRNA in the hypothalamus  [30]. The intraperitoneal injection of leptin down-regulated the expression of these miRNAs of interest in the hypothalamus of ob/ob mice showing the involvement of leptin in the expression of mir-383, mir-384-3p and mir-488  [30]. Altogether, above collected data strongly suggest an interaction between miRNAs and leptin signaling in the hypothalamus. However, the role of miRNAs in the leptin sensitivity in hypothalamic melanocortin system has never been assessed. Because the deletion of the two alleles of Dicer in POMC-expressing cells led to a postnatal ablation of POMC neurons  [27, 28] , we used a mice model where one allele of Dicer gene in the POMC cells was inactivated (PDKO).
Physiological studies The body weights of PDKO and control male mice were measured weekly. PDKO mice and control littermates were used for simultaneous assessments daily changes in food intake and whole-body metabolic profile in an open-flow indirect calorimeter (Oxylet System, Bioseb, France) as described previously  [54, 55]. Data were collected for two days to confirm acclimation to the calorimetry chambers (stable body weight and food intake), and data from the third day were analyzed. Rates of oxygen consumption (VO 2 , ml/min/kg 0. 75 ) and carbon dioxide production (VCO 2 , ml/min/kg 0. 75 ) were measured for each chamber every 36 min throughout the studies. Energy expenditure (in kcal/day/kg 75  ]/1000), and lipid oxidation (in g/min/kg 0. 75 = [1. 672 × (VO 2 -VCO 2 )]/1000) were calculated.
02 M KPBS (pH 7. 4) containing 1% BSA, 0. 4% Triton X-100, and 4% NGS. Sections were incubated for 48 h at 4°C with a rabbit polyclonal antibody to pSTAT (1:500; Ref: 9145, Cell Signaling, Beverly, MA). After washing, the sections were then incubated for 90 min with goat anti-rabbit immunoglobulin-Alexa 594 conjugate (1:500; Ref: A11012, Life Technologies). In all conditions, sections were finally coverslipped with mounting medium for fluorescence microscope preparation. Sections were observed using a Zeiss LSM 700 confocal microscope associated to ZEN 2012 software or a DXM 1200 Camera (Nikon) coupled to ACT-1 software. For quantitative analysis, cells were counted manually using the Image J analysis system (National Institutes of Health). For the quantification of pSTAT3-immunopositive cells and eGFP-positive neurons in PDKO; Z/EG and control mice, the average number of cells counted in 8 identical sections (from -1.
Acknowledgments A. D. was the recipient of a doctoral fellowship from the  Ministry of Education. The authors are grateful to  C. Airault ,  B. Bariohay ,  K. Poirot  and  N. Moulard  for technical assistance. We also thank the  Aix-Marseille University Microscopy Center CP2M  for the access to confocal Microscopy and the  AVB platform (Analyse et Valorisation de la Biodiversité ,  iSm2 ,  UMR 7313 ,  Marseille ).
A few years ago, the identification of the Lutheran (Lu) adhesion glycoprotein/basal cell adhesion molecule (BCAM) as a cellular receptor for CNF1 provided new insights into the adhesion process of CNF1  [8]. Very recently, the Ig-like domain 2 of Lu/BCAM was confirmed as the main interaction site using protein-protein interaction and competition studies with various different mutants  [9]. The structure of the catalytic domain of CNTF1 has been available since 2001, yet lacks the region of interest (before position 720). Nonetheless, it is possible to complete it using a threading approach and in silico mutations to precisely explain the results of such experimental mutations. The structure of the catalytic domain of CNF1 is available as PDB ID 1HQ0  [6]. It corresponds to the positions 720 to 1014.
When no structure is available, various different approaches are accessible to tackle this, namely comparative modeling, threading, and de novo approaches  [10]. Using PSI-BLAST  [11]  and ORION  [12, 13] , a good structural template covering the region of positions 657-734 was identified. The corresponding part of the polygalacturonase inhibiting protein (PDB ID 1OGQ  [14] ), positions 100 to 174 shared slightly higher than 50% similarity with excellent coverage, i. e. , nearly no gaps. The prediction of the secondary structure using PSI-PRED correlates very well, emphasizing the quality of the selection of this template to complete CNF1 catalytic domain. A multiple sequence alignment of the different protein chains was generated by ClustalOmega  [15]  and used with Modeller  [16]  to build the structural models.
One hundred structural models were generated and the best DOPE score was selected and analyzed using classical approaches  [17, 18]. The structure of the catalytic domain of CNF1 is available as PDB ID 1HQ0  [6]. It corresponds to the positions 720 to 1014. When no structure is available, various different approaches are accessible to tackle this, namely comparative modeling, threading, and de novo approaches  [10]. Using PSI-BLAST  [11]  and ORION  [12, 13] , a good structural template covering the region of positions 657-734 was identified. The corresponding part of the polygalacturonase inhibiting protein (PDB ID 1OGQ  [14] ), positions 100 to 174 shared slightly higher than 50% similarity with excellent coverage, i. e. , nearly no gaps. The prediction of the secondary structure using PSI-PRED correlates very well, emphasizing the quality of the selection of this template to complete CNF1 catalytic domain.
A multiple sequence alignment of the different protein chains was generated by ClustalOmega  [15]  and used with Modeller  [16]  to build the structural models. One hundred structural models were generated and the best DOPE score was selected and analyzed using classical approaches  [17, 18]. The different mutations were performed with PyMOL 1. 7. 1 software  [19]  using the SCWRL 4. 0 tool  [20]  for (i) simple mutant S720N, (ii) double mutants S720N and S723E, and (iii) double mutants K726S and S727I. Amino acid accessibilities were computed using the DSSP 2. 2. 1 tool  [21]  and VLDP webserver  [22]. Figure  2  shows different visualizations of the wild-type structural models and mutants, while Table  1  summarizes the change in the accessibility of the residues in this region (additional visualizations can be seen in Supplementary Figures  S1-S4 ).
The different mutations were performed with PyMOL 1. 7. 1 software  [19]  using the SCWRL 4. 0 tool  [20]  for (i) simple mutant S720N, (ii) double mutants S720N and S723E, and (iii) double mutants K726S and S727I. Amino acid accessibilities were computed using the DSSP 2. 2. 1 tool  [21]  and VLDP webserver  [22]. Figure  2  shows different visualizations of the wild-type structural models and mutants, while Table  1  summarizes the change in the accessibility of the residues in this region (additional visualizations can be seen in Supplementary Figures  S1-S4 ). The structure of the catalytic domain of CNF1 is available as PDB ID 1HQ0  [6]. It corresponds to the positions 720 to 1014. When no structure is available, various different approaches are accessible to tackle this, namely comparative modeling, threading, and de novo approaches  [10].
Using PSI-BLAST  [11]  and ORION  [12, 13] , a good structural template covering the region of positions 657-734 was identified. The corresponding part of the polygalacturonase inhibiting protein (PDB ID 1OGQ  [14] ), positions 100 to 174 shared slightly higher than 50% similarity with excellent coverage, i. e. , nearly no gaps. The prediction of the secondary structure using PSI-PRED correlates very well, emphasizing the quality of the selection of this template to complete CNF1 catalytic domain. A multiple sequence alignment of the different protein chains was generated by ClustalOmega  [15]  and used with Modeller  [16]  to build the structural models. One hundred structural models were generated and the best DOPE score was selected and analyzed using classical approaches  [17, 18]. The different mutations were performed with PyMOL 1.