dataesr/scientific-paragraphs-categorization huggingface dataset:
This size was chosen, since it corresponds approximately to the diameter of a PC soma. We then calculated the bouton density as the number of boutons per μm 2 and used a color gradient scale (heat map) to visualize regions with high and low bouton densities. For bouton volume measurements, we first defined the "boundaries of a bouton" along the axon recurrent collateral as the points at which collaterals dilate to twice its axonal diameter. Next we counted the number of fluorescent voxels between these points (or in the case of terminal boutons only from the starting point). Because a single voxel corresponded to a cube of 0. 00004 μm 3 (0. 01 × 0. 01 × 0. 4 μm for x, y, and z dimensions), we were able to calculate the approximated value of bouton volumes. Changes in volume were more evident when we performed 3D-surface reconstructions of each bouton with Osirix software, as it is shown in Figure  3D.
DATA ANALYSIS AND STATISTICS Data analyses were performed using Neuromatic software package and custom routines within the IgorPro (Wavemetrics, Lake Oswego, OR, USA) or Matlab environment. All values are expressed as mean ± SEM. Student's t-test were performed within the Excel software package (Microsoft). The significance level was established at P < 0. 05 (*). Cumulative distributions were compared using the Kolmogorov-Smirnov test. A homoscedasticity test was used to determine whether measurements of synaptic cleft width within a group have equal variances or not (separately for either WT or CB-/-). This was important to justify that the homeostatic rearrangement in synaptic cleft values are indeed genotypically stable changes (low variability for each genotype, independent of the number of samples).
ACKNOWLEDGMENTS  David   Orduz was supported by a postdoctoral fellowship from  ULB. We thank  Michele Authelet, Brussels , for her advices and expertise in EM that was fundamental for this study. Also we thank the imaging platform  LiMif (Belgium)  for help with confocal imaging,  Patrick Bischop, Brussels , for advices on Igor programming and  Andrea Helo, Paris , for help with image analysis on Matlab environment. The help of  Martine Steinauer, Fribourg , for the Western blot analyses is highly appreciated. This study was supported by  FMRE-Belgium, FRS-FNRS (Belgium)  and  Interuniversity Attraction Pole  ( IUAP -P7/10 ) from  Belgian Federal Scientific Affairs.
Informed consent was obtained and procedures were performed in agreement with the Declaration of Helsinki. Whole-genome sequencing was performed as part of the WGS500 Project, whereby 500 individuals with a variety of medical conditions of suspected genetic origin were sequenced to assess the clinical utility of WGS. It was conducted on the Illumina HiSeq2000 at a 30x sequencing depth. Reads were mapped to the human reference genome (hg19) using STAMPY, 2 identification of alleles and variants was performed using the in-house Platypus software  3  and annotated using a pipeline based on ANNOVAR. 4  We searched for heterozygous or homozygous variants in candidate genes: HIF1A, EPAS1 (HIF2A), HIF3A, ARNT (HIF1B), HIF1AN (FIH), EGLN1 (PHD2), EGLN2 (PHD1), EGLN3 (PHD3), VHL, EPO, EPOR, JAK2, HBB, HBA1, HBA2 and BPGM; or for rare coding mutations in any gene.
In parallel, the SF properties of AGN hosts is topic of much current work. Various tracers of the SFR have been applied to AGNs, though traditional ones such as emission lines, UV continuum or optical spectral features can be strongly contaminated by nuclear light, especially among luminous Type I AGNs. Despite the limited sensitivity of current datasets, farinfrared (FIR) wavelengths (> 50 µm) offer the best discrimination between emission from star-formation heated dust and AGN-heated dust, since the latter almost always exhibits a warmer distribution of temperatures, peaking in luminosity at ∼ 20 µm with a sharp drop off to the FIR  (Netzer et al. 2007; Mullaney et al. 2011; Rosario et al. 2012). Herschel-based SFRs of active galaxies across a range of luminosities have been shown to agree reasonably well with those of massive inactive (i.
e. , non-AGN) star-forming galaxies  (Mullaney et al. 2012; Santini et al. 2012; Rovilos et al. 2012; Rosario et al. 2013b,c) , but there is a strong tendency for X-ray bright AGNs to be found in SF hosts  (Rosario et al. 2013b). Therefore, AGN hosts are primarily massive galaxies forming stars at a normal rate given their stellar content. In this work, we present an exploration into connections between the SFR and structure of AGN hosts at intermediate to high redshifts (z = [0. 5, 2. 5]), towards constraining the degree to which AGNs exhibit the relationships known among inactive galaxies (i. e. , those without substantial on-going SMBH accretion). The primary goal of this paper is to lay the groundwork for a more extensive and detailed analysis of such relationships using forthcoming datasets spanning a number of important extragalactic fields.
Despite this, our work is already unique in its approach of testing SFR-structure trends in systems with significant AGN activity across an important range in redshifts. Our study builds on the best available deep extragalactic data at Xray, optical, NIR and FIR wavelengths, and leverages the substantial efforts of the extragalactic community towards building coherent datasets in the GOODS fields. Datasets, selections and methods are introduced in Section 2. We discuss specific structural patterns relevant to the galaxy population at these redshifts and compare them to the AGNs, revealing any differences between these two populations (Section 3). In Section 4, we investigate trends between SFR and structure, again comparing the AGNs to the galaxy population. Finally, in Section 5, we discuss our results in the context of earlier work and weigh them qualitatively against the predictions of key AGN triggering scenarios.
Sample Selection, Datasets and Methods In this work, we concentrate on X-ray selected AGN, since luminous X-ray emission is an unambiguous hallmark of SMBH accretion activity. However, studies of the AGN population and the X-ray background suggest that X-ray selection systematically misses the most obscured AGNs. Our conclusions from this work are strictly applicable only to such AGNs as are traced by X-ray selection. This caveat must be borne in mind when setting this work into the overall context of AGN demographics.
Multi-wavelength photometry and stellar masses We employ multiwavelength galaxy catalogs in both GOODS fields to define a general galaxy sample, the properties of which we will compare to the AGNs. In GOODS-S, we use the updated GOODS-MUSIC database  (Santini et al. 2009; Grazian et al. 2006) , while in GOODS-N we use a catalog developed for the PEP team using a similar methodology  (Berta et al. 2010 (Berta et al. , 2011) )  The former catalog selects galaxies with observed magnitudes in the HST F850LP band < 26 or in the ISAAC K s band < 23. 5, while the latter is primarily selected to have K < 24. 2. In order to exclude a surfeit of faint sources with inaccurately red colors and masses, we apply an additional cut of F850LP < 26 in the GOODS-N catalog. For galaxies with no current spectroscopic redshifts, photometric redshifts were determined by fitting multiwavelength photometry using PEGASE 2.
Acknowledgements. This work is based on observations taken by the  CANDELS Multi-Cycle Treasury Program  with the  NASA/ESA HST , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS5-26555. PACS has been developed by a consortium of institutes led by  MPE (Germany)  and including  UVIE (Austria) ;  KUL, CSL, IMEC (Belgium) ;  CEA, OAMP (France) ;  MPIA (Germany) ;  IFSI, OAP/AOT ,  OAA/CAISMI, LENS, SISSA (Italy) ;  IAC (Spain). This development has been supported by the funding agencies  BMVIT (Austria) ,  ESA-PRODEX (Belgium) ,  CEA/CNES (France) ,  DLR (Germany) ,  ASI (Italy) , and  CICYT/MCYT (Spain). FEB acknowledges support from  Basal-CATA   PFB-06/2007 ,  CONICYT-Chile  (through FONDECYT  1101024 , gemini-conicyt 32120003, "EMBIGGEN" Anillo  ACT1101 ), and Project  IC120009  " Millennium Institute of Astrophysics (MAS) ", funded by the  Iniciativa Cientifica Milenio del Ministerio de Economia ,  Fomento y Turismo.
Visfatin downregulation by TNFα reduced NAD + concentrations and Sirt1 activity in 3T3-L1 adipocytes Physiological consequences of visfatin downregulation were next evaluated. While TNFα treatment had no effect on the secreted quantity of visfatin (data not shown), it significantly reduced the intracellular quantity of visfatin in 3T3-L1 adipocytes (Fig. 3A ). Because this protein is the key enzyme of the NAD + salvage pathway, we measured the concentration of NAD +. As anticipated, the concentration of NAD + was decreased in TNFα-treated adipocytes (Fig. 3B ). We also measured Sirt1 activity because its activity is strongly dependent on NAD +. Using a fluorescence-based assay, we observed a decrease in Sirt1 activity in cells incubated with TNFα (Fig. 3C ). This reduction in Sirt1 activity was independent of Sirt1 mRNA levels, which were not modified by TNFα incubation (Fig.
Visfatin inhibition led to decreased glucose uptake and Akt phosphorylation To study the involvement of visfatin in TNFα-mediated effects on glucose metabolism, we measured 2-deoxyglucose uptake in 3T3-L1 adipocytes treated with TNFα alone or pretreated with FK866. TNFα treatment led to a 28% decrease in insulinstimulated glucose transport compared with transport in control cells (Fig. 6A ). Incubation with FK866 followed by TNFα treatment led to a 29% decrease in insulin-stimulated glucose uptake compared with transport after TNFα treatment alone. Together, these data suggested that visfatin inhibition reinforced the decrease in glucose uptake mediated by TNFα. The impact on insulin signaling was assessed at the downstream level by evaluating the phosphorylation of Akt. Compared with that in control cells, TNFα treatment decreased Akt phosphorylation.
One of the most appealing solutions  [48, 49, 120]  allowing to avoid the data association problem are based on the assumptions: (i) the numbers of received target and clutter measurements in a time step are Poisson distributed (so several measurements may originate from the target), (ii) target extent is modeled by a spatial probability distribution and each target-related measurement is an independent 'random draw' from this spatial distribution (convolved with a sensor model). At each time step a set of sensor measurements is recorded. These measurements could come from either targets or clutter. The basic idea is to figure out how to assigns each measurements to the right target or identify it as a false alarm. The likelihood can then be calculated as derived in  [48, 49, 120] : p(Z k |X k ) ∝ m k i=1 ρ + λ T p z k (i) X k , ( 10 ) where ρ is the clutter density and λ T is the mean value of the number of measurements originated from the targets, m k is the overall number of measurements received at time k and X k is the state of interest.
Group's evolution modeled by a dynamic Bayesian network The underlying models governing the time evolution of the unknown (θ k , e k ) and observed variables Z k , respectively, can be represented using a dynamic Bayesian network (DBN). Thus, the state transitions are specified by the pdf p(θ k , e k |θ k-1 , e k-1 ) (15) while the observations are related to the unknown states via the likelihood  (14). The DBN corresponding to this process is depicted in Fig. 5  where it is assumed that the DBN lacks the splitting and merging moves. The indicator transitions e In  [22] , the filtering pdf p(θ k |Z 1:k ) is approximated by a variant of the sequential MCMC scheme described in Section 2. 2. The respective Matlab code for  [22]  is available on Matlab Central portal  [99]. This algorithm presented in Section 2.
Key advantages of the box particle filter (Box-PF) against the standard PF are in its reduced computational complexity and its suitability for solving high dimensional problems such as those emerging in tracking of groups of hundreds of targets. The Box-PF has a potential to solve group and extended object tracking problems with reduced computational complexity. • Selective/attentive tracking and perception: Future tracking systems would have to cope with possibly a large number of moving objects, each of which is prone to produce more than a single measurement. The raw data from the scene would potentially be acquired simultaneously from many sensors. This amounts to massive data sets that would have to be processed efficiently. One direction for alleviating this problem relies on selective attention -a concept from neuropsychology and cognitive sciences  [13].
We use the parametrization proposed by  Oganov et al (2000) , known to be particularly robust for high-temperature and also high-pressure calculations  (Oganov et al 2000 , Liu et al 2007 , Ito and Toriumi 2010 , Chen et al 2012). This parametrization is described in table 1. Note that partial charges are used and cation-cation interactions are neglected at short range. Simulations have been performed using standard programming packages GULP  (Gale 1997)  and LAMMPS  (Plimpton 1995). Both packages rely on Ewald summation methods (e. g. In't Veld et al 2007) for Coulombic interactions. Ground states properties of Mg-Pv (lattice parameter and elastic properties) were computed using GULP, whereas GSF energies were mostly determined with LAMMPS as discussed in the following section. First principles calculations of γ -surface energies have also been performed within the framework of the DFT using the VASP package  (Kresse and Hafner 1993, Kresse and Furthmüller 1996).
Acknowledgments The authors thank  Christophe Denoual  for helpful discussions and support in the PNG calculations. Computational resources were provided by the  CRI-Université de Lille 1. This work was supported by funding from the  European Research Council  under the  Seventh Framework Programme  ( FP7) ,  ERC  grant no 290424  RheoMan. The authors warmly thank the two anonymous referees and the Journal Board member for crucial improvements in the manuscript.
Atomic Force Microscopy We used a Nanowizard II AFM (JPK Instruments, Berlin, Germany) mounted on a Zeiss microscope (Carl Zeiss, Jena, Germany). This configuration allows to carry out AFM measurements and simultaneously observe the cells using phase contrast or fluorescence modes. This AFM is also equipped with the 'CellHesion' module (JPK Instruments, Berlin, Germany). This module enables a long-range vertical displacement of the stage up to 100 mm which makes force spectroscopy measurements possible including cell-cell interactions. In parallel, a vertical piezotranslator (PIFOC, Physik Instrumente, Karlsruhe, Germany) is mounted on the microscope objective to move the objective concurrently with the microscope stage and focus on cells while carrying out AFM measurements. All the measurements were carried out at 37uC using the Petri Dish Heater (JPK Instruments, Berlin, Germany).
The retraction curve also provides information about the adhesion energy which is the work necessary to detach the cancer cell (shaded area in Figure  1C ). This includes the work done to stretch the cells as well as the work done to break the molecular bonds  [25]. All these parameters (detachment force, rupture force, adhesion energy) are obtained from the force curve using the Image Processing Software (JPK instrument, Berlin, Germany). For each set of conditions, AFM experiments were carried out about 9 times on 3 different days. Unless otherwise stated, data are reported as mean 6 standard error of the mean. All statistical tests were performed using the R software (2. 14 release). Since the data are correlated, we used a Generalized Linear Mixed Model (GLMM). Differences between the parameters calculated on untreated and anti-ICAM-1-treated cells were tested by the mixed function of the afex package in R software.
Adhesion energy and detachment force (cell level) An important aspect to characterize the interaction of cancer cells to ECs is the adhesion energy which involves the whole cell contact area. The adhesion energy is derived through integration of the area below the curve F(z), where F is the force and z is the piezo displacement. The basis line is chosen as the final limiting value, after all bonds are detached. The JPK software allows to choose this value and then performs the integration. Therefore we investigated the adhesion energy as well as the detachment force (absolute value of the minimum force on retracting force curve, see Figure  1B ) versus retraction speed (V). As shown in Figure  3 , these two parameters increase with retraction speed. Regarding the adhesion energy, the most invasive J82 cells present larger values as compared to the T24 or RT112 cells.
Nano-scale SIMS Data Processing Ion species 12 C 14 N 2 and 13 C 14 N 2 were used to detect carbon associated with biomass, as they have higher intensity and are more statistically reliable than 12 C 2 and 13 C 2. The ratio (%) of 13 C 14 N 2/12 C 14 N 2 was used to quantify the uptake of carbon (enrichment over the background). Color ratio image was generated by using OpenMIMS, an ImageJ plugin  [38]  developed by Claude Lechene's Laboratory. OpenMIMS allows the visualization of ratio value in a HSI (Hue-Saturation-Intensity) image  [39, 40]. To compute the average 13 C 14 N 2/12 C 14 N 2 ratio in the cells in each image, the information pertaining to microbial biomass (enriched in 13 C) was separated from the background resin (natural abundance of 13 C) by a mask. The binary mask was generated by adjusting the threshold level on the sum image of 13 CN 2 and 12 CN 2.
Acknowledgments We thank the  PICT-IBiSA imaging facility in the institut Curie ,  Dr. G. Geesey  ( Univ. Montana ) and  S. Gunther  ( NPS ) for help in YNP,  Dr. L. Casillas  ( Univ. PR ) and members of the  Newman Lab (MIT)  for advice and help,  Dr. W-N. Jane ,  Dr. C-Y. Chiu  and  Ms. C-M. Hsu  ( Academia Sinica ) for help with immunogold labeling,  J. Mui  and  Dr. S. K. Sears  ( McGill Univ. ) for sample preparation for Nano-scale SIMS, and former and current members of the  MIT Laboratory for Geomicrobiology and Microbial Sedimentology  for ideas, lab help and support. A. Maheras ,  A. Evans  and  M. S. Sim  helped with the culturing experiments.
These studies confirmed earliest data demonstrating that various subunits of the PSII and light-harvesting polypeptides (LHCII) 1 are phosphorylated. Some of these phosphorylation events were also found to be reversible in response to light or dark transitions. Zolla and co-workers also studied the light-harvesting proteins (LHCI or LHCII) from various monocot and dicot species and determined their intact molecular weights  (28, 29). Whitelegge and co-workers also coupled ESI-MS with reverse-phase chromatography to catalog all detectable proteins in samples of PSII-enriched thylakoid membrane subdomains (grana) from pea and spinach  (30). Only 30 proteins were identified, with important information on the phosphorylation of several PSII subunits. Later, the same group reported a set of 58 nuclear-encoded thylakoid membrane proteins from four plant species  (31)  and assigned experimentally the N-termini of all these proteins.
The partitioning of each protein in these three chloroplast compartments was assessed using a semiquantitative proteomics approach (spectral counting). These data, together with an in depth investigation of the literature, were compiled to provide accurate subchloroplastic localization of previously known and newly identified chloroplast proteins. Among the 500 proteins that were identified in the thylakoid fraction, 220 could be assigned to the thylakoid compartment (see AT_Chloro database at http://www. grenoble. prabi. fr/at_ chloro/). Although proteomic analysis of thylakoids has allowed defining their protein composition, post-translational modifications and localization in the membrane and lumen compartments, a complete survey of the protein composition of the thylakoid subcompartments is still lacking.
1% (v/v) formic acid during 114 min before reaching 72% (v/v) for the last 6 min. MS and MS/MS data were acquired using Xcalibur V2. 1. 0 SP1 (Thermo Fischer Scientific). Spray voltage and heated capillary were respectively set at 1. 4 kV and 200 °C. Survey full-scan MS spectra (m/z ϭ 400 -1600) were acquired in the Orbitrap with a resolution of 60,000 after accumulation of 106 ions (maximum filling time: 500 ms). The twenty most intense ions from the preview survey scan delivered by the Orbitrap were fragmented by collisioninduced dissociation (collision energy 35%) in the LTQ after accumulation of 104 ions (maximum filling time: 100 ms). Database Searching-Peak lists were generated with the Mascot Distiller version 2. 4. 3. 3 software (Matrix Science, Boston, MA) from the LC-MS/MS raw data.
Using the Mascot 2. 4. 0 search engine (Mascot Daemon, Matrix Science), MS/MS spectra were searched against a compilation of the A. thaliana protein database provided by TAIR (nuclear, mitochondrial, and chloroplast genomes; TAIR version 10; 35,386 entries; http://www. arabidopsis. org/) and a homemade list of contaminants frequently observed in proteomics analyses (260 sequences, (  14 )). Both databases were concatenated with the corresponding decoy database in order to calculate false discovery rate. One missed trypsin cleavage was allowed, and trypsin/P was used as the enzyme (cleavage at the C terminus of Lys/Arg). The mass tolerances were 10 ppm for precursor ions and 1. 0 Da for fragment ions. The variable modifications allowed were N-acetylation (protein N termini), methionine oxidation and dioxidation.
A fixed modification was set: carbomidomethyl cystein. Mascot search results were automatically filtered using the home-developed IRMa 1. 31. 1 software  (42). The following parameters were applied: (1) the number of report hits was fixed automatically to retrieve proteins with a p value, as defined by Mascot, such as p Ͻ 0. 05; (2) only peptides ranked first and with an homology threshold such as the p value, as defined by Mascot, corresponding to p Ͻ 0. 05, were kept. Every duplicated peptide sequence was conserved to calculate spectral counts. Consistent protein grouping for all the analyses (same master protein reported) was ensured using the IRMa software. In order to avoid redundancy, only the master proteins (i. e. the protein representing a protein group) were given in the provided results.
For each database search, protein groups were identified with a 1% false discovery rate  (14). Database searching results and raw files were exported in the PRIDE format and deposited to the ProteomeXchange server with identifier PXD000546 and DOI 10. 6019/PXD000546 and PRIDE accession # 31943 (www. proteomexchange. org). Lists of proteins and peptides can also be found in the Supplemental Data file. Database searching results, with annotated spectra (Pride_Exp_Complete_Ac_ 31943. xml), can be visualized using the freely available PRIDEInspector tool (https://code. google. com/p/pride-toolsuite/wiki/PRIDEInspector). Data Processing-Total spectral counts and spectral counts for proteotypic peptides were retrieved from the IRMa filtered results for each of the MS/MS analyses. Weighted spectral counts were calculated as described in Abacus  (43)  and were imported in R (version 3.