dataesr/scientific-paragraphs-categorization huggingface dataset:
STELLAR MASS FUNCTIONS AND THE COSMIC STAR FORMATION HISTORY We proceed by comparing the predicted stellar mass functions in Horizon-AGN to an array of observational data at 0 < z < 6 (Figure  7 ). The grey shaded region in this plot indicates the Horizon-AGN predictions (with the width of the region indicating Poisson uncertainties). The pink dashed line indicates predictions from Horizon-noAGN, a twin simulation without BH feedback. Not unexpectedly, low-mass galaxies are largely unaffected by BH feedback. However, agreement between theory and observation at the high-mass end of the mass function depends strongly on implementing feedback from BHs, confirming the results found in the literature (e. g. Oppenheimer & Davé 2008; Sales et al. 2010; McCarthy et al. 2010; Vogelsberger et al. 2014; Khandai et al.
This agreement is reflected and summarized in the comparison of the cosmic star formation history. Not unexpectedly, Horizon-AGN shows agreement with the observations, tracking both the shape and normalization of the observational data  (Hopkins & Beacom 2006) , although we note that the predictions are close to the upper bound of the parameter space defined by the observational uncertainties at very low redshift (z ∼ < 0. 1). We conclude this section by briefly discussing the performance of similar models in reproducing the observed stellar mass function and cosmic SFH. In the redshift range studied here, most simulations that are similar to Horizon-AGN produce reasonably good agreement with data (e. g. Furlong et al. 2015; Genel et al. 2014; Khandai et al. 2015) , although a detailed comparison between models is difficult, because all models are typically not compared to the same observational datasets at the same redshifts.
However, similar patterns are seen when most simulations are confronted with observational data. While galaxies beyond the knee of the luminosity/stellar-mass functions are well reproduced (due mainly to implementation of AGN feedback, as noted above), models typically overshoot the data at low stellar masses/luminosities at low and intermediate redshifts (z < 3), with the level of the discrepancy varying with the model in question. It is worth noting that the undershoot of the model compared to data seen in Horizon-AGN at high redshift (z > 5) is also seen in other simulations (e. g. Furlong et al. 2015; Genel et al. 2014). Predictions made by these models for the cosmic SFH are generally consistent with observations -while all models reproduce the shape of the cosmic SFH, small systematic offsets are seen in some comparisons (e.
SUMMARY We have compared the predictions of Horizon-AGN, a hydro-dynamical cosmological simulation that uses an adaptive mesh refinement code, to an extensive array of observational data across cosmic time. Our study has focussed on confronting the predicted evolution of luminosity functions, stellar-mass functions, the star formation main sequence, rest-frame UV-optical-near infrared colours and the cosmic star formation history to observational data in the redshift range 0 < z < 6 (around 95% of the lifetime of the Universe). These observables, which are functions of the evolving stellar mass growth of the galaxy population, represent a significant constraint on the methodologies employed by the simulation. We note that, apart from choosing BH feedback parameters that reproduce the local MBH -σ * relations, the simulation is not otherwise calibrated to the local Universe.
Overall, we find that Horizon-AGN offers an excellent tool, both for studying galaxy evolution over cosmic time, and for making predictions for the next generation of galaxy surveys. Figure  9. Calibration of the predicted mass-gas phase metallicity relations to osbervational data. The mass-gas phase metallicity relations predicted by Horizon-AGN are shown in grey. The observational data  (Maiolino et al. 2008; Mannucci et al. 2009 ) to which we perform the calibrations to correct the simulated metallicities are shown in orange. Best fits to the observational datasets are shown using the orange lines. For the z ∼ 3. 5 dataset we also show the individual points to give an indication of the typical scatter around the best-fit lines (we only show one set of points for clarity and because the scatter is similar at all epochs).
The corrected metallicities that are used for the analysis in this study are shown using the black points. ties in agreement with the observed mass-gas phase metallicity relations at z = 0, 0. 7, 2. 5 and 3. 5  (Maiolino et al. 2008; Mannucci et al. 2009) , where fno = 4. 08 -0. 21z -0. 11z 2. As noted before, the observed mass-metallicity relations are calculated using strong line diagnostics (e. g. In Figure  9  we show the mass-gas phase metallicity relations predicted by the simulation using the grey points. The observational data  (Maiolino et al. 2008; Mannucci et al. 2009 ) to which we perform the calibrations to correct the simulated metallicities are shown in orange. Best fits to the observational datasets are shown using the orange lines. For the z ∼ 3. 5 dataset, we also show the individual points to give an indication of the typical scatter around the best-fit lines (we only show one set of points for clarity, and because the scatter is similar at all epochs).
ACKNOWLEDGEMENTS SK acknowledges a  Senior Research Fellowship from Worcester College Oxford. CL is supported by the  ILP LABEX  (under reference  ANR-10-LABX-63  and  ANR-11-IDEX-0004-02 ). TK is supported by the  ERC Advanced  Grant  320596  ' The Emergence of Structure during the Epoch of Reionization '. JD and AS acknowledge funding support from  Adrian Beecroft , the  Oxford Martin School  and the  STFC. This research has used the DiRAC facility, jointly funded by the  STFC  and the  Large Facilities Capital Fund of BIS , and has been partially supported by grant  Spin(e)   ANR-13-BS05-0005  of the  French ANR. This work was granted access to the HPC resources of CINES under the allocations 2013047012, 2014047012 and 2015047012 made by  GENCI. We thank  Stephane Rouberol  for running and maintaining the Horizon cluster hosted by the  Institut d'Astrophysique de Paris  and the  COSMOS2015 team  for allowing us to use their data ahead of publication.
Confocal image post-treatment. Complete image stack deconvolution was performed with Huygens Pro (version 14. 10, Scientific Volume Imaging), and 2D images were generated from a sum intensity projection over a 3D volume cut of 0. 4 µm depth, centered either on the vesicular compartment, when visible, or on a mid-section of the cell. Colocalization analysis was performed by cropping the whole compartment (Rab11 or FIP3), or the whole cell, using Fiji software  (28)  with the Colocalization Threshold plugin. Threshold was automatically determined using the Costes method autothreshold determination (29). Pearson's correlation coefficient was calculated for the analysis. Statistical analyses were carried out by the nonparametrical Mann-Whitney test using Prism software (Graphpad). Images to quantify Lck accumulation at the immunological synapse were acquired at 1µm increments in the z-axis to avoid fluorescence overlap.
Intracellular calcium analysis 3x10 6 cells were incubated in the dark with 40 µM Fluo-3-AM (Life Technologies) 30 min at room temperature, washed once in RPMI 1640 medium containing 1% FCS and resuspended in 2 mL of in the same medium. Variations of free intracellular calcium concentration before and after activation with 10 µg/mL soluble anti-CD3 (UCHT-1) were measured using a FACSCalibur flow cytometer (BD Biosciences) and analyzed using FlowJo v9 software (Tree Star). Incubating cells with 1 mM MnCl 2 or 2 µg/mL A-23187 allowed measuring minimum and maximum Fluo-3-AM signal, respectively, and normalization of the possible differences in Fluo-3 loading by the different cell lines analyzed. Intracellular calcium concentration was calculated using the formula previously described: [Ca2+]i = Kd(F -Fmin)/(Fmax -F), where Kd is 400 nM at vertebrate ionic strength.
Acknowledgments: We are grateful to  Dr A. Danckaert  and the microscopy core facility  Imagopole-Citech  at the  Institut Pasteur  for continuous technical support. We thank  Drs A. Echard  and  S. Etienne-Manneville  ( Institut Pasteur) , and  Dr O. Acuto ,  K. Nika ,  G. Massi  and  A. L. Lanz  ( University of Oxford ) for expression vectors reagents and cell lines, and  Dr A. Graziani  ( Universita Vita-Salute San Rafaele, Milan ) for stimulating discussions. Blood samples from healthy donors were obtained from the  French Blood Bank  ( EFS, Etablissement Français du Sang ) and from the  Institut Pasteur Clinical Investigation and Access to Biological Resources core facility (ICAReB) , in the framework of institutional research and clinical investigation projects approved by the  Ethical Committees.
Similarly, if the p parameters  2,i are jointly significant then the null hypothesis that y does not Granger cause x can be rejected. Granger causality test is based on the concept of causal ordering and assumption as follows: a variable x is said to Granger cause another variable y if past values of x help predict the current level of y given all other appropriate information  (Stern, 2011; Dudzevičiūtė et al. , 2016). All calculations have been made applying Windows-based econometric software Eviews v. 8. 0.
Flow cytometry MCs were centrifuged at 800× g for 15 min at room temperature. Cells (5. 10 5 cells/ml) were resuspended in PBS-EDTA 3 mM. Fc receptors were blocked with Fc blocking reagent (CD16/CD32) for 5 min. Cells were finally processed for staining with PE-labeled rat antimouse CD 117 (eBioscience, Fontenay sous Bois, France), (1/300) and APC-labeled american hamster anti-mouse FcεRI (eBioscience, 1/300) in PBS-EDTA 3 mM before analysis with a FACScalibur (BD Biosciences, Le Pont de Claix, France) equipped with Flowjo software.
Statistical analyses Each experiment was carried out at least three times in independent experiments. The most representative experiment is shown. Results represent the mean ± standard deviation (SD) of at least triplicates of one experiment and were analyzed by two-tailed Student's t-test or ANOVA with Tukey post-hoc test using GraphPad software. Differences in values were considered significant if P < 0. 05 (*P < 0. 05; **P < 0. 01; ***P < 0. 001). For in vivo studies, a minimum of 5 mice has been used at each time point of the kinetics to get significant results.
Acknowledgements We thank  B. Jaulhac  and  L. Zilliox  from the  French National Reference Centre of Borrelia  for the culture of Borrelia strains and  Dr. J. Hovius  ( University Amsterdam, Holland ) for providing OspC-KO B. burgdorferi (s. s. ) 297,  Dr. B. Luft  ( University of Stonybrook, NY, USA ) for providing OspC lipoprotein,  Prof L. Gern  and  Dr. M. Voordouw  ( University of Neuchâtel, Suisse ) for their help in maintaining the tick colony. We also thank  C. Barthel  and  E. Collin  for their technical support for PCR and culture of mouse organs.
Review of microwave window response The theory of reflection and transmission of multiple dielectric slabs is well understood  [2]. Application to vacuum windows in waveguides is more specific, although good literature is available here too, e. g. [3] [4] [5]. In this section a review is given on how to analytically obtain a multilayer disk response including losses inside a waveguide. This is followed by comparison to results from a simulation code with the aim to also assess more complex structures such as double disk tilted windows.
For a circular waveguide with dominant TE 11 mode k cmn = 1. 841/r with r the radius of the waveguide  [8]. The mode-dependent reflection coefficient for TE-modes mn is: mn = 1 -k cmn /k 0 2 - r -k cmn /k 0 2 1 -k cmn /k 0 2 + r -k cmn /k 0 2 (6) The equations have been implemented in a MATLAB ® code which reads d, the disk spacing and r of each section from a spread sheet and computes the S-matrices. While such S-matrices are convenient for lab measurements they are not well suited for matrix manipulation and thus each S-matrix is converted to a T-matrix as suggested in  [5]. The T-matrices are multiplied right to left and the overall product is converted back to an S-matrix. R is extracted as S 11 S * 11 and T is extracted as S 21 S * 21. Losses are included again by replacing the relative permittivity with the complex permittivity.
Human subjects We independently identified two ZBTB18 point mutations in unrelated patients with AnCC and one HNRNPU mutation in a patient with epileptic encephalopathy by, respectively, sequencing 423 genes associated with AnCC (callosome panel) in humans or mice  (Mignot et al. 2016)  or 4813 genes of the Trusight One panel (Illumina). We then collected clinical and molecular data of patients with ZBTB18 and HNRNPU mutations or 1q43q44 microdeletions through members of the EUROEPINOMICS RES consortium, the French Achropuce network (http://www. renapa. univ-montp1. fr/), Genematcher  (Sobreira et al. 2015)  and Decipher (https:// decipher. sanger. ac. uk/)  (Firth et al. 2009 ). This series includes previously reported patients with updated clinical data and previously reported genotypes with unreported clinical data: patients D2, D3, D4, D5 and D8 were reported as patients #7, #5, #3, #9 and #6, respectively, in  (Thierry et al.
2012; Perlman et al. 2013; Thierry et al. 2012). Coordinates of the deletions reported in hg18 were converted into hg19/ GRCh37 with LiftOver (https://genome. ucsc. edu/cgibin/hgLiftOver). Clinical data were collected using a standardized questionnaire directly from the referring clinicians. Microcephaly was considered for patients with an occipitofrontal circumference (OFC) of at least -2. 5 standard deviation (SD) below the mean. A radiologist, a neuropediatrician and a geneticist collegially ascertained brain MRI anomalies. AgCC designates the absence of one or all parts of the CC, DysCC is used for complete CC with an abnormal shape or abnormally small CC, ThCC is used for complete CC with insufficient thickness.
Genes with a pLI ≥0. 9 are considered to be significantly LoF intolerant. HI scores evaluate the probability that the gene is intolerant to haploinsufficiency, calculated from CNV data and integrating genomic, evolutionary and function properties of haploinsufficiency  (Huang et al. 2010). High ranks (e. g. 0-10%) indicate a gene that is likely intolerant to haploinsufficient; low ranks (e. g. 90-100%) indicate a gene that likely tolerates haploinsufficiency. Missense variants were assessed in silico for possible pathogenicity using Alamut Visual 2. 7 (Biointeractive Software, France), PolyPhen-2 (http://genetics. bwh. harvard. edu/pph2) and SIFT (http://sift. bii. a-star. edu. sg). Alignment of microdeletions found in patients with (red bars) and without (blue bars) microcephaly showed that deletions including AKT3 (upper panel) were mostly associated with microcephaly and those excluding AKT3 (lower panel) were mostly associated with normal OFC (a).
Apoptosis Assays Me275, Colo829, and A375 cells were seeded in 12-well plates. The cells were treated for 24 hr with RAFT-c(RGD)4-poro2 at concentrations ranging between 0. 5 and 10 mM. Control cells were treated with RAFT-c(RGD)4 or the vehicle alone. Active Caspase 3 was evaluated by flow cytometry using the Active Caspase 3 Apoptosis Kit (Becton Dickinson) on an Accuri-C6 flow cytometer with a filter (585/42 nm) and the CflowPlus software.
Immunohistology At the end of the treatment, tumors were taken and frozen in Tissue-Tek (Sakura Finetek) and stored at À80 C. Immunohistology was then performed on 7-mm tumor slices. Tumor cell proliferation was evaluated using a rabbit anti-mouse Ki67 antibody (DakoCytomation) associated with the rabbit anti-rat HRP secondary antibody (DakoCytomation) on tumor slices fixed in acetone. Apoptosis was evaluated using a rabbit primary antibody against cleaved caspase 3 (Asp 175) (Cell Signaling) associated with the secondary antibody anti-rabbit IgG HRP (Trueblot). Blood vessels were observed by immunohistology on tumor slices fixed with acetone using a rabbit primary anti-CD31 antibody (Abcam) associated with the secondary antibody antirabbit IgG HRP (Trueblot). All staining was observed using an Olympus BX41 microscope and the AnalySIS software, with a color DP70 camera.
Evaluation of Anti-tumor Immunity At the end of the treatment, the infiltration and functional status of immune cells were evaluated at the tumor site, in the draining lymph nodes (DLNs), in the control lymph nodes (CLNs), and in the spleen by flow cytometry. The organs were digested for 30 min at 37 C with 2 mg/mL collagenase D (Roche Diagnostics). The resultant cell suspensions were washed with PBS with 2% FCS, stained using anti-human antibodies, and submitted to flow cytometry analysis on a FACSCalibur using the CellQuest Pro software (BD). The anti-human CD45, CD3, CD8, CD56 (Beckman), CD69, and CD107 (BD) antibodies were used, as was HLA-A*0201/MelA dextramers (Immudex).
ACKNOWLEDGMENTS Research was conducted with the financial support of the  Institut National du Cancer (INCa) , project  PLBIO PORO-COMBO  ( PLBIO-2010-218 ). This work was also supported by a grant from  La Région Rhône-Alpes  ( Programme CIBLE ). R. J. was a recipient of an  FRM (Fondation pour la Recherche Médicale)  fellowship. Peptide synthesis was performed using the facilities of SynBio3 IBISA platform, Montpellier, supported by ITMO cancer, or using the  ICMG Chemistry Nanobio Platform, Grenoble. In vivo evaluation was performed by the  OPTIMAL Facility , part of the  France Live Imaging program  ( FLI-Grenoble ;  French program  " Investissement d'Avenir "; grant " Infrastructure d'avenir en Biologie Santé ,"  ANR-11-INBS-0006 ).
The article deals with BB-SPICE (SPICE for Biochemical and Biological Systems), an extension of the famous Simulation Program with Integrated Circuit Emphasis (SPICE). BB-SPICE environment is composed of three modules: a new textual and compact description formalism for biological systems, a converter that handles this description and generates the SPICE netlist of the equivalent electronic circuit and NGSPICE which is an open-source SPICE simulator. In addition, the environment provides back and forth interfaces with SBML (System Biology Markup Language), a very common description language used in systems biology. BB-SPICE has been developed in order to bridge the gap between the simulation of biological systems on the one hand and electronics circuits on the other hand. Thus, it is suitable for applications at the interface between both domains, such as development of design tools for synthetic biology and for the virtual prototyping of biosensors and lab-onchip.
Simulation results obtained with BB-SPICE and COPASI (an open-source software used for the simulation of biochemical systems) have been compared on a benchmark of models commonly used in systems biology. Results are in accordance from a quantitative viewpoint but BB-SPICE outclasses COPASI by 1 to 3 orders of magnitude regarding the computation time. Moreover, as our software is based on NGSPICE, it could take profit of incoming updates such as the GPU implementation, of the coupling with powerful analysis and verification tools or of the integration in design automation tools (synthetic biology).
SPICE (Simulation Program with Integrated Circuit Emphasis ) is a general-purpose analog electronic circuit simulator developed at the Electronics Research Laboratory of the University of California, Berkeley by Laurence Nagel in 1970  [1]. As its name suggests, SPICE is dedicated to the design of large scale integrated circuit (IC) and printed circuit boards, to the checking of circuits integrity and to the prediction of circuits behavior. In the past 40 years, SPICE "evolved to become the worldwide standard integrated circuit simulator", as stated in an IEEE Milestone given to this software in 2011. The first commercial SPICE simulator was released in the 80s. Nowadays, the most common ones are PSPICE owned by CADENCE Design Systems and HSPICE owned by Synopsis. Alternatively, there is also a plenty of open source SPICE distributions that have mostly been developed by and for academic research: XSPICE  [2] , NGSPICE  [3] , LTSpice, TinySPICE  [4] , etc The program takes as an input a textual netlist describing the assembly of elementary electronic devices (resistors, capacitors, voltage and current sources, transistors, etc) connected together on electrical nodes.
It translates this list into a set of ordinary differential equations (ODE) with respect to the electrical Kirchhoff laws. The netlist is very simple. This is probably one of the main asset that contributed to the democratization of this language in the industry of microelectronics. Basically, each line of the netlist corresponds to the instantiation of one device. For each, a label which first letter determines the type of device, a list of the nodes to which the device is connected and a list of the parameters of the model are provided  [5, 6]. For instance, the line R1 A B 100 means that a 100-ohm resistor between nodes A and B. Thus, a SPICE netlist is human-readable, can be easily interpreted by a software and can be easily generated from an electronic schematic or from any other software.
Present versions of SPICE involve many simulation and analysis capabilities including steady state (operating point) analysis, transient analysis, linear small-signal frequency domain analysis, parametric sweep, noise analysis, transfer function analysis, pole-zero analysis, stability calculation, etc. SPICE also allows parameterization of design variables to accelerate design automation of integrated circuits and their optimization, as shown  [7]. Although SPICE was originally a software dedicated to the design of electronic circuits, it has already been extended to other fields of physics, such as thermal and electro-thermal simulations  [8, 9] , optics and optoelectronic devices  [10, 11] , mechanical systems  [12]  and microfluidics  [13]. It has also been used to simulate the dynamics of biological systems whose description is based on differential equations  [14] [15] [16].
We propose, in this paper, a way to formalize this approach. In systems biology, description and simulation software tools appeared much more recently. The first and most successful effort in this domain is probably the System Biology Markup Language (SBML)  [17]. SBML is dedicated to the description of a biochemical system (such as a gene regulatory network, a metabolic pathway, etc). A SBML description is an XML file which contains among other a list of parameters, a list of involved chemical species and a list of reactions with, for each, a rate equation. Most of the time, the XML file is generated through a graphical user interface such as SBMLEditor or Virtual Cell  [18]  or picked up from databases such as BioModels  [19]. Nevertheless, due to its relative complexity, SBML models are not easy to read and edit by a user or a third-party software.
Most of the time, they have been developed from scratch  [23] [24] [25] [26] [27]. An alternative approach, which have been recently demonstrated, is to use and readapt existing tools from the domain of microelectronics (Electronic Design Automation or EDA) to synthetic biology  [21, 28]. As simulation is at the heart of EDA processesa prerequisite for the adaptation of EDA tools to biology is the possibility to simulate biological mechanisms with SPICE. Second, there are more and more applications with a direct coupling between a biological or biochemical systems and an electronic devices: bio-sensor, lab-on-chip, live cell-based sensor, DNA chips, etc  [29]. Design automation of such systems requires a unique description language and simulation environment that encompasses all the disciplines involved in such system (electronics, fluidics, mechanics, biology.