dataesr/scientific-paragraphs-categorization huggingface dataset:
e manual peak fitting method might have induced subjectivity and imprecision, as there were no strict means to analyse bone spectra. Also, these methods are not always spectroscopically accurate (i. e. , there are not always the right number of peaks corresponding to the exact composition of the studied material); however, these methods have been validated and are sufficient to conclude the proportion of compounds in the analysed bone. ese methods might be improved by the careful study and translation of the analysis part into an algorithm implemented into custom software. e aim of this technical note was to offer a solution to the automation process of the peak fitting method used in FTIR spectrum analysis of the bone. Software, i. e. , LYOS spectrum analysis, written in Python, has been developed to fully automate the analysis process of healthy human and thin mouse bone section's infrared spectra, even though the software can be used to analyse other types of spectrum.
6. e system has a spatial resolution of 10 µm at typical midinfrared wavelengths. Contribution from air was subtracted from the original spectrum. e commercial software Spectrum (v6. 2. 0. 0055) provided by Perkin-Elmer and Grams/AI 8. 0 Spectroscopy Software provided by the ermo Electron Corporation offer various preprocessing options and customisation for the automatic peak fitting process. e mandatory methods used in these software have been implemented and improved for automation in the custom Python software and are described below, such as a transformation from transmittance to absorbance values, a spectrum component removal (to extract the components of interest from a raw spectrum), and a baseline correction method (to remove the high-scale effects of a bone FTIRM measure).
Peak Fitting. e peak fitting method is one of the methods used in the conventional FTIRM spectrum analysis  [18]  (least-squares method with trust region reflective algorithm to find the best suited parameters). e curve fitting function used is curve_fit from the scipy. optimize module version 1. 0. 0. Usually, only a first guess state for each Gaussian peak is given for the manual method, allowing the peaks to place everywhere with any characteristics (position, FWHM, and height). is first guess-only method often induces inaccurate results and forces the operator to execute the method multiple times in order to find a valid fit. e automation process is implemented by adding boundaries to find each peak's parameter. ese boundaries are carefully chosen for the solution to be unique (restricting the plurality of solutions induced by the manual method with first state guessing only).
Acknowledgments is study was partly funded by the  ANR  project  MULTIPS  ( ANR-13-BS09-0006 ) and by the  ANR  project  LYSBONE  ( ANR-15-CE14-0010 ), which provided the mice. e authors would like to thank  Pr. Gérard Panczer  from  Université Claude Bernard Lyon 1  for his expert advice in FTIR analysis and his scientific review of the manuscript.
Regional or global Functional groups of zooplankton and micronekton are simulated and used with physical and biogeochemical variables to define the habitats, movements, and key population dynamic processes of targeted fish species. Fishing impact is simulated through catch and effort data. A statistical optimization approach uses all available data (catch by size, tagging data, larvae density, acoustic estimates) to estimate model parameters. Flexible; typically 1 / 12 • grid and daily timestep, or 1-2 • grid × monthly time step. Three vertical layers of epipelagic and mesopelagic ocean. One zooplankton and several micronekton functional groups defined based on their vertical behaviour and one to several targeted species with their fisheries. Lehodey et al. (2008 Lehodey et al. ( , 2010) )  APECOSM A 3-D dynamic energy budgetbased Eulerian model of sizestructured marine populations and communities based on environmentally driven individual bio-energetics, trophic interactions, and behaviours that are upscaled to populations and communities.
Modelled fishing mortality rates, catches, or effort can vary over time and space and can be spatially explicit or applied at local, regional, or basin scales. Most databases on global fishing catch or effort can be spatially disaggregated to match the scales represented in (global) marine ecosystem models. The difference between such data sources may be considerable; see Fig. S3  for an example of the difference in global catches over time between two databases. As examples of approaches taken by individual Fish-MIP models, the EcoOcean model applies fishing as an effort term based on the Sea Around Us Project (SAUP) effort database  (Anticamara et al. , 2011; Christensen et al. , 2015; Watson et al. , 2013) , the DBEM model uses an alternate catch reconstruction database  (Watson, 2017) , BOATS has a dynamic bioeconomic approach using SAUP catch price data to simulate spatially resolved changes in fishing effort over time based on individual fishers attempting to optimize their outcomes  (Carozza et al.
, 2017) , and SS-DBEM represents maximum sustainable yield without explicitly calculating fishing mortality  (Fernandes et al. , 2016 (Fernandes et al. , , 2017;; Mullon et al. , 2016)  using SAUP data supplemented by other sources (the ICES data collections at www. ices. dk/marine-data/dataset-collections/Pages/ Fish-catch-and-stock-assessment. aspx and the RAM legacy stock assessment database at www. ramlegacy. org). This last example further demonstrates how fishing impacts may be modelled implicitly. Regional models have typically used yet different data sources, which are often finely resolved observer-derived local datasets with highly taxonomically resolved information. In the first round of Fish-MIP, to maximize the participation of marine ecosystem models, we decided to allow models to implement fishing according to their own standard method to produce realistic historical transients, followed by constant fishing impacts for future scenarios.
That is, models continued to use their specific catch, effort, or other forcing data (often but not always SAUP data for global models), but with simple standardized scenarios imposed. For historical simulations (to 2005), biomass removal based on reported catches or historical effort levels was imposed to reconstruct the historical level of fishing  (Christensen et al. , 2015)  or assumptions were made about average fishing mortality in historical periods  (Cheung et al. , 2016b). As above, this was based on each Fish-MIP model's specified fishing database. In addition to the standardized scenario, an optional scenario was suggested with no fishing (zero effort or mortality or fisheries-induced). Some models included no representation of fishing and thus only ran this optional scenario of zero fishing.
All of these details matter when we are seeking to develop effective and informative compar-isons and were not all readily documented or accessible at the outset. However, our comments should not be taken to imply that every detail needs to be harmonized among models; in fact, for equitable comparison, it may be desirable to retain much of the diversity of model specifications. In any case, carefully specified information must be shared on those aspects that are harmonized -be it variable names and definitions, scenarios, or datasets -and those aspects that necessarily remain idiosyncratic. For example, 3-D models can use oceanic depth profiles that differ from ESM outputs. Ultimately, modellers used their specific depth profile and we accepted this as a potential confounding factor (in the same way as having 2-D and 3-D models).
S4. All files were to be saved in netcdf format with a. nc4 extension (a conversion script for. csv files was made available at http://vre2. dkrz. de). Full details on outputs, including the conventions for file naming, were made available in the ISIMIP 2A simulation protocol at https://www. isimip. org/protocol/#isimip2a, and the instruc-Table  6. All experiments (historical and future, standardized, and optional) for the global and regional fisheries and marine ecosystem models participating in the first round of Fish-MIP. Runs in bold are prioritized (Tier 1), those in roman preferred but optional (Tier 2), and those in italics optional (Tier 3); this is to allow modellers with limited computational resources to participate and prioritize. Note that the CMIP5-based runs (non-reanalysis) are continuous from historical into the future, reducing the total number of runs.
The core simulations from the initial round are as follows. Historical runs. For historical runs for both global and regional models, the top priority (Tier 1) was one run each with all the climate datasets (reanalysis based, CMIP5 based) with no fishing (zero effort or mortality) and ocean acidification (time-varying pH). The lower priority (Tier 2) was to run the same set of climate data with time-varying fishing effort or mortality (see Table  6 ). Future runs. For future runs with both global and regional models, the top priority (Tier 1) was one run each with all the climate datasets (CMIP5 based) with no fishing (zero effort or mortality) and ocean acidification (time-varying pH) for both the RCP 2. 6 and 8. 5 scenarios. The second priority (Tier 2) was to run the same set of climate data with default fishing effort or mortality (kept constant at 2005 levels) (see Table  6 ).
The protocol is described in this paper, the Supplement, and can also be downloaded from https://www. isimip. org/protocol/#isimip2a (for simulation round 2A). The Fish-MIP website is https: //www. isimip. org/gettingstarted/marine-ecosystems-fisheries/. Forcing data from CMIP5 used for the Fish-MIP simulation round 2A are available on the ISIMIP servers (https://www. isimip. org/gettingstarted/#how-to-join-isimip); fisheries forcing data for specific fished model runs and models are available by contacting individual Fish-MIP modelling groups. Fish-MIP results from simulation round 2A are publicly available for regional  (Eddy et al. , 2018)  and for global  (Tittensor et al. , 2018)  model outputs. The Supplement related to this article is available online at https://doi. org/10. 5194/gmd-11-1421-2018supplement.
Acknowledgements. Financial support was provided by the  German Federal Ministry of Education and Research (BMBF ; grant no. 01LS1201A1 ) through the  Inter-Sectoral Impact Model Intercomparison  Project (ISIMIP). We thank  Reg Watson  for supporting CMIP5 data preparation, and we thank  John Dunne  and  Keith Lindsay  for helping provide us with access to ESM outputs. Derek P. Tittensor  thanks the  Kanne Rassmussen Foundation, Denmark  and the  Cambridge Conservation Initiative  (grant  CCI-05-14-018 ) for financial support that facilitated work on this paper. Additional financial support for the CMIP5 input data preparation was provided by the  Australian Research Council Discovery project  ( DP140101377 ). Heike K. Lotze  acknowledges financial support from the  Natural Sciences and Engineering Research Council (NSERC)  of  Canada.
Julia L. Blanchard  acknowledges the  UK Natural Environment Research Council and Department for Environment, Food and Rural Affairs  (grant number  NE/L003279/1 ). Susa Niiranen  acknowledges support from the  NordForsk -funded project  Green Growth Based on Marine Resources: Ecological and Socio-Economic Constraints. Marta Coll  was partially funded by the  European Commission  through the  Marie Curie Career Integration Grant Fellowships  ( PCIG10-GA-2011-303534 ) to the  BIOWEB  project. Simon Jennings  acknowledges support from the  UK Department of Environment, Food and Rural Affairs. Jose A. Fernandes  received funding from the  European Union   Horizon 2020 research and innovation programme  under grant number  35 678193  ( Climate Change  and  European Aquatic Resources ) and further funding through the  Gipuzkoa Talent Fellowships programme , by the  Gipuzkoa Provincial Council, Spain.
A Probabilistic Asymptotic Graph One of the limitations of Boolean models is the lack of quantitative details: while the state transition graph describes all possible dynamical behaviors, it gives no indication as to which trajectory is more likely to be observed under a given set of initial conditions. To circumvent this problem, Boolean models can be combined with probabilistic frameworks that account for biological perturbations and variability in the logical rules  (Shmulevich et al. , 2002; Mori et al. , 2015). Another approach is to exploit the Markov chain description of the transition graph associated to the asynchronous Boolean model  (Calzone et al. , 2010; Stoll et al. , 2017). Based on this description,  Stoll et al. (2017)  developed the MaBoSS software, which then applies Gillespie algorithm to produce continuous time trajectories.
The main results are presented in Figure  2  and summarized below. All computations were made with Matlab R2016b, The MathWorks, Inc. First, we compare the respective sizes N cr and N as of the cross and the asymptotic graphs (ie. their number of vertices). Figures  2A, B  show respectively the evolution of log 2 (N cr ) and log 2 (N as ) with respect to the connectivity of the two modules. Obviously, both N cr and N as are below N = 2 20 , which is the size of the full transition graph of the interconnected network. The cross graph, which captures both the transient and the asymptotic dynamics of the interconnection is relatively large, however its size seems to vary greatly with the modules' connectivity. When the connectivity increases, implying a highly modular interconnection, the ratio N cr /N can reach very small values, emphasizing the interest of the cross graph to efficiently store the dynamics of large, modular interconnected networks.
Tungsten (W) and Carbon (C) are, respectively, used as the plasma-facing materials for upper and lower divertors in EAST. This provides an opportunity to study the plasma performance with both W and C divertors simultaneously. In this work, simulations are carried out by using the edge fluidplasma/kinetic-neutral code SOLPS5. 0-EIRENE99, with emphasis on the C impurity transport and its effect on the power radiation and erosion of the upper W divertor target. Three typical magnetic field equilibrium configurations, i. e. , lower single null, upper single null (USN), and double null (DN), are studied. The main deuterium (D) plasma and C impurities at each ionization state and their neutrals are considered in the simulation. The SOLPS output such as averaged incident ion energy and particle flux density of each species are used to calculate the erosion rate of W walls using the empirical formula for physical sputtering yield.
8] [39] [40] [41] [42] [43] [44] [45] [46] [47]  In this work, we present detailed modeling studies of effect of C impurities on the power dissipation in the divertor/scrape-off layer (SOL) region and the erosion of the W upper divertor by the C created at lower divertor in EAST, by using the Scrape-Off Layer Plasma Simulation (SOLPS) code (SOLPS5. 0/EIRENE99). In Sec. II, a brief description of the SOLPS code package and modeling setup for EAST are given. In Sec. III, the transport of C and power radiated by C for LSN, USN, and DN magnetic field equilibrium configurations are systematically studied. The upper W target erosion by C impurities is also assessed. The conclusions and discussions are summarized in Sec. IV.
II. SIMULATION MODEL In this work, the SOLPS5. 0/EIRENE99 code package, a coupled version of the multi-fluid transport code B2. 5  48  and the kinetic neutral transport code EIRENE (1999 version),  49  is applied for the modeling study, with fuel D. The electrons and ions for both D and C at each ionization state are handled by the B2. 5 code,  48  while the neutrals (D, C and D 2 ) are tracked by the EIRENE code,  50  in which ionization, charge exchange, dissociation, elastic collision, and recombination processes are taken into account. B2. 5 is written in a curvilinear orthogonal coordinate system. It has two dimensions: poloidal coordinate and radial coordinate orthogonal to the flux surface. 48 B2. 5 solves a set of continuity and parallel momentum equations for each ion species, and energy equations for calculating the ion temperature T i and electron temperature T e.
The standard sheath boundary condition at the target is applied: for the momentum equation, a strict Bohm criterion, mach number (M) ¼ 1 flow at the plasma-sheath boundary is enforced for the parallel fluid velocity; for the density, zero gradient is used; for the energy, the parallel heat flux is computed as cCT (for both ions and electrons). These boundary conditions are the same as in Refs. 41 and 43. When ions hit the wall, a part of them are reflected as recycling neutrals and sent back into the simulation domain. There are two parts of the recycled particles: reflected D atom and thermal release D 2 , and the total particle recycling rate (R) is fixed. The particle reflection rate (R D ) is determined by the TRIM database, see Ref. 49 for details. The rest of the recycled particles are D 2 molecules via thermal release.
23, 26 Moreover, the D þ may become to a more important contributor to the W sources than other impurities during ELMs. 25, 26. DISCUSSION AND CONCLUSIONS Simulations have been done using the SOLPS5. 0/ EIRENE99 code package to study the transport of carbon impurities and its effects on the divertor plasma and tungsten erosion on EAST, which has both a graphite lower divertor target and tungsten upper divertor target simultaneously. The C impurity source is from the erosion of a lower graphite divertor target. Three typical magnetic field equilibrium configurations, i. e. , LSN, USN, and DN, have been simulated. In the simulations, electrons and ions (D and C) at each ionization state are handled by the B2. 5 code, while the neutrals (D, C, and D 2 ) are tracked by the EIRENE code. No W species have been simulated by SOLPS.
ACKNOWLEDGMENTS This work was supported by the  National Key R&D Program of China , Grant Nos. 2017YFA0402500  and  2017YFE0300400 , the  National Natural Science Foundation of China  under Grant No. 11775044 , and the  Fundamental Research Funds for the Central Universities , Grant No. DUT17LK30  and AHNFS under Contract No. 1808085J07. The views and opinions expressed herein do not necessarily reflect those of the  ITER Organization.
Using the analytical conditions described above to process our samples, no loss of any particular lymphocyte population was noted, allowing further data analysis (Fig. 1 ). The number of viable cells available after the whole process was adequate for a relevant mass cytometry analysis. CyTOF analysis in septic shock patients. Due to limited access to CyTOF platform, we restrained the number of samples analysed by mass cytometry to 5 healthy volunteers and 5 septic patients. As our mass cytometry panel was designed for studying mononuclear cells, we selected the 5 septic shock samples presenting with the highest proportion and number of mononuclear cells in peripheral blood mononuclear cells (PBMC) layer after Ficoll. Clinical and biological data for the 5 septic shock patients are presented in Supplementary Table  S2.
Median [IQR] age of the healthy volunteers (HV) was 61 [53-63] years old and 40% were male. The analysis pipeline used throughout the study is illustrated in Fig. 2. To characterize and visualize mononuclear cells' subpopulations in the observational cohort, we ran a SPADE clustering analysis. SPADE algorithm first downsamples data to capture rare populations. Then it hierarchically clusters phenotypically similar cells into "nodes" and represents these nodes and their relationships in a minimum spanning tree format (i. e. SPADE tree). The main issue with SPADE analysis is the definition of the optimal number of cell clusters. This is difficult to determine empirically. Firstly, to address this problem, we determined with X-shift algorithm the optimized cluster number for our dataset, as described elsewhere  12.
X-shift algorithm uses weighted k-nearest-neighbour density estimation to define the optimal number of cell clusters in high dimensional data. For this, X-shift evaluates the impact of different numbers of nearest neighbours on the number of cell clusters, with the optimal number of cell clusters defined at the "switch point" between under-clustering and over-fragmenting data. In our dataset, optimal cluster number was found at 78. Secondly, from this result we ran a SPADE analysis seeking 78 nodes. For further analysis, only nodes with an average proportion ≥0. 5% of total cells were selected, in order to exclude sparse clusters where low cell numbers could potentially increase the error of reported medians. This threshold resulted in exclusion of approximately 6% of total cells, thus retaining 51 nodes for further analysis (see Supplementary Fig.
Thawing step Proportion in Ly (%) Figure  1. No deletion of a particular lymphocyte sub-populations is noted during the whole mass cytometry analysis process. Before freezing (white boxes) and after thawing (grey boxes) processes, proportions of lymphocyte sub-populations in mononuclear cells were analysed by flow cytometry in 10 samples from healthy volunteers and 11 samples from septic shock patients. Analysis pipeline used throughout the study. The study was built through several steps: set up of mass cytometry analysis, process of samples on mass cytometer and data analysis. In this last step, several multidimensional data analysis tools were used, such as SPADE and viSNE, to highlight differences between septic shock patients and healthy donors in terms of cellular abundance and expression levels of exhaustion, activation and differentiation markers.
Thus, by nature, as a proof of concept study, our work has some limitations. In particular, we included small numbers of HV and septic shock patients. However, even in such a small cohort, non-supervised analyses of high dimensional data strikingly succeeded in highlighting both well-established (e. g. , low mHLA-DR) and novel characteristics (e. g. , increased plasma cells) of sepsis-induced immunosuppression. This illustrates the power of CyTOF technology. Furthermore, CyTOF was able to identify one septic patient with a singular immune pattern compared to others. This was confirmed by unusually high mHLA-DR expression measured by traditional flow cytometry. Again, this exemplifies CyTOF capacity to detect heterogeneity within septic shock patients' immune patterns. Thus, upon analysis of larger cohorts, CyTOF could be relevant to reveal new features of sepsis pathophysiology on the one hand, and to stratify patients according to their immune status and trajectories over time on the other hand.
This latter point is an absolute requirement in future studies evaluating immunomodulating therapies in clinical cohorts. Analysing data from more patients would also open the possibility to use new interesting algorithms and software tools, as Citrus, which are able to automatically identify immune signatures in cellular populations  12, 25. Beyond the present study, there are several limiting factors for mass cytometry implementation in clinical studies. Indeed, CyTOF is a time consuming method, requiring 3 days of experiment from freezing to data acquisition. High dimensional data leads to days of data analysis, involving innovative bioinformatics tools such as SPADE and viSNE and representing analytical challenge for non-experts. However, mass cytometry represents a powerful tool to characterize immune alterations and observe the individual variability that must be targeted when developing immunomodulating therapies in context of personalised medicine.