dataesr/scientific-paragraphs-categorization huggingface dataset:
With this purpose, we make use of the recently developed GRASP (Generalized Retrieval of Aerosol and Surface Properties; see  Dubovik et al. , 2014)  algorithm and software. The code implements statistically optimised fitting of diverse observations using multi-term least square method (LSM) concept (e. g. see  Dubovik and King, 2000; Dubovik, 2004). Correspondingly the retrieval is organised as a solution search in continuous space of solutions without traditional use of pre-calculated look-up tables. The GRASP algorithm is highly versatile and can be applied to a large variety of remote sensing measurements (sun photometer, lidars, satellite imagers, etc). The GRASP concept, which originated from the  Dubovik and King (2000)  algorithm, has been successfully used for 15 years to process observations of AErosol RObotic NETwork (AERONET) groundbased sun/sky-radiometers  (Holben et al.
This approach allowed us to compare the results obtained by the GRASP-AOD inversion with the standard AERONET inversion. Consequently, the simulation tests proposed in Sect. 3 are done using the wavelengths in the AERONET network, and the main analysis with real measurements in Sect. 4 corresponds to AERONET data. However, we emphasise that the code is not restricted to AERONET measurements, and some example applications of the code to moon-photometer data are also shown in Sect. 4. Finally we present some retrievals from data obtained at different heights during the ChArMEx campaign  (Mallet et al. , 2016)  with the new airborne sun-tracking photometer PLASMA  (Karol et al. , 2013)  fully designed at the LOA (Laboratoire d'Optique Atmosphérique, University of Lille).
Inversion strategy (GRASP-AOD) As commented in the introduction, we make use of the recently developed GRASP (Generalized Retrieval of Aerosol and Surface Properties; see  Dubovik et al. , 2014)  algorithm and software (more information and a free version of the code can be obtained in http://www. grasp-open. com/) to assess the potential of spectral aerosol optical depth measurement for characterising aerosol properties. The main concept of GRASP is the multi-term least square method (LSM) (for details of the inversion methodology see  Dubovik and King, 2000, Dubovik, 2004  and also and also  King and Dubovik, 2013). GRASP is designed to be applied to a broad number of remote sensing measurements (e. g. sun photometer, lidars, satellite images). The multi-term LSM method solves the following system of equations:      f * = f (a) + f 0 * = Ga + g a * = a + a.
(1) The first equation in Eq. (  1 ) describes the physical relation between the set of measurements f and the vector of unknowns a. The symbols f denote the uncertainty in the real measurements f *. Note that, in our case, the set of measurements only contains the spectral aerosol optical depths (defined in Eq. A2). In GRASP code the atmospheric aerosol is modelled as an ensemble of randomly oriented spheroids and the aerosol optical depth is modelled as follows: τ a (λ) = ln max ln min ln r max ln r min C ext (λ, n, k, r) ν(r) • dn( ) d ln( ) dV (r) d ln(r) d ln(r)d ln( ), (2) where ν(r) is the volume of particle, C ext is the cross section of extinction, λ is wavelength and n and k are real and imaginary parts of the refractive index. The aerosols are approximated as spheroids  (Mishchenko et al.
(  1 ) is given by the minimisation of the following quadratic form: 2 (a) = (f (a) -f * ) T W f -1 (f (a) -f * ) + γ g a T G T W g -1 Ga + γ a (a -a * ) T W a -1 (a -a * ) , (4) where the weighting matrices W and the Lagrange parameters γ are defined as follows: W f = 1 2 f C f ; W g = 1 2 g C g ; W a = 1 2 a C a ; γ g = 2 f 2 g ; γ a = 2 f 2 a , (5) where 2 f and 2 a are the first diagonal elements of the corresponding covariance matrices C f and C a respectively. During the retrieval, we assume that all input data have log-normal error distribution  (Dubovik and King, 2000; Dubovik, 2004) , which means that the measurements and the retrieved parameters are used in the logarithm space. Finally, it should be indicated that one of the recent success of GRASP code has been the easy adaptation of the multi-pixel retrieval concept in the methodology proposed by multi-term LSM (Eq.
(2002a)  and depicted in the summarising formulas, in Table  1  of that study, which are the basis of our aerosol models here. The other three aerosol cases, which are represented in the right panel, do not present this property (as can be gained from the figure or directly from the values in Table  1 ). The simulated aerosol optical depth values obtained by running the GRASP forward module with the values from Table  1  are shown in Table  2. We have also included the resulting Ångström exponent, which is computed as a linear regression of τ a (440), τ a (675) and τ a (870). In the last two columns, the values of the fine-mode aerosol optical depth at 500 nm and the effective radius are depicted, since they will be analysed in our different sensitivity studies (as derived secondary products).
Initial guess variation In this section, we compare the sensitivity of the inversion results to the initial guess. More specifically, we test three different initial guesses for each of the six retrieved parameters, which results in a total of 729 inversions (3 6 ) for the 17 aerosol examples. Note that we need not verify that the initial guesses are consistent with the inversion results; rather, we analyse the stability of the code with respect to the initial guess. That is, a perfect retrieval code would give us the same correct results, independently of the initial guess. Table  4  shows the default initial guesses used for the first iteration of the GRASP-AOD code, which depend upon the Ångström exponent and the aerosol optical depth at 440 nm. Note that these initial guesses can be modified if ancillary information for specific applications exists.
007 and 0. 013 with respect to the values obtained in the tests with all the wavelengths (Table  7 ). In those tests, the standard deviations were around 0. 008-0. 009, while here, the standard deviation of τ f (500) exceeds 0. 02 for some aerosol cases. In relative terms, the increments of the uncertainty in τ f (500), by reducing the spectral range, increases from 5 to 10 % for the cases with τ a (440) = 0. 3 and from 3-4 to 5-7 % for the cases with τ a (440) = 0. 9. Figure  8. The effect of τ a errors on size distributions retrieved with the GRASP-AOD code for the cases of GSFC0 (low aerosol load conditions τ a (440) = 0. 1) and SOLV4 (high aerosol load conditions τ a (440) = 1. 5). As in Fig. 5 , black solid lines are used to illustrate the reference values, while black dashed lines represent the retrievals without errors (self-consistency study).
Red and blue lines are used to indicate the cases for which all the wavelengths present the errors of + i andi respectively. Note that in the case of GSFC0, those two cases do not pass our residuals restrictions (i. e. we require total residual < 0. 014 and relative residual < 15 %). Shaded grey area illustrates all the retrievals for the multiple combinations of random errors. Table  8. Results obtained from the simulation of errors in τ a on GRASP-AOD code for the cases with the lowest and the highest aerosol loads. As in Table  7 , the first six columns contain the parameters representing the bimodal log-normal size distribution (volume median radius (r V f , r V c [µm]), geometric standard deviation (σ V f and σ V c ) and volume concentration (C V f , C V c [µm 3 µm -2 ])). The following columns contain two secondary products derived from the GRASP-AOD inversion: fine-mode aerosol optical depth at 500 nm (τ f (500)) and the effective radius (r eff  [µm] ) (reference values in Table  3 ).
Retrieval analysis Following the scheme from Fig. 3 , GRASP-AOD code has been applied to the optical depth values for the aerosol cases GSFC, ZAMB and SOLV (Table  2 ), considering variations of ±σ and ±2σ (see  Dubovik et al. , 2002a)  from the original values of their refractive index (in Table  1 , only the cases with τ a (440) = 0. 3, 0. 6 and 0. 9) during the inversion procedure. Table  11  presents the retrieved values of the fine-mode aerosol optical depth at 500 nm for variations of ±2σ in the real and in the complex part of the refractive index. The maximum difference between the retrieved values with and without the variations is observed for the case SOLV3 and is equal to 0. 005; however, the differences are typically around 0. 001-0. 002. A similar result is obtained for the rest of the wavelengths, although it is not presented here.
In the example presented here, we use the data from the PLASMA airborne sun photometer during the ChArMEx/ADRIMED summer campaign that took place from 11 June to 5 July 2013 over the western Mediterranean  (Mallet et al. , 2016). The PLASMA airborne sun photometer was installed in the ATR-42 French research aircraft, which was operated from Sardinia (Italy) and participated in the 18 flights of the campaign. The experimental set-up also involved several ground-based measurement sites on different Mediterranean islands (Corsica, Lampedusa, Minorca and Sicily) and additional measurements from lidar and sun photometers performed on alert during aircraft operations (at Granada and Barcelona). The meteorological conditions observed during the campaign (moderate temperatures and southern flows) were not favourable to producing large concentrations of local polluted smoke particles.
Conclusions The main goal of the present work was to show the potential of retrieving the total column aerosol size distributions from spectral optical depth measurements without the aid of coincident radiance measurements, and estimating a set of secondary aerosol properties (e. g. effective radius or fine-mode fraction of aerosol optical depth) derived from it. The limited information content in spectral τ a measurements results in the necessity of using a priori constraints. The utilisation of the GRASP public software allowed us to test and evaluate different aerosol model descriptions and a priori constraints. The current analysis indicates that bimodal log-normal size distributions and a priori estimates of refractive indices and sphericity parameter provide a practically efficient retrieval.
The comparison of coarse-mode parameters shows larger differences, however, especially at the sites with a prevailing fine mode. In these cases differences of 0. 2-0. 3 µm in r V c are normally obtained, although some extreme differences of up to 0. 5 µm are also observed. The comparison improves significantly when limiting the study to those sites characterised by the predominance of desert dust aerosol, with differences typically under 0. 2 µm between GRASP-AOD and AERONET. In relative terms, these differences are under 25 % for all the dust cases, which agrees with the uncertainty estimated from the sensitivity tests. The effective radius shows average differences around 10 % and it does not depend on the fine-or coarse-mode predominance. Finally, we emphasise that the use of GRASP-AOD code is not restricted to the standard AERONET sun-photometer measurements.
Acknowledgements. The research has been supported by the  Labex CaPPA : the  CaPPA project (Chemical and Physical Properties of the Atmosphere ) is funded by the  French National Research Agency (ANR)  through the  PIA (Programme d'Investissement d'Avenir)  under contract " ANR-11-LABX-0005-01 " and by the  Regional Council "Nord Pas de Calais -Picardie " and the  European Funds for Regional Economic Development (FEDER)  Acknowledgements are addressed to  ESA/ESRIN  for funding through the IDEAS+ (Instrument Data quality Evaluation and Analysis Service) contract. This research has also received funding from the  French National Research Agency (ANR)  project  ADRIMED  (contract  ANR-11-BS56-0006 ). This work is part of the  ChArMEx  project supported by  ADEME ,  CEA ,  CNRS-INSU  and  Météo-France through the multidisciplinary programme MISTRALS (Mediterranean Integrated Studies aT Regional And Local Scales ).
The authors acknowledge the funding provided by the  European Union  ( H2020- INFRAIA-2014INFRAIA- -2015 ) ) under grant agreement no. 654109  ( ACTRIS-2 ). Financial support was also provided by the  Spanish MINECO  ( CTM2015-66742-R ). We thank the AERONET principal investigators and site managers at sites  Solar Village  ( Brent Holben  and  Steve Wilcox ),  GSFC  ( Brent Holben  and  Jon Rodriguez ),  Bahrain  ( Brent Holben  and  Andreas Goroch ),  Mexico City (Amando Leyva Contreras and Hector R. Estevez) ,  Lanai  ( Brent Holben ,  Chuck McClain  and  Robert Frouin ),  Mongu  ( Brent Holben  and  Mukufute Mukulabai ),  Rome Tor Vergata  ( Gian Paolo Gobbi ) and  Lampedusa (Daniela Meloni ). We thank the  AERONET, Service National d'Observation PHOTONS/AERONET ,  INSU/CNRS ,  RIMA  and  WRC  staff for their scientific and technical support.
Data availability. The AOD data used for the sensitivity studies in Sect. 3 were generated by the aerosol properties depicted in Table  1. The simulated aerosol optical depth values used in the main analyses are shown in Table  2. The AOD values used in Sect. 4 "Real Cases" are publicly available on the AERONET web page (https://aeronet. gsfc. nasa. gov/). GRASP inversion algorithm software used in this work is free and publicly available at http://www. grasp-open. com. Moreover, a web application was developed to directly use GRASP-AOD inversion without needing to install all of the GRASP code. The application can be found at www. grasp-open. com/aod-inversion.
Gut Microbial Signature for IBS Severity and Association With Clinical and Microbial Parameters To evaluate the robustness of the microbial signature for IBS severity in relationship to other clinical parameters, we investigated the relative abundance of OTUs along with clinical data in a co-inertia analysis as shown in Figure  5. Clinical parameters included age, BMI, HAD anxiety, HAD depression, exhaled CH 4 and H 2 , IBS subtypes, stool consistency (Bristol Stool Form) and frequency, and OATT. The first 2 co-inertia components explained more than 50% of co-variation between the IBS severity signature microbial OTUs and clinical parameters. The IBS-SSS was confirmed to be the most important factor contributing to variation in the full data set along the first co-inertia component (PC1) (Figure  5A ).
Gut Microbial Signature for IBS Severity and Association With Diet and Use of Medications A total of 111 individuals (89 IBS and 22 healthy) followed a 4-day food diary to assess nutrient intake. Average daily intakes were calculated for energy, proportion of fat, carbohydrates, fiber, protein, and total intake of FODMAPs (Supplementary Table  5 ). By using a co-inertia analysis followed by a Monte Carlo test (see Supplementary Material), the overall association between dietary data and the gut microbial signature for IBS severity was tested, and no significant association could be detected (RV, 0. 10; P >. 05), indicating that the microbial signature for IBS severity is independent of overall nutrient intake, as well as intake of FODMAPs. Similar analyses regarding the influence of medications (Supplementary Table  6 ), including laxatives or bulking agents, acid suppressants (mainly proton pump inhibitors), antidiarrheals, and antidepressants drugs, did not show any significant associations with the microbial signature for IBS severity.
The diary included details regarding cooking methods, ingredients, brands of foods (if appropriate), time points for meals, and quantity consumed in grams or household measurements. Patients were given written instructions to enable accurate completion of the food record. The subjects were instructed to consume their usual diet. Different food items and beverages were entered in DIETIST XP version 3. 1 (Kostdata. se, Stockholm, Sweden), which converts food items into nutrients and energy amounts. Composite foods (eg, casseroles) were split into ingredients (food items). DIETIST XP software covers approximately 1600 foods and 52 nutrients. DIETIST XP is designed to estimate macronutrients and micronutrients and energy intake. From the food records, average daily intakes were calculated for energy, proportion of fat, carbohydrates, protein, and FODMAPs.
Microbial Composition Assessment Quality filtering was performed using SDM software. 6 eads were filtered further for minimal and maximal length, any ambiguous nucleotides, barcode and primer errors, and homopolymeric nucleotide runs. The default criteria parameter adapted to the 454 sequencing platform were provided by LotuS. High-quality sequence criterion (read average quality, 27; minimal sequence length, 250; no ambiguous bases, maximum of homopolymer, 8; no mismatch allowed in primers barcode and primers, windows quality threshold average of 25 from quality window of 50 bases) was used to build OTUs. High-quality and midquality sequences were mapped to count the occurrence of established OTUs in a single sample. OTU clustering at 97% identity was performed with UPARSE, which embedded UCHIME as chimera reads filterer.
Acknowledgments The authors acknowledge  Anne Druesne  and  Chenhong Zhang  for technical assistance with DNA extraction and Johan van  Hylckama Vlieg  for valuable discussions. Martin Balvers  and  Jolanda Lamberts  are deeply acknowledged for their bioinformatics support in microbiota analysis. The quantitative polymerase chain reaction used was operated under Yakult License (YIFSCAN technology, Tokyo, Japan). The authors thank  Crea Nostra  (www. creanostra. fr) for the graphic summary icons in Figure 6.
Conflicts of interest These authors disclose the following:  Boris Le Nevé ,  Muriel Derrien ,  Rémi Brazeilles ,  Stéphanie Cools-Portier , and  Julien Tap  are employees of  Danone Research ;  Muriel Simrén  has received unrestricted research grants from  Danone  and  AstraZeneca , and has served as a consultant/advisory board member for  AstraZeneca ,  Danone ,  Novartis ,  Almirall, Albiroe, Shire, Nestlé, Glycom , and  Chr Hansen , and has served on the speaker's bureau for Takeda, Tillotts,  Shire ,  Almirall ,  Menarini , and  Danone ;  Lena Öhman  has received financial support for research from  Danone Research , has served as a consultant for  Genetic Analyses , and has received lecture fees from  AbbVie and Takeda ;  Joël Doré  has received financial support for research from  Danone Research ,  Pfizer , and  PiLeJe , and has served as a consultant/ advisory board member for  Danone Research, AlphaWasserman ,  Enterome Bioscience , and  MaaT Pharma ; and  Hans Törnblom  has served as a consultant/advisory board member for  Almirall ,  Allergan ,  Danone , and  Shire , and has been on the speakers' bureau for Tillotts,  Takeda ,  Shire , and  Almirall.
In this paper we present a new Platform for Retinal ANalysis And Simulation called PRANAS (https://pranas. inria. fr). It was designed as a user-friendly tool dedicated to neuroscientist community in a large sense, i. e. , not only experienced computational neuroscientists. It has two main goals, analyse retina data, especially spatio-temporal correlations, and simulate the spike response of the retina to a visual flow. This makes this tool a unique platform to better understand how the retina works. The first goal of PRANAS is to provide methods to analyse retinal recordings at single cell and population levels. With the advent of new techniques, the recording of the simultaneous activity of groups of neurons provides a critical database to unravel the role of specific neural assemblies in spike coding.
However, they require time and expertise to be implemented efficiently, making them hard to use. The idea of developing a new software came from our motivation to share these recent developments with the neuroscience community in a broad sense. PRANAS integrates all our expertise in terms of spike trains statistical analysis. Note that other methods analyzing correlations in massively parallel data using completely different approaches have also been proposed  (Hillar and Effenberger, 2015; Takahashi et al. , 2015; Torre et al. , 2016; Quaglio et al. , 2017). The second goal of PRANAS is to provide a customizable retina simulator which could evolve in synergy with experimental data analysis. Currently, there is a large and expanding body of literature concerning models of retinal processing.
More precisely, the PRANAS platform has integrated and extended the VIRTUAL RETINA simulator  (Wohrer and Kornprobst, 2009)   1  initially developed in our team to do large scale retina simulations. VIRTUAL RETINA has been used in several theoretical studies  (Masquelier, 2012; Mohemmed et al. , 2012; Basalyga et al. , 2013; Doutsi et al. , 2015a,b; Vance et al. , 2015). This paper, aiming at presenting this new platform PRANAS, is organized as follows. In Section 2, we give an overview of PRANAS and compare it with a selection of other tools currently available focusing on spike train analysis methods. In Section 3, we present the main features of the software. Illustrations and step by step procedures are given in several cases allowing readers to reproduce them. In Section 4, we discuss future developments.
GENERAL PRESENTATION PRANAS targets a broad community of scientists interested in exploring spike coding, in particular at retina level. It provides new tools for analyzing spike trains at the population level and several methods to generate them, either with a prescribed statistics (including spatio-temporal correlations) or by emulating the retinal response to a visual scene. This is done through a user-friendly Graphical User Interface (GUI). PRANAS runs on multiple platforms (Linux, Mac OS, Windows) and it supports parallel architectures. The software is provided as binary code or as source code on request upon acceptance on the terms of the license given on the website (http://pranas. inria. fr). Documentation, tutorials, and samples of spike trains are also available. The GUI of PRANAS (version 1.
0. 0) has three main panels, as shown in Figure  1   where the user can export, show/hide grid and spikes and select neurons graphically. Finally, another convenient feature of the software is that user can save what he has done in term of analysis in a HDF5 file  (Zordan et al. , 2015)  (File>Save) and load it later to continue the work. PRANAS is implemented in C++. It has its own dedicated libraries. It also uses other libraries for storage (HDF5), analysis (GSL, SFMT), display and GUI (Qt4, Qwt, VTK, CImg). Some parts of the software, especially the statistical estimations, run on multiple processors thanks to OpenMP framework. The software takes all the available processors in the machine automatically, without any interaction with the user. Parallelization allows to boost heavy computations and save processing time and memory.
To our best knowledge there is no other platform integrating together the functionalities proposed by PRANAS, to both analyse and stimulate retinal activities. There are however several platforms performing efficient spike train analysis. Table  1  provides a qualitative comparison between PRANAS and a selection of such tools for spike train analysis. We note that the implementation of PRANAS as a stand-alone application in C++/Qt4 rather than a library (such as, e. g. , the FIND toolbox) makes this tool readily available without the need of an external interpreter, such as Python or the commercially available Matlab suite (MathWorks, Natick, VA). Thus, access to PRANAS functions from a scripted analysis is performed via command line calls (see Section 4).