dataesr/scientific-paragraphs-categorization huggingface dataset:
Database DTB1 A numerical database named DTB1 includes computations of aggregates and oblate or prolate cylinders in a fixed orientation. Firstly, radiative transfer simulations were performed with the T-Matrix algorithm for fixed oriented particles. We simulated monodispersed prolate and oblate cylinders with surface equivalent sphere diameter (D e ) between 3 and 7 µm (increment of 1) and EPS between 0. 6 and 1. 6 (increment of 0. 1). These D e and EPS ranges were observed on Thalassiosira pseudonana from scanning electron microscopy. We used a mean complex refractive index for phytoplankton cells of m = 1. 05 + i0. 01 (  [36] ). Secondly, computations were performed for 17 different cell aggregates. They were composed of 2, 3, 4, 5, 8 and 9 spheres arranged as indicated in Table  1. Each sphere composing the cluster had a diameter of 4 µm and a refractive index of : m = 1.
Database DTB2 A second database named DTB2 is made up of 590,000 computations, carried out with the ScattnLay code. An exhaustive review of the internal structure of phytoplankton cells was performed by  Bernard et al. , 2009 ( [8] ). We used this review to define the relevant refractive indices and the relative proportions of the different layers. Homogeneous spheres are used as the reference model to discuss the impact of the cell heterogeneity. The values of the real part of the refractive index range from 1. 03 to 1. 09 with 0. 01 increments. Such values corresponds to cells with a high water content (  [16, 20, [43] [44] [45] [46] ). The imaginary part spans from 0. 001 to 0. 015 in increments of 2. 857. 10 -4. The lower limit represents weakly absorbing cells, whereas the upper limit represents the extremely absorbing cells having photosynthetic pigments  ( [6,14,15,20,47] ).
Indeed, this relationship was established from computations according to Eq. (  5 ) without considering Z 13. For small cylinders, ovoids or aggregates as THAL and CHLAM, it is important to check if Z 13 has a significant impact on the sideward and/or backward cross sections (Eq. (  8 )). For this purpose, the DTB1 database is used (see section 2. 5). Figure  3  shows the ratio Z 13 /Z 11 (%) as a function of the azimuth and zenith angles for two cylinders with D e = 6 µm. The first one is a prolate with an EPS of 0. 7 (Fig. 3a ) and the second one is an oblate with an EPS of 1. 3 (Fig. 3b ). The Z 13 /Z 11 ratio (absolute value) varies between 0% and ±100%. It shows that, in some cases, Z 13 is not negligible as compared to Z 11. The ratio Z 13 /Z  are null for C 9. On the contrary, and as expected, the integrated Z 13 values are not null for C 8a.
Comparison of the measured and theoretical values of forward and sideward cross sections The first step consists in checking that the Cytosense estimates of σ FSC and σ SSC for the cultures, were consistent with values as predicted by theory. The comparison exercise was based on radiative transfer simulations because for phytoplankton cultures, in situ measurements of forward or sideward cross sections, particle by particle, are not available in literature. As explained previously, the T-Matrix code for oblate and prolate cylinders is limited to ρ < 10, ) simulated according to different models. The models plotted are: homogeneous sphere (gray), 80% cyto -18. 625% chl -1. 375% Si (red) and 80% cyto -15% chl -5% Si (green). The dotted lines are PDF derived from the Cytosense measurements of THAL, equivalent at the Fig.
6. The red dotted vertical lines correspond to the median of Cytosense measurements. which correspond to D <8 µm. This code is not used for the comparison exercise because it does not allow to encompass entirely the particle size distribution (PSD) of the sample. For this comparison, we used only computations of ScattnLay code (DTB2). For THAL, the Cytosense measurements were compared with computations of three-layer spheres (80% cyto -18. 625% chl -1. 375% Si and 80% cyto -15% chl -5% Si ) standing for particles with a silica frustule (Fig. 8 ). For CHLAM, they were compared with computations of two-layered spheres only (Fig. 9 ). As the particle size impacts the cross section, simulations were multiplied by the particle size distribution measured on the sample by the Cytosense (Figs. 6c  and 7c ).
20 µm 2 using the 80% cyto -18. 625% chl -1. 375% Si model. For heterogeneous spheres, the variation of the refractive index inside the cell influences internal and external electromagnetic fields (  [8, 52] ). It implies more multiple internal reflections that increase the proportion of side or backscattering light and so decrease the proportion of forward scattering light. For σ SSC , the position and the sharpness of the PFD maximum vary with the considered model. It highlights that the sideward scattering as the backscattering, is more influenced by the cell structure than the forward scattering (  [5, 23, 34, 53] ). These results obtained from a very large and complete database are conform with previous studies (  [5, 54] ). For CHLAM, lowest values of σ FSC and σ SSC were not featured by the simulations; they represent 9% and 16% of CHLAM data, respectively.
The backscattering cross section For each particle, the sideward cross section is converted into the backscattering cross section (σ b b ) from a theoretical relationship as in Duforêt-Gaurier et al. , 2015 : σ b b (488) = 10 -0. 6 × σ SSC (488) 1. 09 (R 2 = 0. 9). (11) The Eq. (  11 ) was established from the Duforêt-Gaurier et al. , 2015's theoretical database including 450,000 simulations from the ScattnLay code. In this study, 140,000 study cases were added to the existing database to consider three-layered sphere with (80% cyto -15% chl -5% Si ) and silica beads with a refractive index of 1. 09 (only silica beads with a refractive index of 1. 07 were previously considered in the Duforêt-Gaurier et al. , 2015's database). This explains why the coefficients of Eq. (  11 ) are slightly different from the coefficients of the Duforêt-Gaurier et al.
It is rather difficult to quantify the impact of the wavelength. Actually, the backscattering coefficient, and consequently the cross section, do not follow a linear curve with the wavelength (Fig. 3  of  [28] ). For applications in natural waters, a specific relationship for diatoms could be used. It will required to re-calculate P3 from a larger database including three-layered sphere computations accounting for different thicknesses for the silica wall. Nevertheless, the use of the global parameterization is more straightforward as it required only to identify living cells. The use of a specific relationship would require a more detailed analysis of the cytograms to isolate carefully diatom cells.
Acknowledgments The authors gratefully acknowledge the support from the  French Space Agency (CNES)  through the  TROPHY  project ( CNES/TOSCA program ) and the  PHYTOCOT  project ( CNES/TOSCA program ). William Moutier  is supported by the  French Ministry of Higher Education  through a PhD grant. The authors thank  Dr. Annick Bricaud  ( Laboratoire d'Océanologie de Villefranche, France ),  Dr. Natacha Guiselin  and  Anne-Hélène Rêve  ( Laboratoire d'Océanologie et de Géoscience, France ) for their precious advice. Thanks to  Laurent Brutier  ( LOG/ULCO ),  Arnaud Cauvin  and  Michel Laréal  for their technical help.
As shown in Table  1 , most of the 25 RNA-seq-based mRNA editing screening studies performed on vertebrates have not considered matched genomic DNA sequences to detect mRNA recoding, but rather have considered either a consensus genomic sequence for the species studied, or expressed sequence tag (EST) databases to remove false positives arising from potential genomic polymorphisms, therefore occulting unreferenced individual variations  (Bahn et al. 2011; Danecek et al. 2012; Gu et al. 2012; Ramaswami et al. 2012; Cattenoz et al. 2013; Lagarrigue et al. 2013; Chen 2013; Bazak et al. 2014; Blanc et al. 2014; Toung et al. 2014; Sakurai et al. 2014; Chan et al. 2014; Zhang and Xiao 2015). More strikingly, while it is fully acknowledged that filtering on minor allele frequency is required to select high-quality genomic polymorphisms (The 1000 Genomes Project Consortium 2010, 2012), some mRNA editing screening studies still consider that reproducibility across biological replicates is not a mandatory criterion for considering a difference between DNA and RNA as a reliable editing event  (Picardi et al.
Computational analyses When not specified, analyses were performed with in-house Perl, Python and R scripts. Genomic sequence analyses: DNA sequences were aligned to the latest chicken genome assembly (Galgal4) using BWA v0. 7. 0  (Li and Durbin 2009)  (Command: bwa aln). Sequences were then filtered based on mapping quality (Command: samtools view -bS -q 30). SAMtools v0. 1. 19 (H. Li et al. 2009a ) rmdup (Command: samtools rmdup) was used to remove possible PCR and optical duplicates. mRNA sequence analyses: mRNA sequences were aligned with Tophat v2. 0. 5  (Kim et al. 2013)  on the chicken reference genome Galgal4 as described in  (Frésard et al. 2014 ) (Command: tophat-min-intronlength 3-max-intron-length 25000-max-deletion-length 1 -mateinner-dist 200-read-realign-edit-dist 0-microexon-search).
Validation assays and editing yield quantification DNA Sanger sequencing and RNA pyro-sequencing: To assess whether the DNA genotype of candidate editing events was homozygous or not, we performed Sanger sequencing on the liver genomic DNA from the eight animals. We then assessed the mRNA genotype at these candidate sites on a PyroMark Q24 pyro-sequencer (Qiagen, Valencia, CA). Primers were designed with PyroMark Assay Design software (Supporting Information, Table  S2 ). PCR products were prepared using the PyroMark PCR Kit (Qiagen, Valencia, CA) following the manufacturer's instructions. Data were analyzed with PyroMark Q24 v1. 0. 10 using default parameters. Experimental designs used to test the effect of age, sex, genetic background, and feeding on mRNA editing level: To measure the impact of age, sex, genotype, and feeding on mRNA editing level, we used different independent experimental designs with animals contrasted for these factors: (1) Genotype, broilers (N = 8) and layers (N = 8) in two unrelated experimental designs (i.
The editing level was computed as the ratio between signals for the mRNA edited allele and allele on genomic DNA. We finally tested the effect of each factor on RNA editing level at the five selected sites using a two-sided unpaired homoscedastic Student t-test. Statistical analyses were performed on R 3. 2. 0 using t. test (two sided unpaired homoscedastic Student t-test) functions from the stats package and graphical visualizations plotted using the ggplot2 package. In silico prediction of RNA editing impact on protein structure and function To predict the putative effect of RNA editing on protein structure and function, we first identified genomic structures likely to be impacted using Ensembl v71 Variant Effect Predictor  (McLaren et al. 2010). Focusing on missense coding editing events, we then recovered orthologous protein sequences from Gallus gallus, Bos taurus, Rattus norvegicus, Mus musculus and Homo sapiens to carry out multi-alignment.
High-throughput sequence analyses Liver DNA and liver and white adipose tissue RNA were extracted from eight 16-week-old male chickens, and paired-end sequenced on an Illumina HiSeq2000. After alignment on the current genome assembly Galgal4, and filtering on mapping quality and multimapping, we conserved an average of 157 million DNA-seq reads, 30 million liver mRNA-seq reads and 38 million adipose RNA-seq reads per sample. On average, 93. 5% of the genome was covered by at least 15 DNA-seq reads, polymorphisms are defined considering either genomic variant databases such as dbSNPs, or strain-specific consensus genomic sequence in the case of studies based on clonal mouse strains. b Ratio between the number of biological replicates considered for reporting a candidate difference between DNA and mRNA as a true mRNA editing event and the total number of biological replicates available in the study for a given cell type.
Validation of candidate mRNA editing events To assess the validity of some of the edited positions detected using the high-throughput screening approach, we performed Sanger sequencing on DNA to confirm their homozygous genotype and pyrosequencing on RNA to validate the mRNA base recoding at the corresponding position on the transcriptome. The validation revealed that noncanonical mRNA editing events were false positives either related to genomic SNPs undetected using genomic DNA-seq data for the one in WAT, or to unbalanced allelic expression not detected through mRNA-seq data for the one in liver (Table  2 ). Following the same approach, we selected five canonical events for validation: two out of the three candidates detected in both tissues, two specific to WAT, and one liver-specific.
Functional characterization of liver and white adipose editomes Functional annotation of the 25 unique canonical mRNA editing events using Variant Effect Predictor revealed that most of them were located in noncoding regions, since 81% and 70% were situated in either 5 kb gene flanking regions, intronic regions or intergenic regions in WAT and liver, respectively (Figure  5 ). Only five unique mRNA editing events were annotated as coding. Among these, four were likely to impact the mature protein: one common to both tissues on COG3, two WATspecific on CES1 and FLNB, and one liver-specific on KCMA1 (Table  2 ). Focusing on these four missense mRNA editing events, we conducted a fine functional annotation analysis using the SIFT software to assess the impact of the amino acid substitution on these proteins.
2015) , consisting of looking for mRNA sequences spanning edited sites in raw genomic DNA sequences, and confirmed its efficiency. In our study, up to 8% of the initial candidate differences between RNA and DNA were false positives related to errors and assembly issues in the chicken genome. This result suggests that thousands of edited sites reported in primates and mice mRNA editing screening studies could be partly attributed to false positives resulting from spurious handling of multimapped mRNA-seq short reads. More strikingly, a huge proportion of mRNA editing screening studies are solely based on mRNA-seq data, and do not consider individual matched genomic DNA-seq data for the samples analyzed. In these studies, candidate edited sites are filtered using the positions of known SNPs referenced in databases such as dbSNP, rather than considering individual polymorphisms, thereby fully occulting inevitable individual specific genomic variations.
ACKNOWLEDGMENTS We thank the members of the  INRA experimental poultry unit  ( UE1295 PEAT, Nouzilly, France ) for producing and rearing the chickens. This work was funded by  INRA  (programs SOSrnaSeq 2010, F1rnaSEQ 2012, ELASETIC 2013) and the  French National Research Agency  ( ANR, program  Fatinteger  2012-2015 ). P. -F. R. 's graduate work was cofunded by  INRA  (www. inra. fr/en)and  Région Bretagne  (www. bretagne. fr). L. F. 's graduate work was cofunded by  Animal Genetics Division at INRA  (www. inra. fr/en)and  Région Midi-Pyrénées  (www. midipyrenees. fr). LITERATURE CITED.
Gasification kinetic analyses Once the effect of temperature was evaluated, the kinetic analysis of the gasification process of the two biomass manure samples here tested was performed. This analysis supposes the first step for the industrial process modelling. Furthermore, knowledge of the kinetics is important for a correct design and product yield control  [44]. Figures  4  and 5  show the quality of the fitting of the different models here considered to the experimental data of the gasification process at 800, 850 and 900 °C for samples char Pre and char SW, respectively. Tables SS1-SS6 summarize the obtained gasification parameters and the statistical significance of each model for samples char Pre and char SW at different gasification temperatures. In this work, the goodness of a model has been evaluated by means of the error obtained between the theoretical   values (values given by the proposed model) and the experimental ones, as well as by the corresponding value of v 2.
Acknowledgements Authors acknowledge the financial support from the  Regional Government of Castilla-La Mancha  (Project  PEII-2014-007-P ) and also from the  University of Castilla-La Mancha of Spain  ( UCLM grant ). The authors also thank to the  Institut de recherches sur la catalyse et l'environnement of Lyon  and the  Centre National en E ´lectrochimie et en Technologies Environnementales (CNETE)  for its in-kind contribution.
A FRAMEWORK FOR THE DEVELOPMENT OF A SMART SYSTEM FOR HAPTIC QUALITY CONTROL One framework used in order to develop a smart system is the KREM model  (Zanni-Merk, 2015) , that has been successfully used in several different domains  (Zanni-Merk et al. , 2015; Gartiser et al. , 2014). Conventionally, a smart system is composed of a fact base and a rule base, on which various types of reasoning can be made. But the observation of the drawbacks of this classic architecture (the difficulties in eliciting expert knowledge, mainly because experts operate tacit knowledge, and basically, the noncompleteness of this elicitation  (Milton, 2008) ) led to the proposal of this model, based on the use of semantic technologies. Semantic technologies use methods from automatic language processing, machine learning and knowledge representation to build the ontologies and the rules that will enable its implementation.
Experience will complete the available knowledge and rules. Finally, meta-knowledge will directly interact with the rules and the experience to indicate which rules (coming from experience or from the initial rule set) need to be launched according to the context of the problem to solve. A modular architecture, such as KREM, is one of the main architectural design pattern for large and complex systems. In this pattern, each module or component has a specific functionality providing separation of concerns that, in turn, support re-use or replacement (i. e. changes in a single module would not affect the others, permitting the continuous operation of the system). Moreover the communication between modules needs to be based on well defined interfaces to provide low coupling. Formalising and structuring Knowledge are the first steps of the development of the Smart System.
Formalization of Knowledge about the Haptic Domain The sense of touch has been widely studied at a biological level in order to understand how it works. A summary of the perception process is presented hereafter. Considering the description of sensations, only few studies have proposed to analyse and select descriptors. Moreover, these studies usually focused on specific types of application and material. A more general and generic approach is proposed here. It intends to formalize and structure knowledge about haptics by gathering relevant vocabulary and relations from multiple sources, such as sensory analysis studies as well as vocabulary databases, and extracting sensation classes.
Formalizing Haptic Sensations Considering the diversity of the vocabulary used in order to describe haptic sensations, there is a strong need for the formalisation of the way these sensations are described. For instance, studies performed in the context of visual quality control  (Baudet, 2012; Maire et al. , 2013)  demonstrated the feasibility of reducing the list of descriptors used and therefore formalizing visual aspect anomalies. The proposed method makes use of the semantic characteristics of the descriptors in order to extract generic categories of haptic sensations. In particular, a classification of the usual descriptors was performed. First, semantic relations between descriptors were used in order to group them. Synonym and antonym links were drawn from semantic databases like Wordnet 2 and the Thesaurus 3.
CONCLUSIONS AND PERSPECTIVES This work sets the basis for the development of a Smart System for haptic quality control. The main architecture of the System was presented with the use of the KREM model which provides a separation of the modules involved in the development of this system, while still enabling interaction between them. This allows for more flexibility in the development, considering future applications of the system, in particular regarding the integration of experience. The Knowledge component of this architecture was explored and detailed in this paper, with the proposition of a general ontological structure adapted to the constraints of the project. The high-level SSN ontology was used in order to structure knowledge corresponding to the multiple domains involved in the development of a Smart System for haptic quality control.
The domain ontology integrating haptic knowledge was specifically presented. The formalization of haptic knowledge was first detailed with the proposition of generic elementary haptic sensations. Haptic knowledge was then conceptualised into the proposed Haptic Perception ontology and aligned with the SSN ontology. The next steps of the development of a Smart System for haptic quality control include the develop-ment of the Meta-knowledge and Rules modules of the KREM architecture. In particular, the extraction of control rules will be realized through the application of the proposed formalization of haptic knowledge on case studies. While expert knowledge enabled a validation of the semantic analysis, the industrial testing being performed will provide more specific evaluation material. Moreover, the establishment of the influence of the application context will enable to select adapted rules.
ACKNOWLEDGEMENTS This work has been done within a thesis project funded by the  French technological research association (ANRT)  as well as the company INEVA 5. This work is the result of a collaboration between three parties, which are all acknowledged here: the company INEVA, the  INSA de Strasbourg  (with the  ICube laboratory)  and the  University of Savoie Mont Blanc  (with the SYMME laboratory).
Introduction Due to its proximity with the brain, cerebrospinal fluid (CSF) could be a medium of choice for the discovery of biomarkers of neurological and psychiatric diseases using untargeted analytical approaches. Objectives This study explored the CSF lipidome in order to generate a robust mass spectral database using an untargeted lipidomic approach. Methods Cerebrospinal fluid samples from 45 individuals were analyzed by liquid chromatography coupled to highresolution mass spectrometry method (LC-HRMS). A dedicated data processing workflow was implemented using XCMS software and adapted filters to select reliable features. In addition, an automatic annotation using an in silico lipid database and several MS/MS experiments were performed to identify CSF lipid species. Results Using this complete workflow, 771 analytically relevant monoisotopic lipid species corresponding to 550 unique lipids which represent five major lipid families (i.
e. , free fatty acids, sphingolipids, glycerophospholipids, glycerolipids, and sterol lipids) were detected and annotated. In addition, MS/MS experiments enabled to improve the annotation of 304 lipid species. Thanks to LC-HRMS, it was possible to discriminate between isobaric and also isomeric lipid species; and interestingly, our study showed that isobaric ions represent about 50 % of the total annotated lipid species in the human CSF. Conclusion This work provides an extensive LC/HRMS database of the human CSF lipidome which constitutes a relevant foundation for future studies aimed at finding biomarkers of neurological disorders.