dataesr/scientific-paragraphs-categorization huggingface dataset:
How data was acquired Chemical titration (for CO 2 quantification) and luminescence-based detection (for O 2 detection) implemented in dedicated experimental set-ups Data format Analyzed, ready to use Experimental factors Samples considered are model and real food products without any pre-treatment except addition of sodium azide to avoid microbial growth Experimental features Solubility is measured by quantifying the concentration of dissolved gas in a sample in equilibrium with a fix and controlled partial pressure. Diffusivity is identified from an experimental diffusion kinetic curve by using a mathematical model and appropriate numerical treatment (algorithm of optimization).
Data type Table  URL. Oxygen optical sensors (Presens GmbH, Regensberg, Germany) were used to monitor O 2 partial pressure. This measurement is based on dynamic luminescence quenching. Due to an excitation flash emitted through an optical fibre, the luminophore contained in the sensor goes into an excited state and thus emits fluorescence backscatter signal, which is detected by the optical fibre. If the luminophore is in contact with an oxygen molecule, the backscatter signal is changed due to a dynamic quenching of luminescence. The change in the backscatter signal permits to detect the O 2 partial pressure in the medium. Two different set-ups exist (1) an invasive O 2 -sensitive optical sensor made of a syringe probe (micro-sensors, Presens GmbH, Regensburg, Germany) connected to the optical fibre and oxygen metre (Oxy-4 micro, Presens) and (2) a non-invasive oxygen sensor made of a dot of 5 mm of diameter that can be stuck on the wall of a transparent container and measurement is then made through the transparent container.
Two types of kinetic could be obtained: (1) CO 2 space-dependent profile in the cylindrical sample after its slicing and CO 2 quantification in each slice or (2) CO 2 time-dependent profile after CO 2 quantification in each thin slice (one slice corresponding at one time of kinetic)  [4, 6]. Numerical treatment. For both O 2 and CO 2 , diffusivities are identified by fitting a dedicated mathematical model to the experimental kinetic curve (space-dependent profile or time-dependent profile). This identification step is performed using a routine ("lsqnonlin") of Matlab s software.
Consequently, research on viral response and antiviral immunity of Anopheles has been limited to ONNV infections  [14, [23] [24] [25] [26] [27] , but research using ONNV requires sophisticated biosecurity conditions. Identification of Anopheles RNA viruses that could be used as low-biosecurity model systems would facilitate studies of Anopheles antiviral immunity and mosquito-virus interactions. In the current study, we used small RNA deep-sequence datasets to reconstruct more than 90% of a novel Dicistrovirus, and several segments of a previously unknown Cypovirus. We verified their infection prevalence within the Anopheles colony where they were discovered. Oral infection experiments were used to evaluate the species-specificity of these newly discovered viral strains and their ability to be transmitted to the progeny.
Results Four datasets of small RNA sequence generated from pooled An. coluzzii (Ngousso colony) mosquito midguts were analyzed using the Metavisitor pipeline  [28] , a suite of open source bioinformatic tools designed to simplify virus diagnostic, discovery and genome reconstruction on a Galaxy framework  [29]. Two novel viruses were detected in the mosquitoes, a Cypovirus (genus: Cypovirus, sub-family: Spinareovirinae, family: Reoviridae), and a Dicistrovirus (genus: Cripavirus, family: Dicistroviridae, order: Picornavirales).
Detection of a Cypovirus We detected a transcript (accession number KU169880) of 892 nt aligning to segment 10 of Culex restuans Cypovirus (CrCPV)  [30]  and to Uranotaenia sapphirina Cypovirus (UsCPV)  [31]  with 91% and 83% identity, respectively. Multiple alignments of the nucleic sequences with Clustal O  [32]  indicated that it was not a chimeric segment of the two known viruses. This segment possesses an intact 237 aa ORF that codes for a complete polyhedrin protein with 96% and 90% amino acid identity to the polyhedrin of CrCPV and UsCPV, respectively. Cypovirus genomes are comprised of 10 to 11 segments of double strand RNA, each coding for a different viral protein. The polyhedrin, protein, coded by segment 10, is highly conserved among Cypoviruses. A second contig of 3889 nt was reconstructed (accession number KU169879) containing an ORF coding for a 1246 aa protein that has 34% identity with the RNA dependent RNA polymerase (RdRp) of Bombyx mori Cypovirus 1 (ACX54961.
1) over 98% of its sequence. Realignment of the original small RNA reads to the contig shows a dicing profile with the read length peaking at 21nt in all four datasets (Fig  1 ). This is the characteristic product of viral replication intermediates processed by the siRNA pathway  [33] , indicating that the reconstructed sequences belong to an actively replicating virus infecting the mosquitoes. Phylogenetic analysis of the predicted polymerase and polyhedrin peptide sequences were performed using the available sequences for the different Cypovirus species (Fig  2 ). Neighborjoining trees show that reconstructed RdRp and polyhedrin sequences are related but distinct from known Cypoviruses. The CPVs have been classified into 16 types by the International Committee on Taxonomy of Viruses  [34] , or 21 types based on the electrophoretic migration patterns of their dsRNA genome segments  [35].
Detection of a Dicistrovirus Other contigs reconstructed from the same small RNA datasets displayed significant similarity using blastx to Drosophila C virus (DCV) and Cricket paralysis virus (CrPV) from the Distroviridae family. Using additional long paired-end RNAseq datasets (accession number ERS977505), we were able to reconstruct a 8919 nt viral genomic sequence that completely includes these contigs and contains two ORFs (accession number KU169878). The first ORF (5' ORF) codes for an 1802 aa (nucleotides 391 to 5799 in the reconstructed genome) polyprotein with 48% blastp identity to DCV nonstructural polyprotein. The second ORF (3' ORF) codes for a predicted 852 aa protein (nucleotides 6067 to 8625 on the reconstructed genome) and displays 54% blastp identity to DCV structural polyprotein.
This dicistronic organization of the reconstructed genome is typical of Dicistroviridae with an ORF1 that encodes for nonstructural proteins and an ORF2 that encodes for virion proteins (reviewed in  [36] ). Dicistroviridae are related to the Picornaviridae, which encode the same proteins in a monocistronic manner as one polyprotein, and to the Comoviridae, which encode the two ORFs but on two monocistronic RNAs. Realignment of the small RNA reads on the reconstructed genome revealed, as for AnCPV, a dicing profile of virus replication intermediates, yielding virus-derived siRNAs with a read length peaking at 21 nt in all four datasets (Fig  4 ). These results are evidence for a previously unknown, actively replicating virus. Phylogenetic tree reconstruction of non-structural proteins from Dicistroviridae shows that the discovered virus is related to known Dicistoviruses and groups with the Cripavirus genus (Fig  5 ).
Viral prevalence in An. coluzzii Ngousso mosquitoes. Total RNA from midgut and carcass of individual mosquitoes was tested by RT-PCR for infection with AnCV and AnCPV. Salivary gland RNA was extracted from individual mosquitoes different from those used for the carcass and midgut dissection. Percent infected represents the total number of mosquitoes with infection in either carcass or midgut. ND, not done. Data available in S2 File. conservation of the AnCV RNA-dependent RNA polymerase with its orthologs of the Drosophila C virus (DCV) and Cricket paralysis virus (CrPV). The novel Dicistrovirus AnCV can infect some Anopheles but not Aedes mosquitoes, and appears not to induce high mortality nor detectable paralysis phenotypes in mosquitoes infected per os. These data suggest that infection phenotype and range of this virus resemble that of DCV and not CrPV.
RNA preparation and deep sequencing Pools of 30 An. coluzzii Ngousso colony mosquito midguts were homogenized in TRIzol (Ambion) and total RNA was extracted following the supplied protocol. Small-RNA libraries were prepared from the small RNA fraction using TruSeq Small RNA Library reagents (Illumina). Libraries were sequenced on an Illumina Hiseq 2500 in a multiplexed 51 +7 bases single read using a TruSeq SR cluster kit v3 cBot HS, and a TruSeq SBS kit v3 HS 50 cycles (Illumina). Primary analysis of the sequences was performed with Casava software (v1. 8 Illumina). Library preparation and sequencing was performed by ARK Genomics at the Roslin Institute (University of Edinburgh). Data are available in the EBI European Nucleotide Archive (http://www. ebi. ac. uk/ena/) under study accession number ERP012577 and sample accession numbers: ERX1180340, ERX1180341, ERX1180342 and ERX1180343.
Long paired-end RNA libraries were prepared and sequenced by the Institut Pasteur Transcriptomics and Epigenomics core facility using standard long paired-end rapid run protocol on an Illumina Hiseq 2500. Primary analysis of the sequences was performed with Casava software (v1. 8 Illumina). Long paired-end sequencing data are available in the EBI European Nucleotide Archive (http://www. ebi. ac. uk/ena/) under accession numbers: ERX1220645 and ERX1220646.
Bioinformatic virus discovery Bioinformatic analysis was performed with the Galaxy  [29, 46, 47]  Mississippi server instance hosted by the ARTbio facility using the Metavisitor workflows (v1. 0)  [28]. Briefly, small RNA sequencing reads were clipped of the adapter sequence, converted to fasta format and concatenated. Then reads were converted to a weighted fasta format to reduce computing load. Reads were then depleted by successive bowtie alignments on the genomes of the known organisms present in the dataset. Unaligned unique reads were then used for the subsequent contig assembly. Velvet and Oases were used to reconstruct longer contigs with k-mers ranging from 15 to 29  [48, 49]. The NCBI BLAST tool suite was used to search a BLAST database created from the nucleotide sequences deposited in the NCBI repository with the reconstructed virus contigs, for related homologies.
Phylogenetic analysis Basic multiple alignment of the polyhedrin segment sequences was performed using Clustal O (v1. 2. 1) and sequence accession numbers DQ212785. 1 and AY876384. 1. Basic amino acid comparison for the polyhedrin protein of AnCPV (segment 10, KU169880) was performed against ABA61432. 1 and AAW80667. 1 using Serial Cloner (v2. 6. 1, http://serialbasics. free. fr). MUSCLE multiple alignment and evolutionary analyses were conducted in MEGA (Tamura et al. , 2011) with 1000 bootstrap replicates. Because non-conserved regions could possibly influence the phylogenetic tree reconstructions, gaps were removed using the 'Complete Deletion' option in MEGA. Phylogenetic reconstructions using neighbor joining (NJ), maximum parsimony (MP) or maximum likelihood (ML) showed the same topology for each dataset.
Trees constructed with partial deletion are also included (S2 Fig). EMBL-EBI Transeq and Sixpack online tools (http://www. ebi. ac. uk/Tools/st/) were used to translate nucleic acid sequences to the corresponding peptide sequences. The multiple alignments for Fig 2 and S1 Fig are based on the amino acid sequences given in S3 and S4 Files. Accession numbers for the RdRp alignment: YP_009111328. 1 (Dendrolimus punctatus cypovirus 22), NP_149147. 1 (Lymantria dispar cypovirus 1), ABB51571. 1 (Heliothis armigera cypovirus 14), AF291683_3 (Trichoplusia ni cytoplasmic polyhedrosis virus 15), DQ192251. 1_1 (Operophtera brumata cypovirus 19), AJC97790. 1 (Thaumetopoea pityocampa cypovirus 5), AHJ14791. 1 (Inachis io cypovirus 2), ACA53380. 1 (Choristoneura occidentalis cypovirus 16), ADH10220. 1 (Antheraea mylitta cypovirus 4).
Accession numbers for the polyhedrin alignment: AAA50751. 1 (Heliothis armigera cypovirus 8), AJC97797. 1 (Thaumetopoea pityocampa cypovirus 5), NP_066381. 1 (Trichoplusia ni cypovirus 15), ABB17224. 1 (Operophtera brumata cypovirus 19), AAY96324. 1 (Heliothis assulta cypovirus 14), ACX54964. 1 (Bombyx mori cypovirus 1), ABB17220. 1 (Operophtera brumata cypovirus 18), AAW80667. 1 (Uranotaenia sapphirina cypovirus 17), YP_009002596. 1 (Inachis io cypovirus 2), ABW87644. 1 (Choristoneura occidentalis cypovirus 16), ABH85367. 1 (Simulium ubiquitum cypovirus 20), AAP44510. 1 (Antheraea proylei cypovirus 4), AIY60608. 1 (Dendrolimus punctatus cypovirus 22). The amino acid sequences used for the multiple alignments for Fig 5 and S2 Fig are given in S4 Fig. Accession numbers for the amino acid sequences used: ALS55295 (Anopheles C virus), NP_647481.
This work received financial support to KDV from the  European Research Council  (http://erc. europa. eu/funding-and-grants),Support for frontier research, Advanced Grant # 323173  AnoPath;  European Commission  (http://ec. europa. eu/research/fp7/index_en. cfm), FP7 Infrastructures #228421 Infravec ; and  French Laboratoire d'Excellence  (http:// www. enseignementsuprecherche. gouv. fr/cid55551/investissements-avenir-projets-labex-parregiondomaine. html), and " Integrative Biology of Emerging Infectious Diseases " # ANR-10-LABX-62-IBEID. This work received financial support to CA from the  Agence Nationale de la Recherche  grant  ANR-13-BSV2-0007  « Plastisipi ». The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
All sequence files are available from the EBI European Nucleotide Archive database (http://www. ebi. ac. uk/ena/) under study accession number ERP012577, and sample accession numbers: ERX1180340, ERX1180341, ERX1180342, ERX1180343, ERX1220645, ERX1220646 and ERS977505). All assembled sequences are available from NCBI (accession numbers: KU169878, KU169879 and KU169880). Study Galaxy workflows are available at: (https:// mississippi. snv. jussieu. fr/u/drosofff/p/anophelesviruses).
The dust radiance is proportional to U min × A V,DL. Our best fit of the renormalization factor as a function of U min is an intermediate solution between the radiance and the model column density (A V,DL ). The scaling factor in Eq. 8 increases with U min but with a slope smaller than 1. Figure  9  shows that our renormalization is a better fit of the data than the radiance. The ratio is the slope obtained fitting A V,QSO versus A V,DL in each U min bin (see text). It is approximated by a linear function of U min.
FIR SEDs per unit of optical extinction The DL model parameter U min and the QSO data are used to compress the FIR IRAS and sub-mm Planck observations of the diffuse ISM to a set of 20 SEDs normalized per A V , which we present and discuss. The parameter U min is mainly determined by the wavelength where the SED peaks; as a corollary, SEDs for different values of U min differ significantly. The A V values obtained from the QSO analysis, A V,QSO , allow us to normalize the observed SEDs (per unit of optical extinction) and generate a one-parameter family of I ν /A V. This family is indexed by the U min parameter; the QSO lines of sight are grouped according to the fitted Galactic U min value. We divide the sample of good QSOs in 20 bins, containing 11 212 QSOs each 15. To obtain the I ν /A V values we proceed as follows.
8. 3. Renormalization of A V,DL in molecular clouds. In the diffuse ISM analysis, we concluded that U min is tracing variations in the radiation field and the dust opacity. Both phenomena are also present in molecular clouds, but their relative contribution in determining the SED peak (and therefore U min ) need not be the same as in the diffuse ISM. Therefore, a renormalization of the DL A V based on 2MASS data may be different from that determined using the QSOs. In Fig. 16  we compare the DL and 2MASS A V estimates for pixels from the five cloud complexes with 1 < A V,2M < 5. Similarly to Fig. 9 , but using the 2MASS A V estimates instead of those from QSOs, we plot the ratio A V,DL / A V,2M as a function of the fitted U min. For each U min value, the solid curve corresponds to the best fit slope of the A V,DL versus A V,2M values (i.
This discrepancy will be present for all dust models based on fixed dust optical properties, possibly with a different magnitude depending of the details of the specific model. Fanciullo et al. (2015)  have used three dust models, the DL,  Compiègne et al. (2011)  and  Jones et al. (2013)  models, to fit the Planck SEDs in Table  3  and compare results. We note that the SEDs listed in the Table and used by  Fanciullo et al. (2015)  are the DL model results obtained fitting the same Planck and IRAS data as in Pl-MBB (see Sect. 2). This study, a follow-up of our data analysis, confirms our interpretation of our DL modelling in two ways. First, the mean ratio between the model and QSO A V values is different for each model. The best match between model and data is obtained for the model of  Jones et al.
The DL model assumes A V = f (Σ M d ) = k × Σ M d , with k = 0. 74 × 10 -5 mag M -1 kpc 2. Our proposed renormalizations are a first step towards a functional renormalization by extending A V = k × Σ M d into A V = g(U min ) × k × Σ M d , where we take g(U min ) to be a linear function of U min. Due to the larger scatter in the QSO A V estimates, only a simple linear function g(U min ) can be robustly estimated in the diffuse ISM. In molecular clouds, where the data are less noisy, one could find a smooth function g (U min ), which better matches the A V,DL / A V,2M fit for each U min (i. e. in Figure  16 , the solid curve flattens for U min > 0. 8, departing from its linear fit). Unfortunately any renormalization procedure, while leading to a more accurate A V estimate, does not provide any further insight into the dust physical properties.
We provide a one-parameter family of SEDs per unit of dust optical extinction in the diffuse ISM. These SEDs, which relate the dust emission and absorption properties, are independent of the dust/gas ratio or problems inferring total H column density from observations. The next generation of dust models will need to reproduce these new SED estimates. We propose an empirical renormalization of the DL A V map as a function of the DL U min parameter. The proposed renormalization ( A V,RQ ), derived to match the QSO A V estimates for the diffuse ISM, also brings into agreement the DL A V estimates with those derived from stellar colours for the |b| > 5 deg sky using the Pan-STARRS1 data (SGF), and towards nearby molecular clouds in the 0 < A V < 5 range using the 2MASS survey. We propose a second renormalized DL A V estimate ( A V,RC ) tailored to trace the A V estimates in molecular clouds more precisely.
Acknowledgements. The development of Planck has been supported by:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MICINN , JA, and  RES (Spain) ;  Tekes, AoF , and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ; and  PRACE (EU). A description of the Planck Collaboration and a list of its members, including the technical or scientific activities in which they have been involved, can be found at http://www. sciops. esa. int/index. php?project=planck&page=Planck_Collaboration. The research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  /  ERC  grant agreement n  267934.
Difficulties Comparison in this task with languages: English and Arabic Although the same method is applied for both languages: English and Arabic, the level of difficulty is different when treating both of them. On the resources level, for the English language, we can find many "free" and "available on-line" datasets of sentiment lexicons and labeled tweets (e. g. Sentiment140 with 1600k records). For the Arabic language, the resources are limited and our data-set of sentiment lexicons and labeled tweet is of 16K records only. On the language characters level, the Arabic language needed special treatment for the sentiment lexicons files, and for using the web search engines (e. g. using coding: UTF-8). On the language use and diversity level, there are 22 Arabic-speaking countries but the people of these countries speak their own "mutant-Arabic" languages (dialects), mostly influenced by other languages (e.
, 2003; Wilson et al. , 2005). We used the positive, negative words only. 7. And we also use the test file's data of Semeval-2013 Task2 (subtaskA) 3  with positive and negative annotated tweets. And based on the test results, we used the sentiment lexicons 1,3,4,5 and 7 of the ones previously mentioned, which gave the best results. For the Arabic language, we are using the below manual constructed sentiment lexicons: 1. Arabic Sentiment Tweets Dataset 4  , a set of Arabic tweets containing over 10,000 entries. 2. Twitter data-set for Arabic Sentiment Analysis 5  , 1000 positive tweets and 1000 negative ones on various topics such as: politics and arts. 3. LABR Lexicons 6. 4. NRC Hashtag Sentiment Lexicon in many languages  (Mohammad and Turney, 2013). The sentiment lexicons and labeled tweets we are taking as a base for our method do not include all the needed phrases and words.
Arabic Negative words: We first tested our method using Bing Search Engine API 78. But the use of the "near:" operator, to restrict the distance between search phrases, did not work as expected. For example when we search Search API is applied on 5% only of the file's phrases (since those 5% were the only phrases not found in our data-set). And in case of no results returned from Google Search API, the phrase is classified Neutral and the value 0. 5 is given as its sentiment intensity. We test our "Arabic language system" using Arabic development data provided by SemEval-2016 Task7 (200 of Arabic phrases), where we used Google Search API on 20% of the phrases (since those 20% were not found in our data-set). The results are in Table  3. We can notice that the use of Google Search API did not increase the values and that would be due to our choice in Arabic positive and negative words included in the SO equation.
Subsequent analysis of the gene expression data was carried out in R (http://www. r-project. org) using Bioconductor packages (www. bioconductor. org)  68. An empirical Bayes moderated paired t-test was applied 66 using the 'limma' package  67  and p-values were adjusted  71. Principal component analysis revealed that one of the 5 SN samples clustered further away than all other samples. This and the matching VTA sample were removed and the statistical analysis re-performed. The genes were ranked according to adjusted p-value and the 50 most significant genes ordered by log 2 -fold change between the SN and VTA. In situ hybridization on mouse and human tissue. Oligonucleotide and riboprobe sequences are listed in Supplementary Table  S3. Radioactive in situ hybridization was performed on coronal cryosections derived from mouse and human brains as previously described  69.
Midbrain sections at the SN level from control (n = 2; case number 3549, 8401) and PD patients (n = 3; case number 3392, 3490, 5874) (reference: GIE Neuro-CEB BB-0033-00011) were used. The pathology reports stated Diffuse Lewy body disease (DLB) for PD patient case number 3392 and 5874, and DLB + Alzheimer's disease: Braak IV and Thal amyloid phase 1 + grain disease for PD case number 3490. Whole-cell current-clamp recordings were carried out in n = 10 cells (n = 6 mice). (15 steps of 12. 5 pA from -150 pA to + 200 pA). Data analysis was performed using custom-written scripts in Matlab. For detection of evoked action potentials, the peak finder routine (Nathanael Yoder, http://www. mathworks. com/matlabcentral/ fileexchange/25500-peakfinder-x0-sel-thresh-extrema-includeendpoints-interpolate) was used in combination with the custom scripts.