dataesr/scientific-paragraphs-categorization huggingface dataset:
To gain insight into the biological processes overrepresented, we performed Gene Ontology (GO) category enrichment analysis (Additional file 2). The most significant enriched GO categories were the ones linked to the cholesterol pathway (GO: 003399: response to lipid, p = 3. 36E-08; GO: 0010033: response to organic substance, p = 1. 84E-08; GO: 0016126: sterol biosynthetic process, p = 7. 86E-06). We thus examined genes involved in cholesterol biosynthesis (PW0000454 in the Rat Genome Database, http:// rgd. mcw. edu/pathway/PW0000454/). Eleven of 18 genes from this pathway were significantly down-regulated after cholesterol increase (Table  1 ). Six genes (Ebp, Fdps, Hmgcr, Lss, Nsdhl and Pvmk) were not differentially expressed. We detected Dhcr24 in control conditions only, suggesting that its expression is strongly downregulated after cholesterol treatment.
The number and size of endosomes were quantified with the ICY software as described  [30]  (Figure 2C   used κ-means to obtain three clusters: small, medium and large (Figure  2E ). In the control condition, the small, medium and large size classes represented 88. 7, 10. 4 and 0. 9% of all endosomes, respectively. After cholesterol treatment, 84. 5, 14. 0 and 1. 5% of endosomes were in the large, medium and large size classes, respectively. The distribution of endosome sizes was significantly shifted towards higher size after membrane cholesterol loading (p = 2. 8 e-07, χ 2 ). We confirmed this change in the morphology of early endosomes in synaptic-competent cortical neurons kept in culture for 14 days (DIV14) and treated similarly with cholesterol (Additional file 6). We used transmission electron microscopy (TEM) to examine enlarged endosomes in more detail.
Z-stacks of neurons were acquired on a Fluoview FV1000 confocal microscope (Olympus, Tokyo, Japan) with the 543 nm line of a He/Ne laser. Fluorescence was collected with a 60× plan apochromat immersion oil objective (NA 1. 35) between 560-660 nm. The mean endosome size and the mean endosome number per neuron were analyzed with ICY software  [54]. Between 13 and 17 neurons were analyzed for each experiment. Each experiment was independently repeated 3 times.
4 mM final concentration). Live videomicroscopy was carried out using an Axiovert 200 microscope (Zeiss, Jena, Germany) with a PL APO oil × 63 objective of numerical aperture of 1. 40. Records were made with a Photometrics Evolve 512 camera (Roper Scientific, Trenton, NJ, USA) controlled by Metamorph software (Molecular Devices, Sunnyvale, CA, USA). Stacks were acquired at 37°C. Images were collected in stream set at 1 × 1 binning with an exposure time of 200 ms. Kymographs were generated and analyzed with a homemade ImageJ plugin, KymoToolbox, available upon request (contact: Fabrice. Cordelieres@curie. fr). Segmental velocities were defined as the speed a particle travels in one direction without a pause or a reversal in the direction of movement.
Microarray data analysis Microarray data were acquired on a ScanArray GX (Perkin Elmer, Waltham, MA, USA) with a resolution of 5 μm and analyzed with Mapix 5. 0. 0 software (Innopsys, Carbonne, France). For each sample, raw data consisted of the Median Feature Intensity -Median Background Feature (F-B) at 532 nm wavelength. These raw data were log2-transformed and quantile-normalized under the R freeware (www. r-project. org). The statistical analysis was performed on these normalized data under the R freeware. All the microarray data have been deposited on the GEO database under the accession number GSE46221. Gene Ontology (GO) category enrichment analysis was realized using the web-based GOrilla application  [55, 56]  (http://cbl-gorilla. cs. technion. ac. il/). Statistical significance was set to a p value <0.
Statistical analysis Most statistical calculations were performed using Graph-Pad Prism software (version 5. 0). All the data are given as mean ± SEM. κ-means analysis of fluorescently-labelled endosome size was performed using the statistic analysis toolbox of Matlab software (version R2012a). Endosome sizes under control conditions were distributed into three size classes defined by the κ-means algorithm using the squared Euclidian distances. This iterative partitioning minimizes the sum, over all clusters, of the within-cluster sums of point-to-cluster-centroid distances. Each endosome was assigned an index corresponding to one of the three clusters defined in the iterative loop (small, medium, large). The partition of endosome sizes after cholesterol treatment in these three classes was then determined.
The χ 2 comparison test of the two distributions of endosome size was made with Matlab sofware (version R2012a) using the following formula: x 2 ¼ X i¼3 i¼1 n ij observed -n ij expected À Á 2 =n ij expected with i (1 to 3) as the size categories (small, medium, large) and j (1 to 2) as the two groups (control and treated). n is the number of neurons falling into each class. The degree of freedom was (n i -1) × (n j -1) = 2.
Acknowledgements The authors gratefully acknowledge  Dr. Frederic Checler  ( Institut de Pharmacologie Moléculaire et Cellulaire, Valbonne, France ) for his kind gift of APP plasmid. The authors wish to thank the imaging facilities  'Plateforme d'Imagerie Cellulaire Pitié Salpêtrière' (Hopital de la Pitie-Salpetriere, Paris)  and ' Plateforme IBiSA d'Imagerie Cellulaire et Tissulaire'  ( Institut Curie, Orsay ). The authors thank  Dr. Gilbert Di Paolo  and  Dr. Richard Miles  for fruitful discussions and for reading the manuscript. This work was supported by funding from  ANR CholAD, ICM Foundation ,  FACCTS (France And Chicago Collaborating In The Sciences) ,  Fondation Claude Pompidou ,  Fondation Philippe Chatrier  and the program " Investissements d'avenir "  ANR-11-INBS-0011.
At present, six complete K. pneumoniae subsp. pneumoniae genomes are available on the public databases, namely K. pneumoniae strains MGH 78578 (multidrugresistant [MDR] bacterium isolated from a patient with pneumonia  [5] ), NTUH-K2044 (a hypermucoviscosity (HV)-positive isolate obtained from a patient with liver abscess and meningitis  [3] ), 342 (N 2 -fixer plant endophyte still bearing pathogenic potential against mice  [4] ), HS11286 (MDR clinical isolate  [6] ), KCTC 2242 (a 2,3-butanediol producer  [7] ) and 1084 (an HV-negative clinical isolate causing liver abscess  [8] ). Other 115 ongoing K. pneumoniae sequencing projects are available as scaffolds/contigs at the NCBI Genome database (http:// www. ncbi. nlm. nih. gov/genome/genomes/815). The availability of closed genomes is important towards understanding the K.
nlm. nih. gov/bioproject) under project accession ID PRJNA78291. The G + C content of the Kp13 chromosome is 57. 5%, which is in agreement with values previously reported for NTUH-K2044 (57. 7%), MGH 78578 (57. 5%) and 342 (57. 3%) (Table  1 ). However, the plasmidial sequences present a much lower G + C content of 49. 1 ± 4. 4% (mean and standard deviation for the six replicons) suggesting the existence of DNA acquired during events of horizontal gene transfer (HGT), an observation that is corroborated by the number of mobile genetic elements (MGEs) such as transposons and phage sequences found in these plasmids. The number of transfer RNAs (tRNAs) identified in Kp13 was 86, which is identical to those of MGH 78578 and NTUH-K2044, but different than that reported in the strain 342 that carries 88 tRNAs genes (Table  1 ).
RGP2 contains genes for transposases and a possible phage integrase (KP16268) as well as hypothetical genes which do not allow for any inference on a possible metabolic role. RGP3 is 41 kbp in length and represents a phage insertion in the Kp13 chromosome, with the majority of its CDSs sharing greater identity to those of Salmonella phage SPN1S [GenBank:NC_016761]. A second, large bacteriophagerelated RGP was detected in the Kp13 chromosome (RGP9, Table  2 ) and part of this region exhibits remarkable sequence similarity to that of K. oxytoca ϕKO2 prophage  [18]. Apart from phage sequences, a possible antibiotic resistance cluster that contains genes coding for transcriptional regulators (KP04065, KP04061), an antibiotic biosynthesis monooxygenase (KP04063) and a glyoxalase/bleomicin dioxygenase (KP04064) were found, but further experimental studies are warranted to establish the relevance of this region to the MDR phenotype displayed by Kp13.
The bla CTX-M-2 gene encodes an ESBL commonly observed in Enterobacteriaceae isolates from humans, swine and poultry manure, although this gene has also been identified in P. aeruginosa, Vibrio cholerae and Acinetobacter baumannii clinical isolates  [44]. In Kp13, bla CTX-M-2 is found downstream a class 1 integrase CDS (KP03119) and adjacent to the Orf513-coding gene (KP03126), also named ISCR1, which possibly mediates the dissemination of this gene  [45]. As previously described  [45] , this integron contains a duplication of its 3′ segment where truncated qacE (between CDSs KP18328 and KP03130) and sul1 (KP03130 and KP03134) were found. The overall architecture of the bla CTX-M-2 region in Kp13 is closer to that described for the class 1 integron harboring bla CTX-M-59 (termed In0506) recovered from patients in São Paulo, Brazil  [46] , with which the Kp13 sequence shares 99% identity (BLASTN against GenBank accession no.
These enzymes transfer methyl radicals to certain nitrogenous bases located at the A site of 16S rRNA in the 30S portion of the ribosome and confers resistance to most clinically-relevant aminoglycosides  [52]. Kp13 harbored the rmtG gene (KP01427) in plasmid pKP13f, which encodes a 16S rRNA methyltransferase recently described from a K. pneumoniae clinical isolate from Brazil  [53]. Immediately upstream this gene a putative tRNA ribosyltransferase CDS (KP01428) was found. A CDS annotated as a transposase (KP01425) is located close to the 3′-end of rmtG, and search against the ISFinder database classified this sequence in the IS91 family, although it is unclear whether this transposon is involved in the mobilization of rmtG. Figure  4  Genetic context of bla KPC-2 in K. pneumoniae Kp13. Genes are depicted as horizontal arrows (orientations indicate their transcriptional direction).
Interstrain variation at the nucleotide level may relate to lifestyle-specific adaptations SNPs were determined for three comparisons using the complete chromosome sequence of Kp13 as reference aligned against: i) the complete chromosome sequence of strain MGH 78578, ii) complete chromosome sequence of strain NTUH-K2044 and iii) complete chromosome sequence of strain 342. The number of SNPs falling within non-coding and coding regions (non-synonymous, synonymous and non-sense) between each comparison are listed in Figure  5A. It can be seen that strain 342 accumulated a total number of mutations much larger than the other two strains in comparison to Kp13 (data not shown). This result is in accordance with the proposed hypothesis that this strain should be reclassified as K. variicola  [62].
MLST analysis Multilocus sequence typing was performed as previously described  [68]. Briefly, the trimmed nucleotide sequences of seven housekeeping genes namely rpoB, gapA, mdh, pgi, phoE, infB and tonB were analyzed using the Klebsiella pneumoniae MLST Database, available at http://www. pasteur. fr/recherche/genopole/PF8/ mlst/Kpneumoniae. html. For each allele was given a number that combined yielded the sequence type (ST). The goeBURST algorithm was employed to generate a minimum spanning tree using the information of isolates available and the PHYLOViZ tool  [69]. Clonal complexes included STs sharing at least 5 identical alleles between their representatives.
Comparative genomic analyses The MicroScope platform  [70, 71]  provided some of the tools used for the comparative genomic analyses, such as the RGPFinder and Phyloprofile modules for determination of regions of genomic plasticity (RGPs) and unique/shared gene content identification, respectively. RGPs are defined as DNA segments over 5 kbp possibly related to events of horizontal exchanges, and they are identified using a series of constraints such as G + C% deviation, compositional biases as measured by the tools Alien_Hunter  [72]  and SIGI-HMM  [73] , synteny breaks and proximity to tRNAs. In these analyses, the Kp13 chromosome was set as the reference and RGPs were searched relative to those of strains 342, MGH 78578 and NTUH-K2044. Each identified RGP was manually inspected based on its genomic context and conserved genes located at the flanks of the region.
Elements characteristic of horizontal transference events such as tRNAs, transposases and prophage sequences were also searched for within each region. EasyFig  [74]  was used to generate comparative figures of specific regions, while the BLAST Ring Image Generator 0. 95  [75]  was used to plot the chromosome map as well as arrange and visualize the RGPs within the overall Kp13 chromosome context. In order to identify unique and shared gene content, the PhyloProfile Exploration tool within MaGe was used, and the following criteria from Jenssen et al. [76]  were adopted to define orthology between two translated open reading frames: (i) the smaller protein covering at least 80% the length of the larger (minLrap ≥ 0. 8); (ii) protein identity ≥ 35%. A Venn diagram of the unique/ shared gene content was generated with a custom R script using the package VennDiagram  [77].
Chromosomal architecture comparison between K. pneumoniae strains were carried out using the progressi-veMauve algorithm implemented in Mauve 2. 3. 1  [78]  at its default parameters. For this analysis, sequences from the chromosome of strains 342 [GenBank:CP000964], MGH 78578 [GenBank:CP000647] and NTUH-K2044 [GenBank:AP006725] were downloaded from the NCBI website and used as input to Mauve. Phage-related sequences were searched using PHAST  [79] , while T4SS-related sequences were scanned for using the AtlasT4SS database  [80]. The investigation of the resistance and virulence repertoire was facilitated by BLASTX sequence comparison to the Antibiotic Resistance Genes Database  [81] , as well as throughout the manual annotation of the Kp13 genome, when CDSs related to these functions were flagged.
Insertion sequences were characterized using the ISFinder database (http://www-is. biotoul. fr/) [82]. SNP analysis was carried out as previously described  [83]. Firstly, three primary SNP-call sets were generated from comparisons between Kp13 and the strains MGH 78578, NTUH-K2044 and 342. Then, scripts in PERL were written to function as quality filters to acquire the most reliable SNP-call sets for the comparisons. Alignments were made using the NUCmer algorithm from the MUMmer v. 3. 0 package  [84]. SNPs were also classified regarding their location on intergenic region or CDS (with its corresponding annotated function and related COG category)  [83].
Acknowledgements We thank  Alex Sandro Mundstein  and  Oberdan de Lima Cunha  for carrying out the automatic genome annotation at the SABIA platform, as well as  Rangel Celso Souza  for preparing the GenBank flat files for submission. PIPR had a Masters scholarship from  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) ,  Brazil , and is currently a recipient of a PhD scholarship from this agency. ACG would like to thank the  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ,  Brazil  (Process number:  307816/2009-5 ). MFN thanks the  CNPq, Brazil  (Process number:  309370/2009-4 ) and the  Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) ,  Brazil  (Process number:  E-26/102. 214/2009 ).
Nucleotide sequence accession numbers The NCBI BioProject accession of K. pneumoniae Kp13 is PRJNA78291. The sequences reported here have been deposited in the GenBank database (http://www. ncbi. nlm. nih. gov/Genbank) under accession number [Gen-Bank: CP003999] for the chromosome. The six plasmids are available under accession numbers [GenBank: CP003996] (pKP13a), [GenBank: CP003994] (pKP13b), [GenBank: CP003995], (pKP13c), [GenBank: CP003997] (pKP13d), [GenBank: CP003998] (pKP13e) and [GenBank: CP004000] (pKP13f).
8 98 694 76. 8 0 pKPN6 4,259 41. 4 5 551 64. 7 0 pKPN7 3,478 45. 7 5 343 49. 3 0 K. pneumoniae 342 Chromosome 5,641,239 57. 3 5,425 915 88. 0 88 [4] Plasmids pKP187 187,922 47. 2 230 608 74. 5 0 pKP91 91,096 51. 1 113 635 78. 8 0 ‡ Number of CDSs according to the submitted GenBank flatfile downloaded from NCBI for each studied genome.
0) RGP7 2,278,838 -2,293,758 14. 9 17 46% s. b. ; dGC%; transposases K. pneumoniae HS11286 (91%, 0. 0) RGP8 2,961,023 -2,987,614 26. 6 20 60% s. b. ; T6SS; dGC% K. pneumoniae KCTC 2242 (98%, 0. 0) RGP9 3,155,750 -3,244,119 88. 4 111 52% s. b. ; dGC%; phage K. pneumoniae MGH 78578 (29%, 0. 0) RGP10 3,425,935 -3,436,504 10. 6 9 48% s. b. ; dGC%; transposases K. pneumoniae MGH 78578 (75%, 0. 0) RGP11 3,898,258 -3,906,459 8. 2 9 54% s. b. ; transposases K. pneumoniae MGH 78578 (100%, 0. 0) RGP12 4,204,889 -4,234,405 29. 5 33 48% s. b. ; dGC%; tRNA; transposases K. pneumoniae KCTC 2242 (43%, 0. 0) RGP13 4,660,071 -4,695,371 35. 3 32 50% s. b. ; dGC%; tRNA; transposases K. pneumoniae KCTC 2242 (60%, 0. 0) *BLASTN performed against the 'nr' database at the NCBI website; dGC%, deviation from mean chromosomal GC content; s.
Other climate variables Since its launch onboard Metop-A in October 2006, IASI contributes to the establishment of robust long-term data records of several essential climate variables (ECVs) as defined by GCOS: (i) clouds: cloud physical properties using the atmospheric opacity of the CO 2 absorbing channels and cirrus bulk microphysical properties based on the spectral variation between 8 and 12 µm; (ii) greenhouse gases: midtropospheric columns of CO 2 and CH 4  (Crevoisier et al. , 2009a (Crevoisier et al. , , b, 2013)) ; (iii) dust aerosols: aerosol optical depth, altitude and radius  (Peyridieu et al. , 2013; Klüser et al. , 2012; Clarisse et al. , 2010) ; (iv) continental surface characteristics: skin temperature and spectral emissivity  (Capelle et al. , 2012; Zhou et al. , 2011). IASI has the potential to monitor the evolution of these variables in the long term, to assess potential trends, and to detect signatures of specific climate events, such as ENSO or other sources of climate variability.
Surface spectral emissivity The MultiSpectral Method (MSM) currently used for retrieving mean surface skin temperature and emissivity spectra from 3. 7 to 14 µm at a resolution of 0. 05 µm from IASI  (Péquignot et al. , 2008; Capelle et al. , 2012)  was used to evaluate the expected performance of each scenario on the retrieval of spectral emissivity. As described in these papers, the standard deviation of the method is evaluated using a large set of synthetic simulations comprised of a randomly selected atmospheric situation from the TIGR database, an emissivity spectrum from a laboratory emissivity database and a surface temperature randomly selected within a realistic range. Figure  10  displays the mean of the difference between the retrieved emissivity spectra and the reference ones averaged over the set of simulations for the IRS1a, b and c scenarios.
Acknowledgements. This research has received funding from  CNES. The authors particularly wish to thank the  IASI-NG Project Team at CNES  and the  EPS-SG team at EUMETSAT  for fruitful discussions. Calculations were performed at IDRIS, the computer centre of  CNRS, and at ClimServ , the data and computer centre of IPSL. Edited by:  A. Lambert The publication of this article is financed by  CNRS-INSU.
Participants Participants in the WPA-WHO survey were 4887 psychiatrists from 44 countries. The WHO-IUPsyS sample included 2155 psychologists from 23 countries. The sample for the present study was derived from the combined WHO-WPA and WHO-IUPsyS surveys (N = 7042). Participants were included only if they responded in English or Spanish and had answered at least one of the four questions that allowed for open-ended responses. A total of 3895 participants from 35 countries (see Table  1 ) responded to the respective survey in English or Spanish (55. 3% of the total combined sample). Of the participants who responded in English or Spanish, 673 (17. 3%) did not answer any of the four open ended questions, or gave answers that were uninterpretable or not specific enough to analyze. Data from 3222 participants (82.
In the subsequent WHO-IUPsyS survey  (Evans et al. , 2013) , the more generic versions of the questions about problematic categories and categories that should be added, as well as the question on the need for a national classification, were administered to members of all participating national psychological associations. For the present analysis, data from the ICD-specific versions of the questions on problematic categories and categories that should be added were combined with data from the more generic versions.
In fact, a major revision and simplification of the classification of personality disorders has been proposed for ICD-11  (Tyrer, Crawford, & Mulder, 2011) , consistent with these findings. These data also suggest a need for training efforts related to this area of the ICD-11. Complex post-traumatic stress disorder was the most frequent diagnosis suggested for inclusion in the classification of mental disorders, especially by psychologists. Participants reported that the diagnostic system needed to better account for the consequences of complex trauma and the differentiated processes that can arise when people experience extreme or prolonged traumatic stressful events from which escape is difficult or impossible. This is consistent with proposals for disorders specifically related to stress in ICD-11  (Maercker et al.
This process is intended to improve the clinical utility and global applicability of the resulting classification, and therefore reduce the need for national adaptations. This would enhance the comparability of data from around the world. Moreover, important and empirically-supported variations in disorder presentation will be described as a specific part of the diagnostic guidance to be provided for ICD-11 Mental and Behavioural Disorders. After the material for the core classification has been developed, a separate examination will be conducted to determine the need for additional categories that are specific to particular cultural contexts.
Acknowledgements The  World Health Organization Department of Mental Health  and  Substance Abuse  has received direct support that contributed to the conduct of this study from several sources: The  International Union of Psychological Science , the  National Institute of Mental Health (USA) , the  World Psychiatric Association , the  Spanish Foundation of Psychiatry and Mental Health (Spain) , and the  Santander Bank UAM/UNAM endowed Chair for Psychiatry (Spain/Mexico). Further thanks are due to  Dr. Jared Keeley  for his very helpful comments on earlier versions of this article.
Background: Type 1 diabetes (T1D) is an autoimmune disease, while type 2 (T2D) and gestational diabetes (GDM) are considered metabolic disturbances. In a previous study evaluating the transcript profiling of peripheral mononuclear blood cells obtained from T1D, T2D and GDM patients we showed that the gene profile of T1D patients was closer to GDM than to T2D. To understand the influence of demographical, clinical, laboratory, pathogenetic and treatment features on the diabetes transcript profiling, we performed an analysis integrating these features with the gene expression profiles of the annotated genes included in databases containing information regarding GWAS and immune cell expression signatures. Methods: Samples from 56 (19 T1D, 20 T2D, and 17 GDM) patients were hybridized to whole genome one-color Agilent 4x44k microarrays.