dataesr/scientific-paragraphs-categorization huggingface dataset:
Expression of miR390/TAS3 Pathway Components during Root Nodule Symbiosis The miR390a of M. truncatula was described previously by  Jagadeeswaran et al. (2009)  and annotated in the miRBase database  (Kozomara and Griffiths-Jones, 2014). However, a BLASTN search using the 21-nucleotide sequence of the M. truncatula miR390a identified two loci in the M. truncatula genome (Medtr4g014400 and Medtr3g031300) that were named as MIR390a and MIR390b, respectively. The precursors of miR390 produced by both genes (premiR390a and premiR390b) form thermodynamically stable stem-loop secondary structures according to RNA fold  (Gruber et al. , 2008)  and produce mature miRNAs that are indistinguishable at the nucleotide level (Supplemental Fig. S1 ). Thus, quantification of the mature miR390 accounts for miRNAs processed from both precursors.
Expression values were normalized to HIS3L, which has been validated by GNORM software  (Vandesompele et al. , 2002) , as reported previously by  Ariel et al. (2010)  and  Reynoso et al. (2013). Quantification of miR390 or tasiARFs was performed using the stem-loop RT-qPCR methods described by  Varkonyi-Gasic and Hellens (2011). The MtmiR390, MttasiARFs, and MtU6 stem-loop reverse transcription primers listed in Supplemental Table  S1  were used in the reverse transcription reactions. MtmiR390 F, MttasiARFs F, or MtU6 F were used in combination with the common primer R in the quantitative PCR (Supplemental Table  S1 ) for quantification of the mature miR390a/b, the tasiARFs, or U6 transcript, respectively. PCR products were cloned and sequenced to confirm their identity. Statistical significance between samples was determined by unpaired two-tailed Student's t test comparing means 6 SE of at least two biological replicates for each data point.
Accession Numbers Sequence data from this article can be found in the National Center for Biotechnology Information GenBank, Mt4. 0v1, or miRBase databases under the following accession numbers: miR390a/b (MI0005586), tasiARFs (Medtr2g033380), premiR390a (Medtr4g014400), premiR390b (Medtr3g031300), TAS3 (Medtr2g033380), ARF2 (Medtr8g100050), ARF3 (Medtr2g014770), ARF4 (Medtr4g060460), NSP1 (Medtr8g020840), NSP2 (Medtr3g072710), NIN (Medtr5g099060), ERN1 (Medtr7g085810), CRE1 (Medtr8g106150), and ENOD40 (X80264).
ACKNOWLEDGMENTS We thank  Pascal Ratet  and  Jean Malo Couzigou  from the  Institute of Plant Sciences Paris-Saclay ,  Centre National de la Recherche Scientifique-Institut National de la Recherche Agronomique , for sharing DR5:RFP stable transgenic lines before publication;  René Geurts  from the  Department of Plant Sciences, Wageningen University and Research , for the nsp1 seeds;  Giles Oldroyd  from the  John Innes Centre  for the nsp2-1 seeds;  Jacques Batut  from  LIPM, Institut National de la Recherche Agronomique-Centre National de la Recherche Scientifique , for providing the S. meliloti strain expressing RFP;  Andreas Niebel  for fruitful discussions; and  Claudio Mazo ,  Silvana Tongiani , and  Paula Giménez  from the  Instituto de Biotecnología y Biología Molecular-CONICET  for technical assistance.
1 Work in the M. E. Z and F. A. B. lab was supported by  ANPCyT ,  Argentina  ( PICT 2007-00095  and  PICT 2013-0384 ). Work in the M. C. lab was supported by the  "Laboratoire d'Excellence Saclay Plant Sciences" (SPS, ANR ,  France ,  ANR-10-LABX-40 ; http://www6. inra. fr/saclay-plant-sciences). M. E. Z. and F. A. B. are members of  CONICET. K. V. H. is the recipient of a CONICET Fellowship. M. A. R. was the recipient of a CONICET Fellowship. P. B. -S. was the recipient of a  Marie Curie Postdoctoral IE Fellowship  ( European Commission , MEDEPIMIR, no. PIEF-GA-2010-273743 ).
We investigate the star formation properties of a large sample of ∼2300 X-ray-selected Type 2 Active Galactic Nuclei (AGNs) host galaxies out to z ∼ 3 in the Chandra COSMOS Legacy Survey in order to understand the connection between the star formation and nuclear activity. Making use of the existing multi-wavelength photometric data available in the COSMOS field, we perform a multi-component modeling from far-infrared to near-ultraviolet using a nuclear dust torus model, a stellar population model and a starburst model of the spectral energy distributions (SEDs). Through detailed analysis of SEDs, we derive the stellar masses and the star formation rates (SFRs) of Type 2 AGN host galaxies. The stellar mass of our sample is in the range 9 < log M stellar /M ⊙ < 12 with uncertainties of ∼0. 19 dex.
2008 (Donley et al. , 2012;; Lehmer et al. 2012; Stern et al. 2012). Therefore, the Xray emission is a relatively clean signal from the nuclear component. Furthermore, using the AGN sample with the large, uniform X-ray depth and coherent observations in the COSMOS field, we can minimize the systematic selection effects (e. g. Lauer et al. 2007; Rosario et al. 2013 ;   Caplar et al. 2015). The already existing extensive compilation of multi-wavelength data in the COSMOS field allows us to investigate AGN host galaxies to have a better understanding of nuclear activity and its connection with star formation. In this paper, we investigate the properties of AGN host galaxies in the Chandra COSMOS-Legacy survey. Since the SMBH-powered emission contributes significantly to the ultraviolet-to-optical parts of the spectra of Type 1 AGNs (e.
g. Elvis et al. 2012; Hao et al. 2013) , it is extremely difficult to determine reliable stellar mass for Type 1 AGNs (e. g. Maiolino et al. 2010). A detailed analysis of the Type 1 AGN host galaxies in the Chandra COSMOS Legacy Survey, including spectroscopic analysis in the optical and near-infrared wavelength ranges, will be presented in a second paper,  Suh et al. (in preparation). Thus, we focus on the non-broad-line and/or obscured AGN host galaxies based either on the spectroscopic or the photometric classification. Hereafter, we refer to non-broad-line and/or obscured sources as "Type 2" AGNs. We utilize multi-wavelength data from nearultraviolet to far-infrared of a large sample of AGNs in the Chandra COSMOS Legacy field. We use the Spectral Energy Distribution (SED) fitting to disentangle the galaxy and nuclear contributions in order to measure the stellar masses and the SFRs accurately.
Multi-wavelength dataset We compile the SEDs of our sample of Type 2 AGNs from near-ultraviolet (near-UV; 2300 Å) to far-infrared (far-IR; 500µm) wavelengths using the multi-wavelength photometric data available in the COSMOS field. Specifically, we use the most recent photometric catalog from  Laigle et al. (2016)  including the GALEX near-UV band, CFHT U band, five Subaru Suprime-Cam bands (B, V, r, i, z + ), four UltraVista bands (Y, H, J, Ks), and four   2010; Griffin et al. 2010). We limit the work to only those objects with at least five detected photometric data points (∼91%; 2056/2267), to guarantee a reliable measurement of the SED fits.
SPECTRAL ENERGY DISTRIBUTION FITTING To derive the physical properties of AGN host galaxies, we developed a 3-component SED fitting technique which allows to disentangle the nuclear emission from the stellar light. Over the far-IR to near-UV wavelength coverage, we decompose the entire SED into a nuclear AGN dusty obscuring structure (i. e. , torus), a host galaxy with stellar populations, and a starburst component, which is crucial for estimating reliable physical properties of host galaxies such as galaxy mass and SFR. The method used here is similar to the one applied by  Lusso et al. (2011)  and  Bongiorno et al. (2012)  on the XMM-COSMOS dataset, with significant improvements, including the Bayesian method described in the following sections.
Multi-component SED fitting We developed a 3-component SED fitting procedure in which the observed photometric data is fitted at a fixed redshift of the source with a large grid of models obtained by combining the templates described above. The observed flux can be expressed as the sum of 3 components as f obs = C 1 f stellarpopulation + C 2 f AGN + C 3 f starburst (1) where the C 1 , C 2 , and C 3 are coefficients that reproduce the observed data by χ 2 minimization. The best-fit SED solution could be a stellar population with a negligible contribution from AGN/starburst components, or a stellar population with the central AGN component, or a stellar population with starburst component, or a stellar population with both AGN and starburst components. The fit is performed differently for sources detected in the far-IR and those that are not.
ACKNOWLEDGMENTS This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No 694925). We were partially supported by MINECO (Spain) through Grant Nos. FIS2012-35719-C02-01, FIS2013-42840-P, FIS2015-65078-C2, FIS2016-76359-P (contract partially funded by FEDER), and by the Junta de Extremadura (Spain) through Grant No. GRU10158 (partially funded by FEDER). Our simulations were carried out at the BIFI supercomputing center (using the Memento and Cierzo clusters), at the TGCC supercomputing center in Bruyères-le-Châtel (using the Curie computer, under the allocation 2015-056870 made by GENCI) and at ICCAEx supercomputer center in Badajoz (GrInFishpc and ICCAExhpc). We thank the staff at BIFI, TGCC and ICCAEx supercomputing centers for their assistance.
RESULTS The UPS-targeted library. A UPS library suitable for PPI detection via the HT-GPCA method was first constructed by assembling 590 cDNAs encoding 558 unique human UPS factors (see Table  S1  in the supplemental material) cloned in fusion with the N-terminal Glc1 fragment of the G. princeps luciferase  (29). The coverage of this library was estimated in comparison with an in silico human UPS database, designated the Human UPS, that was obtained by combining the existing DuDe and UUCE ubiquitindedicated databases (5, 30) (Table  S1 ). Our UPS library covers 50% of the human E1-activating enzymes, 64% of the E2 ubiquitin-conjugating enzymes, 46% of the non-cullin-based E3 ubiquitin ligases, 28% of the cullin-based E3 ubiquitin ligases, and 52% of the DUBs (Fig. 1 ). With few exceptions (E3-DWD, DUB-ULP), the distribution of the different subcategories of E3 ubiquitin ligases and of DUBs in the UPS library is comparable to that in the Human UPS (Fig.
Evaluation of the same matrix of host factors for interactions with multiple pathogen proteins provides rigorous comparative interaction mapping, as applied here to the human UPS and the PB2 protein of IAVs. Importantly, the various intrinsic properties of pathogen proteins are not problematic for the comparative aspect of this interaction mapping, as demonstrated in this study with the PB2 proteins of five different IAV strains. Paramount issues for this systematic interactomics strategy are the quality and exhaustiveness of the library representing a subarray of the human proteome. The UPS-dedicated library that was assembled and carefully characterized contained 558 unique UPS factors out of the 1,277 described in databases  (29) , covering one-third to two-thirds of the different UPS categories (E1, E2, various E3 ligase classes).
The relative UPS library composition parallels the database-derived human UPS composition, notably regarding the E3 ligases or their substrate recognition factors (SRFs). This is of key importance since E3 ligases give the human UPS its complexity and flexibility in protein targeting and are consequently the major targets of viruses  (8). The UPS-dedicated library thus offers an accurate representation of the global human UPS, ready to use for mapping of interactions with any protein by HT-GPCA. The same strategy can be implemented with other subarrays of the proteome, provided that HT-GPCA-compatible libraries are available. Smaller libraries dedicated to the alpha/beta interferon, NF-B, and transforming growth factor beta pathways actually exist and have been used to benchmark the HT-GPCA by using a human-human protein interaction matrix  (28).
The targeting of several DUBs also suggests that PB2 might interfere with the versatility of the ubiquitination process. Finally, PB2 appears to broadly target UPS proteins without subcategory preference (Fig. S4 ), possibly reflecting a wide manipulation of the UPS during IAV infection. Virus-host protein interaction networks can be deduced from the binary PPI data sets obtained. A PB2-UPS interaction map encompassing all of the strains tested was plugged into the human interactome recovered from the BioGRID PPI database. Some of the PB2 targets interact with each other (Fig. 6a ), underlying an intricate interplay where PB2 binds to UPS proteins that already act in complexes. When all human factors interacting with the UPS partners of PB2 are considered, an elaborate interaction map emerges (Fig.
The SRF of cullin1-based complex FBXL19 has been shown to target the receptor of interleukin-33 (IL-33), ST2L, for degradation, thereby limiting the inflammatory effects of IL-33  (36). Its association with the PB2 protein could alter inflammation in response to IAV infection. Alternatively, we suspect that the binding of PB2 to the SRF of CRL ligases could rewire the E3 ligase complex toward novel cellular or viral factors for ubiquitination. The PCH2 protein is a component of a complex required to maintain the transcriptionally repressive state of many cellular genes. Its interaction with PB2 might be involved in the colocalization of the viral RNPs to inactive chromatin that has been detected at late times of IAV infection  (37). The OTUD6A DUB hydrolyzes all ubiquitin chains but the K48-linked chains but has been barely described from a functional point of view, while PAN2 is an inactive DUB with a huge panel of interacting partners in PPI databases, which could therefore act through binding to other factors.
Its current virulence in humans is therefore not defined, but it is considered a low-pathogenicity strain. The ex vivo passages in cell lines do not reflect virus circulation in the human population, so that this strain has been considered in our study only according to its circulation time in humans. The position of the purely avian PB2 away from the other human-infecting avian strains in the interaction dendrogram, which suggests that PB2-UPS interaction profiles might be relevant for detection of the ability of avian strains to infect humans before they acquire human-to-human transmission potential. Therefore, the PB2-UPS interplay may help predict the potential of IAV strains emerging from avian reservoirs to infect humans. The UPS of Gallus gallus appears, judging from the UUCD database, to be less complex than the human UPS  (5).
Whisker length corresponds to 1. 5 times the interquartile range (IQR) that is equal to the difference between the upper and lower quartiles (IQR ϭ Q3 Ϫ Q1). The upper whisker, defined by the third quartile (Q3) plus 1. 5 times the IQR, was defined as the PT as follows: PT ϭ Q3 ϩ 1. 5(IQR). The R software  (40)  was used to make calculations with the boxplot function. The scripts used are available upon request. (ii) Determination of the confidence interval. To estimate the significance of an NLR for a given protein-protein pair by comparison to the RRS sampling signal, a confidence interval was calculated for the RRS data set, considering the estimated standard error (SE) and a confidence level of 99. 73% (i. e. , a risk ␣ of 0. 27%) by using the expression ( Ϫ t. SE) ϩ ( ϩ t. SE), where t is the critical value for a two-sided Student test and for n Ϫ 1 degrees of freedom.
In a normally distributed population, a 99. 73% confidence interval corresponds to the mean Ϯ 3 standard deviations. We considered the NLR of a new sample to be statistically significantly different from the RRS if its value was larger than the upper bound of the confidence interval determined for the RRS data set. The R software  (40)  was used to make calculations with the t. test function. The scripts used are available upon request. (iii) Hierarchical clustering. NLRs for each PB2 were normalized by using z scores calculated as follows: z score ϭ (NLR Ϫ mean NLR for PB2)/standard deviation. The distance matrix was calculated on the basis of z scores with the R software (40) by using euclidian distance parameter of the dist function. Clustering was then performed by the classical ward. D method with the R software (42) by using the hclust function.
ACKNOWLEDGMENTS We thank  Marc Vidal  and  David Hill  of the CCSB at the  Dana-Farber Cancer Institute (Boston, MA ) for providing the human ORFeome collection. We are grateful to  Véronique Guyot-Biquand  ( Université de Rennes 1, UMR CNRS 6552 Ethology Animale et Humaine, Rennes, France ) for her advice concerning statistical analyses of the data and to  Shirit Einav  for her suggestions and critical reading of the manuscript. This work was supported by the  Institut Pasteur  in the framework of PTR call 2015 (PTR no. 546 ),  EU   FP7  PREDEMICS (grant agreement  278433 ), and  Labex IBEID. E. B. and M. D. were recipients of a MENRT Ph. D. fellowship, J. P. was a recipient of a Ph. D. fellowship from the  Ligue Nationale Contre le Cancer and Alsace Contre le Cancer. M. K. received funding from the  European Union   Horizon 2020 research and innovation program  under  Marie Skłodowska-Curie  grant agreement  665850.
Objective The absence of a gold standard for scleroderma renal crisis (SRC) has hindered our understanding of this problem. The objective of this scoping review was to identify the criteria used to define SRC in order to guide the development of a consensus definition for SRC. Methods We conducted a search in three databases: Medline, Embase and non-Ovid Pubmed. Papers were eligible for inclusion if they were full-length articles in English whose main topic was SRC or scleroderma renal disease. Two reviewers independently screened eligible papers for final study selection. Data was extracted using a customized form. A web-based survey of members of the Scleroderma Clinical Trials Consortium was used to identify unpublished definitions of SRC.
Data sources and search strategy The comprehensive search was implemented on June 17, 2016, in three electronic databases including MEDLINE (Ovid) (1946-present), EMBASE (Ovid)  (1947-present)  and Pubmed (1966-present) by one author (SH), with the assistance of a professional librarian. No limits on date, language, subject or type were placed on the database search. The search query was constructed to capture articles that addressed the topics of renal insufficiency or malignant hypertension in SSc. The search query was tailored to the specific requirements of each database (Supplementary File 1). The reference lists of 15 pre-selected relevant review articles were manually searched to identify any further studies not yet captured. A "snowball" technique was also adopted in which citations within articles were searched if they appeared relevant to the review  [8].
Citation management Duplicate citations were initially removed in Ovid for citations from Medline and EMBASE. Citations from Ovid and Pubmed were then imported into the bibliographic manager EndNote X7. 4 (Thomson Reuters) and duplicate citations were further removed manually following a 12-step method of de-duplication  [9]. Citations were finally imported into Microsoft Excel 2010 for title and abstract relevance screening and data extraction of full articles. Additional duplicates were removed when found later in the process.
Data characterization All citations deemed relevant after title and abstract screening were procured for subsequent review of the full-text article. Papers that could not be obtained through institutional holdings available to the primary author were requested through interlibrary loans. Data from included papers were charted by one reviewer (SH), using a pretested data charting form in Microsoft Excel 2010. Extracted data included: author(s), year of publication, publication type, main topic of the study, and data relevant to the specific objectives of this scoping review, i. e. 1) formal definitions of SRC used in the literature, 2) clinical features or predictors that have been shown to characterize SRC (limiting this question to original studies of at least 50 SSc subjects), and 3) items that distinguish SRC from its differential diagnoses.
Data summary and presentation Tables and charts were produced to compile the data. Descriptive statistics were used to summarize the results. A list of candidate items to guide the development of a consensus definition for SRC was created. Results of the scoping review were presented to the Scleroderma Clinical Trials Consortium SRC working group at the American College of Rheumatology annual meeting on November 12, 2016 in Washington, D. C. , USA.
DISCUSSION We conducted a rigorous scoping review of the literature to identify variables that were used to define, characterize or predict SRC. Definitions were heterogeneous and reported in a minority of studies. Several patient-specific, SSc-specific and SRC-specific variables predicted the presence or risk of developing SRC in SSc; however, included studies were of variable methodologic quality, with a number of limitations, including small, selected samples, retrospective designs, and paucity of controlled data or multivariate analyses. Nonetheless, we used this evidence base to generate a comprehensive list of potential candidate items that could be used to develop a definition for SRC (Table  3 ). Given that we limited our search of the published literature to full-text articles published in English and whose main topic was SRC or scleroderma renal disease, it is possible that we missed some published definitions of SRC in other languages or in papers where SRC was a secondary interest.
ACKNOWLEDGEMENTS *Investigators of the  Scleroderma Clinical Trials Consortium Scleroderma Renal Crisis Working Group :  M. Baron ,  Montreal, Quebec, Canada ;  T. Frech ,  Salt Lake City, Utah, United States ;  C. Ghossein ,  Chicago, Illinois, United States ;  S. Johnson ,  Toronto, Ontario, Canada; L. Mouthon, Paris, France; S. Proudman, Adelaide, South Australia; V. Steen, Washington, District of Columbia, United States ;  J. Varga ,  Chicago, Illinois, United States.
Filipin staining may facilitate diagnosis in uncertain cases. Recommendations are provided for psychiatrists, neuro-ophthalmologists, and radiologists, and on screening within specific at-risk patient cohorts. The NP-C diagnostic algorithm has been updated and simplified. Summary: This publication provides expert recommendations for clinicians who may see patients presenting with the signs and symptoms of NP-C, including general practitioners, pediatricians, neurologists, and psychiatrists. Neurol Clin Pract 2017;7:499-511 N iemann-Pick disease type C (NP-C) is a rare neurovisceral lysosomal disorder caused by autosomal recessive mutations in NPC1 or NPC2 (95% and ;4% of patients). 1, 2 Analysis of 4 independent exome sequencing databases gave a conservative estimated incidence of 1/92,104 for NP-C1 and 1/2,858,998 for NP-C2, in agreement with recent clinical data.
Molecular genetics and NP-C diagnosis • Table  2  shows an approach to the interpretation of molecular genetics results. • Online databases should be used to record and assess the pathogenicity of mutations (e. g. , NPC-db2 database in Tübingen; see medgen. medizin. uni-tuebingen. de/NPC-db2/ index. php). • Adherence to the American College of Medical Genetics and Genomics guidelines is recommended for assessment and reporting of new mutations.