dataesr/scientific-paragraphs-categorization huggingface dataset:
The cleavage assay mixtures (14 ml final volume) contained 200 fmoles of radiolabeled double-stranded oligonucleotides and 30 mg of proteins from human whole-cell extract, in the incubation buffer [20 mM Tris-HCl (pH 7. 6), 140 mM NaCl, 4 mM EDTA (pH 8) and 8% glycerol]. The reactions were performed at 37 C for various time and stopped by addition of stop-buffer [0. 5% sodium dodecyl sulphate (SDS) and 50 mM EDTA (pH 8)] and by incubation with proteinase K (0. 8 mg/ml, Eurobio) for 1 h at 37 C. After phenol/chloroform extraction and ethanol precipitation, samples were electrophoresed on a 12% denaturing polyacrylamide gel for 1 h at room temperature at 18. 75 V/cm. The reaction products were visualized and quantified using Molecular Dynamics Storm 820 PhosphorImager and ImageQuant 5. 2 software (Molecular Dynamics).
FUNDING Centre National de la Recherche Scientifique (CNRS)  (to E. S. , J. P. R. and Y. S. );  Institut Curie  (to E. S. and Y. S. );  Electricite´de France (EDF)  (to E. S. and J. P. R. );  Agence Nationale de la Recherche  [ ANR-09-PIRI-0022-01  to E. S. and J. P. R. ]. Funding for open access charge:  Institut Curie.
Msx and Dlx Coexpression Analyses, in silico Previous evidences indicate that Dlx5 binds to homeodomainresponsive elements in a proximal region of the Msx2 promoter, and thereby regulates its transcription  [62, 63]. To further support this possibility, we have used a human-mouse co-expression network, generated using published profiling datasets  [67, 68]  and found that DLX5 and MSX genes are connected, i. e. each ranks in the first 1% of the co-expression lists of the other (p,0. 01, data not shown). Next, we screened conserved regions of the vertebrate genome for the presence of consensus Dlx DNA-binding sites, as defined by the Dlx5 PWM  [69]  present in the Jaspar database (accession Nu PH0024. 1) [70]  and reported in Table  S3. As the PWM for Dlx5 reported in Jaspar is not highly informative, it is not surprising that a total 565,995 putative binding sites were initially identified in the mouse genome.
Limb Phenotype of Msx2;Dlx5;Dlx6 Triple Mutants To reveal possible functional interactions between the Dlx and the Msx gene products during limb development, we generated TKO mice with genotype Msx1 2/2 ;Dlx5 2/2 ;Dlx6 2/2 or Msx2 2/ 2 ;Dlx5 2/2 ;Dlx6 2/2. TKO newborns were obtained at a frequency lower than expected, and all died shortly after birth, due to severe craniofacial malformations and consequent breathing impairment. Quadruple knock-out embryos were never obtained in spite of several attempts. We examined the limb skeleton in the TKO animals at two ages: E14. 5 (with Alcian-Blue) and E18. 5/birth (with Alcian-Blue and Alizarine-Red, which stain, respectively, cartilages and mineralized bones). In Msx2;Dlx5;Dlx6 TKO animals, the HLs were severely affected (Fig. 3G, H ) whereas no evident alteration was observed in the FLs (data not shown).
Acknowledgments We thank  Dr. Rolf Zeller  ( Univ. of Basel, Switzerland)  for providing probes and reagents. We thank  Dr. Ferdinando DiCunto  ( Univ. of Torino, Italy ) for bioinformatic analyses. We also thank  Dr. Denis Duboule  ( Geneva, Switzerland ),  Drs. Massimo Santoro  and  Emilio Hirsch  ( Univ. of Torino, Italy ) for helpful comment on the manuscript.
Air and water stable isotope measurements from four Greenland deep ice cores (GRIP, GISP2, NGRIP and NEEM) are investigated over a series of Dansgaard-Oeschger events (DO 8, 9 and 10), which are representative of glacial millennial scale variability. Combined with firn modeling, air isotope data allow us to quantify abrupt temperature increases for each drill site (1σ = 0. 6 • C for NEEM, GRIP and GISP2, 1. 5 • C for NGRIP). Our data show that the magnitude of stadial-interstadial temperature increase is up to 2 • C larger in central and North Greenland than in northwest Greenland: i. e. , for DO 8, a magnitude of +8. 8 • C is inferred, which is significantly smaller than the +11. 1 • C inferred at GISP2. The same spatial pattern is seen for accumulation increases. This pattern is coherent with climate simulations in response to reduced sea-ice extent in the Nordic seas.
, 2000; Bereiter et al. , 2012; Ahn et al. , 2012). This model-data mismatch motivates us to strengthen the description of the magnitude and spatial patterns of DO temperature changes, using different ice core sites. An improved regional description of past changes in Greenland climate will also be needed for the comparison with regional climate models recently equipped with water stable isotopes  (Sjolte et al. , 2011). So far, no systematic comparison of the signature of DO events in water stable isotopes and temperature (from gas isotopes) has been conducted over an array of drilling sites. This is the main target of this study. In 2010, bedrock was reached at the North Eemian Ice Core Drilling (NEEM) site, northwest (NW) Greenland. A new deep ice core, 2. 5 km long, is now available  (Dahl-Jensen et al.
, 2013). In this paper, we present new data from the NEEM ice core, together with existing and new measurements conducted on the GISP2 (Greenland Ice Sheet Project 2) and NGRIP ice cores on DO events 8 to 10. The location of these drilling sites is depicted in Fig. 1  and their present-day characteristics are summarized in Table  1  (see also  Johnsen et al. , 2001). At present, the main source of NEEM precipitation is located in the North Atlantic, between 30 • N and 50 • N  (Steen-Larsen et al. , 2011). The recent interannual variability of water stable isotopes (δ 18 O, δD) shows similarities with the variability of the Baffin Bay sea-ice extent. Unlike central Greenland where snow falls year round, NW Greenland precipitation occur predominantly in summer according to snow pit studies  (Shuman et al.
Conclusions and perspectives Air and water stable isotopes measurements from four Greenland deep ice cores (GISP2, GRIP, NGRIP and NEEM) have been investigated over a series of Dansgaard-Oeschger events (DO 8, 9 and 10), which are representative of glacial millennial scale variability. We have presented the first δ 15 N data from the NEEM core and combined them with new and previously published δ 15 N data from NGRIP, GRIP and GISP2. Combined with firn modeling, air isotope data allow us to quantify abrupt temperature increases for each ice core site. For DO 8, the reconstructed temperature increase is 8. 8 • C for NEEM, 10. 4 • C for NGRIP, and 11. 1 • C for GISP2. Our data show that for DO 8 and 10, the magnitude of GS-GI increase is up to 2 • C larger in central (GISP2) and North Greenland (NGRIP) than in NW Greenland (NEEM).
A1 Validation of the Goujon firnification model for present day at NEEM We use the present-day characteristics of the firn at NEEM to validate the Goujon firnification model. During the 2008 summer field season, a shallow core was drilled at the S2 site at NEEM. Firn air was sampled at different depths from the surface to 80 m depth in this borehole (for more details see  Buizert et al. , 2012). From these air samples, δ 15 N was measured at LSCE (Fig. A1 ). The increasing δ 15 N with depth reflects the gravitational fractionation. Given the vertical resolution in the data, we do not see a clear convective zone. Below 62 m depth, δ 15 N is constant: the nondiffusive zone is reached. We thus have a LID of 62 m at NEEM for present-day according to these δ 15 N data only. In  Buizert et al. (2012) , using measurements of different gases in the firn and several diffusion models, the S2 borehole is described as follows: a convective zone of 3 m, a diffusive zone of 59 m down to 63 m depth (LID), and a nondiffusive zone down to 78.
Acknowledgements. We thank  Michelle Koutnik ,  Jesper Sjolte ,  Aurélien Quiquet  and  Catherine Ritz  for fruitful discussions and furthermore  Dan Israel  for his great help with statistics. NEEM is directed and organized by the  Center for Ice and Climate at the Niels Bohr Institute  and  US NSF, Office of Polar Programs. It is supported by funding agencies and institutions in 14 countries:  Belgium (FNRS-CFB and FWO) ,  Canada (GSC), China (CAS) ,  Denmark (FIST), France (IPEV, CNRS/INSU, CEA and ANR) ,  Germany (AWI) ,  Iceland (RannIs), Japan (NIPR) ,  Korea (KOPRI) , The  Netherlands (NWO/ALW), Sweden (VR) ,  Switzerland (SNF) ,  UK (NERC)  and the  USA (US NSF, Office of Polar Programs ). LSCE analytical work has been funded by the  ANR VMC   NEEM  project. We also thank the  University of Copenhagen  and the  CNRS/INSU/LEFE program.
Related literature For the isotypic lead analogue, see:  Alhakmi et al. (2013). For compounds with related structures, see:  Adam et al. (2009); Assani et al. (2011a,b,c) ;  Moore & Ito (1979). For applications of related compounds, see  : Trad et al. (2010). For the byproduct phase, see:  Moore & Araki (1973). For bond-valence analysis, see:  Brown & Altermatt (1985). Data collection: APEX2  (Bruker, 2009) ; cell refinement: SAINT  (Bruker, 2009) ; data reduction: SAINT; program(s) used to solve structure: SHELXS97  (Sheldrick, 2008) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and DIAMOND  (Brandenburg, 2006) ; software used to prepare material for publication: publCIF  (Westrip, 2010).
Computing details Data collection: APEX2  (Bruker, 2009) ; cell refinement: SAINT  (Bruker, 2009) ; data reduction: SAINT  (Bruker, 2009) ; program(s) used to solve structure: SHELXS97  (Sheldrick, 2008) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and DIAMOND  (Brandenburg, 2006) ; software used to prepare material for publication: publCIF  (Westrip, 2010).
Materials and methods We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria when performing this study. The MEDLINE and EBM (Evidence-Based Medicine) Reviews/Cochrane Central Register of Controlled Trials databases were searched using both the Ovid and PubMed search engines, covering the period from 1950 to 2009. Search terms included "macrolides", "asthma", and "children", with a focus on the subtopics "treatment" and "therapy". A review of the bibliographies of selected references was also performed.
Study selection and data extraction Inclusion criteria were a clinical diagnosis of asthma, age 2-18 years, a macrolide antibiotic used as an adjunctive controller, oral corticosteroid use with dosage determined before and after initiation of macrolide therapy, and FEV 1 as percent predicted measured before and after initiation of macrolide therapy. Studies were excluded if presented as a review article or meta-analysis or if patients with chronic respiratory conditions other than asthma, such as cystic fibrosis, were included. The studies recovered from the database searches were screened by title, abstract, and article type. The methods sections from those studies that survived the initial cull were blindly reviewed for compliance with our inclusion criteria by MD and RL.
The following online tools were used to compare the patient breakpoints and control sequences: RepeatMasker (http://www. repeatmasker. org/), GEECEE (http://150. 185. 138. 86/cgi-bin/ emboss/geecee), Fuzznuc (http://150. 185. 138. 86/cgi-bin/emboss/ fuzznuc)  [Vissers et al. , 2009] , MEME (http://meme. sdsc. edu/ meme/intro. html), and the database Non-B DB (http://nonb. abcc. ncifcrf. gov /apps/site/default). Low copy repeats (LCRs) were identified using the segmental duplication track on the UCSC genome browser (http://www. genome. ucsc. edu).
SNaPshot reactions were purified using Shrimp Alkaline Phosphatase (SAP), loaded on an ABI-Prism 3100 Avant capillary electrophoresis instrument with a GS120-Liz marker and analyzed using the GeneScan ver 3. 7 software (Applied Biosystems). Additional methods are available in the Supporting Information. LMNB1 gene variants have been submitted to the Leiden Open Variation Database (www. lovd. nl/LMNB1).
Analysis of Genomic Architecture of Duplication Breakpoints We analyzed the genomic architecture of the centromeric and telomeric breakpoints in the 16 independent duplications to determine if they played a role in the rearrangement process. The triplication junction was not included in these analyses, as it seemed to be the result of an event independent from the LMNB1 duplication. We did not identify any LCR (also known as segmental duplications) in the genomic region surrounding the LMNB1 gene within 100 kb of any of the duplication breakpoints. Thirteen of the 32 analyzed breakpoints (41%) were within repetitive sequences, as defined by the Repeat Masker software (Fig. 2 , Table  1 ). In four patients, both the proximal and distal junctions were located in repetitive elements. Among these, the duplication in family A4 had two AluY repeats sharing ∼90% identity at both ends.
Acknowledgments We are indebted to all family members who participated in the study and the  United Leukodystrophy Foundation (ULF)  for assistance in patient recruitment. We would like to thank  Urvashi Surti , and  Naftali Kaminsky  for use of their facilities for aCGH. We are grateful to  Jadaranka Milosevic  and  Katie Ketchum , for assistance with aCGH experiments. Zhiyong (Josh) Wang of Agilent technologies  is acknowledged for the design of the LMNB1 CGH array. We thank  Phillip Hastings  for a critical reading of the manuscript,  Eleanor Feingold  and  Dan Weeks  for advice on statistical analysis. Disclosure statement: The authors declare no conflict of interest.
This paper describes new advances in the exploitation of oxygen A-band measurements from POLDER3 sensor onboard PARASOL, satellite platform within the A-Train. These developments result from not only an account of the dependence of POLDER oxygen parameters to cloud optical thickness τ and to the scene's geometrical conditions but also, and more importantly, from the finer understanding of the sensitivity of these parameters to cloud vertical extent. This sensitivity is made possible thanks to the multidirectional character of POLDER measurements. In the case of monolayer clouds that represent most of cloudy conditions, new oxygen parameters are obtained and calibrated from POLDER3 data colocalized with the measurements of the two active sensors of the A-Train: CALIOP/CALIPSO and CPR/CloudSat.
The present paper follows the study of  Ferlay et al. (2010). Based on an enhanced understanding of the sensitivities of POLDER oxygen pressure and on an extended database, we show here how we can gain further information about cloud pressures and vertical extent. This paper is organized as follows. In Sect. , 2, the characteristics and principle of POLDER oxygen A-band measurements and pressure are reiterated, and the known bias and sensitivity of POLDER oxygen pressure products are presented. In Sect. 3, the other A-Train data used in this study are detailed, as are the statistics of the cloud population on which our study focuses. In Sect. 4, we explain the principle for getting estimates of cloud top and middle pressures and the associated results. In Sect. 5, the strength and H 2 O H 2 O O 2 1: Atmospheric transmittance in the oxygen A band region at the resof 5 cm -1 ( 0.
POLDER cloud oxygen pressure principle and algorithm In this study we use data obtained from measurements of the POLDER3 sensor onboard the PARASOL platform within the Afternoon satellite constellation (A-Train,  Stephens et al. (2002) ). PARASOL was launched in 2004. Its orbit was lowered for the first time in December 2009, and then again in November 2011. Since then, PARASOL has not performed as many measurements coincident with other A-Train satellites as previously, though the POLDER3 sensor still works perfectly. POLDER cloud oxygen pressure is inferred from multidirectional measurements in two large spectral channels located in the oxygen A band, centered at around 763 and 765 nm. Their values for full width at half maximum (FWHM) are respectively 10 and 40 nm. Figure  1  illustrates the spectral variability of the atmospheric absorption in this domain as well as the POLDER response filters for the two channels.
, 1999). More precisely, cloud oxygen pressure appears to be close to the pressure of the geometrical middle of cloud layer. This has been observed with SCIAMACHY data  (Wang et   Oxygen pressure (hPa) θ v × cos ∆ϕ ( ˚) CBP CMP CTP Liquid case, τ = 10, H = 2 km, SZA = 60 Fig. 2. Variation of simulated POLDER directional cloud oxygen pressure with the viewing zenith angles θ v in the solar plane ( ϕ is the measurement's relative azimuth). Characteristics of the case study are indicated in the title. Horizontal lines indicate the level of cloud top, middle, and base pressures (denoted respectively by CTP, CMP, and CBP). The angularly averaged oxygen pressure is here 667 hPa and the angular standard deviation 11 hPa. Discontinuities of the directional cloud oxygen pressure at -60 • and -20 • are signatures of cloud scattering phase function.
A-Train dataset used In the next section we present new inferences obtained from POLDER cloud oxygen pressure level 2 parameters P O 2 and σ P O 2 : new cloud pressures, and an estimate of the cloud geometrical thickness. The dataset that was used for this study is presented in this section. POLDER level 2 data were sampled under the CloudSat/CALIPSO track in order to get "true" cloud vertical locations from the lidar and radar echoes. The CPR radar onboard CloudSat and the CALIOP lidar onboard CALIPSO do indeed have complementarity sensitivities to detect thin and thick scattering layers. This intercomparison filters daytime only CloudSat/CALIPSO data such as POLDER oxygen pressure parameters. CALTRACK level 2 data belong to the pixels nearest to the lidar shots sampled at 5 km. They are delivered by the ICARE thematic center as ICARE Multi-Sensor products (web address: http://www.
icare. univ-lille1. fr/). The horizontal resolution of POLDER level 2 data corresponds to the size of POLDER super-pixel (18×21 km 2 ). Level 2 CloudSat 2B GEOPROF LIDAR data have a horizontal resolution of 2. 5 × 1. 4 km 2 , and they provide cloud base and top altitudes (LAYERBASE and LAY-ERTOP) of no more than five cloud layers n in each atmospheric column. From these altitudes, we obtain cloud geometrical extent denoted by H , as well as cloud base, top, and middle pressures (denoted by CBP, CTP, and CMP, respectively). The conversion from altitude to pressure is performed thanks to a local conversion index that has been added to the CALTRACK L2 2B GEOPROF LIDAR files. Pressures in the atmospheric column come from meteorological reanalyses  (Bloom et al. , 2005)  produced by the NASA Global Modeling and Assimilation Office (GMAO), and are available with CALIPSO CAL LID L2 05kmCPro files.
GIGA will target 7000 noninsect/nonnematode species, with an emphasis on marine taxa because of the unrivaled phyletic diversity in the oceans. Priorities for selecting invertebrates for sequencing will include, but are not restricted to, their phylogenetic placement; relevance to organismal, ecological, and conservation research; and their importance to fisheries and human health. We highlight benefits of sequencing both whole genomes (DNA) and transcriptomes and also suggest policies for genomic-level data access and sharing based on transparency and inclusiveness. The GIGA Web site (http://giga. nova. edu) has been launched to facilitate this collaborative venture.
Therefore, our group-the Global Invertebrate Genomics Alliance (GIGA)-represents a concerted effort toward sequencing invertebrate genomes/transcriptomes and developing informatics tools, resources, tissue repositories, and databases, which will be made publicly available. The GIGA consortium herein reviews the phylogenetic status and adaptive and developmental features of potential species for whole-genome sequencing of invertebrate phyla. Careful taxon selection, the development of data standards, and facilitating comparative approaches will maximize the utility of genomes generated. Collecting whole-genome data is still a nontrivial task, both technologically and financially, and the computational constraints on assembling, annotating, and analyzing genomic data remain considerable. Therefore, GIGA also proposes a series of criteria (outlined below) for prioritizing invertebrate whole-genome sequencing projects, including standards for nominating species for whole-genome sequencing, specimen preparation, and processing, as well as general policies governing the distribution of genomic data as resources for the broader scientific community.
Because international and interinstitutional cooperation is essential for long-term success, the new GIGA Web site will be used to foster cooperative research projects. For now, the GIGA Web site can serve as a community nexus to link projects and collaborators, but it could also eventually expand to host multiple shared data sets or interactive genome browsers. The broad scope of GIGA also necessitates growth in the genomics-enabled community overall. Sequencing and analyzing the large amount of resulting data pose significant bioinformatic and computational challenges and will require the identification and creation of shared bioinformatics infrastructure resources.
Why Focus on Invertebrates? Phylogeny Given the diversity of noninsect/nonnematode/nonvertebrate animals and the variety of invertebrate body plans, a first criterion for selecting taxa is phylogenetic. The taxa selected represent 36 metazoan phyla (Figure  1 ), acknowledging that conferring this taxonomic rank on a group can be dynamic and controversial. Whole or nearly whole-genome sequences (most genomes only asymptotically approach completeness but never fully reach it, Eddy 2013) have been published or submitted to pubic databases for one or a few representatives of 15 phyla (Porifera, Ctenophora, Cnidaria, Placozoa, Cephalochordata, Tunicata, Craniata, Echinodermata, Hemichordata, Nematoda, Arthropoda, Annelida, Mollusca, Platyhelminthes, and Rotifera), but there are currently no published genomes for the other 21 invertebrate phyla.
Policy A main goal of GIGA is to build an international multidisciplinary community to pursue comparative invertebrate genomic studies. We seek to develop tools to enable and facilitate genomic research and encourage collaboration. We will develop standards that ensure data quality, comparability, and integration. By coordinating sample collecting and sequencing efforts among invertebrate biologists, we aim to avoid duplication of effort and leverage resources more efficiently. We envision a scientific commons where shared resources, data, data standards, and innovations move the generation and analysis of invertebrate genomic data to a level that likely could not be achieved with the traditional piecemeal singleinvestigator-driven approach. GIGA embraces a transparent process of project coordination, collaboration, and data sharing that is designed to be fair to all involved parties.
The ENCODE project may be emulated in this regard  (Birney 2012). We are committed to the rapid release of genomic data, minimizing the period of knowledge latency prior to public release while protecting the rights of data product developers  (Contreras 2010). The data accepted as part of GIGA resources will undergo quality control steps that will follow preestablished and evolving standards (see Standards section) prior to data release. Efforts such as those of  Albertin et al. (2012)  have addressed data sharing issues relevant to GIGA and other large-scale genomics consortia. We also recognize the existence and formation of other recent genome science initiatives and coordination networks and will synchronize efforts with such groups through future projects. Because GIGA is an international consortium of scientists, agencies, and institutions, we will also abide by the rules of global funding agencies for data release (e.