dataesr/scientific-paragraphs-categorization huggingface dataset:
The obtained sequences were aligned onto human RefSeq database (ftp://ftp. ncbi. nih. gov/refseq) using SOAP software  [21]. Only uniquely mapped sequences to RefSeq genes were kept for subsequent analysis. For each mRNA sample, we generated on average a total 26 M raw reads. On average, 84% of the reads matched known genes, and ~16,000 genes had expression level data. Raw data are publicly available through the NCBI GEO database [GSE63190].
Gene ontology analysis Gene ontology (GO) enrichment analysis provides all GO terms that are significantly enriched in a list of DEGs by aligning GO terms to the database (http://www. geneontology. org/) and KEGG pathways (http://www. genome. jp/ kegg/). Lists of differentially expressed genes were clustered and calculate by Cluster and Java Treeview software. Transcripts, which had missing values in at least one experiment, were removed from the analysis. Pathway and gene interaction analysis was performed with Ingenuity Pathway analysis software.
Oil Red staining Adherent pre-adipocytes and differentiated adipocytes were fixed with 4% paraformaldehyde for 30 min at room temperature (RT). After one washing step with PBS, cell layers were washed with 60% isopropanol once. Oil Red solution was added for 5 min at RT and rinsed off with diH 2 0 until clear. Stained adipocytes were assessed under a light microscope and images were analyzed using Adobe Photoshop software.
We then plotted the expression values for DEGs identified by comparing SGBS D14 with D28 along with the undifferentiated and differentiated samples of PAZ6 and SW872, respectively. Clustering analysis based on centered correlation was performed for the remaining 284 transcripts and data were displayed with Treeview software (Figure  7c ). Interestingly, SGBS D14 and PAZ6 D14 samples clustered (See figure on previous page. ) Figure  4  Human SGBS adipocytes display features of white and brown adipocytes respectively. (a) Oil Red staining was carried out as described above and the presence of stained lipid droplets at D14, D21 and D28 was assessed at a magnification of 20×. (b and c) Premature and differentiated SGBS cells were co-stained with mitotracker (green) to test the abundance of mitochondria, lipidtox (red, in b) for neutral lipid droplets or anti-UCP1 antibodies (red, in c) and DAPI (blue) to visualize nuclei.
Single-channel images were overlayed and processed by Photoshop software. All scale bars are reported. together suggesting common gene expression patterns. SGBS and PAZ6 pre-adipocytes formed another cluster as well as differentiated SW872, which clustered along with their undifferentiated counterpart. Further analysis of a small subset of genes, which is exclusively shared between SGBS D14 and PAZ14 samples (blue boxes, Figure  7c ) is depicted in Figure  7d , and shown as a headmap image (upper panel). We subjected the 41 genes to pathway analysis and found even in this small subset of genes PPARγ as the top upstream regulator, which is significantly inhibited in SGBS D28, compared to SGBS D14 cells (p value 1. 33E-28) along with nine downstream targets (namely ApoE, SCD, GPAM, PLIN1, FASN, AGPAT2, DGAT2, LIPE and NR1H3) which are shown in Figure  7d , lower panel.
In our study, PAZ6 and SGBS differentiation media were also enriched with rosiglitazone, as this is a standard formulation and widely used. We therefore depleted rosiglitazone from the SGBS medium to investigate whether our observation was mainly attributed to its presence. Nevertheless, (See figure on previous page. ) Figure  7  Next-generation whole genome RNA sequencing elucidates the activation of key pathways in three human adipocytes upon differentiation. RNA was isolated as described above and subjected to next generation sequencing analysis. Comparison of SGBS cells at D14 and D28 revealed 485 DEGs with fold change >2 and FDR <0. 001. Genes were queried against know annotation databases and gene ontology analysis was performed. (a) 10 topinvolved pathways are shown; among those PPARγ and adipocytokine signaling are the two most significantly involved pathways (b).
(c) Expression levels of 284 DEGs with no absence calls between SGBS S14 and D28 samples were clustered for all three human adipocytes and visualized by Treeview software. Two clusters highlighted in blue are characteristic for SGBS D14, representative of the brownish stage, and differentiated, brown PAZ6 cells on D14. (d) 41 DEGs are displayed separately using Treeview software (upper panel). PPARγ gene interaction networks were generated with Ingenuity Pathway analysis software (lower panel). Log2 ratios are reported based on the comparison between SGBS D14 vs. SBGS D28. even in absence of rosiglitazone we noticed upregulation of BAT markers such as UCP1 and PPARγ in SGBS cells at D14, which then declined up to D28 (data not shown). We therefore conclude that we are not promoting the phenotype observed in maturating SGBS cells by drug treatment, but rather are seeing an intrinsic property of the SGBS adipocyte cell line itself.
Acknowledgments This work was supported by  Weill Cornell Medical College in Qatar , and by a grant from the  Qatar National Research Fund  ( NPRP 4-294-3-092 ). The contents are solely the responsibility of the authors and do not necessarily represent the official views of the  Qatar National Research Fund. The authors would like to thank  Dr. Ravi Mamtani  and  Dr. Albert Lowenfels  for their fruitful discussions and critics. We greatly honor and appreciate the contribution of  Dr. Strosberg  and  Dr. Zilberfarb  who sadly passed away during the preparation of this study.
Single-channel images were overlayed and processed by Photoshop software. All scale bars are reported. (c) RNA from SGBS cells, which were subjected to cold-environment for 4-6 h, was isolated using the Qiagen lipid tissue mini kit and cDNA synthesis was carried out. Quantitative real-time PCR was performed by SYBR green detection method and expression levels are normalized for D0 baseline expression levels. All experiments were done in triplicates and data are derived from at least three independent experiments. (* p <0. 01, ** p < 0. 001).
A huge swath of parameter space in the context of the Minimal Supersymmetric Standard Model (MSSM) has been ruled at after run I of the LHC. Various exclusion contours in the m g -m χ0 1 plane were derived by the experimental collaborations, all based on three-body gluino decay topologies. These limits are however extremely model dependent and do not always reflect the level of exclusion. If the gluino-neutralino spectrum is compressed, then the current mass limits can be drastically reduced. In such situations, the radiative decay of the gluino g → g χ0 1 can be dominant and used as a sensitive probe of small mass splittings. We examine the sensitivity of constraints of some Run I experimental searches on this decay after recasting them within the MadAnalysis5 framework. The recasted searches are now part of the MadAnalysis5 Public Analysis Database.
We use the MadAnalysis5  [83, 84]  framework to derive exclusion contours in the m g -m χ0 1 plane, deduced from a Simplified Model approach, after recasting ATLAS and CMS monojet and multijets analyses. We provide in our work a full quantitative exploration of this plane and the quoted ATLAS 95% CL upper bound  [6]  for fixed m g and m χ0 1 rather serves to us as a cross check on our results, which give us quite confidence in our procedure with which we derived our own mass limits. These recasted analyses are now available on the MadAnalysis5 Public Analysis Database (PAD)  [85]. The last section is devoted to the determination of a search strategy at 13 TeV to investigate the prospects for discovering the gluino through its radiative decay at run II of the LHC and then we conclude.
3 fb -1 of collected data. The recasted CMS-SUS-13-012 SUSY search was already available in the MadAnalysis5 Public Analysis Database  [85]  and can be found at  [91]. The two remaining searches were implemented and validated for the present work and are now available at  [92, 93]. For the sake of clarity, we present in the Appendix A a comparison between the official cut flows and our reimplementations of the ATLAS monojet  [88]  and multijet  [89]  analyses within MadAnalysis5 (MA5). From Tables 4,7 and 8 the selection cuts of each respective analysis can be read off. For the monojet search, see Table  4 , one can see that the agreement is not very good at the level of the preselection cuts. The discrepancy originates already from the E miss T > 100 GeV cut applied at the reconstructed level.
The MADGRAPH samples are produced using the AUET2B tune  [99, 100]  1. The MLM  [102]  matching scheme is used, and a MADGRAPH k T measure cut-off and a PYTHIA  [103]  jet measure cut-off both set to 0. 25 times the mass scale of the SUSY particles produced in the hard process. Hadronisation is performed using PYTHIA-6. 426 [103]  as incorporated in the MADGRAPH-5 package and detector effects are simulated with the 'MA5tune' of DELPHES-3  [104]  as described in  [84]. The number of events are then normalised to the correct luminosity after including cross sections at the next-to-leading order and next-to-leading logarithm accuracy  [105] , as tabulated by the LHC SUSY Cross Section Working Group [106] For the statistical interpretation, we make use of the module exclusion CLs. py provided in the MadAnalysis5 recasting tools.
Adopting a Simplified Model approach, where the gluino and the neutralino χ0 1 are the only relevant superparticles accessible at the LHC, and BR(g → g χ0 1 ) = 100%, we reinterpreted some ATLAS and CMS 8 TeV SUSY searches to derive 95% CL exclusion contours in the m g -m χ0 1 plane. This decay process is particularly interesting to probe small ∆M. The reinterpretation of the ATLAS and CMS analyses has been performed through the MadAnalysis5 framework  [83, 84]  and publicly available on its Public Analysis Database web page  [95]. The recasted monojet analysis enables us to illustrate that, had it been used by the experimental collaborations to constrain the loop-induced gluino decay, this search is sensitive to degenerate gluino-LSP scenarios. In turn, degenerate situations with gluino masses up to m g ≃ 750 GeV could have been excluded.
A Cutflow for the ATLAS monojet analysis We give below the cut flow table for the comparison between the ATLAS monojet analysis and the recasted MA5 one. The monojets analysis targets the flavour-violating decay t → c χ0 1 with a branching fraction of 100% using a monojet and c-tagged strategies. We only recasted the monojet strategy. The dataset corresponds to 20. 3 fb.
B Cutflow for the ATLAS multi-jet analysis This ATLAS analysis  [89]  searches for new physics in the 0 lepton + multi-jets (2-6 jets) + missing energy (E miss T ) final state. The dataset corresponds to 20. 3 fb -1 of integrated luminosity at √ s = 8 TeV. In the context of supersymmetry, the analysis targets gluino pair production (gg), squark pair production (q q), and squark gluino production(qg). For each of the production modes the following cases are investigated; 2jm 2jt 3j 4jl-4jl 4jt decay to LSP (q q) (q q) (g q) (q q) (gg) (gg) direct direct direct direct direct direct m q/g 475 1000 1612 400 800 1425 m χ± Table  5 : The benchmark scenarios used for the valiaion of the 10 signal regions. For the direct squark pair or gluino pair production, the rest of the spectrum is decoupled.
Acknowledgements We thank our experimental colleagues  Jamie Boyd ,  Tommaso Lari  and  Jalal Abdallah  for additional information on the ATLAS analyses. We appreciated greatly the help of  Sabine Kraml  for her comments and suggestions on the manuscript and of Bryan Zaldivar concerning the monojet analysis. GC would also like to thank  Mathieu Aurousseau  for the help with ROOT. The work of GC is supported by the  Theory-LHC-France initiative of CNRS/IN2P3. The work of DS is supported by the  French ANR , project  DMAstroLHC ,  ANR-12-BS05-0006 , and by the  Investissements d'avenir, Labex ENIGMASS.
The raw CCD images were cleaned using IRAF 1 software following bias subtraction, flat fielding and cosmic ray removal. The identification of point sources was performed with the DAOFIND task. Following  Stetson (1987) , we have used the roundness limits of -1 to +1 and sharpness limits of 0. 2 to +1 to eliminate bad pixels brightness enhancements and the extended sources such as background galaxies from the point source catalog. The photometry by PSF fitting was done using ALLSTAR task of DAOPHOT package  (Stetson 1992). The instrumental magnitudes were converted to standard magnitudes following the procedure outlined by  Stetson (1987). A total of 13 stars of SA 92 field were used to estimate atmospheric extinction and transformation coefficients. The estimated extinction coefficients in B, V and I are 0.
Near-Infrared Data form UKDISS and 2MASS Near-IR JHK photometric data towards NGC 2282 were acquired from the UKIRT Infrared Deep Sky Survey (UKIDSS, Lawrence et al. 2007), which were taken during the UKIDSS Galactic Plane Survey (GPS)  (Lucas et al. 2008; data release 6). The UKIDSS GPS has saturation limits at J = 13. 25, H = 12. 75 and K = 12. 0 mag, respectively  (Lucas et al. 2008). We therefore, replaced the saturated stars with the 2MASS PSC data. We set the 2MASS limit 0. 5 magnitudes fainter than UKIDSS saturation limits following  Alexander et al. (2013). In total, 150 saturated sources in UKIDSS catalog were replaced by 2MASS sources.
Spitzer-IRAC data from warm mission The IRAC observations in 3. 6 and 4. 5 µm bands (channels 1 and 2) were available in the Spitzer archive program (Program ID: 61071; PI: Whitney, Barbara A). The data sets were taken towards NGC 2282 on May 25, 2011 at various dithered positions and with integration time of 0. 4 and 10. 4 sec per dither. The basic calibrated Data (version S18. 18. 0) were downloaded from Spitzer archive 2. The raw data were processed and the final mosaic frames were created using MOPEX (version 18. 5. 0) with an image scale of 1. 2 arcsec pixel -1. We performed point response function (PRF) fitting method using APEX tool provided by Spitzer Science centre on all the Spitzer IRAC images to extract the magnitudes of point sources. The detailed procedure of source detection and magnitude extraction is described in  Jose et al.
(2013). We adopted zero point flux densities of 280. 9 and 179. 7 Jy for the 3. 6 and 4. 5 µm bands, respectively, following the Warm Spitzer Observer Manual. We finally detected 3049 and 2341 number of sources within 7 ′ radius around the cluster in IRAC 3. 6 and 4. 5 µm bands, respectively. The IRAC data of two bands were matched with a radial matching tolerance of 1. 2 arcsec. Thus our final IRAC catalog contains 3304 sources, of which, 2085 sources are detected in both bands.
Multiwavelength Catalog The multiwavelength catalog was built by cross-matching all the catalogs described in the above sections except IPHAS data. The JHK and IRAC data were matched with a matching radius of 2 ′′. Before this, we performed several test matches by increasing the matching radius in step of 0. 1 ′′ from 1. 0 ′′ to 3. 5 ′′. We found that the matching radius of 2 ′′ is sufficient for cross-matching two catalogs. In few cases, we got more than one matching sources, and we have taken the closest one as the best match. Following the same method, we adopted the matching radius of 2 ′′ to match between IR and optical catalog. Finally, the results of each match have been visually inspected in Optical, UKIDSS, 2MASS and  Spitzer images. Our final catalog contains total 5601 number of sources within 7 ′ radius around the cluster, but all of them do not have detection in all wavelengths.
Stellar density and cluster radius In order to understand the spatial structure of the cluster, we generated the stellar surface density map using the nearest neighborhood technique and following the method intro- duced by  Casertano & Hut (1985). Briefly, the stellar density σ(i, j) inside a cell of an uniform grid with centre at the coordinates (i, j) is σ(i, j) = N-1 πr 2 N (i,j) where, rN is the distance from the centre of the cell to the N th nearest source. Value of N is allowed to vary depending upon how smallest scale structures of the field are interested to study. In Fig. 3. we have shown the surface density map which is estimated using the K-band data (K 17. 5 mag with photometric uncertainty of < 0. 1) for the stars towards NGC 2282 region overlaid on the UKIDSS K-band image. This map has been obtained using a grid size of 10 ′′ × 10 ′′ and N=9.
Reddening towards the Region The extinction in an embedded cluster is distributed nonuniformly. It is important to know the spatial variation of extinction of the cluster to characterize the cluster members. We estimated the K-band extinction towards NGC 2282 using 2MASS and UKIDSS data within an area ∼ 10 ′ × 10 ′ , to understand the local extinction towards the region. We measured the AK value using (H -K) colours of the stars. The sources without infrared excess ( i. e. , background dwarfs and non-excess sources within the field) were used to generate the extinction map. Following Gutermuth et al. (  2005 ), (8)B0. 5Ve (1)B5V (2)G8V (3)K1V (4)F7V (5)F9V (  6 )F0V (7)B2V  we used the grid method to measure the mean value of AK (see  Jose et al. 2013 for details). Briefly, we divided the region of our interest into small grids of size 10 ′′ × 10 ′′.
Identification and Classification of YSOs We identify and classify the YSOs towards NGC 2282 based on their IR colours. Following  Gutermuth et al. (2008 Gutermuth et al. ( , 2009)) , we used IRAC 3. 6 and 4. 5 µm band data along with H and K data to identify and classify the YSOs. The main limitations for the identification of YSOs based on the IR colours arise from the contaminations of different non-stellar sources in IR detections such as, extragalactic objects like polycyclic aromatic hydrocarbon (PAH) emitting and star-forming galaxies, active galactic nuclei (AGN), unresolved knots of shock emission, PAH-emission contaminated apertures, etc. These sources have considerable IR colours, which could mimic the colours of YSOs in colour-colour (CC) diagram. A number of candidate YSOs could be missed in the mid-IR bands due to the limited sensitivity of IRAC observations.
Selection of Hα Emission Stars from Slitless Spectroscopy and IPHAS Photometry Using slitless spectroscopy, Hα emission-line stars were visually identified from their enhancement over the continuum. We identified 16 Hα emitting sources and they are listed in Table  4. Of the 16, 14 sources have JHK data sets. Ten Hα emission stars are detected as YSOs from H, K, 3. 6 and 4. 5 µm colours (1 Class I, 9 Class II). All the Hα emission stars are plotted in the near-IR colour-colour diagram shown in 9a. The star ID 8 (see Table  3 ), which is classified as a B0. 5 Ve star in the optical slit-spectroscopy with strong Hα emission, is also detected with the slitless spectroscopy and falling in the 'P' region of JHK CC diagram (See Fig. 9a ). Fig. 10  presents the IPHAS (ri/r -Hα) CC diagram towards NGC 2282.
1989). We have detected total 1050 objects from our observations within 3. 15 arcmin of the cluster radius. While in similar depth of observations and area on reference field, we have detected 856 stars. After removing the field star contribution, the number of objects associated the cluster area would be ∼ 203. The number of candidate YSOs from H, K, 3. 6, 4. 5 µm data is 72 in the cluster area, while from J, H, K data the number is 40. All disc bearing Hα stars are detected as the IR excesses sources. Hence, the total number of IR excess sources within NGC 2282 is 112. Thus we estimated the disc fraction of NGC 2282 as ∼ 58% ± 6%. If we consider the IR excess sources from mid-IR data only, the disc fraction is ∼ 37% ± 5%. This disc fraction estimated in NGC 2282 is significantly larger than that of  Horner et al.
ACKNOWLEDGMENTS We thank the anonymous referee for valuable comments which further improved the quality of the paper. This research work is financially supported by  S N Bose National Centre for Basic Sciences under Department of Science and Technology, Govt. of India. This publication makes use of data from The  UKIRT Infrared Deep Sky Survey or UKIDSS  which is a next generation near-infrared sky survey using the wide field camera (WFCAM) on the  United Kingdom Infrared Telescope on Mauna Kea in Hawaii. We also use data from  Two-micron All-Sky Survey  ( 2MASS ), which is a joint project of the  University of Massachusetts and Infrared Processing and Analysis Center/California Institute of Technology , funded by  National Aeronautics and Space Administration  and the  National Science Foundation.
To clarify the role of oxidative stress in the development of the grade 3 phenotype, the effects of the mitochondria-targeted antioxidant MitoQ, the natural antioxidant ascorbic acid (vitamin C) and a water-soluble vitamin E analogue (Trolox) were assessed. Unexpectedly, none of these three antioxidants decreased the prevalence of the grade 3 phenotype significantly (p > 0. 05, Student's t-test) (Fig. 4M ,N and Supplementary Fig. S19 ). These results strongly suggested that although oxidative stress is produced during the development of the grade 3 phenotype, this process is not essential to the pathophysiology of this grade of OPP. Moreover, our data indicated that the effect of GSH on the recovery of the grade 3 phenotype was mediated through an oxidative stress-independent mechanism. Development and validation of animal models for rapid screening of molecular libraries to identify potential medical countermeasures against OPP are some of the primary priorities of NATO countries  11.
Behaviour. Basal locomotor activity and the VMR of 8 dpf zebrafish larvae were analysed using a DanioVision system running EthoVision XT 9 software (Noldus, Wageningen, the Netherlands) essentially as described elsewhere (see Supplementary Methods for additional details). For TMR, startle responses were recorded with a high-speed Photron Fastcam SA3 camera (Photron USA, Inc. , San Diego, CA, USA), and the kinematics of the response was analysed using the Flote software package (Supplementary Methods).
Generalized linear models were used to test for differential expression based on comparing each treatment against the control. Genes with a FDR-adjusted p-value ≤ 0. 05 were considered differentially expressed. Gage v2. 14. 4  36  was used to find significantly perturbed KEGG pathways. Each comparison was tested for significant pathways using the above-estimated gene expression levels. Any pathway with a FDR-adjusted p-value ≤ 0. 05 was considered significant. These sequencing data are archived in the NCBI Short Read Archive with the BioProject accession number PRJNA285816. Measurement of oxygen consumption in zebrafish larvae homogenates. Respiration of zebrafish homogenates (400-500 μ g protein) was measured at 28 °C by high-resolution respirometry (Supplementary Methods). Measurement of the adenine nucleotide levels.
AMP, ADP and ATP levels were determined by reverse-phase HPLC using a C18 column (Supplementary Methods). Concentration-response analysis. EC50 and LC50 values were obtained by fitting responses relative to control treatments (R) to the nonlinear allosteric decay regression model (see Supplementary Methods for additional details). Data analysis. Statistical analysis was performed using SPSS software v22 (IBM, USA). The data are presented as the mean ± SEM unless stated otherwise. Pairwise statistical significance was determined with Student's t-test, one-way ANOVA or Mann-Whitney rank sum test as appropriate. The results were considered significant at p < 0. 05 unless otherwise indicated.