dataesr/scientific-paragraphs-categorization huggingface dataset:
1 for cirrus to be detected and, consequently, may underestimate the actual cirrus occurrence frequency. Lidar measurements provide accurate information on the vertical distribution of cirrus and, therefore, are now used to develop highly resolved cirrus database. The objective of this study is to construct a cirrus climatology over the period 1996-2007 using a ground-based Rayleigh-Mie-Raman lidar, use this unique dataset to investigate the lidar capabilities to describe their decadal evolutions and compare these results to those obtained from the spaceborne lidar CALIOP which Published by Copernicus Publications on behalf of the European Geosciences Union. provides global vertical profiling information of clouds since June 2006. Some studies have investigated cloud classification based on their macrophysical properties  (Desbois et al.
, 1982; Keckhut et al. , 2006). This kind of investigations should be useful to identify the different formation processes and their parameterisation in general circulation models. Also to further investigate the lidar dataset, a statistical multivariate analysis based on morphologic cloud information derived on a lidar sub-set has been performed. Section 2 describes the design of the ground-based and spaceborne lidar instruments. Section 3 concerns the retrieval of cirrus clouds parameters. Climatology and data processing to identify the different cirrus cloud classes are presented in Sect. 4. Finally investigations about the cirrus clouds longterm evolution and discussion about their origin are respectively presented in Sect. 5 and Sect. 6.
The cirrus channel used a 20 cm telescope and an optical fibre of 1 mm diameter. The field-of-view is equal to 1 mrad. Because the measurements were also dedicated to stratospheric aerosol analyses, an electronic shutter system has been added to reduce the induced noise due to initial burst. Due to this electronic shutter, clouds are only detected above 6 km. For Raman channels, the backscattered signals are collected by a telescope of 80 cm. The backscattered signals are collected by optical fibres mounted on the focal plane of these telescopes and transferred to the optical ensemble. Counts from 8000 shots (160 s) are pre-accumulated in 75 m (0. 5 µs) bin intervals and stored to constitute the raw data. Temperatures used throughout the paper in relation with OHP lidar data were obtained from radiosondes (Vaïsala RS-80 -now modem), which are launched daily from Nîmes (∼ 100 km from OHP) by the French meteorological centre (Météo-France) at mid-night, during lidar operations.
CALIOP spaceborne lidar The CALIOP lidar, set on the Cloud Aerosol and Infrared Pathfinder Satellite Observations (CALIPSO) satellite, is designed to provide global vertical profiling information of clouds and aerosols. It was launched in April 2006 into a sunsynchronous orbit as part of the A-Train satellite constellation. A brief description is given here; details of the CALIOP instrument can be found in  Hunt et al. (2009). CALIOP is a 2-wavelength (532 nm and 1064 nm) polarization sensitive lidar. Its laser generates optical pulses of ∼ 20 ns with energy of 110 mJ at each of the two wavelengths and a repetition rate of 20. 16 Hz. Backscatter signals are collected by a 1 m diameter telescope. The fundamental sampling resolution of the lidar is 30 m vertical and 335 m horizontal. The high quality of processed CALIPSO data products has been demonstrated through validation campaigns  (Winker et al.
OHP Ground-based lidar Between 1996 and 2007, the lidar recorded about 7000 h of measurements. To build the cirrus clouds database, as in  Hoareau et al. (2009)  we adjusted the integration time according to discontinuities in the optical thickness time series. An example is provided in Fig. 1. We followed an iterative method designed to find the multiple change points in value series  (Lanzante et al. , 1996). The first step of the sampling methodology is to identify the presence of clouds based in the optical thickness time series retrieved in the altitude range 6-15 km (which correspond to the extreme values of base  and top altitude of cirrus clouds) using the pre-accumulated lidar signals (over 2 min 40 s). We then temporally integrate the pre-accumulated lidar signals between discontinuities (points in the optical thickness time series where a statistical change is observed), over periods that represent quasistationary conditions.
Cirrus clouds climatology As noted in Sect. 3. 2 the ground-based lidar at OHP performed systematic measurements over 12 yr  (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007). Due to the irregularity of OHP measurements after 2007 due to frequent instrumental failures, their sampling might not be statistically representative of cirrus cloud properties and we will not use them. Based on the methodology used to construct the cirrus clouds database (see Sect. 3. 1), over the 1996-2007 period around 3300 profiles (with cirrus in ∼ 1850 cirrus profiles) have been obtained, with a total of ∼ 280 profiles per month over this period (Fig. 2 ). The total measurements time is of ∼ 7000 h including ∼ 2650 h where cirrus clouds have been observed. Fewer measurements were obtained in 1996 and 2007.
CALIOP has measured a mean of ∼ 270 profiles (with cirrus in ∼ 90 cirrus profiles) per month during nighttime from June 2006 through December 2012 (Fig. 3 ). That means around 3250 profiles per year, except in 2006. Although the data sampling is different for both instruments, it is interesting to compare the results obtained for both databases. Contrary to the adaptive sampling used for OHP data, CALIOP uses three finite integration periods that depend on the detection or not of a cloud (5 km, 20 km or 80 km, see Sect. 3. 2).
Cirrus clouds occurrence Over the OHP, cirrus clouds occur ∼ 37 % of the observation time. Subvisible cirrus (SVC, with optical thickness < 0. 03) are observed ∼ 38 % of this time, or ∼ 14 % of the total observation time. These results are different from those of  Goldfarb et al. (2001) , hereafter G01, who reported occurrences of ∼ 54 % for cirrus clouds and ∼ 25 % for SVC over the OHP for 1997-1999. These significant differences can be explained by definitions of cloud occurrences. G01 calculates cirrus occurrence from the number of nightly measurements with cirrus divided by the total number of nightly measurements. Here, cirrus occurrence is defined as the ratio of cirrus detection time on the total measurement time. A re-analysis of the G01 dataset for the 1997-1999 period using our methodology gives a cirrus occurrence frequency of ∼ 37 % over the OHP.
Characteristic Reference We find no clear relation between monthly averages of cirrus occurrence and mid-cloud temperature when all cirrus clouds are considered. Correlation coefficients between both are ∼ -0. 2 from OHP lidar data and ∼ -0. 5 from CALIOP data (Fig. 8 ). However, we find higher correlations when different classes are considered: -0. 72 (OHP) and -0. 93 (CALIOP) for class 1. Occurrence, for this class, increases by ∼ 5-10 % when the mid-cloud temperature decreases by ∼ 10 • C. The same trend is observed for class 2, even if the correlation between both parameters is weaker (r = -0. 52 and -0. 57, respectively for OHP and CALIOP data). Finally a good correlation was found between the occurrence of class 3 cirrus clouds and the mid-cloud temperature (r = 0. 83 and 0. 72, respectively for OHP and CALIOP data).
68 km, in good agreement with M11, SC01 and CALIOP data (10. 77 ± 0. 9 km, 10. 97 ± 1. 72 km and 10. 4 ± 1. 62 km, respectively) but smaller than the one reported by G07 (11. 7 ± 0. 9 km). Globally, cirrus clouds physical parameters over the OHP are similar to results obtained by M11, and most parameters agree well with other studies. To further analyse cirrus cloud properties derived from CALIOP over the OHP and those derived from the OHP lidar, we also compared both datasets  (1996-2007 and 2006-2012, respectively  for OHP lidar and CALIOP) during the coincident period  (June 2006 to June 2007). During this period, the difference observed between both datasets decrease. CTH and CBH observed from the OHP lidar are higher than those observed from CALIOP by ∼ 0. 1 km on average (∼ 0. 5 km for the extended period).
For CT, difference between both datasets is similar for the extended and coincident period and keeps inferior to 0. 1 km. As discussed previously (Sect. 4. 1), three distinct classes of cirrus clouds have been retrieved from the OHP lidar and CALIOP observations: a first class of thin cirrus (∼ 1 km) in the middle troposphere (mid-height of ∼ 8 km), a second one thicker (∼ 3 km) at ∼ 10 km and a last one with a thickness of ∼ 1 km at ∼ 11 km. Considering the different classes re- trieved during the extended period, monthly means of physical parameters for each class have been examined (Fig. 9 ). The variability of CT for each class is relatively constant across months, with a mean monthly variation of ∼ 0. 05 km around the mean CT for the class 1, of ∼ 0. 1 km for the class 3 and the largest, ∼ 0.
Dependence of cirrus cloud properties on temperature The dependence of cirrus clouds optical depth and geometrical thickness on mid-cloud temperature is presented in Fig. 10. Considering all cirrus clouds, clouds are thickest (∼ 2 km) near ∼ -42. 5 • C, and thinner at warmer and colder temperatures (Fig. 10 , bottom). This is true for OHP lidar and CALIOP data over the OHP. These results are in agreement with SC01 and G07, but disagree with M11, who found that clouds were thicker at warmer temperatures. The largest optical depths are observed at ∼ 42. 5 • C, and are smaller for warmer and colder temperatures. At colder temperatures (near -65 • C), optical depths are small compared to warm temperatures (near -30 • C) although cloud thickness for these temperatures is similar (∼ 1 km). These trends appear in CALIOP and OHP lidar data, however optical depths from CALIOP are generally larger than those from the OHP lidar.
Summary and discussion This study provides an analysis of cirrus cloud properties at midlatitude in the southern part of France from groundbased and spaceborne lidars. Based on the ground-based lidar database which spans more than 12 yr, a climatology of cirrus cloud properties and their evolution over time have been presented and compared to CALIOP over the OHP site and other mid-latitude climatological studies. Our results show that cirrus occur ∼ 37 % of the total observation time and subvisible cirrus represent ∼ 38 % of these observations, or ∼ 14 % of the total observation time. We have also shown overall cirrus occurrence remain quasi-constant across seasons. CALIOP and the ground-based lidar lead to similar results, with a mean difference in occurrence of ∼ 5 % between both instruments.
Acknowledgements. These systematic lidar observations are performed with the pluri-annual support of  CNES  and  INSU. Authors thank  CNES  ( Centre National d'Etudes Spatiales ) for the direct support of this research in the frame of the CALIOP mission through the funding support of  Christophe Hoareau. These  cloud monitoring activities are coordinated in France within the  SOERE ROSEA  project supported by the  Alliance Allenvi. CALIPSO data were produced by the  NASA Langley Research Center Atmospheric Data Center , and obtained through the  ICARE  thematic pole.
15, 21, 35 arvested cells were analyzed using a FACScan flow cytometer and data were processed using the CellQuestPro software (Becton-Dickinson, Le Pont de Claix, France). The percentage of eGFP-positive cells was determined using nontransfected cells as the baseline for autofluorescence. For luciferase, reporter expression was determined by luminescence using the ONE-Glo™ Luciferase Assay System Kit (Promega). Screening. The screening facility was composed of a TECAN GENESIS 200 workstation completed with a nine-stacker Carousel with barcod reader, a Te-shake 2-position plate shaker and a 6-position Dark Incubator (TECAN), a Kendro 42-position Cytomat 2C cell incubator (THERMO), a TECAN Power-Washer 384 with a 96-well wash head, and a FLUOstar Optima plate reader (BMG Lab Technologies, Ortenberg, Germany).
The platform is controlled with the TECAN Gemini 4. 00 and FACTS 4. 81 softwares. Luminescence was measured with the FLUOStar Optima Plate reader. After cell treatment, raw data were exported to an excel file and compared with bioactive and bioinactive controls. The statistical significance of each well in the 96-well plate assay was assessed by calculating the Z′ factor using DMSO as the bioinactive control and TSA as the bioactive one for anti-TPE activity. This factor takes into account the data variation associated with the signal measurement and reveals the overall quality of the assay. The Z′ factor was calculated using the following equation:  18  (1) C+ is the bioactive control (TSA), C-is the bioinactive control (DMSO); μ, the mean of fluorescence intensity and σ, the SD. A DMSO/TSA ratio of 2.
Acknowledgments. We acknowledge the facilities of IFR 128 (microscopy (PLATIM) and Flow Cytometry) and IRCAN (microscopy (PICMI) and cytometry (CYTOMED)). We also thank the  ChemAxon company  (http://www. chemaxon. com)which has allowed the academic TAMIS software team ( S. Wieczorek ,  S. Aci ,  C. Charavay  and  S. Roy ) and  C. Barette  to freely use the MarvinView package, a helpful  Graphic User Interface  for viewing structure files and query results. A. B was the recipient of a fellowship from  Association Française contre les Myopathies (AFM)  and  Fondation pour la Recherche Médicale (FRM). NB is the recipient of a fellowship from  Association Française contre les Myopathies (AFM). This work was supported by grants from  AFM  (to EG and FM), the  Ligue Nationale contre le Cancer  (to EG, équipe labelisée) and the  National Natural Science Foundation of China  (Grant Numbers:  81000875 ,  81270433 ) (to JY).
We further used information on metabolome and lipidome data from the Human Metabolome Database (HMDB) and LIPID MAPS Lipidomics Gateway, respectively. Model driven simulations, network-dependent analysis and conditionspecific transcriptome data allowed for model refinement. iAdipocytes1809 was used for the analysis of gene expression data obtained from subjects with different body mass indexes in the Swedish Obese Subjects (SOS) Sib Pair study and other adipose tissue relevant clinical data such as uptake/secretion rates in lean and obese subjects. Employing iAdipocytes1809 for integration of transcriptome and fluxome enables understanding of adipocyte metabolism in obese subjects compared with lean subjects. Furthermore, the results of the study lead to identification of molecular mechanisms underlying obesity and its adverse outcomes.
With the objective of gaining new insight into adipocyte metabolism at the genome level, we first used human antibodies to evaluate the presence/absence of 14 077 proteins in adipocytes found in breast and two different soft tissue samples and checked their presence call with previously published adipocyte proteome data. Second, we manually reconstructed a high-quality, simulation ready GEM for adipocytes by using all adipocyte-specific proteome data. The model is based on previously published GEMs but also on publicly available databases on metabolism. Third, we employed the functional GEM for the analysis of microarrays that profile the gene expression from subcutaneous adipose tissue (SAT) of subjects from the Swedish Obese Subjects (SOS) Sib Pair Study, which includes nuclear families with body mass index (BMI)-discordant sibling pairs (BMI differ-enceX10 kg/m 2 ).
Here, we expanded the coverage of the HPA so as to define the protein profiles of adipocytes found in breast and two different soft tissues and examined the spatial distribution and the relative abundance of proteins encoded by 14 077 genes in adipocytes (Supplementary Dataset 1a). Soft tissue cores with 1 mm diameter were sampled from different locations of the human body and included in tissue microarrays (TMAs) together with breast tissue core as previously described  (Kampf et al, 2012). A total of 17 296 high-throughput generated affinity-purified antibodies (Supplementary Dataset 1a) against 14 077 proteins were arranged according to abundance of the corresponding protein target. Immunohistochemically stained sections from TMAs blocks were scanned in high-resolution scanners and separated to individual spot images to represent each core.
Here, proteins with strong, moderate and weak relative expression were included in the reconstruction process of the GEM for adipocytes. It is important to point out that the absolute levels of each protein have not been determined and could vary by orders of magnitude. The high-resolution images together with annotation of the presence or absence of a particular protein target in adipocytes are publically available through the HPA (http://www. proteinatlas. org). The proteome data generated for adipocytes found in breast and two different soft tissues were merged with previously published proteome data as presented in Figure  2A  (Supplementary Dataset 1b). In total, we have proteome evidence for the presence/absence of proteins associated with 14 337 genes in adipocytes and together with the genes in our Human Metabolic Reaction (HMR) database (see later), we cover 98% of the enzymes (Supplementary Dataset 1c) reported in the HPA database (Figure  2B ).
Here, proteins were classified into eight different compartments following our HMR database standard  (Agren et al, 2012) : cytosol, nucleus, endoplasmic reticulum (ER), Golgi apparatus (GA), peroxisome, lysosome, mitochondria and extracellular space. Reactions were assigned to compartments through their association with proteins in these different compartments. We assigned a confidence score ranging from one to three for each protein due the availability of knowledge in HPA and Uniprot (Supplementary Dataset 2a). Proteins localized in the aggresome, centrosome and cytoskeletons were assigned to the cytoplasm, whereas proteins in cell junctions and focal adhesions were assigned to the extracellular compartment (Supplementary Dataset 2b). Lysosome and peroxisome were merged into vesicles in the HPA data, but proteins associated with vesicles in our model were separated based on Uniprot data.
Transcriptome data for SAT and its correlation with proteome data Gene expression in SATof 304 subjects involved in the SOS Sib Pair Study was analyzed by using Affymetrix U133 Plus 2. 0 microarrays (Affymetrix, Santa Clara, CA, USA). The participants of the study, 209 female and 95 male subjects, were divided into three different groups according to their BMI: lean (18. 5oBMI o25), overweight (25p BMIo30) and obese (30pBMI) (Supplementary Table  S1 ). Our study considers the differences in male and female obese subjects compared with lean male and female subjects independently. Differentially expressed genes between obese, overweight and lean subjects for male and female subjects were identified and differentially expressed genes on male and female obese subjects were incorporated during the reconstruction process of GEM for adipocytes to analyze gene expression data from the SOS Sib Pair Study.
This was done as the transcriptome data for SAT represent not only adipocytes but also other cell types; including immune cells and preadipocytes linked with different BMIs. The analysis demonstrates that some of the metabolic and signaling pathways found to be enriched in adipocytes based on the proteome data also show significant changes in gene expression between lean and obese subjects, both in males and in females. Similarly, we find that BP:GO terms that are enriched based on the proteome data (assessed with DAVID;  Huang et al, 2009 ) also show enrichment based on the transcriptome data (Supplementary Dataset 3). Thus, enriched BP:GO terms such as post-translational protein modification, cellular protein metabolic process, lipid metabolic process, cellular lipid metabolic process and FA metabolic process are found both from the adipocyte-specific proteome data and from comparison of expression data for lean and obese subjects.
Reconstruction of the adipocytes GEM iAdipocytes1809 GEMs are reconstructed based on high-throughput data such as genome, transcriptome, proteome, metabolome and Figure  2  Adipocytes proteome data and its correlation with transcriptome data. (A) Proteins encoded by 14 077 genes were annotated for their presence or absence in adipocytes found in breast and two different soft tissues sampled from different locations within the body using antibodies generated within the Human Protein Atlas (HPA) project. The proteome evidence for adipocytes in each tissue sample and previously published adipocytes proteome data were merged and there are proteome evidence for the presence/absence of proteins associated with 14 337 genes in adipocytes. (B) The here generated proteome data combined with published adipocytes specific proteome data encodes for a total of 7340 genes that were used to reconstruct a comprehensive genome-scale metabolic model (GEM) for adipocytes, iAdipocytes1809.
The Venn diagram shows how these genes overlap with genes in an updated version of the Human Metabolic Reaction (HMR) database and enzymes in the HPA. ( þ ) indicates the presence of protein encoded genes whereas ( À ) means the absence of protein encoded genes in adipocytes based on the here generated HPA data. (C) Enrichment of KEGG pathways is presented (P-value from hypergeometric distribution is used) to check the coverage of here generated adipocyte-specific proteome data found in breast, soft tissue 1 (Soft-1), soft tissue 2 (Soft-2) and all adipocyte-specific proteome data. The enrichment of differentially expressed genes in subcutaneous adipose tissue (SAT) of male and female obese subjects compared with lean subjects was identified in the enriched KEGG pathways in all adipocyte-specific proteome data.
First, metabolism of lipids and lipoproteins in Reactome, a manually curated and peer-reviewed pathway database  (Croft et al, 2011) , was merged into HepatoNet1. Second, the resulting network was combined with the evidence-based generic human models Recon1  (Duarte et al, 2007)  and the compartmentalized EHMN  (Hao et al, 2010). This combined reaction list resulted in an updated version of our HMR database that is available at http://www. metabolicatlas. org. The HMR database contains 6000 metabolites in 8 different compartments (3160 unique metabolites), 8100 reactions and 3668 genes associated with those reactions. HMR includes all of the genes and gene-associated reactions in HepatoNet1, Recon1 and EHMN and other isolated gene-associated reactions, and represents the most comprehensive human reaction database for genome-scale modeling (Figure  3B ).
Third, the existence of each protein coding genes associated with a reaction in HMR was assessed for the presence or absence in adipocytes using previously published and the here generated adipocyte-specific proteome data. Differentially expressed genes and associated reactions in obese subjects compared with lean subjects were also included in the model by checking their functionality. This process provided us with a list of reactions that occur in adipocytes. Gaps in the resulting network were filled using our recently developed iHuman1512 metabolic network  (Agren et al, 2012) , public databases such as KEGG  (Kanehisa et al, 2010)  and HumanCyc  (Romero et al, 2005)  and manual evaluation of the literature about adipocyte metabolism. This gap filling resulted in generation of iAdipocytes1809 that is a functional and fully connected GEM for adipocytes (Figure  3A ).