dataesr/scientific-paragraphs-categorization huggingface dataset:
2. 2. The underlying selection of the horizontal and vertical positioning of the monitoring sites in the CHIMERE grid is the same as in  Bréon et al. (2015). Because the gradient selection depends on modelled wind speed and direction, the observation space y and thus y f and H samp depend on the meteorological simulations (ECMWF or Meso-NH/TEB). We   Latoska (2009)  disaggregated reported emission totals for France for 2005 into a 1 × 1 arcmin grid with the use of various proxies for the distribution of emitting activities such as population census, traffic intensity and land cover. We used monthly, weekly and hourly temporal profiles for different emission sectors from the IER inventory for Europe as described by  Vogel et al. (2010)  to disaggregate annual emissions to hourly emissions. The IER and AIRPARIF emission inventories are two largely independent datasets.
3 MtCO 2 month -1 ). A prior estimate based on the budgets from the IER inventory is also used for the sensitivity tests. 1  summarises the acronyms and settings of the different sensitivity tests. et al. (2015)  analysed the skill of the inversion by comparing the fit between measured and modelled CO 2 gradients, which is a first indicator of the reliability of the inverted emissions. In Table  2 , statistical comparisons between the selected measured gradients and results from the initial and reference inversion, respectively, are provided. It demonstrates that both inversions strongly increase the consistency between model and measurements compared to the prior simulations (Table  2 ). Of note is that the statistics of both the prior and posterior model-data misfits are smaller for the initial inversion than for the reference inversion.
3d with b ). Although significant differences in December and April emissions are still apparent between using NE gradients or using SW gradients, and in January and February emissions between using GON-GIF or MON-GIF, these differences are smaller than in the initial inversion. Now, even when assimilating only NE gradients, the four months with the largest inverted emissions correspond to the four coldest ones with the highest heating degrees of the year (November to February), though the assimilation of NE gradients still leads to smaller emissions in December than in November, January and February. One may argue that the improvements of the reference over the initial inversion reflect the assimilation of a smaller dataset, and therefore are due to smaller corrections. However, results from the reference and initial inversion are closer to each other if only SW gradients are assimilated than if only NE gradients are assimilated.
Sensitivity of the Paris emission budgets to mapping and variations at hourly scale   1 ). Thus, this experiment also includes results when using the IER inventory to build both the 6 h budgets in x b and H map = H map IER  (INV_IER). It provides estimates when the inversion relies entirely on the IER inventory to define these parameters and ignores the existence of the AIRPARIF inventory. This situation is similar to that in cities, where no local inventory is available. We have less confidence in the posterior estimates from such an inversion, since the IER in-ventory does not rely on the same amount of local data as the AIRPARIF inventory. INV_mapIER regularly predicts lower monthly budgets than the reference inversion, except in June, July, September and November. The corresponding differences are relatively small and do not exceed 0.
At the annual scale, estimates differ by 8. 2 MtCO 2 yr -1 (Table  1 ). The differences between the two inventories are due to both the differences in the emission model and the driving activity data used. The two inventories correspond to two different years  (2005 vs. 2008 ). However, this hardly explains the amplitude of the difference between the two inventories by itself. The decrease in the total emission in France between 2005 and 2008 was approximately 5 % (see, e. g. CITEPA, 2015). Here, the difference in total emissions in Île-de-France between the IER and AIRPARIF 2008 inventory, however, is about 14 %. Results of the inversion using IER for both the prior emission budgets and the emissions' spatial distribution (INV_IER) reflect these large prior discrepancies. Indeed, monthly and annual budgets of Paris' fossil fuel CO 2 emissions estimated by INV_IER are larger than that from the reference inversion and from INV_mapIER (Fig.
In contrast, the annual emission budget is increased when a flat diurnal cycle and a prior estimate of the annual emission budget that is smaller than that from AIRPARIF 2010 (i. e. of 36 MtCO 2 yr -1 ) is used. This can reveal an error in the mean diurnal cycle of the emissions from AIRPARIF 2008, which the inversion could not correct for, since data are only assimilated during the afternoon. Moreover, we define the uncertainties in the prior emission estimates in terms of relative rather than absolute uncertainty. Consequently, using the diurnal cycle of the emissions from AIRPARIF 2008 in the prior estimate of the 6 h mean emissions and higher (smaller) prior emissions at monthly to annual scale leads to higher (smaller) prior uncertainties, and thus to a stronger (weaker) constraint from the atmospheric measurements, resulting in a stronger (weaker) decrease in the emissions.
However, many cities do not have such local inventories. Bréon et al. (2015)  have shown that the selection of afternoon data provides little constraint on night-time emissions. This is problematic since the diurnal cycle is highly uncertain in inventories. The differences between the results from FLAT_mD and FLAT_mH indicate that the poor representation of the diurnal cycle in FLAT_mH has a large impact on the inverted monthly emissions. As highlighted above, the inversions, based on the diurnal cycle from AIRPARIF 2008, generally tend to decrease the prior estimates which can also be viewed as an impact from errors in this diurnal cycle. New approaches and techniques are needed to provide a direct constraint on the night-time emissions or to better extrapolate the information from daytime to night-time data to solve for this problem.
The month-to-month variability is thus often driven by the variability of the data availability. Results at the monthly scale are thus not systematically consistent with the different sensitivity tests. Monthly estimates can be weakened by missing or over-weighting high variations in the emissions over short time periods (e. g. due to a cold event). One can hope that this limitation could be overcome by an expansion of the observation network with stations all around the Paris urban area, which could ensure a continuous monitoring of the cross-city CO 2 gradients. In December, the number of assimilated data are relatively high for both the reference inversion and INV_MNH. However, while the inversions increase the emissions compared to AIRPARIF 2008 during December when using ECMWF data and all gradients (SW and NE gradients), this is not the case when assimilating subsets of the cross-city gradients only, or when using Meso-NH/TB.
For the same reason, it would be inappropriate with our limited network to narrow the wind direction ranges to select gradients to less than 15 • of the transect between the downwind and upwind sites, even though, in principle, it would strengthen the decrease of the signature from fluxes outside the urban area. The expansion of the network, in particular a full encirclement of the city with at least 8 sites (given that the wind ranges for the selection of gradients between one upwind site and one downwind site cover 30 • in this study) should strengthen the results and could allow for application of such new techniques that result in a stricter gradient selection. However, relying on such a measurement expansion may not be sufficient. Exploiting more information from the available dataset without violating or undermining our assumptions on the selection of cross-city gradients is a requirement to strengthen the observational constraint of the inversion.
The Paris observation network has been set back since September 2014 in the framework of the Carbocount City and Le CO 2 Parisien projects. Both projects aim to deploy more measurement sites than the CO 2 -MEGAPARIS project. However, relying on such a measurement network expansion may not be sufficient. New methods should be developed to exploit urban measurements  (Wu et al. , 2016)  which would allow to solve for the spatial distribution of the emissions, which does not seem possible with the current monitoring network of peri-urban sites. This in turn could help in assimilating data that do not necessarily bear the signature of the emissions from a large part of the city. Finally, developing methods to exploit morning, evening and night-time data would be necessary to constrain night-time fluxes.
Acknowledgements. This study was conducted within the European Carbocount  City  project funded by  European Institute of Technology's Climate KIC program. Grégoire Broquet 's research was funded and supported by the  Chaire industrielle BridGES , a joint research program between  Thalès Alenia Space, Veolia  and the parent institutions of LSCE (CEA, CNRS, UVSQ). Thanks are also due to  Balendra Thiruchittampalan  and  Felix R. Vogel  for providing the IER inventory. We also thank the latter for providing the temporal profiles to disaggregate this inventory's annual emissions to hourly emissions. Edited by: E. Harris Reviewed by:  J. Turnbull  and one anonymous referee.
The temporal profiles and spatial distributions of this inventory are analysed same day in 2008. For sensitivity tests (see Sect. 3. 3. 2), the emission com- ponent of H map is alternatively built based on a national emission inventory for 2005 compiled by the Institut für En- ergiewirtschaft und Rationelle Energieanwendung (IER) of the University of Stuttgart, Germany. samp ini-ECM , H samp ref-ECM , H samp lag-ECM or H samp ref-MNH depending on the inversion cases. 2. 3. 2 Emissions outside Île-de-France and model boundary conditions y f encompasses the signature of fossil fuel CO 2 emissions outside the Paris region but within the modelling domain, Bréon et al. (2015)ng domain's CO 2 boundary conditions and that of the 30-day simulations initial conditions. The signature of the emissions outside the Paris region but within the modelling domain are simulated by CHIMERE using fossil fuel CO 2 emissions from the EDGAR database (Janssens-inBréon et al.
Wind data The data under study are zonal (west-east) and meridional (north-south) surface wind components {u t , v t } at 10 m a. s. l. (above sea level) extracted from the ERA-Interim data set produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). It can be freely downloaded from the url http://apps. ecmwf. int/datasets/data/ interim-full-daily/ and used for scientific purposes. We focus on gridded locations between latitudes 46. 5 and 48 • N and longitudes 6. 75 and 10. 5 • W (15×7 grid points; see Fig. 1 ). The data set we have extracted consists of 32 December-January blocks of wind data from December 1979 to January 2011 picked every 6 h. Furthermore, the statistical inference is based on the assumption that the 32 December-January blocks of wind components are 32 independent realizations of the same stationary process, a reasonable assumption given the strong interannual variability of the wintertime atmospheric dynamics at such a local scale.
To derive these regimes, we use the same methodology as in  Cattiaux et al. (2013). We perform a k-means clustering on the 3607 daily mean maps of 500 mb geopotential height (Z500) anomalies (i. e. , mean-corrected fields) over the North Atlantic/European sector (90 • W-30 • E, 20-80 • N) corresponding to days of December, January, and February 1981-2010. Daily Z500 data are downloaded from the ERA-Interim archive. In order to reduce the computational time, the k-means algorithm is performed on the first 10 principal components (PCs) of the Z500 anomalies time series. These PCs are time series corresponding to the projections of the Z500 anomalies onto the empirical orthogonal functions (EOFs), which are eigenvectors of the spatial covariance matrix of the Z500 field. Such a decomposition enables extraction of the main modes of variability of the spatiotemporal process; here, the first 10 EOFs explain 90 % of the total variance.
2b ). Furthermore, gene ontology (GO) analysis revealed the majority of proteins to be localized in mitochondria and associated with mitochondrial function (see Fig. 2c ). Subsequent pathway analysis in the KEGG database  21  revealed a significant enrichment in oxidative phosphorylation and other mitochondrial metabolic pathways (see Fig. 2d ). We performed a gene-set enrichment analysis using publicly available datasets to explore the physiological relevance of this set of proteins. Thereby we found a significant association with genes negatively regulated by PGC-1α knock out and positively regulated by PPARγ agonist treatment in rodent studies (see Fig. 2e ), confirming the functional importance of this protein cluster for brown adipose tissue biology. Collectively, our protein abundance profile demonstrates that next to a general difference in mitochondrial content, a specific set of proteins is differentially regulated in white and brown adipose tissue.
4c ), suggesting that the coupled respiratory machinery can influence the rate of uncoupling. In conclusion, we observed that both the coupled and   Each timepoint represents a four minute measurement cycle, at the dotted lines the annotated compounds were injected. Data are presented as the mean +/-SEM (n = 6). Oligomycin was used for ATP synthase inhibition, bongkrekic acid for adenine nucleotide transporter inhibition, both were used to assess coupled respiration. cAMP was used to activate the brown adipocytes. Rotenone and antimycin A block OXPHOS complex I and III respectively and were used to assess non-mitochondrial respiration. Coupled respiration is mitochondrial respiration affected by oligomycin or bongkrekic acid; uncoupled respiration is mitochondrial respiration not affected by either inhibitor.
Sample preparation for MS analysis. Samples were prepared with the TMT10plex Isobaric Mass Tag Labeling Kit (Thermo Scientific, Lot number QD212963) and the Pierce High pH Reversed-Phase Peptide Fractionation Kit (Thermo Scientific) according to the manufacturer's instructions. For details see Supplementary experimental procedures. The labeling efficiency of this method was tested with the TMTzero label reagent (Thermo Scientific) followed by mass spectrometric analysis and determined to be higher than 98% (data not shown). Mass Spectrometric data acquisition. Data acquisition was essentially performed as described  39  , for details see Supplementary experimental procedures. Mass spectrometric data analysis. The acquired raw data was analyzed with Proteome Discoverer 2. 1 (Thermo Scientific), for complete settings see Supplementary experimental procedures  41.
Acknowledgements We thank  Dr. Corina Schmid  for excellent assistance with the figure preparation and  Dr. Christoph Müller  for assistance editing the manuscript. The ETH  Zurich Research Grant  ( ETH-45 12-2 ), the  D-HEST BioMedical Proteomics Platform (BMPP) , the scientific grant agency of the  Slovak Academy of Sciences  (VEGA # 2/0180/15 ) as well as the SNF contributed funding to this work.
With this kinetic model in mind, the barriers of the ratelimiting process were evaluated, i. e. E lim (see Table  2 ), which is an estimator of the efficiency of the temperature-dependent quenching of photoluminescence. To obtain the values of E a , E b and E c in Scheme 1, the geometries of the 3 MLCT and 3 MC states, of the TS and of the 1 GS/ 3 MC MECP stationary points of 1-6 were optimized using the B3LYP functional (see the Computational details in the ESI †). Next, their relative energies were evaluated. Key data are collected in Table  2. The energy proles of selected complexes along the deactivation coordinate are shown in Fig. 1. In this series, 1 and 2 follow the E a [ E c kinetic scenario and they possess the largest E lim values (ca. 0. 3 eV, see for example the energetic prole of 1 in Fig.
Acknowledgements I acknowledge the  European Research Council (ERC)  and the  Région des Pays de La Loire  for my post-doctoral grant (Marches -278845). Research support by the  COST Actions CM1305 ECOSTBio (Explicit Control Over Spin-States in Technology and Biochemistry)  and  CM1202 PERSPECT-H 2  O (Supramolecular Chart 2 Chemical structure of the validator complexes 7 and 8.
Ten micrometre sections were cut on a cryostat and deposited on Superfrost slides for observation. RT-qPCR analysis. One microgram of total mRNAs (from tissues or cell culture) was reverse transcribed for 1 h at 37 °C with 5 pmol of random hexamers primers, 200 units reverse transcriptase (M-MLV RT, M1701, Promega), 2 mM dNTPs and 20 units RNAsin (N2615, Promega). One microlitre of a one-tenth dilution of cDNA was used in each qPCR. Except for VegfA and Connexina43 that were amplified with Taqman chemistry as described in ref. 41, all other reactions were conducted with SYBR qPCR Premix Ex Taq II Tli RNase H þ (TAKRR820W, Takara). Primer pairs are listed in Supplementary Data 3. For each experiment and primer pairs, efficiency of PCR reactions was evaluated by amplification of serial dilutions of a mix of cDNAs.
The signals were quantified with a DNR MF ChemiBis 3. 2 camera system and Multi Gauge software suite (Fujifilm). Expression of the phospho-proteins was normalized to expression of the corresponding total protein. Full western-blot pictures are provided as Supplementary Fig. 9. Microarray analysis of gene expression. Adrenal gene expression profiles for four 6-week-old Sf1:Cre;Prkar1aFl/Fl and four wild-type littermates were analysed using Affymetrix Mouse Gene 2. 0 ST Arrays (Raw and processed data are deposited on NCBI GEO platform). Gene expression was normalized by RMA (Affy R package) and genotype comparisons were performed with t-test. All the P values were adjusted by the Benjamini-Hochberg correction method. Volcano plot was generated with R and displays À Log10(adjusted P value) as a function of Log2(fold change in knockout versus WT).
Genes with adjusted P value o0. 05 and Log 2 fold changes o À 1 (blue dots) or 41 (red dots) were considered significantly down and upregulated, respectively. The WNT pathway targets and regulators list was curated from WNT Homepage (http://web. stanford. edu/group/ nusselab/cgi-bin/wnt/) and Pubmed searches. They are appended to this manuscript as Supplementary Data 1. Heatmaps were generated with R and represent colour-coded individual median centred gene expression levels (in the Log2 space) in four WT and four knockout adrenals. Genes were ordered according to the mean fold change (Log2) in knockout versus WT. Only genes with adjusted P values o0. 05 are shown in these representations. Patients' data. Gene expression and clinical data from adrenocortical carcinoma patients were extracted from mRNA sequencing data available from Broad Institute GDAC Firehose (TCGA data version 2015_03_26, http://gdac.
broadinstitute. org). These include 79 adrenal cortex carcinomas. All the calculations were performed on Log2 values of RSEM normalized read counts. Mutational status was retrieved from cBioPortal (http://cbioportal. org). Heatmaps were generated with R. WNT gene signature was determined as the geometric mean of the Log2 values of RSEM normalized read counts for WNT target genes LEF1, AXIN2 and APCDD1. Statistical analyses. Statistical analyses of biological data were performed with R and GraphPad Prism 5.
Acknowledgements We thank  Khirredine Ouchen ,  Sandrine Plantade  and  Philippe Mazuel  for animal care,  Jean-Paul Saru  for western blot analyses,  Christelle Damon-Soubeyrand  for management of histological analyses facilities (Anipath Clermont),  Corinne Belville  for RNA quality analyses, Sebastien Jacques and  Florent Dumont  ( Plateforme Genomic ,  Inserm   U1016-CNRS UMR8104 -UniversiteParis Descartes)  for expert contributions,  Clara Panzolini  for Lac Z staining and  Michelina Plateroti  ( CGPMC, Lyon ) for helpful discussions. This work was funded through institutional support from  Centre National de la Recherche Scientifique, Institut National de la Santeet de la Recherche Medicale, Universite Blaise Pascal, Universited'Auvergne  and through grants from  Fondation de France ,  La Ligue Contre le Cancer  ( Allier & Puy de Do  ˆme committees),  Fondation   ARC, Societe Franc¸aise d'Endocrinologie (young researcher grant  to C.
D. ) and  Agence Nationale de la Recherche  ( ANR-14-CE12-0007-01-DevMiCar ). Generation of Wnt4 floxed mice by S. V. and J. S. was supported by grants from the  Academy of Finland  ( 206038 ,  121647 ) the  Centre of Excellence Grant   2012-2017  of the  Academy of Finland  ( 251314 ), the  Sigrid Juselius ,  Finnish Cancer Research , the  European Community 's  Seventh Framework Programme  [ FP7/2007-2013]  under grant agreement  FP7-HEALTH-F5-2012-INNOVATION-1 EURenOmics 305608. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Moreover, public array data websites are currently available, such as Genevestigator (https://genevestigator. com) and Bio-Array Resource (http://bar. utoronto. ca/), which provide array experiments data from many plant species in ready and easy to use formats. Steps and parameters of some types of microarrays used in gene expression profiling. Probes are selected for regular genome coverage or coding sequence studies (top left). They are synthesized directly on quartz slides or spotted on glass chips. Next, RNA is extracted from the samples of interest (top right), labeled with one or two dyes (depending on the microarrays type and labeling method), and hybridized to the probes (e. g. , two colors for traditional microarrays or one color for Affymetrix or Illumina or other types). Samples are hybridized to separate chips if they are labeled with a single dye.
Despite the central role that antibodies play in the adaptive immune system and in biotechnology, much remains unknown about the quantitative relationship between an antibody's amino acid sequence and its antigen binding affinity. Here we describe a new experimental approach, called Tite-Seq, that is capable of measuring binding titration curves and corresponding affinities for thousands of variant antibodies in parallel. The measurement of titration curves eliminates the confounding effects of antibody expression and stability that arise in standard deep mutational scanning assays. We demonstrate Tite-Seq on the CDR1H and CDR3H regions of a well-studied scFv antibody. Our data shed light on the structural basis for antigen binding affinity and suggests a role for secondary CDR loops in establishing antibody stability.
This enables KD measurements for thousands of variant antibodies in parallel. We note that the Tite-Seq results illustrated in panel E were simulated using three bins under idealized experimental conditions, as described in Appendix A. The inference of binding curves from real Tite-Seq data is more involved than this panel might suggest, due to the multiple sources of experimental noise that must be accounted for. Yeast display construct and antibody libraries. (A) Co-crystal structure of the 4-4-20 (WT) antibody from  [39]  (PDB code 1FLR). The CDR1H and CDR3H regions are colored blue and red, respectively. (B) The yeast display scFv construct from  [7]  that was used in this study. Antibody-bound antigen (fluorescein) was visualized using PE dye. The amount of surface-expressed protein was separately visualized using BV dye.