dataesr/scientific-paragraphs-categorization huggingface dataset:
Ozone profile trends over the period 2000 to 2016 from several merged satellite ozone data sets and from ground-based data measured by four techniques at stations of the Network for the Detection of Atmospheric Composition Change indicate significant ozone increases in the upper stratosphere, between 35 and 48 km altitude (5 and 1 hPa). Near 2 hPa (42 km), ozone has been increasing by about 1. 5 % per decade in the tropics (20 • S to 20 • N), and by 2 to 2. 5 % per decade in the 35 to 60 • latitude bands of both hemispheres. At levels below 35 km (5 hPa), 2000 to 2016 ozone trends are smaller and not statistically significant. The observed trend profiles are consistent with expectations from chemistry climate model simulations. This study confirms positive trends of upper stratospheric ozone already reported, e.
, 2016) , termed SBUV-NOAA in the following. The two data sets rely on the same SBUV instruments, but differ in the approach taken for merging their individual records (see also  Frith et al. , 2017). Ozone profiles with higher vertical resolution (about 2 km), but also with sparser coverage, were provided by the satellite-borne Stratospheric Aerosol and Gas Experiments (SAGE I and SAGE II) and the Halogen Occultation Experiment (HALOE). These instruments measured in solar occultation geometry from 1979 to about 1982 (SAGE I), from late 1984 to 2005 (SAGE II), and from 1991 to 2005 (HALOE); see, e. g. ,  Damadeo et al. (2013 Damadeo et al. ( , 2014) )  and  Remsberg (2008). Since 2002, the Optical Spectrograph and InfraRed Imaging System (OSIRIS) measures ozone profiles from ultraviolet light scattered in limb geometry  (McLinden et al.
, 2015, newer version 2. 20 used here)  and in the Stratospheric Water and Ozone Satellite Homogenized data set (SWOOSH,  Davis et al. , 2016). GOZCARDS (v2. 20) and SWOOSH (v2. 6) are very similar (compare Fig. 1 ). Both rely to a large degree on the ozone records from SAGE  II (1984 II ( to 2005, version 7, version 7 ) and Aura-MLS (2004 to present, version 4. 2). Both adjust ozone values from other satellites to those from SAGE II. GOZCARDS additionally uses SAGE I (version 5. 9_rev) to extend the ozone record back to 1979. For the period from August 2002 to April 2012, ozone profiles were also measured by the SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHar-tographY), GOMOS (Global Ozone Monitoring by Occultation of Stars) and MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) instruments on board the European ENVISAT satellite.
(2013). Note that in  Hubert et al. (2016) , the OSIRIS V5. 07 ozone data did exhibit a significant drift, up to 8 % per decade near 40 km, which is also apparent in  Harris et al. (2015). This drift has been corrected in the revised and updated OSIRIS V5. 10 data set used here  (Bourassa et al. , 2017). Drifts of most SBUV instruments are less than 3 to 5 % per decade, and are not statistically significant  (Kramarova et al. , 2013). Similarly,  Rahpoe et al. (2015)  report that drifts of several limb-viewing instruments including ACE-FTS, MIPAS, and OSIRIS are typically less than 3 % per decade (even for older processing versions) and are not statistically significant. For the current study, newer data sets with reduced drifts were available (especially OSIRIS), and older data sets with apparent large drifts were not used (SAGE-GOMOS).
In the simulations, the ozone increase is enhanced by overall cooling of the stratosphere due to increasing greenhouse gases over the entire 1978 to 2016 period (see also  Jonsson et al. , 2004; Randel et al. , 2016). All observational data sets show similar fluctuations from year to year, usually within 1 or 2 % of each other. They also indicate similar long-term tendencies, usually within ±2 % per decade of each other, and are comparable to the CCMVal-2 simulations. Generally, the station data show larger variations than the zonal means from the satellite data. This is not surprising, given the sparser temporal sampling of most ground-based data (lidar, Umkehr, and FTIR all require clear sky). Also, the low density of stations will generally result in more variability compared to the smoother wide-band zonal means from the satellite records.
The magnitude of all trends is represented by the colour scale. Grey shading indicates regions where trends are not statistically significant (95 % confidence level, |TR| ≤ 2σ ). All satellite data sets show significant ozone increases in the extratropical upper stratosphere, above 10 to 5 hPa (30 to 35 km). Some show significant ozone increases also in the tropical upper stratosphere. At levels between 50 and 10 hPa (20 and 30 km), trends are generally not significant, except for islands of significant trends near 20 or 50 hPa in some data sets, mostly in the Southern Hemisphere. Most data sets (but not SAGE-MIPAS-OMPS) also show significant ozone decline in the tropical lowermost stratosphere below 100 hPa (16 km). However, satellite measurements in this region can have large uncertainties and need very careful consideration, both in tropics and extratropics (see also  Tegtmeier et al.
Comparable patterns, but slightly smaller increases are also reported in Fig. 5  of  Harris et al. (2015)  for the 1998 to 2012 period (but only between 60 • S and 60 • N). Compared to that figure, the SAGE-OSIRIS trends here have changed considerably because of improved data (see also  Bourassa et al. , 2017). The SWOOSH trends have increased in magnitude. The SAGE-GOMOS data, which had shown large, and probably unrealistic ozone decline polewards of 40 • latitude in Fig. 5  of  Harris et al. (2015)  are not used here. A specific look at zonal mean trends from all satellite and ground-based data sets is given in Fig. 4. The basis for these trend calculations is zonal band anomaly time series as in Fig. 1. In Fig. 4 , almost all individual data sets show increasing ozone between 5 and 1 hPa, with trends between 0 and 4 % per decade.
(2015)  compared to  WMO (2014). One problem with the standard weighted mean is that its uncertainty does not depend on the spread of the individual trends (because of Gaussian error propagation). Therefore,  Harris et al. (2015)  also considered the joint distribution approach (J ). There, the uncertainty of the mean trend is essentially given by the SD σ between the individual trends (which includes possible instrument drifts), divided by √ n, where n is the number of data sets. Strictly, n should be the number of statistically independent data sets. However, since most merged data sets use the same SBUV, SAGE, MLS, or OMPS instruments, these data sets are not independent. Also, trend calculation by multiple linear regression uses the same approach and the same proxies for all data sets, which may further reduce independence between the individual trend estimates.
Simulations show that this process will take many more decades. In order to verify that ozone recovery continues as expected, reliable longterm observations from multiple independent platforms will remain crucial for many years to come. Data availability. The satellite ozone records used in this study are available from the sources given in Table  1. The ground-based data are available from the NDACC data base at www. ndacc. org; see also Table  2. The proxy time series used for trend analysis are available from the sources given in Table  4.
Acknowledgements. The authors gratefully acknowledge the extremely important contribution from staff at the stations, who run and fix the ground-based systems, and from the many people involved in the satellite measurements. Funding by national and supranational agencies is also gratefully acknowledged. Some of the data sets were calculated with resources provided by the  North-German Supercomputing Alliance (HLRN). The merged SAGE-ESA CCI-OMPS data set has been created in the framework of the  ESA Ozone_cci  project. OHP NDACC lidar measurements are funded by  CNRS  and  CNES. Work performed at the  Jet Propulsion Laboratory, California Institute of Technology , was done under contract with the  National Aeronautics and Space Administration. NOAA supports and funds a major part of the Dobson Umkehr measurements, in collaboration with funding and work done by Meteoswiss, Switzerland;  NIWA, New Zealand ; the  Australian Bureau of Meteorology ; CNRS, France; and the  University of Fairbanks, Alaska.
Distinct gene signatures in microglia subsets during development and EAE We have previously shown that numbers and proportions of CD11c + microglia dramatically increase during EAE, and the data suggested that they might play a protective role during the disease  (Wlodarczyk et al, 2014 (Wlodarczyk et al, , 2015)). To assess whether microglial subsets from developing CNS and mice with symptomatic EAE are similar, we compared transcriptomes of sorted CD11c + and CD11c À microglia from PN4-6 CNS and symptomatic EAE (grades 3-5) as well as of total naı ¨ve adult microglia (Dataset EV1). Microglial markers (Aif1, Itgam, Cx3cr1, Csf1r) and signature genes  (Butovsky et al, 2014; Bennett et al, 2016 ) (Spi1, Irf8, Olfml3, Hexb, Fcrls, Tgfbr1, P2ry12, Siglech, Tmem119) were similarly expressed in both CD11c + and CD11c À neonatal and EAE microglia populations as in adult microglia (not shown).
CD11c + microglia from neonates and mice with EAE strongly upregulated Itgax expression, confirming high purity of sorted cells and validating the RNA-seq assay (not shown). Moreover, we identified 20 genes (Itgax, Gpnmb, Spp1, Igf1, Colec12, Ccl5, Ak4, Lox, Mmp12, Cpeb1, Ntn1, Clec7a, Saa3, Ahnak2, Fabp5, Hpse, Gm26902, Cspg4, Fam20c, and Stra6 l) that were associated with the CD11c + microglia population. In order to identify gene expression profiles associated with microglia from the different conditions, weighted gene co-expression network analysis (WGCNA) was applied to the RNA-seq data. In WGCNA, genes are clustered according to co-expression and a network with seven co-expression modules was identified  (Fig 3B). From each module, the Module Eigengene (ME) was calculated, which is the first principal component and functions as a representative of the module.
Clusters of repopulating microglia in adulthood contain CD11c + microglia In contrast to developing CNS, where depletion of microglia has never exceeded 50%  (Ueno et al, 2013) , near-total ablation has been shown for adult mice  (Bruttger et al, 2015). Recently, some of us demonstrated that 7 days after microglial ablation from adult CNS, using a CX3CR1 CreER iDTR model, micro-clusters containing highly proliferating microglia were present throughout the CNS  (Bruttger et al, 2015). We further analyzed these repopulating cells by RNAseq and showed overexpression of Itgax (CD11c) and other genes Interestingly, virtually all microglia in the clusters were nestin-positive (not shown). Next, we determined the overlap between the WGCNA modules and up-and downregulated genes of the repopulating microglia.
Cell populations were gated based on isotype-matched control antibodies as CD45 dim CD11b + CD11c À (CD11c À microglia), CD45 dim CD11b + CD11c + (CD11c + microglia) and sorted on a FACSAria TM III cell sorter (BD Biosciences) or data were collected on an LSRII TM flow cytometer (BD Biosciences) and analyzed using FACSDiva TM software version 6. 1. 2 (BD Biosciences). For the analysis of nestin expression after extracellular staining (as above), cells were fixed and permeabilized using BD Cytofix/Cytoperm TM according to the manufacturer's protocol and stained with anti-nestin and corresponding isotype control (Clone 307501; R&D Systems).
RNA sequencing RNA quality check was done using an Agilent bioanalyzer, and only high-quality samples were used (RIN > 7) for sequencing. Sequence libraries were prepared using the Illumina Truseq RNA sample preparation kit. RNA sequencing was performed at the Genome Analysis Facility of the University Medical Center Groningen with an Illumina Hiseq 2500. Single-read 50-bp sequencing was done, aligned to the ensemble reference genome using the Star 2. 3. 1 l aligner  (Dobin et al, 2013) , allowing for two mismatches. Samtools 0. 1. 19 was used to sort the aligned reads  (Roussos et al, 2012). Quantification was done using HT-seq count 0. 5. 4  (Anders et al, 2015). Between 6 and 11 million uniquely aligned reads per sample were generated. Count data were loaded in R and analyzed with the EdgeR BioConductor Package  (Robinson et al, 2010).
Weighted gene co-expression network analysis (WGCNA) WGCNA  (Langfelder & Horvath, 2008)  was applied to the count per million (CPM) expression data. A co-expression network was constructed with a beta value of 20. Genes were clustered into branches of highly expressed genes, and modules were identified with the tree cut algorithm with the additional PAM stage. Six binary variables were generated that were used to calculate the module trait relationships in which all groups were set to zero with the exception of particular groups of interest: control (1's for microglia obtained from healthy control brain), CD11c (1's for both EAE CD11c and neonatal CD11c), EAE (1's for CD11c negative and microglia obtained from EAE brains), neonatal (1's for CD11c negative and microglia obtained from neonatal brains), CD11c EAE, and CD11c neonatal.
Acknowledgements This work was supported by grants from  Lundbeckfonden ,  Danish Council for Independent Research ,  Danish MS Society , and  Warwara Larsens Fond. We thank  Dina Arengoth ,  Pia Nyborg Nielsen ,  Kirstine Nolling Jensen , and  Nieske Brouwer  for expert technical assistance and  Inger Andersen  and  Lars Vitved  for help with cell sorting. The bioimaging experiments reported in this paper were performed at DaMBIC, a bioimaging research core facility, at the  University of Southern Denmark. DaMBIC was established by an equipment grant from the  Danish Agency for Science Technology and Innovation  and by internal funding from the  University of Southern Denmark.
Data availability The datasets of RNA-seq from adult, EAE, and neonatal microglia are deposited in Gene Expression Omnibus (GEO): GSE78809, and will become available via the Glia Open Access Database  (Holtman et al, 2015) : www. goad. education. The datasets of RNA-seq of repopulating microglia are described in  Bruttger et al (2015)  and deposited in GEO: GSE68376. Expanded View for this article is available online.
Numerical Simulation Results We consider a 2D computational domain in the T cell zone of lymph node of about 100 µm × 100 µm. The corresponding number of T cells in the computational domain is ∼3 × 10 3 with the proportions of CD4 + and CD8 + T cells being 2:1 and the number of APCs ranging from 30-300 cells. The maximal number of HIV-specific T cells in the computational domain is assumed to be ∼3 × 10 2. This corresponds to ∼10% of the lymph node space that can be occupied by T cells. The average CPU time of the acute phase of HIV simulation was around 3 h on a computer with four cores and 6 GB of RAM. The code was written under C++ in the Object Oriented Programming (OOP) style. The characteristic units of the model are the minute for time and the domain length for space. Further details of the numerical methods are described in the Appendix to  [50].
The numerical code implementing the model is available upon request to the first author or can be directly assessed at https://github. com/MPS7/MultiScale-HIV. Reaction-diffusion equations were solved using the Alternating Direction Implicit (API) algorithm. We set the initial values for the four fields described by reaction-diffusion equations to zero. Dirichlet conditions were prescribed to the four boundaries of the computational domain. Naive CD4 + and CD8 + T cells are periodically introduced to the middle of the domain when there is available space.
We then focused on results obtained with 1M7 (Figs  4C  and EV5 ) for direct comparison with the previously published structure model of HOTAIR  (Somarowthu et al, 2015). Our reactivity map was used as constrains to model HOTAIR secondary structure using the software RNAstructure  (Deigan et al, 2009). The most stable secondary structure model predicted based on our 1M7 reactivities is slightly distinct from the previous report (Fig EV5 ); nonetheless, we observed a good overlap between the two structures as shown for the D1 domain  (Fig 4C). In summary, the consistency with Somarowthu's thorough HOTAIR structure probing makes us confident that HOTAIR RNA is folded in a similar structure in both studies. Finally, we determined whether our in vitro results hold true in a cellular context. To address this question, we performed RIP pulling down RNAs interacting with EZH2 in our different cell models.
Electrophoretic mobility shift assay Five nanomolar refolded RNA was incubated with increasing concentration of PRC2 in binding buffer (50 mM Tris-HCl, pH 7. 5 at 25°C, 100 mM KCl, 5 mM MgCl 2 , 0. 5 mM ZnCl 2 , 0. 1 mM CaCl 2 , 2 mM 2-mercaptoethanol, 0. 1 mg/ml BSA, 0. 1 mg/ml fragmented yeast tRNA, 5% v/v glycerol, 0. 025% w/v bromophenol blue, and 0. 025% w/v xylene cyanol) at 30°C for 30 min. Samples were cooled to 4°C for 10 min and loaded on a 0. 7% agarose gel in 1× TBE buffer at 4°C. Gels were vacuum-dried for 45 min at 80°C on a nylon membrane and two sheets of Whatman 3-mm chromatography paper. Dried gels were exposed to phosphorimaging plates, and signal acquisition was performed with a Typhoon Trio phosphorimager (GE Healthcare). Densitometry was carried out with ImageJ software and data fitted to a sigmoidal binding curve with Prism Software.
RNA-seq analysis Total RNA from MDA-MB-231 cell lines was isolated by TRIzol extraction and quality-verified by Bionalyzer. Isolated RNA was used to prepare cDNA libraries and amplified with primers containing sequences required for the Illumina platform. PCR products were cleaned and subjected to 100-bp paired-end sequencing on an Illumina Hi-seq 2500. Sequenced reads from duplicate samples were assembled on the human genome hg19, using tophat_2. 0. 6  (Kim et al, 2013). The Htseq software (v0. 6. 0. ) was used to define the number of reads associated with each gene. TMM normalization from the edgeR package v3. 6. 2  (Robinson & Oshlack, 2010)  was first applied. As described in the guideline of limma R package v3. 20. 4, normalized counts were processed by the voom method  (Law et al, 2014)  to convert them into log 2 counts per million with associated precision weights.
The bioinformatics script provided by the Weeks laboratory was adapted for Ion Torrent output files by A. Saadi and Y. Ponty (manuscript in preparation). To generate the HOTAIR secondary structure maps using the software RNAstructure, SHAPE 1M7 reactivity was used to provide pseudo-energy constraints, while VARNA software was used to visualize the predicted structure  (Darty et al, 2009). Resulting structures were manually evaluated for match with NMIA probing data. SHAPE reactivities are listed in the source data for Fig 4C.
Acknowledgements We thank  Dr. Breathnach Richard  for providing the pFLAG_NLS_MS2-MS2 plasmid,  Margaux Charruel  for technical help,  Armelle Luscan  for providing EED and SUZ12 targeting vectors,  Anne-Catherine Dock-Bregeon  for advices on RNA SHAPE,  Afaf Saadi  and  Yann Ponty  for help with processing the raw SHAPE-MaP Data, and  Delphine Allouche  for advices on the SHAPE-MaP experiments. We also thank  Drs. Edith Heard ,  Antoine Graindorge ,  Serena Sanulli ,  Daniel Holoch , and  Mythili Ganapathi  for critical reading of the manuscript. Work in RM laboratory is funded by an  ERC-Stg (REPODDID)  grant and the  Institut National du Cancer (INCa , grant  2012-1-PLBIO ). High-throughput sequencing has been performed by the  NGS platform of the Institut Curie  and supported by the grants  ANR-10-EQPX-03  and  ANR10-INBS-09-08  from the  Agence Nationale de le Recherche  (investissements d'avenir) and by the  Canceropôle Ile-de-France.
The Canadian Nuclear Safety Commission (CNSC) collaborates with the IRSN and CanmetMINING laboratories to perform experimental and theoretical research on the mechanical behaviour of the Tournemire shale. Using the data from creep tests, and monotonic and cyclic triaxial tests performed at CanmetMINING Laboratories, we developed constitutive relationships for the mechanical behaviour of the Tournemire shale. The model is based on the theory of plasticity, and takes into consideration the inherent anisotropy due to the existence of bedding planes, hardening behaviour before the peak strength, and viscosity.
Many researchers took into account the time-dependency by developing constitutive relationships within the theoretical framework of viscoelasticity, or elasto-viscoplasticity D r a f t e-Doc 4879168 3 (e. g. ,  Simo 1987; Fafard et al. 2001; Rouabhi et al. 2007; Pellet et al. 2009; Xiong et al. 2014). The present study shows a systematic development of a constitutive model using data from a comprehensive triaxial testing program performed collaboratively by the authors. Tournemire shale samples from the IRSN's Tournemire Underground Research Laboratory (URL) in France were tested. The main physical phenomena found from the tests were inherent anisotropy due to bedding, stiffness and strength degradation, strain hardening, and energy dissipation found in cyclic tests. Most existing models would only consider one or two of the above characteristics; the present model integrates all of them together.
A constitutive model for the Tournemire shale The comprehensive test data obtained from the laboratory test program summarized above allow us to formulate a constitutive model that would capture the main physical features found during those tests: -The existence of three regions of the stress-strain curves as described above. -The directional dependence of the mechanical behaviour with respect to the bedding orientation. -The time-dependent behaviour that is evidenced from creep tests and the existence of important energy dissipation observed in cyclic triaxial tests. It is also assumed that the stress-strain response from the triaxial tests represents the drained behaviour of the shale. The samples were obtained at natural water contents that correspond to D r a f t e-Doc 4879168 degrees of saturation higher than 90%.
2016). A limited number of creep tests on shales under hydrostatic loading were recently reported. Villamor Lora and Ghazanfari (2015) tested unsaturated shale specimens and found that creep strain under hydrostatic loading is relatively small (approximately 20% ) compared to creep strain for similar deviatoric loading levels. Sone (2012)  attempted to perform creep tests under hydrostatic loading, but found that the data were difficult to interpret due to experimental uncertainty caused by erratic instrument responses to the change in confining pressure. For the above reasons, the authors adopted the simplifying assumption that viscosity is solely dependent on the deviatoric component of stress. As only one viscous branch is considered, the integral deviatoric stress can be rewritten as = 2 ‫‬ ቀ‫ܩ‬ + ‫ܩ‬ ଵ exp (- ௧ି௧ᇱ ఛ భ )ቁ డ డ௧ ᇲ ‫′ݐ݀‬ ௧ (11) The above equation reduces to another form in the absence of viscosity, i.
FEM model for triaxial test A 3-D FEM model for the triaxial test was developed with the commercial finite element software COMSOL Multiphysics (Version 5. 1) in order to solve the equations of static equilibrium. The FEM model has a height of 133 mm and a diameter of 61 mm in order to exactly represent the dimensions of the test samples  (Abdi et al. 2015). The FEM mesh of the model is shown in Figure  15. Tetrahedral elements with quadratic shape functions were used. The bottom plane was assigned with fixed displacement in all directions while the side surfaces were subjected to a confining pressure Pc. The upper boundary was prescribed with timedependent displacement in the vertical direction. The evolution of vertical stress was monitored throughout the simulation for comparison with the test data.