dataesr/scientific-paragraphs-categorization huggingface dataset:
Real-Time qPCR and Analysis of Expression Data qPCR was performed using Power SYBR R Green PCR Master Mix (Applied Biosystems, Darmstadt, Germany) on an ABI Prism 7300 Real Time PCR System (Applied Biosystems). Due to the large number of samples, qPCRs were performed on two 96-well plates for each target gene. For comparability, the normothermic ZT16 group was applied to all plates as interplate calibrator. Hprt was selected as reference gene, based on the stability of expression values across all samples. All samples were run in triplicates (for 5 biological replicates per group), using 1 µl cDNA as template in a reaction volume of 20 µl, and a series of six 10-fold dilutions of specific standard plasmids were used to generate the standard curve to calculate qPCR efficiencies. Additionally, a no-template control was included on each plate in duplicates for each target gene.
Quantification was performed with the following cycling parameters for 40 cycles: 50 First evaluation of qPCR results was carried out using the 7300 System Software v1. 4. 0 (ABI Prism, Applied Biosystems) and subsequently exported to Microsoft Excel 2010 to identify differences in expression levels using the CT method. All statistical testings and figures were done with SigmaPlot 12. 5 (Systat Software Inc. ). All results were statistically analyzed by two-way ANOVA with time of day (Zeitgebertime) and metabolic state (torpid/normothermic) as factors, followed by Tukey's test for pairwise comparison of relative expression levels between torpid and normothermic hamsters and within the torpid and normothermic groups.
Assessments of the global water cycle, freshwater quality, biodiversity, and non-CO 2 greenhouse gases would also benefit from an improved land-use representation. The overall objective of this article is to review three important challenges faced in connecting models to assess land use-climate interactions and feedbacks, discuss the underlying mechanisms and constraints that have hampered improved representations until now, and propose pathways to improve the land-use representation. We review recent literature from the land use, land cover, carbon cycle, and climate modeling communities and support our arguments using illustrative analysis of satellite land-cover products and outputs of the land-use change model CLUMondo  (Van Asselen and Verburg, 2013). Each of the following sections presents one of the three challenges we identify to be crucial in future land use-climate interaction studies and reviews the issue and its implications for the results of modeling studies, based on previously published literature and in the context of the widely applied Land-Use Harmonization (LUH) dataset published by  Hurtt et al.
However, observational data on LULCC are not available on the global scale with the required temporal and spatial resolution, consistency, and historical coverage  (Verburg et al. , 2011). Instead, models are utilized to represent global land use and produce the required land-use change time series. Land-use modeling is typically split up into historical backcasting approaches and future scenario modeling. Both forward-and backward-looking models apply a range of different modeling approaches as well as different assumptions about drivers and the spatial allocation of landuse changes  (National Research Council, 2014; Yang et al. , 2014) , and they are often initialized with different representations of present-day land use  (Prestele et al. , 2016). Thus, even the models within one community (future or historical) do not provide consistent information on land use and landuse change over time, and a variety of independent datasets on a spatially explicit or world regional level are provided to the user community (e.
g. , climate modeling) (see Supplement Sect. S1 and Table  S1  for examples of the historical data). These historical and future datasets are not connected and consistent in the transition period and entail a variety of uncertainties (Klein Goldewijk and Verburg, 2013) (Fig. 1 ). In consequence, these datasets disagree about the amount and the spatial pattern of land affected by human activity. Moreover, varying detail in classification systems, inconsistent definition of individual categories (e. g. , forest or pasture), and individual model aggregation techniques, amplify the discrepancies among models  (Alexander et al. , 2017; Prestele et al. , 2016).
Current approach to providing consistent data: the Land-Use Harmonization (LUH) project Large efforts have been undertaken to connect the different sources of land-use data and provide consistent time series for climate modeling applications during the fifth phase of the Coupled Model Intercomparison Project (CMIP5;  Taylor et al. , 2012)  by the LUH project  (Hurtt et al. , 2011). The resulting dataset (hereafter referred to as LUH data) is commonly used in modeling studies dealing with land use-climate interactions and feedbacks. It has recently been updated for the upcoming sixth phase of the Coupled Model Intercomparison Project (CMIP6;  Eyring et al. , 2016; Lawrence et al. , 2016)  and data for the historical period have been published (hereafter referred to as LUH2). Due to the lack of comprehensive documentation of the updated version at the time this paper was written and as, to our best knowledge, the points we demonstrate using LUH will still be valid with the new product, we primarily refer to the CMIP5 version in the remainder of this paper.
Hurtt et al. (2011)  extended their Global Land-use Model (GLM;  Hurtt et al. , 2006)  to produce a consistent time series of land-use states (fraction of each land-use category in a grid cell) and transitions (changes between land-use categories in a grid cell) for the time period 1500-2100. The cropland, pasture, and wood harvest projections of four IAMs were smoothly connected to the History Database of the Global Environment (HYDE) historical reconstruction of agricultural land use  (Klein Goldewijk et al. , 2011)  and historical wood harvest estimates by applying the decadal spatial patterns from the projections onto the HYDE map of 2005 (Fig. 1 ). This harmonization process tries to conserve the original patterns, rate, and location of change as much as possible and to reduce the differences between the models due to definition of cropland, pasture, and wood harvest.
2. 3 Open issues in the LUH data and their implications for climate change assessments The first major uncertainty of the LUH data evolves from the exclusive consideration of the HYDE baseline dataset for the historical period. The HYDE reconstruction is erroneously regarded as observational data rather than as model output accompanied by various sources of uncertainty (Klein Goldewijk and  Verburg, 2013). Importantly, the LUH2 data will additionally include the HYDE low and high estimates of land use for the historical period  (Lawrence et al. , 2016). However, alternative spatially explicit reconstructions have been proposed  (Kaplan et al. , 2010; Pongratz et al. , 2008; Ramankutty and Foley, 1999)  (see Supplement Sect. S1 and Table  S1  for additional information on these reconstructions), and have been shown to differ substantially in terms of both the total cultivated area and spatial pattern over time  (Meiyappan and Jain, 2012 ).
These differences originate in the scarcity of historical input data (i. e. , mainly population estimates) for historical times, the assumption about the functional relationship between population density and land use (e. g. , linear or nonlinear), and the allocation scheme used to distribute regional or national estimates of agricultural land to specific grid cell locations (Klein Goldewijk and  Verburg, 2013). The uncertainty about land-use history has several implications for land use-climate interactions  (Brovkin et al. , 2004). For instance,  Meiyappan et al. (2015)  found the difference in cumulative land-use emissions among three historical reconstructions for the 21st century modeled by one TBM to be about 18 PgC or ∼ 11 % of the mean land-use emission. Another study, using three commonly used net land-use datasets in one TBM, revealed differences of about 20 PgC or ∼ 9 % of the mean land-use emission since 1750  (Bayer et al.
Excluding wood harvest from the LUH data restricts the occurrence of gross changes to the areas of shifting cultivation. However, our analysis of CLUMondo output (Fig. 3 ), along with the European analysis of  Fuchs et al. (2015a) , suggests substantial amounts of gross change (below the 0. 5 • LUH grid) also in the temperate zone and the high latitudes. Consequently, the LUH approach heavily depends on the resolution of the original landuse data (provided by IAMs or historical reconstructions) and their ability to represent land-use change dynamics on a subgrid scale. The data-based approach avoids the process uncertainty that hinders high-resolution model projections of land use, but is limited to the time period where empirical data through remote sensing is available. Additional sources such as historical land-use and land-cover maps and statistics  (Fuchs et al.
Example: spatial heterogeneity of cropland transitions in the CLUMondo model As it is not possible to compare the land-use allocation strategies of TBMs with historical change data on a global scale due to the lack of accurate global land-use and landcover products (though products with higher resolution (up to ∼ 30 m), more frequent temporal coverage, and increasing thematic detail are just emerging;  Ban et al. , 2015) , we additionally tested to what extent cropland expansion simulated by the land-use change model CLUMondo  (Eitelberg et al. , 2016; Van Asselen and Verburg, 2013)  represents one or more of the simplified algorithms currently considered in TBMs (Table  1 ). CLUMondo models the spatial distribution of land systems over time, instead of land use and land cover directly. Land systems are characterized by, in addition to other factors, a mosaic of land use and land cover within each grid cell.
g. ,  Poulter et al. (2011) , as a background vegetation map on which agricultural land is imposed. Due to inaccuracies in global remote sensing land-cover products and differences in historical reconstructions (as discussed in Sect. 2), fractions of agricultural land on a grid scale are subject to differences between the background map and the external land-use dataset. Consequently, the PFT composition outside the prescribed agricultural land can represent either the real heterogeneity in natural vegetation or represent a mix of natural and anthropogenic land cover due to differences in the datasets. However, these cases are difficult to distinguish and empirically justified transition matrices, together with more accurate present-day land-cover products, would provide a useful tool for reducing uncertainties due to allocation decisions in ESMs.
Tackling uncertainties in the harmonization The LUH  (Hurtt et al. , 2011)   The high computational demands of complex ESMs probably do not allow for multiple runs including all the uncertainties in land-use forcing. However, to derive robust results from climate model intercomparisons, a sufficient quantification of uncertainty in the land-use forcing dataset is urgently required. If this proves impractical through ESM simulations, we recommend utilizing less computationally expensive models such as DGVMs and offline LSMs to assess the full range of uncertainty and to determine a limited set of simulations, which appears to significantly affect biogeochemical cycles and climate. These can be subsequently used to test the uncertainty range in ESMs. Simultaneously, we suggest that the land-use and remotesensing communities should engage to reduce uncertainties in land-use and land-cover products by 1.
Acknowledgements. The research in this paper has been supported by the  European Research Council  under the European Union's  Seventh Framework Programme  project  LUC4C  (grant no. 603542 ),  ERC  grant  GLOLAND  (no. 311819 ), and  BiodivERsA  project  TALE  (no. 832. 14. 006 ) funded by the  Dutch National Science Foundation (NWO). This research contributes to the  Global Land Programme  (www. glp. earth). This is paper number  26  of the  Birmingham Institute of Forest Research. Edited by: R. A. P. Perdigão Reviewed by:  J. Hall  and two anonymous referees.
Data availability. The illustrative analysis in Sects. 3 and 4 is based on CLUMondo simulations  (Eitelberg et al. , 2016). CLU-Mondo source code and simulation results are available from http: //www. environmentalgeography. nl/site/data-models/. The Supplement related to this article is available online at doi:10. 5194/esd-8-369-2017-supplement.
Such an approach would ensure a comprehensive coverage of the uncertainties accumulating across temporal and spatial scales prior to feeding land-use data into climate models and allow for testing of climate model sensitivity to different realizations of land-cover and land-use information. has allowed the inclusion of an- thropogenic impacts on the land surface for the first time in the CMIP5 climate change assessments. As we have shown in Sect. 2, three major sources of uncertainty, which in- clude the uncertainty about land-use history, inconsistencies in present-day land-use estimates, and structural differences across IAMs and LUCMs, are poorly addressed through the almost exclusive implementation of the LUH dataset within the climate modeling community. A wider range of harmo- nized time series is therefore likely to substantially influence the outcomes of studies on land use-climate interactions.
Sample size The required sample size was calculated using a hypothesis of a 10% abstinence rate at the 6-month follow-up (similar to the rate observed in the StopAdvisor trial. 29 Given a rate of 10% in the control group, a sample of 1500 subjects per group is required to show an OR of 1. 5 (ie, a rate of 14% in the intervention group) with a power of 90% (α 0. 05, bilateral test), meaning a total of 3000 persons. 30 ecruitment Subjects will be recruited as the e-TIS website becomes operational and over three full months ( January-March 2017). The study will start in January 2017 and end in July 2018. Data will be collected over 12 months. Recruitment will be via France's national health insurance fund's website Ameli: http://www. ameli-sante. fr. Subjects will log on to the Ameli website where they see a banner for the study.
Other data collection The measures in both arms will be internet-based except for data relating to e-TIS components which only concerns the intervention arm (E). Data will be collected from four sources: an inclusion questionnaire (technical variables), an initial selfreporting questionnaire at T0, three follow-up selfreporting questionnaires (T+3, 6 and 12 months) and routine collection via the internet platform of e-TIS. In the T0 questionnaire, the data collected will be differentiated according to the entry point into the intervention (1-4). In the follow-up questionnaire, the data collected will be differentiated according the participant's status: has stopped smoking or not. Table  3  describes how each measure will be recorded. At each follow-up point, an email and text message will be sent twice as a reminder.
Analysis plan The efficacy will be analysed using blind analysis by comparison at 3, 6, 12 months in both arms using the primary and secondary endpoints. In the main analysis, data will be analysed by intention to treat and then by per protocol analyses. For the main analysis, those participants lost to follow-up (those who don't answer the questionnaires) will be considered smokers as recommended. 12 33 41 For the secondary analysis, we will only consider those who will not be lost to follow-up. The efficacy analysis will be blinded to the randomisation group, but the processes and mechanisms by their nature will be analysed openly. The proportion of quitters in each arm will be estimated, as well as an OR and its 95% CI by logistic univariate regression. We will also conduct an analysis on efficacy in subgroups using the following predefined variables: socioprofessional classification, sex, age, point of entry onto the intervention.
From this we will be able to identify the most common pathway used through the intervention. To identify cluster of participants following similar pathway we will use the SAS Proc Traj. 45 This procedure is a specialised application of finite mixture modelling designed to identify clusters of individuals following similar progressions of an outcome over time (or trajectory). Outcome variable will be smoking status (ie, abstinence, quit attempts); timevarying dependent covariables will be BCTs used, progress through the modules and other factors measured during follow-up. Stage 3: analyse the influence of user characteristics, processes, context and exposure to BCTs on the outcome The clusters developed stage 2 will be used as dependent variables in a model designed to analyse the influence of users' characteristics (eg, sociodemographic, dependency, motivation, quit attempts or experiences, added support, contextual factors) on the trajectory.
Ethical considerations and dissemination Participants must give their informed consent to participate in the study. They will be informed that they can refuse and drop out at any time. Subjects in the control arm will be asked to register to the e-TIS website once they have been deemed suitable for treatment via an initial evaluation. The data collected and processed in this study will be performed so in compliance with the Act of 6 January 1978 on Data Processing, Data Files and The study protocol was reviewed by the ethical and deontological institutional review board of the Institut National de Veille Sanitaire (INVS) on 18 April 2016. All the proposals and recommendations put forward by the ethics committee have been followed and integrated into the amended version of the protocol.
5 to E16. 5. In control fetuses, we observed 2042 genes differentially expressed between E15. 5 and E16. 5 (Figure  3d ; Supplementary Table  1c ). Phenotype and pathway analyses for these 2042 DEGs using WebGestalt 36 confirmed relevance of the identified genes for SkM development or function (Supplementary Figures  3c  and d ). Intersection with the 193 E16. 5 DEGs revealed an overlap of 125 genes (Figure  3d ). Therefore, the absence of Spin1 prevents adequate expression of a subset of genes regulated during the fetal phase of myogenesis in control mice. To corroborate aberrant fetal myogenesis in Spin1 M5 mice, we compared the E16. 5 DEGs with transcripts previously observed to be differentially expressed in embryonic or fetal myotubes. 37 Of the 27 transcripts more highly expressed in embryonic (compared with fetal) myotubes, only one (Tnnc1) overlapped with the E16.
5 DEGs (Figure  3e ). Importantly, eight out of thirteen transcripts more highly expressed in fetal myotubes (Myh8, Tnni2, Tnnt3, Atp2a1, Casq1, Pvalb, Ckm, and Eno3) were downregulated DEGs in Spin1 M5 mice at E16. 5 (Figure  3e ). Together, our data provided evidence for an impaired progression of fetal myogenesis in Spin1 M5 mice. Identification of deregulated SkM functional networks in Spin1 M5 fetuses. To identify genes accounting for SkM defects in Spin1 M5 fetuses, we performed phenotype and pathway analyses for the E16. 5 DEGs using WebGestalt  36  (Figures  4a  and b ). These analyses revealed terms related to SkM function, metabolism, and pathology. We grouped the genes associated with the phenotype and pathway terms according to functional networks previously proposed in systems biology analyses for SkM  38, 39  (Figure  4c ).
To validate Myod1 promoter occupancy of Spin1 target genes, we analyzed a previously deposited Myod1 ChIPseq data set for C2C12 myoblasts (GEO data set GSE36024), which uncovered Myod1 peaks at the promoters of 14612 genes (Figure  5d ). Intersection with Spin1occupied genes showed a large overlap of 11695 direct targets of Spin1 and Myod1 (Figure  5d ). Comparison with the E16. 5 DEGs identified 82 direct Spin1/Myod1 targets (Figure  5d ). Inspection of ChIP-seq tracks confirmed the presence of Spin1, H3K4me3, and Myod1 at promoters of SkM functional, metabolic, and regulatory genes (Supplementary Figure  4b ) as well as the promoters of Myf5, Myod1, Myog, Msc, and Hes6 (Figure  5e ). Together, our data suggest that deregulated bHLH transcription factors affect Spin1/Myod1-dependent target gene transcription thereby accounting, at least in part, for SkM defects in Spin1 M5 fetuses.
Data were analyzed using the 2(-Delta Delta C (T)) method. 59 NA-seq analyses for E15. 5 and E16. 5 were performed with SkM dissected from front and hind limb of three Spin1 M5 and four control fetuses. For RNA-seq analysis of mice at three weeks of age (P21), TA isolated from four Spin1 M5 and four control mice was used. RNA was isolated as described above, except that minced tissue in TRIzol was further homogenized with QIA shredder spin columns (Qiagen, Hilden, Germany). RNA quality was determined using the RNA 6000 Nano Kit (Agilent, Waldbronn, Germany) on an Agilent 2100 Bioanalyzer. RNA with a RIN above 8. 0 was sequenced at the DKFZ core facility (Heidelberg, Germany) or the Deep Sequencing Unit (Max-Planck-Institute of Immunology and Epigenetics, Freiburg) using the standard Illumina protocol.
Chromatin was incubated with antibody for 10 h followed by 3 h of incubation with protein A-conjugated beads. 1% of the original chromatin was used as input. After elution from beads, ChIP and input samples were reverse crosslinked, and DNA purified with MinElute columns (Qiagen, 28004). Libraries were prepared using the NEBNext Ultra DNA library preparation kit (NEB, E7370S) according to the manufacturer's instruction and without size selection. Adapter-ligated fragments were amplified with 12 PCR cycles and sequenced on the Illumina HiSeq 2500 platform by the Deep Sequencing Unit of the Max-Planck-Institute of Immunology and Epigenetics (Freiburg). Paired-end reads were mapped to the mouse reference genome (mm10) using bowtie 2 66 with default parameters. Data were further analyzed using the peak finding algorithm of MACS 1.
4. 2 67 using input as control. All peaks with a false discovery rate 41% were excluded from further analyses. The uniquely mapped reads were used to generate genome-wide intensity profiles, which were visualized using the integrative genomics viewer (IGV). 68 Peaks were annotated and overlaps between different peak files were calculated with HOMER. 61 The genomic features (promoter, exon, intron, 5′ or 3′ untranslated region, and intergenic region) were defined and calculated using Refseq and HOMER. Myod1 chromatin association in C2C12 myoblasts was analyzed using a previously deposited GEO data set (GSE36024). Venn diagrams were generated with the help of Venny. 69 Intensity profiles for Spin1 and H3K4me3 gene promoter occupancy were analyzed with SeqMINER. 70 O data availability. The RNA and DNA sequencing data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus  71  and are accessible through GEO Series accession number GSE92539.
5% periodic acid solution (Sigma, 3951), stained with Schiff's reagent (Sigma, 3952016), dehydrated, and mounted using Roti-Histokitt (Roth). Statistics. Transcriptome and cistrome data were analyzed as described above. Statistical significance of gene set intersections was evaluated by a hypergeometric test using the program 'R' (http://www. R-project. org) [phyper (N12-1, N1, N-N1, N2, lower. tail = FALSE) with N1 (genes in set 1), N2 (genes in set 2), N12 (genes in intersection), and N (genome size)]. The enrichment factor (R) was calculated according to R = (N × N12)/(N1 × N2). Other data are presented as the mean value or percentage change +S. D. Comparisons between two data sets were made using the two-tailed Student's t-test for parametric data and the Wilcoxon signed-rank test for nonparametric data.