dataesr/scientific-paragraphs-categorization huggingface dataset:
Influence of fractional behaviour on computation time The use of fractional behaviour leads to an increase of computation time. For the MAP simulation (six fractional behaviours), the wall clock time is increased by a factor of 1. 95 compared to the DEF simulation (one building use and be-haviour). Our approach is therefore about a factor of 3 faster than using six tiles for the entire TEB scheme, which would increase the computation time by about a factor of 6. However, it has to be kept in mind that computation time is very specific to the model set-up, application and computer. The simulations were performed using mpi on three Intel Core i7-2920XM CPU (2. 50 GHz) processors of a notebook with a Linux operating system. Different results for the computational performance might be obtained for other applications of TEB or on a different computer.
Conclusions In this article, we presented how we enhance the Town Energy Balance TEB in order to take into account fractional building use and human behaviour, and how we deal with behaviours like heating, air conditioning, ventilation and shading. These can influence the building energy consumption, which can in turn be an important contributor to the anthropogenic heat flux. We show how we determine and initialise behaviour-related input parameters of TEB for France by combining information available from administrative datasets and surveys. We evaluated the enhanced version of TEB for the CAPITOUL campaign and demonstrated that a more detailed representation of building use and human behaviours improves the spatio-temporal variability of the simulated building energy consumption. Future work on human behaviour-related aspects in TEB is needed to address two main issues.
Data on building use, equipment and human behaviour might be derived from inventories, sector-wide energy consumption data, OpenStreetMap or surveys. The TEB developments we describe in this article lay the groundwork for simulations of urban climate coupled to building energy consumption. In future studies, the feedbacks between the building energy consumption and the ur-ban climate should be investigated in simulations where TEB is coupled to an atmospheric model. Especially for the winter season, it would be useful to quantify the feedbacks between heating energy consumption and the urban heat island, since this has not been done much. The building energy consumption is often related to emissions of pollutants and CO 2. These emissions could be parametrised in the atmospheric models as a function of the simulated building energy consumption and the type of heating and air conditioning system.
(Schoetter et al. , 2017EB code is available in open source via the surface modelling platform SURFEX (http://www. cnrm-game-meteo. fr/surfex/). It is updated relatively infrequently (every 3 to 6 months). The developments presented here are not yet included in the latest version. However, their inclusion is planned. In the meantime, the routines modified with respect to SURFEX v. 8. 0 as well as the run directories of the model experiments described in Sect. 4. 2. 3 can be retrieved via https://zenodo. org/record/818632(Schoetteret al. , 2017, https://doi. org/10. 5281/zenodo. 818632).
Acknowledgements. We thank  Grégoire Pigeon  for providing original code and data required for the construction of the inventory of building energy consumption and for discussions concerning the building energy model in TEB. Erwan Bocher  and  Gwendall Petit  are acknowledged for making available data characterising urban morphology via the MApUCE toolbox in the OrbisGis software. Alexandre Amossé  is acknowledged for providing data on urban typology. Pauline Crombette ,  Julia Hidalgo  and  Najla Touati  are acknowledged for providing data on urban vegetation. This work has received support from the  French National Agency of Research  for the project applied Modelling and urbAn Planning laws:  Urban Climate  and  Energy (MApUCE)  with the reference  ANR-13-VBDU-0004. Edited by:  Jatin Kala  Reviewed by: three anonymous referees.
(Schoetter et al. , 2017EB code is available in open source via the surface modelling platform SURFEX (http://www. cnrm-game-meteo. fr/surfex/). It is updated relatively infrequently (every 3 to 6 months). The developments presented here are not yet included in the latest version. However, their inclusion is planned. In the meantime, the routines modified with respect to SURFEX v. 8. 0 as well as the run directories of the model experiments described in Sect. 4. 2. 3 can be retrieved via https://zenodo. org/record/818632(Schoetteret al. , 2017, https://doi. org/10. 5281/zenodo. 818632). R. Schoetter et al. : Human behaviour in TEB Appendix A: Representation of human behaviours in TEB In this section, we describe how we represent different hu- man behaviours in TEB. Due to the non-linearity of most involved processes, all model equations presented in this ap- pendix are solved separately for each fractional building use and behaviour.
However, as JU1580 and N2 differ at many loci besides drh-1, including some infection response genes  [11, 12] , we decided to test this further by performing microarray analysis to compare infection induced genes in N2, a drh-1 knockout in the N2 strain, JU1580, and JU1580 carrying a transgene containing the N2 drh-1 locus (JU1580 drh-1 rescue). Hierarchical clustering of differentially regulated genes recapitulated our earlier findings allowing us to identify a set of genes upregulated upon viral infection that are likely dependent on DRH-1 activity (S1 Fig). Thus, we conclude that there is a specific transcriptional response to viral infection downstream of viral RNA recognition. To get at the nature of the signaling downstream of viral recognition, DRH-1dependent or other, we extracted the promoters from the set of genes upregulated in N2 after infection and searched for associated motifs using the motif-prediction software MEME  [15].
We then compared these motifs to known transcription factor DNA binding motifs in the JASPAR core database using Tomtom  [16, 17]. Remarkably, we identified an enriched motif with similarity to the one of STAT transcription factors  (Fig 1B) , well known to have a conserved role in antiviral defence in mammals. There are two STAT homologs, STA-1 and STA-2 in C. elegans  (Fig 1C). STA-1 has all classical functional STAT domains: a coiled-coil domain for protein-protein interaction, a DNA binding domain, a SH2 domain and a putative tyrosine phosphorylation motif  [18]. STA-2 is similar but lacks the coiled-coil domain as well as the tyrosine phosphorylation motif  [19]. Consistent with this, a phylogenetic tree with C. elegans STA-1 and STA-2 and representative STATs from other organisms suggests that STA-1 is closely related to mammalian STATs but that STA-2 is more divergent (Fig  1D ).
Through GFP::STA-1 ChIPseq, we identified 2133 STA-1 peaks across the genome. STA-1 was enriched close to transcription start sites (TSS) of about 20% of genes with an enriched binding peak ~200bp upstream of the TSS (Fig  3D ). These data are in agreement with STA-1 acting as a specific DNA-binding transcription factor to regulate gene expression. To test further the association of STA-1 binding with the antiviral gene expression response we used the sequences surrounding the STA-1 peaks in order to search for enriched motifs. Importantly, we recovered an enriched motif that was nearly identical to the motif predicted from our gene expression analysis (Fig  1B ) and matching the mammalian IRF and STAT motifs in the JASPAR core database and the consensus core interferon sensitive response element (ISRE) TTCNNTTT (Fig 3E  , 3F )  [22].
elegans  [26, 27]. To identify potential kinases upstream of STA-1, we performed an RNAi screen, testing genes with the serine/threonine/tyrosine-protein kinase catalytic domain IPR001245  [28]. The C. elegans genome encodes 176 proteins with this domain, 116 of which were available as clones as part of C. elegans genome-wide RNAi libraries  [29, 30]. Following RNAi by feeding, we infected animals with the Orsay virus and then quantified viral load for each of the 116 candidates and additional controls (Fig 5B  , 5C ). Using a stringent cut-off (|Z-score| > 3) we only identified a single regulator of viral load, sid-3. RNAi of sid-3 resulted in ~100fold reduction of viral RNA accumulation compared to control (Fig  5C ). We confirmed that sid-3 is required for sensitivity to viral infection by testing two independent deletion mutants of sid-3 (Fig 6A  , 6B ).
Microarray Microarray data were processed using rma and annotations were obtained through the Bioconductor pipeline in the R programming environment. Differentially regulated genes in any condition identified using t-test, p<0. 01 and a 2-fold difference upon infection. Data are available in the S1 File. Hierarchical clustering on the drh-1 array was carried out on the set of differentially regulated genes using the hclust function in R and Ward's method.
Motif analysis To specifically identify potential viral response genes we normalized the array by cul-6 as this removed variation in the final level of infection. Qualitatively this identified similar trends to those previously published  [14] , such as putative antibacterial response genes upregulated specifically in JU1580 and drh-1 upon infection. We identified genes that were >40% induced relative to cul-6 and used BiomaRt to download the upstream sequences (S2 File). These were used as an input to Meme attempting to find 0 or 1 site per gene. We screened these manually to avoid spurious hits and then compared potential motifs identified to the JASPAR core database. To test enrichment of STAT motifs in STA-1 ChIP-seq peaks we used FIMO to search for the STAT-1 core motif within the 500bp sequences upstream of genes containing a STA-1 binding site, applying a false-discovery rate cutoff of 0.
RNA seq Animals were grown on 50mm NGM plates, infected or not and harvested as described in the VIral infection methods section. Total RNA was extracted using Trisure (Bioline, UK). RNA library preparation was performed with the NEBNext ® Ultra™ RNA Library Prep Kit for Illumina ® with total RNA purified with the NEBNext Poly(A) mRNA Magnetic Isolation Module, according to manufacturer's instructions (NEB, USA). RNA-seq analysis. RNAseq data was aligned to the C. elegans transcriptome WS190 using bowtie2. Counts were obtained from resulting bam files using bedtools  [36]  and these were used to generate normalized data tables using DESeq  [37].
To generate combined peak calls, we used the modified IDR procedure (https://www. encodeproject. org/software/idr/) with an IDR threshold of 0. 05 to combine replicates (S4 Table ). The pipeline for generating IDR peaks is avilable here: https://github. com/Przemol/biokludge/blob/master/macs2_idr/macs2_idr. ipy.
Acknowledgments We thank  David Jordan  for scientific discussion and help with data visualization,  Jérémie Le Pen  for scientific discussion and contribution to reagents,  Alyson Ashe  for contribution to reagents,  Alex Appert  for technical support with ChIP sequencing experiment,  Kay Harnish  and  Sylviane Moss  for high-throughput sequencing. We thank the  Caenorhabditis Genetics Center, University of Minnesota  for providing some strains used in this study.
Identification of Causal Variants using MetaboChip and Functional Priors We derived causal probabilities for each variant in the following way. We first obtained fine-mapping data of variants at 39 T2D loci (including ARAP1) from the Metabochip. 32 For each of the 49 distinct association signals at the 39 loci, we calculated the Bayesian posterior odds for all variants at each signal using the approach of Wakefield. 33 5] [36] [37]  We thus obtained regulatory chromatin state data previously generated in 12 cell types which included 9 ENCODE cell types (Gm12878, HepG2, HUVEC, Hsmm, hESC, Hmec, NHLF, NHEK, and K562), pancreatic islets (PancIslt), and pre-and mature-adipocytes (hASCt1, hASCt4). 32, 34, 38, 39 For each cell type, we used active enhancer (EnhA), weak enhancer (EnhWk), and active promoter (TssA) elements for a total of 36 chromatin annotations.
IMIDIA Samples Expression data were acquired and normalized from islet organ donors (81 healthy, 19 T2D) or partial pancreatectomy-laser microdissection samples (32 non-diabetic, 35 T2D) from the IMIDIA consortium (Web Resources; M. Solimena, personal communication) with appropriate permissions from donors and/or families. In brief, genotyping analysis was performed on the DNA from the same subjects using HumanOmni 2. 5-8 beadchip from Illumina using a standard Infinium genotyping protocol. Standard quality control assessment was carried out on the genotyping data using PLINK (Web Resources). 46 cis-eQTL analysis was performed with a freely available program called matrix eQTL. 47 inear model was used as a parameter for the analysis with age and gender as covariates. A ''window size'' of 100 kb was used as the cisDist; this distance represents the maximum distance at which gene-SNP pair is considered local.
In Vivo Physiological Studies All studies were performed on male mice except when data were comparable between genders, in which case results from males and females were combined to gain statistical power, as indicated. The American Journal of Human Genetics 100, 238-256, February 2, 2017 241 Intraperitoneal Glucose Tolerance Test Mice fasted overnight (16 hr) were intraperitoneally injected at ~10 am with glucose, 1 g/kg mouse weight. Blood from the tail vein was obtained at 0, 15, 30, 45, 60, 90, and 120 min after injection. Blood glucose levels were measured with the Accu-Chek Aviva glucometer (Roche).
A similar experiment was performed with ARAP1 promoter 2, which is the most active in islets (Figure  S2 ). We also observed an association between the previous index SNP at this locus (rs1552224) which is located in the 5 0 UTR of ARAP1 and sites located across the entire locus. cis-eQTLs for STARD10 and ARAP1 Are Not Detected in the Liver Given the characteristic effect of this locus on apparent proinsulin processing and the importance of liver, where STARD10 is also highly expressed, for proinsulin clearance, we assessed data from previous reports  64, 65  to determine whether similar T2D-GWAS coincident cis-eQTLs were observed for this tissue. We examined publicly available datasets including samples from 97 (GTex)  64  and 600 (STARNET)  65  samples, respectively. In contrast to the situation in islets, the previously reported cis-eQTLs for STARD10 in the liver were in very low LD (r 2 ~0.
1) with the identified T2D locus, and no liver cis-eQTL was observed for ARAP1. In order to further validate the above results, we searched for cis-eQTLs for these genes in a separate cohort of liver biopsies from 186 female subjects in the ABOS (''Atlas Biologique de l'ObesiteSevere'') cohort (ClinicalTrials: NCT01129297)  66  collection. Since none of the variants in the credible set were detected in Metabochip samples after QC checks (see Material and Methods), we used proxy SNPs for four of the five variants (rs140130268, rs148527516, rs7103836, rs76550717). None of these proxies displayed any significant association with probes for ARAP1 or STARD10. Defective Glucose Homeostasis and Insulin Secretion in Mice Deleted Selectively for StarD10 in the b Cell Mice lacking StarD10 selectively in the b cell (bStarD10 KO) were generated by crossing StarD10 floxed mice, in which LoxP sites were present at either side of exon 3, to mice carrying an Ins1Cre allele.
Acknowledgments G. A. R. was supported by a  Wellcome Trust   Senior Investigator Award  ( WT098424AIA ), an  MRC Programme  ( MR/J0003042/1 ),  Biotechnology and Biological Sciences Research Council  ( BB/ J015873/1 ),  Diabetes UK  ( BDA/11/0004210 ,  BDA/15/0005275 ),  Imperial Confidence in Concept (ICiC)  grants, and a  Royal Society Wolfson Research Merit Award. G. R. C. received a Fellowship from the  SocieteFrancophone du Diabe  `te. This work was also supported by an  MRC   Experimental Challenge grant  ( MR/L020149/1 ) to the DIVA consortium and by the  Innovative Medicines Initiative Joint Undertaking  under grant agreement  155005  (IMIDIA), resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and  European Federation of Pharmaceutical Industries and Associations (EFPIA)  companies.
P. E. M. was supported by a 2016-2017 Killam Annual Professorship. K. J. G. is supported by  NIH  grant  P30-DK063491. Received: July 25, 2016 Accepted: December 20, 2016 Published: January 26, 2017  Web Resources BioGPS , http://biogps. org/ClinicalTrials. gov,http://clinicaltrials. govDNase and ChIP pipeline, http://userweb. molbiol. ox. ac. uk/ public/telenius/PipeSite. htmlEuropean Nucleotide Archive, http://www. ebi. ac. uk/enaFastQC, http://www. bioinformatics. babraham. ac. uk/projects/fastqcIMIDIA, http://www. imidia. orgInternational  Mouse Phenotyping Consortium , http://www. mousephenotype. org/Islet regulome, http://gattaca. imppc. org/isletregulome/homeKnockout Mouse Project (KOMP) Repository, https://www. komp. org/OMIM, http://www. omim. org/PLINK, http://pngu. mgh. harvard.
Construction of the Sometimes I wonder if the design task should be handed wholesale to the team behind the Ikea instruction manuals: if they can convey in pictograms how to put up a Billy bookcase anywhere in the world, they can surely tell someone in 10,000 years' time not to dig in a certain place. The New Mexico facility is due to be sealed in 2038. The present plans for marking the site involve a berm with a core of salt, enclosing the above-ground footprint of the repository. Buried in the berm will be radar reflectors, magnets and a 'Storage Room', constructed around a stone slab too big to be removed via the chamber entrance. Data will be inscribed on to the slab including maps, time lines, and scientific details of the waste and its risks, written in all current official UN languages, and what that experience feels like -I'm thinking about the ' ambient hum of guilt' in particular.
, 2016). Cytosolic mouse PIWI proteins MIWI and MILI are piRNA-guided endoribonucleases or slicers that cleave target transcripts for silencing  (De Fazio et al. , 2011; Reuter et al. , 2011) , while nuclear PIWI protein MIWI2 is proposed to recruit the histone or DNA methylation machinery to target genomic loci for transcriptional repression  (Aravin et al. , 2008; Kojima-Kita et al. , 2016; Kuramochi-Miyagawa et al. , 2008). Mobile genetic elements or transposons constitute the universal target of the piRNA pathway  (Ghildiyal and Zamore, 2009). In the mouse embryonic male germline, MILI and MIWI2 are loaded with repetitive transposon-silencing piRNAs  (Aravin et al. , 2007 (Aravin et al. , , 2008)). However, transposons are not the only targets for piRNAs in the adult germline, where MILI and MIWI bind an abundant set of non-repetitive unique sequences that are collectively called pachytene piRNAs  (Aravin et al.
, 2006; Girard et al. , 2006; Li et al. , 2013; Vourekas et al. , 2015). These are shown to promote selective removal of transposon or cellular mRNAs by guiding PIWI slicing or facilitating the recruitment of the mRNA deadenylation machinery, or by regulating translation  (Castaneda et al. , 2014; Goh et al. , 2015; Gou et al. , 2014; Reuter et al. , 2011; Zhang et al. , 2015). Thus, both transposon and cellular mRNAs are targets of the mouse piRNA pathway, and their regulation is essential for spermatogenesis and fertility in male mice  (Pillai and Chuma, 2012). Large genomic regions called piRNA clusters are sources for a majority of the piRNAs  (Aravin et al. , 2006 (Aravin et al. , , 2008;; Girard et al. , 2006). In addition to clusters, individual transposons and a selected set of cellular transcripts are also used as substrates for piRNA biogenesis.
, 2015; Saxe et al. , 2013; Yang et al. , 2016). Although MILI slicing loads both MILI and MIWI2 with the target-derived piRNAs  (Yang et al. , 2016) , MIWI2 is totally dependent on this pathway to acquire its small RNA guides  (Aravin et al. , 2008; De Fazio et al. , 2011; Kuramochi-Miyagawa et al. , 2008). The use of PIWI slicing as an initiator of piRNA biogenesis on a transcript is mechanistically challenging, as slicing is also used for target destruction. How the slicer cleavage fragment is safely handed over to the biogenesis machinery in the mouse male germline is not currently understood. Attesting to the complexity of the pathway, a number of piRNA biogenesis factors are exclusively required for slicertriggered piRNA biogenesis. These include the RNA helicase Mouse Vasa Homolog (MVH)  (Kuramochi-Miyagawa et al.
MVH mutation does not affect distribution and abundance of MILI and MIWI piRNAs, and MVH piRNA abundance correlates better with MIWI piRNAs than those of MILI piRNAs. (K) Apart from piRNAs, long reads of 51 nt (corresponds to maximum sequencing length; black arrowhead) were also found in MVH complexes. Genome annotations for these are displayed. While the origin of the mutant (Mvh +/KI ) long reads resembles that of pachytene piRNAs, the 51-mers of the wild-type (Mvh +/+ ) preferentially come from repeats. The proportion of non-annotated reads mapping to the top pachytene piRNA clusters is shown in the pie charts. (L) The plots show the 5 0 end distance between targeting piRNAs and produced piRNAs or MVH-bound 51-mers (MVH long reads) calculated over 500 cellular transcripts that are the top targets for antisense piRNAs.
Approximately 20% of the 51-mers are pre-piRNA intermediates, as they share the same 5 0 ends as pachytene piRNAs (Figure  4I ). Next, we examined the 51-mers that arise from genic regions. Since pachytene piRNAs are shown to regulate cellular mRNAs by slicing  (Goh et al. , 2015; Zhang et al. , 2015) , we examined whether some of the 51-mers could be products of slicer activity on mRNAs (STAR Methods). This reveals that many of the 51-mers could indeed be slicer cleavage products generated by pachytene piRNA-guided MIWI or MILI slicing of genic mRNAs (Figures  4L  and S5E ) or L1 transposon transcripts (Figure  S5F ). Given the lack of pachytene piRNA biogenesis defect, we propose that the infertility observed in the Mvh +/KI mutant is likely due to formation of stalled MVH complexes, preventing normal function of pachytene piRNAs in promoting spermiogenesis.
The dominant-negative effect of the ATPase mutation in MVH reveals a poorly appreciated role for MVH in the adult germline. Pachytene piRNAs in MILI and MIWI are shown to target complementary transposon and cellular mRNAs for slicing  (Goh et al. , 2015; Reuter et al. , 2011; Zhang et al. , 2015). Partial complementarity base pairing is also shown to be used for targeting cellular transcripts for degradation by recruitment of the deadenylation machinery  (Gou et al. , 2014). This raises the possibility that pachytene piRNAs might recognize thousands of transcripts and tag them for elimination during spermiogenesis, a process that progressively leads to emptying of cytosolic contents and condensation of the chromatin. Our finding that the dominantnegative ATPase mutant MVH traps PIWI proteins MILI and MIWI (Figure  4E ), pachytene piRNAs (Figure  4F ), and slicer products of transposon and genic mRNAs (Figures  4L  and S5F ) is suggestive of a potential role for MVH in their function.