dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2014). The SMOS and CS2SMOS retrievals are almost identical for that region, which is caused in part by the better coverage of the SMOS retrieval in that region. In addition, this area is dominated by thin ice, leading to a higher weighting of the SMOS retrieval due to the lower uncertainties (Fig. 2 ). The scatter diagrams reveal a significantly better agreement of the AEM mean thickness measurements with the CS2SMOS, WM AEM data are compared with the optimal interpolation product (CS2SMOS), the weighted mean (WM), the SMOS retrieval and the monthly CryoSat-2 thickness retrieval (CS2). AEM measurements are averaged on the 25 km EASE2 grid, providing mean and modal total thickness within a grid cell. AEM measurements in the scatter plots are capped at 5 m, while in (a) one mean AEM grid value exceeds the limit.
and SMOS retrievals (RMSD = 0. 27-0. 31 m, r = 0. 61-0. 73) than with the CS2 retrieval (RMSD = 0. 97, r = -0. 35). Hence, the reduction in RMSD considering CS2SMOS or WM compared to CS2 is roughly 0. 7 m. The observed bias with respect to the mean AEM thickness is -0. 25 m for CS2SMOS, -0. 17 for WM and -0. 24 m for SMOS, suggesting a bias towards thinner ice. The maps and scatter diagrams indicate that the CS2SMOS, WM and SMOS retrievals capture small thickness gradients visible in the AEM thickness data. This comparison provides evidence that using SMOS data in areas with a thin ice regime will reduce the RMSD and the mean bias when compared to the CS2 product.
Conclusions We presented methods to carry out the first joint data merging of CryoSat-2 sea-ice thickness fields and thin ice thickness estimates obtained from the L-band radiometer onboard the Soil Moisture and Ocean Salinity satellite. While CS2 lacks the capability to observe thin ice, SMOS is restricted to ice regimes thinner than about 1 m. We used two approaches for merging CS2 and SMOS ice thickness data: a weighted mean and an optimal interpolation scheme based on weekly CS2 and SMOS ice thickness grids. While the weighted mean product only provides estimates at grid cells where observations are available, the OI product (CS2SMOS) provides weekly Arctic-wide sea-ice thickness estimates with corresponding uncertainty estimates. We have shown that the merged products have the capability to allow for weekly thickness estimates that are sensitive to the entire thickness range, using the complementary sensitivity of the individual products to different thickness regimes.
Moreover, the weekly merged products benefit from increased coverage at lower latitudes in conjunction with higher temporal resolution compared to the CS2 retrieval, which is important for observing ice growth during the freeze-up. In particular, the usage of the combined product will improve thickness retrievals in all areas with thin ice, which we have demonstrated using case studies from the Barents Sea during spring 2014 and Beaufort Sea during spring 2016. Comparisons with airborne electromagnetic thickness measurements reveal a reduction in root mean square deviation of about 0. 7 m for CS2SMOS and WM, compared to the CS2 thickness retrieval in the Barents Sea. Moreover, the comparison shows that retrievals that use SMOS data seem to capture small thickness gradients in thin ice regimes, whereas the CS2 retrieval is very noisy.
In the Barents Sea, the CS2 retrieval overestimates mean thin ice thickness by 0. 8 m, while CS2SMOS, WM and SMOS underestimate it by about 0. 2 m. The comparison with the AEM data has also revealed that WM represents a good estimate in regions where weekly data of SMOS and CS2 are available. For the observation of thicker multiyear ice (> 1 m) and mixed ice regimes as in the Beaufort Sea 2016, the CS2 product has the lowest bias, although limitations in capturing high thickness gradients due to heavily deformed ice exist. CS2SMOS, however, exclusively provides weekly ice thickness estimates covering the entire Arctic and combining CS2 and SMOS data. The OI approach used in this study can be adopted to merge sea-ice thickness or freeboard data sets derived from other satellite missions, such as the recently launched European Space Agency mission Sentinel-3, which carries a Ku-band radar altimeter similar to SIRAL onboard CS2.
Acknowledgements. This work has been conducted in the framework of the  European Space Agency  project  SMOS+ Sea Ice  (contracts  4000101476/10/NL/CT  and  4000112022/14/I-AM ). Lars Kaleschke  was responsible for the coordination and design of the  SMOS+ Sea Ice  project. Many thanks go to  Matthias Drusch  and the entire SMOS+ Sea Ice team. Moreover, this study is associated with the  Deutsche Forschungsgemeintschaft  ( DFG EXC177 ) and the  German Federal Ministry of Economics and Technology  (grant  50EE1008 ). CryoSat-2/SMOS data from 2010 to 2016 are provided by http://www. meereisportal. de(grant  REKLIM-2013-04 ). Jennifer King  was funded by the  Norwegian research council project "CORESAT"  ( NFR  project number  222681 ). The helicopter work in the Barents Sea was supported by the  Norwegian Polar Institute (NPI).
Data availability. The weekly updated CS2SMOS product (http://data. seaiceportal. de/data/cs2smos/version1. 3/n/), including the weighted means, and the monthly updated CryoSat-2 product are provided at http://data. seaiceportal. de/data/cryosat2/ version1. 2/n/. The SMOS ice thickness data are provided at https://icdc. cen. uni-hamburg. de/thredds/catalog/ftpthredds/ smos_sea_ice_thickness/v3/catalog. html. Sea-ice concentration and sea-ice type data are provided by OSI SAF via ftp://osisaf. met. no/archive. Barents Sea AEM data are available via https://doi. org/10. 21334/npolar. 2016. ee8f4f8d (King et al. , 2016).
Biological replicates (4 to 6) were performed for each group. Tissues were finely cut with a razor blade and total RNA was extracted using TRIzol reagent (Invitrogen). An additional DNAse treatment (Euromedex) was added before RNA purification by phenol/chloroform extraction and ethanol precipitation. cDNA synthesis was performed either on 0. 5 μ g of total RNA (iScript Reverse transcription Supermix for RT-qPCR kit; Bio-Rad) or on 2 μ g of total RNA for strand-specific reverse transcription (SuperScript II Reverse Transcriptase; Invitrogen or High-Capacity cDNA Reverse Transcription Kit; Appliedbiosystems). Gene-specific primers are available upon request. qRT-PCR analysis was performed on a Bio-Rad iCycler System (CFX) using SsoAdvanced SYBR Green Supermix (Bio-Rad). qRT-PCR conditions were 30 s at 95 °C, followed by 45 cycles of 5 s at 95 °C and 20 s at 63 °C.
RT controls were performed by the omission of RNA template or RT enzyme. A specific standard curve was performed in parallel for each gene, and each sample was quantified in duplicate. Data were analyzed by gene regression using iCycler software and normalized to Gapdh or 36B4 levels. ChIP-seq. ChIP-seq data was previously generated 2 : ChIP-seq reads were aligned to the mouse reference genome (GRCm38/mm10) using Bowtie v0. 12. 8 using the following parameters -m 1 -strata -best. Only uniquely aligned reads have been retained for further analyses. Peak calling was performed using either MACS v1. 4. 2 or SICER v1. 1  38. MACS was used to detect peaks into RNAPII data using default parameters except for -g mm. SICER was used to detect islands into H3K27ac data using the script SICER. sh with the following parameters: Species: mm10, Threshold for redundancy allowed for chip reads: 1, Threshold for redundancy allowed for control reads: 1, Window size: 200 bps, Effective genome size as a fraction of the reference genome of mm10: 0.
77, Gap size: 600 bps, Evalue for identification of candidate islands that exhibit clustering: 1000, False discovery rate controlling significance: 10 -2. Fragment size was set according to the value assessed by Homer v4. 7. 2 makeTagDirectory. Peaks/Islands were annotated using Homer v4. 7. 2 with annotations extracted from Ensembl v78. RNAseq. RNA-seq data was previously generated 2 : RNAseq reads were aligned onto mouse rRNA sequences using bowtie 39 release 0. 12. 7. Reads that do not map to rRNA sequences were mapped onto the mouse reference genome (GRCm38/mm10) using Tophat2 release 2. 0. 10 40. Only uniquely aligned reads have been retained for further analyses. Gene expression was quantified using HTSeq 41 release 0. 5. 4p3 and gene annotations from Ensembl release 78. Read counts were normalized across libraries with the method proposed by Anders et al.
42. Comparison between R6/1 and WT samples was performed using the method proposed by Love et al. 43  , implemented in the DESeq2 Bioconductor library (release 1. 0. 19). Adjustment for multiple testing was performed using a method previously described  44. eRNA identification. In order to identify reads from putative eRNA, we removed split-mapped reads and reads that overlap (≥ 1 bp on the opposite strand) genes annotated by Ensembl (release 78) as: IG_C_gene, IGD_gene, IG_J_gene, IG_LV_gene, IG_V_pseudogene, Mt_rRNA, Mt_tRNA, polymorphic_pseudogene, pro-tein_coding, pseudogene, rRNA, TR_V_gene and TR_V_pseudogene. We extended the region corresponding to those genes to 3 kb upstream of the transcription start site and 10 kb downstream of the transcript end site, in order to minimize signal from polymerase read-through from genic transcripts (as previously described ref.
19). IntersectBed from BEDTools release 2. 21. 0 was used for this purpose  45. This overlap was performed on the opposite strand as the library preparation protocol we used to construct these RNAseq libraries leads to sequence the strand generated during first strand cDNA synthesis. On those filtered RNAseq reads, we then detected the location of putative eRNA as genomic regions enriched in RNAseq reads. We thus used a method used for ChIP-seq peak detection: we used MACS v1. 4. 2  46  with the following parameters -keep-dup = all -nomodel -nolambda -p 1e-4 -g mm. The parameter -shiftsize was set according to the value assessed by Homer v4. 7. 2  47  makeTagDirectory. Motif analysis. Motifs searching of known motifs (Jaspar 2014 motif database) was made using FIMO  48  within sequences of up and down eRNA.
FIMO results were then processed by a custom Perl script that computed the number of occurrence of each motif. To assess the enrichment of motifs within the regions of interest, the same analysis was done n times (n = 100) on randomly selected eRNA regions. We chose to use randomly selected eRNA regions as controls to compute an expected distribution of motif occurrence and to correct for the nucleotide composition bias that could occur specifically in eRNA sequences. Region size distribution of the randomly selected eRNA was the same as for the up and down eRNA of interest. The significance of the motif occurrence was estimated through the computation of a Z-score. The Z-score was computed this way: = -µ σ z (x )/ where: x is the observed value (number of motif occurrence) -μ is the mean of the number of occurrences (computed on randomly selected data) -σ is the standard deviation of the number of occurrences of motifs (computed on randomly selected data) From the Z-scores, P values for each motif were computed.
The P values were corrected for multi-testing using a method previously described  44. Statistical analysis was done with custom R scripts. Gene ontology (GO) analysis. Functional enrichments analyses were performed using the tools DAVID and/or GREAT  22, 49. For analyses with GREAT, defaults setting were used. Whole Mouse genome was used as background. Top-enriched terms are shown (P values < 0. 05 were considered). Integrated analyses. GalaxEast (galaxeast. fr) was used to integrate data relative to eRNA, mRNA, H3K27ac (including super-enhancers) and RNAPII signals. Data access. RNA-seq and ChIP-seq data used for eRNA analysis are accessible through GEO (accession number GSE59572).
Acknowledgements We thank  Ms Carole Strittmater  for animal housing and genotyping. This work was supported by funds from  Centre National de la Recherche Scientifique (CNRS) ,  Agence Nationale de la Recherche  ( ANR-2011-JSV6-003-01  to KM), and  European Huntington's Disease Network (EHDN). A. A. is a post-doctoral fellow supported by  EHDN. Sequencing was performed by the  GenomEast Platform , a member of the ' France Génomique' consortium  ( ANR-10-INBS-0009 ).
Acknowledgements This work was supported by the computing facility center CALMIP of the  University Paul Sabatier of Toulouse France. This work was supported by  DSTL  and  DGA  through a joint UK-France Ph. D. studentship, and by  EPSRC  through grant  EP/J011797/1. O. L. M. acknowledges support through an  EPSRC  fellowship  EP/J016918/1. A. A acknowledges support from  ANR  through grant  ANR-14-CE26-0013.
HT22 cells were transfected for 48 h with a GFP-tagged CRISPR plasmid (pSpCas9_BB_2A-GFP (PX458); U6248BE310_1; GenScript, Piscataway, New Jersey, USA) with a specific gRNA against GRP75 and sorted for high GFP fluorescence, excluding dead cells via DAPI staining, and giving rise to one clonal colony (1-1; KO). GRP75 protein expression was controlled by western blot. Changes on DNA level were investigated by specific primers. To minimize the number of off target genes, the gRNA sequence was chosen using the CRISPR design database (crispr. mit. edu).
Following overnight incubation, PVDF membranes were washed 3 times with 0. 05% TBS-Tween and incubated with corresponding secondary HRP-labeled antibodies (Vector Laboratories, Burlingame, CA, USA). Protein expression was detected by chemiluminescence using the Chemidoc software (Bio-Rad, Munich, Germany) and quantified using the Quantity One software (Bio-Rad). In situ proximity ligation assay ER-mitochondria interactions were analyzed using an optimized in situ proximity ligation assay (PLA) targeting the IP 3 R/GRP75/VDAC1 complex at the MAM interface, as previously described. 74, 75 Briefly, HT22 cells were cultured on 35 mm glass bottom dishes (MatTek, Ashland, MA, USA). After fixation with 4% paraformaldehyde for 10 min, the cells were permeabilized using 0. 3% PBS/Triton X-100 for 30 min.
Each fluorescent dot represents an interaction between VDAC1 and IP 3 R1. Preparations were mounted in Duolink II mounting medium containing DAPI 18 (Sigma-Aldrich) and analyzed with a Zeiss inversed fluorescent microscope at 63 × magnification. Quantification of signals (number of red dots per cell) was done using the BlobFinder software. Experiments were performed in triplicate, n = 10 pictures per conditions.
In addition, cell death was analyzed by annexin V (early apoptosis) and propidium iodide (late apoptosis) double staining using the annexin-V-FITC detection kit (Promokine, Heidelberg, Germany) followed by flow cytometric analysis with excitation at 488 nm and detection 530 nm (green) and 680 nm (red). Data were recorded from 1 × 10 4 cells in triplicate per condition.
Mitochondrial Ca 2+ overload Changes in mitochondrial Ca 2+ were determined by the rhodamine-2acetoxymethylester dye (Rho2-AM, Life Technologies, Carlsbad, CA, USA). Cells were harvested and incubated with 2 μM dye in DMEM without serum for 25-30 min followed by incubation in DMEM for 25-30 min at room temperature in the dark. The fluorescence was excited at 552 nm and detected at 581 nm using the Guava Easy Cite 6-2L system (Merck Millipore, Darmstadt, Germany). Data were recorded from 1 × 10 4 cells in triplicate per condition.
Cytosolic Ca 2+ entry Changes in cytosolic Ca 2+ were determined by a Ca 2+ dye (Ca 2+ Green 5N, Life Technologies). Cells were harvested and incubated with 2 μM dye for 30 min at room temperature. The fluorescence was excited at 506 nm and detected at 532 nm using the Guava Easy Cite 6-2L system (Merck Millipore). Data were recorded from 1 × 10 4 cells in triplicate per condition.
Mitochondrial superoxide (ROS) formation Mitochondrial ROS formation was assessed by the MitoSOX dye (Invitrogen). Cells were incubated with 2. 5 μM MitoSOX dye for 30 min at 37 °C and harvested afterwards. Fluorescence was excited at 488 nm and detected at 690/50 nm. Data were recorded from 1 × 10 4 cells in triplicate per condition. Measurement of the mitochondrial membrane potential (Δψ m ) Loss of the Δψ m was evaluated by staining with TMRE (tetramethylrhodamine-ethyl ester; Invitrogen) dye. Cells were harvested and incubated 30 min with 0. 2 μM TMRE at 37 °C. TMRE fluorescence was excited at 488 nm and detected at 690/50 nm. Data were recorded from 1 × 10 4 cells in triplicate per condition. Seahorse XF analysis HT22 cells were grown and treated with glutamate in Seahorse XF 96-well plates (Seahorse Biosystems, Agilent Technolgies, Waldbronn, Germany).
Lipid peroxidation Lipid peroxidation after 8 h of glutamate exposure was analyzed by staining with 2 μM BODIPY dye (Invitrogen) for 60 min at 37 °C. A shift in BODIPY fluorescence from red to green was assessed by excitation at 488 nm and detection with a 525/30 nm band pass filter and a 690/50 nm band pass filter. Data were recorded from 1 × 10 4 cells in triplicate per condition.
ACKNOWLEDGEMENTS We thank  Katharina Elsässer  for her excellent technical support and advice. This work was supported by a grant from the  Deutsche Forschungsgemeinschaft, DFG  ( DO 1525/3-1 ) and, in parts, by the von-Behring- Röntgen Stiftung. AMD is the recipient of a  Rosalind Franklin Fellowship  co-funded by  European Union  and  University of Groningen.
We globally obtained a high correlation between biological replicates (70% correlation on average), with the exception of zeitgeber time (ZT; ZT0, lights on; ZT12, lights off) 21 due to a potential contamination of one sample during the nuclei preparation (Figure  S1C ). Among the detected proteins with known subcellular localization in Uniprot  (UniProt Consortium, 2015) , around 75% were known to localize in the nucleus or to shuttle between the nucleus and cytoplasm. Moreover, 93% of the ob-tained MS raw signal was from nuclear/shuttling proteins  (Figures S1D and S1E) , and more than 80% of all identified proteins were nuclear according to the COMPARTMENTS database  (Binder et al. , 2014)  (Figure  S1F ). Compared with existing compendia of nuclear proteomes, for instance, the experimentally determined 824 proteins in  Kislinger et al.
(2006)   (Figure S1G) , the computationally defined proteins ($3,500) in  Bauer et al. (2011) and Fink et al. (2008) , and the approximately 4,220 proteins annotated as nuclear in Uniprot, our liver data achieved higher coverage. In fact, we covered nearly 70% of all known proteins expressed in the liver nucleus (Figure  1A ) and close to 90% for proteins annotated as parts of nuclear compartments in mouse liver  (Figures 1A and S1H). The coverage for proteins involved in important nuclear functions, such as transcription factors (TFs), transcriptional co-regulators, and RNA processing proteins, was above 60% (Figure  1B ). To study temporal regulations, we first analyzed the nuclear accumulation of protein involved in circadian rhythms. We could detect all the known components of the core clock, as well as clock-controlled TFs (e.
The two biological replicates are shown as ZT0-ZT21 (replica 1) and ZT24-ZT45 (replica 2). MS and WB data are normalized to the temporal mean, and the 16 time points show the mean and SEM from two independent biological samples. Naphtol blue-black staining of the membranes was used as a loading control (LC) and serves as a reference for normalization of the quantified values. (M) Examples of rhythmic proteins in C2 (FASN, ACACA, and ALB) detected in the nucleus by confocal microscopy. Secondary antibodies alone were used as negative control and the nuclear pore complex NUP98 as positive control. Nuclei were stained and confocal z stack images were acquired at 0. 20 mm intervals (see z stacks in Figure  S2K ). Vertical white scale bars represent 1 mm. (N) Densitometry analyses for proteins FASN (peak time = 23.
, 2014)  (Figures  6C  and S6F ). Many of the transcriptional co-regulators with acetylase and deacetylase, or methylase and demethylase activities, quantified in our dataset showed a rhythmic nuclear accumulation, with peak phases between ZT6 and ZT12 (Figures  S6G  and S6H ). We tested for correlations between the rhythm of these enzymes and global changes in histone acetylation and methylation by WB (Figure  S6I ). Despite a general decrease in histone modification near ZT10, we did not detect clear rhythm of these modifications, consistent with genome-wide chromatin immunoprecipitation (ChIP) studies on those histone marks (Le  Martelot et al. , 2012; Vollmers et al. , 2012). In sum, the measured accumulation rhythms of many transcription regulators significantly help understanding the origins of diurnal rhythms in transcription.
SILAC-Based MS Analysis of Nuclear Proteomics and Phosphoproteomics Tandem MS (MS/MS)-based SILAC proteomic analysis is mostly done as previously described  (Mauvoisin et al. , 2014)  (details in Supplemental Experimental Procedures). Raw MS data and search engine outputs were deposited in the ProteomeXchange Consortium (proteomexchange. org) via the PRIDE partner repository with the identifiers ProteomeXchange: PXD003818 for nuclear proteomics and ProteomeXchange: PXD004191 for nuclear phosphoproteomics.
ACKNOWLEDGMENTS This research was supported by the  Swiss National Science Foundation  (through individual research grant  31003A-153340  to F. N. ), the  Ecole Polytechnique Federale de Lausanne (EPFL) , the  European Research Council  (through individual starting grants  ERC-2010-StG-260988  to F. G. and ERC-2010-StG-260667 to F. N. ), and the  Leenaards Foundation  (to F. G. and F. N. ). We thank  Laura Symul  for her help on figure artwork. We thank  Jessica Sordet-Dessimoz  at the  Histology Core Facility, EPFL , for the assistance in IHC. D. M. , E. M. , F. A. , A. N. G. , L. D. , F. S. , A. P. , M. K. , and F. G. are employees of NestleInstitute of Health Sciences S. A.
Cochrane Database of Systematic Reviews Logically, implementation strategies should address determinants of practice, that is, factors that prevent or enable improvements. However, our understanding of how to identify determinants and match appropriate implementation strategies to identified determinants is limited  (Baker 2015; Wensing 2011) , although there are several frameworks or checklists designed to facilitate this  (Flottorp 2013; Krause 2014). Table  2  shows examples of how di erent implementation strategies might work by addressing di erent determinants of organisational change, healthcare worker practice, and utilisation of health services by healthcare recipients.
Search methods for identification of reviews We searched Health Systems Evidence in November 2010 using the following filters: 1. health system topics = implementation arrangements; 2. type of synthesis = systematic review or Cochrane Review; 3. type of question = e ectiveness; and 4. publication date range = 2000 to 2010. We conducted subsequent searches using PDQ ('pretty darn quick')-Evidence, which was launched in 2012. We searched PDQ up to 17 December 2016, using the filter 'Systematic Reviews' with no Cochrane Library Trusted evidence. Informed decisions. Better health. Cochrane Database of Systematic Reviews other restrictions. We updated that search, excluding records that were entered into PDQ-Evidence prior to the date of the previous search. PDQ-Evidence is a database of evidence for decisions about health systems, which is derived from the Epistemonikos database of systematic reviews  (Rada 2013).