dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2005; Woodruff et al. , 2010). Only slight differences exist around the tropical and south Atlantic between the used versions of ISPD: In particular, data from the island of St. Helena in the central South Atlantic are available in V 3. 2. 6 that were not yet present in V2. Furthermore, both reanalyses assimilate hurricane "best track" surface pressure values from the International Best Track Archive for Climate Stewardship (IBTrACS;  Knapp et al. , 2010). Very recent investigations by one of the co-authors at ECMWF have shown that many hurricane track pressure observations were erroneously discarded in ERA-20C, especially in the first half of the 20 th century. Fig. S1  shows that, for the years 1900 to 1949, 20CR fitted the tropical cyclone central pressure estimates from IBTrACS well, whereas ERA-20C did not (note that the number of IBTrACS hurricane track pressure observations has increased a lot between the time of the creation of 20CR and the time of creation of ERA-20C, explaining the different numbers of collocations in the figure).
Further investigations into the source of this problem identified the quality control step in ERA-20C that checks for constant time series in order to reject the data from stuck pressure sensors as cause. This procedure is inadequate for observations of tropical cyclone central pressure, where the intensity may be constant for several days, while only the cyclone position changes. However, we emphasise that this issue should not affect our analysis, since the Meteor observations north of 10 °N were done exclusively from April to May 1925 and from February to May 1927, outside the North Atlantic hurricane season. In addition to surface pressure and unlike 20CR, ERA-20C assimilates surface marine winds. Further differences between the input datasets to the reanalyses exist in the applied boundary conditions: Whereas 20CR uses sea surface temperature and sea ice from HadISST1.
1  (Rayner et al. , 2003) , ERA-20C uses one of the 10 equally plausible sea surface temperature/sea ice evolutions from the more recent dataset HadISST2. 1. 0. 0 (http://www. metoffice. gov. uk/ hadobs/hadisst2/;  Rayner et al. , manuscript in preparation). And whereas during the period of this study, 20CR uses monthly volcanic aerosols, bi-annual averages of the time-varying global mean CO 2 concentration, and annual values of the incoming solar radiation as described in  Saha et al. (2010) , ERA-20C applies a transient atmospheric forcing and land surface evolution as prescribed in the CMIP5 experiments  (WCRP, 2011). Hersbach et al. (2015)  give a complete description of the model setup. Fig. 4  shows the global distribution of synoptic pressure stations from the International Surface Pressure Data Bank (ISPD; http://rda.
ucar. edu/datasets/ds132. 0/) from which data have been assimilated into both reanalyses during the German Atlantic Expedition (exemplarily showing the year 1926 from V2; 1925 and 1927 look very similar). The number of stations is limited to five around the tropical and South Atlantic (two at the southern tip of South America, one in northern South America, one on the Canary Islands, and one in central East Africa). As mentioned above, the newer ISPD version used in ERA-20C additionally has data from St. Helena. To illustrate the density of assimilated ship surface pressure observations contained in the International Comprehensive Ocean-Atmosphere Data Set (ICOADS, version 2. 5; http://icoads. noaa. gov/; Worley et al. , 2005; Woodruff et al. , 2010) , Fig. 5  shows the cumulative distribution of sea-level pressure observations in ICOADS for the year 1926.
Because they are not using any upper-air data, both 20CR and ERA-20C are completely independent from the ERA-CLIM observational upper-air dataset. Their intercomparison presents an interesting assessment of the reliability of the reanalyses as well as the quality of the observational data, particularly in the case of the sparsely observed regions (even in terms of a surface quantity like sea-level pressure) traversed by the Meteor. To date, no published reanalysis has made use of the considerable amount of historical upper-air data before 1948, even though reanalyses are expected to significantly profit from assimilating further historical upperair data.
Methods Here, we use the 2 °× 2 °temperature, pressure, wind, and specific humidity data on pressure levels extracted from the ensemble mean analysis of 20CR and from the ERA-20C deterministic run for the launch times and locations of the observations in the observational dataset. For 20CR, ERA-20C, and an NCEP/NCAR 50-Year Reanalysis (NNR;  Kistler et al. , 2001)  fourtimes daily 30-year climatology (1981-2010, available on http://www. esrl. noaa. gov/psd/data/gridded/data. ncep. reanalysis. derived. pressure. html), the extraction in time was performed by taking the reanalysis data at the closest available reanalysis time to the launch time of the ascents. For kite ascents, which sometimes took several hours for ascent and descent, observation levels with a difference > 3 h between the launch time of the ascent and the observation were discarded to ensure comparability.
altitude. The values connected to wrong altitudes were discarded from the study, the wrong pressure values in the observations were corrected by going back to images of the original source. Both the observational and the extracted reanalysis data were first pooled into latitude-height bins (size 10 °× 500 m, and 10 °× 1, 000 m for levels above 4,000 m asl in the case of pilot balloon data, centred on whole tens of degrees and multiples of 250 and 500 m, respectively). Then, anomalies of the observations and of reanalysis fields were calculated relative to the NNR climatology, interpolated (as in the case of the 20CR/ERA-20C data) to the times and locations of the observations. The horizontal archived resolution of NNR is 2. 5 °on 17 levels in the vertical. 20CR, ERA-20C and the observations are then compared by 1) calculating the time mean difference (bias) b X of the reanalyses relative to the observations and by 2) calculating Spearman rank correlation coefficients r S ,X between the time series of the anomalies of parameter X in the reanalyses and the observations.
Figs. 6d  and 7d  show the zonally averaged Spearman rank correlation coefficients between the 20CR/ ERA-20C and Meteor pressure anomalies. 43/44 out of 44 altitude-latitude bins with N > 15 and significant results (S i = 90 %, z test) display positive correlations. The mean correlation coefficients are similar and higher than those for temperature anomalies with 0. 73/0. 71. The correlation values are higher on average south of 25 °S (20CR: mean 0. 82, median 0. 91, ERA-20C: mean 0. 78, median 0. 84), but the difference between the datasets in the region between 25 °S and 25 °N is not as large as in the case of temperature (20CR: mean 0. 68, median 0. 75, ERA-20C: mean 0. 66, median 0. 75). The pressure biases of ERA-20C relative to 20CR (Fig. S3c ) look similar than those of ERA-20C relative to observations in the latitude band 35 °S-5 °S, implying that 20CR generally has a smaller pressure bias than ERA-20C.
Anomaly correlations between ERA-20C and 20CR are generally medium to high (Fig. S3d ). Scatter plots of raw pressure values from the reanalyses against those from the observations show results very close to the 1:1 line for both datasets (Fig. 8  middle row), as expected from the very strong relationship between altitude and atmospheric pressure. The regression of 20CR exhibits a slightly smaller scatter (r = 1. 000) than that of ERA-20C (r = 0. 999). Also the intercept is larger for ERA-20C (+2. 6 hPa) compared to 20CR (+0. 5 hPa). For both reanalyses, the largest differences relative to the observations tend to occur at the highest pressure values, i. e. closer to the surface. For pressure, the Taylor diagrams (Fig. 9cd ) confirm the very high (Pearson's) correlations of the total values for both ERA-20C and particularly 20CR in the extratropics and in the Tropics.
2 g/kg and b S H = -0. 6 g/kg). Comparing the variability (not shown), it may be surprising that the standard deviations of the time mean reanalysisobservation differences (rms) calculated over these bins are equal (0. 7 g/kg) for both datasets, and are slightly smaller than the estimated 20CR ensemble mean uncertainty (time mean ensemble spread over the same bins is 1. 1 g/kg). Over most of the study region, the ERA-20C humidity anomalies agree better with the observed than 20CR. Figs. 6f and 7f show the zonally averaged Spearman rank correlation coefficients between the 20CR/ERA-20C and Meteor specific humidity anomalies. Whereas only 29 out of 37 altitude-latitude bins with N > 15 and significant results (S i = 90 %, z test) display nonnegative correlations in the case of 20CR, this number is higher (35) for ERA-20C.
Conclusions and outlook This paper compares a recently digitised, rich source of historical upper-air data for the tropical and South Atlantic during the mid-1920s (ca. 1,000 profiles), obtained on board the research vessel Meteor during the German Atlantic Expedition, with two totally independent reanalyses (20CR, ERA-20C). The value of the newly available atmospheric profiles lies in their potential to be used in future reanalysis projects, to better constrain the past state of the atmosphere in an otherwise sparsely observed region of the globe. The reanalyses themselves differ only slightly in their synoptic input data (besides updated datasets, mainly additional marine winds in ERA-20C), while they differ more strongly with respect to the boundary conditions, atmospheric models, and assimilation techniques.
For zonal wind, the estimated 20CR ensemble mean uncertainty is only a little smaller than what would be expected from a conservatively estimated error of 3 m/s (see Table  2 ) for observed wind speed. The really good agreement between the rms statistics and the 20CR uncertainty conversely implies that the 20CR ensemble appears to be reliable compared to these independent observational data taken from many different climate regimes. 4. Spearman rank anomaly correlations (S i = 90 %) between the reanalyses and observations (Table  1 , time and spatial averages over the latitudinal ranges 65 °S-25 °S and 25 °S-25 °N): (a) Generally, 20CR and ERA-20C show comparable anomaly correlations for all parameters, with the highest values found for pressure and temperature, when accounting for using an ensemble mean instead of a single model realisation.
Furthermore, the statistically significant differences are mostly outside the estimated 2 × σ total error of the observations in the significant bins, at least up to relative observation errors of 15-20 %. Finally, differences of both signs do exist which cannot be explained by rather constant measurement biases. However, even though such errors seem large at a first glance, they cannot be totally excluded in the historical data. All upper-air data digitised in ERA-CLIM will be quality controlled in more detail and included in the Comprehensive Historical Upper-Air Network (CHUAN;  Stickler et al. , 2010). This observation database will serve future reanalysis projects at ECMWF and other centres. The digitisation of the catalogued, historical data is being continued in the framework of ERA-CLIM2, the follow-up project to ERA-CLIM, which started in January 2014.
Acknowledgments AS was funded by the  EU   FP7  projects  ERA-CLIM (Grant 265229 ) and  ERA-CLIM2 (Grant 607029 ). The digitisation of the historical Meteor upper-air data was financed through ERA-CLIM. SB and RW received funding from the  SNF  project  EVALUATE  ( SNF 200021-130407 ). The 20CR data are from the  NOAA ESRL  website. The ERA-20C data are available from the ECMWF website. GPC is supported by the  US Department of Energy Office of Science (BER)  and the  NOAA Climate Program Office.
Technical support and guidance are provided to the programme managers and volunteers by MoH-UNICEF on data collection, analysis and reporting. In each Kampala district, a programme manager is responsible for implementing, coordinating and supervising the activities of the volunteers involved in the immunisation programme. The community volunteers are recruited from the RC pool of active volunteers by the programme manager and by public announcements (482 volunteers deployed in Kampala West and 1060 in Kampala East during the second round of the immunisation campaign). 'Active' in this case is defined as a volunteer who has repeatedly shown interest in taking part in different RC activities or who has had previous experience in community health. A contract is signed and the individual volunteer's skills and expectations are noted.
Data collection Realist evaluation is method neutral; the choice of the data collection is determined by the nature of the research question and the initial PT. 34 Two of the authors visited Kampala East and West, and collected data through document review, individual interviews, focus group discussions (FDGs) and observation. The data collection took place between March 2014 and December 2014. First, information on the overall RC organisational context was collected through a review of URCS documents and discussion with the URCS staff. Second, information was collected locally at each case site. We conducted interviews with volunteers and programme managers to describe the RC management and the organisational context, and to understand how these shape the motivation and performance of volunteers.
Data analysis During the data analysis, the initial PT guided the identification of emerging themes. We used the constant comparative analysis technique. 40 Data were classified and organised according to key themes, concepts and emergent patterns. Furthermore, drawing on Ragin's recommendations on comparative case study research,  41  each case was first examined as a whole and the two cases were then compared with each other. The analysis was conducted as follows. First, a search for potential patterns was carried out. We searched for the link between observed outcomes and the intervention through mechanisms that were triggered for specific actors in specific contexts. 34 This step included five main activities: ▸ The interviews were transcribed verbatim and the first author in collaboration with the co-authors read through the transcripts to identify patterns relevant to the main research questions.
▸ A predefined coding system based on the initial PT (volunteer characteristics, managerial practices and leadership styles, extrinsic and intrinsic motivation, satisfaction of psychological needs, organisational culture, value and mission, etc) was used. The coding tree evolved as relevant issues emerged from the analysis, for instance, the variation of drivers of motivation in function of time spent with the RC. ▸ Next, data were coded to themes, and a The selected volunteers had the option to decline participating in the study. They were informed that nonparticipation in the study would not affect their further work for the URCS. Written consent was sought from each respondent before the interviews. Approval for audio recording of interviews was sought separately. A code was given to questionnaire and interview transcripts to ensure confidentiality of all opinions expressed by participants.
Acknowledgements The authors wish to first thank the  Uganda Red Cross Society  and its staff for welcoming the study and facilitating it. They are especially grateful for the work and the help of  Alex Onzima ,  Joram Musinguzi  and  Umaru Ntambi. Special thanks go also to volunteers  Joseph Ssenkumba ,  Ibrahim Nkonge  and  Akankwasa Keneth Kategaya , for providing translations of interviews on site and overall help with the interview organisation. The authors would also like to acknowledge the inputs of the  IFRC Community Health and Innovation unit , and the  IFRC volunteering department , especially  Ayham Alomari  and  Shaun Hazeldine. Finally , they thank  Vivienne Seabright  for English language editing of the manuscript.
Detailed LC-MS/MS analysis, data search, and validation Each purified proteasome sample or glycerol gradient fraction was precipitated with 20% TCA and washed with acetone. Samples were boiled 30 min at 95°C in Laemmli buffer to denature proteins and reverse formaldehyde cross-link, as previously optimized  (Fabre et al, 2013). Proteins were alkylated with 100 mM chloroacetamide for 30 min at room temperature in the dark. Proteins were concentrated in a single band on a 12% acrylamide SDS-PAGE gel and visualized by colloidal Coomassie Blue staining. One-shot analysis of the entire mixture was performed. A single band, containing the whole sample, was cut and washed in 50 mM ammonium bicarbonate for 15 min at 37°C followed by a second wash in 50 mM ammonium bicarbonate, acetonitrile (1:1) for 15 min at 37°C, and a final dehydration in 100% ACN.
Protein hits were automatically validated with a false discovery rate (FDR) of 1% on proteins and 5% on peptides (minimum peptide length of 6 amino acids). To evaluate false-positive rates, all the initial database searches were performed using the 'decoy' option of Mascot, that is, the data were searched against a combined database containing the real specified protein sequences (target database, Swiss-Prot human, release 2013_01, 20,232 entries) and the corresponding reversed protein sequences (decoy database). Mascot file parsing and quantification (MFPaQ) used the same criteria to validate decoy and target hits, calculated the false discovery rate [FDR = number of validated decoy hits/(number of validated target hits + number of validated decoy hits) × 100]. Proteins identified with exactly the same set of peptides were grouped, and only one member of the protein group was reported (the one that we considered as the most significant according to the functional description given in the UniProt Knowledgebase).
Highly homologous protein hits, that is, proteins identified with top ranking MS/MS queries also assigned to another protein hit of higher score (red, non-bold peptides), were detected by the MFPaQ software (Mouton-Barbosa et al, 2010) and were considered as individual hits and included in the final list only if they were additionally assigned a specific top ranking (red and bold) peptide of score higher than 30 (P-value < 0. 05).
Data quantification Relative quantification of proteins Quantification of proteins was performed using the label-free module implemented in the MFPaQ v4. 0. 0 software (http:// mfpaq. sourceforge. net/) (Bouyssie et al, 2007; Mouton-Barbosa et al, 2010; Gautier et al, 2012). For each sample, the software uses the validated identification results and extracts ion chromatograms (XIC) of the identified peptide ions in the corresponding raw nano-LC-MS files, based on their experimentally measured retention time (RT) and monoisotopic m/z values. The time value used for this process is retrieved from Mascot result files, based on an MS2 event matching to the peptide ion. If several MS2 events were matched to a given peptide ion, the software checks the intensity of each corresponding precursor peak in the previous MS survey scan.
The heat-map was created using the R package software (version 3. 1. 0) and the graphic package ggplot2 (version 0. 9. 3. 1). The two unsupervised clustering analyses (principal component analysis, PCA, and agglomerative hierarchical clustering, AHC) were performed from normalized PAIs, across the 24 proteasome purifications, of proteasome subunits or known interacting proteins using the XLSTAT software  (version 2012. 3. 01). The PAIs were normalized by setting the highest PAI measured for a given protein in the proteasome purifications to one. The PCA was of Pearson (n) type. For the AHC, the Pearson correlation coefficient was used to search for similarities using an unweighted pair-group average agglomeration method.
Acknowledgements We thank  Marc Dubois  for the creation of the heat-map and the  PRIDE Team  for help with depositing the data. The authors also would like to thank the  ARC (Association pour la Recherche sur le Cancer) foundation  for a PhD fellowship to BF. This project was supported in part by the  Région Midi-Pyrénées, European funds (Fonds Européens de Développement Régional, FEDER) ,  Toulouse Métropole , and by the  French Ministry of Research  with the  Investissement d'Avenir Infrastructures Nationales en Biologie et Santé program  ( ProFI, Proteomics French Infrastructure  project,  ANR-10-INBS-08 ).
2% formic acid) and 5% solvent B (80% aceto- nitrile, 0. 2% formic acid). Peptides were eluted using a 5-50% gradient of solvent B during 160 min at a 300 nl/min flow rate. The LTQ-The Mascot Daemon software (version 2. 3. 2; Matrix Science, London, UK) was used to perform database searches, using the Extract_msn. exe macro provided with Xcalibur (version 2. 0 SR2; Thermo Fisher Scientific) to generate peaklists. The following parameters were set for creation of the peaklists: parent ions in the mass range 400-4,500, no grouping of MS/MS scans, and threshold at 1,000. A peaklist was created for each analyzed fraction, and individual Mascot (version 2. 3. 01) searches were performed for each fraction. The mass tolerances in MS and MS/MS were set to 5 ppm and 0. 8 Da, respectively, and the instrument setting was specified as 'ESI-TRAP'.
Hi-C Chromosome conformation capture experiments were performed as described elsewhere  (Marbouty et al. , 2014). Sequencing data were processed into genome-wide contact maps (4 kb bins) that were normalized using the sequential component normalization procedure described previously  (Marbouty et al. , 2014). Raw contact maps are available on the Sequence Read Archive (SRA) under bioproject-ID PRJNA289589 biosample-ID SAMN03856048. Full details are provided in Supplemental Experimental Procedures. (Nicolas et al. , 2012). Genomic annotations of highly expressed genes (light blue rectangles), horizontal gene transfer elements (yellow rectangles), and rRNA genes (purple asterisks) are indicated. Blue, yellow and purple vertical lines represent barriers between domains coinciding with these elements.
From top to bottom: normalized genomewide contact map (HM1320 in rich media); Directionality index analysis; GC content (4kb window); Enrichment for the Rok protein (Smits and Grossman, 2010); transcription signal  (Rasmussen et al 2009). 20 significant barriers were detected at the following positions (in kb):  160, 390, 530, 740, 940, 1120, 1210, 1520, 1700, 1880, 2050, 2150, 2270, 2650, 2880, 3020, 3170, 3460, 3670  and 3960 along the chromosome. For transcription analysis, we mapped against the BS168 reference the reads of a RNAseq dataset [SRX315309] obtained with a B. subtilis wildtype strain growing in LB genome and binned the raw signal into 2kb windows. The 5 % of these 2kb bins that presented the highest transcription signal were considered as HEG (104 in total). Whereas among the 422 10kb bins in the HM1320 contact map (Figure  1a ), 73 contained a HEG (17.
Vertical and horizontal axis represent chromosomal and genomic positions centered on the terminus of replication, with the left and right arms represented by redtoyellow and bluetogreen gradients (from ori to ter), respectively. The origin is represented as pink semicircles. This color code is conserved across all panels of all figures to facilitate reading. III. To compare CIDs between backgrounds, we first aligned the two genomes against one another, to identify the major differences between them (panel III). Reference genomes were aligned using the MUMmer software 3. 0  (Kurtz et al. , 2004)  and represented as Dotplot alignments (see Supplemental Experimental Procedures). The four largest deletions (numbered 1 -4) present in the PY79 background as compared to BS168 are indicated on the right.