dataesr/scientific-paragraphs-categorization huggingface dataset:
C. 2. Instrumental noise The instrumental noise of Planck is rich in statisticallyanisotropic structure, as discussed in Planck Collaboration VI (2014), which is sourced by the following. (1) Striping effects, due to residual noise correlations on long time scales after destriping. (2) Correlations along the scan direction induced by the deconvolution of the detector time response and the low-pass rolloff filter. (3) Large variations in the pixel noise level due to the larger density of pixel hits near the Ecliptic poles. All of these effects can be avoided by forming estimators using pairs of data with independent noise realizations, as we have done in Sect. 7. 3, although our baseline results do not use such cross-correlations. Here we discuss the size of the noise meanfield. Items (  1 ) and (  2 ) are difficult to study analytically.
Acknowledgements. The development of Planck has been supported by:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MICINN, JA  and  RES (Spain) ;  Tekes, AoF  and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ; and  PRACE (EU). Dipartimento di Fisica ,  Università degli Studi di Trieste, via A. Valerio 2, Trieste, Italy Dipartimento di Fisica, Università di Roma Tor Vergata, Via della Ricerca Scientifica, 1, Roma, Italy Discovery Center, Niels Bohr Institute, Blegdamsvej 17, Copenhagen ,  Denmark Dpto. Astrofísica ,  Universidad de La Laguna (ULL), E-38206 La Laguna, Tenerife ,  Spain European Southern Observatory ,  ESO Vitacura, Alonso de Cordova 3107, Vitacura, Casilla 19001, Santiago, Chile European Space Agency, ESAC ,  Planck Science Office, Camino bajo del Castillo , s/n,  Urbanización Villafranca del Castillo ,  Villanueva de la Cañada, Madrid, Spain European Space Agency, ESTEC, Keplerlaan 1, 2201 AZ Noordwijk, The Netherlands Finnish Centre for Astronomy with ESO (FINCA), University of Turku, Väisäläntie 20, FIN-21500, Piikkiö, Finland Haverford College Astronomy Department, 370 Lancaster Avenue, Haverford, Pennsylvania, U.
Classification and features 16S rRNA gene analysis The single genomic 16S rRNA gene sequence of S. mucosus DSM 16094 T was compared with the Greengenes database for determining the weighted relative frequencies of taxa and (truncated) keywords as previously described  [14]. The most frequently occurring genera were Salipiger (21. 2%), Pelagibaca (17. 1%), Roseovarius (17. 0%), Marinovum (13. 1%) and Roseobacter (9. 5%) (30 hits in total). Regarding the single hit to sequences from members of the species, the average identity within high scoring pairs (HSPs) was 100. 0%, whereas the average coverage by HSPs was 97. 2%. Regarding the two hits to sequences from other members of the genus, the average identity within HSPs was 98. 4%, whereas the average coverage by HSPs was 99. 0%. Among all other species, the one yielding the highest score was 'Salipiger bermudensis' (DQ178660), which corresponded to an identity of 96.
8% and a HSP coverage of 99. 9%. (Note that the Greengenes database uses the INSDC (= EMBL/NCBI/DDBJ) annotation, which is not an authoritative source for nomenclature or classification). The highestscoring environmental sequence was AB302369 Figure  1  shows the phylogenetic neighborhood of (Greengenes short name 'Hydrocarbon-Degrading Indonesian Seawater seawater isolate B44-2B44-2 str. B44-2'), which showed an identity of 97. 4% and an HSP coverage of 95. 4%. The most frequently occurring keywords within the labels of all environmental samples that yielded hits were 'aquat, rank' (4. 9%), 'microbi' (3. 7%), 'harbour, newport' (3. 3%), 'water' (2. 7%) and 'seawat' (2. 4%) (219 hits in total) and in line with the habitat from which strain A3 T was isolated. Environmental samples that yielded hits of a higher score than the highest scoring species were not found.
The plates were sealed with parafilm to avoid a loss of fluid. The exported measurement data were further analyzed with the opm package for R  [30, 31] , using its functionality for statistically estimating parameters from the respiration curves such as the maximum height, and automatically translating these values into negative, ambiguous, and positive reactions. The reactions were recorded in three individual biological replicates. Phylogenetic tree highlighting the position of S. mucosus relative to the type strains of the type species of the other genera within the family Rhodobacteraceae. The tree was inferred from 1,331 aligned characters of the 16S rRNA gene sequence under the maximum likelihood (ML) criterion as previously described  [14]. Rooting was done initially using the midpoint method  [15]  and then checked for its agreement with the current classification (Table  1 ).
Genome project history The genome of S. mucosus DSM 16094 T was sequenced as a part of the DFG funded project TRR51 "Ecology, Physiology and Molecular Biology of the Roseobacter clade: Towards a Systems Biology Understanding of a Globally Important Clade of Marine Bacteria". The strain was chosen for genome sequencing according the Genomic Encyclopedia of Bacteria and Archaea (GEBA) criteria  [12, 13]. Project information can found in the Genomes OnLine Database  [16]. The genome sequence is deposited in GenBank and the Integrated Microbial Genomes database (IMG)  [38]. A summary of the project information is shown in Table  2.
Plasmids and phages Genome sequencing of S. mucosus DSM 16094 T resulted in 30 scaffolds. In the species description, it was reported that this strain contains at least seven plasmids (550, 467, 184, 140. 8, 110. 6, 98. 2 and 30. 8 kb)  [7]. However, the identification of plasmids in the genome was difficult because typical replication modules comprising the characteristic replicase and the adjacent parAB partitioning operon are missing  [41]. Nevertheless, comprehensive BLASTP searches with plasmid replicases from Rhodobacterales revealed the presence of three RepA and two RepB genes, whereas RepABC-type and DnaA-like replicases were absent from the genome sequence. General genomic features of the chromosome and four putative extrachromosomal replicons are listed in Table  5 , whereas locus tags of the replicases and the large virB4 and virD4 genes of type IV secretion systems are presented in Table  6.
Here we have surveyed the variability and suitability of PM tissue with the purpose of isolating and characterizing postsynaptic protein complexes using proteomics. We report that standard brain quality indicators from tissue banks poorly predict the integrity of the postsynaptic proteome and we have developed a new index that is a robust correlate of synapse proteome integrity allowing proteomic analysis of MASC from PM samples. These studies and their data resources reveal the importance of human MASC in a large number of human diseases. The new approaches will also facilitate isolation of other synapse multiprotein complexes and their direct study in diseased tissue. All the data generated in this study is freely available in the G2Cdb database (http://www. genes2cognition. org/ publications/human-masc).
The fact that limbic areas anatomically close to the cortex (cingulate gyrus: BA23 and BA24) show high HUSPIR values in the 4 brains analysed gives further evidence for a better preservation of the synaptic proteome in more anterior brain regions. Finally, this data also indicates that when working with non-cortical brain areas HUSPIR should be measured for each brain sample included in the study. The HUSPIR value of one cortical region has a good predictive value for other cortical regions of the same brain, as shown in Figure  2C.
Human MASC proteins in disease To understand the relevance of hMASC to human disease we searched online genetics databases and the published human genetics literature for evidence of hMASC genes involved in brain disorders (see methods). Our approach involved: i) identification of relevant genes and relevant diseases, ii) allocation of diseases into specific categories according to the International classification of Disease System (ICD-10), iii) statistical analysis of the representation of disease categories, and iv) comparison of hMASC with hPSD to examine the relative importance of hMASC, as compared with the whole postsynaptic density. According to OMIM database of inherited diseases  [26]  66 hMASC genes are involved with 93 genetic diseases. Fifty-one of these are nervous system diseases with 47 specific to the CNS.
1% formic acid) on a BEH C18 analytical column (1. 7 μm, 75 μm id × 10 cm,) (Waters). The LTQ-Orbitrap Velos was operated with a cycle of one MS (in the Orbitrap) acquired at a resolution of 60,000 at m/z 400, with the top 10 most abundant multiplycharged (2+ and higher) ions in a given chromatographic window subjected to MS/MS fragmentation in the linear ion trap. An FTMS target values of 1e 6 and an ion trap MSn target value of 1e 4 was used and with the lock mass (445. 120025) enabled. Maximum FTMS scan accumulation time of 250 ms and maximum ion trap MSn scan accumulation time of 100 ms were used. Dynamic exclusion was enabled with a repeat duration of 45 s with an exclusion list of 500 and exclusion duration of 30 s. MS data was analysed using MaxQuant  [34]  version 1. 1. 1. 36. Data was searched against a Human IPI sequence database (v3.
Diseases in chapter VI (Diseases of the Nervous System) were divided into more specific blocks within that chapter. To analyse the relevance of postsynaptic genes in syndromic or non-syndromic intellectual disability (ID or NSID) a list of genes known to cause these disorders was generated (Additional file 6). To create this database all genes linked to the terms "Mental Retardation" or "Intellectual Disability" were extracted from GeneCards  [36]  and added to a list of genes causing NSID curated from the scientific literature. Binomial statistics were used to calculate enrichment of postsynaptic components in ID or NSID. Text-mining and expert curation of MASC gene mutation associations to human nervous system disorders PubMed abstracts up to August 2014 were searched for Human MASC gene mutations associated to disease, using a natural language processing approach to information extraction provided in the Linguamatics I2E platform  [37].
Acknowledgements Human NSB biopsies: This study was approved by  Lothian Region Ethics Committee /2004/4/16 , and informed consent was obtained from all donors. We thank the tissue donors, without whom this study would have been impossible. Human PM samples: PM samples were obtained from  MRC Edinburgh Brain Bank (Edinburgh). We thank the tissue donors, without whom this study would have been impossible. AB was funded by  Proyectos de investigación no orientada ,  BFU2012-34398 ,  Career Integration  grant  CIG, Ref. 304111 ,  Subprograma Ramón y Cajal ,  RYC-2011-08391. AB is thankful to  Dr. Ignasi Gich -IR-HSCSP  for support on biostatistical analysis. CGM was funded by  NIHR Academic Clinical Fellowship. SG, LVL, MDC, MOC CS, MR and JC were supported by the  Medical Research Council , the  Wellcome Trust  and  European Union  programs (Project GENCODYS no.
Comparisons of the same [PSI + ] strain grown in rich and selective media showed large differences in gene expression pattern, precluding statistically valid comparisons. However, a comparison of [PSI + ] and [PSI + ] + Cter in the same growth conditions revealed that 47 genes were downregulated in [PSI + ], whereas only six genes were upregulated (Figure  S2C ; Table  S1C ). This is a very similar to that observed for [PSI + ] and [psi-] in rich medium. We investigated the functional impact of [PSI + ], using AMIGO software to search for Gene Ontology term enrichment  (Carbon et al. , 2009). Three functional categories were particularly abundant among [PSI + ] downtranslated genes: glycogen biosynthesis, carbohydrate biosynthesis, and oxidation-reduction processes. Using the list of genes (Table  S1A ), we completed this functional classification by manually sorting all the downregulated genes as a function of either their published physiological functions or their known regulators.
(A) Functional annotation was carried out in two steps. We first used AMIGO to identify Gene Ontology (GO) terms displaying enrichment in the list. We then manually searched for genes reported to be linked to other biological functions but not present in the GO database (e. g. , common regulatory elements). A category is created if at least five different genes are attributed to the same biological pathway or activity. The three categories identified with AMIGO are indicated with a p value score. *A gene involved in prion maintenance: the absence of TSA2 increases the frequency of de novo [PSI + ] formation. (B) With the list of genes with translational ambiguities in the [PSI + ] strain, we used AMIGO software to identify GO term enrichment for biological functions. We used standard settings (i.
ACKNOWLEDGMENTS English usage was corrected by  Alex Edelman & Associates. This work benefited from the facilities and expertise of the IMAGIF high-throughput sequencing platform ( Centre de Recherche de Gif , www. imagif. cnrs. fr). The bioinformatics analysis was performed by the e-bio platform (http://ebio. u-psud. fr/). We would like to thank  J. P. Rousset  for critical reading of the manuscript. This work and R. L. are supported by a grant from the  ANR  ( ANR-2011-BSV6-011 ) to O. N.
The short-chain non-methane hydrocarbons (NMHC) are mostly emitted into the atmosphere by anthropogenic processes. Recent studies have pointed out a tight linkage between the atmospheric mole fractions of the NMHC ethane and the atmospheric growth rate of methane. Consequently, atmospheric NMHC are valuable indicators for tracking changes in anthropogenic emissions, photochemical ozone production, and greenhouse gases. This study investigates the 1950-2010 Northern Hemisphere atmospheric C 2 -C 5 NMHC ethane, propane, i-butane, nbutane, i-pentane, and n-pentane by (a) reconstructing atmospheric mole fractions of these trace gases using firn air extracted from three boreholes in 2008 and 2009 at the North Greenland Eemian Ice Drilling (NEEM) site and applying state-of-the-art models of trace gas transport in firn, and by (b) considering eight years of ambient NMHC monitoring data from five Arctic sites within the NOAA Global Monitoring Division (GMD) Cooperative Air Sampling Network.
Data quality control and filtering Figure  2  shows firn air NMHC measurements from all boreholes and laboratories after removal of data that were clearly influenced by analytical problems during measurement. While the different boreholes and data reported from the three laboratories agreed on the main features of the depth mole fraction profiles, some samples appeared to be affected by contamination. Because the number of available data points at any given depth level in a single borehole is small, the different data sets were combined to allow for better statistics for outlier testing. This approach is well justified for the 2008 EU borehole, as the INSTAAR and MPI-UEA data sets can be placed on the same calibration scale given that both laboratories were audited and found to meet the data quality criteria of the WCC.
The differences in gas age distributions between the 2008 EU and US boreholes are relatively small  (Buizert et al. , 2012)  and are not expected to result in significant differences in NMHC mole fraction profiles with depth. Data from the 2008 US borehole were thus used to help test four suspected outliers from the 2008 EU borehole. The NMHC depth profiles are also expected to be very similar for the 2008 and 2009 boreholes (based on gas age distributions). Data from 2008 boreholes were thus used to help test two suspected outliers in the 2009 borehole. Suspected outliers or spurious data points were rejected by applying Peirce's criterion  (Ross, 2003). Additionally, if a suspected outlier did not meet the criterion but was very close to meeting it and the same flask showed elevated values for other species, it was also rejected.
Furthermore, several points were rejected because mole fraction results deviated from the seasonal cycle behavior of the species and values seen at adjacent depth levels. Overall, applying the data filtering resulted in 6, 5, 1, 1, 3, and 6 eliminated data points for ethane, propane, i-butane, n-butane, i-pentane, and n-pentane, respectively. For the 2008 EU borehole, the MPI-UEA data were placed on the INSTAAR scale by determining the average offset between the two data sets. These two data sets were then averaged, and the combined data set was used for atmospheric history reconstructions. Data from the 2008 US borehole and the 2009 borehole were treated separately for the atmospheric history reconstructions (Sect. 3. 2). The NOAA-INSTAAR network data were subjected to data curve fitting protocols, then filtered for outliers, and fit to a function comprised of a harmonic component and 10 244 Fig.
2. Firn air depth profiles of ethane, propane, i-butane, n-butane, i-pentane and n-pentane (in pmol mol -1 ) from the three boreholes. Two sets of samples were collected from the EU hole and analyzed independently by two laboratories as indicated in the figure legend. Data points that failed the quality control filters and that were excluded from the data set used for atmospheric trend reconstruction are shown as open symbols. polynomial term as described by  Thoning et al. (1989)  and  Masarie and Tans (1995). Short-term variations in the data were smoothed by applying a Fourier-transform algorithm and long-term trends were determined after removing the short-term fluctuations and the annual harmonic cycle component. After applying the quality control filters, approximately 500 data points remained for each site and NMHC.
An overview and compilation of several emissions databases (including EDGAR, RETRO, and regional scale databases) and reconstruction of historical trends were presented by  Lamarque et al. (2010). That study presented estimates for total VOC, which besides NMHC includes an array of other volatile organic compounds, such as aromatics, oxygenated VOC and organic acids. Nonetheless, NMHC constituted the largest fraction of considered VOC. Assuming that the relative distribution of contributing compounds has remained relatively constant, the NEEMdata reconstructed NMHC trends should provide a reasonable VOC surrogate for comparison and evaluation of these emission estimates. As the NEEM observations are deemed to be mostly representative of Northern Hemisphere (NH) NMHC emissions and atmospheric composition, emission estimates from NH source regions were extracted from the  Lamarque et al.
(2010)  emissions database and compiled. Results from the emissions database and the NEEM firn data in Fig. 11  show increasing levels in the middle part of the 20th century followed by a stabilization and decline during the latter part. The magnitude and timing of these trends, however, are markedly different. The total NMHC carbon atom mole fraction from the NEEM data (black trace in the top panel) increased by approximately 50 % after 1950, reaching a maximum around 1980, and has since declined to close to year 1950 levels. The rate of decline appears to be somewhat faster than the rate of increase in the earlier part of the record. Total NH VOC emissions represented by the database (lower panel, blue data) show a relatively larger rate of increase (∼ doubling), reaching a maximum around 1990, and leveling off at a much slower rate thereafter.
11  and scaled on the right x axis, show much better agreement between the timing of the emissions maximum and the NEEM reconstructed NMHC trends. There is, however, a notable discrepancy in the magnitude of the relative signal, with the emissions increase being more than two times as large as the increase seen in the reconstructed NMHC ambient air mole fractions. It should be noted that ∼ 70 % of the total NMHC signal is constituted by ethane and propane, compounds with lifetimes on the order of > 1 week. Consequently, this comparison may be flawed by the fact that gas species retrieved from the firn air are on average longer lived than VOC species considered in the emissions database. This comparison suggests that emission reductions have likely been more effective for shorterlived compounds (higher molecular weight VOC) than for the longer-lived species (ethane, propane).
The NGRIP data were made available to us, and NEEM + North GRIP multi-site constrained scenario reconstructions were performed (see Supplement Fig. 3  and Supplement Note about NEEM-North GRIP comparison). The results for all species are consistent within error bars for the NEEM-only based scenarios, and the ethane peak date remains in the early 1970s. When results from the NEEM boreholes and data from the different laboratories are considered individually (Fig. S1a ), they all result in ethane peak times prior to 1980. The firn record at NEEM has been analyzed for more species than any other site, making the age of the firn air at NEEM the best constrained. However, it should be noted that while we determined an earlier peak time consistently in our modeling results, the peak of the ethane maximum is relatively flat and our determined ethane peak time and the  Worton et al.
The relatively lower decline rate seen in the ethane trend in the firn data (1. 0 % for ethane vs. 2. 0-2. 7 % for the other NMHC) is not noticeable in the results from the flask data set; however, this may be due to the relatively high uncertainty in the network data trend due to the shortness of the record. Table  3  also lists for comparison the NMHC trend determinations from the  Aydin et al. (2011)  and  Worton et al. (2012)  reconstructions. The  Worton et al. (2012)  trend results tend to be at the higher end, with five of the six NMHC decline rates being larger compared to our and the  Aydin et al. (2011)  findings. This is most notable for ethane, where the  Worton et al. (2012)  trend is ∼ two times the one determined in the two other studies. Notably, the peak ethane firn values in  Worton et al.
NMHC-CO relationship Historic CO mole fractions were determined in parallel with the NMHC measurements. An in-depth presentation of the NEEM CO data and interpretation can be found in  Petrenko et al. (2013). Here, we compare the reconstructed histories of CO and ethane from the NEEM firn air analyses. Because Northern Hemispheric CO is also associated primarily with anthropogenic emissions, particularly from combustion sources, correlations of CO with NMHC can provide a useful indicator of anthropogenically emitted hydrocarbons. Additionally, the OH radical is the major sink for both compound classes and CO and ethane are oxidized at rates that are less than a factor of two different:  Atkinson, 2003) ; k OH + CO = 1. 44 × 10 -13 cm 3 molecule -1 s -1  (Atkinson et al. , 2004). A comparison of the CO and NMHC depth profiles shows similar behavior at depth, as well as similar seasonality.
The increase in ethane mole fractions corresponds to a period of rapidly increasing natural gas production in the United States, with a local maximum around 1970. While the more recent decline in CO suggests decreased emissions from fossil fuel combustion, the faster declines in atmospheric concentrations of ethane relative to CO determined from the firn analysis may reflect reductions in ethane emissions through further emission controls in natural gas production and transport technology. The NEEM simulated NMHC histories were compared with results from two other previously 922 published firn air borehole retrievals from other Greenland sites and with data from one ambient 923 air-monitoring program. Overall good agreement between these independent studies was 924 Fig. 12. Comparison of reconstructed histories of ethane and carbon monoxide from the NEEM firn air analyses.