dataesr/scientific-paragraphs-categorization huggingface dataset:
To generate the E distributions shown in Fig. 3 , we solved the model assuming that r(E) follows a Gaussian distribution with the following initial conditions: OC = 15. 5%, µE = 150. 3 kJ mol -1 and sE = 17. 7 kJ mol -1 ; that is, the median values for all retained samples in our database with OC ≥ 10% (n = 6). We again emphasize that our choice of initial distribution shape will not qualitatively influence µE and sE evolution. To generate the %OC vs. µE and %OC vs. sE trajectories shown in Fig. 4 , we used the canonically assumed initial OC content for recently produced organic matter of 50%. We then simultaneously determined the initial µE and sE values that minimize the root mean square error (RMSE) between theory and observations, again assuming that r(E) follows a Gaussian distribution.
Acknowledgements: This work is dedicated to  John Hayes , whose knowledge, support, and inspiration was always invaluable. We thank the  National Ocean Sciences Accelerator Mass Spectrometer  staff, especially  A. McNichol ,  A. Gagnon , and  M. Lardie-Gaylord  for RPO assistance;  P. Raymond ,  E. Kyzivat ,  R. Spencer , and  A. Stubbins  for supplying sample material;  B. Rosenheim ,  C. Schafer , and  X. Zhang  for providing access to raw data from published manuscripts;  A. Pearson ,  D. Johnston , and  A. Piasecki  for comments and discussions. This research was supported by: the  NSF   Graduate Research Fellowship Program  grant number  2012126152  (J. D. H. );  NASA Astrobiology  grant number  NNA13AA90A  and  NSF  grant number  EAR-1338810  (D. H. R); the  NSF-IGERT  in  Cross Scale Biogeochemistry and Climate at Cornell University  (K.
C yr. Nonetheless, inclusion of OCpetro does artificially increase µE, sE, and 14 C-based reservoir ages relative to purely biospheric OC. Because samples retained in our dataset might contain small OCpetro contributions (i. e. , less than 5% of total OC), these results should be taken as maximum values. Statistical analyses. Because scatterplots generally contain uncertainty in both X and Y variables, all linear relationships presented in this study (i. e. , Extended Data Fig. 2) were fit using reduced major axis regression 58. Selective preservation predictions. Finally, we developed a model to predict the evolution of µE and sE if OC preservation were controlled exclusively by selective preservation. This model predicts r(t,E), the mass of OC remaining at time t that is associated with activation energy E, as a function of G(t), the total mass of OC remaining at time t.
Results We recently showed that in the absence of external constraints (no task, no reward), CA1 activity self-organizes into sequences of neuronal activation ("run sequences") representing distance or duration during spontaneous run episodes in head-fixed mice running on a nonmotorized, uncued treadmill  (5). After expanding this dataset with additional calcium imaging experiments, we further analyzed the spatiotemporal representation in run sequences from 34 imaging sessions (n = 7 mice). In these experiments, mice were head-fixed under the objective of a microscope. Expression of calcium indicator GCaMP5, 6m, or 6f was virally induced, and surgery was performed to provide a chronic optical access to the pyramidal cell layer of dorsal CA1  (5, 21). During the 20-to 30-min imaging sessions, 140 ± 47 neurons were identified as being active within the 400 × 400 μm 2 field of view; 39 ± 11% of them were activated in run sequences (Fig.
ACKNOWLEDGMENTS. This project received funding from the  European Research Council  under  FP7  and  H2020 Program Grants 242842  and  646925 ;  German Research Foundation  Grant  RE 3657/1-1 ;  William Harvey International Translational Research Academy Co -funding of regional, national and international programmes from  European Commission (COFUND)  Grant  PCOFUND-GA-2013-608765  (to S. R. ); and  Aix-Marseille Initiative d'Excellence (A*MIDEX ) Grant  ANR-11-IDEX-0001-02 , funded by the  Programme Investissements d'Avenir  (to D. A. -G. and A. T. ).
Gel analysis Digital images of 2D-gels were acquired using ChemiDoc™ XRS imaging system (Bio-Rad) with internal calibration. The acquisition parameters were 300 dpi and epi white illumination. Gel analysis was performed by adopting PDQuest software (Bio-Rad) for spot detection, according to manufacturer's protocol. Spot volume normalization, in the various 2-DE maps, was carried out using the relative spot volumes (% Vol). Initially, automatic spot detection was performed, followed by manual editing for spot splitting and noise removal. The gels containing the largest number of protein spots for each phase were chosen as the reference gels. All other gels were matched with the reference gel by placing user landmarks on approximately 10% of major and minor protein spots, which were visualized to assist in automatic matching.
HR-LC-MS/MS The 1D protein spots were analyzed using 6550 i-Funnel QTOF-LC-MS/MS coupled with 1260 In nity Nano pump and 1260 Cap pump along with 1260 Chip-cube (Agilent Technologies). The peptides were fractionated along with Solvent A (0. 1% formic acid in milliQ water) and Solvent B (90% acetonitrile + 0. 1% formic acid + 10% milliQ water). For MS measurements, we employed the positive-ion mode with mass range of up to m/z 4000 with the resolution setting 60,000 at m/z 400. The proteins were identi ed by comparison with the SWISS-PROT database entries. Search parameters for MS data were, species: Homo sapiens; Protein Mass: 0-500 kDa; Protein pI: 3-10; Enzyme: trypsin; Miscleavage: 1; Mass type: monoisotopic; Charge state: MH+; precursor and product mass tolerance +/-50 and +/-100ppm, respectively; Fixed modi cation: carbamidomethylation of Cystine (C); Variable modi cations: oxidation of methionine (M).
All the mass spectrometry data have been deposited in the ProteomeXchangeConsortium  [21]  PRIDE (http://proteomecentral. proteomexchange. org) partner repository with the dataset identi er PXD004511. The protein dataset of ovulation (Supplementary le. S1) and the post-ovulation (Supplementary le. S2) was obtained and listed. MALD TOF/TOF analysis 2D protein spots were processed using an automated gel cutter and processor (Shimadzu, Xcise™). The gel spots were washed and destained with 50% ACN and 50 mM NH4HCO3 (Solvent 1), and subjected to in-gel digestion with 30 μL of solvent 7 (50 μl of trypsin stock solution in 4 ml of 50 mM of NH4HCO3) for 2 hr at 37 ºC. ZipTips (C18) were wetted and conditioned with 50% ACN and 0. 05% TFA (Solvent 5) and 0. 1% TFA (Solvent 3), respectively. Cleaved peptides bound to the C-18 resin were desalted using 0.
Data processing The acquired mass spectra were processed using DataExplorer® software, and the mono-isotopic peptide masses were assigned and used in the database search. The protein identi cation was analyzed against Homo sapiens protein sequence inMASCOT database search (http//www. matrixscience. com) using SWISS-PROT database entries. Modi cation of cysteine by carbamidomethylation and methionine by oxidation was allowed. The precursor and product mass tolerance were set as +/-50 and +/-100 ppm, respectively. Two or more unique peptides for each protein were taken for con rmation of the protein present in the sample.
Ovulation speci c proteins Several functionally important ovulation phase-speci c proteins were identi ed and listed. Table  1  shows the list of 30 functionally important proteins related to reproduction during ovulation phase. The biological functions of protein were deduced from Software Tool for Researching Annotations of Proteins (STRAP 1. 5 online database) (http://www. bumc. bu. edu/cardiovascularproteomics/cpctools/strap/); the theoretical pIs and monoisotopic molecular weights were calculated adopting the procedure in Swiss-Prot -ExPASy website (http://web. expasy. org/compute_pi). Cystatin-S,Disintegrin and Metalloproteinase domain-containing protein 7, TANK-binding kinase 1 and Exportin 7 appeared to be predominant, having more number of peptide identi cations.
Gene ontology The unknown, uncharacterized and hypothetical proteins were identi ed and removed. Further, the repeated proteins in the protein datasets were removed. GO entries were predicted using the GI number of proteins in UNIPROT database. After re nement of datasets the ovulation and post-ovulation phases contained 154 and 117 GO entries, respectively. The gene ontology clearly revealed more UNIPROT entries and greater percentage of annotation during the ovulation phase than the other phases.
2D-Gel electrophoresis The salivary protein expression pro les were assessed during pre-ovulation, ovulation, and post-ovulation phases of menstrual cycle (Figure  4A-C ). In Commassie-stained 2D gels, protein spots were present in the pI range 4-7 and the molecular weight between 14-97 kDa. The spot analyses carried out during different phases using PDQuest software (Bio-rad) showed nearly 50 in each phase. Particularly, there were more number of spots during ovulation phase, and many of them had higher densities compared to the other phases. Additionally, we found that the quantitative differences in the spots were high in 14. 5 to 21 kDa spot region of ovulatory phase proteins compared to the other phases. It was noticed that a few low molecular weight protein spots were speci cally expressed during the ovulation phase (Figure  5A ).
Identi cation of proteins from selected spots Sixteen spots with signi cant difference in intensities i. e. , 2-fold increase, were excised from 2D gel of ovulation phase salivary proteins. The spots were subjected to trypsin digestion and submitted to mass spectrometry. The monoisotopic mass were searched in protein database of human from MASCOT search to identify the full length sequence. In case of spot 1, it closely matched with cystatin-S family protein with 71% sequence coverage and eight matched peptides (Figure  6A  & B, Table  2 ). The differentially expressed spots were identi ed as (Table. 3): Cystatin-S, Prolactin-inducible protein, Cystatin-A,Cystatin-SN, BPI fold-containing family A member 2, Alpha-tubulin N-acetyltransferase 1, Carbonic anhydrase 6, Protein LEG1 homolog, Hemoglobin subunit beta, and Pancreatic alpha-amylase.
More speci cally, Carbonic anhydrase is present during all phases of animals exhibiting the estrous cycle. Indeed, there is a signi cant expression of Carbonic anhydrase in cattle, camel and goat  [28]. In the human, recent studies have con rmed that secretion of speci c peptides/proteins is different in the pediatric age compared to adults  [29]. The functional annotation of the identi ed proteins obtained from STRAP database showed that these proteins are associated with binding property and regulatory function. In addition, these extracellular proteins would play important role during the time of ovulation. It is to be noted that proteins having binding property are widely present along with volatiles in body uids and facilitate chemical communication during the estrusperiod  [30, 31]. A more recent report showed that in estrus cycling mammals binding protein are abundant during estrus compared to the other phases  [32].
Acknowledgments G. S. (First author) thanks the  Indian Council of Medical Research (ICMR-New Delhi, India ; No. 45/5/2014-BIO/BMS ) for the  Senior Research Fellowship. G. A. of the article thanks to the University Grants Commission,  New Delhi , for the award of  UGC-BSR Faculty Fellow. Manali Jadhav  and  Pradnya Nikam  thank  SAIF ,  IIT-Bombay, Powai, Mumbai , for help in the LC-MS/MS analysis. The authors thank  PRIDE Team  for processing the data in the MS, which have been deposited with the ProteomeXchange Consortium.
OBSERVATIONS AND DATA REDUCTION 2. 1. Chandra RXJ0821. 0+0752 was observed for 29 ks on 2014-12-15 (ObsID 17194) and 37 ks on 2014-12-28 (ObsID 17563) using the ACIS-S3 detector on the Chandra X-ray Observatory. The observations were reprocessed using CIAO version 4. 5 and CALDB version 4. 6. 7. We applied charge transfer inefficiency and time-dependent gain corrections to the level 1 event files, which were then filtered to remove photons with bad grades. Periods affected by flares were identified and filtered using the LC_CLEAN script. The final exposure time of the cleaned data was 63. 5 ks. The final background-subtracted image, shown in Fig. 1  (left), was created by reprojecting the observations to match the position of the longest exposure (ObsID 17563) and summing all events in the 0.
7. 0  (McMullin et al. 2007 ) using the pipeline reduction scripts. Continuum-subtracted data cubes were created using UVCON-TSUB and CLEAN. Images of the line emission were reconstructed using Briggs weighting with a robust parameter of 2. An additional uv tapering was used to smooth the CO(3-2) image on scales below 0. 1 arcsec. The final CO(1-0) and CO(3-2) data cubes had synthesized beams of 0. 61 × 0. 59 (P. A. -70. 4 • ) and 0. 21 × 0. 165 (P. A. 37. 2 • ), respectively. The CO(1-0) and CO(3-2) images were binned to 3 and 5 km s -1 velocity channels, respectively. The RMS noise in the linefree channels were 0. 5 and 1. 1 mJy beam -1 , respectively.
Without an image of the radio source we cannot conclusively determine if this feature is an X-ray cavity. The existing radio data shows no extended structure on 2 -5 arcsec scales at 1. 4 or 5 GHz, although the radio emission is offset along the direction of the X-ray filament  (Bayer-Kim et al. 2002). Our ALMA imaging shows that the continuum emission at 98. 8 and 304. 6 GHz is similarly offset from the BCG nucleus and is coincident with the molecular gas. The lack of radio emission from the galactic center indicates that the AGN is currently quiescent, so the surface brightness depression may simply be a lack of bright emission when contrasted with the X-ray filament. Nevertheless, we measure the size of the surface brightness depression and compute its energetics should it be a real cavity.
CONCLUSIONS In this work we have performed a morphological analysis of ALMA CO(1-0) and CO(3-2) observations and presented a new 63. 5 ks Chandra X-ray observation of the cool core cluster RXJ0821. 0+0752. This extends the previous analysis of the same ALMA data conducted in V17, where the analysis focused on using the line intensities, along with a 13 CO(3-2) detection, to constrain the CO-to-H 2 conversion factor. The entire 10 10 M supply of molecular gas is located in an 8 kpc long filament spatially offset from the galactic center by about 4 kpc. The emission is concentrated in two bright peaks surrounded by a diffuse, 2 -5 kpc wide envelope. It is coincident with a bright plume of X-ray emission situated alongside a putative X-ray cavity. The narrow spread in both position and velocity suggests that the molecular gas formed relatively quickly and in a single cycle of cooling.
ADNI Participants and study design This is a cross-sectional study in which CSF sTREM2 was measured in 1031 participants of the ADNI project. Among them, 4 individuals did not have the AD core biomarkers measurements and were further excluded from the analysis, rendering a study sample of 1027 subjects. The CSF sTREM2 measurements were uploaded to the ADNI database (http://adni. loni. usc. edu) on 16/03/2018 and the data used in this study was downloaded on 21/03/ 2018. The ADNI project (http://www. loni. usc. edu/) is a multicenter longitudinal study led by Principal Investigator Michael W Weiner with the main goal to develop and validate biomarkers for subject selection and as surrogate outcome measures in late-onset AD  [30]. The institutional review boards (IRB) of all participating centers approved the procedures of the study and all participants or surrogates provided informed consent.
Alzheimer's disease CSF core biomarkers and CSF sTREM2 measurements In the present study, we used the AD CSF core biomarkers measurements performed with the Elecsys® total-tau CSF, the Elecsys® phosphotau(181P) CSF and the Elecsys® β-amyloid(1-42) CSF immunoassays on a cobas e 601 instrument  [39, 40]. The data is available in the 'UPENNBIOMK9. csv' file in the ADNI database. These immunoassays are for investigational use only. They are currently under development by Roche Diagnostics and not commercially available yet. The analyte measuring ranges (lower technical limit to upper technical limit) of these assays are the following: 80 to 1300 pg/ml for total-Tau CSF, 8 to 120 pg/ml for phosphor-Tau(181P) CSF, and 200 to 1700 pg/ml for Elecsys® β-Amyloid(1-42) CSF immunoassays. The measuring range of the Elecsys® β-Amyloid(1-42) CSF immunoassay beyond the upper technical limit has not been formally established.
Therefore use of values above the upper technical limit, which are provided based on an extrapolation of the calibration curve, is restricted to exploratory research purposes and is excluded for clinical decision making or for the derivation of medical decision points. CSF sTREM2 measurements were done with a MSD platform-based assay, previously reported and validated  [2, 25, 26]. A comprehensive description of the assay is shown in Supplementary methods (see Additional file 1: Supplementary methods). The CSF sTREM2 measurements are publicly available in the ADNI database.
The analysis with and without outliers rendered similar results. For CSF Aβ 1-42 , the analyses were performed using both the truncated values at the upper technical limit and the exploratory measurements available based on the extrapolation of the calibration curve. In the main text, we report the results using the extrapolated measurements, but using the truncated ones yielded similar results. Statistical analysis was performed in SPSS IBM, version 20. 0, and the free statistical software R (http:// www. r-project. org/). Figures were built using GraphPad Prism or free statistical software R. All tests were 2-tailed, with a significance level of α = 0. 05.
Association of CSF sTREM2 with genetic and demographical data We studied a total of 1027 participants of the ADNI study. The demographical and clinical characteristics of the whole study population are described in Table  S1  (see Additional file 1). Among the participants studied, 43 (4. 2 %) had a known TREM2 rare variant (see reference  [41]  for a comprehensive review of the pathogenicity of each variant). The overall mean levels of CSF sTREM2 of these individuals (M = 3913 pg/ml, SD = 3548, n = 43) were significantly lower than the rest of ADNI participants without a TREM2 rare variant (M = 4136 pg/ml, SD = 2171, n = 984; F 1,1024 = 6. 77, P = 0. 009, η p 2 = 0. 007; Table  2 , Fig. 1 ) in a one-way ANCOVA adjusted for age. However, CSF sTREM2 varied considerably between TREM2 variants (F 4,1019 = 8.
Nat. Neursci in press) and are therefore overrepresented in the Preclinical AD A +TN-group. Nevertheless, we are cognizant of the fact that this is an observational study and the findings reported herein do not elucidate precise mechanisms. Further work with longitudinal data is needed to address whether the stage-dependent changes in CSF sTREM2 predict a better or worse clinical outcome. In conclusion, the present study represents the first attempt to study CSF sTREM2 based on the A/T/N classification framework. We demonstrate in the ADNI cohort that the increase in CSF sTREM2 which occurs in early stages parallels the increase in biomarkers of tau pathology and neurodegeneration. In contrast, Aβ in the absence of tau deposition and neurodegeneration is associated with lower CSF sTREM2.
Acknowledgements We would like to thank all the researchers and participants in the ADNI initiative. We also thank  Nicole Exner  for technical assistance and  Nicholas Ashton  for critically reading the manuscript and helpful discussion. Data used in preparation of this article were obtained from the  Alzheimer's Disease Neuroimaging Initiative  (ADNI) database (adni. loni. usc. edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni. loni. usc. edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List. pdf.
Results Characteristics of selected B. cenocepacia strains selected for comparative analyses Two hundred forty-six of the 303 genomes (either full or draft) of B. cenocepacia strains available on the NCBI database, at the time of this study, are clinical isolates (Additional file 1: Figure  S1 ). They were sampled from patients suffering from CF, from other pathologies or from healthy patients. The isolates also vary according to the source of biological sample they originate from. Most clinical isolates were obtained from sputum or blood samples, but some were also isolated from hospital equipment  [30]  as B. cenocepacia is resistant to many common antibiotics as well as several sanitizers  [31, 32]. The remaining B. cenocepacia strains with available genomes come from environmental sources.
Core-genome phylogenetic analysis The genomes of 31 B. cenocepacia strains were compared and their core-genome extracted (Additional file 4: Table  S1 ; refer to Methods section for details on strain selection). The resulting 1057 conserved genes were aligned and studied in a phylogenetic analysis using the Maximum Likelihood method (Fig. 1 ). This result was validated through a Bayesian prediction using the BEAST software (Additional file 2: Figure  S2 ). Both approaches yielded comparable reconstructions. An additional tree resulting from a Neighbor Joining analysis with 1000 bootstrap repetitions is also available (Additional file 3: Figure  S3 ). Among the 31 strains labelled as B. cenocepacia, three (869 T2, DDS 22E-1 and DWS 37E-2) fall outside the main clade. The 28 other strains fall within three main clades.
No closer ANI was found with any of the phylogenetically closest Burkholderia species (data not shown). Similarly, the three strains of the third clade do not display any ANI ≥ 95% with B. cenocepacia. Their closest Burkholderia relative is B. cenocepacia strain FL-5-3-30-S1-D7 with 94% ANI value. Finally, three strains that were originally described as B. cenocepacia show closer identity with other species (Additional file 5: Table  S2 ). Strain 869 T2 should be affiliated to the Burkholderia seminalis taxon (98. 99% identity with 88. 85% cover). Strain DDS 22E-1 shares high ANI scores with Burkholderia pseudomultivorans (97. 57% identity with 80. 85% cover) while strain DWS 37E-2 is related to Burkholderia latens with 99. 01% homology and 89. 92% cover. Two genome alignment methods were used for the ANI analyses, one based on BLAST+ (ANIb) and the other on MUMmer (ANIm).
Analysis of core-genome features involved in hostadaptation We analyzed the core-genome of B. cenocepacia s. s. and looked for genes that are strictly absent from the coregenome of Burkholderia sp. nov. and reciprocally. This list was further curated from genes with convergent functions when those were successfully annotated. The coregenome of Burkholderia sp. nov. comprises 150 genes that are missing from B. cenocepacia whereas the latter harbors Fig. 2  Whole-genome comparisons of 31 B. cenocepacia strains. The calculations were performed using the Python module PYANI  [49]. Two major identity clusters are formed. The bottom cluster consists of B. cenocepacia strains and the second cluster consists of Burkholderia sp. nov. strains. One minor identity cluster is formed by the three outlier strains (Bp9038, CEIB_S5-2, Bp8974) and the last three strains are neither genetically related to B.