dataesr/scientific-paragraphs-categorization huggingface dataset:
Fewer pathways were related to the formation of ECM. A significant upregulation of inflammatory signalling pathways, such as for neutrophil degranulation and the innate immune system, was also observed (supplementary figure  S2b ). We further analysed the proteomics data to investigate the dynamics of different cell types underlying the observed changes in protein levels. Human lung tissue is mainly composed of immune, mesenchymal, endothelial and epithelial cells. The LGEA web portal has established a database of gene expression of each cell type (which is also age-dependent, from neonatal to adult stages) in both mouse and human lungs  [39, 40]. In this database, the RNA-seq of flow-sorted human lung cells have been designated as CD45 + immune cells, CD45 -/PECAM -/VECadherin -/EpCAM + mixed epithelial cells, CD45 -/PECAM + / VECadherin + mixed endothelial cells and CD45 -/PECAM -/VECadherin -/EpCAM -mixed mesenchymal cells.
The parent list can be downloaded from the LGEA database under "LungSortedCells". We compared (supplementary table  S3a ) significantly changing (Log2FC⩾0. 5 and ⩽-0. 5 and false discovery rate (FDR) p⩽0. 05) proteins on day 13 versus day 1 to the list of proteins attributed to different cell classes (in LGEA  [40]  analysis). Supplementary figure  S2c  shows markers for four broadly categorised major cell types, namely, epithelial, endothelial, mesenchymal and immune cell types, which are typically present in lung tissue on day 13 in our culture system. Based on the LGEA database we also curated a list of commonly used lung cell-type markers (supplementary figure  S2c , supplementary table  S3 ). Of note, advanced glycosylation end-product specific receptor (AGER) (alveolar type 1) and surfactant protein C (SFTPC) (alveolar type 2) were both significantly downregulated in our ex vivo culture, while SFTPB, another alveolar type 2 marker, showed significant upregulation on day 13 compared to day 1.
The 109 proteins significantly regulated (supplementary figure  S4a ) upon TGF-β1 treatment were curated against the LGEA database for cell type-specific markers (supplementary table  S5 ). This analysis showed that the majority of the significantly downregulated proteins (39 out of 44) are specific for epithelial cells MMP9 MMP3 MMP28 MMP2 MMP19 MMP14 MMP12 MMP10 MMP1 COL8A1 COL7A1 COL6A6 COL6A5 COL6A3 COL6A2 COL6A1 COL5A2 COL5A1 COL4A3 COL4A2 COL3A1 COL1A2 COL1A1 COL18A1 COL16A1 COL15A1 COL14A1 COL12A1 Days in ex vivo culture while those upregulated (64 out of 65) are specific for mesenchymal cells, suggesting that TGF-β1 stimulation might induce an EMT-based pro-fibrotic signalling system  [5]  under the culture conditions used here. To confirm that it is EMT requires further analyses. Both ELISAs for culture supernatant and the proteomic analyses of the cellular fraction showed high upregulation of collagens upon TGF-β1 stimulation (figure  4a, b ).
For MMPi-stimulated hPCLS, seven donors were analysed. Donor identification numbers: LT08, LT13, LT18, LT19, LT35, LT36, LT37, LT38, LT39, LT40, LT44, ET01, ET02, ET03, ET04, ET05, ET06. The ELISA data from donors LT08, LT13, LT18, LT35, LT36, LT37 on day 13 are the same in figures 4a and 6c, d. The data are reused in this figure to represent unequivocal upregulation of PRO-C1 and FBN-C across all donors. NS: nonsignificant. analysed, concomitant treatment with MMPi resulted in a consistently higher fibrillar collagen signal compared to TGF-β1 treatment alone. Together, these data confirm that metalloproteinase expression/ activity is a rate-limiting factor in the hPCLS model system characterised here for deposition of fibrillar collagen upon TGF-β1 stimulation.
Discussion hPCLS have emerged as a model system with high potential for recapitulating pathophysiological conditions close to that of human physiology  [11, 19]. However, characterisation of the inherent molecular changes that such a system undergoes in ex vivo culture has not been reported to date. In this study, we report for the first time proteomic and metabolomic changes of hPCLS over 2 weeks of ex vivo culture. This is significantly longer than the 7 days previously reported  [11, 19]. These cell-type data (supplementary figure  S2c ) were derived from curation of our phenotypic protein expression data to that of RNA-seq data available in the LGEA database. Comparison of the LGEA database sequencing list and recently published single-cell sequencing results of human lung tissue  [66] [67] [68]  shows that some markers classified in the LGEA database as cell type-specific are actually present in more than one cell subtype (e.
Acknowledgements: Technical assistance from  Christa Stolp  from  Biomaterial Bank Heidelberg (BMBH)  in tissue assembling is gratefully acknowledged. We would also like to acknowledge the help of  Vikki Barrett  ( GSK Stevenage ) for help in training with Krumdieck. We sincerely thank  Katrin Strohmer  and  Anna Rutkowska-Klute  ( Cellzome-GSK ) for their help in setting up the SHG imaging. The Pepperkok team and all the EMBL core facilities are also acknowledged for their support, manuscript preparation and fruitful discussions for data analysis.
Statistical analysis Study staff at participating centers entered echocardiographic data into an electronic capture system. The Sponsor monitored it before it was sent to the ECL. Comparisons of baseline and procedural characteristics between the subset of patients analyzed and the rest of the SOURCE 3 cohort were conducted using the Wilcoxon sum rank test for the continuous variables and the Fisher exact test for categorical variables. Echocardiographic parameters were compared between discharge and at 1 year, using paired analysis with the Wilcoxon sum rank test. Mean gradient, effective orifice area (EOA), and GLS were compared (baseline vs. discharge and baseline vs. 1 year), using a paired analysis with the t test.
Baseline and procedural data A total of 276 patients were enrolled in the echocardiographic analysis between July 2014 and October 2015 in 16 European centers. In summary, patient baseline characteristics were a mean age of 80. 8 years and a mean EuroSCORE II of 4. 6 ± 3. 98 (Table  1 ). The latter was statistically lower than the mean EuroSCORE II of the SOURCE 3 patients not included in this sub-analysis (p = 0. 002), as more Echo patients had a logistic EuroSCORE of < 10%, compared with other patients of the SOURCE 3 cohort (35. 0% vs. 28. 2%; p = 0. 041), and fewer echo patients had a logistic EuroSCORE of > 30% compared with other patients of the cohort (10. 2% vs. 16. 6%; p = 0. 011). Most other baseline clinical characteristics and comorbidities were comparable between the subset of patients analyzed and the rest of the SOURCE 3 cohort, except for hypertension (72.
Single Crystal X-ray Diffraction (SCXRD) Crystals of complexes I, II and III suitable for single crystal X-ray diffraction were mounted with Fomblin© in a cryoloop. Data was collected on a BRUKER D8 QUEST diffractometer with graphite-monochromated radiation (Mo Kα, λ = 0. 7107 Å), at 293 K. The X-ray generator was operated at 50 kV and 30 mA, and the X-ray data collection was monitored by the APEX3  [55]  program. All data were corrected for Lorentzian, polarization and absorption effects using SAINT +  [56]  and SADABS  [57]  programs. SHELXT  [58]  was used for structure solution and SHELXL-2014/7  [59]  was used for full matrix least-squares refinement of F 2. These three programs are included in the package of programs called WINGX version 2014. 1  [60, 61]. Non-hydrogen atoms were refined anisotropically.
Powder X-ray Diffraction (PXRD) Powder X-ray diffraction data were collected on a D8 Advance Bruker AXS diffractometer with a Linxeye-XE detector and a copper radiation source (Cu Kα, λ = 1. 5406 Å), operated at 40 kV and 30 mA, considering the 2θ range from 3-60 •. The program MERCURY 4. 3. 1  [62]  was used to obtain the diffraction patterns calculated from single-crystal data. The purity of the bulk was verified by comparing the calculated and observed powder X-ray diffraction patterns.
Variable Temperature Powder X-ray Diffraction (VT-PXRD) The study of structural transitions with temperature the synthesised compounds was performed in a Bruker D8 ADVANCE powder diffractometer equipped with a SSD160 detector and a copper radiation source (Cu Kα, λ = 1. 5406 Å), operated at 40 kV and 40 mA, considering the 2θ range from 6-40 •. An Anton Paar HTK-16 high temperature chamber (RT-1400 • C) was coupled to this diffractometer. The diffractograms were acquired for five different specific temperatures for each compound during the total scan time of 171 s, with 300 s of delay for temperature stabilization, using a step size of 0. 02 •. The percentage of crystallinity was determined using the software DIFFRAC. EVA  [64].
Search Strategy This review conforms to the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (Table  S1 ). 44 A protocol was registered on PROSPERO (International Prospective Register of Systematic Reviews) (CRD42017081974). A systematic search of the MEDLINE, EMBASE, and SCOPUS electronic databases was performed from database inception without language restrictions; the last search was performed on March 3, 2019 (search string Table  S2 ). Studies from database inception were eligible, as we had no a priori justification to limit studies to a certain era. The authors declare that all supporting data are available within the article and its online supplementary files (Data S1).
Exclusion Criteria Studies were excluded if the study was performed on animal subjects; BPV was quantified during the acute or subacute stage (ie, <4 weeks) of stroke or TIA; BPV was reported in the context of BP instability, stress reactivity, or postural change, such as experimental designs with a stress challenge, orthostatic change, or head-up tilt-table testing; the cohort was designed to test the prognosis of coronary revascularization or renal denervation, or the cohort was designed to assess dementia, Parkinson disease, leukodystrophy, or other degenerative neurological disorder prognosis; the report described a case study; effect size data could not be extracted, calculated, or provided by the study authors; or the study reported only brain atrophy or brain volume. Inconsistencies in brain imaging terminology are well documented.
Literature Screening Three reviewers (P. J. T. , K. G. , and M. O. ) independently screened titles and abstracts of all the retrieved bibliographical records for eligibility using a data abstraction protocol. 49  hand search was performed of the articles selected for fulltext review and of narrative reviews,  [50] [51] [52] [53]  supplementing the electronic search. In the case of title/abstract review disagreements, the study was subjected to full-text review. After determination of the study eligibility, data extraction was undertaken independently by 3 reviewers (P. D. P. , K. G. , and E. J. L. L. ) and verified by a fourth reviewer (P. J. T. ) to reduce reviewer errors, reduce bias, and achieve consensus. The data extracted pertained to study identification (first author, year of publication, country where recruitment took place, and name of study or trial), study design characteristics (sample size and study design) of the population under study (age, sex, use of antihypertensive medication, and prevalence of stroke), BP and BPV exposure (type of measure, BPV metrics, duration of BP measures, and observation period), CSVD adjudication (subtypes and definition and imaging methods), effect size (most adjusted effect size or raw numbers) adjustment for covariates (list of variables), and funding (grant numbers or acknowledgement).
A request for additional published, unpublished, and in progress articles was sent through the Variability in Blood Pressure and Brain Health consortium (members listed in acknowledgements). 54 The principal investigators of eligible studies were contacted to clarify published and unpublished data and duplicate articles. Study authors were contacted via e-mail to request the unreported data. If the initial e-mail was not replied to within 2 weeks, a second e-mail was subsequently sent. Confirmation of ineligible data was provided by 1 author,  55  eligibility was confirmed for 2 studies,  56, 57  and additional unpublished data were provided by 2 studies. 58, 59 sk of Bias The RTI item bank was used to identify methodological bias for primary outcomes 1 and 2 for all studies. 60 Risk of bias assessment was undertaken by 2 reviewers (E.
CSVD Grouping and Outcome Adjudication 1. Primary outcome odds for CSVD attributable to BPV: If BPV's association with CSVD was reported for >1 CSVD subtype, we prioritized the analysis with the largest number of CSVD end points, handling CSVD subtypes in stratified analyses. If models were adjusted, we extracted data from the most adjusted model. In instances where different levels of adjustment were made for mean and BPV data, we prioritized data from the same model to ensure equivalence in covariate adjustment. When >1 BPV metric was reported for CSVD subtypes, we prioritized the most adjusted model and models with the largest number of participants. If multiple metrics were available after applying this hierarchy, we extracted both outcomes and reported in sensitivity analyses. 2. Difference between groups in CSVD: If BPV meanAESD was reported in different CSVD pathological conditions or severities, the subgroup with the highest N was used, prioritizing statistical power over severity,  62  ensuring one effect size was analyzed per study rather than collapsing CSVD groups.
Mean BP Mean BP data were extracted for eligible studies. Odds ratios (ORs) and risk ratios for CSVD were standardized to 10/5mm Hg increase, multiplying the log of the summary statistic by 10/5, depending on whether systolic BP or diastolic BP was being modeled. It was not possible to standardize the OR effect sizes per SD increase because of inconsistent reporting in the original studies. Between CSVD group differences in BP were modeled with standardized mean differences (SMDs); thus, no standardization was necessary.
6 -SD method; mean BP extracted from Table  6. 9 SBP only). CV data are also shown here in our ancillary analysis. BPV data for CMBs was only reported using pulse pressure metrics and not systolic or diastolic BPV. The periventricular odds ratios were used to calculate d. The Spearman's rho correlations with WMH reported in Table  8. 8 were deemed unsuitable for pooling. In the study by Yamaguchi  27  the systolic CV [Table  3 , total sample] was prioritized over the SD values for CSVD progression, as the systolic CV was presented in a multivariable adjusted model. The diastolic BPV analyses for CSVD progression also utilized CV to remain consistent (adjusted, model 2). Analysis of CSVD progression was restricted to the sample with CSVD (n=174) and adjusted CV-BPV analysis. In the study by Yang et al.
28  the association between BPV with CSVD was available for EPVS in basal ganglia (Table  4 , pg. 9) but not EPVS in the white matter. We extracted CV data (24-hours) as this data was available for systolic and diastolic BPV. For between group differences we compared EPVS in the basal ganglia (grade 3 versus 1), estimating the Mean from Fig 1 pg. 7 to calculate a t-statistic and SMD based on the reported p value (only median and IQR values were reported in Table  3 ). In the study by Yang et al. 29  data total CSVD burden was extracted from Table  3  (Model 2) and the rationale was that both mean and BPV metrics were reported in this adjusted model. Data extracted for our primary analysis was for the SD method (CV and weighted SD also shown in our ancillary analysis). For comparative purposes with mean SBP, the ancillary analyses extracted data from Model 2 (including day vs.
Circular Dichroism (CD) Spectroscopy CD spectra and CD-monitored denaturation/renaturation experiments were recorded-using a quartz cuvette from Hellma (1 cm path length and 3 mL internal volume)-on a Jasco J-715 spectropolarimeter supplied with a Peltier-type temperature control system (model PTC-348WI). The following CD parameters were used: 220-320 nm range, data pitch 1 nm, band width 2 nm, response 4 s, scanning speed 100 nm/min, 3 accumulations, with the proper baseline subtraction  [78]. All the investigated oligonucleotides were dissolved in the selected saline conditions at 2 µM concentration. Thermal melting/annealing curves were recorded following the CD signal intensity-at the maximum ellipticity-as a function of the temperature with a scan rate of 1 • C/min. In parallel, CD spectra in 220-320 nm spectral window and 5 • C steps were also recorded in the 15-95 • C temperature range for both the selected buffer solutions.
t. , at constant 200 V for 2. 5 h, then stained with GelGreen Nucleic Acid Stain (supplemented with 0. 1 M NaCl) for 30 min and finally visualized with a UV transilluminator (BioRad ChemiDoc XRS). Each experiment was performed at least 5 times. The intensity of the DNA bands on the gel, at each collected time, was then calculated by using the FiJi software and normalized with respect to the initial one (untreated oligonucleotides). Percentages of the remaining intact oligonucleotide are reported as mean values ± SD for the multiple determinations. Half-life times of each oligonucleotide (t 1/2 ) was obtained by fitting the values with an equation for first order kinetics.
UV and CD spectroscopic data revealed that the overall folding topology of the cyclic NU172 variants was unaffected by the cyclization and that all the investigated aptamers folded into duplex-quadruplex structures similar to that of the linear aptamer, with the G4 module always maintaining its peculiar antiparallel G4 conformation. In both the investigated phosphate buffer solutions, the cyclic analogues showed G4 structures with generally improved thermal stability compared to those found for unmodified NU172. In addition, all these cyclic variants proved to be more resistant to nuclease digestion than their natural counterpart, showing cycNU172-Pro as the most stable derivative against enzymatic degradation, with a three-fold higher half-life compared to linear NU172. Thus, the analysis of the cyclic NU172 derivatives further confirmed that the cyclization approach can be a convenient strategy to effectively reduce the susceptibility to nucleases and improve both the serum and thermal stability of bimodular aptamers with respect to their linear parent compounds.
Educational poverty was measured by the percentage of entrants to the last year in primary school relative to the total number of children in the same age as that for enrolling in that school grade  [48]. Monetary poverty was measured by the percentage of the population below the national poverty line per country  [49]. The outcome variable was the percentage of 3-5-yearold children with ECC. This data was collected in a previous study  [26]  through comprehensive search of the World Health Organization database and the existing literature without restriction by gender or language. The literature search used systematic methodology and was conducted in several databases reported in the primary study, using the definition of ECC adopted by the American Academy of Pediatric Dentistry: ECC was defined as one or more decayed, missing due to decay or filled primary tooth surfaces in children less than 72 months of age  [50].
The percentage of children with ECC per country was calculated by adding the number of those affected by ECC in all relevant studies and dividing this by the total number of children examined and multiplying by hundred. We calculated the number and percentage of LICs and MICs from which we were able to extract information on all the variables (Additional file 1: Appendix 2). The numerator for the percentage calculated was the number of the countries for which data for all the study variables were extracted. The denominator was the number of countries in each income category listed by the World Bank for 2017  [51]. We also calculated means and standard deviations for the indicators of poverty. The indicator for health (percentage of children receiving ORT and food after diarrhea), information (number of mobile subscriptions per 100 persons) and education (percentage of entrants to last grade of primary school) were reversed to reflect that the higher the prevalence the higher the poverty in line with the other measures of poverty used for this study.
Model 2 included the seven indicators of poverty together. Model 3 assessed the relationship with monetary poverty. Model 4 included the seven indicators of poverty, and monetary poverty. We calculated regression coefficients (B) and 95% confidence intervals (CI) in addition to adjusted R 2 as a measure of model ability to explain the variation among LICs and MICs in the percentage of 3-5-year-old children with ECC. Statistical analysis was performed using IBM SPSS for Windows version 22. 0 (IBM Corp. , Armonk, N. Y. , USA). Significance level was set at <5%.
This combination also explained more variation than combining all the seven indicators of poverty along with he tmonetary poverty indicator. The study hypothesis was therefore, only partially sustained. One of the strengths of the study was its use of large datasets collated from measures derived from the global surveys such as the Demographic Health Survey and MIC, which provide high quality data  [53]. We also did not use a summative index for poverty thereby reducing the risk for discounting item-level distinctions, and showing clearly that different measures of poverty do not have the same impact on ECC. Despite the strengths of the study, the study findings need to be interpreted with caution because of the possible fallacies associated with ecological studies likes ours. The proxy measures of poverty were not all age specific.
We also did not control for traditional risk factors for ECC like oral hygiene, sugar consumption, tooth brushing, use of fluoride and access to oral healthcare as these remain largely unknown due to lack of country-level data. In addition, because of data availability issues, our sample consisted of mostly MICs and few LICs. The differences in the distribution of poverty indicators between MICs and LICs may have affected our findings. We used data that generally targets non high-income countries (HICs) implying that our findings should not be generalized to HICs. In spite of the study limitations, the findings provide insights that can help in generating hypotheses for further studies on ECC and poverty. In the present study, monetary poverty was associated with less ECC in univariate regression.
Relative peptide quantification based on label-free LC-MALDI. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium  [38]  via the PRIDE  [39]  partner repository with the dataset identifier PXD014121 and 10. 6019/PXD014121. Measurements were performed mostly as described in  [35]. Ratios and t-test p-values for all (m/z ; retention time) coordinates were calculated using were likely to lower the apparent relative amounts of individual proteins of interest in the corresponding samples. In order to avoid such bias, proteins found increased in iATDC5 cells at these time points were cross-validated by calculations of their changes in iATDC5 cells between successive time points, from day 14 to 35. All allowed calculations were exported to proteinscape 2. 4 server.