dataesr/scientific-paragraphs-categorization huggingface dataset:
Each simulation begins at 12:00 UT on 12 March 2012, and ends 48 h later. To reduce computing costs, we activated the third grid only at 10:00 UT on 13 March and recorded model outputs every 7. 5 min after that. This data record frequency corresponds to the volume scans produced by the IP-Met S-band radar. These are used to validate the cloud-top models. To ensure numerical stability, the simulation integration time step varies between 2 and 10 s for the coarsest grid. It is 5 times smaller for the second grid and 25 times lower for the third grid. Invoking the radiation module has a time resolution of 300-500 s. The ECMWF operational analyses with 1. 0 • spatial resolution initialise all simulations and force the first grid's boundary conditions every 6 h. Following the work of  Liu et al.
Validation against TRO-Pico balloon-borne measurements The WV and particle measurements performed in the vicinity of overshoots in the frame of the TRO-Pico campaign establish a well-documented database to validate model simulations. For our study, as the balloon-borne measurements belong to a moment several hours after the overshooting event -this time interval between the overshooting event and the balloon-borne measurements is indicated as δt om hereafter; the simulation validation strategy is as follows. We observe the modelled overshooting plume at 17. 2 and 17. 8 km altitudes, respectively, where FLASH-B and Pico-SDLA hygrometers captured the WV local enhancements (see  Khaykin et al. , 2016). Then, after the same δt om , we investigate the WV enhancement at these levels in the model.
COHORTS As part of the UK TTP registry, patients were consented for DNA analysis (MREC: 08/H0810/54) (see Supplemental Materials). Patients on the UK TTP registry were screened for the clinical diagnosis, and confirmed with an ADAMTS13 level <10IU/dL at diagnosis (utilising FRETS methodology)  (8)  and the presence of an anti-ADAMTS13 autoantibody. (2, 3) The French replication cohort TTP samples were obtained from the French Reference Centre for TMA (CNR-MAT) and informed consent was obtained from each patient with confirmed iTTP (see above criteria) (Institutional Review Board of Pitié Salpêtrière Hospital, ClinicalTrials. gov, NCT00426686). The European control genotypes were obtained from the Wellcome Trust Case Control Consortium (WTCCC), both the 1958 British Birth Cohort and National Blood Service control samples.
(9) In addition, controls were used from the Illumina reference panel  (10)  and Oxford controls. (11, 12) NOTYPING, QUALITY CONTROL AND IMPUTATION TTP samples were genotyped on the Illumina Human Omni Express SNP chips and controls were genotyped on different SNP chips (see Supplementary Methodology). Pre-imputation quality control was performed in all data sets separately, and then in a combined cohort (Supplemental Figure  1 ). Quality control was performed for individuals and SNPs. Individuals were selected for further analysis by European ancestry principal component analysis (PCA) (see Supplemental Figure  2 ). Only SNPs present in all data sets were subsequently analysed. Genome-wide imputation was performed on markers that had passed quality control, and were present in all datasets using Beagle (version 5.
GWAS AND LOCI CHARACTERISATION Genome wide association testing was performed using SNP & Variation Suite v8, using logistic regression with principal component correction. (14, 15) The logistic regression p-values, odds ratios were calculated in addition to lambda inflation factors, and QQ plots are shown (Supplemental Figure  3 ). A standardised genome wide significance level of 5x10 -8 was applied. (15) For discovery and replication analysis meta-data please contact the authors. Conditional analyses were undertaken using a full versus reduced regression model. Lead SNPs at each locus were used as conditional inputs to determine independence, with results plotted using Locus Zoom software. (16) putation of HLA types was performed utilising SNP2HLA with previously genotyped markers. (17) Imputed HLA types were excluded if the R 2 (confidence) was <0.
80. Conditional analyses were subsequently performed as described above. Expression quantitative trait locus (eQTL) analysis was performed to associate identified SNPs with differential gene expression. (18) Additional markers in linkage disequilibrium with the lead SNP at the Chromosome 3 locus were identified by LDlink (https://ldlink. nci. nih. gov). (19) Functional annotation of the haploblock was performed using ChipSeq data via the UCSC genome browser (https://genome. ucsc. edu). Binding sites of transcription factors (highlighted through genome annotation) were obtained from FactorBook  (20)  , and position weight matrix (PWM) binding motifs generated. Binding motifs were generated using Mast-Meme. (21).
CONDITIONAL ANALYSIS To investigate for independent signal conditional analysis was undertaken using a full versus reduced regression model in SVS. Lead SNPs were used as conditional inputs to determine independence, with results plotted using Locus Zoom software. (16) A IMPUTATION HLA imputation was performed utilising SNP2HLA to impute HLA types using previously genotyped markers. (17)  Imputed HLA types were excluded if the DR2 (confidence) was <0. 80. Conditional analysis was subsequently performed, using the previously described method. To validate our HLA imputation, we compared imputed HLA types in a subset of serologically HLA typed individuals (n=17), and found a concordance of >80%.
LD-LINK Additional markers in linkage disequilibrium with our lead SNP were identified by LDlink (https://ldlink. nci. nih. gov). (20) LD between variants rs71767581 and rs9884090 was supported using the NIHR BioResource-Rare Diseases dataset, comprising whole genome sequences from 6,588 European individuals. (21) NCTIONAL ANNOTATION Functional annotation of the haploblock was performed using the UCSC genome browser (https://genome. ucsc. edu)  (22, 23) , to identify functional important variants. Functional annotations, Chip-Seq data and expression data to identify functionally important variants such as missense variants or regulatory variants.
ACKNOWLEDGEMENTS This Study was supported by research funding from  Shire/Takeda  to  Professor Marie Scully. We would like to thank  Drs Fairfax ,  Makino , and  Knight  from the  University of Oxford  for sharing ethnically matched control data. We would also like to acknowledge the significant contribution of our collaborators within in UK TTP registry and the  French Reference  Centre for Thrombotic Microangiopathies, who are listed below:.
Control SNP Quality Control is summarised below; • Illumina Ethnicity Controls -QC was performed on 711,320 SNPs, and post QC 531,093 SNPs remained. • Oxford Controls -QC was performed on 712,878 SNPs, and post QC 567,947 SNPs remained. • British Birth Cohort -QC was performed on 1,066,003 SNPs, and post QC 722,672 SNPs remained. • National Blood Service Cohort -QC was performed on 1,066,003 SNPs, and post QC 736,251 SNPs remained. The UK and French datasets were combined with separate control datasets, and the above per-SNP QC performed on the merged datasets • UK Discover Cohort -The UK TTP cohort (n=241) was combined with control datasets (Illumina Ethnicity, Oxford and British Birth cohorts) (n=3200) for overlapping SNPs (n=337,088). • French Replication Cohort -The French TTP cohort (n=112) was combined with control datasets (National Blood Service cohort) (n=2603) for overlapping SNPs (n=334,756).
IMPUTATION Genotype data was imputed using Beagle version 5. 0, utilising the 1000 Genome European CEU reference population (Supplemental Figure  1  for QC). (10) Cases and controls were imputed together using individuals and markers that had previously passed stringent QC. Following imputation filtering was performed using bcftools  (11)  (https://samtools. github. io/bcftools/bcftools. html), and markers with a Dosage Rsquared (DR2) less than 0. 80 were removed, and imputed genotype data was also re-filtered per SNP using SNP & Variation Suite,  (7)  details listed below: • The UK TTP cohort and control data sets were imputed, with indels and SNPs with DR2<0. 80 excluded. Post QC 3,649,349 remained for analysis, and.
Melting curve protocol was 1 min at 95°C, followed by 30s at 58°C, then 0. 11°C/s to 95°C followed by 10s at 40°C. We used the LinRegPCR software package (version 2016. 0) (Ruijter et al. , 2009)  to correct for baseline fluorescence, to set a window of linearity for each amplicon group and to calculate well-specific reaction efficiencies and Cq values  (Fairlie et al. , 2016). A constant fluorescence threshold was set within the window of linearity for each amplicon group, calculated using the average Cq across all three plates. The threshold values used were 0. 304 and 0. 394, and the average efficiency across all plates was 1. 89 for both the B2M and telomere amplicon groups. Samples were excluded from further analysis if the coefficient of variation (CV) across triplicate Cq values for either amplicon was > 5%, or if at least one of their triplicate reactions had an efficiency that was 5% higher or lower than the mean efficiency across all wells on that plate for the respective amplicon.
The results did not reveal any difference in feces moisture content between sites (mean moisture content ± SD: 43 ± 7% vs. 44 ± 11% at Chizé and Trois Fontaines, respectively). Overall, FGM concentrations were similar in Chizé and Trois Fontaines (Chizé: 973. 98ng/g, 95%  CI [716. 55; 1231. 42] ; Trois Fontaines: 800. 59ng/g, 95%  CI [649. 60; 951. 47] ). One male from the TF population had an extremely low non-physiological (and unexplained) FGM level in 2017 (8 ng/g, see Fig. S1 ) and was removed from the analyses. Individual concentrations of FGMs in 2016 and 2017 were correlated (r = 0. 42, p = 0. 02, n = 30, Fig. 1 ). However, this relationship was strongly influenced by a single male in Chizé, which had the highest level of FGMs in the dataset in both 2016 and 2017. Once this individual was removed, individual concentrations of FGMs in 2016 and 2017 were no longer correlated (r = 0.
We repeated the analyses excluding individuals sampled in their first year of life (i. e. at about eight months of age, Tables  S2  and S4 ), because telomere attrition may be faster during early life compared to adulthood  (Fairlie et al. , 2016) , and including the birth cohort (2003 to 2015) as a random effect (Table  S5 ) to control for non-independence among animals born in the same year using linear mixed effects models (using the R-package lme4). In all cases, results were qualitatively unchanged (Tables  S2, S4  and S5 ). Model selection was based on the Akaike Information Criterion (AIC) and we retained the model with the lowest AIC, except when the difference in AIC (ΔAIC) between two competing models was less than 2, in which case we retained the simplest model  (Burnham and Anderson 2002).
Funding J. F. L. , J. M. G. , E. G. F. , B. R. and L. C. were supported by a grant from the  Agence Nationale de la Recherche  ( ANR-15-CE32-0002-01 ). This work was performed within the framework of the  LABEX ECOFECT  ( ANR-11-LABX-0048 ), within the  program 'Investissements d'Avenir'  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). D. H. N. and H. F. were supported by a  BBSRC  grant no. ( BB/L020769/1 ).
Additional details about the experiment design can be found in  (22, 23,  and methods, instrumental setup and observing Strategy). α Centauri was observed from 2019 May 23 to 2019 June 11. The nightly conditions can be found in Supplementary Table  1. An additional night of data was taken on 2019 June 27. Enough time separates the collection of these data from the initial observations such that orbital motion complicates combining these with the rest of the data  (33, 34). However, this extra night provides a useful astrometric check for brighter planet candidates. Including these data, we collected a cumulative exposure time of approximately 100 hours, out of which 23 hours were not used because of mediocre data quality as a result of high sky background, coronagraph misalignment, or AO problems.
With no prior orbital constraints, the NEAR data reach a maximum completeness of ∼80% for Jovian-sized planets, or ~85% for slightly larger (i. e. , inflated) planets. The maximum completeness is less than unity since the projected separation can be smaller than a given semi-major axis, or behind the persistence stripes. At the extreme end of the detection limits, there is a ∼1-10% chance of detecting a warm R~3 R⊕ planet orbiting α Centauri A. For comparison, the plausible radii range of rocky exoplanets extends up to R~1. 75 R⊕  (35, 36). Discussion: The primary goal of the NEAR campaign is to demonstrate the capabilities of thermal infrared exoplanet imaging. The results showed that the sensitivity is background limited and follows a signal to noise ratio (SNR) ∝√ relation in image regions far from the center (≥7 λ/D).
1 au from α Centauri A and with an elongation on the order of ~0. 1 arcsec-consistent with orbital motion of a planet in an i~70º orbit throughout the nineteen-day campaign. Notably, the detection of C1 is repeatable in multiple independent subsets of the data (see Supplementary Fig. 2 ), which makes it unlikely to be a random false positive. Based on pre-imaging conducted a decade prior to the NEAR campaign, we can exclude the possibility that C1 is a background source (see 44 and supplementary methods, pre-imaging for background sources). Therefore, we consider C1 to be a plausible exoplanet and/or exozodiacal disk candidate. While C1 cannot be explained by presently known systematic artifacts, an independent experiment is necessary to exclude this third possibility. RV observations exclude the presence of Msini ≥ 53 M⊕ planets within the habitable zone α Centauri A  (14).
Simulated Planet Injection and Retrieval Tests To compute the image sensitivity, we performed extensive point source injection and recovery tests (see Figs. 3  & 4 ). For the injected PSF, we utilized the off-axis PSF of α Centauri A and included a scaling correction for the transmission of the AGPM  (14). The PSFs were injected prior to high-pass filtering, after which we completed the remaining data processing steps (PSF subtraction, etc. ). We measured the signal within an aperture of two pixels in diameter centered on the location of the injected source. This diameter corresponds to ~0. 3×FWHM, which is smaller than typically used. This provides a larger number of apertures that are non-contaminated by persistence. To measure the noise, we blocked the regions of the image affected by detector persistence and a 12-pixel wide box centered on the injected source and calculated the mean and standard deviation of fluxes within all non-overlapping apertures at the same separation.
Bernhard Brandl ,  Ryan Endsley ,  Rachel Fernandes ,  Kaitlin Kratter ,  Christian Marois ,  Michael Meyer , and  Maxwell Moe  for fruitful discussions and advice. The results reported herein benefited from collaborations and/or information exchange within  NASA's Nexus for Exoplanet System Science (NExSS)  research coordination network sponsored by  NASA's Science Mission Directorate. This work was supported by the  National Centre of Competence in Research PlanetS  supported by the  Swiss National Science Foundation , by the  Fonds de la Recherche Scientifique -FNRS  under Grant no F. 4504. 18, by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement no  819155 ) and under the European Union's  Seventh Framework Program  (grant agreement no  337569 ), and by the  Wallonia-Brussels Federation  (grant for Concerted Research Actions).
KW acknowledges support from  NASA  through the  NASA Hubble Fellowship  grant  HST-HF2-51472. 001-A  awarded by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Incorporated , under  NASA  contract  NAS5-26555. AB and SPQ acknowledge the financial support of the  Swiss National Science Foundation. GR was supported by an  NSF   Astronomy and Astrophysics Postdoctoral Fellowship  under award  AST-1602444.
GSE emerges as a powerful senolytic agent when applied at high concentrations Given the prominent efficacy of GSE in reducing the SASP expression, we next interrogated the potential of this natural product in killing senescent cells at higher concentrations. To this end, we measured the percentage of senescent cells after treatment with increasing concentrations of GSE. Data from SA-β-Gal staining indicated that senescent cells were not significantly eliminated until GSE concentration reached 0. 75 μg/ml (Fig. 2a-b ). The efficacy of GSE in killing senescent cells was further increased with increasing concentrations, which reached a threshold (20% cell survival) when GSE was applied at 3. 75 μg/ml, and did not further augment even at higher concentrations such as 7. 50 μg/ml (Fig. 2a-b ). We evaluated the potential of GSE as a senolytic agent with alternative methods.
We used MitoSOX Red, a mitochondrial superoxide indicator  24  , to probe intercellular changes, and found GSE1 promoted the generation of mitochondrial ROS in senescent, but not proliferating cells (Fig. 2g ). Thus, our data consistently supported the prominent capacity of GSE in targeting senescent cells via induction of cell apoptosis and exacerbation of mitochondrial stress under in vitro conditions. Grape seeds amount to 38%-52% on a dry matter basis of grapes, one of the most widely grown fruit crops worldwide, and constitute a handy source of antioxidant compounds  25. Since GSE exhibits remarkable senescencetargeting capacity, the phytochemical composition of GSE deserves thorough investigation. We applied high pressure liquid chromatography (HPLC) coupled to a quadrupole time-of-flight mass spectrometer (QTOF-MS) and equipped with an electrospray ionization (ESI) interface.
The cells were subcutaneously implanted to the hind flank of experimental mice with non-obese diabetes and severe combined immunodeficiency (NOD/SCID). Animals were measured for tumor size at the end of an 8-week period, with tissues acquired for pathological appraisal. Compared with tumors comprising PC3 cancer cells and naïve PSC27 stromal cells, xenografts composed of PC3 cells and senescent PSC27 cells exhibited significantly increased volume (P < 0. 001), basically confirming the promoting effects of senescent cells in tumor progression (Extended Data Fig. 5a ). To closely mimick clinical conditions, we experimentally designed a preclinical regimen incorporating genotoxic therapeutics and/or senolytics (Fig. 5a ). Two weeks after subcutaneous implantation when stable uptake of tumors in vivo was observed, a single dose of MIT or placebo was delivered to animals at the 1 st day of 3 rd , 5 th and 7 th week until end of the 8-week regimen and (Extended Data Fig.
We then assessed the impact of preclinical treatments on the physical parameters of mice. As expected, WBI significantly compromised the exercise capacity and muscle strength as measured by treadmill and grip strength assays in the vehicle group (Extended Data Fig. 8i, j ). In a sharp contrast, PCC1 administration provided substantial benefits by markedly recovering these capacities. More importantly, PCC1 treatment showed a prominent tendency to increase the survival rate (Extended Data Fig. 8k ). Our results support that PCC1-induced elimination of SA-β-Gal-positive senescent cells may be an effective strategy to alleviate senescence-related physical regression and reduce mortality in settings of premature aging triggered by environmental stressors such as cytotoxic therapy. Given the substansive effects of PCC1 treatment on accelerated aging, we next sought to define the impact of senescent cells on physical function in naturally aging animals, which indeed more closely mimick the pathophysiological changes in aging process per se.
These data indicate that PCC1 can significantly decrease the risk of age-associated mortality in old mice. We next asked whether the reduced death rate in aged animals comes at the cost of an increased late-life morbidity, we measured physical function in experimental mice treated with PCC1 or vehicle monthly until death. Despite the longer remaining lifespan in PCC1-treated mice, physical function in the last 2 months of life was not significantly lower compared to vehicle-treated mice (Fig. 7n ). Upon autopsy, the incidence of several age-related pathologies, tumor burden and cause of death, were not significantly different between PCC1-and vehicle-treated mice (Fig. 7o  and Extended Data Fig. 10a, b ). However, expression of the SASP was effectively reduced in solid organs, largely compatible with the decline of circulating levels of IL6, CSF2 and MCP1, representative SASP markers in the peripheral blood (Extended Data Fig.
SA-β-galactosidase staining, BrdU incorporation and apoptosis assays. SA-β-Gal staining assay was carried out with a SA-β-Gal staining kit (Beyotime) according to the manufacturer's instruction. Alternatively, cells were incubated with X-Gal staining solution (1 mg/ml X-Gal, Thermo Scientific) with 5 mM K3[Fe(CN)6] and 5 mM K4[Fe(CN)6] for 12-16 h at 37°C. The percentage of SA-β-Gal positive cells was estimated by counting at least 100 cells per replicate sample facilitated by the "point picker" tool of ImageJ software (NIH). Flow SAβ-Gal staining assay was performed by flow cytometry using an ImaGene Green C12FDG lacZ gene expression kit from Molecular Probes (Life Technologies), according to the manufacturer's instructions. To examine the proliferation or senescence status of cells, a BrdU incorporation assay (10 μM, Yeasen) was performed for 16-18 h and cells were subject to immunofluorescence staining with an anti-BrdU antibody (1:400 dilution, Cell Signaling, cat no.
Intracellular reactive oxygen species measurement. The intracellular reactive oxygen species (ROS) level was determined using a ROS Assay Kit (Beyotime) which uses dichloro-dihydro-fluorescein diacetate (DCFH-DA) as a probe. Briefly, cells were cultured in 6-well plates for 24 h at 37°C and were then washed twice with serum-free medium. Medium containing 10 µM DCFH-DA was added. Cells were then incubated for 20 min at 37°C, with light avoided during incubation. After incubation, the cells were washed thrice with serum-free medium, then observed and photographed using a fluorescence microscope (Nikon). The fluorescence intensity was quantitatively measured using ImageJ software (v1. 51, NIH).
05. The top GO terms of GO-Biological Progress (BP) and GO-Cellular Component (CC) were selected for presentation. Protein-protein interaction (PPI) analysis was performed with STRING 3. 0. The specific proteins meeting the criteria, were imported to NetworkAnalyst (http://www. networkanalyst. ca). A minimum interaction network was chosen for further hub and module analysis. were mixed at a ratio of 1:4, with each in vitro recombinant comprising 1. 25 × 10 6 total cells prior to subcutaneous implantation as described previously  14, 37.
The frozen sections were washed thrice with PBS and incubated with SA-β-Gal staining solution (Beyotime) overnight at 37 °C. After completion of SA-β-Gal staining, sections were stained with eosin for 1-2 min, rinsed under running water for 1 min, differentiated in 1% acid alcohol for 10-20 s, and washed again under running water for 1 min. Sections were dehydrated in increasing concentrations of alcohol and cleared in xylene. After drying, samples were examined under a bright-field microscope. Prostate, lung, liver and kidney frozen sections stained with SA-β-Gal were quantified by ImageJ software (NIH) to measure the SA-β-Gal + area. The total area was quantified by eosin-positive area, while the relative SA-β-Gal + cells were calculated with the SA-β-Gal + area divided by the total area.