dataesr/scientific-paragraphs-categorization huggingface dataset:
Here, by comparison with gastric lipase in complex with a phosphonate inhibitor (  [33] , PDB id: 1k8q), we identified Val55 as the second NH group involved in the oxyanion hole. The Tli3 AIEC immunity protein is predicted to be translated as a 274-residue long pr The catalytic triad is located at the bottom of a crevice ~30-Å deep and ~20 × 15 Å (Figure  2A ). Surprisingly, the walls of the crevice, as well as the surface residues around it, are not particularly hydrophobic. A Dali search with Tle3 AIEC structure returned hits with a palmitoyl thioesterase (PDB id 1pja; Z = 12. 1; r. m. s. d. = 3. 3 Å; aligned 187) and a lipase (PDB id 5h6g; Z = 11. 4; r. m. s. d. = 3. 1; aligned 176). Indeed, most residues belonging to the α/β-hydrolase fold core, i. e. , the six β-strands and the seven α-helices, are aligned.
Data Collection and Processing The SeMet Tle3 AIEC /Tli3 AIEC data were collected at Proxima 2 using a Pilatus 6M detector at a wavelength of 0. 9791 Å. Before data collection, the crystals were soaked in reservoir solution supplemented with 12% (v/v) ethylene glycerol for a few seconds and then flash-cooled in liquid nitrogen. All data were processed using XDS  [47]. The final data-collection and processing statistics are provided in Table  1. A total of 375,390 measured reflections were merged into 78,970 unique reflections. The merged dataset was 98. 9% complete to 3. 6 Å resolution. The relevant data collection statistics are provided in Table  1.
Acknowledgments: We thank  Nicolas Barnich  ( M2iSH ,  Unversité Clermont Auvergne)  for providing the LF82 AIEC strain. We thank the members of Soleil synchrotron. We acknowledge  UCSF ChimeraX  for molecular graphics developed by the  Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco , with support from  National Institutes of Health   R01-GM129325  and the  Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases.
Models can be either static (Models 0 and 2) with fixed parameters or dynamic (Models 1, 4, 6, 7, and 8) with parameters depending on AOD to represent variations in particle sizes and in the ratio of fine to coarse modes  (Remer and Kaufman, 1998). Parameters are given in Table  1  for all models. The microphysical properties in Table  1  were used to calculate the optical properties required by the RTM used in iAERUS-GEO (i. e. , 𝑃(𝜉), 𝜏, 𝜔) to perform calculations of TOL reflectance. Calculations were done with the MOPSMAP (Modelled optical properties of ensembles of aerosol particles) software  (Gasteiger and Wiegner, 2018)  taking into account the SEVIRI spectral responses and results were stored in look-up tables. A Mie code was used for spherical particles whereas the T-matrix code by  Mishchenko and Travis (1998)  was used for spheroids assuming the aspect ratio distribution described by  Dubovik et al.
Input data The following inputs were used in this work for the processing of SEVIRI data: 1. Cloud masks were computed from SEVIRI data with the software from the EUMETSAT Satellite Application Facility on Support to Nowcasting and Very Short Range Forecasting (http://www. nwcsaf. org/). Detection of clouds is done with the current version of the algorithm that was originally proposed by  Derrien and Le Gleau (2005). 2. Meteorological parameters (i. e. , total column water vapor, total column ozone, surface pressure, wind speed, and wind direction) were obtained from ERA5 reanalyses of the European Centre for Medium-Range Weather Forecasts  (Hersbach et al. , 2020). The original 3-h data were temporally interpolated to match the 15-min frequency of SEVIRI. Following  Ceamanos et al. (2021) , SEVIRI radiances were recalibrated to account for the systematic biases found by  Meirink et al.
GRASP/POLDER The GRASP/POLDER product provides state-of-the-art satellite observations of AOD from the combination of POLDER measurements and the retrieval algorithm GRASP. POLDER was a multi-polarization, multi-angular, and multi-spectral imager on the PARASOL LEO satellite which was operative from 2005 to 2013. GRASP performs optimal retrieval of aerosolsurface properties from multi-dimensional remote sensing observations such as POLDER's  (Dubovik et al. , 2011). The GRASP/POLDER data (hereafter simply referred to GRASP) that were used in this study correspond to the version "Models", which was found to provide the best estimates of total AOD with respect to other versions of GRASP and other satellite products  (Chen et al. , 2020). For this study we downloaded data corresponding to Version 2.
1 and quality-assured Level-2 (available over a sinusoidal projection at approximately 6 km of resolution) for years 2012 and 2013 from https://www. grasp- Second, a nearest neighbor approach was used to select the GRASP pixel corresponding to each AERONET site. This was done to perform a pixel-wise comparison between GRASP and iAERUS-GEO retrievals, as the latter data are at a resolution of ~5-7 km over most of AERONET sites used in this work. According to  Chen et al. (2020) , the average AOD of the box with size up to 9 × 9 pixels was used for some coastal and island sites for which GRASP retrievals were found to be missing.
DT-DB/MODIS A Level-3 daily global aerosol data set based on the combination of products MOD08_D3_v6. 1 and MYD08_D3_v6. 1 was also used in this work. We used the variable named AOD_550_Dark_Target_Deep_Blue_Combined provided by the NASA Earth Observations (https://neo. sci. gsfc. nasa. gov/) at a resolution of 0. 1°. This data set results from the combination of the Dark Target (DT) and Deep Blue (DB) algorithms to provide state-of-the-art AOD retrievals from MODIS for a maximized spatial coverage  (Hsu et al. , 2013; Levy et al. , 2013). The combined DT and DB product (hereafter referred to as DT-DB) provides total AOD at 550 nm from Terra and Aqua, with an overpass time around 10:30 a. m. and 1:30 p. m. , respectively.
3AODtotal, AODDU > 0. 3AODtotal, and AODOM + AODBC > 0. 3AODtotal (Fig. C1c  for May). The lower threshold values used here are justified by the less reliable AOD estimates obtained with Model 0 unless pure sea salt aerosols are present. Remaining pixels were assigned to Model 0. Author contributions. XC developed the retrieval algorithm with contributions of DC, AG, JG, AL, JLA, and IK. BS, XC, and DC developed the scientific code. XC and BS performed the data processing with the support of JR. XC and SM designed the experiments for validation. XC analyzed the results and prepared the manuscript with contributions from SM, JLA, JR, AG, AL, JG, and IK. Competing interests. The contact author has declared that none of the authors has any competing interests. Acknowledgments. We thank the investigators and their staff for establishing and maintaining the AERONET sites used in this work.
A reassessment of the eclogite outcrops from which this Silurian age is derived is therefore essential. This contribution presents geochronological results obtained in two eclogite samples and one closely associated felsic diatexite (Supporting Information S1) from a locality where a previous study suggested a Silurian age for the eclogite facies metamorphism. U-Pb isotopes of zircon, apatite, and rutile separated from the eclogites are studied in situ using LA-ICP-MS. The isotopic results are interpreted in the framework of an already published petrological study (de Hoÿm de  Marien et al. , 2020)  complemented by new petrographic data and the determination of microinclusions in zircon by Raman spectroscopy, together with trace-element data in zircon and rutile.
| Geochemistry Samples for whole-rock chemistry were cleaned from weathering alteration, successively crushed in a jaw and a roller crusher, and reduced in powder with a planetary crusher in an agate bowl. Powders were analysed at the Geochemical and Petrographical Research Center in Nancy, France (SARM laboratory, CNRS-CRPG), for major and trace elements using Inductively Coupled Plasma Optical Emission Spectrometry and Mass Spectrometry (ICP-MS) respectively. FeO (vs. Fe 2 O 3 ) was analysed by wet titration. Whole-rock geochemical data are accessible in Supporting Information S2.
, 2014). The instrumental conditions are reported in Tables  S1-S3 , and details for each analytical sessions may be found in the first sheet of Supporting Information S3 and S4 (BR18 and BR19eclogite samples and BR60-diatexites sample). Further information on the dating protocol can be found in  Nosenzo et al. (2022)  for zircon,  Lotout et al. (2020)  for rutile, and in  Pochon et al. (2016)  for apatite. Zircon, apatite, and rutile were analysed during several analytical sessions with distinct analytical conditions (beam size, repetition rate, fluence, and analysed masses) summarized in Supporting Information S3 and S4. Data were corrected for U-Pb and Th-Pb fractionation and for the mass bias by standard bracketing with repeated measurements of the GJ-1 zircon standard  (Jackson et al.
, 2004) , Madagascar apatite standard  (Thomson et al. , 2018) , or R10 rutile standard  (Luvizotto et al. , 2009). In addition, zircon, apatite, and rutile reference materials were measured along with the unknowns to monitor the accuracy of the analyses. For the sake of clarity, the ages indicated for the standard in the following text (and Tables  S1-S3 ) were calculated using all the data (obtained during different analytical session), and the ages produced during individual analytical session are indicated in Supporting Information S3 and S4. McClure  (Schoene & Bowring, 2006; 524. 5 ± 4. 5 ; n = 13; MSWD = 0. 66) and Durango  (McDowell et al. , 2005 ; 32 ± 0. 5; n = 14; MWSD = 1. 2) apatite reference materials were measured along with the apatite analyses. Accuracy during the rutile analyses was monitored using rutile R19  (Zack et al.
A 100Â objective and a 2400 L/mm grating were used for the analyses resulting in spatial and spectral resolution of $1 μm and 1 cm À1 , respectively. Calibration of the spectrometer was based on the Si 520. 5 ± 0. 5 cm À1 peak. The spectra were recorded using the Wire 4. 2 software in the aluminosilicate network domain ($100 to 1300 cm À1 Raman shifts), and the water domain ($3000 to 3800 cm À1 ) was checked when needed. Acquisition times varied from 10 to 300 s depending on the laser power condition and the nature of the analysed mineral phase.
ACKNOWLEDGEMENTS This work beneficiated the support of the  French Geological Survey  ( BRGM ) as well as an internal  Czech Geological Survey  grant (# 311190 ), accorded to  L. de Hoÿm de Marien. X. Le Coz  is acknowledged for the production of the thin sections and  Y. Lepagnot  for the mineral separation. We are grateful for the assistance in SEM observations and EDS analyses of the staff of the  CMEBA facility (ScanMAT, University of Rennes ), which received financial support from the  Région Bretagne  and the  European Union  ( CPER-FEDER 2007-2014 ). We are also grateful to  J. Langlade  and  F. Schiavi , for their assistance during the EPMA analyses ( Microsonde Ouest, IFRMER, Brest-Plouzané, France ) and Raman spectroscopy analyses ( Laboratoire Magmas et Volcans, Clermont-Ferrand, France ), respectively.
Population Analysis The Analysis and Research in Cancers of the Digestive System (ARCAD) is a worldwide collaboration of clinicians, statisticians, and scientists who specialize in gastrointestinal malignancies  [17]. The database created by the ARCAD Foundation contains patient-level data from clinical trials that enrolled patients with mCRC from 1997 to 2013. All clinical trials included in the ARCAD database had their study protocols approved by their respective independent ethics committees and the institutional review boards of participating institutions. All patients provided written informed consent to the respective clinical trials that they enrolled in. In the present analysis, patients from first-line trials in the ARCAD database with cycle-by-cycle tumor measurement information and corresponding overall survival data were included.
4. The image-based assessment schedule was slightly different across trials; therefore, we considered any assessments that occurred between baseline and 12 weeks from registration. If multiple assessments were available, we chose the assessment that was closest to 12 weeks and included the complete set of tumor measurements. The 12-week measurements used in this analysis comprise the harmonized data after the above data processing. The RECIST measurements (per RECIST 1. 1) used in this analysis were calculated using this harmonized 12-week measurement data without considering new lesion information.
Results Among all patients included in the ARCAD database, 10,648 patients from 14 trials were excluded because they were not enrolled in 1st-line trials, 11,017 patients from 17 trials were excluded because they did not have individual lesion data available at the time of analysis, and an additional 6152 patients from the remaining 17 trials (Table  S1 ) were excluded for the following reasons: no baseline measurements, only 1 targeted lesion recorded at baseline, no re-staging measurement within 12 weeks of enrollment, not all lesions were evaluated during re-staging, progression due to non-target lesion at the 1st re-staging, or no additional survival information available post-re-staging. Finally, 10,551 patients enrolled in 17 mCRC 1st-line trials were included in this analysis (Figure  2 ).
lesions, or more progressing lesions. To understand the additional prognostic effect of LBR12 among patients with the same RECIST 1. 1 classification, we investigated the association between LBR12 and OS in patient subgroups defined by RECIST 1. 1. The goodness of fit of the survival models was measured by concordance statistics. [23]  A 2-sided p-value of < 0. 05 was considered statistically significant for all tests. No adjustments were made for multiple comparisons since all analyses were considered exploratory. All analyses were performed using SAS software version 9. 4 (SAS Institute, Cary, NC, USA).
Results Among all patients included in the ARCAD database, 10,648 patients from 14 trials were excluded because they were not enrolled in 1st-line trials, 11,017 patients from 17 trials were excluded because they did not have individual lesion data available at the time of analysis, and an additional 6152 patients from the remaining 17 trials (Table  S1 ) were excluded for the following reasons: no baseline measurements, only 1 targeted lesion recorded at baseline, no re-staging measurement within 12 weeks of enrollment, not all lesions were evaluated during re-staging, progression due to non-target lesion at the 1st restaging, or no additional survival information available post-re-staging. Finally, 10,551 patients enrolled in 17 mCRC 1st-line trials were included in this analysis (Figure  2 ).
There were no changes in treatment for patients in the included trials who experienced either partial or complete response; both continued with their current therapy. Since this was the first attempt to develop new lesion-based response criteria, we only focused on the 12-week tumor measurement, which was typically based on the 1st or 2nd re-staging scan in this population. It is conceivable that if all the data points from the re-staging scan measurements were used, more complete responses would potentially be included, which would better describe the acquired mechanisms of resistance and "temporal" heterogeneity. The same logic behind the creation of the current lesion-based response criteria would also still apply. In the ARCAD database, there are currently no data from clinical trials utilizing immunotherapy; therefore, we could not evaluate the performance of the proposed criteria for patients who received immunotherapy.
Given that mixed responses have been observed in patients with lung cancer receiving immunotherapy  [34] [35] [36] , we conjectured that the new lesion-based criteria would be able to identify patients with mixed responses beyond RECIST 1. 1. There will need to be additional analyses to evaluate whether the association with OS observed in this analysis can be generalized to other treatment options, including immunotherapy, and disease settings. Our study has several limitations. All patients included in this analysis were deemed eligible and appropriate for participation in clinical trials, therefore, they may not be representative of a real-world population. However, the analysis was conducted using data from international multicenter clinical trials, meaning it is likely to be more representative and generalizable compared to studies at single institutions or in the Eastern or Western world.
Supplementary Materials: The following supporting information can be downloaded at: https:// www. mdpi. com/article/10. 3390/cancers15164117/s1. Figure  S1. Patient classifications by LBR12 vs. RECIST 1. 1. Table  S1. Information of clinical trials included in this study. Table  S2. Patient characteristics by whether patients were included in the analysis population. Table  S3   Institutional Review Board Statement: Individual trials were approved through each country's mechanisms at the time the trials were done. The ARCAD database collaboration research protocol was approved by the Mayo Clinic Institution Review Board. Individual patient data from all trials were collected and the analyses were done at an independent statistical center at Mayo Clinic, Rochester, MN, USA. The cutoff date for this analysis was 5 February 2021.
Data Availability Statement: Restrictions apply to the availability of the data. The sharing of individual patient data from each participating trial is subject to the policies and procedures of the institutions and groups that conducted the original studies. Data were obtained from the ARCAD Foundation (https://www. fondationarcad. org/en/, accessed on 11 August 2023). Acknowledgments: This work is dedicated to the memory of Daniel J. Sargent. Dan was one of the world's foremost experts in biostatistics and oncology. He brought together disparate investigators and established data sharing across academia and industry internationally. His groundbreaking initiatives of integrating large collections of databases enabled research to answer questions otherwise beyond statistical possibility, to design important new clinical studies, to make regulatory observations, and to set new standards.
Study-level summary statistics quality control GWAS summary statistics from each participating study were processed centrally with a quality control (QC) workflow implemented in Snakemake 64 following the procedure described by Winkler T, et al  65. Variants with more than two alleles, regression coefficients (log odds ratio or log hazard ratios) >10, standard error of the coefficients >10, minor allele frequency (MAF) <1%, imputation (INFO) score <0. 6, or effective allele count (, calculated as 2 × 𝑀𝐴𝐹 × (1 -𝑀𝐴𝐹) × 𝑁 #$%&'( × 𝐼𝑁𝐹𝑂, of <50 were excluded from the analysis. Remaining variants with allele frequency difference >0. 2 as compared against ancestry-specific reference panels were further excluded. The reference panels were derived from whole-genome sequence data from HRC and 1000 genomes project phase 3 version 5 (1000Gp3v5) for European ancestry, or 1000Gp3v5 for East Asian, South Asian, African, and Admixed American (Hispanic) ancestries.
Identification of genomic susceptibility loci across HF subtypes Genomic susceptibility loci for HF were identified using sets of conditionally independent variants identified through a chromosome-wide stepwise conditional-joint analysis using Genome-wide Complex Trait Analysis (GCTA) software  68. Conditionally independent variants across 4 HF phenotypes with joint P value < 5 x 10 -8 that are physically located within 500 kilobases of each other were aggregated into one set. A genomic locus was then defined as the genomic region within 500 kilobases upstream and downstream of the farthest variants of each aggregated set of conditionally independent variants. The identified genomic loci were labelled with an incremental one-based integer sequence based on phenotype order (HF, ni-HF, ni-HFrEF, and ni-HFpEF), chromosome, and base pair positions.
A genomic locus was declared as novel if all conditionally independent variants within the locus and any of the sentinel variants reported at P < 5 x 10 8 in previous GWAS of HF  [1] [2] [3] [4] [5]  were physically located more than 250 kb away and not in LD (𝑅. < 0. 2). To model LD between genetic variants, we used a reference panel derived from individual-level imputed genotype data of a randomly sampled 10,000 UK Biobank participants with an ancestry composition proportionally matched to the present meta-analysis (Hispanic ancestry was not included due to unavailability in UK Biobank and small proportion in the overall sample). Further, association between a locus and a HF subtype was categorised based on P values at cut-off values of P < 5 x 10 -8 (genome-wide significant), P < 0. 05 / number of identified loci (replicated at Bonferroni-adjusted threshold), P < 0.
Genetic architecture assessment To assess genetic architecture and polygenicity across HF phenotypes, we compared quantiles of the expected and observed genome-wide genetic association P-values using quantile-quantile (QQ plot) and calculated genomic control coefficient (𝜆 !" ). An early inflation on QQ plot with 𝜆 !" > 1. 1 suggests genome-wide genomic inflation, which may be due to polygenic genetic architecture or confounding by population stratification. To distinguish between the two, we calculated 𝜆 !" assuming 1000 participants (𝜆 !"/0111 ), and estimated LD score regression slope using LDSC software  69  , whereby values close to 1 indicate that any observed genomic inflation is likely due to polygenic genetic architecture. To minimise confounding by population structure, LDSC regression was performed using European ancestry meta-analysis subset and LD reference panel.
We investigated the distribution of allelic effect on HF across the spectrum of allele frequency by plotting the risk ratio per additional minor allele as a function of minor allele frequency of conditionally independent variants. To assess the shape of the relationship, we fitted two separate local polynomial regression with locally estimated scatterplot smoothing (LOESS) for groups of variants associated with increased and decreased risk of HF. To increase precision of the fitted regression, a sub-genome-wide threshold association at a false discovery rate (FDR) <1%, estimated using the qvalue package in R  70  , was used to identify conditionally independent variants.
SNP-based heritability estimation The proportion of variance in HF risk explained by common SNPs, i. e. SNP-based heritability (ℎ 𝑆𝑁𝑃 2 ), was estimated from GWAS meta-analysis summary statistics using Linkage-Disequilibrium Adjusted Kinships (LDAK) SumHer software  71  with LDAK-Thin and BLD-LDAK heritability models  72. The ℎ 678. estimates were calculated on a liability scale, which assumes that a binary phenotype has an underlying continuous liability, and above a certain liability threshold an individual becomes affected  73. To obtain a more accurate estimate, we used the European ancestry meta-analysis summary statistics with pre-computed tagging files derived from 2,000 individuals White British individuals, and used a correction for sample prevalence by calculating the effective sample size assuming an equal number of cases and controls  74.