dataesr/scientific-paragraphs-categorization huggingface dataset:
It is worth noting that the fit quality in BEC studies is not expected to be perfect. Due to the ad hoc descriptions of the unknown nonfemtoscopic background contribution as well as the compromise between the fit quality and interpretability of the measured correlation parameters, the obtained χ 2 /ndf values are often larger than unity. The cluster contribution is expected to be larger for the OS pairs than for the SS ones due to the charge conservation in processes contributing to the cluster formation. For this reason, the amplitude of the cluster contribution for the SS correlation functions is Table  2 : Results of the global fits to the OS correlation functions using Eq. 9 to parametrize the cluster contribution in the pPb and Pbp data. Dataset A 0 [GeV] n A σ 0 [GeV] σ 1 [GeV] pPb 2.
25 to 0. 50 (which corresponds to the maximum value available in the dataset after the preselection). The fractions of fake tracks in the selected pion sample and of signal pairs containing a fake track (values determined using the simulation) for those two criteria are ≤1% (see Table  4 ). pPb dataset Pbp dataset Contribution σ syst (R) [%] σ syst (λ) [%] σ syst (R) [%] σ syst (λ) [%] Background The contributions of the fake tracks and the pion selection criteria optimization to the systematic uncertainty are calculated as the absolute difference between the results obtained with the modified selection requirements and the central ones. In a limited number of bins, those inputs constitute the most important contributions to the total systematic uncertainty, together with the one related to the background scaling.
Results The correlation parameters are determined by performing fits to the SS correlation functions in each individual N VELO bin using Eq. 7. In this procedure, the parameters characterising the cluster contribution and the background scaling are fixed to the values measured in the previous steps of the analysis. The fits are performed in the full range of Q variable (0. 05-2. 00 GeV) in the constructed correlation functions. Example results of the final fits to the SS correlation functions are presented in Fig. 2. Correlation parameters determined from fits to the SS correlation functions using Eq. 7 in the N VELO bins for the pPb and Pbp datasets are presented in Table  5. The fit quality in BEC studies is not expected to be ideal due to various assumptions in the signal parametrization and the unknown theoretical parameterization of the nonfemtoscopic background effects.
The results are complementary to the measurements performed at LHC energies in central rapidity regions  [55, 56, 59 ]. The measured behaviour of the correlation parameters is compatible with observations from other experiments at LHC. In general, the correlation radius becomes larger with increasing event multiplicity, while the intercept parameter displays the opposite behaviour. The determined R (λ) parameters vary within 1-4 fm (∼0. 40-0. 85) depending on the N VELO interval. Correlation parameters determined in the BEC studies for the pp  [52] , pPb and Pbp collisions at LHCb are illustrated in Figs. 3  and 4. As it is observed in Fig. 3  the measured correlation radii scale linearly with the cube root of the reconstructed charged-particle multiplicity. A simple fit illustrating this relationship is performed for different datasets (pp, pPb and Pbp).
Methods: PACIFIC-R (NCT03798535) is an ongoing, international, retrospective study of patients who started durvalumab (intravenously; 10 mg/kg every 2 wk) within an early access program between September 2017 and December 2018. The primary end points are investigatorassessed rwPFS and overall survival (analyzed by Kaplan-Meier method). Results: As of November 30, 2020, the full analysis set comprised 1399 patients from 11 countries (median followup duration, 23. 5 mo). Patients received durvalumab for a median of 11. 0 months. Median rwPFS was 21. 7 months (95% confidence interval: 19. 1-24. 5). RwPFS was numerically longer among patients who received concurrent versus sequential CRT (median, 23. 7 versus 19. 3 mo) and among patients with programmed cell death-ligand 1 expression greater than or equal to 1% versus less than 1% (22.
Introduction 2] [3]  The historic standard of care (SoC) for patients with unresectable, stage III NSCLC was platinum-based chemotherapy administered concurrently with radiotherapy (cCRT), followed by active surveillance. ] [10] [11] [12] [13]  This changed after the primary data readouts from the phase 3 PA-CIFIC trial (NCT02125461). 14, 15 n PACIFIC, up to 12 months of consolidation therapy with durvalumab (an inhibitor of programmed cell death-ligand 1 [PD-L1]  16  ) significantly improved progression-free survival (PFS) and OS versus placebo in patients with unresectable, stage III NSCLC and no disease progression after definitive cCRT. 14, 15 Consolidation durvalumab also exhibited a manageable safety profile, and patient-reported outcomes were comparable with placebo. 14, 15, 17 9] [20]  At the most recent update, median PFS (measured from random assignment) with durvalumab versus placebo was 16.
e. , consolidation durvalumab following CRT). Once the primary results from PACIFIC were available, an early access program (EAP) was started to provide ethical access to durvalumab. PACIFIC-R (NCT03798535) subsequently enrolled patients who received durvalumab through the EAP with the aim of providing the first real-world data on the use and effectiveness of the PACIFIC regimen. This includes data for patients who received sequential CRT (sCRT) and patients with PD-L1 expression less than 1%. A preliminary safety analysis from PACIFIC-R, based on the first 3 months of treatment using data from the first of several preplanned, retrospective chart extractions (spaced in a 5-y period), provided early evidence of the real-world tolerability of the PACIFIC regimen. 27 Here, we report more comprehensive analyses from PACIFIC-R, based on the second planned chart extraction (with approximately 2 y of follow-up), including treatment characteristics and a preplanned analysis of real-world PFS (rwPFS), as well as a preliminary OS analysis.
In accordance with regulatory requirements, a country was eligible to enter PACIFIC-R once the EAP had closed in that country. To be enrolled, patients must have started durvalumab within the EAP between September 2017 and December 2018 and have provided informed consent for data to be retrieved from their medical records. Patients who died during or after the EAP and before PACIFIC-R enrollment were eligible where local laws allowed for a consent waiver, or nextof-kin consent, provided all other entry criteria were met. Patients who received durvalumab in clinical studies were excluded.
Statistical Analyses Analyses were based on the full analysis set (all eligible, enrolled patients), or subgroups thereof, and were descriptive in nature with summary statistics for continuous variables or numbers and frequency for calculation of categorical variables. Missing values were not imputed. All analyses in this report were based on the second planned chart extraction from PACIFIC-R (extraction end date: November 30, 2020). The timing of this extraction was based on an estimate of when there would be enough observed progression events to determine median rwPFS (and corresponding 95% CI) for the full analysis set. The rwPFS and OS data were censored for patients lost to follow-up (i. e. , still alive as of their last visit or contact before the database cutoff). Medians and landmark rates were calculated by Kaplan-Meier method and corresponding 95% CIs were calculated by Greenwood's method.
Although Spain did not participate in PACIFIC-R, Spanish data were sourced from an externally sponsored, locally initiated study with the same design & study materials (NCT04285866). As regulatory restrictions in Spain allowed only one data extraction, it was decided that data collection should be timed to allow for sufficient PFS maturity. Ultimately, the timing of data collection for the Spanish study was in line with the second planned chart extraction from PACIFIC-R (also timed for sufficient PFS maturity). Therefore, the Spanish data set was integrated for the analyses reported in this article (after internal quality review by AstraZeneca) but will not be integrated for analyses based on future PACIFIC-R chart extractions. November 30, 2020, was the last date of data entry for the analyses reported in this article; data cleaning was performed up to a database cutoff date of April 8, 2021, for the main PACIFIC-R cohort and July 2, 2021, for the Spanish data set.
Characteristics of Durvalumab Treatment The median time to start of durvalumab from the end of RT was 56. 0 days (1. 8 months) (range: À35 to 981 d [À1. 1 to 32. 2 mo]; n ¼ 1365) in the full analysis set; one patient started durvalumab before finishing RT. Overall, 30. 1% of the patients started durvalumab within 42 days (and 1. 2% within 14 d) of finishing RT; meanwhile, 14. 4% and 1. 0% started more than 3 months and more than 6 months after finishing RT, respectively. At the time of database cutoff, the median total treatment duration (including the duration of dose interruptions) was 334.
Preplanned Analysis of RwPFS At the time of the database cutoff, 737 of 1399 patients (52. 7%) had either experienced disease progression (n ¼ 659) or had died without documentation of progression (n ¼ 78); progression was determined per RECIST in 458 of 659 patients (69. 5%), per investigator's assessment in 171 of 659 patients (25. 9%), and by unknown means in 30 of 659 patients (4. 6%). Median rwPFS was 21. 7 months (95% CI: 19. 1-24. 5) in the full analysis set (Fig. 2 ); 62. 2% (95% CI: 59. 6-64. 6) and 48. 2% (95% CI: 45. 4-50. 9) of the patients were estimated to be alive and free of progression at 12 and 24 months, respectively. Subgroup analyses were performed to evaluate possible associations between rwPFS and prognostic factors of interest. As found in Figure  3A    among patients with known EGFR mutations (median, 11.
28 Together, the findings of these studies suggest that durvalumab after sCRT could be a reasonable treatment strategy for patients who are considered unsuitable for cCRT; the benefit of this strategy is currently being investigated in the phase 3 PACIFIC-5 trial (NCT03706690). Better outcomes were also observed among patients with PD-L1 expression greater than or equal to 1% compared with less than 1%, consistent with observations from PACIFIC. 20, 35 Nevertheless, favorable rwPFS outcomes were still observed among patients with PD-L1 expression less than 1% (median, 15. 6 mo). Patients with PD-L1 expression on less than 1% of TCs are excluded from the European Medicines Agency label based on an exploratory, post hoc analysis  21, 24  ; no restrictions regarding PD-L1 status are applied in other regions, including the United States.
47 We are uncertain of the factors underpinning this observation, but preclinical evidence suggests that administering PD-L1 inhibitors as close as possible to CRT may increase effectiveness. 43 evertheless, it should be acknowledged that the timing of durvalumab initiation after CRT may correlate with other clinical factors that influence survival outcomes. The ongoing phase 3 PACIFIC-2 trial (NCT03519971) is investigating concurrent administration of durvalumab with cCRT. The median rwPFS reported in PACIFIC-R is longer than the median PFS reported with durvalumab in PA-CIFIC (16. 8 mo). 14 This may seem unexpected as, owing to strict enrollment criteria, clinical trial cohorts are typically healthier than real-world populations. Several factors can contribute to overestimation of PFS in the real-world setting.
Figure2. Real-world PFS in the full analysis set. Shown is a Kaplan-Meier distribution of real-world PFS in the full analysis set. The tick marks represent censored observations, and the dashed lines represent 12-, 18-, and 24-month landmark analyses. At the time of the database cutoff, the median duration of follow-up for patients who remained censored for PFS was 23. 0 months (range: 0. 0-35. 6 mo); 10 patients (0. 7%) were lost to follow-up. CI, confidence interval; PFS, progression-free survival.
Dr. Garassino  reports receiving grants from  AstraZeneca  and  Merck ; personal fees from  AstraZeneca ,  Merck ,  Roche ,  Bristol Myers Squibb ,  Celgene ,  Boehringer Ingelheim ,  Novartis ,  GlaxoSmithKline ,  Pfizer ,  Eli Lilly , and  Takeda. Dr. McDonald  reports receiving support from  AstraZeneca  during the conduct of the study; consulting fees and payment or honoraria from  AstraZeneca. Dr. Filippi  reports receiving personal fees and nonfinancial support from  AstraZeneca ,  Roche ,  Merck Sharp & Dohme ,  Ipsen , and  RefleXion Medical Inc. ; research grants paid to the institution and travel support from  AstraZeneca. Dr. Smit  reports receiving personal fees for participation on advisory boards for  Merck Sharp & Dohme ,  Bristol Myers Squibb , and  AstraZeneca. Dr. Peters  reports having participation as a subinvestigator in trials (institutional financial support for clinical trials) sponsored by  Amgen ,  AstraZeneca ,  Biodesix ,  Boehringer Ingelheim ,  Bristol Myers Squibb, Clovis ,  GlaxoSmithKline ,  Illumina ,  Eli Lilly ,  Merck Sharp & Dohme ,  Merck Serono, Mirati ,  Novartis ,  Pfizer ,  Phosplatin Therapeutics , and  Roche/Genentech ; having participation on an advisory board for, and consulting fees paid to the institution from,  AbbVie ,  Amgen, AstraZeneca, Bayer, Beigene, Biocartis, Boehringer Ingelheim, Bristol Myers Squibb, Clovis, Daiichi Sankyo, Debiopharm , ecancer,  Eli Lilly , and Takeda; and receiving support for attending meetings and/or travel paid to the institution from  Astra-Zeneca ,  Bristol Myers Squibb ,  Daiichi Sankyo ,  Eli Lilly ,  Merck Sharp & Dohme ,  Novartis ,  Pfizer ,  Roche/Genentech , and  Takeda.
We identified 34 (65%) of the 52 (2x26) consensus HTR extremities (the very last bases) as belonging to a TE, and 87% of the extremities had a detected TE within 1,000 bp, suggesting a possible role for TEs in the horizontal transfer of some of these regions. After the removal of all TEs of known and unknown families identified in the P. nalgiovense and P. salamii reference genomes ("repeat-masked" analyses), three putative HTRs had lengths below 500 bp and were therefore discarded for subsequent analyses (Table  S4 ). We identified 283 coding genes in the remaining 23 putative HTRs, 199 of which yielded hits in one of the egg-NOG databases, mostly with the fungal egg-NOG database (87. 4%, 173 genes), suggesting that most of the putative HTRs identified originated from fungi. The other 25 genes had best hits in the eukaryote egg-NOG database (12.
1%) or the principal egg-NOG database (0. 5%). They could, therefore, also be of fungal origin, but better annotated in more phylogenetically distant groups (e. g. mammals), resulting in a best match at a higher taxonomical level. We describe below only the presence/absence and content of the putative HTRs in the repeatmasked case; we provide the results for the unmasked and TE-masked datasets in Figs. S4-5 , in which highly similar patterns are observed. Most P. nalgiovense strains contained the same putative HTRs, whereas the diversity was greater in P. salamii strains, with some strains even carrying none of the putative HTRs (Fig. 2A ; Figs. S4-5 ). The number and length of the putative HTRs largely accounted for the differences in genome size between P. salamii strains (adjusted R 2 : 0.
PCRs were performed using a Bio-Rad thermal cycler with an initial denaturation of 2 min at 94°C, 40 cycles of 30 s denaturing at 94°C, 30 s annealing at 54°C and 40 s extension at 72°C with a final extension step at 72°C for 5 min. PCR fragments were sequenced at GENEWIZ Europe and sequences blasted (Blastn) against public databases (nucleotide collection nr/nt) for species identifications  72, 73.
Sequencing was performed by the UCSD IGM Genomics Facility La Jolla, CA, USA. The complete genome assemblies were performed using the SMRT analysis system v2. 3. 0 (Pacific Biosciences). Genome sequences were de novo assembled using the HGAP v3  74  protocol, with a minimum seed read length of 1000, genome size of 30 Mb, target coverage of 20, and an overlap error rate of 0. 03. Polished contigs were further error corrected using Quiver v1. A summary of raw data and assembly statistics is reported in Table  S1. The protein-coding gene models were predicted with EuGene v4. 2a trained for Penicillium  23, 75, 76  and their putative function with eggNOG-mapper online  77  and the eggNOG 4. 5. 1. general database  78.
Short-read assembly Short-read sequencing data were checked using FastQC (v0. 11. 6)  79. No adapter sequence could be detected. A first assembly round was performed using SOAPdenovo v2. 04  80  and a k-mer value of 71. To confirm species identity, we recovered part of the calmodulin and βtubulin genes and of the ITS regions using blastn v2. 2. 29+  73  and the corresponding P. nalgiovense genes as references (accessions JX996974. 1, KX928936. 1 and KC009791. 1), then identified species by comparing the partial genes obtained with the NCBI nucleotide database (nt) using blastn v2. 2. 29+  72, 73. We then compared all the assembled contigs and scaffolds to the NCBI nucleotide database (nt) using blastn v2. 2. 29+  72, 73  to identify those contaminated by other species. Among the 55 genomes sequenced, we detected three P.
It is a rather conservative approach, as the annotation of the potential horizontally transferred regions identified in this study did not match the contaminants identified at this step (see Results). With the reads cleaned from contaminants, we did a second assembly round with SPAdes v3. 11. 0  83  and default k-mer parameters (21, 33, 55, 77), and then removed scaffolds shorter than 2,000 bp. We checked assembly quality with QUAST v5. 0. 2  84. We performed pairwise genome alignments with NUCmer v3. 1 and mummerplot v3. 5 from the MUMmer3 package  85  with default parameters to detect large genomic rearrangements between strains within each species, and duplications within genomes. We predicted the genes as previously described for long-read assemblies. All the genomes were deposited on ENA under the study accession number PRJEB44534 (Table  S1 ).
Population genetic analyses We mapped cleaned sequencing reads to the reference genomes LCP06525 (P. salamii) and LCP06099 (FM193; P. nalgiovense; accession numbers HG815136-HG815288 and HG815290-HG816004  13  ) using Stampy with default parameters (v1. 0. 32)  86. SNPs were called using the genome analysis toolkit GATK (v4. 1. 0. 0)  87  with the RealignerTargetCreator, IndelRealigner and HaplotypeCaller functions using default parameters except that we used a ploidy of one. We filtered out variant call formats (VCF) files as follows: quality (with low qual-tag), quality by depth (QD < 2. 0), mapping quality (MQ < 40), Fisher strand bias (FS > 60; a statistic to detect strand bias in variant calling in reverse and forward strands), mapping quality rank sum (MQRankSum < 12. 5; to test read quality differences between reference and alternative alleles) and read position rank (ReadPosRank < 8.
6. 2  89. We built population trees using RAxML (v8. 2. 7)  90  , run with the general time-reversible model of nucleotide substitution with the gamma model of rate heterogeneity (GTRGAMMA) and 100 bootstraps. In order to analyze population subdivision, we filtered our SNP dataset with bcftools (v1. 7)  91  to keep only distant SNPs to avoid too strong LD (r 2 lower than 0. 2 in a window of 1kbp), with a minor allele present in at least two individuals per species (minimal allele frequency of 0. 05). We then ran STRUCTURE (v2. 3. 4)  92  with 20,000 iterations after 10,000 iterations as burn-in, for a number of populations (K) varying from 2 to 10, with 10 independent runs for each K. We combined the results with the R package pophelper v2. 3. 0  93. We used the full fasta alignment built to estimate the population tree to run a principal component analysis following the code used in Konishi et al.
For this purpose, we identified TE present in P. salamii and P. nalgiovense reference genomes (LCP06525 and LCP06099-FM193) with RepeatModeler v1. 0. 11  101. This software identifies TE families based on high frequency patterns but also relationship between the different elements and compare them to the Dfam library (v3. 0)  102  , allowing some of the families to be formerly associated with known TE families in the five species present in the library (human, mouse, zebrafish, fruit fly, nematode), like LINE, LTR-Copia… We analyzed the putative HTRs with two databases: one based on all the TE identified in the P. salamii and P. nalgiovense reference genomes, and one based only on repeats associated to known TE families. Given the absence of fungi reference in the Dfam library, it is highly probable that the TE identified but not associated with any known family are indeed TE, simply not annotated, but we cannot entirely rule out the possibility of any other duplicated sequence.
We therefore looked at the 76 genes identified in these regions, none of them could be annotated. To take this small uncertainty into consideration, we either used the entire HTRs or masked all the TE present in the first database with RepeatMasker v4. 0. 7  103  for analyses not specifically focusing on TEs and mentioned those HTRs as "repeatmasked". When the second database was used, masked sequences of the putative HTRs were identified as "TE-masked". We then searched for the presence of the reference set of HTRs in all P. nalgiovense and P. salamii genomes using blastn v2. 2. 29+  72  , as well as in 68 other genomes from 54 species (four Aspergillus spp. , Monascus purpureus and 49 other Penicillium spp. ), using two different genomes for each species whenever possible (Table  S1 -S2).
One genome of each of P. biforme and P. egyptiacum, and five genomes of P. olsonii were assembled from Illumina HiSeq reads for this analysis, with the method described above (Table  S1 ). The other genomes were retrieved from public databases (Table  S2 ). When hits were obtained with previously identified HTRs, we checked the proportion of each putative HTR present in the genome, its percentage of identity with the reference HTR sequence, its position in the genome by looking at flanking regions and whether it was fragmented on different scaffolds. To confirm the split and avoid confusion with potential assembly errors, we considered a region to be split only when the region was present on two different scaffolds and distant by at least 10% more base pairs than the distance separating the two parts in the reference sequence.
We ran the tests for P. nalgiovense using P. chrysogenum as an outgroup and for P. salamii using P. olsonii as an outgroup. For this goal, we applied the method described above for the phylogeny reconstruction to obtain singlecopy genes present in all the P. nalgiovense strains and five P. chrysogenum on one dataset, and in all the P. salamii strains and five P. olsonii on a second dataset, allowing one missing sequence per alignment. From these orthologous gene alignments, we recovered the number of synonymous and non-synonymous sites, and among each category the number of fixed and polymorphic sites in each of P. salamii and P. nalgiovense gene with the MKtest program of libsequence  110. We estimated the mean number of synonymous and polymorphic sites by performing a weighted average on the polydNdS output, these two functions belonging to the package "analysis" of libsequence  110.