dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The research leading to these results has received funding from  AIRC  under  5  per  Mille 2018 -ID. 21073  project - P. I. Maio Michele ,  G. L. Anichini Andrea ,  G. L. Ceccarelli Michele. The research leading to these results has received funding from  AIRC  under  IG -ID. 21846  project - P. I. Ceccarelli Michele. This work was also supported by the  Italian Ministry of Research  Grant  PRIN 2017XJ38A4_004. The authors wish to tank the  EPigenetic Immune-oncology Consortium AIRC (EPICA)  investigators ( Giuseppe Palmieri ,  Daniela Massi  and  Ulrich Pfeffer ) for helpful discussion of data and critical review of the manuscript.
Data Availability The processed NanoString dare are available via the GEO accession number: GSE211645 [https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE211645; token mjghgimevhmpbkx]. Raw data of Whole Exome Sequencing, RNA-Sequencing and Reduced Representation Bisulfite Sequencing are available on the European Genome-phenome Archive under accession number EGAS00001006736.
at 65 °C for 16 h. Samples were scanned at maximum scan resolution capabilities (555 FOV) using the nCounter Digital Analyzer. Quality control of samples, data normalization and data analysis were performed using nSolver software 4. 0 (NanoString Technologies). ICR/GIE validation in independent cohortsMolecular and clinical data for three independent immunogenomic datasets of melanoma patients treated with ICI 4, 39, 40  were obtained from cBioportal 77. A total of 83 patients for which all required data were available (gene expression, mutation/neoantigen loads, treatment response and overall survival) were selected and grouped in responder (CR: complete response, PR: partial response, SD: stable disease, LB: long-term benefits) and non-responder (PD: progression disease, NB: minimal or no-benefits) according to the treatment outcome as described in the corresponding original studies.
| MATERIAL S AND ME THODS The Antiretroviral Therapy Cohort Collaboration (ART-CC) combines data from cohorts of PWH in Europe and North America. Ethics committees or institutional review boards approved the individual cohorts, which used standardised data collection methods and regularly followed-up patients. The 14 cohorts included in these analyses were those with data on both grams of alcohol use per day (recorded from 6 months before starting ART) and baseline HCV RNA testing data available for over 50 patients. Included PWH started ART between 2001 and 2017 when aged ≥16 years old,  22  and had a CD4 count and HIV-1 RNA viral load measurement between 3 months before and 2 weeks after starting their first combination ART regimen. We excluded those with known hepatitis B virus (HBV) infection, defined as testing positive for hepatitis B surface antigen (HBsAg) at baseline: data on HBV testing were unavailable for one cohort that was included.
| Alcohol data Data on self-reported alcohol use were recorded through questionnaires that varied between cohorts and included both alcohol use disorders identification test consumption (AUDIT-C) and non-AUDIT-C measures. AUDIT-C is a three-item questionnaire which returns a score from 0 to 12 where increasing score means higher risk alcohol use. Non-AUDIT-C cohorts recorded the self-reported number of drinks/units per week/day. These data were harmonised into grams of alcohol per day taking the mid-points of categories for cohorts that measured alcohol use in categories (see supplementary materials for further information). Distributions of alcohol use within cohorts were examined, following which they were categorised as 0 , 0. 1-20. 0 and > 20. 0 g per day, to enable analyses to be combined across cohorts, while still accounting for no alcohol use, low to medium alcohol use and high alcohol use.
| Mortality data Cohorts gathered information on mortality through linkage with vital statistics agencies and hospitals or physician report, and the active follow-up of participants. We adapted the Coding of Death in HIV (CoDe) project protocol (https://www. chip. dk/Tools-Stand ards/ CoDe/About) to classify causes of death, as described previously,  23  with causes of death further categorised into groups and tabulated against HCV/alcohol use status.
| Statistical analyses Patients were followed-up from ART start (baseline) until the earliest of death, loss to follow-up or cohort-specific database administrative censoring (mostly in 2019). Patients with a gap of greater than 1 year between the date last known to be alive and administrative censoring were considered lost to follow-up and censored 6 months after their last recorded measurement. We fitted Cox models with baseline hazards stratified by cohort to estimate associations of alcohol use with all-cause mortality according to HCV status (negative, positive) by including interaction terms. account for the availability of DAAs from 2014 onwards. The covariates included were self-reported HIV acquisition mode (sex between men, IDU, heterosexual sex, other/unknown), sex (male or female) and variables time-updated at times of HCV cure, clearance, or infection: prior AIDS events (binary), as well as age (years), CD4 count (cells/μL) and log HIV-1 RNA (copies/mL).
| Strengths and limitations This analysis was based on multiple cohorts of PWH spanning many countries across the US and Europe. However, there were several limitations. Firstly, individuals' alcohol use will vary over time, but our analyses were based on a single report of alcohol use for each individual, so we may miss changes in alcohol use. We performed a sensitivity analysis to investigate the effect of restricting the window of alcohol use values to between 6 months before and 3 months after ART start and found similar results. Second, alcohol data were collected in different ways by different cohorts, and detailed analyses were required to harmonise these data (see supplementary materials), which may have resulted in the use of broad categories that do not capture enough variation in alcohol use.
Self-reported alcohol use may be affected by recall bias and perceived desirability of lower alcohol use, so the percentage of people in the highest alcohol use category may be an underestimate. Equally, the percentage of people self-reporting as not drinking alcohol may be an overestimate. However, we have no evidence to suggest that any misreporting of drinking was not equivalent among both PWH with and without HCV, so our overall conclusions regarding the interaction between alcohol use and HCV status would likely remain unchanged. Due to a lack of data, we were unable to adjust our regression models for potential variables that may explain non-drinking such as religious or cultural reasons, and previous diagnoses of alcohol use disorders. The database close date for most cohorts (~2019) occurred 5 years after the start of the DAA-era of HCV treatment, so some HCV treatment information is for interferon-based treatments, which are now rarely used in high-income settings.
Strong postglacial population expansion revealed through whole genome sequences We generated 30X coverage whole genome resequencing data for 71 Coho salmon representing 14 populations distributed from California to some of the most upstream populations of the Porcupine River in Yukon, Canada (Fig  1A ). We also included several outgroup species namely the Sockeye salmon (O. nerka), Chinook salmon (O. tshawytscha), Pink salmon (O. Gorbuscha), Rainbow trout (O. mykiss) and Atlantic salmon (Salmo salar). These genomes were used to identify the ancestral and derived alleles in Coho and/or testing correlation(s) between genomic load estimates and recombination rate (S1-S3 Tables). To explore the amount of clustering among individuals a PCA was performed (S1A and S1B Fig). The PCA illustrates that populations are spatially structured following both latitudinal and longitudinal gradients.
The first axis strongly correlates with both latitude (Pearson r = 0. 97, p < 2e -16 ) and longitude (Pearson r = -0. 82, p < 2e -16 ), as expected when space drives population structure  [45]. Moreover, admixture analyses (S1B Fig) revealed that Coho populations were generally structured at the river/watershed level. These observations corroborate our previous inferences based on lower resolution genomic dataset  [42, 43]. Map produced in R using the World data from Maps package with public domain data available at  [47]. B) Average Tajima's D values computed along the genome and summarized for each sample locality. Populations are ordered from distance to the river mouth and from the South to the North (i) California, (ii) Cascadia, (iii) British-Columbia (BC), (iv) Haida-Gwaii, (v) Salmon River (Fraser Basin) and (vi) Alaska.
Range expansion have reduced the efficacy of selection at range margin We predicted that selection efficacy will be reduced at the expansion front and in populations with a lower N e. To test this, we measured three parameters, namely the rate of non-adaptive and adaptive amino-acid substitutions relative to neutral divergence (ω NA and ω A , respectively; with ω A = d N /d S -ω NA ) and the proportion of non-synonymous amino-acid substitution that result from positive selection (α = ω A /(d N /d S )) using the software Grapes  [56]  using maximum likelihood estimation under a population genetic model. A general expectation is that populations at the expansion margin will exhibit a higher rate of non-adaptive substitutions (ω NA ), a lower rate of adaptive substitutions (ω A ) and a lower proportion of amino-acid substitutions (α).
Sampling design for Coho salmon We sampled and sequenced 71 individuals representing 14 populations distributed from California to Alaska (S1 Table ). A set of 55 individuals was sequenced on an Illumina HiSeq2500 platform  [38]  and the other 16 individuals were sequenced on a NovaSeq6000 S4 platform using paired-end 150 bp reads. Reads were processed using fastp for trimming  [87] , and mapped to the most recent Coho reference genome (https://www. ncbi. nlm. nih. gov/assembly/ GCF_002021735. 2/) using bwa mem v2  [88]. Reads with a minimum quality below 20 were discarded with samtools v1. 7. Duplicates were removed with picard (http://broadinstitute. github. io/picard/). SNP calling was performed using GATK v4. 2  [89]  using our pipeline available at github. com/quentinrougemont/gatk_haplotype/.
We generated a Haplotype gVCF for each sample individually, combined all gVCF and then performed a joint genotyping. We checked the variants quality score of our data and filtered our genotypes according to their quality following GATK best practices and based on quantiles distributions of quality metrics. We excluded all sites that did not match the following criterion: MQ < 30, QD < 2, FS > 60, MQRankSum < -20, ReadPosRankSum < 10, ReadPosRankSum > 10. We also excluded multiallelic SNPs, as well as indels. Genotypes with a depth lower than 6 or higher than 100 reads were also excluded to remove low confidence genotypes potentially associated with paralogs. Finally, we also generated a separate vcf file using the-all-sites option to obtain a file with invariant position to reconstruct sequence data (see the Genetic load estimation in Coho salmon and related species section).
A total of 14,701,439 SNPs were identified without missing data. Population structure was evaluated using a principal component analysis (PCA) performed using Ade4  [90]  on a set of LD-pruned SNPs without missing data (1,739,037 SNPs) identified stringently with plink1. 9  [91]  (command indep-pairwise 100 50 0. 1). Outgroup dataset. In order to test the generality of the relationship observed between π N / π S and GC3 we took advantage of the newly assembled reference genomes and resequencing data from other closely related Pacific salmonid species with similar demographic histories  [92] [93] [94]. Sockeye salmon published by  [92]  were retrieved from NCBI PRJNA530256. Samples with a high number of individuals per ecotype (Sockeye and Kokanee) were chosen from the NCBI table. We retained a total of 5 Kokanee populations and 5 Sockeye populations (from Fraser & Columbia watershed described in S1 and S3 Tables).
Three Chinook salmon samples were provided by B. Koop (also available at NCBI PRJNA694998). Additionally, 11 samples of "even" and 10 samples of "odd" pink salmon (O. gorbuscha) were downloaded from PRJNA 556728 and included in the analysis. Here "even" and "odd" refers to Salmon returning to their natal rivers in "even" and "odd" years to spawn, leading to a temporal isolation of these ecotypes  [93]. These were indeed clearly separated based on a PCA. Finally, a number of rainbow trout available from NCBI PRJNA386519 were used (n = 19 from 3 random populations showing genomic differentiation based on a PCA). Each sample was downloaded and mapped onto its species' reference genome downloaded from NCBI and using the exact same procedure as described above relying on fastp, bwa-mem2, picard and GATK 4.
Ancestral and derived alleles identification To accurately recover the ancestral allelic states, we used three outgroup species; including the chinook salmon and rainbow trout sample (see above, n = 5 for rainbow trout) plus data from Atlantic salmon (n = 5, SRP059652). Each individual was aligned against the Coho salmon V2 genome (GCF_002021745. 2) using GATK with the same procedure as above and calling every SNP using the-all-site mode to obtain invariant positions. We then determined the ancestral state of each SNP if 1) the SNP was homozygous in at least 90% of the individuals from our three outgroups, and 2) matched one of the two alleles identified in Coho salmon. Otherwise, the site was inferred as missing and was not used in subsequent analyses of the load. In addition, we reconstructed a consensus ancestral fasta sequence using the doFasta option from angsd  [95].
Demographic reconstruction We first tested our prediction that genetic diversity (observed heterozygosity H o ) decreases towards the North following our «out of Cascadia» model previously inferred  [42, 43]. Conversely, we verified that the β ST coefficient, a measure of both genetic differentiation and ancestrality  [46]  increases northwards the North, as expected due to isolation by distance. β ST and observed heterozygosity (H o ) were measured using the hierfstat R package  [96]  Details about β ST computation are provided in Weir & Goudet  [46]  and H o was computed following  Nei (1987)   [97]  as 1 -∑ k ∑ i Pkii/np with Pkii the proportion of homozygotes i in sample k and np the number of samples. Oceanic coastal distances were computed using the marmap package  [98]  and waterway distance was computed using ArcGIS.
Then we summed the 2 distances to obtain the distance to the most likely refugia that we identified in our previous studies  [42, 43]. A shapefile of the rivers used here is available at https://github. com/QuentinRougemont/ selection_efficacy/tree/main/00. raw_data. Under postglacial expansion from a single refugium, the general hypothesis would be that all sampled populations should follow a common temporal trajectory of a population decline (bottleneck due to founder events by few individuals) followed by a (strong) increase in N e corresponding to the expansion phase. To test this hypothesis, we inferred temporal changes in N e using SMC++  [50]. SMC++ works similarly to the PSMC model but takes into account the Site Frequency Spectrum (SFS) and is better suited to large sample sizes. Estimates of changes in population size were performed for all populations.
We used a MAF of 5% in each population, keeping between 3. 7 and 6. 4 million SNPs with populations having undergone stronger bottleneck/founding events displaying the lowest amount of variation. We estimated LD decay by plotting LD (R 2 ) against physical distance measured in base pairs in ggplot2 [100] package in R. We observed slight discrepancies between our SMC++ estimates of divergence time and our previous work based on the site frequency spectrum  [42]. To investigate this, we performed a new set of inference based on the unfolded joint site frequency spectrum (jSFS) using @a@i [101] and a new set of refined models as detailed in supp. S1 Note and S8 and S9 Tables. To reconstruct the population expansion routes we computed Weir and Cockerham [102] F ST values among populations using Vcftools [103] and constructed a phylogenetic population tree using the R package ape  [104].
Population-scaled recombination rate Statistical phasing of the Coho whole genome sequences was performed using the Shapeit software [106], considering all individuals at once. We then estimated effective recombination rates (ρ = 4. N e. r where r represents the recombination rate per generation and N e is the effective population size) along the genome using LDHat [107]. Phased genotypes were converted into LDHat format using vcftools after excluding SNPs with MAF < 10% since rare variants are not informative for such inferences. Following the guidelines of the LDHat manual, the genome was split into fragments of 2,000 SNPs with overlapping windows of 500 SNPs. We measured recombination rates independently for each population. Differences in the distribution of population-scaled recombination was visualized using violin plot (S13 Fig) and statistically tested using Tuckey HSD tests in R (S5 Table ).
To obtain suitable data, we converted the quality filtered whole genome vcf file into a fasta file for each population containing sequence information for each individual in the population (using vcf2fasta available at https://github. com/QuentinRougemont/selection_efficacy/tree/main/08. piNpiS/). This file was then converted into a site-frequency spectrum using bppml  [110]. We required at least 10 sites without gap to keep a given site (-gapN_site), with a maximum proportion of gap of 0. 5 (-gapN_seq), a minimum number of complete codon of 6 (-min_nb_codon), we did not allow frameshift (remove_frameshift parameter), we also required a sample size of 8 (-sample_size), the number of initial/terminal position in which Stop codons or FrameShift codons are tolerated was set to 20 (-tolerance_zone), we did not allow Stop or FrameShift codons between the two tolerance zones (-allow_internal_bc parameter).
Differences in load for region of residual tetraploidy in Coho salmon π N /π S comparison. In the Coho reference genome, Chromosomes 1 to 30 are considered to represent generally diploid chromosomes, whereas chromosomes 31 to 38 represent those with a clear signal of residual tetraploid  [41]. We took advantage of this specificity regarding chromosome evolution to contrast the load for the 3700 genes in regions of residual tetraploidy (averaged across all genes for each population). For diploid regions, we generated 200 datasets of 4,000 genes randomly sampled and then estimated the load for each of these datasets.
Acknowledgments We are grateful to  Alysse Perreault-Payette  and Berenice  Bougas  for help in DNA extraction of additional individuals for whole genome sequencing. We thank  Benoit Nabholz  for the initial development of the π N /π S C++ app we used here. We are grateful to the genomic platform at  Compute Canada (Canada) ,  IBIS (Canada) , and  MBB (Montpellier, France)  for providing computational capacity as well as  Bird (Nantes, France ) for data storage. This research was carried out in conjunction with EPIC4 (Enhanced Production in Coho: Culture, Community, Catch), a project supported in part by  University Laval, Ressources Aquatiques Quebec (RAQ) , and the  Government of Canada  through  Genome Canada ,  Genome British Columbia , and  Genome Que  ´bec.
To 'validate' a name would thus be only one of the two possible mechanisms to make this name validly published according to Rule 27. In any case, the genus name Borrelia was not 'validated' a second time. In contrast, the valid publication of the new combinations in the genus Borreliella  [15, 16]  for validly published Borrelia species names did not render these unavailable for use, i. e. these Borrelia species names remained validly published and legitimate  [18]. Scholars who prefer Borrelia in its original circumscription can still use the Borrelia synonyms of the Borreliella names and nevertheless be in accordance with the ICNP  [20]. Accordingly, the perceived problem regarding the Borreliella names is actually restricted to the use of these names in publications and particularly in public databases  [17].
The curators of some public databases may misunderstand the ICNP as indicating that the latest validly published name must be treated as the correct name, or may just regard this as a database policy to be followed  [18]. Fixing this issue, if it is perceived to be problematic, is a matter of negotiation with these public databases  [17]. It does not have a direct connection to the ICNP  [12] , which does not regulate taxonomy. For the reasons given above, the Judicial Commission does not consider it possible to grant this Request for an Opinion. As in the case of Opinion 122, this conclusion can, and has to, be reached without resorting to any consideration of the taxonomic merits (or lack thereof) of the underlying work  [15]. The Judicial Commission thus voted to deny this request  [14].
Lactobacillus fornicalis The JCM responded that the 16S rRNA gene sequence for JCM 12512 T does not fit to Y18654 but does not have phenotypic data for the deposit, which was obtained from CIP (Mitsuo Sakamoto, personal communication, 9 June 2022). The JCM catalogue further indicates that CIP 106679 T was directly obtained from Dr. Dicks. The sequence AB290883, obtained from JCM 12512 T , confirms the mismatch; it shows a high similarity to EF468099, obtained from the type strain of Lactobacillus plantarum. The ATCC catalogue indicates that ATCC 700934 T was deposited by Dr. Dicks and that 16S rRNA gene sequencing pointed to a strain of Lactobacillus plantarum. This was confirmed by ATCC staff (Shimaz Hashimdeen, personal communication, 13 June 2022); apparently ATCC 700934 T showed 99. 97 % 16S rRNA gene sequence similarity to ATCC 14917 T , a type strain deposit of Lactobacillus plantarum.
Acknowledgements The Judicial Commission thanks  Dr. Mitsuo Sakamoto  (JCM),  Dr. Shimaz Hashimdeen  (ATCC),  Estelle Boulanger  and  Dr. Olivier Chesneau  (CIP),  Dr. Rüdiger Pukall  (DSMZ), and  Prof. Dr. Edward R. B. Moore  ( CCUG ) for providing information on deposits in their respective collections (affecting Opinion  125 ). The five Opinions were ratified by the voting members of the  International Committee on Systematics of Prokaryotes , which is gratefully acknowledged.
Previous attempts to link candidate genes to acetyltransferase activity include a study in which expression differences of candidate acetyltransferase genes failed to correlate with the presence/absence of acetates in the two closely related and sympatrically occurring species H. virescens and H. subflexa  21. Also notable is a study in which 34 candidate genes from Agrotis segetum failed to yield acetate esters in a yeast expression system  16. Misleadingly, each of these 34 genes is now annotated as "fatty alcohol acetyltransferase" in GenBank (Accession Numbers KJ579206-KJ579239). This precedent has led to paralogs in other species being named as acetyltransferases, despite the absence of any biochemical evidence (e. g. , Helicoverpa armigera (Accession Numbers MF706167-MF706196) and H.