dataesr/scientific-paragraphs-categorization huggingface dataset:
Heat-notburn devices, electronic cigarettes, NRT and non-tobacco pouches are around 50% complete, with the epidemiological analysis accounting for most of the missing data points. As such, their position on the relative risk hierarchy is determined mostly by the LCR analysis. The lowest data completeness scores are cigarillos and cut tobacco, which have only 20% of the data points complete. For cut tobacco, the dataset has mostly been assumed from combustible cigarettes in both the LCR and epidemiological analysis and taking account of this assumption the data completeness score is 100%. For cigarillos, most of the dataset for the LCR (toxin emissions) analysis was assumed from combustible cigarettes and there is no data for this product in the epidemiological analysis. Overall, data completeness has increased from 2020, but there are still significant gaps to fill and therefore, there remains a level of uncertainty due to missing data in the scores of most products.
Therefore, it does not represent the potential risk associated with higher consumption levels of the products or dual/poly-use of the products. Fourthly, the epidemiological meta-analysis combines odds ratios for several diseases, which effectively generalizes a multitude of diseases to "cancer" and "non-cancer" categories. While generalization of multiple datasets is not unprecedented in meta-analyses, where calculation of odds ratios for all-cause mortality often involves a similar process, it is important to note that these values must be interpreted carefully. For example, if the generalized cancer risk ratio is 2, the lung cancer ratio may still be much higher or the risk for stomach cancer much lower, as is the case for combustible cigarettes. The risk ratios derived here are meaningless at the level of individual diseases and should not be used as an indicator for specific disease risk.
5  Third, the epidemiological risk estimates for dipping are mischaracterized, the authors include risk estimates from use of products from non-US countries, that are vastly different from the US dip product. While the search criteria for the literature review on epidemiological evidence is reasonable, the authors do not accurately characterize the disease risks from the use of US dip. The study identifies dip risk from a mix of U. S. , European, and some global cancer risk estimates as 2. 06 (1. 38 -3. 09) and non-cancer risk as 1. 305 (1. 15 -1. 48). The authors conflate the disease risk estimates instance, the unit risk for formaldehyde is validated against a cohort study of industrial workers exposed to formaldehyde (OEHHA, 2011). As such, the lifetime cancer risk analysis is validated against the same type of data as the epidemiological analysis and the comparison of the two datasets is not entirely incoherent.
2. Comment: Biomarkers of exposure. Response: Thank you for raising this point about the biomarkers of exposure (BoE) data. There is certainly an argument to be made for including this data as it can account for factors that are lacking in the toxin emissions-based lifetime cancer risk analysis, such as those mentioned: differences in administration routes, absorption, distribution, metabolism, and excretion. We did extract this data and assess it for inclusion in the relative risk hierarchy but decided against this for three main reasons. Firstly, the BoE measurement gives a snapshot of the levels of biomarkers excreted by users of nicotine products at the moment when the sample is taken. BoE measurements are known to show intra-individual variation depending on the elimination half-life of the marker and the intervals between exposure events, meaning that a sample taken one hour after exposure to a nicotine product may give a different biomarker profile compared with a sample taken 4 hours after exposure in the same person, and may not be representative of the peak biomarker level after exposure (Aylward et al.
I hope that this explanation clarifies why the biomarker of exposure data was not explored to a greater extent in the paper and why it was not further pursued it in this update. While we agree that BoE studies reveal valuable information about toxin exposure in users of different products, it was not suitable for integration with the rest of the dataset included in the relative risk hierarchy. Comment: Differentiating U. S. smokeless tobacco from other varieties. Response: This comment is consistent with a key piece of feedback we have received from other parts of the scientific community over the last 2 years when discussing this work. An update of this study was already underway since late 2021, in which the dipping and chewing tobacco categories had been limited to data from U. S. varieties and an additional category was created for smokeless tobacco from the rest of the world, as well as for bidis.
Taken together, this apparent adaptive plasticity may explain the presence of P. calceolata in many different oceanic environments, however, an exhaustive analysis of the genetic capacity of this species and the in situ characterization of its ecological niche is lacking. Here we sequenced, assembled and annotated the Pelagomonas calceolata genome, with a combination of long-and short reads. We examined its genomic structure and gene content relative to other unicellular phytoplankton. We used this genome to detect P. calceolata in environmental datasets of Tara expeditions across all oceans to characterize its ecological niche and to identify the environmental conditions controlling its relative abundance. Finally, environmental expression levels of genes involved in nitrogen compounds and iron uptake and metabolism were studied.
Results Chromosome-scale assembly and annotation of the P. calceolata genome. To measure the abundance of P. calceolata in the oceans and study its genetic capacity to grow in different environmental conditions, we sequenced and assembled the genome of P. calceolata RCC100 using long reads of Oxford Nanopore Technologies (ONT) and Illumina short reads. Using the k-mer distribution of short reads, the genome was estimated to be homozygous with a size of 31 Mb (Supplementary Fig. S1a ). The ONT long reads were assembled with Flye into six nuclear contigs for a total of 32. 4 Mb, 1 plastid circular contig (90 Kb) and 1 mitochondrial circular contig (39 Kb) (Fig. 1 , Supplementary Fig. S1b, c , and Supplementary Data 1). Two large and highly similar duplicated regions (>99% of identity) were detected at the extremity of contig 1 and 5 (393 Kb) and at the extremities of contig 3 and 6 (192 Kb; Supplementary Note 1 and Supplementary Fig.
02 and Pearson 0. 41, P value = 4. 82e -05 in the 0. 8-5 µm and 0. 8-2000 µm size fraction, respectively). We used a general additive model to estimate the contribution of temperature, PAR and iron concentration to P. calceolata relative abundance (Table  2 ). The three factors explain 32. 3% of the variations of P. calceolata abundance in the 0. 8-5 µm size fraction and 56. 8% in the 0. 8-2000 µm size fraction. Finally, we projected the relative abundance of P. calceolata at the end of the century following Frémont et al. methodology  35. We modeled the ecological niche of P. calceolata using the World Ocean Atlas (WOA18) datasets at the time and location of sampling or using the projected climatology in 2099 using the RPC8. 5 scenario (see "Methods"). We used four machinelearning techniques: Generalized Additive Models (GAM), Neural Networks (nn), Random Forest (rf) and Gradient Boosted Trees (bt) and evaluated their performances with two parameters.
The Pearson correlation coefficient indicates the correlation between the model and in situ measurements of P. calceolata abundance. The four machine-learning tools have similar performances based on Pearson's correlations (nn = 0. 676; gam = 0. 621; bt = 0. 683; rf = 0. 694). The second parameter is the root mean square error (rmse) and reflects the magnitude of the errors in the models (the number of standard deviations from the mean). Using this metric the GAM approach is less good (rmse = 1. 04) than the three other tools (nn = 0. 964; bt = 0. 952; rf = 0. 941). These results indicate that we have enough in situ data to capture the global trends on the relative abundance of P. calceolata but these models could be imprecise on the amplitude of abundance variations. In addition, the predictions in the tropical waters are uncertain because this environment in 2099 is out of the range of the training dataset.
2 nmol/l) iron conditions using Tara Oceans metatranscriptomes. P. calceolata has five genes encoding the phytotransferrin ISIP2A involved in Fe 3+ uptake via endosomal vesicles and 2 putative iron-storage protein ISIP3. In iron-poor environments, three ISIP2A and one ISIP3 are overexpressed (Fig. 4a  and Supplementary Data 9). This result indicates that similarly to diatoms, ISIP are upregulated following iron starvation in P. calceolata potentially improving cell growth in low-iron environments. Three genes encode the iron transporter ferroportin but are not differentially expressed according to the environment (Supplementary Fig. S8a ). These proteins are iron exporters in multicellular organisms but their function in microalgae remains to be studied  36. Finally, we identified eight Zinc/iron permeases potentially involved in iron uptake from the environment in the P.
S10 ). We identified three genes carrying the nitrate and nitrite sensing (NIT) domain (IPR013587) in the P. calceolata genome. Using NCBI non-redundant proteins and marine genomic databases (see "Methods"), 60 homologous proteins of the NIT-sensing domains of P. calceolata were identified. These homologs are restricted to the Pelagophyceae class (16 transcriptomes), the Dictyochophyceae class (6 transcriptomes) and one putative cryptophyte transcriptome. The phylogenetic tree of this protein family shows three subfamilies diverging before the Dictyochophyceae/Pelagophyceae separation (Supplementary Fig. S11 ). One P. calceolata protein carries a NIT-sensing domain surrounded by two transmembrane domains suggesting a capacity for external nitrate/nitrite sensing while the two other NIT genes carry a protein-kinase domain (IPR000719) suggesting phosphorylation-based signal transduction dependent on intracellular nitrate or nitrite concentration (Fig.
However, we cannot exclude that the large genome size of P. calceolata compared to bacterial genomes present in the 0. 8-5 µm size fraction overestimates its relative abundance in metagenomic datasets. Further studies may combine microscopic and flow sorting approaches with genomic data to assess the number of cells and the biomass of this organism in the oceans. Our model analysis has revealed a probable increase of P. calceolata relative abundance at the end of the century in high latitudes where the seawater temperature is currently too low for this species. This result is coherent with previous studies suggesting a global increase of phytoplankton in subpolar regions  38, 39. Iron is essential for growth, photosynthesis, primary production, nitrogen fixation and reduction for PPEs  40.
We have shown that the cyanate lyase and urease genes are expressed in many environments but only the cyanate lyase is overexpressed in low-nitrate conditions. These two genes, largely present among phytoplankton lineages, could be significant components of acclimation to low-nitrate environments  45. In addition, A. anophagefferens grow faster on the organic nitrogen substrates (urea and glutamic acid) than on nitrate or ammonium, suggesting that the dominance of Pelagophyceae in low-nitrate environments could be due to an optimized usage of these organic molecules  46. One remarkable feature of the P. calceolata genome is the presence of three genes carrying NIT domains (PF08376). This NIT domain was first described in bacterial nitrite and nitrate sensor proteins  47. This sensor is an alpha-helical protein playing a signal transduction role in regulating gene expression, cell motility and enzyme activity in Klebsiella oxytoca  48.
In contrast, putative extracellular NIT-sensing could be an environmental nitrate or nitrite sensor activated to regulate the expression of genes involved in the acclimation to low-nitrate conditions. In summary, due to its widespread distribution and its high abundance in the open oceans, Pelagomonas calceolata can serve as an ecologically-relevant model to study marine photosynthetic protists. We used the chromosome-scale genome sequence, mostly telomere-to-telomere, generated in this study to estimate its abundance in environmental datasets. We have shown that the P. calceolata genome has specific genomic features potentially explaining its ecological success in open oceans. Compared to the coastal Pelagophyceae A. anophagefferens, the P. calceolata genome contains specific genes involved in the acclimation to lowiron conditions.
The library was sequenced using 101 bp paired-end read chemistry on a HiSeq2000 Illumina sequencer. Low-quality nucleotides (Q < 20) from both ends of the reads were discarded. Illumina sequencing adapters and primer sequences were removed and reads shorter than 30 nucleotides after trimming were discarded. These trimming and cleaning steps were achieved using in-house-designed software based on the FastX package (https://www. genoscope. cns. fr/externe/fastxtend/). The last step identifies and discards read pairs mapped to the phage phiX genome, using the SOAP aligner  50  and the Enterobacteria phage PhiX174 reference sequence (GenBank: NC_001422. 1). This processing, described in ref. 51  , resulted in highquality data. Moreover, ribosomal RNA-like reads were excluded using SortMeRNA  52  2.
1 and SILVA databases. Long-read-based genome assembly. Raw nanopore reads were used for genome assembly. The taxonomic assignation was performed using Centrifuge 53 version 1. 0. 3 to detect potential contamination. Genome size and heterozygosity rate were estimated using Genomescope  54  and Illumina short reads. For the genome assembly, we generated three sets of ONT reads: all the reads, 30× genome coverage with the longest reads and 30× genome coverage of the highest quality reads estimated by the Filtlong tool (https://github. com/rrwick/Filtlong). We applied Filtlong filtering with default parameters using Pelagomonas Illumina short reads as a reference (ONT reads covered by Illumina reads have higher scores). We then applied four different assemblers with default settings, Smartdenovo, Redbean, Flye and Ra on these three sets of reads (Supplementary Data 1)  [55] [56] [57] [58].
After the assembly phase, we selected the best assembly (Flye with all reads) based on the cumulative size and fragmentation. Indeed, the Wtdbg2 and Smartdenovo assembler generated fragmented assemblies with lower N90. Raven and Flye were very close, but only the Flye assembly with all ONT reads contained both the mitochondrial and chloroplastic circular contigs. To display connections that are not present in the contigs file, we used Bandage tool  59. The selected Flye assembly was polished three times using Racon  60  with ONT reads, and two times with Hapo-G  61  and Illumina reads. Gene completeness of the assembly was estimated using the single-copy orthologous gene analysis from BUSCO v5 with the stramenopile dataset version 10 containing 100 genes  28. Repeat masking and GC analyses.
29. 2  66  and the coverage over a non-overlapping window of 2 Kb with Mosdepth version 0. 2. 8  67. Transcriptome assembly. RNA sequencing reads from P. calceolata RCC100 were assembled using Velvet 1. 2. 07 and Oases 0. 2. 08 with a k-mer size of 63 bp  68, 69. Reads were mapped back to the contigs with BWA-mem  70  and only consistent paired-end reads were kept. Uncovered regions were detected and used to identify chimeric contigs. In addition, open reading frames (ORF) and domains were searched using respectively TransDecoder (http://transdecoder. sourceforge. net) and CDDsearch  71. Contig extremities without predicted ORFs or functional domains were removed. Lastly, we used the read strand information to orient RNA contigs. We completed the RNA contigs dataset with the two transcriptome assemblies of the RCC100 strain of P.
calceolata from the Marine Eukaryotes Transcriptomes database (METdb) (http://metdb. sb-roscoff. fr/metdb/)  72. Gene prediction. Nuclear gene prediction was performed using 23,696 Pelagomonadales proteins (mainly A. anophagefferens) downloaded from the NCBI website. Proteins were aligned on the genome in a two-step strategy. First, BLAT  73  (version 36 with default parameters) was used to rapidly localize corresponding putative regions of these proteins on the genome. The best match and the matches with a score greater than or equal to 90% of the best match score were retained. Then, the regions with BLAT alignments were masked and we aligned the same set of proteins using BLAST  74  , which can identify more divergent matches. Second, alignments were refined using Genewise 75 (version 2.
Acknowledgements We thank the commitment of the following people who made this work possible: the  Genoscope/CEA , the  CNRS  (in particular the  Federation de Recherche R2022/Tara Oceans GO-SEE) ,  Marie-José Garet-Delmas  from the  Roscoff Culture Collection  for growing the RCC100 strain,  Claude Scarpelli  for support in high-performance computing and  Linda Sperling  for language editing. Computations were performed using the cobalt HPC machine. We acknowledge the financial support of  FRANCE GENOMIQUE  ( ANR-10-INBS-09-08 ) and  Oceanomics  ( ANR-11-BTBR-0008 ). We also thank the  Tara Expedition Foundation  and their partners for the organization of marine scientific expeditions (http://oceans. taraexpeditions. org). This article is contribution number 139 of  Tara Oceans.
Data availability Pelagomonas calceolata genomic and transcriptomic reads, genome assembly, and gene prediction are available at the ENA (EMBL-EBI) website under the accession number PRJEB47931. P. calceolata transcriptomes are available under the accession number PRJEB34158, runs ERR3497221 and ERR3497222. Tara Oceans and Tara Polar Circle metagenomic sequences are archived at the ENA under the following accession numbers: PRJEB9740, PRJEB9691, PRJEB4352, and PRJEB1787. All other data are available from the corresponding author upon reasonable request.
Data Analysis The 30 Si( 3 He,d) 31 P reaction was performed at Maier-Leibnitz-Laboratorium (MLL) Germany. Ejectiles were momentum analysed using the Q3D spectrometer for different laboratory angles. Deuterons were selected using the focal-plane detectors and experimental details can be found in ref. [6]  where excited states between E x = 6. 8 keV MeV and E x = 8. 1 keV MeV have been studied. Figure  1  shows the deuteron focal plane position spectrum obtained at an angle of θ lab = 12°, containing both bound and unbound levels (Sp=7296 keV) of 31 P which were fitted with skewed Gaussians with same resolution. The red curve shows the best least-square fit of the deuteron spectrum. The experimental resolution achieved was 6. 2 keV (FWHM) at θ lab = 12°, three times better then a previous study of the same reaction  [7].
The products were purified from the P5 and P7 primers by double selection with AMPure beads (Beckman). NGS was performed on an Illumina NextSeq 500 machine. NextSeq reads were trimmed with Cutadapt (version 1. 17). MatLab scripts were developed to analyse results, draw 2D-maps, butterfly plots, violin plots, bar plots, and sequence frequency logos, and are available upon request. The relative integration frequency of a mini-TLC plasmid was calculated as the ratio of the frequency of its attP motif in the integration library, r int (moti f ), to the frequency of the attP motif in the donor library, r lib (moti f ). The integration efficiency, E int (moti f ) was then obtained by multiplying the relative integration frequencies by the global integration frequency of the corresponding library, f int (librar y).
Randomization The randomization will be strati ed on the age group (children  [10;18[, adult [18;60] ) Randomization will be performed centrally through the Ennov Clinical software. , The lists for randomization will be provided by the department of biostatistics of the Hospices Civils de Lyon and built using the permuted block randomization method. The data from the 5 rst treated adults during a period of 10 weeks after titration period (visit V8) will be at rst evaluated by the DSMB and reported to ANSM to decide inclusion of patients < 18 years old.
Data There is a priori criterion for premature drop-out. The patients, regardless of the randomization group, will not be excluded from the study and will be followed up according to the protocol. Outcomes related to 24-hour calciuria measurements will be collected and reported at baseline and before every study visits V 3 , V 4 , V 5 , V 6 , V 8, by the investigator or the authorized persons in the medical eld of participant, as per site practice using a standard of care software. If there is missing data due to a problem during the collection of urine a four-days margin is planned to be able to organize a new collection at the patient's home/center. All study results collected in electronic or paper source documents will be entered in an electronic case report form by the investigator or an authorized person who will be appearing on the tasks delegation sheet, as soon as they are collected (during/after patients' visits).
Data will be coded with respect to data con dentiality. Each form will be dated and signed electronically by the investigator, signifying its agreement with the data entered in the eCRF. The Ennov Clinical system was selected. Control quality of the data is performed both centrally and on site by monitors. Presence, accuracy and conformity of the data is veri ed. Surveillance and emergence of adverse and serious adverse event is performed. All adverse and serious adverse event will be reported in the eCRF.
A p-value less than 5% will be considered as statistically signi cant. Two-sided 95% con dence intervals will be provided for the analyses. The analyses will be performed using the R and SAS software. For the primary outcome, the proportions of patients with normalization of calciuria or decrease of 30% of calciuria (as compared to Baseline, V 1 from W 16 , V 8 ) will be described in both groups. The effect of uconazole on calciuria will be quanti ed and tested through the odds ratio (with its 95% con dence interval), adjusted on the age class (children/adult) and on the level of 1,25(OH) 2 D during the screening period (150-200 pmol/L or >200 pmol/L). A description of the proportions of success depending on the dose of uconazole administered at the end of the titration period will be performed.
An exploratory analysis of the uconazole effect will be performed in the subgroups de ned by the level of 1,25(OH)2D during the screening period (150-200 pmol/L or >200 pmol/L). Secondary endpoints related to evolution over time will be analyzed using mixed effect models. Missing data will generally not be imputed. If there is missing data due to a problem during the collection of urine (carrier, broken tube. ) a four-days margin is planned to be able to organize a new collection at the patient's home/center. For the primary endpoint, a patient without calciuria assessment at W 16 will be considered as a patient without normalization of calciuria. Two sensitivity analyses will be performed. The rst one will be performed by multiple imputations, the outcome of patients without 24-hour calciuria assessment at W 16 will be sampled from a Bernoulli distribution with probability equals to the proportion of calciuria normalization in the same treatment, initial 1,25(OH) 2 D level category and age class groups.