dataesr/scientific-paragraphs-categorization huggingface dataset:
| MPXV genome sequence analysis Raw NGS reads were mapped against the MPXV genome GenBank Accession no. ON563414. 3 using minimap2 software version 2. 23-r1111. 20 This 197 205 base pair-long genome was obtained from a patient sampled in Massachusetts, United States, in May 2022, and is used as a B. 1 reference genome by the Nextclade tool v1. 6. 0 (https://clades. nextstrain. org/). 21 Different methods for variant calling were used according to the NGS technology. In case of Illumina reads, variant calling was performed with freebayes version 1. 3. 5  22  and in case of Nanopore reads it was performed with medaka 1. 4. 4, longshot v0. 4. 5 (https://github. com/pjedge/longshot),  23 and artic_vcf_filter from the artic pipeline (https://artic. readthedocs. io/ en/latest/). MPXV consensus genomes were generated by putting a "N" at all positions covered by less than 4 reads for Illumina sequencing and less than 10 reads for Nanopore sequencing, and by considering all position tags as failed by artic_vcf_filter has "N.
| Phylogenetic analysis The MPXV genomes deposited in the GISAID sequence database (https://gisaid. org/)  24 until 22nd of August, 2022 and harboring the most mutations in common with those obtained in the present study were selected for their incorporation in the phylogeny reconstruction within the limit of maximum two genomes (asides genomes obtained in the present study) by identified phylogenetic cluster. The genomes GenBank accession numbers NC_063383 and ON563414 were added to root the tree. Genome sequences were aligned with MAFFT v7. 505. 25 The genome region corresponding to nucleotide positions 150,551 to 150,576 (relatively to genome NC_063383) was excluded from the analysis because it contains a deletion and repeats that do no allow a robust assembly of NGS reads. The tree was built with the Iqtree tool v2.
| Sanger sequencing of a MPXV genomic fragment To check for results obtained by NGS, two sets of conventional PCR primers were chosen from a set of PCR primer pairs designed to amplify overlapping fragments of the MPXV genome (Supporting Information Methods). PCR were performed according to conditions provided in the Supporting Information Methods. Consensus sequences obtained by Sanger sequencing and NGS were aligned and analyzed using the CodonCode Aligner v. 9. 0 software (https:// www. codoncode. com/aligner/; CodonCode Corporation).
| Analysis of mutational patterns in the MPXV genomes Nucleotide and amino acid mutations were identified for all MPXV genomes obtained in the present study with the Nextclade tool v1. 6. 0 (https://clades. nextstrain. org/)  21 relatively to the genome GenBank Accession no. ON563414. 3, used as a B. 1 reference genome by the Nextclade tool,  21, 28  and to the genome GenBank Accession no. NC_063383. 1 (isolate: MPXV-M5312_HM12_Rivers-001) that belongs to clade IIb and was obtained in Nigeria in August 2018. 3 In addition, we downloaded all B. 1 genomes available as of February 13, 2023 through the Nextstrain web application (https:// nextstrain. org/monkeypox/hmpxv1),  28 hich were curated sequences with their metadata counterparts originating from the NCBI Virus sequence database (https://www.
ncbi. nlm. nih. gov/labs/ virus/). 29 A set of genomes that encompassed all downloaded genomes with a size of at least 90% of that of genome GenBank Accession no. NC_063383. 1 was further used. All these genomes were analyzed with the Nextclade tool (https://clades. nextstrain. org/),  21 en the list of mutations and of genomes that harbor them overall and by lineage were obtained using an in house Python (https://www. python. org/) script.
| Clusters of MPXV genomes based on mutation patterns and phylogenetic analyses We searched in the GISAID sequence database (https://gisaid. org/) for the MPXV genomes the most similar to those obtained in the present study based on mutation patterns and phylogenetic analyses (Figure  1 ). ; Supporting Information: Figure  S6 ). F I G U R E 1 Phylogenetic analysis based on Monkeypox virus genomes obtained in the present study and genomes that are those the most similar to them. Monkeypox virus genomes obtained in the present study are indicated by a blue font and the prefix "hMPXV-IHU" in the label name. The genomes from the GISAID sequence database (https://gisaid. org/ 24 ;) that were found as those the most similar to them are indicated by a green font. For Information on the genomes from the GISAID sequence database (https://gisaid.
54 The presence of a frameshift in the gene encoding DNA-dependent RNA polymerase subunit RPO132, related to the insertion of an adenine in a stretch of 8 such nucleotides, is particularly intriguing considering that this mutation would truncate an informational protein critical for the virus replication cycle. A sequencing artifact needed being ruled out considering that this insertion is located in an homopolymer that are proned to sequencing errors, but the insertion was observed in 47/48 NGS reads and through Sanger sequencing as well. An insertion in this gene was also reported recently in two MPXV genomes obtained from samples collected in Portugal  5  and in the United States. 31 As a of fact, it is very likely that an alternative start codon (at Methionine in position 9) exist for this essential gene as previously reported  31  and this is in fact how this is annotated in the modified VACV Ankara strain as well (GenBank Accession no.
genomes obtained in the present study (GenBank Accession no. OP382478-OP382495) were classified in the B. 1 lineage of clade 3 (or clade IIb according to the WHO classification); two were classified into the B. 1. 1 sublineage, two into the B. 1. 14 lineage, and two into the B. 1. 10 or B. 1. 12 lineages (https:// nextstrain. org/monkeypox/hmpxv1).
pharyngeal samples. We investigated further to which bacterial genome regions corresponded NGS reads identified as sequences of S. aureus and S. pyogenes and generated from two skin lesions, one non genital and one penile, respectively. The 2166 NGS reads identified as of S. aureus corresponded to 1216 (41%) of the 2967 genes and to 504 (21%) of the 2450 intergenic regions of bacterial genome GenBank Accession no. NC_007795. 1 (Supporting Information: Figure A). Besides, the 80 316 NGS reads identified as of S. pyogenes corresponded to 1637 (97%) of the 1693 genes and to 1354 (96%) of the 1414 intergenic regions of bacterial genome GenBank Accession no. NZ_LS483338. 1 (Supporting Information: Figure B). Here, we obtained 18 MPXV genomes, which represents about two thirds of the 29 genomes available from France in GISAID (https:// gisaid.
org/) 24 and in GenBank 29 as of February 16, 2022. At the global level, 5013 genomes have been released in GISAID (https:// gisaid. org/) 24 at that time. All these 18 genomes obtained in our laboratory were found to belong to the B. 1 lineage, as for the case of other viral genomes released for the 2022 outbreak. 5,7 In addition, we identified 4 B. 1 sublineages (B. 1. 1, B. 1. 10, B. 1. 12, and B. 1. 14) encompassing one or two viral genomes. The phylogenomic analyses of the 18 genomes indicate firstly that MPXV detected among patients tested in our institute did not comprise a particular lineage apart from other viruses involved in the 2022 outbreak in nonendemic areas. Second, they show that the genomes the most closely related to those obtained in our laboratory are from Europe and North and South America.
ACKNOWLEDGMENTS We are thankful to  Marion Le Bideau ,  Claudia Andrieu  and  Priscilla Jardot  for their technical help. This work was supported by the  French Government  under the  "Investments for the Future" program  managed by the  National Agency for Research (ANR)  (Méditerranée-Infection  10-IAHU-03 ). Funding sources had no role in the design and conduct of the study, the collection, management, analysis, and interpretation of the data, and the preparation, review, or approval of the manuscript.
DATA AVAILABILITY STATEMENT Viral genomes obtained and analyzed in the present study have been deposited in the GenBank sequence database (https://www. ncbi. nlm. nih. gov/genbank/)  28  under accession numbers OP382478-OP382495, and are available from the IHU Marseille Infection website (https://www. mediterranee-infection. com/acces-ressources/donnees-pour-articles/ genome-monkeypox/). They can also be retrieved online from the EpiPox sheet of GISAID sequence database (https://gisaid. org/) (Elbe et al. , 2017)   using the online search tool with "IHU" and "France" as keywords (GISAID identifiers are as follows: no. EPI_ISL_14863048, EPI_ISL_14863050, EPI_ISL_14863066, EPI_ISL_14621526, EPI_ISL_14621525, EPI_ISL_ 13308160, EPI_ISL_13308158, EPI_ISL_13052287). For Information on the genomes from the GISAID sequence database (https://gisaid.
All 18 MPXV 18 genomes obtained in the present study (GenBank Accession no. OP382478-OP382495) were classified in the B. 1 lineage of clade 3 (or clade IIb according to the WHO classification); two were classified into the B. 1. 1 sublineage, two into the B. 1. 14 lineage, and two into the B. 1. 10 or B. 1. 12 lineages (https:// nextstrain. org/monkeypox/hmpxv1).
against the database k2_standard as available on June 7, 2022. NGS reads identified as corresponding to sequences from Staphylococcus aureus and Strepto-Main features of biological samples, next-generation sequencing data, and Monkeypox virus genomes in the present study. Genome identifier hMPXV-IHU00001 hMPXV-IHU00002 hMPXV-IHU00003 hMPXV-IHU00004 hMPXV-IHU00005 hMPXV-IHU00006 hMPXV-IHU00008 hMPXV-IHU00007 coccus pyogenes, two agents of secondary bacterial infections on skin Monkeypox virus lineage and sublineage B. 1 B. 1. 1 B. 1 B. 1 B. 1 B. 1 B. 1. 12 B. 1 lesions, were selected and used to perform a mapping with the minimap2 software version 2. 23-r1111 against genomes from these bacteria (GenBank accession no. NC_007795. 1 and NZ_LS483338. 1, respectively). Then, mapping coordinates, as well as gene coordinates retrieved from the NCBI GenBank nucleotide sequence database (https://www.
ncbi. nlm. nih. gov/nuccore/ 29 ) for these bacterial Genome coverage (%) 100. 00 99. 98 99. 98 99. 98 99. 98 96. 55 93. 43 99. 96 genomes, were handled using an in house Python (https://www. python. org/) script to determine the distribution of reads between genes and intergenic fragments. Mean sequencing depth (X) 93 166 60 58 65 12 17 67 3 | RESULTS 3. 1 | Selection of MPXV-positive samples used for NGS Between the May 22 and the July 22, 2022 (2 months), 307 patients Number of NGS reads mapped on the genome GenBank Accession no. ON563414. 3 2461 148 297 57 666 50 310 60 224 11 197 19 577 66 461 were tested for the presence of MPXV in the setting of clinical virology diagnosis. This was performed with an in-house real-time PCR assay (qPCR) 16,17 on several clinical samples including mostly skin lesions, and fecal and nasopharyngeal samples.
). The mean Sample type Skin lesion Rectal sample Skin lesion Skin lesion Rectal sample Rectal sample Skin lesion Rectal sample number of NGS reads per sample was 2 474 509 (range, 886 636-5 977 822) (Table 1). After mapping all reads against the genome GenBank Accession no. ON563414. 3 that was obtained from a human specimen collected in May 2022 in the United T A B L E 1 Sampling date June 13, 2022 July 1, 2022 July 4, 2022 July 4, 2022 July 4, 2022 July 4, 2022 July 5, 2022 July 6, 2022 July 7, 2022 States and is used as a B. 1 lineage reference by Nextclade (https:// clades. nextstrain. org/), 21 there remained a mean number of 33 738 (range, 1195-148 297) reads, and mean genome coverage relatively to the genome no. ON563414. 3 was 81. 8 ± 30. 7% (range, 4. 2-100. 0%) (Table 1; Supporting Information Data).
5,32 Mutations in the 3184 MPXV genomes of the B. 1 lineage recovered from GenBankTo get a more comprehensive picture of the mutational patterns in MPXV genomes from the 2022 outbreak in non-endemic countries, we analyzed all curated B. 1 genomes with a size of at least 90% of that of genome GenBank Accession no. NC_063383. 1 that were available as of February 13, 2023 through the Nextstrain web application (https://nextstrain. org/monkeypox/hmpxv1)28and originated from the NCBI Virus sequence database (https:// www. ncbi. nlm. nih. gov/labs/virus/). tion:TablesS2, S3). In addition, compared to proteins encoded by genome ON563414. 3, 3909 different non-synonymous mutations were detected for at least one genome, and 5, 11, 591, 697 and 1611 mutations were detected for at least 100, 50, 10, 5, and 2 genomes, respectively (Supporting Information: Tables S2, S3.
7 | Mutations shared by the 18 MPXV genomes obtained in the present study and the 3184 MPXV genomes of the B. 1 lineage recovered from GenBank 50, 10, 5, and 2 genomes, respectively (Supporting Information: Among the 3,184 genomes, a mean number of 15 genomes (range, 0-230) harbored each of the 35 mutations that were detected in one or two of the 18 viral genomes obtained in the present study relatively to the same genome ON563414. 3 (Supporting Information Table S6). Apart from mutation G74360A harbored by 230 genomes, all 34 other mutations were harbored by between 5 and 50 genomes in 13 cases, between 1 and 5 genomes in 20 cases, and in no genome in two cases. A mean number of 22 genomes (range, 0-230) harbored each of the 21 nonsynonymous mutations that were detected in one or two of the 18 viral genomes obtained in the present study relatively to the same genome ON563414.
Sample size For the primary endpoint, according to French nationally representative data (ESTEBAN), we assume respectively a BP rate control of 50% and 60% in the control and the interventional group. Assuming an equal number of patients in the two groups, a chi-square test with a two-sided signi cance level of 5% and a power of 80% with 10% of patients lost to follow-up, 852 patients are needed to be recruited (426 per arm). Sample size calculation is performed with SAS software (version 9. 4; Institute Carry, NC).
A similar study over a longer period of time would probably provide a better re ection of APNs management on a chronic pathology such as hypertension for example. The delay of the study can also be a problem for the choice of the endpoints. It is di cult to obtain usable and signi cant data for lifestyle modi cations on a six-months period. We chose the smoking status and the weight variation according to BMI categories to assess the adherence of lifestyle changes because these quantitative endpoints can evolve on short period and can be more measurable than alimentation or physical activity. This study is carried out by a recently graduated APN. The level of experience of the professional involved in the study can create a bias. We can suppose that the results would vary with an experienced APN.
The wide range of methods used to investigate subglacial condition highlights the need to collect various proxies of hydro-mechanical conditions to understand the feedback between the development of the subglacial drainage system, rheological changes in the till and consequences for basal motion. In this study, we present the results of a comprehensive, subglacial multi-sensor record, complemented by a cryoseismological dataset, all simultaneously acquired at Kongsvegen, a surge-type glacier in Svalbard. The field instrumentation has been designed to optimize interpretation by co-locating the instruments in a single borehole and accompanying measurements of glacier surface velocity and energy-balance driven estimates of surface runoff. The records span the period from June 2021 to August 2022 and cover two contrasting melt seasons.
As the glacier moves across its bed, the ploughmeter tip is dragged through the sediment, and the device bends which is sensed by the strain gauges. Just above the upper end of the ploughmeter, about 1 m above the glacier bed, we installed a vibrating wire pressure sensor (Geokon 4500SH, <2 kPa accuracy and 0. 5 kPa resolution) to monitor water pressure, p. Sensor readings and data recording is performed using a Campbell Scientific CR1000X datalogger, recording data at one minute intervals. One three-component geophone (DiGOS, 4. 5 Hz) was installed into the ice at ∼1. 5 m below the surface to ensure good coupling and to prevent melt-out during summer. DiGOS datacube, that comprises a digitizer and a datalogger, controlled the sampling rate (100 Hz) and recorded the signals. In this study, we analyse seismicity in the 3-10 Hz frequency band as a proxy for hydraulic conditions  (Bartholomaus et al.
, 2015; Gimbert et al. , 2016; Nanni et al. , 2020; Lindner et al. , 2020; Labedz et al. , 2022). In addition, we used data of two surface stations (KNG6 and KNG7) that recorded positions using Global Navigation Satellite System (GNSS), from which we derive daily surface velocities. The stations are located at distances of ∼ 700 m (KNG6 78. 78067 o N, 13. 15153 o E) and ∼ 3000 m (KNG7, 78. 76770 o N, 13. 23962 o E) upstream of the drill site. Data are recorded at five-seconds intervals continuously between April 1 and September 1 when there is enough sun for the solar panels, and to conserve battery power, only for one hour per day during the rest of the year. Static post-processing is applied to the GNSS data, assuming that the rover station is static for one hour due to the relatively low speed of the glacier.
Overview over the dataset The meteorological conditions extracted from CARRA/AROME-Arctic, the velocity, the runoff, Q, the channel-flow-induced seismic power, P , the water pressure, p and the force acting on the ploughmeter, F , are displayed in Figure  4. We obtain time 245 series covering two melt seasons (2021 and 2022) and one winter period. The meteorological conditions (Figure  4a ) control timing and volume of Q resulting from meltwater production or rainfall (Figure  4b ). We note that the dataset covers two very The channel-flow-induced seismic power P (Fig. 4c , yellow line) follows the variations in Q throughout the recorded period, increasing at the beginning of the two melt seasons and decreasing towards their end. Such behaviour has been previously observed in other settings and confirms the sensitivity of the selected frequency band to Q  (Bartholomaus et al.
Acknowledgements. This project has received support from the  Research Council of Norway  through the projects  MAMMAMIA  (grant no. 301837 ) and  SLIDE  (no. 337228 ) and from the  Faculty of Mathematics and Natural Sciences at the University of Oslo  through the strategic research initiative EarthFlows. We are grateful to the  Governor of Svalbard  for permitting fieldwork at Kongsvegen. The energetic help of  Wenxue Cao ,  Ceslav Czyk ,  Basile De Fleurian ,  Jean-Charles Gallet ,  Adrien Gilbert ,  Stephen Hudson ,  Simon Filhol ,  Urs Fischer ,  Catherine Larose , and  Ashley Morris  during the field campaigns is greatly acknowledged. UN acknowledges  Andreas Köhler  for support in the seismic analysis.
Recently, others have proposed state-space methods to infer viral concentrations from wastewater time series. The underlying "true" viral concentration at time X t is modelled as a first-order auto-regressive (AR1) process  [12, 13]. To account for measurement noise and outliers in the observations, measurements Y t are assumed to be equal to X t plus an independent mean-zero Gaussian observation error. To account for outliers, from time to time Y t is assumed to be replaced by an independent Uniformly distributed random variable that is unrelated to X t. Left-censoring is accounted for by capping Y t at the (known) limit of quantification. Using a Kalman Filter and numerical approximations, the state variable X t is inferred from observation data Y t to produce a smoothed estimate of viral concentration, with outliers removed, that can extend below the known limit of quantification  [12].
In this paper, we propose and test a simpler, more realistic, and more flexible state-space model. Our latent variable is modelled by a first order random walk (RW1) instead of an AR1 process, which reduces the number of model parameters. Instead of randomly replacing observations by a random number, our model generates outliers by assuming observation errors from a heavy-tailed tdistribution. This has the benefit that observations classified as "outliers" can still be informative about viral concentrations. Our model is implemented in the Stan modelling language  [14] , which allows for fast Bayesian inference and straightforward extensions of the model.
Wastewater data source Untreated influent samples were collected from sewage treatment plant sites across England at a frequency of four days a week by the Environmental Monitoring for Health Protection (EMHP) programme, led by the UKHSA. The sampling strategy provides coverage of approximately 40 million people across England. Samples were analysed for SARS-CoV-2 RNA by quantifying the number of copies of the nucleocapsid gene (N1) using RT-qPCR. Concentrations under the limit of detection were assigned a value of -4, to be handled during the data processing pipeline depending on the use case. Only sites sampled 30 times or more (around seven weeks' worth of data) were included; median sample count across sites was 145, ranging from 31 to 323.
A left-censored dynamic linear model In our model, the (unobserved) viral concentration signal X t is modelled as a first-order random walk (RW1) process X t = X t-1 + σ t (2. 4) where t ∼ N(0, 1) is an independent and identically distributed normal random variable for t = 2,. , n. The measured concentrations Y * t are modelled by adding independent measurement noise to X t : Y * t = X t + τ t (2. 5) where the independent measurement error t ∼ t ν has a Student t-distribution with ν degrees of freedom. The actually observed, censored data, are modelled by truncating Y * t at the known censoring threshold t : Y t = max(Y * t , t ) (2. 6) As samples are taken only four times a week, the vector of measurements Y contains data observed at a subset T of all n available time points. We infer the viral concentration X t from Y by Bayesian inference  [17] , i.
The (multiplicative) proportionality constant in Eq 2. 7 is inferred by Markov-Chain Monte-Carlo (MCMC) using the Stan software  [14]. The hyperparameters τ and ν of the measurement process can be interpreted as measurement error variance (larger τ's correspond to noisier measurements), and the tendency to generate outliers (smaller ν's generate greater deviations from measured viral concentrations). Posterior estimates of these parameters are thus interesting for diagnostic purposes, e. g. to identify anomalous sites.
Dynamic linear model (DLM) The DLM was implemented in the open-source programming language Stan  [14] , which provides efficient sampling of probabilistic models via MCMC and other inference algorithms. Code specifying the model is provided in Supplementary Information Figure  S17. MCMC convergence statistics for the fit examples shown in Figure  1  can also be found in the SI (Figure  S4 -S8, Tables  S1-S4 ).
10-fold cross-validation 10-fold cross-validation was performed on the data across the 286 sites that had at least 30 samples. For each iteration: • Raw SARS-CoV-2 N1 gc/L (with no normalisation for flow) was used with a log 10 transformation. • Data were randomly split (90%/10%) into training and test sets • Pre-existing missing values (days when samples were expected but were not collected) were included in the training set but not in the test set. • The censoring threshold was set to a single value log 10 (133. 0 gc/L) for simplicity. In reality the limit of quantification will vary across samples. Y pred, t for the DLM were generated by using Stan to sample from the joint posterior distribution of X 1 ,. , X n and hyperparameters σ, ν, τ, inferred from the training data. We then inferred posterior predictive samples Y pred, t at times t left out during training by adding t-distributed measurement errors to posterior samples of X t , and applying censoring if the sampled observation was below the censoring threshold.