dataesr/scientific-paragraphs-categorization huggingface dataset:
Exploratory analysis of ν and τ Outputs from the DLM Analyses were performed using R statistical software (Version 4. 1. 3) to establish whether the DLM is more likely to observe data variability -characterised by ν and τ outputs -at sites that show greater concentrations of SARS-CoV-2 RNA in wastewater. For this purpose we regressed the median gene copies per litre (gc/L) obtained over all time (log 10 transformed) against mean ν or τ, controlling for the standard deviation of ν and τ, respectively. We obtained the residuals from these linear regression models to identify sites where the ν and τ outputs from the model vary in excess of what is accountable to median gc/L and the posterior standard deviation of ν or τ. Residuals for both models were then mapped to the Lower Layer Super Output Area (LSOA) for a given site using the Simple Features (sf ) package in R  [18]  (Figure  4 ).
Model fit: Simulated data To test the output of the DLM, we first simulated data by generating a random walk with variance parameter σ 2 to model the underlying state, which was then sampled with measurement error parameters τ and ν. Exact values are provided in the code. Any values below a predefined limit l are set to the value of l. These synthetic data were then fit with the DLM. Supplementary Figure  S1  shows that the underlying state X-true is tracked rather well by the X-smoothed estimate and lies within the inferred credible intervals, demonstrating that the model can reliably recover the underlying state from noisy observations in a synthetic dataset.
Model validation Model performance was assessed by comparing the MSE produced by the DLM, KS and a sevenday centered MA over 10 folds of cross-validation (see Methods). The MA represents a simple way to remove noise from data, and is used here as a benchmark for comparison. All three models generated comparable MSE per site (Figure  2 ). However, the DLM and KS can estimate viral concentrations below the censoring threshold and, therefore, provide additional information on value for applications, such as case prevalence estimation (see Applications section). In addition both the DLM and KS provide useful parameters for quantifying uncertainty and outliers within the data (DLM: σ/τ/ν, KS: σ/τ/p outlier ). This is particularly useful to identify sites generating unexpected data. So, while an MA scores equally well in terms of the MSE, the smoothing methods still confer additional advantages.
Results of the test are provided in Supplementary Table  SS5 , however in both cases the run times are small enough that we believe the difference is of little practical significance. The speed difference that is of more practical relevance (although difficult to quantify) is that our model was written in a general purpose modelling framework and so is easier to maintain, modify and adapt than the handcrafted R code of the Kalman Smoother. On the other hand, only the Kalman Smoother is able to quantify the probability of a given sample being an outlier and, therefore, this model will be more desirable for specific use cases. The DLM can only inform on whether a given sample lies outside of a predefined interval of the estimated underlying state, as shown in Figure  1.
Acknowledgments The  United Kingdom Government (Department of Health and Social Care)  funded the sampling, testing, and data analysis of wastewater in England. Obépine funded the work of  Marie Courbariaux  and provided the R code of the modified  Kalman Smoother  and support to use it. ww site code date min date max site reporting name UKENAN AW  TP000254 08/03/2021 30/03/2022 Sudbury UKENAN AW TP000293 08/03/2021 30/03/2022 Wisbech UKENAN AW TP000116 08/03/2021 30/03/2022 Grimsby UKENAN AW TP000261 08/03/2021 30/03/2022 Thetford UKENAN AW TP000286 08/03/2021 30/03/2022 Daventry UKENAN AW TP000051 08/03/2021 30/03/2022 Chalton UKENAN AW TP000041 08/03/2021 30/03/2022 Buckingham UKENAN AW TP000028 08/03/2021 30/03/2022 Brackley UKENAN AW TP000107 08/03/2021 30/03/2022 Northampton UKENAN AW TP000055 08/03/2021 30/03/2022 Chelmsford UKENAN AW TP000067 08/03/2021 30/03/2022 Corby UKENAN AW TP000069 08/03/2021 30/03/2022 Milton Keynes UKENAN AW TP000037 08/03/2021 30/03/2022 Wellingborough UKENAN AW TP000023 08/03/2021 30/03/2022 Boston UKENAN AW TP000026 08/03/2021 30/03/2022 Bourne UKENAN AW TP000078 08/03/2021 30/03/2022 Diss UKENAN AW TP000082 08/03/2021 30/03/2022 Downham Market UKENAN AW TP000096 08/03/2021 30/03/2022 Felixstowe UKENAN AW TP000106 08/03/2021 30/03/2022 Grantham UKENAN AW TP000016 08/03/2021 30/03/2022 Bedford UKENAN AW TP000015 08/03/2021 30/03/2022 Beccles UKENAN AW TP000012 08/03/2021 30/03/2022 Barton- upon -Humber UKENAN AW TP000077 08/03/2021 30/03/2022 Breckland UKENAN AW TP000029 08/03/2021 27/03/2022 Braintree UKENTH TWU TP000123 10/03/2021 30/03/2022 Reigate UKENAN AW TP000237 10/03/2021 30/03/2022 Soham UKENSW WXW TP000086 10/03/2021 30/03/2022 Taunton UKENAN AW TP000194 10/03/2021 30/03/2022 Newmarket UKENAN AW TP000047 10/03/2021 30/03/2022 Bury St.
Acknowledgements This study was partially supported by: the  ANR  projects  COSCREEN  ( ANR-21-CO16-0005 ) and  DATAREDUX  ( ANR-19-CE46-0008-03 ) to VC;  EU Horizon 2020 grant MOOD  ( H2020-874850 ) to EV, CP, VC. We thank  Alain Barrat  and  Mason Porter  for useful discussions.
51-6490-82, eBioscience) using a Cytofix/cytoperm fixation permeabilization kit (Cat no. 554714, BD) according to the manufacturer's instructions. Toxicity was analyzed by using Viobility 405/452 Fixable Dye (Cat no. 130-109-814, from Miltenyi Biotec) according to the manufacturer's instructions. Briefly, the cells were washed twice with PBS before viability fixable dye staining for 15 min at room temperature. Then, the cells were permeabilized with Cytofix/Cytoperm buffer for 20 min, and after two washes with Permash buffer, anti-spike-Alexa 647 was added to the cells for 30 min at 4°C. After staining, the cells were fixed with 2% paraformaldehyde (FPA) and then analyzed on a Fortessa Flux Cytometer. Thirty thousand events were recorded for each condition in triplicate. Analyses were performed using FlowJo Software.
Calu-3a cells, Tryple Express was used (Gibco 12604013). The strain BetaCoV/France/IDF0372/2020 was supplied by the National Reference Centre for Respiratory Viruses hosted by Institut Pasteur (Paris, France) and headed by Pr. Sylvie van der Werf. The human sample from which strain BetaCoV/France/IDF0372/2020 was isolated was provided by Dr. X. Lescure and Pr. Y. Yazdanpanah from the Bichat Hospital, Paris, France. Moreover, the strain BetaCoV/France/IDF0372/2020 was supplied through the European Virus Archive goes Global (Evag) platform, a project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 653316. The virus titer used for all experiments was 3. 75E+6 PFU/mL. All infection experiments were performed in a biosafety level-3 (BLS-3) laboratory at either the CRC (Cordelier Research Center) or UB'L3 facility (TBMcore, Bordeaux).
Acknowledgments R. S. was supported by fellowships from the  Agence Nationale de Recherches sur le Sida et les Hépatites Virales (ANRS). M. L. receives financial support from  Erganéo/INSERM-Transfert  and  ANRS , and P. G. receives financial support from  INSERM ,  Université Paris Cité  and  Fondation Grace de Monaco. We thank the  BL3 platform at Cordeliers Research Center Core Facility  and the BL3 technical manager,  Maxime Lecerf. We thank  Dr. Pierre-Olivier Vidalin  for providing us with the Calu-3a cell line.
For Calu-3a cells, Tryple Express was used (Gibco 12604013). The strain BetaCoV/France/IDF0372/2020 was supplied by the National Reference Centre for Respiratory Viruses hosted by Institut Pasteur (Paris, France) and headed by Pr. Sylvie van der Werf. The human sample from which strain BetaCoV/France/IDF0372/2020 was isolated was provided by Dr. X. Lescure and Pr. Y. Yazdanpanah from the Bichat Hospital, Paris, France. Moreover, the strain BetaCoV/France/IDF0372/2020 was supplied through the European Virus Archive goes Global (Evag) platform, a project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 653316. The virus titer used for all experiments was 3. 75E+6 PFU/mL. All infection experiments were performed in a biosafety level-3 (BLS-3) laboratory at either the CRC (Cordelier Research Center) or UB'L3 facility (TBMcore, Bordeaux).
After sampling, bats were released at the collection site. Bats were morphologically identified as follows: photographs and selected external measurements of every captured bat were taken for identification based on morphological diagnoses in comparison with published descriptions and confirmed features of Rhinolophus thomasi from Cuc Phuong National Park, Northern Vietnam. Morphologically, Rhinolophus thomasi is distinguished from other horseshoe bat species in Vietnam and surrounding countries by a complex of external characteristics including a short lancet with a rudimentary tip; a rounded connecting process; horseshoe does not cover the muzzle completely and has well-developed lateral leaflets; and a lower lip that has three mental grooves  [51, 52]. To confirm the morphological identifications, we took advantage of the concomitant sequencing of bat transcriptome and used the Barcode of Life Data Systems (BOLD) as previously described  [53].
Briefly, all trimmed reads were mapped onto the Cytochrome Oxydase subunit I (COI) database, followed by de novo assembly of mapped reads. The resulting contigs were uploaded to the BOLD Identification System (https://www. boldsystems. org/index. php/IDS_OpenIdEngine) (accessed on 7 August 2023) for species identification. When a discrepancy between BOLD-based and morphology-based species identification was observed, we determined the most probable species by positioning the COI sequence of the bat individual into a phylogenetic tree based on 103 sequences of the N-terminal region of the Cytochrome Oxidase I gene (region of 657 bp), representative of 35 Rhinolophus species.
Molecular and Phylogenetic Analyses A comprehensive database of complete Sarbecovirus genomes was first built based on two recent studies  [23, 55]  and completed with other sequences manually picked from GenBank and GISAID (Table  S2 ) to cover non-human (bat, pangolin, palm civet and ferret badger) sarbecovirus diversity. A total of 221 complete genomes were selected. Several human prototype strains of SARS-CoV and SARS-CoV-2 were also included in the final dataset. For each complete genome, 5 -3 UTRs and intergenic regions were manually removed and genes were concatenated before being translated with C2A. A2C  [56]. Protein sequences were then aligned with MAFFT (in auto mode, global alignment  [57] ) and backtranslated in nucleotides using the original sequences. Given the high variability in Sarbecovirus sequences, this method ensured the correct alignment of selected whole genomes.
The same strategy was applied to align Vietnamese Sarbecovirus genomes. Phylogenetic analyses were performed at the gene level, either for Vietnamese isolates or for the complete Sarbecovirus dataset, with W-IQ-TREE  [58] , using the maximum likelihood reconstruction method and the aBayes branch support parameter, after determination of the best substitution model fitting the data implemented in W-IQ-TREE. Trees were visualized with FigTree  [59]. Identity matrices (expressed in %) were calculated at the whole genome and at each gene nucleotide and amino-acid level using CLC Main Workbench 21. 0. 4 (Qiagen, Hong Kong, China). Recombination analyses were conducted on the Vietnamese or on the complete Sarbecovirus datasets using the "full exploratory recombination scan" implemented in the Recombination Detection Program (RDP v.
In Silico Evaluation of RBD-ACE2 Binding Free Energy Bat ACE2 sequences missing from the databases at the time of analysis (i. e. , Rh. malayanus, Rh. thomasi and Chaerephon plicatus) were reconstructed from our whole host genome sequences and completed with public data collected from NCBI/SRA. Briefly, a custom database comprising ACE2 sequences from Chiroptera or Rhinolophus sp. was created and queried using NCBI/SRA and our data using Diamond BlastX to identify ACE2-associated sequences. The assembly of identified sequences was then performed with Megahit. The ACE2 genes from Rhizomys pruinosus (hoary bamboo rat) and Hystrix brachyura (Malayan porcupine) were similarly retrieved from genome assembly (respectively Gen-Bank GCA_009823505. 1 and GCA_016801275. 1) with BlastN against a custom database of Rodentia ACE2 genes.
2. For each potential RBD-ACE2 complex, we created 200 models of the complex using HADDOCK  [67]. ACE2 and RBDs were first docked into a complex with the help of inter-subunit distance restraints. These restraints were defined between each pair of atoms at the interface of the SARS-CoV-2 RBD and human ACE2 based on the X-ray structure of this complex (PDB code 6M0J  [68] ). The generated models were then refined via molecular dynamics simulations in explicit solvent during the last step of the HADDOCK modeling pipeline.
ACE2-Dependent Entry Assay of Vietnamese Sarbecoviruses To confirm these in silico predictions, we generated lentiviral particles pseudotyped either with the spike protein of one representative strain of Vietnamese sarbecoviruses (i. e. ,  193,  or with SARS-CoV-2 Wuhan (strain Be-taCoV/France/IDF0372/2020, GISAID accession number EPI_ISL_406596). We tested the ability of these pseudotypes to enter into 293T-hACE2 cells (Figure  6 ) and observed that none of the Vietnamese strains were able to enter cells, unlike SARS-CoV-2. This result confirmed the in silico predictions and suggests that the deletion observed in the receptorbinding motif (Figure  S5B ) influences the ability of Vietnamese sarbecoviruses to use the human ACE2 receptor to infect humans.
ACE2-Dependent Entry Assay of Vietnamese Sarbecoviruses To confirm these in silico predictions, we generated lentiviral particles pseudotyped either with the spike protein of one representative strain of Vietnamese sarbecoviruses (i. e. ,  193,  or with SARS-CoV-2 Wuhan (strain BetaCoV/France/IDF0372/2020, GISAID accession number EPI_ISL_406596). We tested the ability of these pseudotypes to enter into 293T-hACE2 cells (Figure  6 ) and observed that none of the Vietnamese strains were able to enter cells, unlike SARS-CoV-2. This result confirmed the in silico predictions and suggests that the deletion observed in the receptor-binding motif (Figure  S5B ) influences the ability of Vietnamese sarbecoviruses to use the human ACE2 receptor to infect humans.
Supplementary Materials: The following supporting information can be downloaded at https: //www. mdpi. com/article/10. 3390/v15091897/s1: Figure  S1 : Amino-acid (lower) and nucleotide (upper) identity matrices of Vietnamese bat sarbecoviruses performed at the gene level. S1 : Detailed sampling of bats. Table  S2 : Acknowledgment of sharing of SARS-CoV-2-related genome sequences from the GISAID database. Table  S3 : Complete identity matrices used to generate the heatmaps presented in Figure  3  (whole genome and spike) and in Figure  S2  (RdRP, N, S-NTD and S-RBD). Table  S4.
Data Availability Statement: The complete genome sequences of Vietnamese sarbecoviruses and bat-associated COIs were deposited in GenBank (accession numbers OR261262-68 and OR427971-77, respectively). GISAID identifier: EPI_SET_230623sh (DOI: 10. 55876/gis8. 230623sh). All genome sequences and associated metadata in this dataset are published in GISAID's EpiCoV database. The alignments used to reconstruct phylogenies presented in this study and the complete RDP5 recombination analysis are openly available in FigShare (DOI: 10. 6084/m9. figshare. 23821428 and 10. 6084/m9. figshare. 23821719, respectively).
Funding: This research was funded by the Institut Pasteur "Covid Taskforce", by the French Agence Nationale de la Recherche ANR (BAT-COR-ASIA project, grant no. ANR-22-CE35-0004) and in part by the H2020 project 101003589 (RECOVER) and the Labex IBEID (ANR-10-LABX62-IBEID). Vu Dinh Thong was supported by the Vietnam Academy of Science and Technology through the project ID VAST04. 03/21-22. Institutional Review Board Statement: The animal study was approved by the Ministry of Health (Vietnam) (protocol code 4026/QD-BYT on the 21 September 2020).
Most health phenomena confirm this inverse care law, including the distribution of SARS-COV-2 in most countries, even though data collection is still ongoing  (5, 6). In France, research on the first wave showed significant spatial heterogeneity in hospital COVID-19 incidence and mortality distribution  (7, 8). The evidence demonstrated that from the onset of the pandemic, the most vulnerable and precarious populations were most affected-especially people born abroad and living in vulnerable neighbourhoods  (9) (10) (11). The corollary to Hart's proposals is the importance of interventions to address this uneven distribution: the famous principle of Marmot's proportional universalism  (12). Numerous articles, books, guides for action, and public health conferences in France have raised this challenge of equity and the need for it to be considered by public health organizations.
The national health insurance staff called on the phone to older people or in precarious situations to suggest they make an appointment for a vaccination. In May 2021, a national workshop, with the presence of people from the Ile-de-France region, was organized by Santé Publique France to reflect on the challenges of vaccination of people in precarious situations  (35)  together. Despite these regional public health actions and the deployment of financial resources like never before, our centre-wide analysis at a university hospital shows that this has not been enough to avoid the inverse care law. We do not have data on a scale as fine as ours to verify that this may have been different elsewhere in the region and vaccination centres deployed outside a hospital facility. However, a study in the Paris region and Marseilles shows that people in precarious situations benefited less from primary vaccination than the general population and had a two-month lag to access it  (27).
The COVID-19 pandemic illustrates that these approaches are indispensable and must be further integrated into training. Our study has some limitations, particularly regarding the data used for January 2021. Some vaccination data could include health workers vaccinating at the study center hospital while residing in Paris. We, therefore, carried out an additional analysis by retaining only those vaccinated over 75 years of age for the first period (January 1st -January 18th 2021) to exclude health workers working in the hospital or the area (Appendix 4). This analysis confirms that the vaccinated people came from favoured neighbourhoods. The implications of this research for public health are numerous, even if they are not necessarily very original. First, it would be helpful to translate policy statements into public health instruments better to combat social inequalities in health  (33).
Seine-Saint-Denis, a territory particularly affected by the pandemic in the Ile de France region, and marked by significant social inequalities in historical health (13) , set up vaccination centres for the general population in several hospitals in the public network and other non-hospital sites in the region (e. g. Stade de France). Given the specific needs of that Study period: The study is based on primary vaccination data from the vaccination centre of the hospital Avicenne AP-HP (Assistance Public Hopitaux de Paris) in Bobigny in Seine-Saint-Denis from January 8th to September 30th, 2021. department and contrary to national doctrine, the ARS had already decided to allocate more Data source: doses than a "simple population distribution" (22). The objective of these centres nested in The vaccination database was obtained from the ORBIS® application.
Introduction Genetic frontotemporal dementia (FTD) is a progressive and heterogeneous neurodegenerative disease most frequently caused by an autosomal dominant genetic mutation in the microtubule-associated protein tau (MAPT), progranulin (GRN), or chromosome 9 open reading frame 72 (C9orf72). 1 Changes in grey and white matter regions measured by magnetic resonance imaging (MRI) have been reported many years before symptoms develop in previous studies,  [2] [3] [4]  but the exact relationship of such brain changes to clinical progression is yet to be fully understood. This is particularly relevant in current research, considering the need for robust biomarkers to allow accurate measurement of disease onset and progression in the context of clinical trials. Using data from the GENetic FTD Initiative (GENFI) cohort, we aimed to localize and quantify the specific pattern of subregional grey and white matter abnormalities in the prodromal and symptomatic stages of genetic FTD, and how these abnormalities relate to progression of symptoms in presymptomatic mutation carriers.
Materials and methods At the time of the fifth data freeze in the GENFI study, 850 participants had been recruited as part of the second phase (03 March 2015-31 May 2019) across 24 centers in the United Kingdom, Canada, Italy, The Netherlands, Sweden, Portugal, Germany, France, Spain, and Belgium, of whom 710 had volumetric T1-and diffusion-weighted MRI acquired on a 3T scanner. Eighty three of these participants were excluded as their scans were of unsuitable quality due to motion, incomplete spatial coverage or other imaging artifacts, for pathology unlikely to be attributed to FTD, or if they were carriers of mutations in one of the rarer genetic causes of FTD. All the remaining 627 participants were known to be either a carrier of a pathogenic expansion in C9orf72 or of a pathogenic mutation in GRN or MAPT (n = 387), or were non-carrier first-degree relatives (n = 240), who therefore acted as controls within the study.
DTI acquisition and processing The preprocessing was carried out with a combination of source tools described below, wrapped up by NiftyPipe (http://cmictig. cs. ucl. ac. uk/wiki/index. php/NiftyPipe) software packages. First, the multiple diffusion-weighted image (DWI) acquisitions were merged with the FMRIB Software Library (FSL, v5. 0. 4). 21 Then, the images were corrected for eddycurrent distortion and motion by performing an affine co-registration between the DWIs and the averaged b0 images, using FSL eddy function. 22 Susceptibility-induced image distortions were subsequently corrected using a unified field map and image-registration-based approach. 23 We used the subjectspecific structural T1-weighted image as the reference space, and the TIV binary mask extracted from GIF to restrict the analyses to the brain, and to improve the registration.
The w-scores have a similar interpretation to Z-scores: in the control group, they have a mean value of 0 and a standard deviation of 1; a w-score of -1. 960 corresponds to the 2. 5th percentile of the controls, -1. 645 to the 5th percentile, -1. 282 to the 10th percentile, and -0. 675 to the 25th percentile. Statistical analyses were performed in Stata v. 14 (Stata Statistical Software: College Station, TX: StataCorp LP) and SPSS v. 26 software (SPSS Inc. , Chicago, IL, USA). First, we calculated non-parametric percentile 95% confidence intervals using bootstrapping with 1000 replicates (as not all variables were normally distributed) to verify whether the GM and WM w-scores in the presymptomatic and symptomatic (as defined by CDR® plus NACC FTLD) subgroups within each genetic groups (C9orf72, MAPT, GRN) were each significantly different from 0, indicating the mean in the genetic group was below the mean of controls (or above 0 in the case of MD).