dataesr/scientific-paragraphs-categorization huggingface dataset:
Detection and Characterization of Genetic Quasispecies The complete genome mapping data generated by the CLC software were exported from the mapping output file as tab separated values (. tsv) files. In these latter files, nucleotides were detected, and corresponding numbers of reads were mentioned for each nucleotide position of the assembled genomes. These data were automatically analyzed using an in-house tool created through the Microsoft Excel software (https: //www. microsoft. com/fr-FR/microsoft-365/excel (accessed on 30 September 2022)) that we named "QuasiS". QuasiS allows superimposing the consensus genome sequence obtained to the Wuhan-Hu-1 isolate genome GenBank Accession no. NC_045512. 2. It further calculates for each position of the genome the intra-sample nucleotide diversity, which is the proportion of reads covering a given position that does not harbor the consensus nucleotide.
In addition, it indicates whether the consensus nucleotides differ from those harbored by the reference genome and calculates the mean, standard deviation, and minimum and maximum values for diversity for the whole genome and the proportion of nucleotide positions covered with more than 50 reads. The positions of the different viral genes are indicated in the QuasiS tool, and genes can be selected and scrutinized individually. Alarms are generated in case of diversity above a defined value, and contiguous positions with such level of diversity are also indicated. Finally, nucleotide diversity and genome coverage by NGS reads are plotted into separated graphics. Based on QuasiS files generated for each clinical sample, the numbers of reads per nucleotide position were used to guide the selection of NGS data to be subsequently characterized, considering only genomes with a mean number of reads per position higher than 50 and a coverage greater than 99% of the genome GenBank Accession no.
Analysis of SARS-CoV-2 Intra-Sample Genetic Diversity at the Genome and Gene Scales An intra-sample nucleotide diversity of 4% at a given nucleotide position was defined as a threshold for a significant diversity, on the basis that this corresponds to at least two reads for a minimum of 50 reads per position, and as we only analyzed genomes for which the mean number of reads per position was higher than 50. For the selected set of SARS-CoV-2 genomes, the mean intra-sample genetic diversity at nucleotide positions within the genome (in %) and the mean NGS depth (in number of reads per nucleotide position) were both plotted through the Microsoft Excel software. To study the correlation between genetic diversity at nucleotide positions either intra-sample or inter-samples, we compared intrasample diversity in the genomes studied here and genetic diversity at the largest available scale that was recovered as a.
tsv file from the Nextstrain website (https://nextstrain. org/ ncov/gisaid/global/all-time (accessed on 30 September 2022))  [10]  that displays genomic epidemiology of SARS-CoV-2. The correlation between the intra-sample genetic diversity in the present study and the inter-patient diversity at the global scale was represented as a graph generated with the Microsoft Excel software. Genome positions were identified as exhibiting nucleotide diversity and were selected if they exhibited an intra-sample diversity >1% and a non-null inter-sample diversity, to detect significant concordances. For a global visualization of the mean intra-sample genetic diversity and of the number of variable positions for all the samples, we performed a hierarchical clustering using the Morpheus web application (https://software.
Data Availability Statement: Viral genomes analyzed in the present study are available from the NCBI GenBank nucleotide sequence database (https://www. ncbi. nlm. nih. gov/genbank/ (accessed on 30 September 2022)) [61]  with no. OP646492-OP646601, and from the GISAID sequence database (https://www. gisaid. org/ (accessed on 30 September 2022))  [62]  using the GISAID online search tool with "IHU" and "France" as keywords and correspondence from Supplementary Table  S1.
Coordinates on the Genome GenBank Accession no. NC_045512. 2 Mean Diversity (%) Number of Gene Positions Exhibiting a Significant (>4%) Diversity in Any of the 110 Samples Number of Positions per 100 Nucleotides ORF1ab 266. 21555 0. 2 3123 0. 15 S 21563. 25384 0. 22 1281 0. 34 ORF3a 25393. 26220 0. 2 105 0. 13 E 26245. 26472 0. 2 31 0. 14 M 26523. 27191 0. 21 68 0. 10 ORF6 27202. 27387 0. 17 26 0. 14 ORF7a 27394. 27759 0. 2 28 0. 08 ORF7b 27756. 27887 0. 19 4 0. 03 ORF8 27894. 28259 0. 21 14 0. 04 N 28274. 29533 0. 34 301 0. 24 ORF10 29558. 29674 0. 2 65 0. 56.
Peptide array and analysis The CTH522 peptide array was designed with triplicates of immobilized 15mer peptides overlapping by 14 amino acids which were printed on functionalized glass slides and incubated with serum samples by JPT Peptide Technologies, Berlin. Briefly, the array was incubated with serum samples collected at week 24 and IgG peptide complexes were visualized using biotinylated goat anti-monkey IgG and Alexa Fluor ® 647 Streptavidin. Slides were scanned with a high-resolution scanner at 635 nm and data reported as arbitrary fluorescence units. The mean of triplicate values was determined, if the coefficient of variation was larger than 0. 5, only the 2 closest values were used. 10% of the maximum intensity signal (65,535) was used as cut-off for a positive response, and mean signals below this threshold were assigned a value corresponding to half the threshold value (3277).
Statistical analysis All statistics were performed with GraphPad Prism software (version 8. 1. 2). The not-normally distributed data were analyzed with non-parametric tests; Kruskal-Wallis test and Dunn's multiple comparison test to determine the significance of the difference between groups. Curves showing percent PCR negative animals were compared with a Log-rank (Mantel-Cox) statistical test. The comparisons were considered statistical significant if the P value was < 0. 05 (P < 0. 05). Further levels of significance are indicated with asterisks *P < 0. 05, **P < 0. 01, ***P < 0. 001. Domaine d'Inteŕet Majeur (DIM) "One Health" program, and the "Programme Investissements d'Avenir" (PIA), managed by the ANR under references ANR-11-INBS-0008 and ANR-10-EQPX-02-01.
Sample, design and setting We conducted a cross-sectional online survey (https:// frama forms. org) in France from April 6 to May 11 2020, i. e. , during the first country-wide lockdown due to COVID-19 pandemic. Following a convenience non-probability sampling method, participants were recruited with online announcements on websites of mental health and mailing lists from caregivers associations, with no incentives. The inclusion criteria included (1) having a relative suffering from a mental illness, (2) being the primary caregiver, (3) speaking French, and (4) being at least 18 years of age. In line with French regulations on health research, no ethics committee approval was required because data collection was anonymous. This study is reported according to the 'Checklist for Reporting Results of Internet E-Surveys' (CHERRIES) statement  [26, 27].
Measures The survey included socio-demographics, relative's diagnosis, and personal and environmental conditions during lockdown (whose data were not analyzed in this study). Participants were also asked whether they had ever benefited from a psychoeducation program ('yes' or 'no'). The Center for Epidemiological Studies Depression (CES-D) scale. Depression symptoms were assessed using the 20-item CES-D scale  [28, 29] , a self-report measure where participants indicate how often over the past week various statements such as I felt lonely or I felt sad applied to them. Each item is rated on a 4-point Likert scale that ranges from 0 (rarely or none of the time) to 3 (most of the time), with total scores ranging from 0 to 60.
Data analysis Network analysis has received increasing attention in psychiatric research over the past years  [33]. This approach includes graphical representations of the relationships between variables such as symptoms and provides the capacity to identify core features of complex networks. The network analysis was computed with RStudio  [34]  based on the methods described by Epskamp et al. 2018  [35]. Data and R-code are available online (https:// osf. io/ 7vhxf/). In network models, variables are represented by 'nodes' connected by 'edges'. All participants (n = 384) were included in the network analysis (there were no missing data for the CES-D, ZBI and BECI scales). Two networks were estimated. The first network (Network 1a) consisted of the ZBI subscales (sum-scores): Negative Emotion/Consequences, Interpersonal Relationship, Time Demand, Patient's Dependence, Self-accusation/guilt; the BECI subscales (sum-scores): Difficult Behaviours, Positive Personal Experiences, Problems with Services, Stigma/ Effects on Family; and the total CES-D score (a total of 10 nodes).
Data were normalized using the non-paranormal transformation  [36]. The second network consisted of CES-D items in place of total CES-D score (Network 1b). The 20 CES-D items were first submitted to the goldbricker function from the R package networktools  [37]  comparing correlations in the network to identify nodes which most likely measure the same underlying construct (i. e. , are colinear). Two nodes (or variables) are deemed to be 'redundant' if the correlations between these two variables and all other variables are highly similar. If any, one variable is selected using the net_reduce function (see  [37] ). Two pairs were found redundant (see Results Sect. 3. 2). Therefore, the second estimated network consisted of 27 nodes (i. e. , ZBI: 5 subscales, BECI: 4 subscales, CES-D: 18 symptoms).
Networks were estimated using the estimateNetwork function in the R package bootnet  [38]  with the "EBICglasso" method computing a Gaussian graphical model with the graphical LASSO  [39]  and extended Bayesian information criterion (EBIC;  [40] ) for model selection. In Gaussian graphical models, the parameters (i. e. , edges) represent the association among two variables, after conditioning on all other variables in the network. The LASSO ('least absolute shrinkage and selection operator'  [41] ) is a regularization technique allowing parameters to be zero, resulting in a sparse network. The penalty parameter lambda (λ; the shrinkage parameter) was selected using the EBIC which involves the hyperparameter gamma (γ; we used the default value 0. 5) to control the level of the penalization.
The centrality index strength was computed to quantify the role of nodes in the network (using centrality functions in the R package qgraph  [42] ). Strength indicates overall connections of each node and is calculated by summing the absolute edge weights that are connected to a specific node. We estimated the accuracy of strength centrality indices using a case-dropping subset bootstrapping approach that determines how many cases can be removed from the network before the results become unstable, and estimated correlation stability coefficients (CS-coefficients). The CS represents the maximum proportion of population that can be dropped with re-calculated indices correlating at least 0. 7 with indices of the initial sample. Networks with reliable centrality should have a CS ≥ 0. 25, ideally ≥ 0.
In this regard, innovative strategies such as joint crisis plan could alleviate the negative impact of the relative's symptoms and behaviors on caregivers  [66]. Finally, being afraid of patient's future and financially stressed were also connected to depression severity (Fig. 2a ), showing their strongest edge weight with sadness and a feeling of failure respectively (Fig. 2b ). Caregiving often includes financial support in terms of travel expenses, funding treatment and providing food; hence, many caregivers express difficulties in providing for their relative's needs  [47]. Our results are also consistent with studies reporting that lower income is associated with higher levels of depressive symptoms among caregivers of individuals with a SMD  [22, 67, 68]. Such findings support the crucial importance of connecting caregivers to social workers so that they benefit from the financial assistance to which they are entitled.
Limitations Some limitations should be mentioned. First, due to the cross-sectional design of our study, it is not possible to conclude for causal relationships between the identified predictors and the severity of depression. Longitudinal studies are needed to better explore this issue. Second, one should keep in mind that our data were collected during the first French global lockdown due to COVID-19. Previous reports have indicated increased depression rates in general population  [82]  and non-clinical samples  [83] [84] [85]  during lockdown. Here, 54. 4% of caregivers presented a possible depressive disorder (CES-D total score ≥ 16), thus higher than the ones reported in caregivers of subjects with bipolar disorder or schizophrenia (respectively, 22-33% and 42%) outside lockdown periods  [66, [79] [80] [81].
Acknowledgements This research was partially supported by  Le Vinatier Hospital. The authors would like to thank all those involved in developing the survey-especially, the  Team of the Center for Caregivers in Psychiatry of Lyon and the family association Unafam (Union Nationale des Amis et Familles de personnes Malades  et/ou handicapées psychiques). We would like to thank the  family associations "Unafam", "Promesses", "Collectif Schizophrénies", "Argos 2001", "Icebergs ", the  Fondation FondaMental , the  French networks "Réseau Transition", "Réhabilitation Psychosociale", "Réseau Profamille " and the associations " Take a BREF", "Métropole Aidante " for their assistance in the promotion of our survey. Finally, we would also like to show our gratitude to the caregivers for taking part to the survey.
However, the majority of studies are based on the use of one polarization state (linear, circular or elliptical)  [23] , without any combination of these different states of polarization. On the other hand, the Mueller matrix is wellknown to describe complete information about all the polarization properties of media and materials  [24]. Some studies using this concept are focused on the particular elements of the Mueller matrix, or else, on all the elements of the Mueller matrix but with a single wavelength or only based on polarization imaging  [25, 26]. This work follows a first article on valorization of the spectral database resulting from experimentation  [27]. In this paper, the main objective was to exploit this database and to explore the potential of VIS-NIR spectroscopy in polarized light combined with the Mueller matrix concept with multivariate analysis based on PLS methods in order to predict absorption and scattering properties through the evaluation of matrix elements.
Spectral Acquisition For all phantoms, diffuse reflectance spectra in polarized light were measured for each of the 49 configurations of both PSG and PSA. Moreover, for each of them, a white diffuse standard (Spectralon ® , SRS-99-010, Labsphere, North Sutton, United States) was applied in order to standardize spectra from non-uniformities of all components of the PoLiS system. Then, each calculated element of the matrix (M11, M22, …, M44) was divided by the corresponding spectral reference in order to obtain a normalized hybrid diffuse reflectance (r11, r22, …, r44) respectively. All data are available in this publication  [27].
Spectral Acquisition For all phantoms, diffuse reflectance spectra in polarized light were measured for each of the 49 configurations of both PSG and PSA. Moreover, for each of them, a white diffuse standard (Spectralon ® , SRS-99-010, Labsphere, North Sutton, United States) was applied in order to standardize spectra from non-uniformities of all components of the PoLiS system. Then, each calculated element of the matrix (M 11 , M 22 ,. , M 44 ) was divided by the corresponding spectral reference in order to obtain a normalized hybrid diffuse reflectance (r 11 , r 22 ,. , r 44 ) respectively. All data are available in this publication  [27].
PLS Models All multivariate data analyses were performed with Matlab software v. R2015b (The Mathworks Inc. , Natick, MA, USA). A partial least square (PLS) model was used to predict the chemical and physical parameters of the liquid optical phantoms. PLS models were built from each element of the diagonal of the Mueller matrix using the whole calibration set (two-thirds of the sample) and a predicting set (one-third of the sample). The number of latent variables (LV) was determined by comparing performances using the leave-oneout cross-validation method  [40]. Basic statistical parameters including the determination coefficient (R 2 ), the root mean standard error of prediction (RMSEP) and the bias were calculated. These main parameters were used to assess the performance of each model for predicting absorber and scatterer concentrations.
PLS Models All multivariate data analyses were performed with Matlab software v. R2015b (The Mathworks Inc. , Natick, MA, USA). A partial least square (PLS) model was used to predict the chemical and physical parameters of the liquid optical phantoms. PLS models were built from each element of the diagonal of the Mueller matrix using the whole calibration set (two-thirds of the sample) and a predicting set (one-third of the sample). The number of latent variables (LV) was determined by comparing performances using the leave-one-out cross-validation method  [40]. Basic statistical parameters including the determination coefficient (R 2 ), the root mean standard error of prediction (RMSE P ) and the bias were calculated. These main parameters were used to assess the performance of each model for predicting absorber and scatterer concentrations.
e. 3000 cases in the USA and 9000 cases in Europe for instance. On the other hand, a recent study based upon six population genomic databases reported that the large number of cases in Japan and Iceland was consistent with a founder effect in these populations. In other countries, the minor allele frequency of pathogenic variants seems relatively low, suggesting that there is no widespread underdiagnosis of this nephropathy  [13]. In France, many cases have been diagnosed with the help of crystalluria, morphoconstitutional kidney stone analysis including Fourier transform infrared (FTIR) spectroscopy and FTIR microscopy evidencing the crystalline phase in kidney biopsies, including kidney graft biopsies  [9, [14] [15] [16]. Measurement of enzyme activity in red blood cell lysates and genetic analyses were usually performed after identification of crystals and one may hypothesize that many patients with undiagnosed chronic kidney disease are affected by APRT deficiency.
Mouse air-pouch model Eight-week old male C57Bl/6J mice were used to perform an air-pouch model (six animals/group). Air pouches were created by two dorsal subcutaneous injections of 3 mL of sterile air (day 0 and day 3), under isoflurane anesthesia. At day 6, PBS (1 mL) in control mice or crystals (2,8-DHA, mCPPD or MSU, 1 mg/mL, diluted in PBS) were injected directly into the air pouch. Six hours after crystal stimulation, air pouch lavages were performed and collected for cytokine quantification and cell isolation. Infiltrated cells were counted and phenotyped by flow cytometry. IL-1β cytokine production in supernatants or air pouch lavages was measured in the 6 animals (one missing data in the MSU group) by using ELISA kit (Invitrogen-Thermo fisher scientific, Waltham, MA, USA).
Flow cytometry 10 5 cells from air pouch lavages were stained with anti-F4/80-APC (clone REA126; Miltenyi Biotechnology, Bergish Gladbach, Germany) and anti-Ly6G-PE mouse monoclonal antibodies and clone 1A8 (Miltenyi Biotechnology, Bergish Gladbach, Germany) for 20 min at 4 °C, washed in PBS and analyzed with a BD FACS Canto II cytometer (BD Biosciences, Franklin Lakes, NJ, USA). Data were analyzed with BDFACS Diva software (BD Biosciences, Franklin Lakes, NJ, USA).
Animals were sacrificed 4 weeks after initiation of the adenine-enriched diet, blood and kidneys were collected. Blood samples were collected to assess serum urea, serum creatinine and serum uric acid levels with an IDS-iSYS automat (Immunodiagnostic Systems Holdings PLC, Pouilly-en-Auxois, France). Left kidneys were fixed in formaldehyde and embedded in paraffin. X-ray computed tomography imaging of the left kidney was performed using a Skyscan 1272 (Bruker, Anvers, Belgium). A 6 µm resolution scale was obtained. Data were reconstructed using N-recon software (Bruker, Anvers, Belgium). The Mimics Innovation suite 20. 0 (Materialise, Leuven, Belgium) was used to perform threedimensional modeling of crystalline deposits in the kidney and quantify crystalline volume. Four-µm thick sections were stained with Masson trichromic solution and Hematoxylin/Eosin standard protocol to reveal 2,8-DHA deposits in polarized light, or with sirius red in picric acid solution to assess fibrosis.
A morphometric analysis of fibrosis tissue surface was performed with the Image J software (NIH) on 5 representative fields at ×200 magnification by using polarized light. For each animal, the mean value has been considered. Crystalline deposits observed in tissue sections were characterized using FTIR microscopy as described above. Four-µm tissue sections were deposited on low-emission microscope slides (MirrIR; Keveley Technologies, Tienta Sciences, Indianapolis, USA) and FTIR hyperspectral images were recorded with a Spectrum spotlight 400 FT-IR imaging system (PerkinElmer, Waltham, USA).
Quantitative RT-PCR mRNA from kidney was extracted using Trizol solution (Life Technologies BRL, Gaithersburg, MD, USA). RNA concentration was measured by using NanoDrop1000 spectrophotometer (Thermo Scientific, Waltham, MA, USA). RT-PCR was performed using SYBR green and specific probes on the light cycler 480 (Roche Diagnostic). Specific primers for TNF-α ForTCTTCTCATTCCTGCTTGTGG, RevATGA-GAGGGAGGCCATTTG and IL1-β ForTGTAATGAAA-GACGGCACACC RevTCTTCTTTGGGTATTGCTTGG, PCR was also carried out for the housekeeping gene HPRT ForGGAGCGGTAGCACCTCCT RevCTG-GTTCATCATCGCTAATCAC to normalize the Q-PCR results, using Roche LightCycler 2. 0 software (Roche Diagnostics Indianapolis, IN, USA). Results are expressed as 2 -∆∆CT , where CT is the cycle threshold number.
Clinical study We conducted a retrospective, single-center, cohort study using routinely collected data in compliance with good clinical practice and the Declaration of Helsinki. According to French law, formal ethics committee approval was not required for this study. Female SCD patients aged ≥18 years with at least one current or previous leg ulcer were recruited from the Red Blood Cell Genetic Diseases Unit (Hôpital Mondor, Créteil, France) between January 2020 and September 2021. Patients' medical histories, treatments, laboratory data and information related to leg ulcers were extracted from medical files. Patients did not undergo a specific medical examination for this study.
Besides, the ARCHS4 database identified these cells as "dendritic cells", "cord blood", "CD34 + cells" or "bone marrow" (Figure  2C ). We also analyzed eGFP + FMC from peripheral blood of post-partum mice after creating a cutaneous wound. This enabled us to describe the fetal cells specifically responding to a maternal wound. When compared to circulating fetal cells at steady state, there were 487 differentially expressed genes (Figure  2D ). Several plasma membrane receptor genes were enriched in FMC from wounded post-partum mice, including Ccr8, Il1r2 and Cxcr5, suggesting that several pathways amplify the recruitment of these cells to damaged skin (Figure  2D ). Canonical pathway analysis showed a significant enrichment of pathways related to CXCR4 signaling and to integrin signaling and cell motility, through ILK, RHOA and the actin cytoskeleton, in post-partum FMC upon wounding (Figure  2E ).