dataesr/scientific-paragraphs-categorization huggingface dataset:
Immunoreactive bands were detected in a Chemidoc system and quantified with the Image Lab software. Protein levels were normalized on β-actin and the ratio between each immunosubunit and its constitutive counterpart was calculated and shown in the graphs of panel D. Four non infected, six infected/untreated and five infected/ treated mice were analyzed. Mice were from one single experiment. Statistical analysis was performed using one-way ANOVA followed by the Dunnett's post-test. *p <. 05; **p <. 01. development, survival, and activation but that is also involved in the growth of some types of B-cell lymphomas  [15]. This observation would partially explain the reduction of plasma cell maturation and lymphadenopathy at 5 weeks p. i. [5, 6]  and would rule out an involvement of NF-kB in modulating cytokine expression and/or immunoproteasome up-regulation.
Acknowledgement The authors would like to thank  Roberto Troiano  and  Serena Brundu  for their technical contribution in some of the analyses. This work was partially supported by the  University of Urbino Carlo Bo  (grant  DIS-B_CRINELLI_PROG_SIC_ALIMENTARE  to RC) and  Ministero dell'Istruzione, dell'Università e della Ricerca (grant DISB_PIERSANTI_FFABR_CTC).
Study population and study design This was a cross-sectional study in which we pooled data from patients with FH who were referred to specialized lipid clinics or who were identified through cascade screening programs. Upon enrollment, all patients were without known coronary heart disease (CHD, defined broadly as asymptomatic adults without a history of ASCVD or CHD, prior revascularization procedures, or symptoms suggestive of ischemia) at the baseline study visit. Included patients had CAC and CTA imaging data and were enrolled at sites from five different countries that have each previously reported on the atherosclerotic plaque burden of patients with FH (i. e. Spain  [20] , the Netherlands [2 cohorts, 21,22] Brazil  [15, 23]  Australia  [24] , and France  [25] ). Individuals were included if they 1) were free of clinical ASCVD at baseline (i.
Detailed information on each of the participating multi-national cohorts including participants' baseline characteristics, eligibility criteria, and CAC/CTA imaging data are presented in Supplemental Tables  1 2 3 4. Lead investigators at all sites were contacted and provided with the study objectives and a standard data collection form to gather pooled baseline demographic and clinical information of enrolled patients with FH, along with their CAC and CTA imaging results. Given that our pooled analysis sought to collect de-identified data from participating sites in aggregate, it was deemed exempt of individual informed consent by the Yale University Institutional Review Board. Each site's separate ethical review information and participant informed consent processes can be found in Supplemental Table  4.
CAC/CTA data At each site, CAC burden was quantified using the Agatston method  [31]  and the extent of CAD was determined based on CTA findings and according to guidelines from the Society of Cardiovascular Computed Tomography (SCCT)  [32]. Participants' CAC scores were stratified into CAC=0, CAC 1-100, 101-400, and CAC >400 ranges, as classified in prior studies and widely-used multi-society guidelines  [10, 33]. We further stratified participants' CAD burden and stenosis severity into obstructive CAD (≥50% stenosis), non-obstructive CAD (<50% stenosis), and no plaque or stenosis. Pooled data were obtained from the standardized data extraction form in the form of aggregate frequencies and percentages. We coalesced these data, along with previously published information (e. g. , eligibility criteria, CAC/CTA imaging information, and study sample characteristics) corresponding with each site, to produce pooled estimates of our study variables.
Covariates Demographic and clinical characteristics were collected in aggregate from each of the participating sites. Demographic information included age and sex, while clinical characteristics included the frequency and proportion of patients on statin, ezetimibe, and/or PCSK9i therapy, along with their pre-treatment and treated LDL-C levels. Additionally, we collected participants' personal history of diabetes mellitus and hypertension along with their current/former smoking status and the presence of xanthomas at the time of their most recent hospital visit. Two participating sites had missing data on pre-treatment LDL-C levels and one site lacked information on participants' presence of xanthomas.
Statistical analysis We presented dichotomous and categorical variables as counts and percentages and continuous variables as mean (± standard deviation [SD]). Given our collection of pooled data in aggregate from each of the participating clinical sites, we ascertained distribution of CAC scores as well as CTA-derived plaque burden (obstructive CAD, non-obstructive CAD, and no plaque) both overall and across the pre-specified CAC ranges. Additionally, we conducted subgroup analyses of CAC and plaque burden stratified by sex and across distinct age subgroups (i. e. , 30-39y, 40-49y, 50-59y, and ≥60y). 1 ). Most patients were heterozygous carriers of a variant in LDLR (88. 3%), followed by APOB (6. 2%) and PCKS9 (0. 4%). The majority of patients without genetically confirmed FH (n = 46, 4. 5%) received a clinical diagnosis of FH based on a "probable" or "definite" score according to the Dutch Lipid Clinics Network diagnostic criteria.
Study limitations Our study has several limitations. First, we lacked patient-level information pertaining to the individualized management of FH, including time of diagnosis, lipoprotein(a) levels, and other lipid concentrations (e. g. , HDL-C, total cholesterol, and triglycerides). Moreover, the pooled data that were obtained in aggregate from participating sites precluded our ability to evaluate demographic and clinical characteristics associated with an absence of CAC and plaque burden/severity, beyond presenting descriptive statistics. Second, all patients included in this analysis were referred to specialized centers that offered CAC/CTA imaging and may not represent the general population with FH, a large group that remains largely undiagnosed and undertreated globally. Third, we used cross-sectional data, therefore, we could not assess incident MACE, temporal trends in atherosclerotic progression, and longitudinal changes in CAD risk.
Declaration of competing interest Dr. Nasir is supported by the Katz Academy for Translational Research. Dr. Santos received honoraria for consulting, speaker activities and research from Abbott, Ache, Amgen, Astra Zeneca, EMS, Hypera, Libbs, Esperion, Kowa, Getz pharma, PTC therapeutics, Novo-Nordisk, Novartis, Merck, MSD, Pfizer, and Sanofi, he also receives a research scholarship from Conselho Nacional de Pesquisa e Desenvolvimento Tecnológico, Brazil, (CNPq) #303734/2018-3. Dr. Watts has received honoraria related to consulting, research and/or speaker activities from: Amgen, Arrowhead, AstraZeneca, Esperion, Kowa, Novartis, and Sanofi/Regeneron. Dr. Hovingh reports research grants from the Netherlands Organization for Scientific Research (vidi 016. 156. 445), CardioVascular Research Initiative, European Union and the Klinkerpad fonds, institutional research support from Aegerion, Amgen, AstraZeneca, Eli Lilly, Genzyme, Ionis, Kowa, Pfizer, Regeneron, Roche, Sanofi, and The Medicines Company; speaker's bureau and consulting fees from Amgen, Aegerion, Sanofi, and Regeneron until April 2019 (fees paid to the academic institution); and part-time employment at Novo-Nordisk A/S, Denmark since April 2019.
The mean depth of coverage per sample was approximately 156. 2. Downstream processing was carried out with the Genome Analysis Toolkit (GATK), SAMtools, and Picard, following documented best practices (http://www. broadinstitute. org/gatk/guide/topic?name=best-practices). The annotation process was based on the latest release of the Gencode database, Gnomad, Clinvar, CADD. Variants were annotated and filtered using the Polyweb software interface designed by the Bioinformatics platform of University Paris Cité.
Intracellular XBP1s (Q3695) and FoxP3 eFluor450 (ThermoFisher, FJK-16s) staining was performed after permeabilization of cells using Foxp3/Transcription Factor Staining Buffer Set (ThermoFisher) according to the manufacturer instruction. Cells were analyzed using the FACS Gallios Navios (Beckman Coulter) or Attune NxT (ThermoFisher) or BD FACSymphony A5 (BD Bioscience). We then analyzed data with FlowJo software (Treestar).
CITE-Sequencing analysis. Sequencing libraries were loaded on an Illumina NovaSeq flow cell at VIB Nucleomics core with sequencing settings according to the recommendations of 10x Genomics, pooled in a 70:20:10 ratio for the combined 3' gene expression, cell surface protein samples and HashTag-Oligo (HTO) data, respectively. The Cell Ranger pipeline (10x Genomics, v5. 0. 0) was used to perform sample demultiplexing and to generate FASTQ files for read 1 and read 2 for the gene expression and cell surface protein libraries. Read 2 of the gene expression libraries was mapped to the reference genome (mouse mm10) using STAR. Subsequent barcode processing, unique molecular identifiers filtering and gene counting was performed using the Cell Ranger suite. CITE-seq reads were quantified using the featurebarcoding functionality.
Each sample was amplified in triplicate in a StepOnePlus real-time PCR system (Applied Biosystems) or CFX 96 system (BioRad). mRNA levels were calculated with StepOne v2. 1 software (Applied Biosystems) or CFX manager software (BioRad) by using the comparative cycle threshold method and normalized to mean expression of the Actb and Gapdh housekeeping genes. Xbp1 ratio is calculated as follows: (Rq Xbp1s/(Rq Xbp1s + Rq Xbp1u))*100. Rq are calculated by using the ΔΔCt method.
Immunosequencing CD138+ cells from the bone marrow of 5 IRE1α +/+ mice and 5 IRE1α R594C/R594C mice were sorted using the EasySep Mouse CD138 positive Selection kit, according to the manufacturer's recommendations (StemCell). CD138+ cells were lysed, and DNA was extracted using the QIAamp DNA micro kit (Qiagen). The immunoSEQ assay uses multiplex PCR (using gene segment specific primers) followed by next generation sequencing to allow identification of CDR3 sequences from recombinant antigen receptor loci. Purified DNA samples were sent to the Adaptive Biotechnologies facility (Seattle, Washington, USA) according to their instructions. We used the online software immunoSEQ for data extraction and analysis.
Acknowledgements We thank  Fabrice Augé ,  Christian Galmiche ,  Delphine Lamon ,  Fabien Lhericel  and  Sophie Reibel-Foisset  for excellent animal care. We thank  Vincent Gies ,  Frédéric Gros ,  Aurélien Guffroy ,  Sophie Jung  and  Thierry Martin  ( INSERM UMR -S1109, Strasbourg University, France ),  Christopher Mueller  ( CNRS UPR 3572, Institute of Molecular and Cellular Biology (IBMC), Strasbourg, France ) and  Sylviane Muller  ( CNRS UMR7242, Biotechnology and Cell Signaling/Strasbourg Drug Discovery and Development Institute (IMS), Strasbourg, France ) for scientific discussions. This work was supported by the  Hôpitaux Universitaires de Strasbourg , by the " Direction de la Recherche Clinique et de l'Innovation " (DRCI, HUS) and by grants from  Strasbourg University (UdS) , by the  Agence Nationale de la Recherche  ( ANR-14-CE14-0026-04 ,  Lumugene ; and  ANR-11-EQPX-022 ).
Quentin Reuschlé  and  Matthieu Moncan  was supported by  Arthritis R&D  ( CIFRE PhD program ). Sophie Janssens  obtained funding from  ERC Consolidator Grant  ( DCRIDDLE-819314 ) and  FWO Program Grants  ( G063218  and  G050622  N). The study was also supported by the  Institut National de la Santé et de la Recherche Médicale (INSERM) , by the  Atip-Avenir , by government grants managed by the  Agence National de la Recherche  as part of the " Investment for the Future" program  ( Institut Hospitalo-Universitaire Imagine , grant  ANR-10-IAHU-01 ,  Recherche Hospitalo-Universitaire , grant  ANR-18-RHUS-0010 ).
The availability of big data created from aggregate databases, such as electronic medical records (EMR) and insurance claims, makes the use of artificial intelligence (AI) and machine learning (ML) a potentially powerful avenue in identifying patient clusters that have phenotypic, immunological, or genetic properties making them more or less likely to respond to specific treatments. This can be valuable information in the selection of enriched patient populations or for the stratification of study samples of clinical trials. The collaboration of academics, industry, clinician groups, insurance organizations will be essential for the creation of these datasets that will have sufficient power to yield valid results from AI and ML. Examples of such initiatives include the LupusNet, 3TR, and EMR datasets, as well as the CorEvitas  [55] , IORRA  [56] , and IMS (IQVIA) registries.
Improved outcome measures are needed to capture the diversity of disease activity in common and less common rheumatic diseases, which can indicate different causal pathways and, hence, response to treatment. More sensitive tools are also required to detect improvements in disease activity in D2T subpopulations, subgroups with less severe baseline disease activity, and those with concomitant immune-modulating medications. The advent of rapidly-advancing AI and ML tools shows promise in identifying new disease-related signals in large tissue and cellspecific omics datasets. Finally, progress in imaging technologies such as ultrasound and MRI may provide new avenues for detecting biomarkers of structural damage onset and progression in rheumatic disease. However, although more sensitive, these techniques are likely to suffer from similar limitations as radiographs in terms of reproducibility and feasibility  [57, 58].
The application of AI and ML tools to large high-quality imaging datasets is an avenue of exploration to circumvent these limitations and, possibly, to detect subtle but clinically-relevant changes, not decipherable through physician observations. In the last decades, advancements in the understanding of the underlying disease pathologies have led to the development of innovative and effective treatments for rheumatic diseases. Despite these improvements, a considerable number of patients do not achieve the expected therapeutic outcomes. The unraveling of complexities of the rheumatic disease process that has contributed to the development of new treatments has also highlighted the phenotypic and physiologic diversity of these conditions. While these insights present hurdles to the development of even more effective treatments, they can be addressed through the continued focus on innovation that has brought us this far.
Data collection Eight countries from Central and Eastern Europe implementing national EU co-financed ORV programmes conducted between 2010 and 2020 volunteered to participate in the study: Bulgaria, Croatia, Estonia, Finland, Greece, Latvia, Lithuania, and Romania (Fig. 1 ). Thus, gathering data from several countries made it possible to provide more powerful statistics and the highest probability to detect tendencies. The collection of harmonised ORV and ecological data was performed in 2021 using purpose-designed questionnaires. All countries followed a standardised programme of ORV i. e. two oral rabies vaccination campaigns performed per year (except one campaign per year performed in Finland), by aerial distribution, one in autumn and one in spring with a density of 20-25 baits per km 2  (European Commission, 2002 , 2015).
Studied factors Tetracycline biomarking and seroconversion can be considered as permanent marking (apart from primary vaccination for serology) and the life span of foxes in nature is generally considered to be globally 2-4 years  (Meia, 1996). To limit the impact of such long marking on the multivariable analysis, seroconversion and bait uptake data were not considered by campaign (autumn and spring) but by year. The responses variables considered were the number of blood /fluid samples of foxes with and without evidence of neutralising antibodies up to a threshold level (i. e. seropositive and seronegative samples) in a first modelling and the number of foxes 'mandibles with and without evidence of biomarker (Tetracycline) in a second one. The technique used to detect the tetracycline biomarker in teeth and bones of fox mandibles is based on an harmonised procedure shared within the EU network by the European Union Reference Laboratory for Rabies.
Since 2011, proficiency testings to evaluate laboratories performances on tetracycline detection are regularly organised  (Robardet et al. , 2012) , they ensure an EU analysis capacity with comparable performances between countries and over time. To be able to detect reasonable-size effects with reasonable power, enough observations per parameter estimated are needed  (Harrell, 2015). Because our database counted 79 observations, and to get a ratio of 12 observation per covariate analysis was performed by selecting seven responses variables hypothesised as the most potentially affecting the explanatory variables. The seven explanatory variables assessed were the following: -Type of oral vaccine used (Fuchsoral; Lysvulpen; Rabigen; Rabitec) named "vaccine_used". -Size of the vaccinated area (continuous variable) /10000 named "areascaled".
5 EU/mL, ELISA BioPro 40 %PB, FAVN, RFFIT) named "sero_type". Some factors for which data were collected were not considered in the analysis. The proportion of compliant titers in vaccine batches was not taken into account as 100 % of vaccine batches tested fulfilled the titer criteria. The adequacy of bait densities (homogeneity of the distribution within the vaccinated area) was not considered in the analysis as 97 % of responses indicated that bait distribution was evaluated by GPS analysis and considered adequate. Blood sample quality was also not included as an explanatory factor because no specific trends in the evolution of blood sample quality during the study period were highlighted by respondents. This factor was also considered to be too subjective to allow a proper and harmonised use in the dataset.
Differences in the Akaike second-order information criterion (AICc) between the best model and all other considered models were calculated to determine the relative ranking of each possible model. The model with the lowest AIC represented the best compromise between residual deviance and the number of variables. When Δi was below two (Δi= difference between AICc and the lowest AICc value), the most parsimonious model (i. e. the one with the fewest variables) was selected. The goodness of fit of the selected model was assessed with graphical exploration (normality, homoscedasticity and linearity of residuals)  (Davison and Snell, 1991). Over-dispersion in models was assessed by analysing standardised GLM residuals using DHARMa package  (Hartig, 2022). When over-dispersion was identified, an ad hoc Williams correction was performed to minimize Type 1 error  (Williams, 1982).
All the data were collected per year over and per participating country. "vaccine_used" is the type of vaccine used during the ORV programme. "sero_type" is the type of elisa test used during the ORV programme. 'ELISA BR03′ is Bio-Rad kit used with 0. 3 as the cut-off value. 'ELISA BR05′ is Bio-Rad kit used with 0. 5 as the cut-off value. "fox_pop" is the number of hunting fox bags during the ORV programme for 10 km 2 (used as indicator of red fox population abundances). "wildboar_pop" is the number of hunting wildboar bags observed during the ORV programme for 10 km 2 (used as indicator of wildboar population abundances). "areascaled" is indicator of the size of the vaccinated area. Odds ratio > 1 reflects a tendency to positively impact the seroprevalences; Odds ratio < 1 reflects a tendency to negatively impact the seroprevalences.
large wild boar population could therefore jeopardise the effectiveness of ORV campaigns by reducing the availability of baits for red foxes  (Musto et al. , 2022). Interestingly, the evolution of wild boar abundances in our dataset did not support the continuous increase in this population. Although considered to be good indicators of population size, hunting data may be biased, for example by the number of hunters (which is known to be decreasing in Europe), or by any particular management campaign which would bias the collection pressure on populations  (Husek et al. , 2021). By example, African swine Fever is or has been present in some participating countries (all countries excepted Croatia and Greece)  (Sauter-Louis et al. , 2021). In this context, increased hunting efforts could have been set up as a mean to limit the spread of the disease, modifying substantially the figures of hunting bags that have been considered here as indicators for population abundances.
Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Emmanuelle Robardet reports financial support was provided by European Commission. Fig. 6. Parameter estimates of the best model selected to explain oral rabies bait uptake variations in foxes based on analysis of foxes sampled after ORV programmes. All the data were collected per year and per participating country. "vaccine_used" is the type of vaccine used during the ORV programme. "Tmax" is the maximum temperature recorded during the ORV programme. "fox_pop" is the number of hunting fox bags during the ORV programme for 10 km 2 (used as indicator of red fox population abundances). "wildboar_pop" is the number of hunting wildboar bags observed during the ORV programme for 10 km 2 (used as indicator of wildboar population abundances).
All protocols were undertaken in compliance with EU Directive 2010/63 EU and the French regulation on the protection of laboratory animals issued on February 1st, 2013. They are part of project number # 2014-0049 and HA0036, which were approved by the Institut Pasteur ethics committee for animal experimentation (Comit e d'Ethique en Exp erimentation Animale CETEA registered under #89) and was authorized under #8562 by the French Ministry of Research, the French Competent Authority. Funding sources This work was funded by Institut Pasteur grant PTR2017-66 to CW and by Agence Nationale de la Recherche (ANR) grant ANR-10-LABX-62-IBEID to IGB. DB was funded by Universit e Paris Cit e (formerly Universit e Paris Diderot) through Doctoral school FIRE (ED FIRE474). IS was part of the Pasteur-Paris University (PPU) International PhD program.
This program received funding from the Institut Carnot Pasteur Microbes & Sant e, and the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement no. 665807. IS additionally benefited from a scholarship "Fin de th ese de science" number FDT201805005258 granted by "Fondation pour la recherche m edicale (FRM)". SP was supported by Universit e Paris Cit e (formerly Universit e Paris Descartes) through doctoral school Bio-SPC (ED BIOSPC). The PE research was funded by the DARRI-Institut Carnot-Microbe et sant e (grant number INNOV-SP10-19) and the ANR grant ANR-21-CE15-0038-01. France-BioImaging infrastructure network is supported by the French National Research Agency (ANR-10eINSBe04, Investments for the future). The use of the Opera Phenix system in the PBI core facility was made possible by the kind financial support of the Institut Pasteur (Paris) and the R egion Ile-de-France (program DIM1Health).