dataesr/scientific-paragraphs-categorization huggingface dataset:
Principle 6: National Registries National registries are important epidemiological tools for assessing the proportion of affected individuals among the general population (prevalence), as well as for measuring the number of new cases diagnosed each year (incidence), detecting areas of low-diagnostic rates, providing insights on diagnostic delay associated with increased morbidity and mortality and studying specific populations or conditions  (31). Beyond this, national and regional registries are recognised to be vital components of any public health programme, and provide data necessary for planning services, monitoring public health, research and the care of individuals  (35). Not only is it necessary to collect national figures but given the scarcity of the individual types of PID, international databases are of importance to ensure that patient numbers needed for clinical research on PID is available.
Currently, the availability and nature of these registries vary greatly. Some countries are equipped with up-to-date national registries (14 countries), while many lack registries altogether  (38 countries). Some countries such as Malaysia  (36) , Vietnam and Algeria have registries based on data from a few medical centres, while others, such as South Korea, have a PID registry connected to only one medical centre. In contrast, in large countries with many medical centres treating PID patients such as Canada and the United States of America, finding ways to centralise the registries is a huge challenge that, if solved, could greatly benefit the PID community.
DISCUSSION The PID Life Index demonstrates that, regardless of global scientific progress with an increasing number of diagnostic tools and better treatment options for patients with PID becoming available, the accessibility and affordability remains uneven throughout the world. While some principles are becoming more widely implemented in some regions, others have much work ahead to reach full implementation. This discrepancy is not only visible between regions, but also comparing countries in the same region. The data shows that some countries do provide a better PID environment than others, but it also confirms that no country surveyed so far reaches a 100% score in the PID Life Index. Consequently, there is still nowhere in the world where the gold standard according to the PID Principles of Care is fully available and accessible to patients.
Nevertheless, many principles or criteria are interlinked and an improvement in one could imply an improvement in the other (e. g. existence of specialised centres in a country would likely improve diagnosis rates and allow for better treatment). It could thus be considered that countries channeling effort into implementing one principle may then improve other principles as well, and thus progress faster than expected. The aim of this project has been to study the global PID environment through the IPOPI network. The invitation to participate was thus sent to IPOPI's national member organisations (67 at the time), and to countries where IPOPI has well-respected medical contacts in the field of PIDs. Although this study offers a first of its kind presentation of key global data on PIDs, there are still PID centers that have not yet contributed to the index.
Availability and Affordability Having diagnosis and treatments both available and covered/ reimbursed is key for patients with PID to access these in practice (3). In most countries, the necessary anti-infectious therapies are available and covered or reimbursed for patients with PID. In comparison, the full range of Ig therapies is only available in a few countries and in some countries, patients do not have access at all, or only on an irregular basis. As these biological medicines are not generic nor interchangeable  (38) , an individualised treatment plan and access to a wide range of products and administration routes are crucial for patients with PID. Despite the statement in the WHO Essential Medicines Lists where immunoglobulins are listed as essential therapies in the treatment of PIDs in adults and paediatric populations  (9, 10) , the data demonstrate that many countries still fail to make this treatment available and affordable in full range to patients.
Patient Centricity The data manifests a vast landscape of national PID patient organisations engaging to improve the environment for PID patients in most of the surveyed countries. These organisations have a significant role to play in healthcare systems, bringing unique perspectives on the impact of diagnosis, treatment, and quality of life. It is therefore not only important to ensure patient-centred care for patients with PID for the well-being of the individual patient (i. e. individualised treatment plans), patient centricity and involvement are also key to reach a gold standard for PID from a more long-term perspective. Importantly, patient-centricity should not only be a symbolic word but put into practice and patient representatives should have a say in decisions and policies affecting their lives.
The PID Life Index provides an integrated worldwide view of the actual situation of PID Principles of Care, which conforms a cornerstone for what needs to be done next and a catalyst for creating new value for the patients. The data aggregated in the Index is the first of its kind for primary immunodeficiencies and offers a good basis for benchmarking, information sharing and for awareness and advocacy purposes. It handles a vast amount of data and offers easy access to essential principles and criteria for patients with PID, supporting the analysis of the current global PID environment and the way forward. Limitations of this tool have been described elsewhere (article pending publication) and include: a nation-wide approach may not suit all countries, discrepancies between policy and how this policy is implemented, country coverage dependent on the existence of a PID patient group or a medical contact of IPOPI and the respondent's personal bias depending on his/her knowledge and good faith.
Flow Cytometry Single cell suspensions prepared from peripheral blood, spleen of humanized mice was stained with surface markers and analyzed on a CyAn ADP (Dako). FITC-conjugated anti-human CD40, CD24, PE-conjugated anti-human CD303, CD38, PE/Cy5conjugated anti-human CD86, IgG, PE/Cy7-conjugated antihuman HLA-DR, PB-conjugated anti-human CD4, IgM, APCconjugated anti-human CD10 and APC/Cy7-conjugated antihuman CD45 were purchased from Biolegend. Pacific orangeconjugated anti-mouse CD45, PE/Texas red-conjugated antihuman CD19 and LIVE/DEAD Fixable Aqua (LD7) Dead Cell Stain Kit were purchased from Invitrogen. Data were analyzed using Summit4. 3 software (Dako).
The cardiac rhythm was used as a clinical criterion for survival graphs. Before bleach treatment, embryos were killed using the anesthetic tricaine at a fatal dosage (500 mg/mL). Whole-genome analysis. S. pettenkoferi strain SP165 was cultivated aerobically at 37°C for 24 h on Columbia sheep blood agar plates (5%) (Becton Dickinson). Following the manufacturer's instructions, genomic DNA was extracted using the DNeasy UltraClean Microbial Kit (QIAGEN). Whole Genome Sequencing (WGS) was carried out using an Illumina MiSeq sequencing equipment (Illumina) using paired-end (PE) read libraries (PE250) made with an Illumina Nextera XT DNA Library Prep Kit (Illumina) according to the manufacturer's instructions. To examine data quality, raw readings were processed using FastQC (v. 0. 11. 9). To eliminate leftover PCR primers and filter low quality bases (Q_score <30) and short reads (<150bp), the Cutadapter tool (v.
1. 16) was used, which was implemented in Python (v. 3. 5. 2). The downstream analysis includes the filtered trimmed reads. Using the CLC genomics workbench 7 (Qiagen), reads were mapped to the S. pettenkoferi FDAAGOS_288 genome (GenBank accession number: GCA_002208805. 2) using default settings; length fraction: 0. 5, similarity fraction: 0. 8. Shovill v1. 1. 0 software on the Galaxy platform was used to process the assembled contigs for microbial genome annotation. The CONTIGuator website was used to produce consensus sequences, utilizing S. pettenkoferi FDAARGOS_288 as the reference. The virulence factor database (VFDB) was utilized to identify virulence factor-encoding genes from genome sequences (https://www. mgc. ac. cn/VFs/)  19. On assembled genomes, antimicrobial resistance genes were acquired from ABRIcate using the ResFinder database  20, 21.
The genome was annotated using PROKKA v1. 14. 5. The origin of replication was determined using GenSkew software (http://genskew. csb. univie. ac. at/) and plasmids were screened using the PlasmidFinder 2. 0 webtool (https://cge. cbs. dtu. dk/services/PlasmidFinder/). WGS was subjected to other investigations, such as circular genome representation (using the BLAST Ring Image Generator program) (BRIG)  22  ). The newly sequenced strain can be found under the BioProject: PRJNA768314 for the raw reads and assembled genomes: SP165 SAMN22027592. Statistical Analyses. GraphPad software package Prism 6. 01 was used to estimate the statistical significance of differences across groups, which is indicated in the relevant figure legends.
Study population The study was based on the French DEvenir des Spondyloarthrites Indifferenciees Recentes (DESIR) cohort, which is a prospective, multicentre (n ¼ 25) cohort (clinical trials. gov ID: NCT01648907)  [25]. The cohort included consecutive patients with inflammatory back pain lasting 3 months but <3 years and with a clinical diagnosis of axSpA according to the treating rheumatologist. Patients who provided work-related data as part of the health-economic section of the DESIR Case Reporting Form (CRF) were included in the study. Both tick-box and free-text information provided in the CRF were used to maximize the information available related to work status and outcomes. Patients were grouped into one of two main categories: working or nonworking, with relevant sub-categories for each.
Ethics The DESIR study was conducted according to good clinical practice guidelines and was approved by the appropriate local medical ethical committees (Comitelde Protection des Personnes Ile de France III) on 17 September 2007 (EUDRACT number: 2007-A00608-45). A detailed description of the study protocol is available at the DESIR website (www. lacohortedesir. fr/desir-inenglish/). The research proposal for this particular analysis was approved by the scientific committee of the DESIR cohort. Elena Nikiphorou et al.
Acknowledgements The DESIR cohort was sponsored by the  Departement de la Recherche Clinique et du Developpement de l'Assistance Publique-Hopitaux de Paris. This study is conducted under the umbrella of the  French Society of Rheumatology  and  INSERM  ( Institut National de la Santeet de la Recherche Medicale ). The database management is performed within the  department of epidemiology and biostatistics  ( Professor Paul Landais ,  D. I. M. ,  Nı ˆmes, France). An unrestricted grant from  Pfizer  was allocated for the 10 years of the follow-up of the recruited patients. The authors would like to thank the.
Baseline data Three hundred forty-four blood donors were enrolled during June-July of 2018-19 with follow-up of 131 in the post-season period November-February. In 2018, 214 participants were included with 93 available for followup, while 130 were included in 2019, with 38 available for follow-up. Four follow-up samples in the 2019 season were unfit for testing due to improper storage of the samples. The median age of the participants was 46 years, and 43. 9% were female. More than half of the participants had one or more risk factors of tick exposure (Table  1 ).
Acknowledgements We would like to thank  Svend Erik Jacobsen  and the staff of the blood bank at the  Hospital of Southern Jutland, Soenderborg , for their invaluable assistance to the project. Caroline Moos  from the  Research and Education Center, Hospital of Southern Jutland  for language revising the manuscript and  Niels Møller Andersen  from the  Department of Clinical Microbiology, Hospital of Southern Jutland , for assistance with making the logistics possible.
Cells were washed with sterile filtered PBS supplemented with 1% bovine serum albumin (PBS-BSA 1%) and filtered through a 40 µm nylon mesh (BD Biosciences). For the detection of ALDH1 or CD44, cells were stained as describe above. Data were analyzed using FlowJo version 8. 8. 6 (Treestar Inc). To analyze immune cells infiltrating F-clone in Balb/c mice, tumors were harvested, dissociated on gentleMACS using the tumor dissociation kit (Miltenyi Biotec) and cells (250,000 cells) were stained with the following antibody panels. T lymphocytes-infiltrating tumors were identified using an antibody panel consisting of CCR4-BV421, CXCR5-BV605, CD8-BV650, CXCR3-BV711, CD45-BV785, CD4-AF488, CD3-PerCP/Cy5. 5, CCR6-PE/Dazzle594 antibodies and Zombie NIR for cell viability. MDSC and macrophage-infiltrating tumors were stained using CD11b-BV510, F4/80-BV605, CD45-BV785, Gr1-PE/Dazzle594, Ly6C-PE/Cy7, Ly6G-APC antibodies and Zombie NIR for cell viability.
M1/M2 cells and Treg cells were monitored in permeabilized cells using FoxP3-BV421, CD11b-BV510, F4/80-BV605, CD45-BV785, CD4-AF488, CD3-PerCP/Cy5. 5, CD38-PE-Dazzle594, CD25-PE/Cy7, and Zombie NIR for cell viability. All antibodies used for tumor-infiltrating immune cells came from Biolegend (San Diego, CA, USA). Data were acquired using a Novocyte cytometer (ACEA Biosciences) and analyzed using Novoexpress software.
Cell pellets were then washed (resuspended in 2. 5 ml PBS, centrifuged at 500 x g for 5 minutes, and then decanted) and resuspended in 500 µl lysis buffer (see above) and kept on ice and protected from light. Immediately before FACS analysis, samples were spiked with 1:500 DAPI solution as a counterstain to assess cell viability (with corresponding no DAPI controls). Samples were then analyzed on a BD FacsAria SORP 6-Laser Cell sorter at the RHLCCC Flow Cytometry Core Facility. Subsequent data were analyzed using FlowJo 10 software.
Growth of 4T1 cells in NGS and Balb/c mice U-clones: 4T1 vCas9 (wt) and CD95 k. o. clones #54 and #69 were infected with pFU-L2G luciferase lentivirus as previously described  (Ceppi et al. , 2014). Then cells (1×10 5 ) of wt or a 1:1 mixture of the CD95 k. o. clones were injected in 100 μl PBS and Matrigel (Cat#3432-010-01, Trevigen) (1:1 ratio) into the fourth mammary fat pad at the base of the nipple into female NODscid-gamma (NSG) mice and Balb/c mice (Jackson lab) according to the Northwestern University Institutional Animal Care and Use Committee (IACUC)-approved protocol. Growth of tumors was monitored weekly by using an IVIS Spectrum in vivo imaging system and luminescence was quantified at the regions of interest (ROI = the same area for each mouse encompassing the entire mammary gland) using the Living Image software.
IHC quantification Stained slides on standard size (26 mm x 76 mm) were scanned using NanoZoomer 2. 0-HT (020234 by Olympus America Inc. ) with scanning mode of x20 (0. 46 µm/pixel) either automatic or semi-automatic set up under SOP (MICR 1. 1. 03) at the department of Pathology and RHLCCC Core Facility of Northwestern University. The digital images were then stored as high definition and were analyzed with NDP (NanoZoomer Digital Pathology, Hamamatsu Inc. ) View software (https://www. hamamatsu. com/us/en/product/type/U12388-01/index. html) for quantification analysis. For identification of individual nuclei in the tissue, we trained a Cell Classifier in the Visiopharm Image analysis Suite (VIS, Horsholm, Denmark) by manually annotating exemplar nuclei that were positive and negative for DAB (DAB+ and DAB-, respectively).
05) were clustered into 6 groups using k-means clustering. Gene ontology analysis was performed for each cluster using the TopGO package (RRID:SCR_014798). Heatmaps were created with the pheatmap package v1. 0. 12 (RRID:SCR_016418). All analyses were carried out using R v3. 6. 3 (RRID:SCR_001905). The GO enrichment analyses shown in Fig. 3B  was performed using the GOrilla gene ontology analysis tool at http://cblgorilla. cs. technion. ac. il using default settings and a p-value cut-off of 10 -6.
Acknowledgement U-clones: We would like to thank  Shanshan Zhang  for performing slide scanning and signal quantification and  Michelle von Locquenghien  for help with the generation of the 4T1 CD95 k. o. clones. F-clones: We would like to thank the  IHC  facility ( Dr. Alain Fautrel  and  Roselyne Viel ,  Rennes, France)  for performing 4T1 tissue staining, slide scanning and signal quantification. The mouse study in NSG mice benefited from the help of  Remy Castellano  and  Emmanuelle Josselin  ( Marseille, France ). This work was funded by grant  R35CA197450  to M. E. P, by  INCa  ( PLBIO18-059 ),  Ligue Contre le Cancer and Fondation de France  ( Price Jean Valade ) for P. L. and by  Lynn Sage Cancer Research Foundation  and  Lynn Sage Scholar  fund for Z. J.
Results Temporal changes in loggerhead sizes. In total, 729 stranded and by-caught loggerhead turtles were recorded in the RTMMF database between 1920 and 2020. Of these, 700 were measured between 1990 and 2020, with sizes (curved carapace length; CCL) varying between 9 and 100 cm, and a mean CCL of 48 (± 14 SD) cm. Overall, 96% of observed individuals measured between 25 and 75 cm. On average, individuals observed during the summer months were larger than those observed in the fall and winter [mean monthly sizes over 50 cm CCL between June and August (peak of the nesting season) compared to less than 45 cm from October to March; Fig. 1 ]. Furthermore, size followed a bimodal distribution with two peaks around 40 and 60 cm (Fig. 2 ). Overall, loggerhead turtle CCL significantly increased over the 1990-2020 time period (Fig.
3A ; linear regression model slope: Estimate = 0. 30, standard error = 0. 080, p-value = 0. 0002). However, when considering different seasons separately, this significant increase in size was only observed during the breeding/nesting season (Fig. 3B ; linear regression model including size data collected from May to September-slope: Estimate = 0. 60, standard error = 0. 10, p-value < 0. 0001) and not in the winter (Fig. 3C ; linear regression model including size data collected from October to April-slope: Estimate = -0. 027, standard error = 0. 11, p-value = 0. 80). Of the 729 stranded and by-caught turtles recorded between 1920 and 2020, 63 were mature individuals (53 during the breeding/nesting season and 10 during the winter season). The first recorded adult (> 70 cm CCL) was observed in 1990 (Fig.
While the cause of death could only be identified for the female found in 2020 (myocarditis), multiple morbidities were recorded, including injuries (healed severed flipper) and the presence of plastic debris in their digestive tracts. Trends in sea surface temperature. Although spatially variable (Fig. 6A ), mean sea surface temperatures (SST) computed for the north-western Mediterranean, based on data extracted from the US National Oceanic and Atmospheric Administration's database  56  , significantly increased between 1982 (beginning of the    1  and 2. FR continental France, Cr Corsica, IT Italy. available SST time series) and 2020 (Fig. 6B ; Mann-Kendall test: p-value = 0. 004). This upward trend in SST was detected both during the summer (Fig. 6C ; Seasonal Mann-Kendall test: p-value = 0.
Discussion Through the analysis of stranding, by-catch and nesting data collected by the RTMMF stranding network, temporal changes in the demographic structure and nesting activity of loggerhead turtles in the French Mediterranean were successfully characterized. Although data collected as early as 1920 have been recorded in the RTMMF database, observation effort prior to 1990 was too low to detect any significant trends. However, based on the last 30 years of data, an increase in the numbers of adult individuals and nesting events could be identified in the French Mediterranean. As previously evidenced  46, 47  , most loggerhead turtles observed in this study between 1990 and 2020 were juveniles, with a mean recorded size of 48 cm CCL. In particular, two peaks, at 40 and 60 cm CCL, were observed in the loggerhead size distribution, reflecting seasonal patterns in the size of individuals frequenting French waters (Fig.
Methods Study area and data collection. Data were collected by the French Mediterranean sea turtle stranding network RTMMF, which operates along the Mediterranean coasts of continental France and Corsica, and is part of the French Sea Turtle Observatory (Observatoire des tortues marines de France métropolitaine) coordinated by the National Museum of Natural History (Muséum National d'Histoire Naturelle; MNHN). As such, its members are authorized to intervene on stranded or by-caught turtles within the framework described in the scientific programme of the Observatory  83, 84. RTMMF observers record all observations of live and dead sea turtles along the French Mediterranean coast. Individuals found alive (stranded or incidentally caught by fishermen) are generally transferred to the closest rescue centre for diagnostic and, when required, rehabilitation, while dead turtles are generally necropsied (depending on the accessibility and state of the carcass).
When possible, biometric data (including Curved Carapace Length-CCL) are collected and the sex of the individuals identified. The RTMMF works closely with rescue centres, participative science programmes and associations, as well as local fishermen, all of which significantly contribute to the collection of sea turtle observations. All observations of live and dead Caretta caretta recorded in continental France and Corsica between 1920 and 2020 were considered in this study.
Characterization of temporal changes in loggerhead size. Although data on loggerhead turtle size have been collected since 1920, few observations (29) had been recorded prior to the 1990s due to inconsistent observation effort. Therefore, only size data (CCL; measured from notch to tip) collected between 1990 and 2020 were analysed to characterize temporal changes in size. Overall, data from 700 measured individuals were used to estimate total and monthly average sizes over the studied time period. The size distribution of loggerhead turtles observed between 1990 and 2020 was first estimated. The effect of year of observation on size (CCL) was then tested with linear regression models. Three different regression models were computed: (1) model including all sizes recorded during the 1990-2020 time period, (2) "summer" model only including size data collected between May and September and (3) "winter" model only including size data from October to April.
Residuals were analysed prior to validating regression models and a significance level of 0. 05 was considered in all tests. All analyses were carried out in the open-source programming environment R  85. Evaluation of trends in the number of observed mature individuals. When considering both males and females, Mediterranean loggerhead turtles reach sexual maturity above 70 cm CCL, on average  35, 86. Therefore, only individuals larger than 70 cm CCL were considered sexually mature in this study. The annual numbers of mature males, females and individuals of undetermined sex observed between 1920 and 2020 were calculated. Review of reproductive activities. Opportunistic observations related to reproduction were extracted from the RTMMF database. These observations were classified into four categories: nesting events (discovery of a nest, eggs and/or hatchlings), nesting attempts (i.