dataesr/scientific-paragraphs-categorization huggingface dataset:
3. 4  [34]. Analyses were conducted choosing a model with admixture, uncorrelated allele frequencies, and a non-uniform ancestry prior ALPHA among clusters, as suggested by Wang  [35]  for uneven samplings. We run 20 replicates for each value of K from K=1 to K=12 (K is the number of inferred genetic groups), with 750000 MCMC after a burnin of 500000. Structure results were summarized and visualized with the web server CLUMPAK  [36]. We used STRUCTURE HARVESTER  [37]  to infer the best value of K, based on both the probability of the data given K  [34]  and the Evanno approach  [38]. The reference database of wild individuals was then prepared based on the following three-step analysis. First, we added to each wild individual the prior information about the genetic cluster most represented in the geographic area from which they were sampled.
Assignment of individuals of unknown origin According to Manel and colleagues  [39]  fully Bayesian methods of assignment, as implemented in STRUCTURE, outperform partially Bayesian methods  [40]  with higher assignment rates and lower assignment error. However, this method considers all populations simultaneously with the drawback of assigning individuals to reference population even if the true population of origin is actually unsampled  [39]. To overcome this problem, Manel and colleagues  [39]  suggests performing both fully Bayesian assignment tests and exclusion tests. We performed assignment tests on 458 confiscated individuals with STRUCTURE using the POPFLAG for individuals in the reference database and activating the "update allele frequencies using only individuals with POPFLAG=1" option under a USEPOPINFO without admixture model.
RESULTS The analysis of the complete wild dataset indicated that K=2 and K=6 were the most supported numbers of clusters. The log probability of data increased sharply from K=1 to K=2 and then more slowly from K=3 to K=6 where it reached a plateau (see Supplementary Material Fig  1). The delta K analysis  [38]  provided two modes at K=2 and K=6, respectively. The first and most evident partition discriminated eastern and western subspecies (see Supplementary Material Fig  2) , whereas the second mode at K=6 suggested a subdivision of T. h. hermanni in 4 groups and of T. h. boettgeri in 2 groups (Fig  1 ). The T. h. hermanni groups were Italian Peninsula (ITP) (all the populations from central and southern Italian Peninsula, except samples from central and southern Calabria), mainland France (FRA), Calabria (CAL) and Mediterranean islands (Sicily, Sardinia, Corsica, Pantelleria) joined with Spain (ISS).
The T. h. boettgeri groups were Greece (GRE) and Bosco Mesola with Croatia and Macedonia (MCM). These results agree with the groups previously obtained by Perez et al. [27, 28, 44, 45] , but with the additional CAL cluster, emerging from an area that was previously unsampled. The analyses carried out in STRUCTURE using the prior population information allowed us to detect the presence of one hybrid and six migrant individuals among wild populations (Tab 1). While the hybrid was from an admixture area between two geographically contiguous clusters and one of the migrants was from the same subspecies, the other five migrants were from the other subspecies (four of them from spatially very distant clusters). Genotypes from these seven individuals were excluded from the reference database. In order to assign the 458 confiscated individuals to the most probable geographic area of provenance, we used K=6 as the optimal K value, and q > 0.
A priority concern that motivated this study and requires urgent solutions is the management of the tortoises kept in captivity in seizure/recovery centres. These animals, usually confiscated from local authorities or found by private citizens far from natural populations and likely escaped from domestic contexts, cannot be released in nature without knowledge of their origin. Their number is increasing, with increasing problems related to their management and health condition. The assembly of a genetic reference database, and the assessment of the most probable geographic origin of captive tortoises, are fundamental steps towards the development of plans of reintroduction in the wild, which will not only reduce the problems and the costs associated with the captive animals, but also re-create wild populations in areas where this species was present in the past but is now extinct.
In addition, the reference database represents a useful forensic tool to investigate the genotype of individuals when their declared origin is legally disputed. Future efforts should be devoted to achieve higher geographic resolution of genetic population structure analyses, and to reduce the fraction of unassigned individuals. These goals could be achieved with one or both of the following strategies. First, to sample still poorly covered areas, in order to get a complete representation of the genetic variation in the whole species' range. Second, to increase the number of informative genetic markers, possibly decreasing the costs. Next Generation Sequencing (NGS) technologies could help in this direction, allowing to develop a panel of diagnostic SNPs to be assessed with the increasingly cheap genotyping methods  [1, 54].
ACKNOWLEDGMENTS Animal handling and sample collection were allowed by the  Ministero dell'Ambiente e della Tutela del Territorio e del Mare  ( 0044068 -4/12/2012-PNM-II; 0001805/PNM -4/2/2015; ISPRA 68754/T-A31 -28/11/2016 ) and the  Regione Autonoma della Sardegna Prot. 4749,Rep. N. 73 07/03/2017. We would like to thank all the colleagues and friends who helped during fieldwork:  Carabinieri per la Tutela dell'Ambiente ;  Centro Regionale di Recupero degli animali selvatici di Bonassai, Centro Recupero Fauna Selvatica Bosco di Ficuzza ;  Centro Recupero Animali Selvatici Formichella ;  Centro Recupero Fauna Selvatica "Stretto di Messina"; Centro Recupero Animali Selvatici Provinciale di Policoro ;  Parco Nazionale dell'Asinara -Area Marina Protetta "Isola dell'Asinara"; Parco Nazionale dell'Aspromonte, Parco Nazionale del Circeo ,  Riserva Naturale Regionale Lecceta di Torino di Sangro ;  Riserva Naturale Bosco della Mesola -Parco Delta del Po ;  Oasi WWF "Lago di Conza.
Additionally, it is unknown whether defective NOD2 signalling influences susceptibility to aspergillosis. Therefore, the present study investigates whether common polymorphisms in NOD2 or its complete deficiency influences susceptibility to IA, and whether it affects the immune response to Aspergillus. We demonstrate that genetic variation in NOD2 in humans and complete Nod2 deficiency in mice protects against IA. In line with this, NOD2 deficiency or its neutralization associates with increased antifungal activity of macrophages and monocytes, conversely NOD2 activation neutralizes fungal killing capacity of phagocytes. Our data collectively highlight a detrimental role for NOD2 receptor in anti-Aspergillus host defence.
8a ). Subsequently, NOD2 gene expression was silenced in human monocyte-derived macrophages (MDMs) to validate that the absence of NOD2 also positively influences fungal killing in human cells. Treatment of MDMs with NOD2 targeting siRNA augmented fungal killing capacity (Fig. 8b ). Since ). e The area under the curve of the reactive oxygen species release of PBMCs from healthy controls and NOD2-deficient patients (controls n = 8, patients n = 4) in response to live Aspergillus conidia (1 × 10 7 /mL) and zymosan (150 µg/mL) measured by luminescence signal from luminol conversion over 1 h. Data are represented as scatter dot plot with median and means were compared using the Mann-Whitney U test the absence of NOD2 positively influences fungal killing we hypothesized that NOD2 activation might impair antifungal host defence.
B3, dilution 1:300) according to the protocols supplied by the manufacturer's. The cells were measured on an FC500 flow cytometer (Beckman Coulter) and the data were analysed using CXP analysis software v2. 2 (Beckman Coulter). The surface dectin-1 expression on human MDMs was assessed following stimulation of with MDP as described above. MDMs were stained in a final volume of 50 μL with FITC-conjugated anti-human CD14 monoclonal antibody (BD; clone TUK 4, dilution 1:20), KromeOrange-conjugated anti-human CD45 monoclonal antibody (Beckman Coulter; clone J. 33, dilution 1:10) and APC-conjugated antihuman dectin-1 monoclonal antibody (R&D, clone 259931, dilution 1:10). CD14 + cells were gated within the population of CD45 + cells and subsequently, the Mean fluorescence intensity (MFI) of Dectin-1 was assessed on the CD14 + /CD45 + cells (Supplementary Figure  4 ).
The cells were measured on a Cytoflex flow cytometer (Beckman Coulter) and the data were analysed using Kaluza software (Beckman Coulter). Aspergillus killing assays. Freshly isolated PBMCs (5 × 10 5 ), human GM-CSF monocyte-derived macrophages (2 × 10 5 ) or murine BMDMs (2 × 10 5 ) were exposed to Aspergillus conidia (2 × 10 6 ) in 96 well plates a final volume of 200 μL. After 24 h at 37 °C, the cells were washed in water and plated in serial dilution on Sabouraud agar plates. CFUs were counted after 24 h at 37 °C. Phagocytosis assays. Human CD14 + monocytes, human MDMs, or BMDMs were plated in 24 flat bottom plates at 5 × 10 5 cells/well. Cells were allowed to phagocytose 5 × 10 6 (MOI 1:10) heat inactivated FITC-labelled conidia for 4 h. Subsequently the fluorescence signal of extracellular non-phagocytosed conidia was quenched using 0.
2% trypan blue. The cells were measured on a Cytoflex flow cytometer (Beckman Coulter) and the data were analysed using Kaluza software (Beckman Coulter). The monocytes that phagocytosed one or more conidia were enumerated by their positivity for the FITC signal, and could be divided into a FITC negative (monocytes that did not engulf conidia) and a FITC positive (monocytes that engulfed conidia) population. Phagocytosis efficiency was assessed as the mean fluorescence intensity of FITC + macrophage population (Supplementary Figure  5 ).
Acknowledgements We thank  C. Wertz  and  M. Fanton D'Andon  for providing Nod2-deficient mice,  M. Schlotter  for organizing patient inclusion,  B. Rösler  for assistance with flowcytometry. We also thank the NOD2-deficient patients for contributing to our study by providing blood samples. M. S. G. was supported by the  Erasmus lifelong learning program. F. L. v. d. V. was supported by the E-rare project  EURO-CMC. M. O. was supported by the  NWO ,  016. 176. 006 ). A. C. and C. C. were supported by the  Northern Portugal Regional Operational Programme  ( NORTE 2020 ), under the Portugal 2020 Partnership Agreement, through the  European Regional Development Fund (FEDER)  ( NORTE-01-0145-FEDER-000013 ), and the  Fundação para a Ciência e Tecnologia (FCT)  ( IF/00735/2014  to A. C. and  SFRH/BPD/96176/2013  to C.
Evaluation of imaging studies All scoring exercises were completed within a web-based environment (CaREArthritis. com) or Research Electronic Data Capture (REDCap)  [13]. REDCap is a secure, web-based application designed to support data capture for research studies. Four raters were musculoskeletal radiologists (DMB, RGL, JLJ, NAC) and one rater was an adult rheumatologist with SIJ imaging expertise (WPM). All images were reviewed in random order and blinded to clinical details. All raters have had extensive training in the interpretation of pelvic radiographs and MRI. Each radiograph was assessed for erosions, sclerosis, joint space narrowing, joint space widening, and ankylosis. Each rater indicated whether the radiograph was globally representative of sacroiliitis (yes or no) and rated confidence in global impression (ordinal scale -4 to 4 with anchors of "definitely no" and "definitely yes").
The ASAS MRImagine consensus-based eCRF for recording MRI data was used to capture the following: rater global impression of acute/ active inflammatory lesions compatible with sacroiliitis (yes/no), rater global impression of structural lesions typical of axial SpA, confidence in that assessment (ordinal scale -4 to 4 with anchors of "definitely no" and "definitely yes"), capsulitis (yes/no), SIJ effusion (yes/no), and enthesitis outside of the SIJ (yes/no)  [17, 18]. Structural lesions on MRI (erosion, sclerosis, fat metaplasia, backfill, ankyloses) were assessed using the Car-eArthritis platform and the SPARCC SIJ structural score (SSS) scoring module. Reliability of the SPARCC SSS has been demonstrated in the pediatric population  [19].
Second, this was a retrospective study using sets of images that could have been obtained at any point in the child's disease course. Perhaps the utility of radiographs is higher with longer disease duration or older age and more closely matches the utility seen in adults in these of prolonged disease exposure. The vast majority of children, however, have relatively short duration of symptoms and disease at the time imaging is ordered. We think our data reflect the typical use of radiographs for routine practice in the evaluation of the sacroiliac joints in children with both suspected and established JSpA. Third, some of the subjects had imaging performed shortly after spondyloarthritis diagnosis as part of a prior study. Our sensitivity analysis investigating this limitation using a sample restricted to those patients imaged specifically because of pain (no prior research subjects) demonstrated that our range of estimates of the radiograph test properties did not vary between the full and restricted samples.
Moreover, at day 28, patients completed a treatment satisfaction questionnaire. Adverse events were collected throughout the study. As this was a pilot study, no proper sample size calculation was performed. For summary statistics, categorical variables were summarized by frequency and percentage for each response category. Continuous variables were summarized using mean and standard deviation (mean ± SD). All local tolerability and efficacy endpoints were summarized per visit. SAS version 9. 3 or above was used for all statistical analyses (SAS Institute Inc. , Cary, NC, USA). The authors do not plan to share any individual deidentified data generated through this presently reported study.
Data collection Inclusions are performed 24 h per day, seven days a week. Each patient admitted for an elective or non-emergent CS during labor is informed before the beginning of the CS and a signed consent is collected. Patients are included in the H group when hemorrhage > 800 mL. The blind injection of the 10-mL vial containing 0, 0. 5, or 1 g of TA is performed over 1 min between T0 and T1 after injection. Blood loss, transfusion, maternal hemodynamics, resuscitation measures, uterotonic and invasive procedure data, and maternal morbidity parameters are recorded before inclusion (T0) and at T30, T60, T120, T360 (30, 60, 120, and 360 min, respectively, after inclusion). A follow-up visit collecting total blood loss and maternal morbidity takes place at day 2 (±12 h) and at day 42 (±14 days) postpartum during a phone interview.
Specific management of the ancillary pharmacobiological study Inclusion criteria Out of the 342 H patients included in the TRACES trial (CS with bleeding > 800 mL), a subgroup of 144 patients will be included in the substudy, for which specific (SGTPA and TA concentration) biological samples and analysis will be collected. The criteria for the inclusion in the substudy are organizational criteria: the sampling and the management of plasma aliquots need daytime conditions. Each patient included in the TRACES trial in these conditions may be included in the TRACES substudy. The 48 NH group (CS with bleeding < 800 mL) inclusions are all expected to be included in the TRACES pharmacobiological substudy to obtain the reference data for the SGTPA.
Data collection and management Data are confidential, in accordance with the law dated 6 January 1978. Data are collected electronically. Data will be analyzed in accordance with methodology described in the MR 06001 form of the French data protection authority by Pr Alain Duhamel's epidemiology and public health biostatistics platform Lille University Hospital Lille. Access to data will be restricted to individuals who are directly involved in the study. Data may be modified by any physician participating in the study or a fellow working with a physician participating in the study. Trial data will be archived for at least 15 years after the trial has ended.
Data collection and management Data will be anonymized in accordance with the law dated 6 January 1978. Data will be collected electronically. Data will be analyzed in accordance with the methodology described in the MR 06001 form of the French data protection authority by Pr Alain Duhamel's epidemiology and public health biostatistics platform Lille University Hospital Lille. Access to data will be restricted to individuals who are directly involved in the study. Data may be modified by any physician participating in the study or a fellow working with a physician participating in the study. Trial data will be archived for at least 15 years after the trial has ended.
Statistical method Sample size calculation The sample size computation is based on the expected difference between the placebo group and the low dose and available data on TA use in active PPH. Given that there is no available data on low TA dosage impact on blood loss reduction after inclusion, and considering a 50% reduction as previously observed after a higher dose administration, the required number of participants is 103 per group for a type I error of 5% and a power of 80%. Considering a maximum of 10% of dropouts or missing data, we will recruit 114 H patients per group for a total of 342 patients. In order to compare the specific fibrinolysis inhibition parameters with the recent thrombin-plasmin potential generation in a well, a subgroup of patients are sampled for these specific biological tests and a complementary group of non-treated, NH CS patients will be included in an observational sequence of clinical and biological assessments.
Statistical analysis Statistical analyses will be independently performed by the Biostatistics Department of University of Lille under the responsibility of Pr. Alain Duhamel. Data will be analyzed using the SAS software (SAS Institute Inc, Cary, NC, USA) and all statistical tests will be performed with a two-tailed alpha risk of 0. 05. A detailed statistical analysis plan will be written and finalized before the database lock. Baseline characteristics will be described for each group. The quantitative variables will be expressed as mean and standard deviation in cases of normal distribution and median and interquartile if not. The normality of distributions will be checked graphically using the Shapiro-Wilk test. Qualitative variables will be expressed as the frequencies and percentages. For the main objective, the blood loss measured in each experimental group (low dose and high dose) will be compared to that of the placebo group by using an analysis of covariance adjusted for baseline blood loss volume and center-effect.
Timing of the biological samples The timing of sampling was T0 (inclusion time when bleeding ≥ 800 mL was diagnosed), T1 (at the end of injection), and T15, T30, T60, T120, T180, and T360 (defined as 15, 30, 60, 120, 180, and 360 min later, respectively). A timed urinary sample was collected in a graduated urinary bag between inclusion (T0) and T120 then T120 and T360, at the end of the observation period of 6 h to measure the cumulative dose of TA excreted in urine. Various timing margins have been allowed for samples' collection: T1 is the reference time and the time of the TA plasmatic concentration peak and no margin will be allowed (sample exactly at the end of the TA injection), The margins allowed for T30, T60, T120, T360 were 5, 5, 10, and 60 min, respectively. The exact timing of the data collection was noted to show the pharmacodynamic analysis.
Statistical method Clinical and biological data were collected using Excel® (Microsoft). The number needed to study in this pilot study was estimated to ten patients in the H group and ten patients in the NH groups. Non-pregnant (NP) values were extracted out of a previous validation study. Qualitative variables were analyzed regarding frequency and percent as quantitative variables were described by median and interquartile or mean and standard deviation. Comparison used non-parametric tests (Mann-Whitney and Kruskal-Wallis tests) and Student's t-test for NH and H mean values. Correlation was evaluated by Spearman coefficient. P < 0. 05 was considered significant. Statistical analyses and graphs were performed using GraphPad Prism® (version 7).
Data acquisition and quantification were performed using MassLynx 4. 1 Software (Waters). Calibration standards were obtained by dilution of the TA stock solution (100 g/L) at 200, 100, 50, 25, and 5 mg/L in water or blank plasma. Results Using TA-spiked plasmas, the limit of detection (LOD) and the lower limit of quantification (LLOQ) were determined at 1. 2 mg/L and 2 mg/L, respectively. Linearity was tested up to 300 mg/L using the calibration range 5-200 mg/L (r 2 = 0. 995). Intra-day and interday precisions were < 3. 80% and 5. 30%, respectively, for a 20-mg/L-spiked sample and < 2. 90% and 4. 15%, respectively, for a 150-mg/L-spiked sample, which indicates that our method is highly precise and reproducible. The analysis of ten TA-free plasma samples and ten TAfree urine samples did not reveal any interference in TA detection, which indicates that our method is selective and specific for TA quantification.
The cumulative fluorescent signals were measured by Ascent Software and used to calculate in Microsoft Excel the first derivative of each value using this mathematical formula: Y'i = (Yi + 1 -Yi-1)/(Xi + 1 -Xi-1) (Yi = the y-axis value, i. e. the fluorescence intensity for a point i; Xi = the x-axis value, i. e. the time for the same point i). These first derivative curves represented the thrombin and plasmin generation rate curves. The first derivative values were then converted into thrombin and plasmin concentrations using a linear calibration curve. The two final curves represented the thrombin and plasmin generated concentration over time. Parameters To describe the proteolytic activity of both thrombin and plasmin, a number of parameters were defined and automatically calculated on a Microsoft Excel® macro program.
Study organization The coordinating team includes the coordinator, the promotor, and the major investigators of each of the centers. A call conference will be organized every two months between the coordinator and the centers, supported by a newsletter reporting inclusion rates and protocol information. Data and consent collection will be monitored by the promotor. An independent safety monitoring committee is recruited to observe blinding safety issues and allow the trial continuation. Data management and statistics will be done by an independent unit. Plans are written in advance for investigators and the sponsor to communicate trial results to participants, healthcare professionals, the public, and other relevant groups (e. g. via publication, reporting in results databases, or other data sharing arrangements).
Image and Data Analysis Open source software Cell Profiler (CP) was used for the image analysis  (Carpenter et al. , 2006). In order to perform automated image analysis in CP, a robust pipeline able to recognize different cell features was built. Data analysis was performed using R, a programming language and software environment for statistical computing and graphics. Potential miss segmentation of cells was detected based on cell area and perimeter. Cells with cell perimeter and area that exceeded 1. 5 quantiles from the median among all replicas for a particular surface were excluded from further analysis. ICAM-1-positive or -negative cells were identified based on a threshold value. The threshold value was determined as the intersection of ICAM-1 cells median intensities distributions of positive and negative controls (Figure  4B ) and corresponded to the 93rd percentile of all ICAM-1 median cell intensities in the negative control (Figure  4B ).