dataesr/scientific-paragraphs-categorization huggingface dataset:
2016-0932). The French Poison Control center network reported from 2012 to 2019, 17 events with fish caught from the Indian Ocean. In a recent report from Germany, the rarely occurring CP outbreaks were reported between 2012-2017, the main reason identified as imports of snappers (Lutjanidae) from the Indian Ocean region mainly from countries like India, Indonesia, and Vietnam. The author emphasized that fishes from the Indian Ocean can cause ciguatera, which has been poorly documented  [81]. Although incidences of CP have not been reported from the Arabian Gulf yet, however, the fishes such as grouper, snapper, emperors, barracuda, jacks, trevallies, kingfish, and tuna form the part of common catch by local fishermen in Arabian Gulf  [123]. The database search in Fish base returned a minimum of 13 (Iraq) to a maximum of 60 (Oman) fishes associated with CP elsewhere in the world  [124]  (Figure  9 ).
Ciguatera Monitoring in Indian Ocean Region Risk assessment and monitoring of Ciguatera in the Indian Ocean region is largely lacking with only a couple of previous attempts from la Reunion  [47]  and Mayotte Island  [61]. In 2014, an algal bloom monitoring system was developed for Kuwait's coastal waters. The OC3M (Aqua-MODIS) and OC4E (ENVISAT-MERIS) algorithms most accurately measured chlorophyll-a concentrations in Kuwait bay. Due to the poor temporal resolution and the decommissioning of ENVISAT-MERIS, Aqua-MODIS data was used for continuous observation. Additionally, algorithms such as Generalized Inherent Optical Properties (GIOP), Garver, Siegel, Maritorena Model, OC2, MODIS fluorescence line height, and the MERIS-based NIR-Red algorithms were attempted, which have a lower accuracy when compared to the OC3M algorithm.
Desk research Data sources included literature reviews, databases, the WHO Global Health Observatory, surveys, interviews and the WHO Non-Communicable Disease Strategy. A scoping review of published articles and grey literature was also performed to extract estimates of the annual costs of maintenance haemodialysis (HD), maintenance peritoneal dialysis (PD) and kidney transplantation in the first year and subsequent years and of the cost ratio of maintenance HD to PD. Since included studies were from different years and from countries with different currencies, reported costs were converted to international dollars (2016 US$) using standard techniques (online supplemental figure  1 ).
Country survey A project leader from each of the 10 ISN regions identified three key stakeholders (a nephrology society leader, a leader of a consumer representative organisation and a policymaker) for each country in his or her region (182 countries total). The stakeholders were subsequently invited by email to participate in the survey (available in English, French and Spanish) via an online portal ( www. redcapcloud. com) between July and September 2018. The survey data were used to provide information on funding for KRT and conservative kidney management, UHC, out-of-pocket costs for KRT, within-country variations in ESKD care delivery and oversight of ESKD care.
Patient Selection From 1 January 2010 to 31 December 2018, 355 consecutive patients who underwent pancreaticoduodenectomy with curative intent for PDAC (n = 288) or DC (n = 67) at the Institut Paoli-Calmettes, Marseille, France were evaluated prospectively, and followed-up until 15 April 2021. Patients with duodenal adenocarcinoma, ampulla adenocarcinoma, pancreatic cystic tumors, neuroendocrine tumors, benign tumors, or uncertain distinction between PDAC and DC were excluded. Data were entered into a clinical database. The study protocol was approved by the institutional review board of the Institute. Participants were informed about the study and provided written consent. Their data are contained in the investigators' declared prospective institutional database (NCT02871336; CNIL n • Sy50955016U).
Study Parameters Numerous clinical variables were evaluated. These included age, sex, body mass index, serum CA 19-9 level (U/mL; at diagnosis after jaundice resolution), biliary stenting, and administration of neoadjuvant treatment. Data on type of surgery (pancreaticoduodenectomy or total pancreatectomy), vascular resection, and enlarged resection (not including gallbladder and spleen) were also noted. Information regarding morbidities according to the Clavien-Dindo classification was recorded  [14] , including postoperative pancreatic fistula  [15] , hemorrhage, postoperative (in-hospital or 30-day) and 90-day mortality rates, perioperative red blood cell (RBC) transfusion rate (from surgery to home discharge), length of hospital stay, and adjuvant treatment administration. No patients were lost to follow-up.
Statistical Analysis Categorical variables were compared using the Fisher's exact test or the chi-squared test, and continuous variables were compared using the student's t-test. Overall survival (OS) was assessed using the Kaplan-Meier method (based on the date of diagnosis and the censoring date, 15 April 2021). Survival curves were compared using the log-rank test, and results were reported as hazard ratios (HRs) with 95% confidence intervals (CIs). Multivariate analysis was performed using stepwise logistic regression, integrating factors identified in the univariate analysis with a significance level of p < 0. 1, unless they were highly clinically significant. Data analyses were performed using GraphPad Prism version 8 (GraphPad Software Inc. , La Jolla, CA, USA) and SPSS ® version 24 (IBM Corp.
A declustering method was applied to separate the time-dependent part of seismicity from the seismic activity related to aftershocks, foreshocks or clusters of earthquakes. Complete periods associated to each class of magnitudes are defined Table  1. Furthermore, we left all besides the magnitudes lower than m 0 = 3. 5M w , considered as not reliable. We assume that the restricted catalog constitutes a complete statistical sample. Our  database finally contains N = 583 earthquake events, from year 1524 until year 2016, whose magnitudes range from 3. 5 until 6. 69. The database is large enough to achieve robust results with all the models presented. Furthermore, the accuracy of magnitude data can sometimes be no better than 0. 5 unit: this can be due to imprecise measurement methods or to bias introduced when converting from various local magnitude scales.
This probability is determined using a model of GMPE that takes into account the model errors due to amplifications and misunderstandings of geological phenomena. λ(IM > x) = λ(M > m min ) × P [IM > x|(m, r, θ)] f M |M >mmin (m)f R (r) dm dr The structure of a PSHA calculation is summarized in Figure  16. In the study, the PSHA model is a simplified but realistic model composed of only one area source. It uses real data (see Date and Resources Section) and a conceptual calculation model respecting the international rules. The tool used for the calculations is OpenQuake  [27]. The PSHA calculations take into account the following epistemic uncertainties: -Ground motion models uncertainty: we selected four Ground Motion Prediction Equations (GMPE). These independent GMPEs are representative of all published database for shallow continental active regions ([4],  [5] ,  [9] ,  [12] ).
Statistical Analysis The safety population included all patients meeting the inclusion criteria receiving at least one dose of glecaprevir/pibrentasvir, including those who were treated off label. The core population included patients from the safety population treated according to the current label at the time of study in accordance with their disease characteristics and with a known drug start date, excluding patients who were DAA-experienced. The core population with sufficient follow-up (CPSFU) included patients from the core population, excluding those who did not have an HCV RNA evaluation after posttreatment day 70 for reasons not related to effectiveness or safety (lost to follow-up or unavailable HCV RNA data). Patients included in the CPSFU had one of the following: HCV RNA data after post-treatment day 70 (not included if the drug end date was unknown), virologic failure (on-treatment virologic failure or post-treatment relapse), discontinued the study because of an AE, and had HCV RNA \ 50 IU/mL at the last measurement.
ACKNOWLEDGEMENTS We thank the participants of these studies. Glecaprevir was identified by  AbbVie and Enanta. Funding. AbbVie sponsored the study; contributed to its design; and participated in the collection, analysis, and interpretation of the data and in the writing, reviewing, and approval of the publication. AbbVie funded the  Rapid Service  and  Open Access  of this publication. All authors had access to relevant data, and participated in the writing, review, and approval of the publication. No honoraria or payments were made for authorship.
MARTY is a C++ computer algebra system specialized for High Energy Physics that can calculate amplitudes, squared amplitudes and Wilson coefficients in a large variety of beyond the Standard Model scenarios up to the one-loop order. It is fully independent of any other framework and its main development guideline is generality, in order to be adapted easily to any type of model. The calculations are fully automated from the Lagrangian up to the generation of the C++ code evaluating the theoretical results (numerically, depending on the model parameters). Once a phenomenological tool chain has been set up -from a Lagrangian to observable analysis -it can be used in a model independent way leaving only model building, with MARTY, as the task to be performed by physicists. Here we present the main steps to build a general new physics model, namely gauge group, particle content, representations, replacements, rotations and symmetry breaking, using the example of a 2 Higgs Doublet Model.
Introduction MARTY is an open source code for symbolic computation in high-energy physics  [1]  and can be downloaded from its website where all related publications, manuals and documentations can be found. Symbolic computations are done using the CSL module of MARTY, a C++ Symbolic computation Library dedicated to this task. We present some of the CSL features in the following but extensive information can be found in the CSL manual. The 2 Higgs Doublet Model (2HDM) is one of the simplest extension of the Standard Model (SM) as it contains only one additional representation, namely another Higgs doublet (four real degrees of freedom) in the  (2)  group. In the SM, the gauge symmetry breaking  (2)  ×  (1)  →  (1)  of the Higgs doublet Φ 1 gives masses to the weak bosons (three longitudinal polarizations for  ± and ) leaving one real degree of freedom, the so-called Higgs boson ℎ 0.
Yukawa couplings Without the SM flavour symmetry, Yukawa couplings are not 3 × 3 matrices, simply scalar constants. Equation  (1)    to couple to only one of the two doublets. This is presented in Table  2 , following conventions defined in Ref. [2]. In the following we consider the 2HDM of type IV, lepton specific. The Yukawa Lagrangian can be built in MARTY using the sample code below. We abbreviated the GetComplexConjugate() CSL function into cc(). For a MARTY fermion, the  0 matrix is implicit and 𝜓 * ≡ ψ = 𝜓 * 𝛾 0. ( 4 ) Indices are always ordered in the same way in quantum tensor fields: • Flavour indices in the order of group definition. • Gauge indices in the order of group definition. • Space-time indices, Dirac or Minkowski for fermions or vector bosons respectively. If only one index has to be given, the curly braces {} can be omitted.
The following sample code demonstrates this procedure for the first doublet, first getting all the required Particle objects: Particle Phi_11 = THDM. getParticle("Phi_11"); Particle Phi_12 = THDM. getParticle("Phi_12"); Particle phi_1 = scalarboson_s("phi_1", THDM); Particle eta_1 = scalarboson_s("eta_1", THDM); Particle rho_1 = scalarboson_s("rho_1", THDM); eta_1->setSelfConjugate(true); // real field rho_1->setSelfConjugate(true); // real field Then making MARTY apply the expansion in the Lagrangian: Expr v_1 = constant_s("v_1"); THDM. replace(Phi_11, phi_1()); THDM. replace(Phi_12, (v_1 + rho_1() + CSL_I*eta_1()) / sqrt_s(2));.
Conclusion and perspectives We demonstrated how to build a model from scratch with MARTY using the example of a 2HDM-like model. We preferred to keep this write-up as brief, simple and pedagogical as possible, therefore some relevant parts such as the flavour structure have been skipped. There is no limitation in MARTY preventing us to generate more complex models and one can find in particular the full 2HDM (types I to IV) together with other (B)SM models in MARTY's source code as explained in the 'Built-in models' chapter of the manual. As mentioned in the Introduction, model building is the only task left once a phenomenological tool chain has been established, as presented in Figure  2. Such a phenomenological tool chain, in green, is mostly model independent and can be implemented only once.
It requires the implementation of the initial MARTY program that calculates specific quantities such as squared amplitudes or Wilson coefficients, and an interface with a phenomenological code to obtain observables from those quantities. Work is in progress in that direction using SuperIso  [6] [7] [8]  for the automated G. Uhlrich of flavour observables in general BSM theories using Wilson coefficients calculated by MARTY at the one-loop level, and SuperIso Relic  [9] [10] [11]  for dark matter observables using squared tree-level amplitudes. One can see that MARTY has two distinct objectives: Calculate theoretical quantities at the one-loop level, but also help physicists to build BSM models. An important part of the development work has been and will still be dedicated to this task, making model building with MARTY as simple and general as possible.
The wearing of the face masks appears as a solution for limiting the spread of COVID-19. In this context, efficient recognition systems are expected for checking that people faces are masked in regulated areas. To perform this task, a large dataset of masked faces is necessary for training deep learning models towards detecting people wearing masks and those not wearing masks. Some large datasets of masked faces are available in the literature. However, at the moment, there are no available large dataset of masked face images that permits to check if detected masked faces are correctly worn or not. Indeed, many people are not correctly wearing their masks due to bad practices, bad behaviors or vulnerability of individuals (e. g. , children, old people). For these reasons, several mask wearing campaigns intend to sensitize people about this problem and good practices.
In this sense, this work proposes three types of masked face detection dataset; namely, the Correctly Masked Face Dataset (CMFD), the Incorrectly Masked Face Dataset (IMFD) and their combination for the global masked face detection (MaskedFace-Net). Realistic masked face datasets are proposed with a twofold objective: i) to detect people having their faces masked or not masked, ii) to detect faces having their masks correctly worn or incorrectly worn (e. g. ; at airport portals or in crowds). To the best of our knowledge, no large dataset of masked faces provides such a granularity of classification towards permitting mask wearing analysis. Moreover, this work globally presents the applied mask-to-face deformable model for permitting the generation of other masked face images, notably with specific masks.
Introduction and motivation The wearing of the face masks appears as a solution for limiting the spread of COVID-19. In this context, efficient recognition systems are expected for checking that people faces are masked in regulated areas. To perform this task, a large dataset of masked faces is necessary for training deep learning models towards detecting people wearing masks and those not wearing masks. In this sense, some large datasets of face images with virus-related protection mask are available in the literature; e. g. the MAsked FAces dataset (MAFA)  [1] , the Real-World Masked Face Dataset (RMFD 2  ) and a masked face recognition dataset  [2]  composed of Masked Face Detection Dataset (MFDD), Real-world Masked Face Recognition Dataset (RMFRD) and Simulated Masked Face Recognition Dataset (SMFRD).
Besides, many people are not correctly wearing their masks due to bad practices, bad behaviors or vulnerability of individuals (e. g. , children, old people). In this sense, several mask wearing campaigns intend to sensitize people about this problem and good practices  [3] [4] [5] [6]. In  [7] , a mobile application "CheckYourMask" has been designed towards permitting people to check if their mask is correctly worn or not by taking a selfie. The creation of a dataset with correctly/incorrectly worn mask classes has been suggested. In  [8] , a dataset composed of images with individual or multiple masked faces (853 images) has been proposed towards creating detection model taking into account the improperly masked faces. In the present case, we propose a relatively large dataset of 137,016 masked face images that is divided into two masked face categories; correctly worn and incorrectly worn (see samples in Fig.
1a  and Fig. 1b , respectively). Specifically, this work proposes three types of masked face detection dataset; namely, the Correctly Masked Face Dataset (CMFD), the Incorrectly Masked Face Dataset (IMFD) and their combination (MaskedFace-Net) for the masked face detection (see dataset structure in Fig. 2a ). Realistic masked face datasets are proposed with a twofold objective: i) to detect people having their faces masked or not masked, ii) to detect faces having their masked correctly worn or incorrectly worn (e. g. ; at airport portals or in crowds). To best of our knowledge, no large dataset of masked faces provides such a granularity of classification towards permitting mask wearing analysis. Moreover, this work globally presents the applied mask-to-face deformable model for permitting the generation of other masked face images, notably with specific masks.
Applied mask-to-face deformable model The dataset of face images Flickr-Faces-HQ 3 (FFHQ) has been selected as a base for creating an enhanced dataset MaskedFace-Net composed of correctly and incorrectly masked face images. Indeed, FFHQ contains 70000 high-quality images of human faces in PNG images at 1024 × 1024 resolution and is free of use. The FFHQ dataset offers a lot of variety in terms of age, ethnicity, viewpoint, lighting, and image background. It was originally created as a benchmark for generative adversarial networks (GAN)  [9]. Our MaskedFace-Net dataset has been created by defining a mask-to-face deformable model. A pseudo-code of the global principle for generating MaskedFace-Net is shown in Fig. 2b  with respect to outputs depicted in Fig. 2a. For each face image of FFHQ (e.
e. mask correctly worn), mask only covering the nose and mouth; mask only covering mouth and chin and mask under the mouth (i. e. three cases of mask incorrectly worn). For each type of mask-to-face mapping (CMFD, IMFD1, IMFD2 or IMFD3), a subset of 12 facial key points is retained from the 68 landmarks automatically detected; then matched to the 12 mask key points. By this way, the mask can fit specific areas of the face for each targeted case. Hence, a mask-to-face deformable mask model has been created to generate MaskedFace-Net. Moreover, each targeted case can have up to 2 key points of the mask (amongst 12 key points) that have their locations randomly displaced in a limited perimeter. In particular, this tolerance allows to act on the height of the mask on the face and then to bring more realism to the generated dataset.
Finally, a homography transformation which relies on the defined point-to-point correspondence of landmarks between mask image and face image is applied for mapping mask pixels over the targeted facial areas. Instances of produced face landmarks and corresponding mask-to-face mapping are displayed for each type in Fig. 4a   3 see "dataset of face images Flickr-Faces-HQ (FFHQ)" https://github. com/NVlabs/ffhq-dataset. 4 see "Facial point annotations" https://ibug. doc. ic. ac. uk/resources/facial-point-annotations/. 5      We emphasize that a raw mask-to-face mapping has been applied to the FFHQ dataset. In particular, no images have been filtered according to specific parameters (e. g. age). However, the file naming of MaskedFace-Net includes the one given by the FFHQ dataset. Hence, correspondence in between the FFHQ and MaskedFace-Net can be established towards related filtering.
It is worth mentioning that the minimum age for mask wearing depends on applicable laws in concerned countries. For instance, the mask wearing is compulsory from 6 years old in Spain, 11 years old in France, 12 years old in Belgium under certain conditions  [11]. Between 2 and 11 years old, opinions differ  [12]. Since FFHQ contains face images of all ages, it is also the case for masked face image of MaskedFace-Net. Such datasets could then be exploited for detecting children in crowds that wear a mask under the recommended limit of age.
Conclusion A large dataset of 137,016 quality masked face images has been produced and made available online. MaskedFace-Net can be seen as a benchmark dataset for creating machine learning models related to the mask wearing analysis; notably, detecting the presence of mask or not over detected face images, the correct or incorrect wearing for detected masked faces. MaskedFace-Net can then be used for enhancing vision-based monitoring systems towards several applications such as checking the respect of laws related to the mask wearing or generating crowd statistics. Moreover, the method used for the generation of MaskedFace-Net has been described for permitting the generation of masked face images by using other types of mask.
DNA barcoding The standard COI barcode fragment was sequenced for all specimens to obtain calibrations for divergence time estimates (see below). For each sample, the cocktail of M13 tailed primers and the PCR conditions described in Germain et al. [38]  were used on 4μL of DNA obtained with WGA. Unpurified PCR products were sent to Eurofins Genomics for Sanger sequencing. Forward and reverse strands were assembled using Geneious 10. 2. 2  [39]. Consensuses were aligned with MAFFT-7. 313 (linsi option)  [40]  and translated to amino acids using MEGA 7  [41]  to detect frame-shift mutations and premature stop codons, which may indicate the https://doi. org/10. 1371/journal. pntd. 0009479. g001 presence of pseudogenes. Sequences were merged with all barcodes available in BOLD  [42]  for the genus Phlebotomus (last access December 4, 2019) and IQ-TREE v1.
Preparation and sequencing of RAD library In silico digestion of the genome of Ph. papatasi available on GenBank (AJVK00000000. 1) was performed using the recognition site of the PstI enzyme to determine the number of expected cut-sites and calculate the maximum number of samples to be sequenced on a single lane of an Illumina HiSeq 2500. 51,712 cut sites (103,424 RAD markers) were inferred. Considering that ca 150,000,000 passing filter reads would be usable to construct RAD loci according to our experience (after removal of reads with mutation in barcodes /restriction sites and PCR duplicates), 48 samples were included in the library to get a sequencing depth per marker of ca 30X (only 32 samples were analysed for this study, the 16 others were sequenced for another purpose). Library construction followed by Baird et al.