dataesr/scientific-paragraphs-categorization huggingface dataset:
(vi) Strains indicated with * cluster differently compared with other methods, those indicated with ** do not represent the strain as reported in GenBank. The dissimilarity scale is presented at the bottom. Bootstrap values higher than 50% from a 2,000 replicate analysis are shown in percentages at the internodes. Clusters strongly supported are indicated in bold, those less supported are indicated by dotted lines. For each recognised cluster, the number of strains in the total of 207 available sequences is given between brackets. between L. guyanensis and L. panamensis is not a priority in clinical practice  [36]. Separating L. braziliensis from L. peruviana is considered more relevant, because L. braziliensis potentially causes mucocutaneous complications, while L. peruviana generally does not  [34].
In practice, BLAST searches are often used for identification purposes, on the basis of hsp70 sequences found in public databases such as GenBank. We have found several instances where the species designation reported in these databases was incorrect. For example, in entries JX021425 up to JX021443 and JX970993 up to JX970996, several erroneous L. donovani sequences are reported, even disagreeing with the species assignment as listed in the related publication  [23]. Two entries were here shown related to Leptomonas rather than Leishmania (Figure  3  and supplementaty dendrogram). Two entries were determined from the L. infantum type strain MHOM/FR/78/LEM75: Y08020 and X85798. Both sequences clearly grouped with L. tropica, unlike all genuine L. infantum sequences in our analysis. This illustrates that one should be extremely careful when using sequences that have not been quality-controlled for species typing by comparison with other sequences from the same species, as this could result in incorrect typing outcomes, with potential adverse consequences for the patient.
Conclusion We present in this paper a complete validation and globally applicable standardised protocol for the use of hsp70 sequences in Leishmania typing. As this validation includes a detailed comparison with other species identification methods currently used in various laboratories, we feel that implementation of the here presented typing strategy in a clinical diagnostic laboratory should be straight-forward, and could entail the validation of only the sequencing process itself rather than the actual species assignment, in view of this report. We intend to further promote our strategy, to identify additional strains with linked clinical information, and to establish a global database of circulating Leishmania parasites.
Literature search strategy Scientific literature for the purposes of this review was searched in May 2012 by all participating co-authors, sourcing the PubMed and Scopus databases. An electronic search was conducted among articles from 1970 until recently, as well as a few relevant older references, cross-referencing the following combination of keywords: 'leishmaniasis' and 'Phlebotomus' and 'emergence' and 'reservoirs'. Titles relevant to the scope of this review (an updated view of the establishment and spread of the leishmaniases in Europe with respect to newly emerging and re-emerging foci and the incrimination of neglected and new reservoir hosts) were obtained in full text and selected for inclusion. Unpublished data and titles from non-peer-reviewed literature were not considered. fragile nature of sandflies in comparison to the rather robust invasive mosquito species, a shift of sandfly occurrence to northern areas of Europe, traditionally regarded as Leishmania-free, was recently well documented.
A multicentre, multinational surveillance has started analysing leishmaniasis treatment protocols and treatment outcomes with respect to the infecting parasite genotype or species. All patients with parasitologically confirmed cutaneous or mucosal leishmaniasis are included in the participating centres. The clinical data (patient data, country where the lesion was acquired, localisation and description of the lesion, etc. ) are assessed in a questionnaire and documented before and after treatment. Each physician applies their routine treatment schedules. However, suggestions for treatment guidance will be offered to all physicians. Patients will be followed at least until the lesion has healed. Follow-up examinations will be done according to the current guidelines.
Acknowledgements To all the healthcare staff who are taking part in the surveillance and control areas of the disease and patient care. To  E. García-Puente  for their special contribution regarding case notifications from Fuenlabrada hospital. To  M. Pichiule  (MIR) and to the  Public Health Alerts Service  for their contribution to the design and drafting of the extended epidemiological survey. To the veterinarians from the  Public Health 9 Territorial Service  ( R. Artero ,  A. Basagoiti ,  M. Bentolila ,  M. Daza ,  J. Fernández ,  A. Ferrer ,  M. García ,  J. M. Obradors  and  A. San Martín ) for their participation in control actions. Also to Public Health 10 Territorial Service (particularly  D. Alves  and  L. Gutiérrez ) and to the  Environmental Health Service  (particularly  C. Escacena  and  J.
Acknowledgements We would like to give thanks to our colleagues from the  Subdirección General de Sanidad Ambiental y Epidemiología, Dirección General de Atención Primaria y Servicio de Epidemiología, and Servicio de Salud Pública del Área 9, Comunidad de Madrid (Spain) , for their enthusiastic and collective nature during our collaboration on the studies of this outbreak. We would also like to thank our colleagues from referral hospitals which routinely contribute to our L. infantum cryo-bank. Last but not least we want to express our sincere gratitude and appreciation to the  WHO Department for the Control of Neglected Tropical Diseases (HTM/NTD/IDM) ,  Leishmaniasis Control Program , for its continuous support to the  WHO Collaborating Centre for Leishmaniasis  at the  Instituto de Salud Carlos III.
Acknowledgements This study is a contribution to project  POII09-0141-8176  ( JCCM  and  EU-FEDER)  and  EU  grants  ANTIGONE (278976 ) and  APHAEA (EMIDA ERA-NET ). We wish to thank  Dr. V. Alzaga ,  Dr. J. Vicente ,  Dr. I. G. Fernández-de-Mera ,  Dr. P. Acevedo ,  O. Rodríguez  and  F. Talavera  from 'IREC' and  S. Zanet  and  A. Trisciuoglio  from  'Facoltà di Medicina Veterinaria di Torino ' for their help with hare sampling and lab work. F. Ruiz-Fons  acknowledges a  Juan de la Cierva contract (Ministerio de Economía y Competitividad ).
Acknowledgements The authors would like to thank  Ingrid Felger  ( Swiss Tropical and Public Health Institute, Basel, Switzerland ),  Aldert Bart  ( Academic Medical Centre, Amsterdam, The Netherlands ),  Mark Bailey  ( Army Medical Directorate, Camberley, UK ), and  Ricardo Lleonart  ( INDICASAT, Panama City, Panama ) for providing DNA from strains and clinical samples. We acknowledge the constructive input from all colleagues of the LeishMan consortium. We appreciate the help of  Eva De Clercq  ( Université Catholique de Louvain, Louvain-la-Neuve, Belgium ) for the construction of Figure 2. Salary support of GVDA was provided by the  Belgian Directorate General for Development cooperation  (third framework agreement with ITM).
Acknowledgements The authors would like to thank  Prof. Luigi Gradoni  for advice and continuous support and  Maribel Jiménez  for reviewing the paper and providing important information. This study was partially funded by  EU  grant  GOCE-2003-010284  EDENext and is catalogued by the EDENext Steering Committee as EDEN056 (http://www. edenext. eu). The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the  European Commission.
• Standardisation -a coordinated research effort will improve database quality for supra-national analysis through uniform methodology, reporting scheme, and common denominators (e. g. data on drug sales). • Flexibility -a coordinated research effort will allow rapid incorporation of newly emerging biotherapies and rare infections other than leishmaniasis. The leishmaniases are dynamic diseases and the circumstances of transmis- sion are continually changing in relation to environ- mental, demographic and human behavioural factors. Changes in the habitat of the natural hosts and vec- tors, immunosuppressive conditions (like infection with human immunodeficiency virus (HIV) or organ transplantation-associated therapies in humans) and the consequences of war, all contribute to the trans- formation of the epidemiology of leishmaniasis.
Attacks usually last from a few days to several weeks. The most frequent manifestations observed in TRAPS include fever, myalgia with underlying fasciitis, migratory skin rashes, and abdominal pain. The inflammatory attacks can also be associated with pericarditis, arthralgia, arthritis, and ocular manifestations (conjunctivitis, periorbital edema). Like in other autoinflammatory disorders, sustained elevation of inflammatory markers can lead to AA amyloidosis  [2]. Numerous sequence variations, corresponding mostly to missense mutations have been identified in the TNFRSF1A gene (Infevers database, http://fmf. igh. cnrs. fr/ISSAID/infevers/). Many of them involve highly conserved cysteine residues, which are important for disulfide bond formation, and TNFR1 proper folding. These mutations are usually found in familial forms of TRAPS and associated with high disease penetrance  [3] [4].
Molecular Analysis gDNA was extracted from peripheral blood leukocytes using a commercial kit (FlexiGene, Qiagen). Exons and flanking intronic sequences of TNFRSF1A were amplified by PCR, purified, sequenced with the Big Dye Terminator sequencing kit (Applied Biosystems), and run on an ABI 37306l automated sequencer. Sequences were analyzed with SeqScape software (Applied Biosystems). The ten exons of the gene were screened in one family member. In relatives, the presence of the R92W mutation was searched for and confirmed by forward and reverse sequencing.
Prediction of TNFR1 3-dimensional (3D) Structure and Electrostatic Properties Models of wild-type and mutated TNFR1 3D structures were generated using the receptor monomer A chain from the Protein Data Bank crystal structure 1EXT as template. Homology modeling was performed using Modeller software v. 9. 8  [20]. Accuracy of output structures from Modeller were further assessed using Procheck v. 3. 4. 3  [21]. Graphic displays were generated using the PyMOL Molecular Graphic System v0. 99. Electrostatic properties of the models were predicted with the vacuum electrostatics PyMOL plug-in.
After cell washing, fluorescence was acquired and analysed with a FACSCalibur flow cytometer (Becton Dickinson), using the CellQuest Pro Analysis software (Becton Dickinson). Dead cells were excluded using 7-aminoactinomycine D (Sigma-Aldrich). To perform intracellular staining, cells were fixed and permeabilized with the Fix and Perm kit (Invitrogen), according to the manufacturer's instructions.
These data demonstrate that R92W and R92P result in a dramatic diminution of the pool of sTNFR1, like unambiguous mutations. In contrast, R92Q behaves like the WT protein. We also evaluated the membrane expression of TNFR1 by FACS analysis. We first used a PE-conjugated monoclonal anti-TNFR1 antibody (R&D Systems) used previously by other teams  [7, [22] [23]. Consistent with a previous observation  [22] , the receptor carrying the R92P mutation was not recognized by this antibody (Figure  4 ), so that we could not conclude on its subcellular localization. The subsequent use of a polyclonal antibody (R&D Systems) showed that TNFR1-R92P is indeed expressed in the cytoplasm, whereas very low levels are detectable at the cell surface. As shown in Figure  4A , the percentage of cells expressing the receptor at their surface is higher in the presence of TNFR1-WT, TNFR1-P46L and TNFR1-R92Q than in the presence of TNFR1-T50M, TNFR1-C70S, TNFR1-C73R, TNFR1-R92P and TNFR1-R92W.
Lohse et al. , personal communication in the Infevers database; Ravet et al. [6] ). We contacted these two teams to determine if R92W was also associated with typical TRAPS manifestations in these cases. The patient reported in  [6]  is a man who presented less than one inflammatory episode per month characterized by high fever (40uC) and peritonitis. He experienced his first attacks at the age of 28. It was, however, not possible to get a more precise description of the disease phenotype a posteriori. The patient reported by P. Lohse et al. is a lady who presented with a severe form of the disease. Reassessment of her medical history revealed that she started her manifestations at the age of 14 with 3 to 4 inflammatory episodes per year lasting up to 14 days. Her symptoms included high fever (up to 40uC), myalgia, arthragia, and acute abdominal pain.
Observational data The data used in this study are the ozone and NO 2 columns from the ground-based SAOZ/NDACC stations at the two tropical stations Reunion Island and Bauru and EP-TOMS, GOME, SCIAMACHY and OMI products for the overpasses of the satellites above the SAOZ stations. in Bauru  (Ionov et al. , 2008). The total accuracy on the NO 2 vertical column is estimated to ∼ 10-15 %.
EP-TOMS EP-TOMS, is a 6 wavelengths nadir viewing instrument on the helio-synchronous Earth Probe (EP) satellite, crossing the equator at 11h local time which operated between July 1996 and the end of  2005 (McPeters et al. , 1998)). The data used here are those of the version 8 retrieval algorithm available on (ftp://jwocky. gsfc. nasa. gov/pub/eptoms/ data/overpass/). The ozone measurements are based on a differential pair method at 317. 5 and 331. 2 nm for a SZA smaller than 70 • always available in the tropics  (Wellemeyer et al. , 2004). The estimated accuracy is about 3 to 5 %. Since 2000, EP-TOMS experienced instrumental problems inducing a 3 % bias in total ozone  (Bramstedt et al. , 2003). An empirical correction was performed in version 8 to overcome this bias. In 2002, and depending on the latitude, the estimated error was of 4-10 % (WMO, 2007).
Acknowledgements. The authors thank the personnel of La Reunion and Bauru stations for running the instruments. The SAOZ network is part of NDACC (Network for Detection of Atmospheric Composition Changes). The stations are supported in France by CNRS/INSU and CNES in collaboration with the Observatoire de Physique Atmosph érique de la Reunion (OPAR) and University of La Reunion for the Reunion instrument and IPMET/UNESP for Bauru instrument. The satellite data are availabled trough web access: TOMS (NASA) via the World Data Center for Remote Sensing of the Atmosphere (WDC/RSAT), Ozone Monitoring Instrument (NIVR/KNMI, FMI, NASA) via http://avdc. gsfc. nasa. gov/ and HALOE via http://haloe. gats-inc. com/home/index. php. SCIAMACHY data extraction was carried out with a software tool developed by K.
Chagas disease has a major impact on human health in Latin America and is becoming of global concern due to international migrations. Trypanosoma cruzi, the etiological agent of the disease, is one of the rare human parasites transmitted by the feces of its vector, as it is unable to reach the salivary gland of the insect. This stercorarian transmission is notoriously poorly understood, despite its crucial role in the ecology and evolution of the pathogen and the disease. The objective of this study was to quantify the probability of T. cruzi vectorial transmission to humans, and to use such an estimate to predict human prevalence from entomological data. We developed several models of T. cruzi transmission to estimate the probability of transmission from vector to host. Using datasets from the literature, we estimated the probability of transmission per contact with an infected triatomine to be 5.
8610 24 (95%CI: [2. 6 ; 11. 0]610 24 ). This estimate was consistent across triatomine species, robust to variations in other parameters, and corresponded to 900-4,000 contacts per case. Our models subsequently allowed predicting human prevalence from vector abundance and infection rate in 7/10 independent datasets covering various triatomine species and epidemiological situations. This low probability of T. cruzi transmission reflected well the complex and unlikely mechanism of transmission via insect feces, and allowed predicting human prevalence from basic entomological data. Although a proof of principle study would now be valuable to validate our models' predictive ability in an even broader range of entomological and ecological settings, our quantitative estimate could allow switching the evaluation of disease risk and vector control program from purely entomological indexes to parasitological measures, as commonly done for other major vector borne diseases.
Estimates thus rely on indirect approaches based on a combination of entomological and epidemiological studies at fine temporal and spatial scales. Given the difficulty to collect such integrative datasets, there are currently only three estimates of the probability of stercorarian transmission of T. cruzi to its hosts. This probability was found to be 9|10 {4 per contact with an infected vector for transmission to human  [11] , 2|10 {4 to guinea pigs, a typical domestic host in many Latin American regions  [15] , and 6|10 {2 to opossums, the likely ancestral mammalian host of T. cruzi  [16]. Although these point estimates seem rather consistently low, their usefulness remains limited as there is no information on their confidence intervals, and no sensitivity analysis to uncertainties in the entomological and epidemiological raw data has been performed.
We first used this framework to re-analyze the data from  [11] , which provides entomological (vector abundance, infection rate, feeding frequency and the proportion of blood meals on humans) and epidemiological (incidence of infection in humans) data at the household scale. Next, we adjusted our model to estimate the probability of transmission to humans (i) when entomological and/ or epidemiological data are available at the village rather than at the household scale, and (ii) when epidemiological data consist of human prevalence rather than incidence. These adjustments allowed estimating the probability of stercorarian transmission from four additional datasets. We then performed sensitivity analysis of each estimate to the uncertainties in the measurement of the entomological variables.
Sensitivity analysis of the estimate of the transmission probability The assessment of the probability of transmission T using the indirect approaches described above relies on the estimates of the various quantities appearing in equations 1-3. Since they all are subject to estimation uncertainty (Table  1 ), we performed a sensitivity analysis to such uncertainties in P vj ,b j , F j and N vj by determining the distribution of T estimates that resulted from variations in the raw data. The distributions of plausible estimates were obtained by randomly sampling 1000 values of P vj ,b j and F j in independent zero-truncated normal distributions with mean and standard deviation estimated from the data (Table  1 ). When no information on the variability in measurements was reported by the authors, we used the standard deviation calculated from dataset 2, as this dataset provides the most comprehensive information about these three parameters.
When species-specific information was missing, species-aggregated estimates were used. Since the average values of these parameters were typically larger than 5% (Table  1 ), sampling from an exact zero truncated binomial distribution would not change the results of the sensitivity analysis. The sensitivity analysis to the density of vectors, N vj , was performed in a slightly different manner. Since bug collection tends to underestimate vector densities, a correction factor is usually applied to estimate actual vector densities according to the efficacy of the particular collection method. Two different methods were used in these studies; M1 -timed-manual collection with insecticide spraying or aerosol to kill or dislodge triatomines (datasets 1-4), and M2 -passive surveillance by inhabitants or community participation (dataset 5).
The correction factor of M1 and M2 have been previously estimated to be around 2. 5  [11]  and  10 [21] , respectively. The sensitivity analysis of the estimate of T to variations in N vj was performed by varying the correction factor according to a uniform distribution within a range defined as its value 61. 5. This range was chosen as the maximum possible range to guarantee that samples collected with M1 reflected at least the density of bugs actually found (i. e. for the lowest correction factor to be 2. 5-1. 5 = 1), while keeping the mean of the correction factor equals to the value of 2. 5 that was inferred from field data  [11]. For consistency the same range was applied to other collection methods (see below for the definition of a last method -M3 -used for datasets that allowed evaluation of the model predictive ability).
Predicting human prevalence We then explored the usefulness of our models and our best estimate of the probability of transmission T (i. e. from dataset 1, see results) to predict the prevalence of infection in humans. From the literature, we selected nine additional studies (Table  2 ) reporting data on triatomine density and infection rates, as well as human prevalence of infection, so that predictions of human prevalence could be derived and compared with the observed values. As for the sensitivity analyses above, uncertainties in the raw data were taken into account for the predictions of prevalence of infection in humans. We first constructed a distribution of the number of potentially infectious contacts per human (C j ) accounting for the variability in P vj ,b j , F j and N vj , according to the same zero-truncated normal and uniform distributions as described for the estimates of T.
The standard deviations of the zero-truncated normal distributions were again estimated from the data whenever possible, or we used the standard deviation calculated from the most comprehensive dataset (dataset 2). The range of the uniform distribution was determined according to the Box 1: Models assumptions, applicability and associated caveats. Case 1. When entomological and epidemiological information are available at the household scale, a likelihood estimation is possible. These data include the abundance of vectors (N vj ), the prevalence of infection in vectors (P vj ), the vector biting rate (b j ), the feeding rate on humans (F j ) and the number of individuals in the household (N hj ). The analysis relies on equations 1-4 assuming that: i) All parameters used in the calculation of the risk per household, r j,t , are determined without errors and are constant during the period over which the incidence is measured.
e. highly heterogeneous distribution of vectors, see Appendix S2 for details). Case 3. When information on human incidence is not available, we use a catalytic model to infer incidence values from either prevalence in the total population (equation 6) or prevalence per age class (equation 7). Using this approach we assumed, in addition to all assumptions above, that: v) The human mortality is constant across ages. vi) The entomological observations made at time t must reflect the conditions in which the prevalence was measured. For instance, if using prevalence data of human under 20 years old, then entomological data are assumed to have been constant during the 20 previous years. This last assumption is critical, and to limit potential bias, prevalence in younger groups should be used to best reflect the contemporary entomologic conditions (see table  2 ).