dataesr/scientific-paragraphs-categorization huggingface dataset:
Consequently, we envisage that an important first step in better characterising spatiotemporal heterogeneity will be to map and visualise anthelminthic responses (Table  1 ), inspired by   46. This would provide an overview of the current geographical picture of anthelmintic responses, and spur more detailed analysis of the IPD hosted by the platform to identify drivers of geographical and temporal heterogeneity, particularly with respect to PC history. This process can begin now with existing submissions to the IDDO platform and be updated as new data are contributed.
The IDDO platform intends to facilitate the development of a standard case reporting form (CRF) to foster increased standardisation of protocols and reporting for clinical studies on SCH and STHs (Table  1 ). This will be achieved through engagement with the respective research communities and will be informed by current and future contributions to the platform. The CRF will also integrate the principles of the Clinical Data Interchange Standards Consortium (CDISC) clinical data standards that the platform is using to curate data contributions, further enhancing data interpretability, clarity and interoperability.
Analytical approaches The WHO provides guidance on analysis protocols for calculating anthelmintic efficacy as ERRs on a population-level basis  90. These approaches, however, do not extend to IPD when incorporating covariates of the treatment response. A number of new methods have been proposed in both the human  [19] [20] [21] [22] 121  and veterinary  122, 123  domains for analysing IPD, but there remain no standardised or consensus approach and many unresolved technical questions. For example, how quantitative expressions of the distribution of drug responses among individuals (e. g. , percentage of individuals with a 'satisfactory' or 'reduced' response) relate to more traditional population-level summaries (e. g. , average ERR or cure rate), how accurately can individual suboptimal (or 'reduced') responses be identified, and importantly, be distinguished from response variation not associated with decreased efficacy? should responses measured using (multiple) molecular diagnostics be integrated into analytical frameworks? These q estions will not be answered by the sharing of data per se, but the assemblage of an abundance of IPD will provide the raw material to test different analytical approaches.
This is an excellent article on the subject area of schistosomiasis and soil-transmitted helminthiasis control and elimination involving ensuring decisions, recommendations, and guidelines are driven by strong evidential data. The Infectious Diseases Data Observatory (IDDO) will contribute in no small way to informing the thinking around global technical and priority topics for further research and implementation under schistosomiasis and soil-transmitted helminthiasis by institutions, organizations, and partners including the WHO, and therefore the research agenda. Information from this platform will be most useful to technical advisory groups including the WHO technical advisory group on schistosomiasis and soil-transmitted helminthiasis. The article clearly outlines and articulates a clear agenda and processes to enable the achievement of its goal of establishing a scientific engagement process involving a diverse group of research partners whose efforts are focused on schistosomiasis and soil-transmitted helminthiasis.
The role of the WHO in providing technical leadership in working with countries should however be acknowledged somewhere in the paper. The gaps and the methodology for identifying these gaps are well discussed. However, the paper makes little reference to discussions or information on the currently available schistosomiasis and soil-transmitted helminthiasis subject matter experts, research partners, observatories, data repositories, and other data platforms within this space, and what they do. This can be mentioned without going into great detail about what these platforms offer and their potential role in supporting the research agenda and IDDO. These could be identified and briefly touched on to provide more context with specific examples. The utility and potential for interactions and interoperability between IDDO and other platforms though not a key objective of this paper could serve as an interesting area to be considered.
Noting that the highest burden of schistosomiasis and soil-transmitted helminthiasis occurs in Africa, the ESPEN data portal, and also Global Atlas of Helminth Infection (GAHI), and how these platforms will interact with IDDO will provide more clarity on the functionality and purpose of IDDO while dispelling any views of potential duplication. The value of routine reporting data from country NTD programs in Africa in particular with the highest disease burden and research needs and the role of the Joint Application Package (JAP) for the Preventive Chemotherapy Neglected Tropical Disease (PC-NTDs) reporting including research data has not been captured in the paper. This, in my estimation, could throw some light on the gaps in available or currently used data collection tools employed by countries.
In conclusion, this is a brilliant paper with all the expected elements required for the establishment of the IDDO using individual participant data (IPD). The paper is written in clear technical but easy-to-understand language. Additional comments made may be considered if it is convincing enough which I will leave to the discretion of the writers and editors, however, the article should be accepted for indexing with or without addressing these comments. This review describes the priority research themes of the clinical data platform of the Infectious Diseases Data Observatory. This is an extremely important initiative that aims to promote research and provide a framework for meaningful data sharing and research collaborations aiming to eliminate NTDs. The paper is extremely well written and will be a valuable resource for NTD workers.
Acknowledgements We thank the following people for providing feedback on the content of this manuscript:  Professor Daniel G Colley ,  University of Georgia ;  Dr Thomas Crellen ,  Big Data Institute, University of Oxford ;  Dr Julia Dunn ,  Clinton Health Access Initiative ;  Dr Cristin Fergus ,  Firoz Lalji Institute for Africa, London School of Economics and Political Science ;  Mr Amadou Jallow ,  Ministry of Health , The Gambia;  Dr William Lin ,  EJD Global Health ;  Mr Bakary Sanneh ,  Ministry of Health , The Gambia;  Dr Anthony Solomon ,  World Health Organization.
MW acknowledges funding from  Research England's Connecting Capabilities Fund (Bloomsbury SET  [https://bloomsburyset. org. uk/]; BSA34 ). This work was supported by  Wellcome  ( 208378 , https://doi. org/10. 35802/208378 ; a  Wellcome Biomedical Resources  grant to MW, LTF, JBH, MB and PJG for the Infectious Diseases Data Observatory [https://www. iddo. org/]). MGB acknowledges funding from the  Medical Research Council (MRC)   Centre for Global Infectious Disease Analysis  ( MR/R015600/1 ), jointly funded by the  UK MRC  and the  UK Foreign, Commonwealth & Development Office (FCDO) , under the  MRC/FCDO  Concordat agreement and is also part of the  European and Developing Countries Clinical Trials Partnership  ( EDCTP2 ) programme supported by the  European Union. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
e. , Curculioniphilus buchneri, Wolbachia and Rickettsia) were then sequenced (30 clones; see Additional file 1). We characterized the bacteria hosted by Curculio females by performing phylogenetic analyses using the sequences obtained in this study together with the most similar ones found by Blast in Genbank. Multiple sequences were aligned using MUSCLE software  [46]. The appropriate model of evolution was estimated with jmodeltest  [47]  for each set of sequences considered. Phylogenetic analyses were performed using maximum likelihood (ML) inference with Phyml v3. 0. 1  [48]. The  models selected were GTR + G for Rickettsia (16S, 873 sites) as well as for Spiroplasma (16S + ITS, 1405 sites), HKY85 + I for Serratia (16S, 503 sites), K80 + I for Wolbachia (16S, 340 sites). The robustness of the nodes was assessed with 100 bootstrap replicates.
Additionally, Bayesian phylogenies were performed with MrBayes v3. 1. 2 [49]  using appropriate parameters leading to convergence between two runs. We then compared topologies obtained with the two methods using approximate unbiased (AU)  [50]  and Shimodaira and Hasegawa (SH)  [51]  tests available in the CONSEL program package  [52]. No significant difference was detected between the two topologies for any of the bacteria. To examine the possible co-diversification between Curculioniphilus buchneri, considered as the primary symbiont, and its hosts, we used the method previously described to build the phylogenies based on COI gene sequences retrieved from Genbank for the weevil phylogeny, and on two distinct markers, the 16S rRNA sequence and the GroEL gene, for the bacterial phylogeny. Nine host-symbiont pairs were included in the analysis, among which the four Curculio species studied here, the hazelnut weevil C.
A single clone among 1,358 ones was identified as a Sodalis-like endosymbiont (97,77% of similarity with the Sodalis strain found in C. sikkimensis). It was detected in the pool of C. elephas females sampled at the site 2, and could be a posteriori detected by means of individual PCR diagnostic in one of the 16 females of this pool. We could not detect this symbiont in any of the 562 oak weevils individually screened with the same method. Consequently, we conducted phylogenetic analyses on the five endosymbionts identified (i. e. Curculioniphilus, Wolbachia strains 1 and 2, Rickettsia, Spiroplasma) (Figures  1  and 2 ). For the Curculioniphilus endosymbiont, four distinct sequences were obtained, one for each weevil species. We found statistical evidence of global co-diversification between weevil hosts and their primary symbiont on each dataset (P = 0.
002 for COI-GroEL comparison, P = 0. 005 for COI-16S comparison). In addition, the association of seven of the nine host-symbiont pairs individually tested was found significant (see Figure  1 ). The insignificant associations corresponded to Curculio elephas and C. dentipes with their symbionts, and could be explained by their weakly supported positions in the phylogenetic trees generated either by bootstrap or by Bayesian posterior probabilities. This finding suggests co-diversification between Curculioniphilus and its weevil hosts and implies that this endosymbiont has been acquired by a common ancestor of Curculio spp. and subsequently strictly vertically transmitted. The Wolbachia 16 s rRNA sequences revealed at least two distinct strains. The corresponding phylogeny (Figure  2A ) indicates that one of them belongs to the Wolbachia supergroup A while the other one seems rather positioned at the edge of the Wolbachia supergroup B, according to the way Wolbachia sequences are annotated in Genbank.
While we detected Rickettsia in all four Phylogenies of the Curculio species and their primary symbiont. The phylogeny of Curculioniphilus is based on the GroEL gene, as its topology was better resolved than that obtained with the 16S rRNA sequence. The Curculio phylogeny is based on a 375 bp sequence of the COI gene. The Bayesian trees are shown, as the Maximum-likelihood trees (not shown) exhibit substantially the same topology (see Methods). The bootstrap values for the maximum-likelihood analysis (100 replicates) and the Bayesian posterior probabilities are shown above and below the nodes only if greater than 50 and 0. 50, respectively. The accession numbers of the nucleotide sequences are shown in brackets. Sequences indicated with asterisks were obtained in this study, the other sequences being retrieved from Genbank.
Figure1Phylogenies of the Curculio species and their primary symbiont. The phylogeny of Curculioniphilus is based on the GroEL gene, as its topology was better resolved than that obtained with the 16S rRNA sequence. The Curculio phylogeny is based on a 375 bp sequence of the COI gene. The Bayesian trees are shown, as the Maximum-likelihood trees (not shown) exhibit substantially the same topology (see Methods). The bootstrap values for the maximum-likelihood analysis (100 replicates) and the Bayesian posterior probabilities are shown above and below the nodes only if greater than 50 and 0. 50, respectively. The accession numbers of the nucleotide sequences are shown in brackets. Sequences indicated with asterisks were obtained in this study, the other sequences being retrieved from Genbank.
Acknowledgements We thank  P. F. Pélisson ,  B. Rey  and  F. Débias  for their help on insect community surveys, and  S. Balmand ,  J. Blugeot  and  C. Vincent-Monégat  for their contribution to the molecular and phylogenetic analyses. We are also grateful to  D. Charif  for her statistical advices and we thank two anonymous reviewers that helped us improving an earlier draft of the manuscript. This work was funded by the  French National Research Agency (ANR :  CoCoReCo  project  JC09-470585 ), by the  CNRS , and by  Envirhonalp ('Sante' et Environnement' Environment Cluster ).
8 52 [45] Spixo-16S R TCTGGCATTGCCAACTCTC [45] Sodalis 16S rRNA Sodalis 370 F CGRTRGCGTTAAYAGCGC 0. 2 55 [38] 16SSod590R AACAGACCGCCTGCGTACG [38] *Annealing temperature. † References: (*) this study; (**) nucleotides numbering from Escherichia coli, GenBank accession number J01859.
HIV/AIDS is not a neglected disease. Histoplasmosis is not considered a neglected disease in North America. However, in South America, it should be. It often affects neglected populations and represents a lethal blind spot of the HIV/AIDS data collection systems. Counts of new AIDS cases and AIDS-related deaths are useful to follow the epidemic; however, they overlook the exact cause of death. In the context of the South American pathogen ecology, the systemic mycosis due to Histoplasma capsulatum var. capsulatum is probably on the top of the list of AIDSdefining illnesses and AIDS-related deaths  [1] , yet it is mostly undiagnosed and is not even on the diagnostic algorithm used by a significant proportion of clinicians facing a febrile, severely immunodepressed patient in the region.
During the first four MCs, it is based on ACE observations, but during the last event, 14 April 2006, data are taken from Wind measurements. However, when Wind or OMNI data are also available, they may be used as a means of comparison. An example of observations from the four data sets mentioned above, ACE, Wind and Cluster 1 observations and the 1 min-resolution OMNI data which correspond here to ACE data propagated to the BS nose, is shown in Fig. 2. It displays magnetic field measurements during the 15 h of the 27 January 2003 MC when the M A was low. ACE and Wind measurements have been shifted in time by respectively 45 and 35 min, to fit Cluster 1 time. The IMF direction is given in the GSE frame to which we associated a spherical coordinate system. θ is here the colatitude, ranging between 0 and 180 • and ϕ the longitude, ranging between -180 and 180 •.
The three solar wind data sets (i. e. ACE, Wind and OMNI) display very similar features. As for Cluster 1, it encounters the shock 15 times, and alternately moves from solar wind to the magnetosheath. The shock crossings take place at each sharp variation of the IMF amplitude shown in the first panel of Fig. 2. When Cluster 1 observes an IMF strength several times higher than the other spacecraft, it is located in the magnetosheath. However, whenever Cluster 1 is in the solar wind, it appears clearly that the IMF amplitude and direction are similar on the four data sets. In the following, we use data from the ACE Magnetic Field Experiment (MAG) and Solar Wind Electron Proton Alpha Monitor (SWEPAM)  (Smith et al. , 1998; McComas et al. , 1998)  as inputs for the BS models. These data are shifted in time from L1 to the BS, assuming that the solar wind propagates along the Sun-Earth line at a constant speed and that the structures do not change during their propagation.
For each event, we determine the time shift that will be applied to the entire data set using the measured solar wind speed averaged over the whole event. On the basis of the maximum and minimum speed observed during the interval, we estimated that using a constant time shift throughout an entire event leads to errors of ±5 min on the propagation time. As our data are averaged over 5 min, a constant time shift is a reasonable approximation. As mentioned in Sect. 2. 1, the Wu00 model depends on the magnetosonic Mach number, whereas the other models take into account the Alfvén Mach number. The M ms is calculated using ACE radial proton temperature measurements and assuming an isotropic temperature. Whenever Wind data were also available near L1, it has been checked that the results are not sensitive to the electron temperature.
Acknowledgements. The authors thank  J. Merka  for fruitful discussions on bow shock models,  P. Robert  for providing the MVA software and  K. Andreeova  for her advice on normal determination. We thank the  ACE SWEPAM  and MAG instrument teams and the  CDAWeb  for providing the ACE data. We also acknowledge Cluster FGM and PEACE teams and the  Cluster Active Archive  for providing Cluster data. Topical Editor  R. Nakamura  thanks  J. Merka  and one anonymous referee for their help in evaluating this paper.
Additional corrections of coding-sequence predictions were performed by using RNAseq data and performed in a separate project. The STM3625 chromosome size is 4,132,041 bases, and plasmids are (from p1 to p6) 1,561,715, 551,965, 108,298, 87,722, 8,157, and 12,029 bp, respectively. The genome has a GϩC content average of 58. 20%. The STM3625 genome contains 1 rRNA operon, 45 tRNA genes, and 6,511 protein-coding sequences (CDSs). The symbiotic plasmid (RHI3625p2) contains two nodulation gene operons and three nodD regulators. Two copies of nodA were found, one copy in the nodA1BCSUIJHPQ operon and another copy close to fatty acid modification genes. The pSym also carries the whole nif and fix operons for symbiotic nitrogen fixation, and the symbiotic sigma factor rpoN. Nucleotide sequence accession numbers.
D-HÁ Data collection: CrysAlis CCD (Oxford Diffraction, 2009); cell refinement: CrysAlis CCD; data reduction: CrysAlis RED (Oxford Diffraction, 2009); program(s) used to solve structure: SHELXS97  (Sheldrick, 2008) ; program(s) used to refine structure: SHELXL97  (Sheldrick, 2008) ; molecular graphics: ORTEP-3 for Windows  (Farrugia, 2012)  and Mercury  (Macrae et al. , 2008) ; software used to prepare material for publication: PLATON  (Spek, 2009)  and PARST  (Nardelli, 1995). We are thankful to SSCU, IISc, India for the Oxford Diffraction facility funded under DST-FIST (Level II) and are grateful to the Durban University of Technology for facilities. KNV thanks the NRF South Africa for a DST/NRF Innovation Postdoctoral Fellowship. Supplementary data and figures for this paper are available from the IUCr electronic archives (Reference: LX2286).
Refinement All H atoms were positioned geometrically and refined using a riding model, with C-H = 0. 95 Å for aryl and 0. 99 Å for methylene H atoms. U iso (H) = 1. 2U eq (C) for aryl and methylene H atoms. (Farrugia, 2012)  and Mercury  (Macrae et al. , 2008) ; software used to prepare material for publication: PLATON  (Spek, 2009)  and PARST  (Nardelli, 1995).
We present an updated and quality controlled surface mass balance (SMB) database for the Antarctic ice sheet. Importantly, the database includes formatted metadata, such as measurement technique, elevation, time covered, etc, which allows any user to filter out the data. Here, we discard data with limited spatial and temporal representativeness, too small measurement accuracy, or lack of quality control. Applied to the database, this filtering process gives four times more reliable data than when applied to previously available databases. New data with high spatial resolution are now available over long traverses, and at low elevation in some areas. However, the quality control led to a considerable reduction in the spatial density of data in several regions, particularly over West Antarctica.
Over interior plateaus, where the SMB is low, the spatial density of measurements remains high. This quality controlled dataset was compared to results from ERA-Interim reanalysis to assess whether field data allow us to reconstruct an accurate description of the main SMB distribution features in Antarctica. We identified large areas where data gaps impede model validation: except for very few areas (e. g. , Adelie Land), measurements in the elevation range between 200 m and 1000 m above sea level are not regularly distributed and do not allow a thorough validation of models in such regions with complex topography, where the highest scattering of SMB values is reported. Clearly, increasing the spatial density of field measurements at low elevations, in the Antarctic Peninsula and in West Antarctica is a scientific priority.
Thus, it is crucial to aggregate all available field data to better constrain interpolation techniques based on modeling or remote sensing data. Even though several methods have been developed to assess the SMB in the field (see  Eisen et al. , 2008 , for a review), direct SMB measurements are rare in Antarctica and existing ones generally span a very local area (e. g. , stake and ice core measurements). The size and remoteness of the AIS and the harsh climatic conditions make long-term investigation difficult. All available data have only been compiled once previously by  Vaughan and Russell (1997). This Antarctica database (hereafter referred to as V99) was described in detail by  Vaughan et al. (1999). The V99 database legitimately became a reference for climate studies in Antarctica and was Published by Copernicus Publications on behalf of the European Geosciences Union.
V. Favier et al. : An updated and quality controlled surface mass balance dataset regularly used for model validation (e. g. ,  Van de Berg et al. , 2006; Krinner et al. , 2007 Krinner et al. , , 2008;; Lenaerts et al. , 2012b). However, only partial updates have been undertaken since 1999 (e. g. ,  Magand et al. , 2007; Van de Berg et al. , 2006; Lenaerts et al. , 2012b) , even if important new datasets have been acquired since 1999. For instance, during the last international polar year  2007 -2008 (IPY) (IPY) , several inland traverses were performed with several scientific goals including filling the gaps in SMB measurements. In the framework of the international TASTE-IDEA programs (Trans-Antarctic Scientific Traverse Expeditions -Ice Divide of East Antarctica), isolated measurements and traverses were performed, as from Troll station to South Pole  (Anschütz et al.
, 2009) , from the Swedish Wasa station to the Japanese Syowa station  (Fujita et al. , 2011)  and along the French traverse to Dome C  (Verfaillie et al. , 2012). Based on the V99 database, several authors interpolated the SMB data to the whole AIS. The current surface accumulation value integrated over the grounded ice sheet is generally assumed to range between 143 mm w. e. a -1  (Arthern et al. , 2006)  and 168 mm w. e. a -1 (Van de  Berg et al. , 2006). These two studies are generally considered the most reliable ones:  Arthern et al. (2006)  computations included interpolation methods of remote sensed passive microwave data to accurately fit the observed SMB from the V99 database  (Monaghan et al. , 2006) , and  Van de Berg et al. (2006)  calibrated model results. However, these values should be considered with caution because a reliability check of the V99 data, as proposed by  Magand et al.