dataesr/scientific-paragraphs-categorization huggingface dataset:
e. low number of low-frequency alleles. Moreover, the magnitude and timing of past changes in population size was inferred from the analysis of ITS2 mismatch distributions with ARLEQUIN v 3. 11  [33]. Mismatch distribution were calculated for the whole ITS2 data set and then for several partitioning of the ITS2 data set: (Chile, New Zealand, Western New Zealand, Eastern New Zealand (excluding Chatham Island) and Chatham Island alone). First, Fu's Fs  [42]  and Ramos-Onsins and Rozas's [43] R 2 statistic were estimated and significant departure from mutation-drift equilibrium was tested by 10,000 bootstrap replicates using DnaSP version 5. 10. 01  [44]. Second, the mismatch distribution of each partition was compared to a distribution expected under a model of spatial population expansion by calculating the sum of squared differences between observed and expected distributions and the Harpending's raggedness index  [45].
Genetic diversity Over the whole dataset (592 individuals), nuclear microsatellite loci displayed moderate to high numbers of alleles, ranging from three to 22 for the loci 6C7 and 7D3, respectively. For the 201 ITS2 sequences, characterised by 24 polymorphic sites and four indels, 21 ribotypes were identified. The most genetically diverse population was found in Chatham Island (Table  S1 ). Observed heterozygosity (Ho) and Allele richness (Ae) were significantly higher in New Zealand than in Chile (Mann and Withney, P,0. 05) but no significant differences were encountered for expected heterozygosity (He) and ribotype richness (Re) (Table  S1 ). Allelic and ribotypic richness in Chile (Ae50. 95¡0. 42 and Re50. 52¡0. 37, Table  S1 ) are half of those observed in New Zealand (Ae51. 76¡0. 69 and Re50.
chilensis sequences from Chile deposited in GenBank  [27, 28]. No significant linkage disequilibrium was found among the five microsatellite loci. Most of the nineteen sampled populations were at H-W equilibrium except two populations from Chile and four populations from New Zealand (Table  S1 ). In the population NZ-MOU, the strong heterozygote excess associated with a high number of repeated multilocus genotypes suggests the occurrence of asexual reproduction. The repeated genotypes of this population were thus removed for the analysis of the genetic structure to avoid erroneous individual assignation and flawed reconstruction of topological relationships among populations. The CH-MOL population also displayed numerous repeated genotypes but this was associated with a very low number of alleles at all loci (Table  S1 ).
Genetic structure We found strong evidence for genetic divergence between New Zealand and Chilean populations supported by both model based clustering and graph theory analyses of microsatellite data (Figure  2A  and 2B ). First, there was a remarkable lack of admixture among populations (Figure  2A ). All individuals within a given population were assigned to the same cluster, except for the NZ-PGB population in New Zealand. Second, the topology of the network has 42 edges with two visually identifiable sub-graphs corresponding to the different sides of the South Pacific Ocean (Figure  2B ). The two connections between CH-LEN (Chile) and NZ-CHT and NZ-MOU (New Zealand) form the only link between the two continents revealing significant genetic differentiation between New Zealand and Chile (Figure  2B ; P,0.
0001). Only the POPULATION GRAPH resulting from the exclusion of CH-LEN population located in the Araucanian region reveals two distinct disconnected sub-graphs suggesting that this region could be the unique source of transpacific connectivity. In addition, the Araucanian region hosts the three most genetically diverse populations in Chile for both allele and haplotype richness (i. e. CH-DIC, CH-LEN and CH-TUB; Table  S1 ). In the west Pacific area,   [37] , proportions of individual multilocus genotypes were assigned to each of the 5 virtual genetic clusters indicated by the different colours; (B) based on graph analysis with POPULATION GRAPH software  [40] , differences in node size reflect differences in within population variability whereas edge lengths represent the genetic variation between pairs of samples, there were significantly more edges within New Zealand and Chilean groups than between the two groups (P,0.
Bottleneck test using microsatellite data We found evidence of a bottleneck in Chile where rare alleles (alleles with frequency ,5%) were 10 times less common than in New Zealand (Figure  3 ). New Zealand populations as a whole were near mutation drift equilibrium as revealed by the distribution of allele frequency following the characteristic Lshaped distribution (Figure  3 ). The distribution of allele frequencies in Chile clearly differed from New Zealand even if it did not exactly fit the mode-shifted distribution described by Luikart et al. [41]. However, when the analysis was carried out on each population separately, half of the Chilean populations showed a clear mode-shifted distribution of allele frequencies (5 of the 11 sampled, CH-LEN, CH-NIE, CH-MET, CH-MAU and CH-RMA) while all populations from New Zealand had an L-shaped distribution.
Discussion Gracilaria Chilensis a non-native species in chile: recent range expansion from new zealand Both rbcL and ITS2 data confirmed that specimens of G. chilensis found in New Zealand and Chile are conspecific  [25] , and revealed that G. chilensis, originally described in Chile  [48] , is paradoxically not native to this country. Several lines of evidence support the hypothesis that G. chilensis arrived in Chile as a result of recent trans-oceanic long-distance dispersal from New Zealand. Firstly, G. chilensis is the unique representative species of Gracilaria on the Chilean coast (  [25] , this study) whereas four native species are recorded for both New Zealand and southern and southeastern Australia  [49, 50, 51]. Secondly, the lack of molecular divergence between sequences from New Zealand and Chile (rbcL: only one mitotype encountered; ITS2: the same common ribotype r1 found in New Zealand and Chile) supports the hypothesis of a recent co-ancestry.
This research was supported by the  European Research Cooperation for Development INCO-DEV Programme  ( INCO-EPIFIGHT ICA4-CT-2001-10021 ), research grants IDEALG '' Seaweed biotechnology and bioresources: a french challenge ''  ANR-10-BTBR-04-02  from the  French National Research Agency, CONICYT, Comision Nacional de Investigacion Cientı ´fica y Tecnologica ,  Gobierno de Chile  ( Fondo Nacional de Desarrollo Cientı ´fico y Tecnologico FONDECYT   N˚1090360  and  N˚1130868 ) and  Iniciativa Cientifica Milenio  ( ICM P10-033F ). Additional support came from the  European Network of Excellence 'Marine Genomics' (Algal Node ), from the 'ARCUS' programme of the  French Ministry of Foreign Affairs ,  International Research Network ''Diversity, Evolution and Biotechnology of Marine Algae''  ( GDRI N˚0803 ),  National Institute of Water and Atmospheric Research (NIWA) Coasts & Oceans CORE funding  ( COBR1401 ).
The leishmaniases are neglected tropical diseases of significant public health importance. However, information on their global occurrence is disparate and sparse. This database represents an attempt to collate reported leishmaniasis occurrences from 1960 to 2012. Methodology for the collection of data from the literature, abstraction of case locations and data processing procedures are described here. In addition, strain archives and online data resources were accessed. A total of 12,563 spatially and temporally unique occurrences of both cutaneous and visceral leishmaniasis comprise the database, ranging in geographic scale from villages to states. These data can be used for a variety of mapping and spatial analyses covering multiple resolutions.
Background & Summary The leishmaniases cause a range of clinical symptoms, from cutaneous lesions to visceral, often fatal, complications  1. Considered as one of the 'neglected tropical diseases,' knowledge of this disease is relatively poor, particularly when compared to conditions with similar burden profiles  2, 3. Indeed, the real burden of this disease remains unknown  4. Case survey data such as that presented by Alvar et al. 5  , whilst informative, is prone to biases associated with reporting and national healthcare provisioning. Alternate methods should be employed in conjunction with such data to compensate for these issues and provide different approaches for estimating disease distribution and burden. An example of mapping the leishmaniases is presented in Pigott et al. 6  where the authors were able to estimate the current global distribution of these diseases by using the database of 12,563 unique records of occurrence presented here and statistical modelling to produce a pixel-based assessment of the likelihood of disease occurrence.
This database comprises occurrence records from 1960 to 2012, and represents a significant expansion in data compared to existing databases  7. Database creation and management procedures are outlined below, along with a description of the final database structure. Information for accessing the full database is provided here as well in order to (a) allow for replication of the Pigott et al. 6  study, (b) enable the maps to be improved upon as new modelling methods and datasets become available, (c) provide an additional data source for both local-scale and regional mapping studies and (d) provide information for public health organisations on leishmaniasis in specific regions. All data files are available from Dryad (Data Citation 1).
Data collection PubMed and Web of Knowledge were searched using the keyword 'leish*' for all articles dating up until December 2012. Abstracts for all articles were imported into a bibliographic referencing tool and assessed for relevance, removing articles that did not contain information relating to disease occurrence. From these searches, 4,845 articles were identified and the corresponding texts obtained in full, where possible. Leishmaniasis occurrence, defined as a report indicating one or more confirmed cases of leishmaniasis, within a specific administrative unit or 5 × 5 km pixel in a given calendar year, was divided into two categories representing the two main forms of the disease: cutaneous (including cases of localised cutaneous leishmaniasis, diffuse cutaneous leishmaniasis and mucosal leishmaniasis) and visceral leishmaniasis (including cases of post-kala-azar-dermal leishmaniasis (PKDL)).
Only autochthonous symptomatic cases were included; wherever possible, cases that were imported were traced back to the original source of infection, otherwise they were excluded. If reports of PKDL also included information on prior VL infection, the latter was recorded; if not, the PKDL case was registered in the database as a VL occurrence. Similarly, only confirmed cases were included. Diagnoses via PCR or parasite cultures, serological tests or microscopic identification were included, as were articles indicating non-specific 'laboratory diagnosis'. In addition to this, information was made available from the strain archives of the Centre National de Référence des Leishmanioses (CNR-L) at the Montpellier University Hospital Centre, France. In total, information from 3,465 strains isolated from humans was provided, collected between 1954 and 2013 (in the final database this was restricted to 1960-2012 to be consistent with the literature searches).
These strains have been collected by various groups from around the world, cryopreserved in the International Biological Resources Centre of Leishmania, Montpellier, France and have subsequently undergone isoenzymatic identification  8, 9. In addition, information from GenBank was extracted by searching for Leishmania spp. known to cause disease in humans  10. Informal online information sources, including online news articles and ProMED-mail reports  11  , were provided by HealthMap (http://healthmap. org)  12. The automated system systematically searches various news aggregators, open mailing lists, electronic disease surveillance networks and public health outbreak report feeds. The HealthMap classification system parses out disease and disease-related keywords from the body of these articles as well as classifying the report by one of five classifications-breaking (i.
e. , information relating to an ongoing outbreak), context (an article supplying background information on the disease, or relating to policy or research articles), warning (articles that suggest an outbreak or unusual case load is likely in the near future), not-disease related (false-positives resulting from the disease term classification system), and old news (referencing previous outbreaks). Duplicate articles, such as identical reports issued by different news services, are identified and processed by the system by aggregating contemporary articles together and checking for similarities. Later human review of the automated assignments ensures data quality and identifies opportunities to improve the automated process. Articles included in our dataset were those categorised as 'breaking' with the disease tag 'leishmaniasis' and totalled some 109 reports.
Geo-positioning of data Each article sourced from the literature search was read and the geographic coordinates of the cases described in each article manually extracted. In many cases, either due to multiple places sharing the same name or to differences in spelling of place names (particularly when translating between different languages), additional contextual information from the article was required for accurate positioning. Each case was reported to the highest degree of spatial resolution available based upon the information provided. This ranged from point locations (indicative of a precise location, such as a village), to areas, termed polygon locations, which correspond approximately to districts (specifically an Admin 2 unit as classified by the Food and Agriculture Organization's Global Administrative Unit Layers (GAUL) coding  13  ), and areas corresponding to states or provinces (the first national subdivision, GAUL Admin 1).
For cities or towns, the coordinates of the centre were recorded, unless a specific part of the city (or an explicit latitude and longitude) was described. In the case of district and provincial level data, the approximate centroid of the polygon (obtained using Google Maps; https://maps. google. co. uk) was recorded, to allow for consistent identification in subsequent analyses. Montpellier's CNR-L strain archive has geographic tags associated with it (provided by the original survey teams), and those that included at least a sub-national identifier (ranging from villages to provinces) were geo-positioned using Google Maps by the same rules as with the literaturesourced occurrences. GenBank archived material that had associated spatial tags was similarly geopositioned. HealthMap automatically geopositions the data it parses using a custom-built gazetteer  12.
An algorithm searches for matches between the online article and language-specific terms linked to locations in the geographic reference database, and therefore can identify potential location tags. These matches are then evaluated by a ruleset that attempts to determine the relevance of each location tag (i. e. , to distinguish publication relevant tags from outbreak and disease relevant locations) by assessing the number and position of the location keywords within the text. Certain common misleading phrases, such as 'Brazil nut' and 'guinea pig' are also accounted for in this phase.
Occurrence standardisation As the occurrence database was derived from a wide range of disparate sources, we attempted to standardise the occurrence reports both spatially and temporally. Firstly, using the centroid coordinates, all polygon-level data was assigned to a specific admin unit (as defined by GAUL). Point-level data was similarly aligned to a 5 × 5 km pixel gridded surface of the world. For occurrences that spanned several years, these were disaggregated so that a location that reported cases from 2000 to 2003 (for example) would represent four separate occurrences, one for each year. Occurrence records which were duplicates in both space (either per pixel or per polygon) and time (per calendar year) were removed from the database thus removing the potential for duplicated reports of the same cases.
As such, a unique record in the database is defined as the occurrence of one or more cases in a given location in a specific year. Therefore, irrespective of the number of cases or reports that occur within a given polygon or pixel in a given year, they are summarised as one unique occurrence, i. e. , an area with 500 cases in a given year is equivalent to an area with 5 cases. By focussing on using unique occurrences, we avoided issues of bias associated with oversampling caused by higher reporting in one location over another, and thus gather a more accurate picture of the global distribution of these diseases, which is particularly important when using presence-absence based modelling techniques.
Data Records The database associated with this article (available via Dryad (Data Citation 1) contains the following fields: 1. OCCURRENCE_ID: a unique identifier assigned to each unique occurrence of disease. 2. SOURCE_TYPE: indicating whether sourced from literature, Montpellier CNR-L strain archives, GenBank or HealthMap. 3. LOCATION_TYPE: indicative of point or polygon level data. 4. ADMIN_LEVEL: the administrative level associated with the occurrence. Values are 1 (state or province), 2 (district) and -999 (point). 5. X: the longitudinal coordinate of the point or polygon centroid in decimal degrees (WGS1984 Datum). 6. Y: the latitudinal coordinate of the point or polygon centroid in decimal degrees (WGS1984 Datum). 7. YEAR: the year of occurrence. 8. COUNTRY: the name of the country within which the occurrence lies.
Technical Validation All occurrences were compared to standardised 5 × 5 km pixel grids of the world and checked that they fell on land. Those not on land were automatically repositioned to the nearest land pixel. In addition, all occurrences were referenced with the evidence consensus map produced by Pigott et al. 6  This map is a quantitative measure of the consensus of various sources of evidence for the presence or absence of leishmaniasis within that province, scored on a scale from +100 to -100. Importantly, the dataset used to generate the evidence consensus scores included a wider range of data sources (e. g. , health organisation statuses and national-level case data) and included data that did not meet our geographic precision inclusion criteria for the occurrence dataset as described below.
Occurrences that fell in regions where there was a consensus on disease absence (between -25 and -100) were manually double-checked by the authors to ensure that the latitude and longitude were correct and that the corresponding articles reported a genuine occurrence of leishmaniasis. Finally, any polygon greater in area than one squared degree was removed from the database, as the generalisation of modelling covariates at this scale would be misleading and present a possible source of bias in the model. The final validated database consisted of 12,563 unique occurrences (6,426 for cutaneous leishmaniasis and 6,137 for visceral leishmaniasis) with source locations and occurrence types listed in Table  1. Figs  1  and 2  show the cumulative distributions of unique occurrence records of cutaneous and visceral leishmaniasis over time, subdivided by continent.
Usage Notes This dataset can be used to investigate the spatial epidemiology of leishmaniasis at a range of scales, from sub-national studies to regional assessments. In addition, the framework outlined here can easily be extended to a variety of other diseases, as has already been demonstrated with dengue  14  , and given the potential to automate some of these methods further  15  , the entire process should become increasingly easier and timely. An initial analysis of this dataset was within a niche modelling framework assessing the global distribution of the leishmaniases  6. The code used to carry out technical validation of the dataset and to generate the predictive risk maps presented in Pigott et al. 6  is freely available as an R software package seegSDM from GitHub (https:// github.
com/SEEG-Oxford/seegSDM) and is accompanied by a tutorial for its use. This dataset could be used to assess regional and national variation in disease reporting rates by comparing the density and distribution of occurrence data with existing estimates of case numbers. In areas where there is a lack of regular reporting, occurrences may indicate a potential cryptic burden of disease. Since much of this data is derived independent of centralised healthcare provisioning, it can act as a secondary indicator of disease presence. In addition, the data can be considered in the context of burden estimation, particularly in helping to delineate the spatial limits of leishmaniasis risk. Despite its comparative ubiquity with respect to other forms of epidemiological data, such as disease prevalence, one of the main limitations of disease occurrence data is the potential for sampling bias  16, 17  , where disease occurrence is more likely to be reported from some areas (e.
Acknowledgements D. M. P. is funded by a  Sir Richard Southwood Graduate Scholarship  from the  Department of Zoology at the University of Oxford. S. I. H. is funded by a  Senior Research Fellowship  from the  Wellcome trust  ( 095066 ) which also supports K. A. D. and K. E. B. , N. G. is funded by a grant from the  Bill & Melinda gates Foundation  (# OPP1053338 ). J. P. M. is funded by, and S. I. H. acknowledges the support of, the  International Research Consortium on Dengue Risk Assessment Management and Surveillance (IDAMS, European Commission 7th Framework Programme  (# 21803 ) http://www. idams. eu). Y. B. , F. P. and P. B. acknowledge financial support by the  French Institut de Veille Sanitaire (InVS). J. S. B. , S. R. M. and C. F. acknowledge the funding from  NIH National Library of Medicine  ( R01LM010812 ).
google. com/site/bctnet) offers a wide variety of functions in Matlab and C that calculate most of these measures. The reported study by  Weisz et al. (2014)  provides first evidence for the Win2Con framework. Different sensory areas (here somatosensory regions) are differentially integrated within a network prior to the relevant stimulus, rendering its perception more or less likely. As proposed in the framework, local alpha modifications predicting the perception of an upcoming event (as suggested in the functional inhibition hypothesis) appear to depend on the level of integration between the region showing alpha modulations and the relevant network. In this sense, local excitability is necessary but not sufficient for conscious perception. Pre-established pathways within the relevant network are another prerequisite for NT stimuli to become consciously accessible.
velutina behavior during the tests. The cage was illuminated with red light by means of an incandescent light bulb. Red light allowed filming the hornets while precluding their use of visual cues during the experiments, since, as shown for the closely related species V. crabro, hornets lack red-light sensitive photoreceptors  [56]. The Virtualdub software (http://www. virtualdub. org) was used for recording high quality video at a frame rate of 22 fps.