dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, valuable information exists in internal reports, which is not easily available since it is not indexed in MEDLINE and SCOPUS databases. More complete and precise information pertaining to rabies in NHPs is needed. This information could be obtained through field surveys. We believe a greater effort should be directed toward coordinating and frequently reviewing the need for rabies PEP after exposure to animal species such as NHPs that are not primary reservoirs of rabies. Information obtained in this way should be regularly collected, updated, and made available to the medical community. To this end, efforts towards greater openness and accessibility of information regarding the incidence of rabies in NHPs and its geographic distribution would provide a muchneeded basis for improving and sustaining the public health debate around the risk evaluation of rabies after human exposure to these species.
Thus, the identification of tick-borne parasites increasingly relies on molecular detection approaches. Classically, pathogen detection in ticks is performed by PCR with specific primers. These are designed to amplify conserved microbial sequences in a predefined list of pathogens known to be transmitted by the specific collected tick species, in the specific geographical area of collection. However, this method is not at all suited to detect new or unexpected pathogens  [7, 8]. In addition, because of the relative paucity of available sequence data for tick-borne parasites, most of these techniques rely on the amplification of the 18S genes which are well conserved among parasites, implying an additional sequencing step in order to identify them at the species level. Finally, the amount of available DNA in a tick sample limits such detection to a limited number of PCR tests.
High throughput sequencing and data analysis High throughput sequencing of tick pool samples was performed as previously described  [11]. Briefly, total RNA, which indicates the occurrence of viable and replicating microorganisms, and total DNA, for specific real-time PCR, were separately extracted. Wild and pathogen-free RNA samples were sequenced to a depth of 100 million and 62 million for 101 bp paired-end reads respectively. As there is no publicly available I. ricinus reference genome, we removed those sequences corresponding to the ticks themselves, or to symbiotic or commensal bacteria naturally found in ticks, by subtracting sequences homologous to sequences from the pathogen-free reference sample using the SOAP2 aligner tool. Finally, 7 787 463 remaining reads out of 70 396 392 reads initially obtained from wild ticks, were used for de novo assembly, producing 174 841 contigs.
Confirmation of parasite targets with quantitative PCR Real-time PCR was performed on DNA extracted from each pool of ticks to confirm taxonomic species assignment of NGSderived contigs. Amplification was performed as previously described  [11]  and the primers newly designed for this study, based on the 18S rDNA, hsp70 and CCTeta sequences present in GenBank, are presented in Table  1. Babesia and Theileria DNA used for positive controls were kindly provided by Huseyin Bilgic, Faculty of Vet. Med, Turkey; Laurence Malandrin, ONIRIS, France; Emmanuel Cornillot, Montpellier University, France.
Sequence analysis and phylogenic tree analysis For phylogenetic analysis, the 28S sequence data obtained via NGS (Table  2 ) were aligned and subsequently compared with parasitic species data from GenBank using the phyml v2. 4. 4 software  [13, 14]. Distance matrices were calculated using the General time reversible (GTR) model and bootstrap analysis was performed with 1000 replications  [15]. Plasmodium falciparum, a close apicomplexa was used as an out-group.
Results and Discussion To identify known, novel or unexpected parasites carried by ticks in France, I. ricinus were collected in Eastern France, a wooded region with high tick abundance. Using NGS techniques, 17 contigs were selected following the criteria previously described and are presented in Table  2. Parasites from two distinct genera were identified: Babesia spp. (13 sequences), and possibly Theileria spp. (4 sequences). Other eukaryotic sequences with significant identity to sequences present in the databank corresponded mainly to fungi (Ascomycota) and are not presented here.
These are the most highly represented parasitic sequences in GenBank, but are not the most informative in terms of species assignation. Moreover, this preliminary analysis was performed with short sequences (139-197 bp), which are not located at the same region within the 28S rDNA, therefore no definite species can be identified. Thus, further investigations are now required to clarify whether the identification of new Theileria or Babesia species in France, similar to tropical species, actually corresponds to an expanded geographical distribution of these species, and whether they have a potential pathogenic effect in mammals. Unfortunately, the absence of tick-borne parasite genome data causes difficulties in realizing such phylogenetic studies. However, in spite of the low level of robustness of theses phylogenetic analyses, our results are confirmed by other studies, in particular those demonstrating that the B.
Conclusion The inventory of parasitic RNA content in I. ricinus performed by NGS revealed the presence of expected viable parasites belonging to the Babesia genus, some of them being identified in France for the first time. However, the epidemiological relevance of these results must of course be interpreted with caution. Unfortunately, complete genomic data on tick-borne parasites is scarce, likely due to large genome complexity compared to the relatively small number of research teams in this field. In addition, their small genome size and the strong inter-species conservation of available sequences (essentially 18S rRNA), does not permit clear species identification. Moreover, unknown species with too distant alignment and the fewest database sequences could not be identified in this context.
The increased number of sequences relative to tick-borne parasites in data banks should facilitate an increase in the power of NGS techniques to detect tick-borne parasites in the future. In addition, detecting pathogenic RNA within ticks does not imply that these pathogens are actually transmitted by this arthropod. Therefore competence and epidemiological studies are also required in order to verify whether I. ricinus is implicated in the transmission of those tickborne diseases which are present or emerging in France. And finally, further studies are also required to confirm whether the unexpected Theileria species detected here is actually novel, and whether the detection of parasitic species similar to other tropical species in France, corresponds to increasing geographical species distribution.
This study was partially funded by  EU  grant  FP7-261504  EDENext and is catalogued by the EDENext Steering Committee as EDENext 097 (http://www. edenext. eu). The contents of this publication are the sole responsibility of the authors and don't necessarily reflect the views of the  European Commission. Financial aid was also provided by the  DIM Ile de France , the CoVetLab partner institutes, the  ANSES , the  Animal Health department of INRA  and the  INRA Metaprogram MEM  and the  PathoID  project. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
These coexpressions define networks (Fig. 1 ) that-even though not representative of the world's general population-represent a coarse map of the links connecting the elites that participate of these three important global forums, as social connections often require a shared language. In this paper, we map the global language networks (GLNs) expressed in three large records of linguistic expression, and use the structure of these networks to determine the degree to which each language is global. First, we look at a collection of more than 2. 2 million book translations compiled by UNESCO's Index Translationum project. This dataset allows us to map the network of book translations, which are produced by individuals with a high literary capacity (authors and professional translators) and are shaped by market forces, such as the demand for books in different languages.
Data There is no single GLN because different sets of speakers share different kinds of information across different sets of languages for different purposes. Accordingly, we map three different versions of the GLN using data from Twitter, Wikipedia, and UNESCO's Index Translationum (IT), an international index of printed book translations  (8). Going forward, we note that the resulting networks represent patterns of linguistic coexpression not among the entire human population but among the kinds of speakers and texts that contributed to the respective datasets. The populations are confined to literate speakers and, in turn, to a subset of social media users (Twitter), book translators (Index Translationum), and knowledgeable public-minded specialists (Wikipedia). Additional datasets could be used to map the language networks of other groups as long as these datasets cover the linguistic expression of a large fraction of multilingual speakers.
To that extent, monolingual resources, such as the Chinese microblogging service Sina Weibo, the Russian social network VK, or the Chinese encyclopedia Baidu Baike, do not represent resources that can be used to map connections between global languages. The elites that participate in Twitter, Wikipedia, and book translations are not representative of the entire human population, yet they still represent groups that are worthy of study because elites often drive the cultural, political, technological, and economic processes with which observers of global language patterns are concerned. We compiled our Twitter dataset from more than one billion tweets collected between December 6, 2011, and February 13, 2012. The language of each tweet was detected using the Chromium Compact Language Detector (9), after removing misleading expressions, such as URLs, hashtags, and @ mentions.
We used only tweets that the language detector identified with a certainty score higher than 90% (SI Appendix). Our final dataset consists of nearly 550 million tweets in 73 languages generated by more than 17 million unique users, which represented more than 10% of Twitter's active users at the time the data were collected. Two languages are connected when users that tweet in one language are significantly more likely to also tweet in the other language (Eq. 1). The Wikipedia dataset was compiled from the edit histories of all Wikipedia language editions as recorded by the end of 2011. After removing edits made by Wikipedia's maintenance bots and applying the filters described in SI Appendix, the dataset contains 382 million edits in 238 languages by 2. 5 million unique editors. Here, two languages are connected when users that edit an article in one Wikipedia language edition are significantly more likely to also edit an article in the edition of the other language (Eq.
1). The IT dataset consists of 2. 2 million translations of printed books published between 1979 and 2011 in 150 countries and more than a thousand languages (SI Appendix). The dataset records translations rather than books, so it does not list books that have not been translated. Moreover, the IT dataset counts each translation separately. For example, IT records 22 independent translations of Tolstoy's Anna Karenina from Russian to English. In mapping the network, we treat each independent translation separately, and in this case, count 22 translations from Russian to English. Also we note that the source language of a translation recorded by IT can be different from the language in which the book was originally written. For example, at the time we retrieved the data, the IT recorded 15 translations of The Adventures of Tom Sawyer to Catalan, of which only 13 were translated directly from English; the two other translations came from Spanish and Galician versions.
Results Mapping a Global Language Network. We begin our construction of these three GLNs by identifying the links that are statistically significant with respect to the population of speakers expressed in each dataset. By definition, a statistically significant connection is a connection where the probability of finding a speaker, or record, connecting languages i and j is larger than what we would expect based on the prevalence of these languages alone [P(i,j) > P(i)P(j)]. To determine the strength and significance of each connection, we use the standard methods of ϕ correlation and t statistic, which are defined as follows. Fig. 3. The position of a language in the GLN and the global impact of its speakers. Top row shows the number of people per language (born 1800-1950) with articles in at least 26 Wikipedia language editions as a function of their language's eigenvector centrality in the (A) Twitter GLN, (B) Wikipedia GLN, and (C) book translation GLN.
The bottom row shows the number of people per language (born 1800-1950) listed in Human Accomplishment as a function of their language's eigenvector centrality in (D) Twitter GLN, (E) Wikipedia GLN, and (F) book translation GLN. Size represents the number of speakers for each language, and color intensity represents GDP per capita for the language. All subplots report the adjusted R 2. Let M ij be the matrix representing the number of common users or number of translations from language i to language j. Then the correlation ϕ ij between languages i and j is given by ϕ ij = M ij N -M i M j ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi M i M j ðN -M i Þ À N -M j Á q ; [1] where M i represents the number of multilingual users (or translations) expressed in language i ðM i = P j M ij Þ, and N represents the total number of users or translations in the dataset.
ϕ ij is positive for pairs of languages that co-occur more often than expected based on their representation in the dataset alone and is negative otherwise. To assess the statistical significance of these correlations, we use the t statistic, which is given by t ij = ϕ ij ffiffiffiffiffiffiffiffiffiffiffi D -2 p ffiffiffiffiffiffiffiffiffiffiffiffi ffi 1 -ϕ 2 ij q ; [2] where D -2 represents the degrees of freedom of the correlation. Here we consider D = maxðM i ; M j Þ as it provides more stringent criteria for finding a correlation than using D = N. Finally, we construct our network by considering only links that are statistically significant with P < 0. 01 (t ij > 2. 59 for D > 20; one-tailed). Also, we consider only links for which M ij ≥ 6 to avoid false positives that could emerge due to small statistics.
Node sizes are proportional to the number of speakers (native plus nonnative) of each language  (11). Node colors indicate language families and link colors show the significance of the link (according to its t statistic). Finally, link widths show the total number of co-occurrences or translations (M ij ). The three GLNs share a number of features, even though they capture information about the linguistic expression of different communities. First, the representation or number of expression of each language-number of Twitter users, Wikipedia editors, or translations from a language-correlates strongly across the three datasets (Fig. 2 A-C ). Moreover, the coexpressionsnumber of common twitter users, Wikipedia editors, and average number of book translations from and to a language-are also positively correlated across the three datasets (Fig.
(A) Regression table explaining the number of people (born 1800-1950) of each language about which there are articles in at least 26 Wikipedia language editions as a function of the language's GDP per capita, population, and eigenvector (EV) centrality in each of GLNs. Cultural production rankings: the (B) countries and (C) languages with the largest number of people about which there are articles in at least 26 Wikipedia editions. GLN eigenvector centrality rankings for languages represented in biographies list: top seven languages in (D) the Twitter GLN, (E) the Wikipedia GLN, and (F) the book translation GLN. See SI Appendix for the full lists. networks are the most dissimilar. Finally, we note that compared with the book translation dataset, the two digital datasets (Twitter and Wikipedia) exhibit a larger share of languages associated with developing countries-such as Malay, Filipino, and Swahili-which could indicate that these less formal channels of communication are more inclusive of the populations of developing countries than written books.
The Position of Languages in the GLN. Next, we study the relationship between the position of a language in the GLN and the global cultural influence of its speakers. We measure the position of a language in the GLN using its eigenvector centrality  (12) ; for other centrality measures, see SI Appendix. Eigenvector centrality (which is also the basis for Google's PageRank algorithm) considers the connectivity of a language as well as that of its neighbors, and that of its neighbors' neighbors, in an iterative manner. Hence, eigenvector centrality rewards hubs that are connected to hubs. To measure the global influence of the speakers of a language, we use two datasets that estimate the number of famous people associated with each language. First, we compiled a list of the 4,886 biographies of people who were born between 1800 and 1950 and have articles in at least 26 Wikipedia language editions (data available at pantheon.
media. mit. edu). The list is populated by famous individuals of the arts and sciences, such as Einstein, Darwin, Van Gogh, and Picasso, by popular writers such as Charles Dickens, social activists such as Che Guevara, and politicians, sportsmen, and entrepreneurs. We associated each person with a language using the current language demographics for his or her country of birth. Each famous person in the dataset equals one point, which is distributed across the languages spoken in his or her native country according to its language demographics. For example, a person born in Canada contributes 0. 59 to English and 0. 22 to French. See SI Appendix for a detailed explanation of the conversion and data sources. The second measure of famous people is based on Human Accomplishment, a published volume listing 3,869 individuals that have made significant contributions to the arts and sciences before 1950  (13).
We distributed the contribution of the 1,655 people on this list born between 1800 and 1950 across different languages using the same method used for the Wikipedia dataset (SI Appendix). SI Appendix, Fig. S5  compares these two independent measures of fame by looking at the correlation between the scores reported in Human Accomplishment and the number of different language editions in which a biography is present in Wikipedia. The correlation between both datasets is mild but significant (R 2 = 0. 25, P 0. 001). The mild correlation between the two datasets highlights the robustness of results that hold for both datasets: the differences between the two datasets imply that a result obtained for one does not need to hold for the other merely because of the colinearity of the data. Fig. 3 A-C  shows the bivariate correlation between the number of famous people measured using the Wikipedia dataset and the eigenvector centrality of that language in the Twitter, Wikipedia, and book translation networks.
We only use languages that are present in all three GLNs and that are associated with one or more famous character. The regression table in Fig. 4  compares the effects of several independent variables: the combined effect of its population and income (combined) and the effect eigenvector centrality in each of the GLNs. The variables are introduced sequentially. We use gross domestic product (GDP) to indicate income; see SI Appendix for an explanation of the method used to calculate the population and GDP of a language. With the exception of the Twitter dataset, the correlation between the number of famous people and the eigenvector centrality of a language is similar to or higher than the correlation observed between the number of famous people and the income and population of the language combined using both nested models (column 1 vs.
6   6 and 7) and nonnested statistical models (column 1 vs. columns 3 and 4). For nonnested models, we used a Clarke test and found that eigenvector centrality is a significantly better correlate of fame than the combination of population and income, with P < 0. 01 for almost all combinations. The one exception is the combination of Wikipedia fame and the eigenvector centrality in the book translation GLN, for which both regressions are statistically equivalent. For nested models, we estimate the semipartial correlation and the F test. The semipartial correlation is defined as the difference between the R 2 obtained from a regression with all variables and a regression where the variable in question has been removed. For the Wikipedia fame dataset, we find that the percentage of the variance in the number of famous people explained by the centrality of a language in the Wikipedia and book translation GLNs are, respectively, 7.
5% (F = 22. 97, P < 0. 001) and 7. 7% (F = 23. 48, P < 0. 001) after the effects of income and population have been taken into account. In contrast, the semipartial contributions of income and population together are 5. 1% (F = 7. 74, P < 0. 001) when measured against the Wikipedia GLN, and 11. 3% (F = 17. 32, P < 0. 001) when measured against the book translation GLN. Figs. 3 D-F and 5 show the same analysis for Human Accomplishment. The cultural influence of the languages as reflected in this biographical dataset is best explained by a combination of population, GDP, and the centrality of a language in the book translation network (Fig. 5 ), which accounts for 89% of the variance. Centrality in the Wikipedia GLN or book translation GLN alone explains 76% and 86% of the variance, respectively, and 6.
1% (F = 7. 59, P = 0. 01) and 16. 1% (F = 37. 98, P < 0. 001) at the margin, as measured by the semipartial correlation. The semipartial contribution of income and population in this case is much lower, being only 2. 3% (F = 1. 43, P = 0. 26) and 2. 7% (F = 3. 13, P = 0. 06) when measured, respectively, against the Wikipedia GLN and book translation GLN. Finally, we note that the data cannot distinguish between the hypothesis that speakers translate material from a hub language into their own language because the content produced in the hub language is more noteworthy or the hypothesis that a person has an advantage in the competition for international prominence if he or she is born in a location associated with a hub language. These alternatives are not mutually exclusive, because the two mechanisms are likely to reinforce each other.
One might argue that the peripheral position of Chinese, Hindi, and Arabic in the GLNs stems from biases in the datasets used, such as the underrepresentation of these languages and of some regional languages to which they connect. However, although these languages may be central in other media, their peripheral role in three global forums of recognized importance-Twitter, Wikipedia, and printed book translationsweakens their claim for global influence. Moreover, Chinese, Hindi, or Arabic would not qualify as global hubs even if their connections to regional languages were better documented in our datasets, because a global language also links distant languages and not just local or regional ones. The structure of the three GLNs documented here also raises important questions involving the dynamics of the networks observed.