dataesr/scientific-paragraphs-categorization huggingface dataset:
Understanding in vivo their developmental role should provide useful knowledge to link tissue-specific expression of DLG protein isoforms and NDD development. The comparison between human and mouse DLG2/ Dlg2 genes revealed a significant lack of crossannotation in both reference genomes. For example, DLG2 human exons 1-6 and 10 are unmapped to any mouse exons while these murine exons should exist. Because of the human-mouse genetic overlap and evolutionarily conserved properties, we expect that, as we already manually did for DLG2, an automated cross-species annotation analysis of DNA and protein information from UCSC, NCBI BLAST, and UniProtKB/ Swiss-Prot databases will disclose novel exons in other genes. Classic data-driven strategies study the enrichment in patients compared to controls one region at a time.
Conclusions Our work demonstrates the key importance of two new DLG2 promoters and coding first exons for their association with neurodevelopmental phenotypes. It expands the DLG2 NDD phenotypic spectrum to intellectual disability (GDD/ID, language delay) and behavioral disorders (ASD, ADHD). Through our manual investigation of DLG2/Dlg2 exon mapping, it unveiled the lack of cross-annotation between the human and mouse reference genomes and between nucleotide and protein databases. Our study also emphasizes the importance of tissue-specific integrative studies along the developmental timeline to further explain developmental disorders.
Description of the 29 patients having deletions in the DLG2 7-9 region (Continued) ≥0. 32 (as routinely done at Université Libre de Bruxelles Center for Medical Genetics). Furthermore, DGV and literature structural variations are described categorically as either duplication or deletion; therefore, for each dataset, we converted log2ratios into categorical values using the following rationale: duplication for positive value, deletion for negative value. For the whole genome enrichment analysis, we used the release version dated April 22nd, 2016 for DECIPHER and July 2015 for DGV (see Additional file 1: Supplementary note 2 for details).
Acknowledgements CR and GS thank  Prof. Philippe Lepage  and  Prof. Pierre Smeesters  for their support of this work. This study makes use of data generated by the  DECIPHER Consortium. A full list of centers who contributed to the generation of the data is available from http://decipher. sanger. ac. uk and via email from decipher@sanger. ac. uk. Funding for the project was provided by the  Wellcome Trust.
Availability of data and materials The partial deletions of DLG2 in patients 1 and 2 are publicly available in the DECIPHER database. Patient 1 has been assigned the DECIPHER id 317136 and patient 2 the DECIPHER id 317185. The CFEin7 coding DNA sequence is registered in GenBank with reference KY368395. The CFEin8 coding DNA sequence is registered in GenBank with reference KY368394. The mCFEin1 coding DNA sequence is registered in GenBank with reference KY368397. The mCFEin2 coding DNA sequence is registered in GenBank with reference KY368396.
"CEGA score" documents the conservation score across vertebrates as reported in the Conserved Elements from Genomics Alignments database[63]. The "Type" column reports whether such a region is predicted as promoter (P) or exon (E) by Ensembl (archive 75, Feb 2014) or as novel (N). Coordinates are in hg19 Entry Chr Start End Width Gene name Strand Number of cases Number of control CEGA score Splicing site Type a Chr2 236577649 236583540 5892 AGAP1 + 2 1 31 236579701-2 P b Chr3 114167766 114174803 7038 ZBTB20 - 2 0 832 114173425-6 P c Chr5 14440397 14444098 3702 TRIO + 1 1 192 14441469-70 P d Chr5 58722748 58727155 4408 PDE4D - 1 4 300 58726119-20 N e Chr7 75266093 75269827 3735 HIP1 - 3 2 144 75268368-9 N f Chr11 84147024 84149361 2338 DLG2* - 11 5 557 84148430-1 P a g Chr11 84429842 84432885 3044 DLG2* - 13 2 97 84431338-9 N b h Chr11 84843131 84844944 1814 DLG2 - 3 0 862 84843811-2 P i Chr17 61227923 61231987 4065 TANC2 + 2 6 194 61228741-2 E j Chr22 28832791 28840308 7518 TTC28 - 2 1 553 28838873-4 P k Chr22 36355185 36358538 3354 RBFOX2 - 1 0 NA 36357610-1 P a This region corresponds to HPin8 b This region corresponds to HPin7.
What are the relevant approaches to be considered for a successful dissemination in a scientific project? As exemplified above, CYANOCOST needed to engage a large number of end-users and stakeholders. The value of different dissemination approaches using databases, webpages, social media, flyers, specialized books, special issues in scientific journals, training schools and shortterm scientific missions (STSMs) were considered within the Action, and these approaches were developed, based on responses from the Action participants and stakeholders. The approaches taken by the Action are described in the following text as examples that can be generalised and used in similar projects. A Europe-wide rolling database (https://sites. google. com/site/cyanocost/) which was an extension of the 2005 UNESCO CYANONET database  (Codd et al.
, 2005)  was developed within the Action. The CYANOCOST database contains useful contacts of researchers, organizations, companies involved in this field, with extended profiles, expertise, publications, projects, collaborations and contact details. Links to national risk management policies in use in Europe and in neighbouring states, including guidelines and legislation, are included. The database has been origi-nally designed for use by CYANOCOST participants only but it is expected to be made freely available for the public. In addition to this Europe-wide database, a freely-accessible local database, the Serbian Cyanobacterial Database (https://cloud. pmf. uns. ac. rs/index. php/s/v6tErVCV-cAuaXQN), was developed. It is expected that these databases/repositories will be used as sources of information beyond the lifetime of the Action.
The databases contain material from both pre-CYANOCOST publications and papers arising from the CYANOCOST Action. Based on the CYANOCOST experience, it is recommended that the following matters receive attention during the lifetime of any action or project establishing public databases: i) intellectual property rights and terms of use; ii) accessibility of information and design of the database; iii) mechanisms to ensure correctness of data; iv) continued updating of the database; and v) maintenance of the database. To address the needs of various professional groups, including water managers, the drinking water industry, the environmental and public health sectors, and academia, CYANOCOST outcomes were communicated in the form of the three handbooks mentioned in this paper and two special issues in Aquatic Ecology and Advances in Oceanography and Limnology, i.
, 2013a)  identified a disproportionality between well-covered regions including the USA, Canada, Europe and Australia, versus other (highly vulnerable) areas including much of South America, Asia or Africa  (Codd et al. , 2005 , Merel et al. , 2013a). However, a recent survey has col-N o n -c o m m e r c i a l u s e o n l y lated more information on activities in Africa  (Ndlela et al. , 2016). Further surveys in these continents are needed for truly global mapping of toxic cyanobacteria risks. Even existing information may not be fully accessible  (Merel et al. , 2013b). For example, blooms may eventually be monitored and cyanotoxin analyses performed, but the information may be only locally available and not reported to any recognized national or international database(s). Thus, the occurrence and potential risks of toxic blooms can be considerably underestimated, and future efforts should aim to implement 'open environmental data' principles (e.
ACKNOWLEDGEMENTS This work is supported in part by the  National Basic Research Program of China  ( 973 ) Project # 2014CB340104/3 ,  NSFC  Projects  61335005 ,  61377076 ,  61575142 ,  61431009  and  61671227 , the  United States Army Research Office  grant  W911NF-13-1-0283  and  Shandong Provincial Natural Science Foundation  ( ZR2011FM015 ).
General: The IIDDT-C3 (Mw=58,000 Da; polydispersity, PDI = 2. 4) polymer was supplied by 1-MATERIAL INC and it was used as received. For all the LS and SC films, a 0. 5 mg/mL chloroform solution of IIDDT-C3 polymer was used. The polarized UV-Vis spectra were recorded at room temperature on a Shimadzu UV3101 spectrometer equipped with Melles Griot polarizer. AFM Characterizations were carried out with a Multimode V (Veeco) microscope equipped with a Nanoscope V controller. Commercial silicon cantilevers with a nominal spring constant of 40 N m -1 were used for morphological characterization in intermittent contact mode. Water contact angles (Table  SI -2, Supporting Information) were measured by the sessile drop method on a DSA 100 goniometer (Krüss GmbH); milli-Q water was used and data evaluation was done using the software Drop Shape Analysis.
§ Present address:  Institut National de la Recherche Scientifique (INRS), EMT Center, 1650 Boul. Lionel Boulet ,  J3X1S2  Varennes, CA. ⊥ Present address:  Center for Neuroprosthetics, Interfaculty Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. ACKNOWLEDGMENTS This work was supported by the  EC  through the  Marie Curie IEF GALACTIC  ( PIEF-GA-2013-628563 ), the  FET  project  UPGRADE  ( GA-309056 ), the  ERC  project  SUPRAFUNCTION  ( GA-257305 ), the  Marie Sklodowska-Curie ITN  project  iSwitch  ( GA-642196 ), the  Agence Nationale de la Recherche  through the  LabEx  project  Chemistry of Complex Systems  ( ANR-10-LABX-0026_CSC ), the  International Center for Frontier Research in Chemistry , and the  Italian National  Project  N-CHEM -Flagship NANOMAX.
| Sampling, data collection and plant material Wheat leaf samples were collected during July 2012 from 11 pairs of organically and conventionally managed fields in Sweden. Fields were paired based on proximity and cultivar. Paired fields were adjacent or located between 0. 5 and 9. 5 km from each other and the winter wheat cultivar grown was either 'Olivin' or 'Stava' (Table  1 ; Figure  1 ). At sampling, the leaf below the flag leaf was picked from 10 plants evenly distributed along a 10-m-long diagonal transect starting 5 m into the field. The 10 leaves collected from each field were pooled into one plastic bag and stored at 4°C during sampling and at À20°C in the laboratory until DNA extraction. Leaves were collected using gloves to prevent cross-contamination. The proportion of diseased leaf surface was visually estimated for each pool of ten leaves, which included symptoms of several pathogens.
To capture both endophytic and epiphytic fungi, DNA was extracted from the whole leaf tissue using the DNeasy Plant Mini kit (QIAGEN, Germany) as described previously  (Karlsson et al. , 2014). From each pooled sample of 10 leaves, three extraction replicates were made. A number of additional data were collected in each field (Table  1 ). Wheat developmental stage was estimated according to the Zadoks' scale  (Zadoks, Chang, & Konzak, 1974). At the start and end of the transect, two 0. 25 m 2 squares were laid out and wheat plants and weeds (all plants other than wheat) in each square were cut and separated, and the plant material was dried for 24 hr at 105°C to determine dry mass. We hypothesized that fields with dense crop or weeds would have a different microclimate. The farmers managing the sampled fields were interviewed about the preceding crops, soil tillage, pesticides, fertilization and other measures applied.
These data were also used to calculate an agricultural intensification index for each field based on the index proposed by  Herzog et al. (2006). The original index by Herzog et al. includes three indicators: livestock density, the amount of nitrogen applied and the number of pesticide applications. Only the two latter indicators were applicable in this study and used to calculate the agricultural intensity index. Weather data from weather stations in the area of the sampled fields (6-43 km from the fields, Figure  1 ) were taken from the LANT- MET database (http://www. ffe. slu. se/lm/) (Table  2 ).
, 2013). Clustering into operational taxonomic units (OTUs) approximating fungal species was performed at 1. 5% dissimilarity cut-off. The cutoff level was chosen where basidiomycete yeast species were separated into different OTUs. However, no cut-off will perfectly reflect biological species  (Ryberg, 2015). To test the robustness of the results in relation to the chosen OTU cut-off, the statistical analyses were repeated on data clustered at 3% and 5% dissimilarity cut-off with similar results (Fig. S1 ). Global and per-sample singletons were removed from the data set  (Lindahl et al. , 2013). The most abundant sequence in each OTU was screened against the NCBI nucleotide database (http://www. ncbi. nlm. nih. gov/) using the BLAST algorithm, and the output was checked for nonfungal and erroneous OTUs (hits with identity or coverage   (Kõljalg et al.
The effect of agricultural intensity index on fungal abundance was tested using a GLM. The effect of cropping system on diseased leaf area was tested with a paired t-test as above. The correlation between diseased leaf area and the most abundant pathogen (OTU 0 Zymoseptoria) was tested using Spearman rank order correlation. The effect of cropping system and agricultural intensity index on community composition was tested using GLMs. GLMs are better in handling the mean-variance relationship typical of count data, than To visualize community composition among fields, we performed nonmetric multidimensional scaling (NMDS) using the function metaMDS in the 'VEGAN' package  (Oksanen et al. , 2013). The NMDS was performed on Bray-Curtis dissimilarities calculated from logtransformed data to mitigate the effects of unequal variance between abundant and rare species.
The most abundant OTU in the data set was Zymoseptoria (OTU 0), contributing 46% of the total ITS copies and 6%-75% of the ITS copies per field. This OTU matched several Zymoseptoria species, including Z. tritici (Mycosphaerella graminicola), Z. brevis and Z. pseudotritici. Zymoseptoria tritici is a common pathogen on wheat leaves, causing septoria tritici blotch, while the other two are newly described pathogens on graminicolous hosts, but little is known about their distribution and host range  (Quaedvlieg et al. , 2011; Stukenbrock et al. , 2012). However, symptoms of septoria tritici blotch were observed during sampling, indicating that Z. tritici was present on the leaves. A correlation between visual assessments of diseased leaf area (which included symptoms caused by several pathogens) and the amount of Z.
In (b), the 10 operational taxonomic units (OTUs) with the highest incidence are shown, while less abundant OTUs are grouped into 'Others'. Species hypothesis accession codes in the UNITE database are indicated when available (http://unite. ut. ee/) effect of agricultural intensity index on fungal communities followed a similar pattern. Agricultural intensity index had a negative effect on species richness but no effect on evenness (Table  3b ; Figure  3de ). Fungal abundance was not affected by agricultural intensity index (GLM: deviance = 1. 26, df = 9, p = 0. 29, Figure  3f ), but there was a significant effect of field pair (deviance = 7. 08, df = 10, p =. 002). Agricultural intensity index also had an effect on fungal community composition (Table  4b ).
We found no indication of differing fungal abundance between the two production systems and abundance was not related to OTU richness. Fungal abundance was highly variable between fields, even within field pairs. This highlights the importance of complementing amplicon sequencing data with estimates of fungal biomass to get a more complete view of fungal communities. T A B L E 3 Effect of (a) cropping system and (b) agricultural intensity on the richness and evenness of wheat phyllosphere fungal communities. Species richness is estimated as the number of operational taxonomic units (OTUs) per 10 wheat leaves; Fungal abundance is estimated as internal transcribed spacer (ITS) copies per ng wheat DNA. Eleven field pairs in conventional and organic production were included. Agricultural intensity was measured as an index including the amount of nitrogen applied (kg/ha) and the number of pesticide applications; one field was excluded from this analysis due to missing data.
Eleven pairs of conventionally and organically managed wheat fields were sampled in Sweden. Agricultural intensity was measured as an index including the amount of nitrogen applied (kg/ha) and the number of pesticide applications, one field was excluded from this analysis due to missing data. Data were analysed with multivariate generalized linear models (using the manyglm function in the 'MVABUND' R package) with a negative binomial probability distribution Fields are shown with fitted environmental variables. Pairs are numbered on a south-western gradient starting from the north (Figure  1 ). Ellipses represent 95% confidence areas for organic and conventional groups, respectively. Stress: 0. 14 the fields with the highest estimated fungal biomass, 3O and 1O, Zymoseptoria accounted for 54% and 35% of the fungal community, respectively, so the presence of this pathogen could not fully explain the high amount of fungal DNA on leaves from these fields, as other fungi were also abundant (Figure  2 ).
Furthermore, as organic production has to comply with certain regulations, it can be expected that the cropping practices varies less within the organic group than within the conventional group. This was reflected in our data set as not all the conventional farmers had applied fungicides or fertilizer. We used an agricultural intensity index to directly evaluate the effect of the main cropping practices differing between the two systems. The index includes the number of pesticide applications and the amount of nitrogen applied. As expected, the mean agricultural intensity index was lower for organically managed fields and lower agricultural intensity was associated with higher species richness. Interestingly, the three conventional fields where there was a higher richness than in the organic field in the pair, all tended towards the lower range of the agricultural intensity index of the conventional group.
ACKNOWLEDGEMENTS We would like to thank the farmers for their participation in the study,  Maria Kedmark  and  Rickard Almers  for assistance in the field,  Tina Astor  for critical review of the manuscript and  Johannes Forkman  for advice on statistical analyses. This work was supported by the  Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning  (http://www. formas. se/en/),[grant number  2010-1945 ] and the  SLU Centre for Biological Control  (http://www. slu. se/cbc).
9 1 applications Insecticide - - - 0 0. 2 1 applications Total pesticide - - - 1 2. 4 3 applications Yield (t/ha) 2. 3 3. 2 4. 5 3. 5 5. 2 7 Agricultural <70%). Species identification was performed with the RDP classifier 0 1 3 3 4 4 3 7 8 1 0 0 intensity index b of The Ribosomal Database Project (Wang, Garrity, Tiedje, & Cole, a No. of crops grown during previous 3 years, ley counted as two crops. 2007) and complemented by including reference sequences in b No. of pesticide applications and amount of nitrogen applied (kg/ha) SCATA. The RDP classifier was run with the ITS data set from the were used to calculate the agricultural intensity index. UNITE fungal database (https://unite. ut. ee/) at a confidence level of 80%. The UNITE database is curated by experts.
This paper examines one of the most visible but oddly neglected aspects of the rapidly expanding Global Health (GH) enterprise: its vast literature. Basing our data on the PubMed MeSH term "World Health" (changed to "Global Health" in 2015) and utilizing the citation and funding metadata provided by Web of Science, we analyze nearly 20,000 articles using the software platform CorTexT for the automatic processing of large text corpora. We perform several types of scientometric network analyses, and provide maps displaying inter-citations among journals publishing GH articles, co-authorship among the 292 authors who published 12 or more papers, co-citation analysis of works (articles, books and reports) cited at least 30 times by the papers in our database, and funding sources since 2008. The maps display the social, cognitive, and funding substructure of the GH publication field.
Establishing the Database The literature on GH is both huge and elusive. For one thing the term itself is polysemic. An initial attempt to create a database of publications by searching for the term in Web of Science (title, abstract, keywords) got about 10,000 hits. It soon became apparent, however, that well over 20% of these referred to a common category in Quality of Life instruments denoting the general health status of patients. A smaller number were incidental word combinations meaning "total" and referring to such things as the "global health" budget of a state or province. Rather than cleaning the database, a task that would have involved a myriad of value judgments about what was and was not a GH publication, we settled on a different strategy. PubMed has had since 1972 a Medical Subject Headings (MeSH) term called "World Health".
Few historical or anthropological works would reach our thresholds of inclusion. More seriously, books and reports (grey literature), both numerous and important in this field, are not noted in this source that covers only periodic literature. This creates a serious gap in our data. Our co-citation analyses partially cover this gap by allowing us to gauge the influence of such works among our periodical sources. The result is less than perfect but our MeSH-based strategy has the distinctive advantage of avoiding our own subjective choice of sources that would most certainly affect final results and that could not be reproduced by other scholars. Whatever the limitations of our PubMed database, it is consistent, transparent and reproducible. It reflects the GH publication domain as defined by trained indexers producing the most influential thesaurus of biomedical literature.
PubMed, while equipped with a strong MeSH thesaurus, does not include citation information (both citing and cited publications), a key resource for exploring the dynamics of a domain. For this we need to turn to a database such as Web of Science (WoS), with a relatively weak keyword system, and including fewer biomedical publications (albeit all the most relevant ones as defined by their impact factor). To utilize the benefits of both databases, we located all the PubMed articles that were also included in WoS. This yielded 19,595 texts or nearly twothirds of the PubMed hits. Authored or co-authored by 39,650 individuals, the proportion of PubMed articles in WoS rises considerably with time, reaching 80% around 2014 (see Figure  1 ) as WoS expanded the number of biomedical journals it surveys, and the field gained recognition and found its way into an increasing number of mainstream journals.
Basic statistics Our mapping approach (see below) has the advantage of not reducing figurational complexity  (Elias 1978 ) to a few statistical indicators. Nonetheless, an initial statistical description can provide us with some insights into the content of the database, and serve simultaneously as quality control. Figure  1 , in addition to displaying the relationship between publications listed in PubMed and WoS, clearly illustrates the staggering growth of this body of literature since the late 1990s. Given the relatively small number of publications in the 1980s and 1990s, our focus will be on the post-2000 period. It must be noted that this is a highly unusual body of literature. According to PubMed's own analytical categories, 13. 5 % of the publications consist of "editorial" material.