dataesr/scientific-paragraphs-categorization huggingface dataset:
Optionally, the collectors can ask for their name or a synonym to be entered in an interactive mosquito collection site map presented on the homepage. The homepage also provides background information on the monitoring programme and on mosquitoes in general. In the long-term, it is intended to present mosquito distribution maps. The mosquitoes submitted to the "Mückenatlas" are identified in the laboratory, either morphologically or, in the case of cryptic species and damaged specimens, genetically, and are added to a pinned voucher specimen collection and/or to a DNA collection. The results are fed into the German mosquito database CULBASE where all German research groups currently involved in mosquito field work enter their data. On request, the CULBASE data will be made available to scientists, stakeholders and policy makers.
UK: Mosquito Reporting Scheme/Mosquito Watch The Mosquito Recording Scheme (MRS) was set up by Public Health England (PHE, then the Health Protection Agency, HPA) and the Biological Records Centre (www. brc. ac. uk) in 2005. In the same way as with other species groups, the scheme would provide a national focus for Culicidae data in the UK, and the data would be made publicly accessible via the National Biodiversity Network Gateway (http://www. nbn. org. uk). The MRS built upon a previous mosquito database held by the University of East London which led to distribution maps of the British mosquitoes  [35] [36] [37] [38] [39] [40]. In addition to this founding data resource, the MRS receives datasets from amateur and professional entomologists, museums and universities, and also provides an identification resource for the general public to submit mosquitoes that may be causing a biting nuisance.
The samples are sent to PHE for identification by medical entomologists who respond with information about the species and their habitats. Since 2005, the MRS has received approximately 3,500 submissions in addition to 7,000 records from historical datasets dating back to the 1750s. Whilst there are records for most counties across Great Britain, the majority of records come from the south-eastern and southern counties of England (Figure  2 ). Thirty-four different mosquito species have been recorded through MRS. The most common species are from the Anopheles maculipennis and the Culex pipiens complexes while the dataset also holds records of some very rare British species such as Anopheles algeriensis, Aedes vexans, Ochlerotatus leucomelas, Ochlerotatus sticticus, Orthopodomyia pulcripalpis and Culex modestus.
Thanks to this participatory process, the tiger mosquito was again very present in the media, helping spread the word on individual actions that citizens can apply at their households to prevent breeding and spread. Lessons learnt are shaping the continuation of the project, which is now open to all citizens and includes new citizen engagement elements. The app and server components are licensed as free and open source software. The app is now available in three languages (Catalan, Spanish, English), and technological barriers are mostly eliminated. The project tries to ensure that the privacy of participants is fully protected in all stages. The information collected is not, inherently, of a personal or private nature (e. g. locations, photographs and notes of mosquitoes and breeding sites), which allows working towards a more open approach.
In this sense, users are informed during registration that any information they submit through the app may be made public. In fact, avoiding the collection of personal data facilitates a direct return in a real time web map and sharing data with citizens. Up to now, the app has been downloaded more than 6,000 times and citizens have contributed sending more than 1,400 geolocations of possible tiger mosquito sightings. Between June and September 2014, approximately 150 potential breeding sites were reported, accompanied by some 700 pictures. Data is being validated using novel techniques such as crowd-crafting tasks for pictures, and dynamic and geo-located missions (e. g. encouraging citizens to complement data reports with pictures). Missions permit refining the information sent by citizens in time and space according to specific socio-environmental or scientific interests.
However, only 31% of the reports were on mosquitoes while 69% dealt with other insects. Based on the observation that most of the reports were web-based, the idea emerged to use novel technologies to intensify reporting and enlarge surveillance. In 2013, EID Atlantique developed the first mobile application on mosquitoes (iMoustique®) which allows users to directly transmit a mosquito picture from a cell phone device to a database. As modern mobile devices are equipped with a camera and GPS, users are able to take pictures anytime and anywhere and geo-reference their findings. All mosquito reports are automatically entered into a database including the date of receipt of the report, the reporting mode (web-site form, mail contact, post mail or iMoustique® report), the name of the reporter, her/his department and city, email address and phone number.
Discussion Being fundamental to evidence-based science, active data collection, e. g. by carrying out experimental studies and field work, can be laborious, time-consuming and costintensive cf. [61]. As human and financial resources diminish while data requirement increases with scientific progress, more and more scientists make use of passive ways of data collection. There are two principal approaches of passive data collection in biology, representing various qualities: in case biological, ecological and morphological data on species are needed, data deposited in historical collections may be used. Data may then be extracted from databases (built up by experts or lay people or both), literature collections and voucher specimen/museum collections. In contrast, occurrence and distribution data are usually required to be up-to-date, e.
With regard to data verification, citizen science projects have clear advantages over data acquisition from databases or literature, which just have to be believed in being correct. All described projects are based on elaborate websites and communication both with the participants and the media. Publicity is of major importance, and the special challenge of community-based projects is to appropriately address the public and to keep their interest in participating alive. The media are the most important tools to address citizens and to call their attention to the projects. For the participants there is no remuneration for reporting, collecting and sending mosquitoes; some projects do not even cover the postage when mosquitoes are submitted. Besides education and occasional advice, the compensation for participating is primarily mental.
Conclusions As indicated, passive mosquito surveillance supplements, but does not replace, active surveillance. While passive surveillance can reliably provide presence data over a large geographic region, active surveillance is usually more targeted in its aims and appropriate to collect data of a more specific quality, such as abundances, seasonal activities, breeding site and other ecological characteristics, and provide samples for pathogen screening. Thus, passive surveillance may function as a background alert system for triggering active surveillance when necessary and may design more cost-intensive active surveillance activities in a focused way. While active surveillance is increasingly being standardized on a European level  [61, 64] , there is no international coordination (methods, databases, communication techniques) in passive surveillance so far, although passive surveillance per se has lately been promoted by the ECDC  [64].
The application of the Rasch model on Alzheimer's Disease (AD) ADNI GWAS dataset also allows a coherent interpretation of the results. Our analysis supports the idea that APOE is a major susceptibility gene for AD. In the top genes selected by proposed method, several could be functionally linked to AD. In particular, a pathway analysis of these genes also highlights the metabolism of cholesterol, that is known to play a key role in AD pathogenesis. Interestingly, many of these top genes can be integrated in a hypothetic signalling network.
Implementation with R Several softwares and R packages are available for Rasch model analysis such as ConQuest (https://shop. acer. edu. au/group/CON3), RUMM (www. rummlab. com. au), ltm (cran. r-project. org/package = ltm) and eRM (cran. r-project. org/package = eRm). Considering its flexibility and ease of integration to a pipeline of analysis, we choose to use the eRM R package. The following short R script provides the functions used to obtain the multi-marker Rasch genetic score for each subject of a dataset of interest, where 'Geno' is a data matrix of genoptypes coded by 0, 1 and 2, with subjects in rows and markers in columns: > library(eRM) > rasch. model = PCM(Geno) > score = person. parameter(rasch. model)$theta. table[, 1] If 'Trait' is a binary trait disease coded by 1 for cases and 0 for controls, the association of the multi-marker Rasch genetic score to the disease can then simply be assessed with a logistic model: > glm(Trait * score, family = "binomial") If 'Trait' is a quantitative trait, the association of the multi-marker Rasch genetic score to the disease can then simply be assessed with a linear model: > lm(Trait * score).
Scenario 2: SNPs in moderate Linkage Disequilibrium. To account for SNPs in Linkage Disequilibrium (LD), our simulation model follows an approach based on the diplotype frequencies of real datasets. These frequencies are used as an empirical distribution of the range of possible diplotypes. First, 12 DSLs are simulated independently from the model described in Scenario 1. Then the remaining SNPs are completed based on a real dataset (here the chromosome 6 of the ADNI dataset described below) in order to generate one LD blocks of moderate magnitude (0. 4-0. 7) around each DSL. Simulating this way leads to genetic patterns similar to those found in real data and therefore allows us to finely control the level of LD between SNPs. Scenario 3: SNPs in strong Linkage Disequilibrium. The simulation is the same as for Scenario 2 with the difference that we consider SNPs in strong LD (0.
Application to the Alzheimer ADNI GWAS data Alzheimer's disease (AD) is the most common neurodegenerative disorder and affects more than 35 million people worldwide. It is characterized by brain atrophy reflecting neuronal and synaptic loss and the presence of amyloid plaques and neurofibrillary tangles, leading to a progressive deterioration of cognitive functions involving memory, reason, judgment and orientation  [27]. AD pathogenic mechanisms are still unclear and the disease remains a condition without cure. According to age at onset, two main types of AD are differentiated: Early-Onset AD (EOAD, appears generally before the age of 65, less than 10% of the AD population and clear genetic determinants with mutations found in the APP, PSEN1 and PSEN2 genes) and Late-Onset AD (LOAD, more than 90% of the AD population, appears generally after the age of 65 and has a complex etiology based on genetic and environmental factors)  [28].
By way of illustration, we applied our Rasch-based multi-marker association test to the genes of the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni. loni. usc. edu) [31]. The study population is made up of 359 cases and 226 controls, genotyped with an Illumina Human 610-Quad (= 620901 SNPs). A standard quality control process based on minor allele frequency, Hardy-Weinberg equilibrium, missingness and relatedness excluded 31 cases, 49 controls and 82071 SNPs  [35]. The dataset was also reduced with a minimal loss of information by pruning with Plink (window size = 50 SNPs, shift = of 5 SNPs at each step and threshold correlation coefficient of 0. 2)  [36]. Missing genotypes were imputed with weighted k-Nearest-Neighbors method  [37]. SNPs are considered attached to a gene if they are located within a distance of 20 kb around it.
The curated dataset to analyze comprises 16514 genes. For each gene and each subject, a Rasch-based multi-marker genetic score is computed, and the association of this score to the disease is evaluated by a logistic regression model. The top genes identified by the Rasch analysis were integrated into a hypothetic signalling network. Protein-protein interaction data and functional findings were extracted from QIA-GEN's Ingenuity Pathway Analysis (IPA, QIAGEN Redwood City, www. qiagen. com/ingenuity), manually analysed and supplemented by literature curation.
The application of the Rasch model to the genes of the Alzheimer ADNI GWAS data allowed a coherent interpretation of the data. Our analysis supports that APOE is a major susceptibility gene for AD. In the other top genes, several of them (AEN, ADAMTS12, PSMA5, FXN, NTM, LARP1, WDTC1, SEMA7A, VKORC1L1, COL5A3) can be functionally linked to Alzheimer's disease. A pathway analysis of these genes also highlights the metabolism of cholesterol, that is known to play a key role in AD pathogenesis. All these elements can be integrated in a hypothetic signalling network based on known protein-protein, functional and phenomenological interactions (Fig  4 ). Interestingly, this network could be potentially targeted by acamprosate, a drug that was first approved in 1989 and since then has been widely used to  treat alcohol-dependence  [69].
Second, in some particular cases the estimation of the Rasch model with the eRM R package does not converge and consequently does not provide any results. It happened for instance to 9 genes over the 16514 genes analyzed in the ADNI GWAS data and the reasons of that problem were not clear to us. Finally applying a Rasch model necessitates accessing individual level genetic data. But often, only summary statistics are available for published GWAS. This is a real limitation for most of the existing multimarker methods in order to correctly account for gene size and LD, although some authors have found a solution in using the genotype data from a reference panel such as the 1000 Genomes or the HapMap projects  [77] [78] [79]  which is not applicable here. The application of the Rasch model also opens two opportunities that were not yet considered here.
Acknowledgments We thank  Linda Belo  and  Julien Laffaire  for their critical reviewing of the manuscript. Data collection and sharing for this project was funded by the  Alzheimer's Disease Neuroimaging Initiative (ADNI)  ( National Institutes of Health  Grant  U01 AG024904 ) and  DOD ADNI (Department of Defense  award number  W81XWH-12-2-0012 ). ADNI-PI is Michael W. Weiner, MD(email: Michael. Weiner@ucsf. edu). A complete listing of ADNI investigators can be found at http://adni. loni. usc. edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgment_List. pdf. ADNI is funded by the  National Institute on Aging , the  National Institute of Biomedical Imaging and Bioengineering , and through generous contributions from.
(6) Solve Chinese remainders problems. (  7 ) Compute a p-integral basis of L. The genetic-based routines designed to perform these tasks are much faster than the classical ones, especially for those number fields defined by polynomials with a large genomic tree at some prime. The Magma package +Ideals. m based on this approach may be downloaded from http://www-ma4. upc. edu/∼guardia/+Ideals. html. 6. 7. OM representations of places in a function field. Suppose that K = k(t) is the function field of the projective line P 1 over a field k. An irreducible polynomial f (t, x) ∈ k[t, x], separable over k[t], determines a unique smooth projective curve C as the normalization of the projective closure of the affine curve f (t, x) = 0. The function field of C is L = k(t, x) = K[x]/(f ). Let A = k[t] and denote by A ∞ = k[t -1 ] the local ring at the point at infinity of P 1.
The two genomic clones share similar structural organization with, however, SlARF2A being made of 15 exons while only 14 exons are present in SlARF2B. The isolation of full-length cDNAs corresponding to SlARF2A (2541 bp) and SlARF2B (2490 bp) indicated that the deduced protein sizes are 847 and 830 amino acids, respectively (Table  1 ), and pairwise comparison of the two SlARF2 protein sequences revealed 83. 3% amino acid identity. The search for protein domains in Expasy database (http://prosite. expasy. org/) indicated the presence of highly conserved domains typical of ARFs (Fig 1A ) including the DBD (DNA Binding Domain) and the dimerization domains (protein/protein domain III and IV). Moreover, the analysis of a 2 kb promoter sequence using PLACE/signal search tool (http://www. dna. affrc.
Sequence structure and promoter analysis The structure of the SlARF2A and SlARF2B were determinated using in silico approaches (software: Fancy Gene V1. 4). Protein domains were first predicted on the prosite protein database (http://prosite. expasy. org/). Promoter sequences of SlARF2A and SlARF2B genes were analyzed using PLACE signal scan search software (http://www. dna. affrc. go. jp/PLACE/signalscan. html).
Accession number The sequences of genes used for the qPCR can be found at the website (http://solgenomics. net/ ) under the following solyc numbers: Sl-ERF. A1 (Solyc08g078180), Sl-ERF. A2 (Solyc03g093610), Sl-ERF. A3 (Solyc06g063070), Sl-ERF. B1 (Solyc05g052040), Sl-ERF. B2 (Solyc02g077360), Sl-ERF. B3 (Solyc05g052030), Sl-ERF. C1 (Solyc05g051200), Sl-ERF. C2 (Solyc04g014530), Sl-ERF. C3 (Solyc09g066360), Sl-ERF. C6 (Solyc03g093560), Sl-ERF. D1 (Solyc04g051360), Sl-ERF. D2 (Solyc12g056590), Sl-ERF. D3 (Solyc01g108240), Sl-ERF. D4 (Solyc10g050970), Sl-ERF. E1 (Solyc09g075420), Sl-ERF. E2 (Solyc09g089930), Sl-ERF. E3 (Solyc06g082590), Sl-ERF. E4 (Solyc01g065980), Sl-ERF. F1 (Solyc10g006130), Sl-ERF. F2 (Solyc07g064890), Sl-ERF. F3 (Solyc07g049490), Sl-ERF. F4 (Solyc07g053740), Sl-ERF. F5 (Solyc10g009110), Sl-ERF.
To date, about 1,900 miRNAs (1,881 precursors and 2,588 mature miRNAs; GRCh38 human genome assembly) have been reported in the miRbase database (http://www. mirbase. org/). A substantial number of these miRNAs have dubious annotations and for nearly one-third of miRNA loci, there is no convincing evidence concerning the production of authentic miRNAs (miRbase).
Predictive tools are freely available Predictive tools are necessary to assess the putative presence of miRNA binding motifs. A nonexhaustive list, including information about each program, is proposed in Table  2. Databases collecting all information on miRNAs are listed in Table  3. Although miRBase (http:// www. mirbase. org/) is the most used database and collects links for several predictive pro-grams, others exist. These tools have been developed to predict miRNA targets or the miRNAs that putatively bind to a selected gene. Some of them propose the possibility to input several miRNAs and/or several genes to identify integrated networks. and TargetScan to identify miRNA targets in the 3'UTR of target genes. [100] Cystic Fibrosis in the Light of New Research.
Data obtained were analyzed and compared to the SITVIT2 database. Mean age of the patients was 47. 5 years and 61% were male; 11. 6% were migrants. Of 103 strains (1 isolate/ patient) included, there were 56 distinct spoligotype patterns. Of these, 16 strains (15. 5%) corresponded to orphan strains in the SITVIT2 database, not previously reported. Latin American and Mediterranean (LAM) (34%) and T (33%) lineages were the most prevalent strains, followed by Haarlem lineage (16. 5%). Beijing family was scarcely represented with only two cases (1. 9%), one of them isolated from a Peruvian migrant. The most frequent clustered spoligotypes were SIT33/LAM3 (10. 7%), SIT53/T1 (8. 7%), SIT50/H3 (7. 8%), and SIT37/T3 (6. 8%). We conclude that LAM and T genotypes are the most prevalent genotypes of MTBC in Santiago, Chile, and together correspond to almost two thirds of analyzed strains, which is similar to strain distribution reported from other countries of Latin America.
Patients and bacterial isolates This study describes genotyping results of a total of 103 M. tuberculosis isolates collected from August 2008 to September 2013, the majority of strains (84. 5%) between 2011 and 2013. The specimens were obtained from the same number of pulmonary TB patients, in 22 outpatient clinics and 3 large hospitals in the central area of Santiago city, with a reference TB incidence of 16. 6 per 100,000 population for the year 2012. Our samples represent around 6. 44% of all smear-positive TB cases for that period in the whole Metropolitan Region. Basic demographic data (age, gender and country of origin) was obtained from mandatory reports included in the National Tuberculosis Program registry and anonymized. Strains included newly and previously treated cases, and none of the samples were duplicated from a single patient.
Computer assisted genotype analysis and comparison with databases Spoligotype patterns as octal codes were entered in the SITVIT2 proprietary database of the Institut Pasteur de la Guadeloupe which is an updated version of the previously released SIT-VITWEB database (available online at http://www. pasteur-guadeloupe. fr:8081/SITVIT_ ONLINE/)  [18]. At the time of the analysis on August 2014, the database contained genotypes of about 112,000 M. tuberculosis clinical isolates from about 170 countries of origin. In this database, Spoligotype International Type (SIT) and MIRU International Type (MIT) designates patterns shared by two or more patient isolates, whereas "orphan" designates patterns reported for a single isolate  [18]   [19]. Genotypic lineages were assigned according to the rules described in SITVITWEB  [18]  in which the LSP-based Euro-American lineage (lineage IV) is split in Latin American and Mediterranean (LAM), ill-defined T, Haarlem (H), X, and S lineages.
Importantly, the nomenclature "East-African Indian" (EAI or lineage III) denotes two altogether different M. tuberculosis clades by spoligotyping vs. LSPs  [6] ; EAI by spoligotyping was named "Indo-Oceanic" by LSPs, while EAI by LSPs corresponds to Central-Asian (CAS) in the SIT-VIT2 database  [6]. Note that lineages could be subdivided into sublineages. Data were also analyzed using MIRU-VNTRplus web server (http://www. miru-vntrplus. org/) which is a collection of 186 strains representing the major M. tuberculosis complex lineages  [19][20]. This database contains information of strain species, lineage, country together with copy numbers of 24 MIRU loci, spoligotyping patterns, regions of difference (RD) profiles, single nucleotide polymorphisms (SNPs), drug susceptibility data, and IS6110-RFLP fingerprint images.
Phylogenetic analysis BioNumerics software version 6. 6 (Applied Maths, Sint-Martens-Latem, Belgium) was used to compare spoligotypes and MIRU-VNTRs patterns, by drawing Minimum spanning trees (MSTs) in order to visualize evolutionary relationships between the clinical isolates in our study. MSTs are undirected graphs in which all samples are connected together with the fewest possible connections between nearest neighbors. An Unweighted Pair Group Method with Arithmetic mean (UPGMA) tree was also constructed using MLVA Compare software (http:// www. ridom. de/mlvacompare/) to visualize deeper associations between spoligotypes and MIRU-VNTRs involved in this study.