dataesr/scientific-paragraphs-categorization huggingface dataset:
Random and randomized GRNs A random network associated with a GRN formed by g genes, n TFs and e edges was obtained by randomly placing e edges on an empty network with g nodes, in such a way that the source of each edge was randomly selected from a fixed set of n nodes. The randomized (rewired) GRNs were derived from original GRNs of the corresponding organisms using the 'rewire. edge' function of the igraph package version 1. 0. 1 in R version 3. 24. The number of rewiring iterations for each GRNs was set to ten times the number of edges in the network. One hundred randomized GRNs were generated for each organism or cell type.
Computational analysis of LRCs in E. coli The results reported in the previous section support the idea that LRCs of more than a few genes in length act as strong attenuators of variation. LRCs beyond a few steps in length should be rare in real organisms since the attenuation saturates exponentially as a function of the number of links in the LRC (i. e. increasingly long chains act as increasingly imperfect regulators and exhibit increasingly lower relative effectiveness). To test this hypothesis, we constructed various GRNs from the literature and computed the number of LRCs of different length. The E. coli GRN, obtainable from the RegulonDB database  (13) , is one of the most validated in the literature. Our analysis on this GRN indicates that LRCs are preferentially short and that chains with more than six genes are very uncommon (Figure  3A  and Table  2 ).
' The experimental work of Harbison et al. provides one of the most reliable sources for transcriptional interactions in yeast  (9, 20)  and we reconstructed the GRN of S. cerevisiae from their data. However, it must be noted that other datasets exist with different properties  (31, 32) , thus highlighting the difficulties associated with the experimental derivation of the GRN for this organism and suggesting a perceivable level of noise even in the data that we used. LRCs of moderate length are present in this organism (Fig-  ure 4E ). Nonetheless, they are shorter than expected from random networks. Moreover, compatible with our expectations, the number of transcriptional regulators is higher for genes deeper in the longest LRCs (Figure  4F ). No chaotic motifs are observed in this GRN.
Game species like the red deer have been subjected to anthropogenic impacts for centuries. Translocations are often carried out-sometimes illegally-not only for sporting purposes, but also to increase trophy quality, reduce inbreeding, or mitigate bottlenecks after excessive persecution. Apart from the blurring of large-scale genetic structure, translocations without adequate quarantine measure risk introducing pathogens into potentially immunologically naïve populations. It is therefore important to understand the frequency of clandestine translocations. Identification of nonautochthonous animals and their potential origin is often difficult and, in red deer, has been hampered by the lack of large-scale genotypic datasets for comparison. In the present study, we make use of a recently published European-wide microsatellite dataset to detect and quantify the presence of non-autochthonous red deer in a large population sample (n = 1,780) from Central Europe (Belgium).
, 2010). It would therefore be important to understand the frequency of clandestine translocations. To date, there are few studies directly aiming at identifying the genetic signature of translocation events. Probably, the most extensive such study was that of  Frantz et al. (2006)  who were able to prove the illegal introduction of a small number of deer into a local population in Luxembourg. In general, this kind of analysis is difficult as human-mediated introductions cannot be unequivocally distinguished from natural immigration from a neighboring, but genetically differentiated population. Also, if the origin of a translocated animal is to be determined, DNA samples from ideally all possible source populations need to be available. Because until recently no European-wide dataset based on high-resolution markers such as microsatellites was available, genetic studies on translocations have long been hampered.
In this study, we present an in-depth analysis of regional red deer populations in Belgium-where since 1994 no private individual is legally allowed to translocate wildlife-based on 13 microsatellite loci and 1780 samples and compare this dataset to the recently published European-wide dataset  (Zachos et al. , 2016)  to identify nonindigenous genotypes and to arrive at a quantitative estimate on how large the proportion of potentially translocated individuals is, thus testing claims that few if any European red deer populations are free from introductions  (Hartl et al. , 2003).
| Sample collection and laboratory work Between 2003 and 2009, we collected tissue samples from 1,733 red deer from the southern Walloon part of Belgium, as well as 47 samples from the neighboring Eifel region of Germany (Fig. 1 ; see also  Frantz et al. , 2012  who used about half of our present samples). For ease of reference, we will refer to the dataset as "Belgian" or "Walloon," despite the few German animals. The total forested area of Wallonia amounts to approximately 5,000 km 2 , which corresponds to one-third of the total area of the region. Human population density in the southern part of Wallonia, where our study was based, was mostly between 20 and 90 inhabitants⁄km 2 , depending on the municipality  (Thomas, Frankhauser, & Biernacki, 2008). In 2014, the Walloon red deer population was estimated to have contained approximately 11,000 individuals (Direction de l'Etat Environnemental 2014).
| European reference data We aimed to identify the source population of translocated deer, both to identify illegal introductions and animals that had dispersed naturally, using a European microsatellite reference dataset analyzed by  Zachos et al. (2016). Not considering Belgian animals, it contained genetic profiles of 608 red deer from 26 locations throughout the continent (see Fig. 2 ), including 30 samples from a French deer farm (Boisgervilly). Both the Walloon and the European dataset were genotyped in the same laboratory using the same markers. We obtained a complete 13-loci genetic profile for 571 of the 608 European deer, with the remaining profiles having genotypes at a minimum of 11 loci. Loci Cer14 and ILSTS06 did not amplify in 14 and 10 individuals, respectively. Using the individual-based modal population mixture analysis implemented in BAPS v.
5. 4  (Corander, Waldmann, Marttinen, & Sillanpää, 2004) , the European dataset was inferred to consist of 25 genetic populations  (Zachos et al. , 2016). Seven of these 25 clusters consisted of six individuals or less. These were excluded from the present analysis. Although the animals sampled at a deer farm were assigned to different genetic clusters, for the purposes of the present study, we created a separate reference population containing all the farmed deer. The European reference dataset therefore consisted of 596 non-Belgian individuals belonging to 19 genetic clusters (18 BAPS-defined partitions with N > 6, plus the deer farm; Fig. 2 ).
The individuals that were outliers in the FCA analysis were excluded from the analysis of the population genetic structure of red deer in Belgium (it was later formally tested whether these deer were nonautochthonous using an exclusion test; see below). We analyzed the population genetic structure of the whole Belgian dataset (minus outliers) using two different (Bayesian) genetic clustering algorithms. First, we analyzed the data using the program STRUCTURE v. 2. 3. 1  (Pritchard, Stephens, & Donnelly, 2000). To estimate the number of subpopulations (K), 10 independent runs of K = 1-10 were carried out with 10 6 Markov chain Monte Carlo (MCMC) iterations after a burn-in period of 10 5 iterations, using the model with correlated allele frequencies and assuming admixture. ALPHA, the Dirichlet parameter for the degree of admixture, was allowed to vary between clusters.
After deciding on the most probable number of sub-populations based on the log-likelihood values (and their convergence) associated with each K, we calculated each individual's percentage of membership (q) for each cluster, averaging q over 10 runs. Individuals were assigned to the STRUCTURE cluster for which they had the highest q value. Finally, we also analyzed the data using the spatially explicit genetic clustering method that is implemented in the program BAPS v. 6. 0  (Corander, Sirén, & Arjas, 2008). In addition to the genetic data, the algorithm considers the specific geographic coordinates of each individual and modally assigns each individual to its population of origin. We performed 10 runs for each K = 2-10. We calculated the average assignment proportions for each sampling location and mapped them using ArcGIS 10.
(2016). The size of the pie charts indicates the number of samples collected from a locality, while the pattern of the pie chart indicates the identity of the genetic clusters. "Mesola II" was excluded from the analysis as it contained only six individuals. The entire deer farm (not indicative of geographic location) was considered to be a distinct reference population although its animals were assigned to different BAPS clusters. The locations of the Walloon red deer are indicated by individual sampling locations Bayesian approach of  Rannala and Mountain (1997) implemented in GENECLASS. In order to avoid false inclusion of non-autochthonous individuals in the Belgian reference dataset (not including the 89 outliers), we first calculated the exclusion probability of each Walloon deer for the three STRUCTURE-defined reference populations using a leaveone-out approach where each individual in turn is excluded from the populations during computation.
Furthermore, for each Belgian deer (that was not an outlier), we calculated the probability of belonging to each European reference population and assigned it to its most likely Belgian or European source population. The following Belgian animals were considered to be non-autochthonous and excluded from the reference dataset for further analysis: (1) deer that could be excluded with p <. 01 from all STRUCTURE-defined Belgian reference clusters, (2) animals that were assigned with a (total) score of >0. 85 to one (or two) European reference population(s). Furthermore, reference animals that were assigned with a score of <0. 85 to a Belgian population and with a score of >0. 15 to a European reference population were considered to have at least one non-autochthonous recent ancestor. We choose the 0.
| RESULTS After excluding the 89 outliers identified using an FCA (Fig. 3 ), the loglikelihood values generated by the STRUCTURE analysis supported the presence of three genetic clusters in the Belgian dataset (Fig. S1 , Fig. 1 ). Similarly, the individual-based spatial model implemented in BAPS inferred the presence of three geographically coherent genetic clusters in red deer from Wallonia (Fig. 1 ). Both programs roughly infer the same clustering solution. The spatial BAPS algorithm identified the deer sampled in the eastern half of the study area to form one genetic cluster. The deer in the western half formed two distinct genetic populations with the genetic discontinuities corresponding to a major motorway bisecting the study area (Fig. 1 ). The non-spatial STRUCTURE algorithm suggested a more gradual differentiation between the three clusters.
While one to two loci deviated from Hardy-Weinberg equilibrium (HWE) in each STRUCTURE-defined cluster after correcting for multiple tests, this was the case with zero to three loci in case of the BAPS-derived clusters (Table  S1 ). Locus CSSM66 deviated strongly from HWE in two of the three clusters, independently of the algorithm considered. It was therefore excluded from further analysis, which were based on the STRUCTURE clusters, since fewer loci overall deviated from HWE when considering the partition results of this algorithm (Table  S1 ). After correcting for multiple tests, between one and three pairs of loci were in linkage disequilibrium in each STRUCTUREdefined cluster. Using the leave-one-out exclusion approach, eight deer from the Belgian reference dataset (89 outliers not included) could be excluded (p <.
Altogether 40 of the 89 FCA-outliers could be excluded from all three Walloon clusters at the p <. 01-level (28 animals even with F I G U R E 3 Factorial correspondence analysis of Belgian red deer (N = 1,780). The analysis was based on 13 microsatellite loci. The 89 (5%) outliers were identified using a harmonic mean method. The percentage of the total variation explained by each of the two axes is given. The inset magnifies the interface between the core population and the outliers T A B L E 1 Identification of non-autochthonous Belgian deer. We first removed 89 deer from the Belgian dataset that were outliers (5%) in an FCA analysis. For each animal, we then calculated the probabilities of it belonging to each of the three Belgian reference clusters dataset and to each of the 19 European reference populations by means of assignment tests with the GENECLASS software.
| DISCUSSION Translocations of game animals are often carried out, sometimes illegally (see  Frantz et al. , 2006) , for sporting purposes and to increase trophy quality (in the case of red deer: body mass, size, and number of points in antlers), but apart from blurring natural genetic patterns the potential transmission of diseases is also an imminent danger  (Ferroglio et al. , 2011). Until recently, the unequivocal identification of non-autochthonous red deer and their potential origin within a geographically limited region impeded by the lack of a continent-wide nuclear analysis of the species and difficulties in inter-laboratory comparisons of microsatellite alleles. We genotyped 1,780 red deer from   (2006)  were not able to confidently identify anthropogenic introductions. The present study is therefore, to our knowledge, the first to address this topic in such detail and with such an extensive dataset.
Our dataset confirmed the conclusions by  Frantz et al. (2012)  that the E411 motorway in the west of our study area represented a gene flow barrier to red deer. The motorways in the eastern part of our Belgium study area did not appear to have the same effect (Fig. 2 ). A more detailed landscape genetic study will be necessary to further untangle the effect of geographic distance and landscape features on the population genetic structure of Belgian red deer. The information provided in this study will help to avoid analytical problems related to the inclusion of non-autochthonous animals. The inferred frequency of non-autochthonous individuals was between three and four percent. While this value seems rather low, it corresponds roughly to one in 25-30 red deer, which in total amounts to a large number of animals-approx.
The markers per se have proved suitable for the study of both small-scale and large-scale genetic structuring and for the identification of immigration/introduction events  (Dellicour et al. , 2011; Frantz et al. , 2008 Frantz et al. , , 2012;; Zachos et al. , 2016). Still, analyzing a higher number of loci may have pushed a (small) number of individuals below an exclusion threshold. More generally, a microsatellite dataset will only yield information on events involving a few recent generations, while incidents further back in time are unlikely to be detected (e. g. ,  Frantz et al. , 2013). It is known that red deer have been translocated for many centuries  (Apollonio et al. , 2014; Niethammer, 1963) , and in many cases these interferences with the natural distribution pattern and its demographic, genetic, and evolutionary consequences cannot be uncovered anymore by means of molecular (or any other) approaches.
It is not clear to what extent our thresholds (FCA-outliers, exclusion, and assignment tests) for identifying non-autochthonous animals were appropriate to produce an accurate estimate of the number of non-native deer. We failed to detect some non-autochthonous animals by excluding the 5%-FCA-outliers from the dataset. However, our results suggest that, in principle, the approach allows the identification of introduced animals in the absence of a priori suspects (which was the case in  Frantz et al. , 2006). Removing a larger percentage of all individuals might be necessary to ensure that no non-native animals remain non-included. Only considering animals with an exclusion probability of p <. 001 as being non-native would have reduced our estimate of the number of non-natives, but would in all likelihood have been too conservative (e.
While results by  Frantz et al. (2006)  suggest that the results in the present study might be high compared to other regions, a few south-eastern populations in our European reference dataset appeared to contain non-autochthonous animals (see Fig. 2 ). European-wide phylogeographic studies of red deer based on mtDNA have shown that the (inferred) postglacial genetic pattern of the three lineages (A, B, and C) has generally not been blurred by among-lineage translocations  (Ludt et al. , 2004; Niedziałkowska et al. , 2011; Skog et al. , 2009). The bi-parentally inherited nuclear microsatellites were expected to show less pronounced geographic structuring across Europe because of their higher mutation rates that result in a faster erosion of postglacial demographic signals than with mtDNA. Surprisingly,  Zachos et al.
(2016)  found a clear signal of three genetic clusters at the highest hierarchical level in their multi-locus dataset that was geographically congruent with the one known from mitochondrial DNA phylogeography. Our present study with high enough resolution power (i. e. , a sufficient number of loci and large sample sizes) suggests that, at smaller scales than those usually addressed in phylogeographic analyses, natural structures have been blurred to a certain extent by the introduction of non-native individuals that subsequently reproduced successfully. This is likely to hold true not just for the Belgian red deer, but also for other European populations, including those of other ungulate species.
4) VAT: As for VAP without a new or progressive radiological pulmonary infiltrate. The primary objective of the study is to compare mortality among these different pneumonia categories. Secondary objectives are to compare epidemiological patterns with particular emphasis on individual risk factors, microbiological profile, clinical characteristics and therapeutic interventions (i. e. severity of the disease and duration of antimicrobial therapy), along with other common outcome measures (i. e. length of ICU stay and length of organ support). All consecutive adult patients admitted to the ICU of participating sites and presenting with nosocomial pneumonia are eligible for the study. Enrolment is performed during a 12-month period, with a maximum of 50 cases per centre. ICUs which take part in the ENIRRIs project agree to prospectively collect the data, clearly document in the medical chart all the information required for the data analysis and transfer the data to the principal investigators of the project.
All data are anonymous and only the local principal investigator has access to the decoding list in order to link the enrolment number with the corresponding patient. All infections are microbiologically confirmed: sputum in non-ventilated patients, tracheobronchial aspirates (TBA) in intubated/tracheotomised patients, and/or bronchoscopic or blind bronchoalveolar lavage (BAL), within the first 24 h of clinical diagnosis. Diagnostic thresholds are: BAL ⩾10 4 colony-forming units per mL, and sputum or TBAs ⩾10 5 colony-forming units per mL, or any threshold if the patient had antibiotic treatment. The final goal of this observational study is to recruit at least 1000 patients across 15 countries, in Europe and Latin America (figure  1 ). Study results will be disseminated in the form of annual reports, conference abstracts and peer-reviewed publications.
In light of the paucity of data regarding this incredibly complex scenario, new insights are absolutely warranted in order to increase our knowledge and improve the outcome of ICU patients with lower respiratory tract nosocomial infections. This protocol from the ENIRRIs project, along with future upcoming multidisciplinary educational projects and patient-professional collaborations, will contribute to better clarify the clinical impact of such diseases with the aim being to optimise the management of critically ill patients with nosocomial respiratory infections. Finally, we hope that the results of the ENIRRIs project will help to answer some of the research questions recently posed in the ERS, European Society of Intensive Care Medicine, European Society of Clinical Microbiology and Infectious Diseases and Asociación Latinoamericana del Tórax guidelines for HAP/VAP management  [17].
Additionally, we extracted 4260 de-identified ultrasound reports form the Early Pregnancy Clinic database at the University College London Hospital (UCLH), restricted to ectopic pregnancy cases from October 2006 to April 2014. The lexico-syntactic analysis of these texts was performed using the part-of-speech tagger TreeTagger  [17]  and the Fig. 2  Overview of the design of the knowledge system for ectopic pregnancy: ontology design, reference image collection and application for image annotation term extractor YaTeA (http://search. cpan. org/dist/Lingua-YaTeA/). A total of 40,237 single/multi-word candidate terms were extracted.
Acknowledgements This work was conducted using the Protégé resource, which is supported by grant  GM10331601  from the  National Institute of General Medical Sciences  of the  United States National Institutes of Health. (website: http://protege. stanford. edu)The lexicalization plugin Archonte 4. 2 for Protégé 5, developed by  Laurent Mazuel  and supported by  INSERM   U1142  LIMICS, was used to edit the SKOS multilingual annotations in the ontology. (website: http://github. com/icsupmc/archonte)The authors would like to thank  Olivier Bodenreider  for his expert advice and support throughout the final editing of this manuscript.
Microarray experiments The 8x60K microarray platform accommodating a total of 59,824 probes has been deposited in the GEO database (http://www. ncbi. nlm. nih. gov/geo/) under accession number GPL22720. It was designed in the context of the European project REPROSEED (FP 7-KBBE-2009-1-2-11) that funded the high throughput sequencing of several P. Maximus tissues. Details on the sequencing data, the resulting assembly and the microarray design were reported in S1 File, while the sequences of the 45,488 contigs successfully employed for the P. maximus DNA microarray platform design have been provided in S2 File. At the time of data analysis, the annotation of each contig employed for the microarray design was performed again, by running blastx similarity searches (cut off e-value of <1. 0 E-5) against the updated release of several protein databases.
The best hits against UniProtKB/Swis-sProt high quality proteins (release 2016_10-November 02, 2016), Danio rerio, Drosophila melanogaster, Homo sapiens, Gasterosteus aculeatus, Nematostella vectensis, Capitella teleta, Strongylocentrotus purpuratus, Lottia gigantea and Crassostrea gigas available on Ensembl Genome Browser (release 82, September 2015) and Ensembl Metazoa (release 33, October 2016) provided at least one match for 31,579 (52. 8%) out of the total amount of transcripts. The best blastx hit of each probe against all the selected protein databases is reported in S1 Table. Probe sequences and other details on the microarray platform can be found in the GEO database (http://www. ncbi. nlm. nih. gov/geo/) under accession number GPL22720. Microarray experiments were carried out on a total of 14 samples corresponding to stripped oocytes (n = 6) and spawned oocytes (n = 8).