dataesr/scientific-paragraphs-categorization huggingface dataset:
We then used non-isoform-specific primers to allow RT-PCR amplification and characterization of both Vegfaxxx and putative Vegfaxxxb transcripts (Fig  3 ). The sequences identified are consistent with predicted sequences in databases and findings in other species, including human and mouse, which suggest phylogenetical conservation of Vegfa splicing events  [13, 16]. As assessed by RT-PCR and agarose gel electrophoresis, the transcripts with exon 6 skipping represent the major isoform (b2 band). We also identify four minor isoforms. In the shortest (b3 band), both exons 6 and 7 are skipped. For the three longer isoforms, exon 7 is retained while exon 6 may be retained, or undergo usage of cryptic donor or acceptor sites. This latter isoform (denoted b1c), which was not predicted in sheep, has recently been identified in human tissues  [16].
Therefore, we infer that the epitope recognized by the anti-VEGFAxxxb antibody might be restricted to the sequence LTRKD. Considering that Vegfaxxxb transcripts and isoforms are non-existent or scarce, an alternative explanation for the VEGFAxxxb-like immunostaining might be cross-reactivity of the antibody with a protein bearing a similar epitope. To substantiate this hypothesis, we searched human protein databases using blastp (protein-protein Blast, see  [31] ), which led to the unambiguous identification of 10 proteins harboring an LTRKD motif (S1 Table ). Interestingly, this anti-VEGFAxxxb antibody was also used to detect VEGFAxxxb isoforms in tissues from mouse  [32]  and rat  [33, 34] , while these isoforms would harbor a distinct PLTGKTD motif at their C-term, as already pointed by others  [15, 35].
Taken together, these data reinforce our conclusion that Vegfaxxxb transcripts are absent from the ewe MBH, or expressed to very low levels, i. e. below the sensitivity threshold of our RT-PCR methodology. As alluded to above, our findings in sheep echo multiple failed attempts by different research teams to reveal Vegfaxxxb mRNA in various animal models (cow, mouse and human) and using multiple methodologies  [15, 16, 27, [35] [36] [37]. In the most recent study, Bridgett et al. [16]  used publicly available RNA-seq datasets to search for Vegfaxxxb transcripts in human tissues. Their extensive analysis identified >40000 Vegfa transcripts that cover the junction between exon 7 and exon 8 in >10 different human tissues. None of the reads supported the existence of an exon 8b splice site, hence the existence of Vegfaxxxb mRNA  [16].
Our current analysis of independent MBH/PT RNAseq datasets, which were generated in ewes  [10]  and rams  [9] , fully concurs with findings by Bridgett et al. in human tissues (Table  2 ). This provides further support to the notion that previous detection of Vegfaxxxb mRNA may be accounted for by methodological issues. Indeed, Harris et al  [15]  had already dedicated a thorough study to the identification of Vegfaxxxb in mouse and human tissues and cells and concluded that detection of Vegfaxxxb mRNA might arise as a PCR artefact through "5' tailing". In reply to the study by Harris et al. [15] , Bates et al. [19]  argued that inability to detect Vegfaxxxb mRNA was due to inadequate primer design and lack of appropriate controls. According to Bates et al. , the 3'end of the adequate Vegfaxxxb-specific primer includes 11 bases complementary to exon 7, while the 9 bases at the 5'end are complementary to the putative exon 8b  [19].
Sequencing was performed with an Illumina HiSeq 2000 sequencer (Illumina, San Diego, CA, USA), and data were assembled as described previously  (17). Sequences are available through the PubMLST database, which runs on the BIGSdb platform  (18). WGS data were analyzed using the PubMLST database to determine the sequence types (STs) and clonal complexes (CCs), according to multilocus sequence typing (MLST) analysis. Data on other alleles corresponding to genes of interest for antibiotic susceptibility were also extracted. WGS data were also analyzed using a "gene-by-gene" approach available through the PubMLST Genome Comparator tool, using N. meningitidis core genome MLST (cgMLST) v1. 0, which includes 1,605 core loci  (18). SplitsTree4 v4. 13. 1 was used to visualize the resulting distance matrices as neighbor-net networks  (19).
Accession number(s). Sequences have been deposited in the PubMLST database under accession numbers 42113 to 42156, 42158 to 42160, 42162 to 42170, 42172 to 42177, 42179, 42218 to 42220, 42656, 42661, 42662, 43827 to 43835, 43837 to 43858, and 43860 to 43863 (see Table  S1  in the supplemental material).
The 2 serogroup Y isolates were different and belonged to ST1627 (CC167) and ST1466 (CC174). The 2 serogroup W isolates were of ST11 and ST13257, which both belonged to CC11; ST13257 was a single-locus variant of ST11. Genomic analysis of isolates. To describe the relationships of the isolates, all 105 cultured isolates (for the period of 2010 to 2016) were subjected to cgMLST analysis using the BIGSdb tools in the PubMLST database (see Materials and Methods). The isolates were grouped into several lineages. The major cluster corresponded to the CC32 isolates, which were quite separate from the other serogroup B isolates (Fig. 3A ). The isolates of serogroup B:P1. 19,15:F5-1:CC32 were highly related; therefore, we compared, by cgMLST analysis, the relationships of these isolates from Morocco to all other isolates that demonstrated the same combination and were available in the PubMLST database (accessed 26 December 2017).
19, 15). Moreover, WGS data were also available in the PubMLST database for 1 serogroup B/CC32 isolate from Morocco from 1994 (identification number 26047), which clustered together with the recent isolates (26) (Fig. 2 ). Our data suggest the persistence of these isolates in the region and may further suggest a recent clonal expansion of these isolates; most of the isolates belong to the major ST (ST33), with a few single-locus and double-locus variants. Of interest, the cgMLST analysis showed that these isolates seemed to differ from related isolates reported in other countries, suggesting expansion of a local strain in the Casablanca region. Those related isolates were responsible for large expansions, as reported for Cuba and Brazil, and led to vaccination campaigns  (27). The availability of protein-based vaccines may provide a possible strategy to control the expansion of the isolates.
ACKNOWLEDGMENTS This work was supported by the  Institut Pasteur  ( ACIP grant   A03-14 ). This publication made use of the Neisseria Multilocus Sequence Typing website (https://pubmlst. org/neisseria),developed by  Keith Jolley  and sited at the  University of Oxford  ( 18 ). The development of this site has been funded by the  Wellcome Trust  and the  European Union. We also acknowledge the  PIBNET-P2M  platform at the  Institut Pasteur.
Data quality The DREES verified the completeness of the database. Internal consistency was, first, controlled by focusing on 3 major variables: age, gender and radiological prescriptions so as to detect discrepancies between age and type of ED (e. g. , adult patients seen in a pediatric ED), gender and pathologies (e. g. , male with a strictly female diagnosis) and type of radiological exam and type of ED (e. g. , unconventional imaging procedure [MRI or tomodensitometry] in a structure that does not have such imaging available). For age and gender, less than 0. 001% discrepancy was noted. For radiological exams, a 2% discrepancy was noted. This finding probably also reflects particular organizations, for example a partnership with a nearby imaging center. Then, data quality was assessed by comparing data from the FES and the Oscour 1 network.
The Oscour 1 network was established in 2004 with support of the General Directorate of Health Care Provision, the SFMU and regional partners including the ORU. In 2013, 414 emergency structures participated in the monitoring network, covering nearly 65% of ED visits in France  [27]. Data from EDs involved in the Oscour 1 network are routinely obtained from patient medical records and the emergency passage summary, including sociodemographic, administrative and medical data. The Oscour 1 network and FES data were consistent for ED visits recorded on June 11, 2013. This study was declared to be of public interest by the CNIS (Conseil National d'Information Statistique) and was integrated into the public statistical program (Visa no. 2013X080SA and publication in the official Journal of French Republic September 17, 2013).
Statistical analysis Descriptive analyses were performed. They included the characteristics of the EDs and then the characteristics of ED users. EDs users were classified in two ways: first according to the type of ED visited, then according to their age. Continuous variables, which were non-normally distributed, are reported as median (Q1-Q3). Categorical variables are reported as numbers (%). All statistical analyses were performed with SAS (SAS/STAT Package 2002-2003, SAS Institute Inc. , Cary, NC, USA).
Characteristics of ED users and care Among the 52,018 expected questionnaires (based on the number of ED visits to each ED during the study period, as described in the ED section of the survey) 48,711 patient questionnaires were collected (94%). They were completed by the accompanying person/family member (in most cases, the accompanying person was the parent) in 25% of cases. For 2% of patients, data collection was impossible because the patient was not able to communicate and no accompanying person was available.
Study limitations The main limitation is that data regarding patients were recorded in real time (i. e. , during patient care), which can suggest reporting errors. Second, this study has limitations that are common to this type of study design (declarative surveys) such as reporting bias (including social desirability bias) and representativeness of the sample. This is especially true if the patient-questionnaire was completed with the assistance of a proxy (in our study 25% of patients). But it is important to note that in most cases, the proxy was a family member. Thus, we can assume that the proxy is sufficiently close to the individual to correctly answer, particularly concerning socio-demographic characteristics. In addition, the design of the survey allowed for responses to the ED questionnaire provided by an ED administrator, which reduced the risk of missing data and reporting bias.
Acknowledgments We thank all the members of the  French Society of Emergency Medicine Evaluation and Quality Committee  for their help:  Dr Sandra Bernard ,  Dr Jean Marie Bonnec ,  Dr Vincent Bounes ,  Dr Bahram Chaybany ,  Dr Romain Dufau ,  Dr Sybille Goddet ,  Dr Alban Guibert ,  Dr Romain Hellmann ,  Dr Mohamed Hachelaf ,  Dr Jean Yves Lardeur ,  Dr Philippe Leveau ,  Dr Claire Mauriat ,  Dr Pascale Nelh ,  Dr Carole Paquier ,  Dr Catherine Pradeau ,  Dr Francois Revaux ,  Dr Patrice Serre ,  Dr Jean Pierre Tourtier ,  Dr Gilles Viudes. We also thank all members of the  Frenche Society of Emergency Medicine  who helped filling the questionnaire on the day of the survey:  Abalea L, MD  (Brest),  Abdelhadi M, MD  ( Chalautre la Petite ),  Achouri A, MD (Libourne) ,  Adi O, MD  ( St Jean d'Angely ),  Agbessi A, MD  ( Boulogne Billancourt ),  Ahmed M, MD  (Koungou),  Ait Ali M, MD  (Amboise),  Ait Idir T, MD (Arnas) ,  Ait Oual A, MD  ( St Amand Montrond ),  Akendegue J, MD  ( Bourgoin Jallieu ),  Al Bourgol S, MD (Ambilly) ,  Al Tabchi A, MD  ( Belmont-sur-Vair ),  Alarcon P, MD  ( Maubeuge ),  Alayrangues G, MD  ( St Barthelemy-Antilles ),  Alazia M, MD (Marseille ),  Alazia ML, MD (Marseille ),  Albert J, MD  (Cabestany),  Alexander R, MD  (Appilly),  Allix V, MD  ( Le Mans ),  Amama H, MD  ( Auxerre ),  Amellal F, MD (Narbonne) ,  Amiens E, MD (Issoire) ,  Amrane A, MD  ( Le Blanc Mesnil ),  Ancelin P, MD  ( Mont de Marsan), Andre A, MD (Pontarlier ),  Andritsakis O, MD  ( L'Union ),  Andronikof M, MD  (Clamart), Anfart E,  MD (Biarritz), Arabeyre G, MD (Melun), Arnault F, MD (Vannes), Ascofare IC, MD (Amilly), Asdrubal J, MD (Macon), Auroy V, MD (Pontivy), Bah T, MD (Bordeaux), Bailly B, MD (Chaumont), Ballestrazzi V, MD (Armentieres) ,  Ballouz M, MD  (Provins),  Bandaly F, MD (Vierzon), Banihachemi JJ, MD (Echirolles), Bar C, MD (Brignoles), Barriere P, MD (Nantes) ,  Bartou C, MD  ( Villeneuve-sur-Lot ),  Bassil J, MD  (Laval),  Battaglini P, MD  (Aubagne),  Baudot V, MD  ( Le Pont de Beauvoisin ),  Baugnon D, MD  ( Verdun), Baure JJ, MD (Rochetaillee), Beaujean F, MD (Tulle ),  Bedioui A, MD  ( Almont les Junies ),  Begnis R, MD (Champagnole) ,  Belabbas H, MD  (Ales),  Belkhodja H, MD E, MD  ( St Mande ),  Vicente P, MD  (Cahors),  Vigneron P, MD  (Lorient),  Vinciguerra D, MD  (Toulon),  Violet S, MD  ( Castelnau Le lez ),  Voisin X, MD  (Antibes),  Voituret N, MD  ( Paray le Monial ),  Volait G, MD  ( Le Havre ),  Vrignaud B, MD  (Nantes),  Vuillermoz F, MD  ( Anglars Nozac ),  Wadih S, MD  (Ambres),  Wahiche M, MD  (Privas),  Wargon M, MD  ( Bry-sur-Marne ),  Wiener P, MD  ( Vitre ),  Wingert D, MD  (Wissembourg),  Wohler A, MD (Agen) ,  Wongeczowski O, MD  ( Levallois Perret ),  Wuilmet L, MD  ( Chalons en Champagne ),  Ximenes A, MD  ( Aicirits Camou Suhast ),  Younsi S, MD  ( Vire ),  Yousfi R, MD  ( St Nazaire ),  Zamoum D, MD  ( Mont St Martin ),  Zamour C, MD  (Valence),  Zane D, MD  (Augan),  Zaoui A, MD (Issoudun) ,  Zegar T, MD  (Amiens),  Zerga M, MD  (Chevreville),  Ziade E, MD  ( Le Creusot ),  Zimmermann S, MD  (Assainvillers), Zix AM, MD (Saverne),  Zunino FM, MD  ( Aix en Provence ).
Besides lncRNA, ceRNAs may also include mRNAs, circle RNAs, and pseudogenes  [86]. Despite the fact that the stoichiometric relationship between miRNAs/regulated genes and the affinity of ceRNAs to miRNAs may question the feasibility of gene regulation through changes in ceRNA levels  [87] , several dedicated databases list lncRNA-miRNA interactions  [86, 88]  and the number of original reports on their interactions still increases every year (at least 130 articles on lnc-ceRNAs January-June 2018). Recently, an lncRNA/miRNA/mRNA interactome was reported based on CRC expression data from the TCGA database. These analyses illustrated a ceRNA network of 25 principal miRNAs and 64 lncRNAs, including CRNDE, H19, HULC and UCA1  [17, 89]. As discussed below, UCA1 was reported to interact with 29 miRNAs in several types of cancers.
Association of UCA1 Transcript Expression with Colorectal Cancer Urothelial Cancer Associated 1 (UCA1) was first discovered in bladder cancer  [108]  and its long transcript is also the nominated Cancer Upregulated Drug-Resistant transcript (CUDR)  [109]. Actually, three UCA1 transcript isoforms of 1. 4 kb, 2. 2 kb, and 2. 7 kb have been described, but, in general, the most abundant 1. 4 kb isoform is studied  [110]. Analysis of UCA1 expression and patient survival data from the TCGA dataset shows that its expression was correlated with increased hazard ratio in different types of cancers, in particular with pancreatic adenocarcinoma (Table  2 ,  [111] [112] [113] ). Although several patient studies reported that a high expression of UCA1 is correlated with bad disease prognosis in CRC (the number of patient of these studies N = 80  [114] , N = 54  [115] , N = 90  [116]  and the N = 530 for the Asian meta-analysis  [117] ), no such evidence was observed in the TCGA COAD-READ study (Table  2 ).
* in bold = miRNA target site predicted at UCA1 gene by miRcode  [136] ; ** = number of miRNA-validated targets (identified in ChemiRs/mirTAR database for 3p and 5p mature miRNA  [325] ) *** = Panther Go-Slim biological processes that present a ≥2-fold enrichment for miRNA-validated targets (PANTHER  [326] ; Overrepresentation Test (version 20171205/version 13. 1)). N. A. = miRNA data not available in ChemiRs database. N. S. = No statistically significant results (FDR > 0. 05). "CRC"-column: references of reports implicating miRNAs in CRC.
Enzymatic validation of dual-probe 3 To determine the enzymatic specificity of the dual-probes 1-5 (fragments b and f, and peptides undergoing optimisation as outlined in the ESI †) the probes were incubated at a concentration of 5 µM (unless otherwise stated) with the required recombinant MMP enzymes (Enzo Life Sciences) (active domains of MMP-1, -2, -3, -7, -8, -9, -10, -11, -12 and -13, 30 nM), thrombin (Sigma Aldrich) (5 U ml -1 ), Factor Xa (Sigma Aldrich) (500 nM), Plasmin (Sigma Aldrich) (30 nM), and human neutrophil elastase (30 nM). Where enzymes were added sequentially to dual-probe 3, the first enzyme was incubated with the probe for 60 min prior to the addition of the second enzyme. Data was collected from 10 min incubation of probe with enzyme. Where appropriate enzymes were incubated with specific inhibitors for 30 min at 37 °C prior to addition of the dual-probe 3.
The pan-MMP inhibitor Marimastat (Tocris Biosciences) was used at 20 µM, while the thrombin inhibitor (anti-thrombin III (AT3, Sigma Aldrich)) was pre-incubated at 1 µM. Enzyme free reactions served as a control. Enzymatic reactions were performed in MMP buffer (10 mM CaCl 2 , 6. 1 g Tris-HCl, 8. 6 g NaCl per litre, pH 7. 5) in a final volume of 20 µL. Reactions were performed in duplicate in 384 well plates (Life Technologies) with optically clear plate seals (Thermo Scientific) and repeated thrice (independently). Fluorescence signal was measured for up to 60 min, ex/em 485/528 nm (FAM) and ex/em 640/670 nm (Cy5) using micro-plate reader (BioTek Synergy H1 multi-mode reader). Data was normalised to buffer alone and are presented as fold-change in signal (Relative Fluorescent Units) compared to enzyme-free controls at 10 min.
Acknowledgements We would like to thank  Engineering and Physical Sciences Research Council  ( EPSRC, United Kingdom) Interdisciplinary Research Collaboration grant   EP/K03197X/1  and  Medical Research Council  (under the  Developmental Pathway Funding Scheme  grant number  MR/J014702/1 ) for funding this work. The research leading to these results has received funding from the  European Union Seventh Framework Programme   FP7 2012  under grant agreement no. 326465  (A. M. -F). We would like to thank  Emma Scholefield  and  Sheelagh Duncan  for their technical assistance with the murine model. Data used within this publication can be accessed at http://dx. doi. org/10. 7488/ds/2066.
[ ( F i g. _ 7 ) T D $ F I G ] As most of the works in the literature are dealing with some or one of the TMN, in this work, the total ground energy, the lattice parameter and the electron density of states (EDOS) of all the nitrides of the transition metals of the groups IVb-Vb-VIb in the B1 structure (except CrN) have been calculated with the linear augmented plane wave (LAPW) method within the density functional theory using the Wien2k software  [527] , in order to  deal with all of them using exactly the same computational details and thus being able to compare their differences quantitatively. The LAPW method, which is the most widely used method for the EDOS calculations of TMN, according to Table  1 , expands the Kohn-Sham in atomic-like orbitals inside the atomic (Muffin Tin (MT)) spheres and plane waves in the interstitial region.
B1-VN received less attention and in this review only data from one sample are reported. These data indicate that B1-VN is not inferior to B1-NbN, while having the additional asset of exceptionally low work function as shown in Fig. 26. The combination of the potential plasmonic response in the UV with the low work function of B1-VN might be a strong incentive for the investigation of B1-VN in plasmonics and hot electron applications, however, its real potential for such applications can not be solidly evaluated for the time being due to the lack of data from other research groups that would confirm its reported optical behavior. Finally, B1-MoN and B1-WN are exceptionally and excessively lossy due to both their inherent characteristics as calculated by LAPW and presented in Figs. Fig. 13 , 15, 16a and b, and their metastabe character, which results in the incorporation of a high concentration of point defects to stabilize them.
Acknowledgements P. P. would like to acknowledge  Prof. S. Logothetidis  of  Aristotle University  for his guidance at the initial steps of the nitride research,  Dr G. M. Matenoglou  (now at  Texas A&M University at Qatar ) and  Dr L. E. Koutsokeras  (now at  Cyprus University of Technology ) for their massive experimental work on the growth of conductive nitrides, and  Prof. J. -F. Pierson  of  University of Lorraine -France , and  Prof. D. Gall  of  Rensselaer Polytechnic Institute -USA  for their collaboration in studying ternary nitrides.
Methods We reviewed 2543 elective primary TKAs performed at the Queen Mary Hospital, Hong Kong, from 1993 to 2013. Data were collected by an infection control nurse of the Department of Microbiology who was blinded to the study objectives. The cohort data were collected from the Hong Kong Hospital Authority's Clinical Data Analysis and Reporting System, the Infection Control Team, and the hospital's joint replacement division registry. The keywords used in the data search were 'periprosthetic joint infection' , 'total knee arthroplasty' , and 'surgical site infection'. Revision arthroplasties and knee arthroplasties for malignant conditions were excluded from the study. In patients with a history of native joint infection, elective primary TKA was performed only after eradication of the infection.
Patients with active bacteraemia were also precluded from elective primary TKA until they were infection-free. There were no cases of severe immunosuppression. In relation to infection control, the majority of perioperative protocols for primary TKA were the same throughout the study period. Preoperatively, intravenous antibiotic prophylaxis (1 g of cefazolin) was given within 1 h before skin incision. In patients with penicillin allergy, other antibiotics were prescribed as appropriate. Intra-operatively, laminar airflow and body exhaust systems were used. There was no routine use of antibiotic-loaded cement or postoperative antibiotics. Postoperative wound management was the same throughout the study period. Cohort characteristics, occurrence of PJI, and bacteriological data were retrieved.
Signaling and transcriptional networks are strongly intertwined in the cell, providing multiple feedbacks from one to an other. Availability of computational reconstructions of all types of molecular networks are critical for understanding the connection between genotype and phenotype  [1]. Global reconstructions of the directed causal signaling network structure have appeared only recently in the form of comprehensive molecular interaction databases such as SIGNOR  [7] , SignaLink  [8] , where the pair-wise relations between molecules are oriented. Large-scale cell type-specific reconstructions of transcriptional causal networks have become possible thanks to appearance of Chip-Seq technology  [9, 10]  or advances in computational methodology of transcription factor binding site predictions combined with the data on chromatin accessibility  [11].
To our knowledge, the Google matrix approach or related ideas have been applied before in systems biology mostly to undirected networks, with few exceptions (e. g. ,  [20] ), in order to find activated network modules or to "smooth" high-throughput data  [21] [22] [23] , to establish connection of genes to diseases  [24, 25] , to improve interpretability of genome-wide analyses  [26] [27] [28]  and to compute network-based cancer biomarkers  [29, 30]. PageRank approach has been used to quantify the functional proximity in undirected protein-protein interactions networks  [31]. A comprehensive recent review on the application of random walk with restart approach to biological networks can be found in  [32]. Comparison of the Google matrix algorithm with other approaches for quantifying centrality (e.