dataesr/scientific-paragraphs-categorization huggingface dataset:
Geostrophic velocities derived from hydrographic data suggest that the high-velocity cores have a weak but discernable baroclinic component matching the location of the surface jets. The vertical shear, extent, and location of the jets are also confirmed by direct shipboard velocity measurements. Based on recent underway velocity observations, the multiple jets in the upper layer have a meridional extent of about 900 km, between 42. 5 and 52 S. In accordance with the ADCP survey and surface drifters, the analysis of a high-resolution MDT derived from satellite altimetry reveals that two distinct and continuous high-velocity cores extend from about 47ºS to 42ºS. The jets are separated by a region of relatively steep bottom slope, which suggests a close relationship with the bottom topography.
[27] Acknowledgments. This research was funded by grant CRN2076 from the Inter-American Institute for Global Change Research, which is supported by the US National Science Foundation (Grant GEO-0452325), MINCYT/CONAE grant 001 and ANPCyT grant PICT08-1874. B. C. F. was supported by a fellowship from Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina. Comments from three anonymous referees are gratefully acknowledged.
X-ray structure Fortunately, single crystals of target compounds were grown from water, and X-ray data were obtained for five mimetics, 17c,h,i,j,t. They were fixed in the solid crystalline state as either trifluoroacetate or chloride salt with the compositions 17c,h and 17i,j,tÁH 2 O. After 2 h of boiling of aqueous solutions of compounds 17c and 17h and then their cooling to room temperature, single crystals were grown. They were identified as the hydrates of zwitterionic forms, 17c-TfaOHÁ5H 2 O and 17h-TfaOHÁ2H 2 O. The ORTEP drawings for the formula units are shown in Figure  3. All the mimetics molecules bear three planar fragments, the benzoyl ring, the 1-oxo-1,3-dihydroisoindolyl core with the equivalent bond lengths being similar within themselves, in the parent 5b and in the closely related 3-(1-oxo-1,3-dihydroizoindol-2-yl)propionic acid,  11  and the amide bridge between these moieties.
In vitro biology Functional activity was determined by measuring the inhibition of ADP induced platelet aggregation in human platelet-rich plasma (PRP) by Born's method. 17 Mode of action for some compounds was subsequently revealed in vitro by measuring the ability of compounds to inhibit the binding of fluoresceinisothiocyanate-labeled fibrinogen (FITC-Fg)  18  to a IIb b 3 (in a suspension of human washed platelets). 19 Experimental data (Table  2 ) evidently show high affinities of the compounds 17 for a IIb b 3. RGDS peptide and Tirofiban were used as standard inhibitors. We first investigated the SAR of compounds 17a-e, containing a fragment of (1-oxo-1,3-dihydroisoindol-2-yl)acetic acid. These compounds showed low in vitro antiaggregative activity, except for the leader of the group, mimetic 17d containing 4-piperazine-4-yl-benzoyl, as Arg isostere.
Molecular docking Molecular docking studies have been performed in order to give a microscopic insight into experimentally observed structure-property relationship. FlexX tool from LeadIT package has been used. 20 The X-ray structure of complex of Tirofiban with headpiece of a IIb b 3 integrin (2VDM) has been taken from Protein Data Bank. 21 Structure of the pocket has been prepared and four water molecules have been kept in the cavity: two water molecules, coordinated with Mg 2+ metal ion-dependent adhesive site (MIDAS) of the a IIb b 3 integrin, and two water molecules coordinated with Asp232 residue of the a IIb -subunit. Investigated compounds have been docked in the prepared pocket. Because the interaction of ligands of a IIb b 3 integrin with aAsp224 and MIDAS are essential for compound binding and activity, only best poses which follow these requirements have been taken for further analysis.
Acknowledgments This article is dedicated to the memory of  Dr. Yurii A. Simonov , who took active part in this research. The authors are grateful to  Dr. V. V. Polovinko  ( Enamine LTD, Ukraine ) for the 1 H NMR spectra. We also thank  Dr. A. V. Mazepa  and  Dr. I. M. Rakipov  ( A. V. Bogatsky Physico-Chemical Institute of the National Academy of Sciences of Ukraine ) for the Mass spectra. T. M. Khristova  thanks the  French Embassy in Ukraine  for a PhD fellowship.
Methods: MEDLINE and EMBASE databases from their inception until early 2012 were searched and the corresponding authors of all eligible primary studies invited to contribute their raw data. We developed relevant quality assurance items likely to identify occasions when study validity may have been compromised from several sources. A confidentiality agreement, arrangements for communication and reporting as well as ethical and governance considerations are explained. We have agreement from the corresponding authors of all studies which meet the eligibility criteria and they collectively possess data from more than 17000 patients. We propose, as a provisional analysis plan, to use a multilevel mixed model, using "study" as one of the levels. Such a model can also allow for the within-patient clustering that occurs if a patient contributes data from both feet, although to aid interpretation, we prefer to use patients rather than feet as the unit of analysis.
There are several advantages of IPD meta-analyses, but the ability to conduct a more complete time-to -event analysis, where outcomes can be adjusted for prespecified covariates is key. We propose to conduct a systematic review and meta-analysis of IPD to create a statistical model of independent predicative factors of diabetic foot ulceration  [9] [10] [11]. A detailed assessment of 20 IPD articles conducted as part of a synthesis of IPD reviews identified many challenges associated with this research method and the time-consuming and costly nature requires proper consideration. Fundamental methodological omissions are common: the assessment of the risk of bias arising from the conduct of individual studies is not usually performed and a priori study protocols and ethics are rare  [12]. Our protocol has been informed by this work and we have identified 15 cohort studies through searches of electronic databases.
These cohorts include data from approximately 17,000 patients worldwide and the authors of the original reports have agreed to collaborate in this research by sharing data. This review is central to the development of a global evidence-based strategy for the risk assessment of the foot in patients with diabetes, ensuring future valid recommendations that can reliably inform international clinical guidelines. A flow diagram of the stages involved in a systematic review of IPD can be found in Additional file 1: Appendix 1  [10].
Types of Participants The IPD review will only include data from individuals who are free of foot ulceration at the time of study entry and who have a diagnosis of diabetes mellitus (either type 1 or type 2). Corresponding authors of all identified cohort studies will be contacted and invited to share their data. When we identify studies with patients who had prevalent foot ulcers at the time of recruitment, we will ascertain whether IPD are available for patients who were free of ulceration at the time of recruitment.
Types of studies We will seek data from all cohort studies which included participants who were free of foot ulceration at the time of study recruitment. Our previous work indicates that data collected in older studies could be difficult to obtain and we are aware that some investigators are no longer in possession of their study data (Personal communication, D. Armstrong 2012). Where data are unavailable, details of the study will be presented in aggregate form in the final report. Cohort studies which recruited patients with prevalent and incident foot ulceration will be considered for inclusion where it is possible to separate the data for these patients.
Heterogeneity As with any meta-analysis, heterogeneity must be considered, both from a clinical and statistical viewpoint. First, clinical expertise will be used to decide if it would be meaningful to combine the studies based on the patient demographics, risk factors (symptoms, signs and diagnostic test results), outcome measures and timing of outcome measures (length of follow-up). We will examine histograms of relevant variables from each dataset to check the spread, mean, median, and skewedness, and the consistency of these properties across datasets, before reaching a decision about whether it makes clinical or statistical sense to combine the data. We will also consider relationships between variables using tables and scatter plots. Sources of heterogeneity that particularly concern us are differences between the patient groups with regard to basic demographics and disease spectrum as these may have a strong influence on prognosis and the performance of the tests.
Also important are the various methods used to conduct the tests, which again may lead to marked differences in test performance. Another potentially important source of heterogeneity is length of follow-up as this may impact on the proportion of patients who develop ulceration. These aspects will be carefully detailed during the review process. We are aware that a consensus has not yet been reached about the investigation of heterogeneity in IPD systematic reviews. Therefore we will use conventional methods of investigating heterogeneity on aggregate data generated from the datasets. We shall therefore generate summary measures and use these to create forest plots and compute I  [2]  statistics  [27]. I  [2]  values of 50% and 75% have been used to denote moderate and high levels of variation between studies that are not explainable by chance.
Statistical analysis We propose to use a multi-level mixed model, using "study" as one of the levels. Such a model can also allow for the within-patient clustering that occurs if a patient contributes data from both feet, although to aid interpretation, we prefer to use patients rather than feet as the unit of analysis. We will only attempt this analysis if the results of the investigation of heterogeneity do not rule it out and the model diagnostics are acceptable. As the datasets should contain the date of initial diagnosis of diabetes and the date, if any, of foot ulceration, we propose to use survival analysis. Covariates will be added to the model based on clinical relevance, if there are many possible covariates that could be added given the number of events and patients and there is a danger of model overfitting, the clinicians will be asked to choose a subset of covariates based on their expertise and experience.
Validation of the dataset We intend to undertake both internal and external validation of the prognostic model. For internal validation, we will not divide the datasets into development and validation subsets, as this is a relatively inefficient method of validating prediction models. Instead we shall use bootstrapping as it is less susceptible to bias and leads to more stable model development  [28]. For external validation, we shall reserve one or two of the datasets to test the final model obtained in the main analyses. The reserved datasets will be chosen on the basis of completeness of variables collected so that, we hope, all the variables present in the final model will also be in the reserved datasets, thus requiring no or minimal modification of the final model for external validation purposes.
We also shall look at various characteristics of each dataset such as patient demographics when choosing the reserve datasets to ensure that these datasets are not atypical of the set of datasets. Unfortunately we are currently lacking the data required for a full power calculation. However, as an illustration, assuming that it is possible to split the sample of 17000 evenly in half into patients with and without some prognostic factor, it would be possible to detect a 2% difference in the proportions of patients with foot ulcers in each group with over 90% power. This calculation assumes that the ulceration rate in one group is 0. 10 and 0. 08 in the other. With a Type I error rate of 0. 05, these figures give a power of 99. 53%.
Handling missing data Our method for handling missing data will depend on the extent of the missingness and if the mechanism causing the missingness is known, specifically if they are missing completely at random, or not. If the datasets contain missing data for which there is no explanation, they will be assumed to be 'missing at random  [29]. We will use ICE multiple imputation (ICE programs, Stata 11. 0)  [30] , and include all available patient variables (including the patient outcome: foot ulceration) in the imputation model to help predict missing data for the variables of interest. Twenty imputed datasets will be used and included in the imputation procedure. To test the validity of the imputation, a sensitivity analyses will be performed restricting our cohort to patients without missing data (complete case analysis)  [31].
Ethics and governance The ethics of obtaining data collected from a number of sources which cross international boundaries and different legal systems have been carefully considered and informed by ethics advice issued by the Medical Research Council (UK). This study does not require separate ethical committee approval for the following reasons; Investigators of each of the original studies obtained local ethical committee approval and written, informed patient consent prior for each of the cohorts included in the IPD review. The project seeks anonymised data from which the individuals recruited to the original study cannot be identified  [32]. The value of the IPD analysis will be the production of a global dataset of prognostic factors for diabetic foot disease and the opportunities for new uses will be maximised.
Clinical studies All registrants provided demographic and clinical information including gender, age at diagnosis, symptoms, biochemical, and imaging data as well as family health histories. Paraganglial tumors were classified according to tumor number (solitary or multiple), location  (adrenal, extraadrenal abdominal, thoracic, head, and neck) , and biology (benign or malignant) (Table  1 ). Criteria for malignancy were presence of metastases in lymph nodes or distant tissues  (Tischler 2008). Patients who were identified as having one of the hereditary syndromes underwent clinical reevaluation. The clinical screening program comprised for MEN2 serum calcitonin and parathormone, for VHL magnetic resonance imaging (MRI) of the CNS, MRI of the abdomen and retinoscopy, and for the paraganglioma syndromes (PGL1-4) MRI of the abdomen, thorax, and neck.
Statistical analysis Categorical variables were expressed as absolute and relative frequencies and compared by two-sided c 2 test. Scale variables were expressed as median and range and compared by two-sided Mann-Whitney U test. Censored data were analyzed and plotted by Kaplan-Meier method and compared by two-sided log-rank test. The 95% CI for proportions was calculated according to the modified method of Wilson by Newcombe  (Wilson 1927 , Newcombe 1998 ). Significance level was set to PZ0. 05 for statistical testing. All statistical calculations were carried out with IBM SPSS, version 17 (SPSS, Inc. ).
Phylogenetic analyses For every selected clone, both forward and reverse amplified nucleotide sequences were aligned using ''ClustalW alignment » software included in the DAMBE version 4. 5. 68  [17]. According to Akaike Information Criterion (AIC), different evolutionary models were tested using PAUP software version 4. 0b10 (Sinauer associates, Inc. Publishers, Sunderland, Massachussets). Phylogeny was performed with the neighbor joining method and the best tree was selected after a bootstrap analysis of 1000 replicates. GenBank accession numbers of the gorillas SFV strains used in the multiple alignments are: SFVggo(BANGA) (AY686191), SFVggo(BINTA) (AY686193), SFVggo(CAM7) (AY583782), SFVggo(GORGABCOL) (AY603409), SFVggo(GORGABOMO) (AY603410), SFVggo(KOOB) (AY686194), SFVggo(KUCHI) (AY686192), SFVggo(LP5) (EU527593).
Sequences of the SFV pol-in clones from PBMCs and saliva were deposited in GenBank under the accession numbers: KC602129-KC602164 for LOBAK2, KC602201-KC602238 for BAD456, KF515319-KF515357 for BAD447, KF515358-KF515395 for BAK33, KF515396-KF515431 for BAK74, KF515432-KF515469 for BAK132, KF515470-KF515508 for BAK133, KC602165 to KC602200 for BAK177.
We then quantified SFV DNA load by performing q-PCR on the same samples. SFV DNA load could be quantified in 13/14 PBMC samples and in 6/14 saliva samples (Figure  1 ). Total SFV DNA load in PBMCs and in saliva were very low in most of the samples (7. 1 ± 6. 0 and 2. 4 ± 4. 3 SFV DNA copies/10 5 cells respectively). No correlation was found between SFV DNA loads in the PBMCs and saliva samples Of note, only individuals with long time of infection (>16 years, which is the median) had more than 1 SFV copy/10 5 cells in the saliva (data not shown). The sensitivity of the technique used to detect SFV DNA is higher for our nested-PCR assay (1 copy/150 000 cells)  [10]  than for our q-PCR assay (1 copy/50 000 cells): 3/28 DNA samples (saliva from BAD456 and BAK177 and PBMCs from LOBAK2) were positive by nested-PCR but negative by q-PCR, probably due to a low viral load (Figure  1 ).
Overall, in 8/14 cases, SFV nested-PCR was positive in DNA samples of both PBMCs and saliva derived from SFV infected-individuals (Figure  1  and Table  3 ). Characterization of SFV clones. We generated SFV clones of the pol-in fragment (mean number of 18 clones) when both saliva and PBMCs samples contained SFV DNA (8/14 individuals). At the individual level, the sequences of SFV clones derived from PBMCs versus saliva often differed by only 0. 5% genetic variation (in 5/8 individuals, Figure  2A ). In 1/8 case (BAK177), a higher genetic variation (3. 3%) in the range of the inter-strain variability was found among SFV clones which segregated into two clusters (Figure  2B ). Such data could indicate a co-infection by two SFV strains. For individuals BAD456 and LOBAK2, consensus sequences of PBMCs and saliva SFV clones were not identical and displayed a G/A polymorphism in a GG dinucleotide-context (representing 5/5 and 4/8 nucleotide differences respectively), with the A polymorphism being absent in the 8 SFV strains found in gorillas and available in GenBank (Figures  S1  and S2  provide alignments of SFV sequences, and Figure  2C  and 2D provide corresponding phylogenetic trees).
SFV RNA is not detectable in PBMCs and saliva samples from SFV-infected hunters To determine if PBMCs or cells of the saliva were permissive for SFV infection, we used qRT-PCR targeting viral pol-in RNA, as an indication of viral replication. Since primers in the pol gene were used, our measurements include both SFV genomic RNA and SFV pol transcripts. We checked that this assay could detect SFV RNA from a SFV permissive cell line (the human microglial cell line CHME) infected with the SFV strain from individual BAK74, after appearance of the typical in vitro cytopathic effect (data not shown). We next examined PBMCs and saliva samples of 10/14 and 14/14 SFV-infected individuals respectively (4/14 PBMC samples were not available for RNA extraction). Although cellular RNA was detected in all the PBMC samples tested and in 10/14 saliva samples by GAPDH RT-QPCR, no SFV viral RNA was detected in any of these samples (Table  2 ).
Supporting Information Figure  S1. Analysis of viral sequences from SFV-infected human BAD456. Uncultured PBMCs and saliva samples from BAD456 hunter accidentally infected by a Gorilla gorilla SFV strain were analyzed for the presence of SFV sequences. A total of 18 and 20 different PCR-derived clones are depicted for PBMCs and saliva samples respectively. 20 PBMCs clones (blue) were grouped in "cluster 1" and 18 saliva clones (red) were grouped in "cluster 2" according to phylogenetic analysis (not shown). Their consensus sequences (PBMC-cons and SALIVA-cons, respectively) are also depicted. The 8 reference SFV sequences available in GenBank from gorillas of the Gorilla gorilla species are aligned (green). 5 G-to-A mutations found in the hunter samples in a GG dinucleotide context and absent in the 8/8 gorillas sequences are highlighted in blue.
Their consensus sequences (PBMC-cons1, SALIVAcons and PBMC-cons2 respectively) are also depicted. The 8 reference SFV sequences available in GenBank from gorillas of the Gorilla gorilla species are aligned (green). 4 G-to-A mutations found in a GG dinucleotide context and absent in the gorillas sequences are highlighted in blue and 4 other mutations between PBMCs and saliva clones also present in some gorillas sequences were highlighted in grey. Sequences are aligned to the consensus sequence of PBMC cluster1 samples, which served as a reference due to its high similarity with the gorillas strains. The 425bp-fragment of the pol-in sequence is shown. Due to the low SFV viral load, LOBAK2 clones of each cluster might derive from one to few in vivo SFV copies. Dots represent identical residues.
Only values greater than 50% are shown. The scale of the tree is 0. 01 nucleotides replacement per site. Of note, phylogenetic tree for BAK132 is representative of trees obtained for the other following hunters (BAK33, BAK74, BAK132, BAD447). Sequences accession numbers for BAK132 SFV clones are KF515432-KF515469. Phylogenetic tree for BAK177 indicates the presence of two distinct groups of SFV clones, suggesting a co-infection with two SFV strains. Sequences accession numbers for BAK177 SFV clones are KC602165 to KC602200. For BAD456 and LOBAK2, some clones displayed G to A mutations (G->A) present in a GG dinucleotide context and absent in the 8 NHP gorillas strains. Sequences accession numbers of SFV clones are KC602201 to KC602238 (BAD456) and KC602129 to KC602164 (LOBAK2). Due to very low viral loads, a single cluster of SFV clones within one compartment (PBMCs or saliva) might derive from PCR amplification of a single SFV copy.
R. Rua  was personally supported by the  "Bourse de l'Ecole Normale Supérieure, Faculté Paris Diderot ". E. Betsem  was supported by the  "Association Virus Cancer Prevention " and the  Institut National pour le Cancer. Financial support was also provided by the  Institut Pasteur in Paris. This study has received funding from the  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence « Integrative Biology of Emerging Infectious Diseases »  (grant n°ANR-10-LABX-62-IBEID ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Materials and Methods To retrieve information on human rabies cases in travelers, we first conducted a literature search using the PubMed (MEDLINE) and Scopus databases (http://www. ncbi. nlm. nih. gov/pubmed; www. scopus. com/scopus/home. url), from 1980 to December 2012, cross-referencing the following terms: ''rabies'', ''imported'' and ''travel''. Relevant systematic and narrative reviews were also utilized to obtain useful background information. The reference lists of the systematic reviews and other identified papers were scanned for potentially relevant primary studies that could be considered for inclusion in the review. Additional searches were conducted using the ProMED-mail (http://www. promedmail. org/), Google (http://www. google. fr/), and Yahoo (http://fr. yahoo. com/) general search engines.