dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, additional external rules from publishers are expected to be positively correlated with the data sharing. Finally, the impact of access and benefit sharing regulation (ABS) is expected to depend upon the compatibility with the social production and collaborative economy model. In the case of materials, there is already a long tradition with Material Transfer Agreements for sharing, which explicitly consider access and benefit sharing. Therefore, the impact is expected to be positive or neutral. In contrast, in the case of data, there exists a high degree of legal uncertainly on the way and the possibility to reach a mutual compatibility between the public availability and the implementation of the Protocol. Therefore a negative correlation is expected. To assess the role of these variables on the decision to allow further downstream sharing of materials and/or to deposit data on public genomic databases upon or prior to publishing, the following two regression models were developed: A first model on sharing of materials.
As similar variables are likely to play a role in these 3 sub-models, a joint estimation of the three outcome variables is appropriate. Therefore a multi-variate probit model has been used (for the justification of the choice of a probit, cf. comments below). A second model addresses the early release of genomic data by research and managers of the culture collections, through their deposit on external genomic databases, mainly the public databases from the INSDC (International Nucleotide Sequence Database Collaboration). This second model tests if the early release of genomic data is correlated with: internalized motivations: duty as a core motivation to provide materials (variable: Data prov duty) external rules/incentives for sharing: rules on access and benefit sharing applicable to data and information regulated by the country (variable: ABS country) or the organization (variable: ABS organization); the government imposes the rules on conditions of transfer of data (variable: Gov.
rules data); publishers mandate data provision to public online databases (variable: Publishers' rules); scientists and/or national public authorities are consulted when making decisions on transfer of data (variables Consult nat public authorities and Consult scientists); perceived importance of employers' role in creating willingness to share (variable Perc employers role). Not for profit nature of the organization (variable: For profit) With a view to verifying the consistency of the results with previous studies of the distribution patterns of the culture collections  (Dedeurwaerdere et al. , 2009; Stromberg et al. , 2013) , the following control variables were also included: private sector provision, contributions from public sector research organizations, formal permission for redistribution and reception from other culture collections and the international orientation of the collections.
Data analysis method The decision to share can reasonably be represented by binary response variables (closed question 9 of the questionnaire), but this decision varies with the source of the material. The main three sources of materials in the sample are ''other culture collections'', ''government research institutions'' and ''universities''. We therefore modelled the decision to share materials through three outcome variables. As these decision variables are correlated, we used a multivariate probit, allowing to jointly predict the decision choice when receiving materials from one of these three sources. All explanatory and control variables of the models were derived from the close-end questions of the structured questionnaire (cf. table in Annex A and the list of close-ended survey questions in Annex C).
The networks that play a role in the case of the culture collections are the World Federation and the 5 regional federations that closely collaborate with the World Federation (respectively the 2 Asian federations, the European Federation, the North American Federation and the South American Federation) (acronyms cf. table in Annex A). In the case of data, as discussed above, competitive pressures amongst scientists tend to play an important role in the trade-off with the social production model, as can be witnessed from the significant positive correlation with external incentives (Publishers' rule) and negative correlation with the consultation of the scientists (Consult scientists). Finally, the impact of access and benefit-sharing (ABS) rules on the scientific research commons shows a clear contrast between the case of data and materials.
Indeed, the existence of ABS rules does not have a highly significant impact on the sharing of materials, while ABS rules related to data are correlated to a significant decrease in public data deposit (both for ''Data ABS country'' and ''Data ABS org''). This result can be related to the legal uncertainty surrounding the impact of ABS on data in the collections, in contrast to the case of materials where material transfer agreements for ABS have been discussed and implemented for a long time.
Acknowledgements We acknowledge co-funding of this research from the  European Commission , under the contract of the  EU research projects BIOMOT  ( FP7 grant  agreement  282625 ),  GENCOMMONS  ( ERC grant  agreement  284 ) and  MICRO B3  ( FP7 grant  agreement  28758 ) and co-funding from the  National Science Foundation ,  Belgium (F. R. S. -FNRS/MIS   Incentive Grant on Governing Global Science Commons ). We also thank  Philippe Desmeth  (currently presidentelect of the WFCC) for his insights and support throughout the study. We are also grateful for their assistance in organizing the survey to the organizers of the ICCC-13 conference and the presidents of the  US ,  Latin American  and European networks of culture collections (respectively  Prof. Xiuzhu DONG  ( for ICCC-13 ),  Kevin McClusky  (USCCN),  Nelson Lima (ECCO) ,  Graciela Davel  (FELACC)).
In order to test this functionality over a broad range of concentrations, we analyzed the mainstream aerosol of an e-cigarette connected to the smoking machine and volunteer-exhaled breath after mouth-hold and inhalation topographies of e-cigarette aerosols, respectively. Representative results for PG, nicotine and VG in these three scenarios are shown in Figs. 3 4 5. The total time-span for all the graphs presented here is 400 s, although every data point has been integrated for only 390 ms to get a high time resolution. For the mainstream aerosol test, which is shown in the upper panel (a) of each figure, we set the smoking   Analysis of VOCs in e-cigarette aerosol by PTR-MS machine to 60 mL puff volume, 3 s puff duration and 60 s puff interval. A first strong indicator that contamination is avoided is that within the 60 s interval the concentrations return to baseline levels; none of the three compounds accumulate in the sampling system.
Acknowledgements This project was supported by  Fontem Ventures B. V. , a fully owned subsidiary of  Imperial Tobacco Group. K. Br. received funding through the PIMMS ITN which is supported by the  European Commission 's  7th Framework Programme  under Grant Agreement Number  287382. The authors would like to thank  Dr Rene Gutmann  ( IONICON Analytik GmbH, Innsbruck, Austria ) for his help with the LCU calibrations.
Therefore, the debate concerning the "right to be forgotten" highlights the need for a counterweight to the persistence of digital data. Throughout an individual's life, pictures and other personal data are being shared online by the person him/herself, and data may also be published in a child's early years through "sharenting" (the divulging of children's personal data by their parents). Individuals themselves fuel corporate databases through collaborating to establish a "quantified self"  (Wolf, 2007)  by processing physical activities and health metrics through wearables. This processing of sensitive personal data also raises questions on their secure storage and the purposes for which these data are and could be used. Moreover, as youth are embracing these digital media in their daily lives, it is important to investigate how they deal with their own personal data online.
There is substantial interest among vaccine manufacturers, contract research organisations, regulators and medicines control laboratories in evaluating the method for routine safety testing, and potentially replacing some or all of the existing in vitro and in vivo tests. A major challenge to the realisation of this potential is the identification of a robust, sensitive and specific assay design. A wide range of methods exist for viral metagenomics, many of which are early in their development: multiple options exist for generation of sequencing libraries; several commercial sequencing platforms exist, based on fundamentally different chemistry, with more in development; numerous bioinformatics pipelines are used for sequence classification, both academically and commercially developed; and the databases against which the reads are searched are constantly evolving.
One lab reported difficulty in distinguishing Norovirus strains due to a large number of closely related sequences available in public databases. This lab provided an explanation and reported hits to 'Human norovirus' rather than to serotypes GI and GII. For result plotting, hits to 'Human norovirus' (for this laboratory only) were considered to be to Norovirus GII that had a higher overall rank. Hits to Ad2 and AdC were merged as the reference sequences are identical. Similarly hits to Mastadenovirus F were merged with Ad41.
Issues in bioinformatics and databases It is important that bioinformatics algorithms strike an appropriate balance of speed, sensitivity and specificity. The size of sequencing datasets and reference databases continues to grow, offering obvious advantages in terms of sensitivity; however this has necessitated the development of new sequence classification algorithms to enable data analysis within a reasonable time, at a cost of potentially increased false negative rate  [24] [25] [26] [27]. A range of methods were used in the current study and thresholds for assigning hits varied, with some laboratories reporting detection of a virus on the basis of a single read but others requiring that additional criteria were met. Viral identification stringency should be determined by the context. A low stringency will maximise the chance of detecting novel viruses which may be highly divergent from databases references at the risk of increased false positives, and may be appropriate e.
g. for screening of master cell and virus banks where follow-up testing can be performed. In a routine testing scheme, or where a defined set of contaminants is being screened for, higher stringency may be appropriate to minimise false positives, e. g. due to matches with host sequences similar to viral genes. While the current study focussed on the detection of known viruses, many of the pipelines employed are entirely compatible with virus discovery investigations simply by altering the processing and search parameters. We also observed variation in the databases used for sequence classification. Publicly available databases such as those hosted by NCBI  [28]  are valuable but incomplete and not curated, and consideration is needed of what action should be taken if sequencing reads that are currently non-classified are later identified as being of viral origin.
Acknowledgements We are grateful to the participants of the study for contributing time and resources. We would like to thank  Dr. Rob Anderson ,  Dr. Sophie Collot-Teixeira  and  Dr. Kathryn Doris  at  NIBSC  for provision of reagent  11/242-001  and advice on production and characterisation. We are grateful to the  NIBSC Sales and Dispatch Team , in particular  Mark Harris  and  Paul Bolton  for co-ordination of reagent shipments and thank members of the  FDA/PDA Advanced Virus Detection Technologies Interest Group  for helpful discussions and feedback. This report is independent research which, in part, was commissioned and funded by the  Department of Health Policy Research Programme (NIBSC Regulatory Science Research Unit ,  044/0069 ). Funding reported by participants:  NIH  grant number  R01-HL-105770  to Eric Delwart.
White bar, target viruses detected in both replicates using all reads. Laboratories L01-B, L11, L13 and L15-B performed analysis only on the total read set. Lab Nuclease Extraction Primary lib Seq library Platform Database(s) Primary Blind/targeted identification L01-A Yes Column/silica Ion Torrent Ion Torrent Ion Proton In-house Smith- Blind Waterman/BLAST L01-B Yes Column/silica Ion Torrent Ion Torrent Ion Proton In-house Mapping to Targeted targets L02 Yes Column/silica Fragmentation- Ion Torrent Ion Proton Virus Proprietary Blind ligation RefSeq/in- house L03 DNAse EZ1 cDNA Nextera XT MiSeq nt (Jun 2014) SURPI Blind amplification Nucleic acid extract L04 No Column/silica cDNA synthesis Nextera XT MiSeq NCBI Align to NCBI Blind L05 No Column/silica Adaptor TruSeq HiSeq2500 NCBI Viral Alignment to Blind ligation Genome references Neighbor L06 No Beads Adaptor Custom HiSeq2000 In-house BWA Blind ligation alignment L07 No Phenol/chloroform Confidential Nextera MiSeq Virus Mapping to all Both RefSeq/NCBI viruses L08 No Maxwell None ScriptSeq MiSeq Virus RefSeq/nr BLAST Blind L09 No Column/silica Proprietary Custom 454 In-house Proprietary Not specified (Ref/Seq GenBank derived) L10-A No Column/silica Fragmentation- TruSeq HiSeq2500 GenBank 2013, BLASTn Blind ligation clustered viral partition L10-B No Column/silica Fragmentation- TruSeq HiSeq2500 GenBank 2013, BLASTn Blind ligation clustered viral partition L11 No Column/silica cDNA Nextera XT HiSeq1500 In-house BLAST Blind L12-A No Column/silica Random Nextera XT MiSeq Virus RefSeq/nt BLASTn Blind RT-PCR L12-B No Column/silica MDA/SPIA Nextera XT MiSeq Virus RefSeq/nt BLASTn Blind L13 No Magnetic beads TruSeq TruSeq MiSeq Virus RefSeq BLAST/ Blind CENSUSCOPE L14 DNAse Column/silica Fragmentation- Illumina MiSeq GenBank SLIM Both ligation PCR L15-A* Yes Column/silica MDA/SPIA Nextera XT MiSeq In-house BWA then Targeted BLAST L15-B* Yes Column/silica MDA/SPIA Nextera XT MiSeq In-house BWA then Targeted BLAST Individual laboratories are Fig.
The efficacy of erythropoietin (EPO) can be enhanced by the addition of granulocyte colonystimulating factor (G-CSF), mainly in MDS with ring sideroblasts; the median duration of response to the combined treatment is around 30 months, with some patients responding for more than 10 years  [1]. Patients with a low probability of response are characterized by a transfusion need exceeding 2 units per month combined with a serum EPO level of ≥500 U L À1 [3]. However, some studies have shown that the effect of ESAs is decreased already at serum EPO levels of >100 U L À1  [5, 11, 13]. The French MDS (GFM) group compared overall survival in patients treated with ESAs within clinical studies with patients in the International Prognostic Scoring System (IPSS) database and showed a longer survival in the treated group  [5].
No further adjustments were included. Propensity to receive ESA treatment was modelled using multivariate logistic regression (Supplementary material). Only patients with comparable propensity scores were included in the analyses to estimate the effects of ESA treatment on outcomes (outcome group: n = 860; 474 treated and 386 not treated with ESAs; Fig. 1 ). The relationship between the effects of ESAs and pre-ESA treatment transfusion status was explored with this model. All analyses were performed using the SAS/STATâ software (SAS Institute Inc. , Cary, NC, USA)  [20] , and tests of the proportional hazards assumption were undertaken in all analyses.
Discussion The aim of this study was to analyse treatment patterns of ESAs, as well as their effects on longterm outcome in a large prospectively enrolled and well followed-up cohort of patients with lower-risk MDS. Because of the recruitment of patients from university hospitals as well as from smaller regional units in a wide range of European countries, our data can be viewed as representative of MDS patients in routine clinical practice. The higher median age of patients in the EUMDS registry (74. 4 years) compared to other registries with a majority of patients from university hospitals (e. g. D€ usseldorf, 72 years; Pavia, 65. 3 years [23, 24] ) may be due to the wider recruitment. It may also be a reflection of an ageing population given the more recent establishment of the EUMDS registry.
The plausibility of a country-specific analysis was investigated; however, only two countries had sufficient comparable treated and untreated patients and therefore this issue may be considered in the future as the registry matures. Previous retrospective epidemiological studies on cohorts collected within academic trials, compassionate use cohorts and the IPSS database  [2, 5, 11]  have shown significant survival benefits for ESA-treated compared with untreated patients. It is clear that such studies carry inherent problems regarding patient selection. The prospective noninterventional EUMDS registry has several advantages and disadvantages but as treatment with ESAs is based solely on the physician's choice, it is most likely to be as close to the real-world setting as possible, whilst still retaining control over objective entry and exit variables.
Acknowledgements The authors would like to thank the research group at  Radboud University Medical Center, Nijmegen  for administrative and practical support all local investigators and operational team members for their contribution to the Registry, and  Novartis Pharmacy B. V. Oncology Europe  for financial support. We would like to express specific thanks to the rest of the EUMDS study group:  Ulrich Germing-Germany ; Krzysztof Madry-Poland; Gerwin Huls-the Netherlands;  Aurelia Tatic-Romania ;  Mette Skov Holm-Denmark; Antonio Medina Almeida-Portugal; Aleksandar Savic-Republic of Serbia ;  Njeto cka Gredelj-Simec-Croatia ;  Agnes Guerci-Bresler-France ;  Elisa Luño-Spain ;  Odile Beyne-Rauzy-France ; and  Dominic Culligan-UK.
Financial support The work of the EUMDS Registry for low and intermediate-1 MDS has been supported by an unrestricted educational grant from  Novartis Pharmacy B. V. Europe. This work is part of the  MDS-RIGHT activities , which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  634789  -"Providing the right care to the right patient with  MyeloDysplastic Syndrome  at the right time". W. T. J. , A. G. S. and S. C. are supported by  Bloodwise (formerly Leukaemia & Lymphoma Research). Participants of the EUMDS registry receive financial support from the  European Union 's  Horizon 2020 Research and Innovation Programme  under grant agreement No  634789. 'Providing the right care to the right patient with  MyeloDysplastic Syndrome  at the right time' (MDS-RIGHT).
We start by computing a set W = {w k , k = 1 • • • K} of K parametric motion models that are relevant to different sub-regions of the scene. We extract a large number K of overlapping windows of different sizes, starting with a window encompassing the full image support, and subsequently reducing the size by a half and changing position of the windows with a fixed overlap factor of 50%, covering the whole image for every window size. For the image size of the Sintel dataset, with four levels of window sizes we obtain, for instance, K = 115 windows. For each window, we robustly estimate a parametric warp that captures at best the motion of corresponding scene fragment. Several classic techniques can be used to this end. In our experiments, we combine semi-dense matching, to handle large displacements, with robust affine motion estimation.
Experimental results For the quantitative evaluations reported in this section, we rely on the MPI Sintel dataset 6   [10]. This dataset comprises 69 sequences from the open-source CGI movie Sintel  7  for which ground-truth optical flows and occlusion maps have been computed from the known 3D dynamic structure of the scenes. For all the experiments, we fixed the number of super-pixels J to 700. We found experimentally that having only 2 components for each GMM provides good results in general. Furthermore, the standard deviation of the Gaussian kernels was set to 1. 0, with a window size of 5 pixels. These parameters are related to the local color model variability within the superpixel supports.
Evaluation of the occlusion criterion Comparisons are conducted against DFD-based detection, which is at the heart of approaches based on analyzing violations of the brightness constancy assumption  [4, 40]. For eight sequences of the dataset, we report in Fig. 4  occlusion detection ROC curves (true-positive-rate (TPR) vs. false-positive-rate (FPR)) and area under the ROC curves (AUC), both varying the threshold ε c in the rule (1) and varying ε v in our own framework  (8). It can be seen that our reconstruction-based criterion has more discriminative power than classic DFD criterion in the prospect of occlusion detection. In all regimes and on all the sequences the former outperforms the latter, often by a substantial margin. More interestingly, for most of the sequences, i. e. , "alley 1", "alley 2", "ambush 4", "ambush 5", "ambush 7" and "bamboo 1", the decrease in occlusion detection performance due to flow inaccuracies (with noisy ground-truth or DeepFlow estimate) is less significant for our method.
g. , α v for our method. In a first set of experiments ("Global 69" column), it is manually set at once for all sequences. For  [29]  and  [14] , the corresponding parameters are set to 0. 5, as reported by  [29] ; for our method α v is set to 10; finally, for  [4]   9  and [30] 10  we set the parameters to the values proposed by the respective authors. In a second round ("Oracle 69" column), we tuned the key parameter of each method so as to maximize the F-scores. As it can be appreciated, our reconstruction-based method outperforms all the other reported methods in both settings. Finally, we also present some results (as available) by tuning the parameters to maximize the F-scores only in the 23 training sequences on the final rendering pass of the Sintel dataset ("Oracle Final" column).
In a second round ("Oracle 69" column), we tuned the key parameter of each method so as to maximize the F-scores. As it can be appreciated, our reconstruction-based method outperforms all the other reported methods in both settings. Finally, we also present some results (as available) by tuning the parameters to maximize the F-scores only in the 23 training sequences on the final rendering pass of the Sintel dataset ("Oracle Final" column). We provide in Fig. 5several samples of our results for visual inspection. Despite the complexity of some of these scenes, occlusion maps of good quality are obtained. In particular, extended occlusions produced by large displacements are very well captured, while retaining some details of smaller occluded regions. It is also interesting to examine associated motion model labelings M = m(x) x∈Ω and associated motion flows w m(x) (x) x∈Ω (Fig.
All PCR products were then analysed by electrophoresis on 3 % agarose gels and visualized under UV illumination after staining with ethidium bromide. The size of the amplified fragments was estimated by comparison to a 100 bp ladder marker. Each PCR product was screened for polymorphic banding by GeneTools analysis software (Syngene, UK). The genetic diversity of each marker was analysed by the number of alleles and the expected virtual heterozygosity (H E ), The expected virtual heterozygosity (HE), defining as the probability that a randomly chosen pair of alleles differed from each other can range between 0 and 1, with values close to 1, reflecting high genetic diversity levels in a population  [7]. The formula of expected heterozygosity (HE) is [n/(n -1)] × (1 -∑pi2), where "n" is the number of samples analysed and Pi is the proportion of the parasite population having allele i.