dataesr/scientific-paragraphs-categorization huggingface dataset:
The United Kingdom (UK) is one of the countries with a longer historical background of working on health inequalities  [45, 46]  and, for example, London has a specific strategy to tackle health inequalities  [34]  which has been supported by different governments. This fact is corroborated in this paper because the UK is the country where most published interventions were found. In addition, English people might have more facilities to publish because of the language. We found more papers in Pubmed than in the Sociological Abstracts database probably because the latter tends to focus on international literature in sociology and disciplines traditionally more related to the social and behavioural sciences rather than to health. However, social and economic factors are being considered more and more as determinants of health inequalities so we consider that this trend may change.
The rigorous procedures used have ensured the validity of the data abstraction. However, the review is not exhaustive because we have not been able to identify all relevant evidence and we did not search in the grey literature. We tried to identify all possible papers published in scientific journals by searching in two different databases, one of which is not medical, in order to capture policies or interventions from other sectors. The combination of heterogeneous sources of data adds value to the results. As one of the additional requirements for inclusion was that papers were written in English, Spanish or French, we believe it has brought a significant change in the results since we only had to exclude two documents. We must also take into account that the fact that these policies and interventions have been published does not necessarily mean they are still functioning or effective.
Acknowledgements We would like to express our thanks to  Edwin Ng  for his collaboration in the design of the Sociological Abstracts search strategy, and to all those INEQ-CITIES researchers who contributed ideas included in this paper. This article has been partially funded by the project  INEQ-CITIES, "Socioeconomic inequalities in mortality: evidence and policies of cities of Europe "; project funded by the  Executive Agency for Health and Consumers (Commission of the European Union) , project  n°2008 12 13.
Moreover it is likely that mealiness as a unique sensory parameter reports various biochemical and structural situations in different fruit species. Transcriptome analyses have the potential to screen many gene pathways simultaneously. In recent years, a number of studies have integrated these extensive data sets in order to investigate apple fruit ripening. Most of them were based on 'custom made' microarrays developed from EST apple databases. Lee et al. [15]  used a 3,484 cDNAs array to identify 192 cDNAs involved in the early stages of apple fruit development. Janssen et al. [16]  undertook a similar study on "Royal Gala" and extended to the fruit ripening stages, using a 13,000 gene apple oligonucleotide array. This same array was used previously to identify 17 putative candidate genes regulated by ethylene and involved in the production of aroma compounds in the fruits of transgenic lines of "Royal Gala"  [17].
In silico analysis of the MDP0000245813 protein sequence, confirmed the presence of a processed aminoterminal PRO domain similar to a PME inhibitor domain (PF04043) followed by a complete catalytic PME domain (PF01095). A processing motif (RRLL) was also identified between both domains (Figure  6A ). This latter was described as a cleavage site suggesting the cleavage of the PRO domain during the process of the protein maturation  [31, 33]. The current knowledge on the PME family and these observations support the hypothesis that MdPME2 codes for a pectin methylesterase. In all studied species, genes coding for pectin methylesterases belong to large multigenic families and their members might be involved in different developmental processes  [33, 34]. To determinate if other MdPME genes are involved in development of fruit mealiness, all apple MdPME homologues were identified within the apple v1 genome sequence  [22].
Based on protein similarities to the PME conserved domain (PF01095), 60 genes were identified as potential pectinesterase family members. Molecular phylogenetic analysis showed that the MdPME family is divided in two groups. As observed with the Arabidopsis AtPME familly  [33] , PME proteins from group type I are characterized by the presence of a PRO domain which is absent  Moreover, MdPME2 displayed a unique differential expression pattern during fruit maturation among all MdPMEs annotated genes (Figure  7 ). Few PMEs annotated genes displayed differential expression patterns between mealy and non-mealy apples, but none of them remained consistently differentially expressed during the kinetic of development. Roles of other PMEs in cell wall remodelling during apple maturation is not excluded.
Nevertheless, the softening rate was higher in mealy apples compared with non-mealy apples when assessed with penetrometry. These observations suggest that the rate of softening, rather than the absolute softness of the fruit, may be positively correlated with mealiness. Sensory analysis by a trained panel of four people is time consuming, expensive, difficult to transpose easily to other laboratories and the number of samples that can be analysed is limited. A few reports investigated some assumptions about cell wall modifications and apple fruit mealiness in order to develop a new test to   [22]. At1g53830 is the Arabidopsis thaliana most similar sequence. MDP0000245813 is the complete sequence identified from the 3' RACE analysis. All 3 protein sequences are characterized by the presence of a PRO domain region (PF04043) followed by a putative processing motif  [33, 69].
The PME domain (PF01095) of MDP0000222620 is incomplete. B Confirmation of the cloned gene expression profiles with 2 PME specific primer sets by semi-quantitative RT-PCR for 30 cycles. M20 genomic DNA (DNAg) was used as positive control. Genes expression were studied for pair 2 (M20/M74) during complete fruit kinetic. MDP0000245813 could be amplified with clear difference between mealy (M) and non-mealy (N) genotypes, while there was no amplification with MDP0000222620, and GADPH expression remained at a relatively constant level. quantify mealiness. Assumptions are based mainly on microscopic observations of the fracture surface obtained via a tensile test. Different modes of tissue failure can be observed such as cell fracture or cell-to-cell separation. Mealy apples are characterized by neighbouring cells separating from each other at the level of the middle lamella without any damage  [5].
Cell cohesion test The expert panel scored 158 apples (Additional file 1). The apples were used to develop the cell cohesion test. Three radial cylinders with diameter and thickness of 1 cm each were cut from fresh apple parenchyma. Samples were suspended into 5 ml of tap water in a 50 mL tube, mixed using a vortex at 3000 rpm for 2 min and stained with 50% toluidine blue dye. 40 μl of suspended cells were immediately spotted onto a microscopy slide and visualized with a binocular microscope at 7x magnification (Olympus SZX16). Images were captured with a digital camera. Two replicates per sample were analysed. Automatic counting of individual cells was achieved using the function "Analyze particles" from ImageJ software  [58]  after image conversion from RGB coloured picture to black and white.
RNA extraction, amplification and microarray hybridization Total RNAs were extracted from 4 g of frozen fruit flesh tissue ground in liquid nitrogen using a CTAB extraction buffer as described in  [11]. mRNAs were amplified, labelled and co-hybridized according to  [23]  as following: aRNAs were produced with Message AmpII aRNA amplification kit (Ambion) from 200 ng of total RNA. Then, 5 μg of each aRNAs were retrotranscribed and labelled with either Cyanine-3 or Cyanine-5 fluorescent dye (Interchim, Montluçon, France). Labelled samples were combined as 30 pmol for each dye and cohybridized to the Nimblegen microarray AryANE v1. 0 containing 135000 60-mers oligonucleotide probes as described in  [23]. Deva software (Nimblegen) was used to extract pair-data files from the scanned images, obtained using the MS200 microarray scanner (Roche Nimblegen).
Microarray analyses All statistical analyses were conducted as described  [61]  with the R software (R development Core Team, 2009). Briefly, data were normalized with the Lowess method, and differential expression analyses were carried out using the lmFit function and the Bayes moderated t test using the R package LIMMA  [62]  from the Bioconductor project. To estimate gene expression levels, the normalized expression values were corrected from background. Genes were considered differentially expressed if the t-test P-values of the paired sample were below 1%. Genesis software (http://genome. tugraz. at/genesisclient/ genesisclient_description. shtml) was used to visualize results. Functional classification was based on Mapman ontology  [63]. An enrichment analysis (Wilcoxon rank sum test) was performed with MapMan software.
6 (Bio-Rad). Primers were designed for short and specific amplification of the microarray probe region of the selected sequences with Primer 3 plus software (http://www. bioinformatics. nl/cgi-bin/primer3plus/ primer3plus. cgi) (Additional file 10). For each run, single product amplification was confirmed by melt curve analysis. The amplification efficiency was tested for each primer pairs using a dilution curve method over a 6 point dilution series (from 0. 1 to 0. 002) on a pool of cDNAs containing all genotypes and developmental stages. Primer pairs selected for further analysis have efficiencies above 91%. RT-qPCR was carried out for 5 genes, 2 pairs at different time points. Based on the microarray results, three reference genes with similar expression levels in all samples were selected to calculate a normalization factor: MDP0000645828 (GADPH), MDP0000217860 and MDP0000271281 (respectively annotated as drought-responsive family protein and putative porin).
Malus domestica genome The HMM profile of the Pectinesterase proteins (PF01095) was downloaded from the Pfam database (http://pfam. sanger. ac. uk), and used as a query to search the translated Apple genome (http://www. rosaceae. org/) using the HMMER 3. 0 program with default parameters (hmmer. org). All sequences collected were checked using the NCBI conserved domain database  [65]. Protein coding genes without a complete pectinesterase domain  (EC 3. 1. 1. 11) or with extra and incoherent domain were discarded from further analyses. Multiple protein sequence alignments were performed with the online MUSCLE tool (http://www. ebi. ac. uk/ Tools/msa/muscle/). Phylogenetic analysis was carried out by the neighbor-joining method using MEGA 5 software  [66]  using a p-distance model and pairwise deletion for missing data treatment; 1,000 replicates were used for bootstrap analysis.
The PCR product was purified using the PCR clean-up system (Promega) according to the manufacturer's instructions, and cloned in the pENTR/D-TOPO vector using the pENTR directional TOPO cloning kit (Invitrogen, USA) according to the manufacturer's instructions. One Shot TOP10 chemically competent E. coli cells (Invitrogen) were then transformed by heat shock with 2 μL of the reaction. The transformed bacteria were grown overnight at 37°C on LB agar medium containing 50 μg/ml kanamycin as selective agent. Positive clones were selected by PCR amplification of the inserted cDNA using F and R primers and a plasmid sequenced to confirm the proper orientation of the cloned fragments. All DNA sequencing were performed by Genoscreen (http://www. genoscreen. fr) and MdPME2 sequence was submitted to [GenBank:KM252690].
Figure6cloning and validation by semi-quantitative RT-PCR. A Analysis of MDP0000222620 sequence annotated on Malus x domestica genome [22]. At1g53830 is the Arabidopsis thaliana most similar sequence. MDP0000245813 is the complete sequence identified from the 3' RACE analysis. All 3 protein sequences are characterized by the presence of a PRO domain region (PF04043) followed by a putative processing motif [33, 69]. The PME domain (PF01095) of MDP0000222620 is incomplete. B Confirmation of the cloned gene expression profiles with 2 PME specific primer sets by semi-quantitative RT-PCR for 30 cycles. M20 genomic DNA (DNAg) was used as positive control. Genes expression were studied for pair 2 (M20/M74) during complete fruit kinetic. MDP0000245813 could be amplified with clear difference between mealy (M) and non-mealy (N) genotypes, while there was no amplification with MDP0000222620, and GADPH expression remained at a relatively constant level.
Acknowledgments This work was supported by the  Institut National de la Recherche Agromomique  and by the  Region Pays de la Loire. The authors would like to thank  Roland Robic  for his help in orchard, lab tasting and analyses and all sensorial experts involved in this analyse;  Céline Gourdon  for her contribution in sample preparation and data analysis;  Axelle Bouder  for her help with N. tabacum transformation and confocal observations; the  INRA-Horticultural experimental Facility  ( UEH0449 ) for the apple trees maintenance; the  ANAN and I-MAC  platforms of the  SFR QuaSaV  for respectively microarray and microscopic facilities access; the  BIBS (INRA Nantes ) platform for biochemical analyses;  Dr Marc Lahaye  ( INRA/UR1268 Biopolymères, Interactions, Assemblages ) for his advice on biochemical analyses;  Dr Gregory Mouille  ( IJPB/UMR1318 ) for providing the Agrobacterium tumefaciens C58C1 clone; Dr Alexis Peaucelle  ( IJPB/UMR1318 ) for helpful discussion and  Dr Thomas Baldwin  for critically reading the manuscript.
Availability of supporting data The nucleotide sequence supporting the results of this article is available in the GenBank repository (http:// www. ncbi. nlm. nih. gov/genbank/) under the identifier GenBank:KM252690. The microarray data are available on Gene Expression Omnibus  [68]  database (http:// www. ncbi. nlm. nih. gov/geo/) under the accession number GSE59947. MdPMEs sequence alignment and phylogenic tree data are available on Dryad (http://datadryad. org/) under the DOI number: 10. 5061/dryad. 4337n.
City/Town selection Each country is represented by a member of its EPHE National Coordination Team, which is a member of the EPHE Operational Board. The National Coordination Team is responsible to guide the Local Project Managers, which are in charge of the data collection in the community level. All the countries will follow a standardised protocol for the selection of the EPHE-city and the data collection, which will be described further in this article. The evaluation study, as well as the interventions, will be implemented in a medium-sized city/town, where the population shall belong in a wide range of socioeconomic statuses. The selected city/town should preferably not have implemented many interventions relevant to nutrition and physical activity addressed to the EPHE target group, in order to prevent of not detecting differences between the socio-economic groups.
Data cleaning and analysis plan All the data sets will be checked for missing and doublecrossed values. The missing values will be treated by the multiple imputation method, if necessary. The sample will be divided in two groups, according to the socioeconomic indicator used in the assessment. Descriptive analysis will include appropriate non-parametric tests for comparing means, in order to detect differences in behaviours and determinants between the two socioeconomic groups. The SPSS software 21. 0 (IBM Corp. , Armonk, NY, USA) will be used for all the analyses.
Discussion This article describes the methodology of the effect evaluation of the EPHE project, aimed to reduce the socioeconomic inequalities in selected energy balance-related behaviours. The EPHE evaluation study is a two-year prospective follow-up survey, which will collect self-reported data of the energy balance-related behaviours of 6-8 year olds and their potential family environmental determinants, depending on the socio-economic level of the parents. Little research has been conducted to associate childhood obesity and relevant behavioural determinants with socioeconomic inequalities in the country level  [10]. Nevertheless, obesogenic environments seem to influence more the energy balance-related behaviours of lower socio-economic populations  [12, 28, 29]. Screen exposure of children is inversely associated with parental education  [30] , whereas lower fruit and vegetable intake is observed more frequently in children with low educated parents  [31, 32].
To address major societal challenges and enhance cooperation in research across Europe, the European Commission has initiated and facilitated 'joint programming'. Joint programming is a process by which Member States engage in defining, developing and implementing a common strategic research agenda, based on a shared vision of how to address major societal challenges that no Member State is capable of resolving independently. Setting up a Joint Programming Initiative (JPI) should also contribute to avoiding unnecessary overlap and repetition of research, and enable and enhance the development and use of standardised research methods, procedures and data management. The Determinants of Diet and Physical Activity (DEDIPAC) Knowledge Hub (KH) is the first act of the European JPI 'A Healthy Diet for a Healthy Life'.
The objective of DEDIPAC is to contribute to improving understanding of the determinants of dietary, physical activity and sedentary behaviours. DEDIPAC KH is a multi-disciplinary consortium of 46 consortia and organisations supported by joint programming grants from 12 countries across Europe. The work is divided into three thematic areas: (I) assessment and harmonisation of methods for future research, surveillance and monitoring, and for evaluation of interventions and policies; (II) determinants of dietary, physical activity and sedentary behaviours across the life course and in vulnerable groups; and (III) evaluation and benchmarking of public health and policy interventions aimed at improving dietary, physical activity and sedentary behaviours. In the first three years, DEDIPAC KH will organise, develop, share and harmonise expertise, methods, measures, data and other infrastructure.
Prototyping and piloting innovative assessment methods will be initiated by mapping databases of on-going research. In addition, existing pan-European datasets on reference methods (e. g. independent replications of 24-hour recalls for diet or objective activity monitors for physical activity and sedentary behaviours) will be exploited to identify the main contributors to relevant variation (between countries, risk groups, age, sex, meals) in dietary, physical activity and sedentary behaviours. This will result in the description of the European diversity and at the same time stimulate and elicit potential innovative short-cut measurement methods to be explored in a feasibility pilot study. The range of relevant dietary, physical activity and sedentary behaviours and their determinants is very broad and priorities must be set.
For the latter, we will be using analytical techniques such as multilevel mediation and moderation analysis, structural equation modelling of pooled data, meta-analyses of different studies, and others. In this first phase of DEDIPAC, we will not be collecting primary data. This second TA in DEDIPAC thus aims to understand the determinants of dietary, physical activity and sedentary behaviours, at both the individual and group levels, using a broad multi-disciplinary approach. In service of this multi-disciplinary and inter-behavioural scope, the use of a variety of data sources from different disciplines, focusing on different behaviours, determinants and aggregation levels is essential. There are already many disciplines involved in the Knowledge Hubalthough not all relevant disciplines are fully covered yet.
Novel ways to perform pooled analysis of individual-level data without actually sharing the data will be explored. For instance, EU BioSHaRE established a password-protected web portal that facilitated remote and federated analyses of the harmonised datasets conducted at each participating institution using DataSHIELD software  [17]. Another example of data sharing and harmonisation is the International Children's Accelerometer Database (ICAD) that pools youth accelerometer data and reanalyses raw data files in order to harmonize data between different studies  [18]. ICAD is closely linked to DEDIPAC and will serve as an example for other harmonisation exercises within DEDIPAC. Further examination of these and other approaches to data harmonisation and sharing is one of the research infrastructure issue areas in which DEDIPAC aims to make a difference, paving the way for European collaboration and JPI's in the years to come.
TA 3 will utilise the DEDIPAC-toolbox developed in TA 1 and the individual, socio-cultural-and environmentallevel determinants of dietary, physical activity and sedentary behaviours described by TA 2 to develop an evaluation framework and open-access database in which policy and multi-component interventions will be described. This will include their evaluation in terms of reach, effectiveness, adoption, implementation and maintenance. This pan-European framework will then be pilot tested in ongoing interventions or policies in a number of DEDIPAC Member States and will be adapted accordingly. Eventually, the framework will outline evidence-and practice-based policy and multi-component intervention options. In order to fulfil the aforementioned aims, the draft evaluation framework will first be pilot-tested by 19 partners across nine countries using 'natural experiments' or policies that are currently being implemented or have recently been implemented.
Acknowledgements This work is supported by the  Joint Programming Initiative 'Healthy Diet for a Healthy Life'. The funding agencies supporting this work are (in alphabetical order of participating Member States): Austria:  Austrian Federal Ministry of Science and Research; Belgium: Research Foundation -Flanders ; Finland:  Finnish Funding Agency for Technology and Innovation (Tekes) ; France:  Institut National de la Recherche Agronomique (INRA) ;  Germany: Federal Ministry of Education and Research; Italy: Ministry of Education, University and Research/ Ministry of Agriculture Food and Forestry Policies ; Ireland: The Health Research Board (HRB); The Netherlands: The  Netherlands Organisation for Health Research and Development (ZonMw) ;  Norway: The Research Council of Norway , Division for Society and Health; Poland:  The National Centre for Research and Development; Spain: Carlos III Institute of Health (ISCIII) ; The United Kingdom: The  Medical Research Council (MRC).
Methods: We used paediatric safety data from a multi-site, multi-country clinical study conducted in seven African countries (Burkina Faso, Gabon, Nigeria, Rwanda, Uganda, Zambia, and Mozambique). Each site compared three out of four ACTs, namely amodiaquine-artesunate (ASAQ), dihydroartemisinin-piperaquine (DHAPQ), artemether-lumefantrine (AL) or chlorproguanil/dapsone and artesunate (CD+A). We examine two pharmacovigilance signal detection methods, namely proportional reporting ratio and Bayesian Confidence Propagation Neural Network on the clinical safety dataset. Results: Among the 4,116 children (6-59 months old) enrolled and followed up for 28 days post treatment, a total of 6,238 adverse events were reported resulting into 346 drug-event combinations. Nine signals were generated both by proportional reporting ratio and Bayesian Confidence Propagation Neural Network.