dataesr/scientific-paragraphs-categorization huggingface dataset:
p27 regulates the expression of genes involved in signal transduction As it can be seen in Fig  5  one of the more represented transcriptional program putatively regulated by p27 is the intracellular signaling cascade. In addition to the 52 genes included in this group by the DAVID program, the manual annotation of the genes identified by the ChIP-seq analysis, has allowed to increase the number of genes of this group until 107 (S3 Fig). The list of these genes includes ligands, receptors, G-proteins, GEFs, GAPS, kinases, phosphatases and cyclases. Thus, we aimed to validate the interaction of p27 with the specific regions described in the ChIP-seq analysis for a group of these genes. Validation was performed by ChIP using an anti-p27 antibody (see methods) followed by qPCR using primers specific for these sequences.
p27 regulates cell adhesion Cell adhesion was also a transcriptional program regulated by p27. ChIP-seq analysis revealed that 32 genes belonging to this group were annotated. Interestingly, cell adhesion was the most significantly enriched term in GO analysis and Kegg pathways that were altered in p27 -/-cells  [16]. Thus, we aimed to determine by qPCR whether the expression of a number of genes found in the ChIP-seq analysis was altered in p27 deficient MEFs. As shown in Fig 8A a group of genes including: Ctnnd2, Col12a1, Ncam1, Nedd9, and Pcdh9, were up-regulated in p27 -/- cells whereas the genes Amigo2 and Itga8 were down-regulated. These data supports that p27 is involved in the transcriptional regulation of genes involved in cell adhesion and that have p27-BSs in their vicinity. To examine the behavior of p27 deficient cells in terms of adhesion we used the Roche xCELLigence real-time cell analysis system.
Thus, p27 wt and p27 -/-MEFs were trypsinized and the same number of cells was plated on xCELLigence plates. Normalized cell index data obtained few hours after cell plating were plotted over time axis. As observed in Fig  8B , at different times post-plating, p27 -/-MEFs had greater adhesion capacity to culture The effect of p27 silencing on transcription was determined by luciferase assays using a pGL3 vector including the p27-BSs annotated to Mefc2, Sox6 and Shox2. P27WT and p27 -/-cells were co-transfected with the pGL3 vector and the CMV-βGal vector. Luciferase assay were performed as described under material and methods. Results are the mean value ± SD of three independent experiments. Statistical analyses were performed using the t-student's test. (**) P<0. 01 and (***) P<0. 001. https://doi.
org/10. 1371/journal. pone. 0187891. g006 Fig 7. p27 regulates the expression of cell signaling genes. (A) Analysis of the association of p27 with specific p27 binding sites of the detailed cell signaling genes. Interaction was determined by ChIP followed by qPCR using specific primers in p27 WT and p27 -/-MEFs. ChIP data were quantified using the comparative delta Ct method (see methods). Ct values resulting from p27 WT and p27 -/-MEFs were normalized both versus the input and subsequently versus no-antibody control. Results are expressed as relative enrichment of values from p27 WT versus those from p27 -/-. Results are the mean value ± SD of three independent experiments. Statistical analyses were performed using Prism 6. 0 software (GraphPad Software, Inc. , Ca,USA) and the P-values were calculated using two-tailed t-test.
Acknowledgments We would like to thank to  Dr. Pedro M. Fernandez  for the kind gift of the anti-AHR antibody. This work was supported by grants from the  Ministerio de Economia y competitividad (MINECO)   SAF2012-38078 ,  SAF2015-64244-R, La Caixa 2016-052. 407and from the  Instituto de Salud Carlos   III RD12/0036/0054. AB is supported by grants from the  Fondation ARC pour.
This paper examines the association between income, income inequalities and health inequalities in Europe. The contribution of this paper is to study different hypotheses linking self-perceived health status and income, allowing for the identification of different mechanisms in income-related health inequalities. Using data from the Survey of Health, Ageing and Retirement in Europe (15 countries), we take the advantage of the cross-sectional and longitudinal nature of this rich database to make robust results. The analyses (coefficient estimates as well as average marginal effects) strongly support two hypotheses by showing that (i) income has a positive and concave effect on health (Absolute Income Hypothesis); (ii) income inequalities in a country affect all members in a society (strong version of the Income Inequality Hypothesis).
Literature review: the relationship between income inequalities and health Inequalities in health refer to the close relationship between health and membership in a group characterized by incomes, where income is an individual social determinant. This section formally presents the three hypotheses mentioned in the introduction, as well as some related literature. We should mention that, in this literature review, we transcribe terminology employed by authors which reflects causal relationships even if cross-sectional databases are used or some endogeneity might be at play.
The survey The Survey of Health, Ageing and Retirement in Europe (SHARE) is a multidisciplinary and cross-national panel database of micro data on health, socio-economic status and social and family networks of more than 123,000 individuals aged 50 and over from many European countries and Israel  [7]. Since 2004, SHARE asks questions throughout Europe to a sample of households with at least one member who is 50 and older. These households are re-interviewed every two years in the panel. SHARE is part of a context of an ageing population. It is the European Commission which has identified the need for scientific knowledge about ageing people in Europe. 3 In fact, people of the European Innovation Partnership on Active and Health Ageing project estimate that in 2050, one in three Europeans will be over 60 years old and one in ten will be over 85 years old.
The SHARE survey was then constructed in the different European countries under the leadership of Professor Axel Börsch-Supan. In addition, SHARE is harmonized with the Health and Retirement Study (in the United States -HRS) and the English Longitudinal Study of Ageing (UK -ELSA). The first wave (2004-2005, 27,014 individuals) and the second one  (2006-2007, 34,393 individuals)  were used to collect data on health status, medical consumption, socio-economic status and living conditions. The 2008-2009 survey (Wave 3 -"SHARELIFE") was extended to life stories by collecting information on the history of the respondents. The number of participants increased from 12 countries in wave 1, to 15 (+ Ireland, Israel, Poland and Czech Republic) in wave 2, and the third wave contains information about 14 countries.
We do not make our analysis using directly the pooled database since all the control explanatory variables are not available in each waves, which is a limitation of this database. Moreover, we also focus on the pooled database (waves 1, 2, 4 [4-6] and 5) in order to make our results more robust (the third wave is not considered in the pooled database since it does not contain the same information as the other ones). The advantage of the SHARE database is that it has many individual variables on health, socioeconomic status and income to perform this research. However, researchers should be also aware of the potential disadvantage of this database. Indeed, Börsch-Supan et al. [7]  explain that in some waves there are a relative low response rates and moderate levels of attrition (even though the overall response rate is high compared to other European and US surveys 4 ) which are due to the economic crisis faced by some countries, implying a decrease in the participation rates.
Descriptive statistics -an overview In this paper, the data used are from the fifth wave of the SHARE survey. This wave includes responses from 63,626 respondents aged 50 and over, living in 15 different countries. Thus, this survey aims to provide information on health, income, activities and other features of the elderly. In one hand, the variable of interest is the health which is defined in the database as the self-perceived health status. Individuals are asked to classify their health using ordered qualitative labels from "poor" to "excellent. The Fig. 1  characterizes the distribution of the health variable among individuals aged 50 and older by gender for all countries. As we can see the majority of inhabitants reports being in a good health. In the other hand, one of our main determinant of health is the income.
This variable can be seen as a proxy for well-being, that is to say a factor which allows individuals to improve the living standards. In the database, it corresponds to the sum of individual imputed Fig. 1  Self-perceived health in Europe income for all household components. Figure  2  shows the distribution of income of people aged 50 and over in the fifth wave where the mean is about 36,000e. Moreover, the income inequality hypothesis includes an indicator for the measurement of income inequalities (see Fig. 3 ). In this paper, we use either the Gini index or the Theil index. The mean of the Gini index in Europe is 0. 39 which corresponds to a rather egalitarian society. The mean of the Theil index in Europe is 0. 33 which is also rather egalitarian. In our analysis we include others variables such as the age, the marital status, the education, the job situation, dummies for the countries and the gender, and the GDP of the countries (see Tables  2, 3 , 4 and 5 in the Appendix for further information).
Economic results and discussion Table  1  reports coefficient estimates for all estimated ordered probit models when income inequalities are measured using the Theil index. 8 The fifth wave gives us access to 63,626 observations and we also display results of the pooled database for sake of robustness (see Table  6  in the Appendix section). Results in the first column reports the estimated coefficients for the absolute income hypothesis while results in columns two and three provide tests of both the strong version and the weak version of the income inequality hypothesis. Coefficients of individual income and income squared provide support for all the hypotheses that there is a positive and concave relationship between income and self-perceived health status. Indeed, coefficients associated to the income variable are all positive and significant and coefficients associated to the income squared variable are all negative and significant.
Robustness checks As a sake of robustness, we also make our entire analysis using the pooled database (see Table  6  in the Appendix section) and the results are very similar to the ones obtained with the fifth wave of the survey. To give more support to the concavity assumption, we compute, for all three hypotheses, the marginal effects at mean 10 of income on the five outcomes. Results, reported at the end of Table  1 , are all significant. On one hand, for the first two outcomes, income has a negative effect on the probability to report either a poor health or a fair health status. On the other hand, there is a positive effect of income on the probability to report being in a good, very good and excellent health (outcomes three to five). These results are obtained following the ordered probit regressions of the three hypotheses, where the quadratic effect of income is investigated (see Eqs.
This study is one of the first analyzing this relationship through different hypotheses at the same time using the SHARE survey which is a rich database, containing a lot of information on elderly people and countries simultaneously. We find evidence supporting the absolute income hypothesis which states that people with higher incomes have better health outcomes. We also find evidence supporting the strong version of income inequality hypothesis which argues that inequality affects all members in a society equivalently. In this hypothesis, we find that when there are high income inequalities in a country, people aged 50 and over feel less healthy. However, we do not find evidence supporting the weak version of income inequality hypothesis which states that only the least well-off are hurt by income inequalities in a society.
On one hand, 25 percent of total income is shared in the same way by 75 percent of the population, and on he other hand, the remaining 25 percent of the total income is divided by the remaining 25 percent of the population. 6 It is this normalized index that we use hereafter and that we name the Theil index. 7 Once h * i crosses a certain value you report fair, then poor, then good, then very good, then excellent health. 8 Results associated to the Gini coefficient are not provided here but they are very similar and available upon request. 9 Source: OECD website. 10 We look at the average individual of the database and compute the marginal effects. 11 We do not include the ones for the income inequality hypothesis (both versions) since the results are very similar and do not change the main conclusion, but these are available upon request.
Acknowledgements "This paper uses data from SHARE Waves 1, 2, 4 and 5 (DOIs: 10. 6103/SHARE. w1. 260, 10. 6103/SHARE. w2. 260, 10. 6103/SHARE. w4. 111, 10. 6103/SHARE. w5. 100). The SHARE data collection has been primarily funded by the  European Commission  through  FP5  ( QLK6-CT-2001-00360 ),  FP6  ( SHARE-I3 :  RII-CT-2006-062193 , COMPARE:  CIT5-CT-2005-028857 , SHARELIFE:  CIT4-CT-2006-028812 ) and  FP7  ( SHARE-PREP: N 211909 ,  SHARE-LEAP: N 227822 ,  SHARE M4: N 261982 ). Additional funding from the  German Ministry of Education and Research , the  U. S. National Institute on Ageing  ( U01 AG09740-13S2 ,  P01 AG005842 ,  P01 AG08291 ,  P30 AG12815 ,  R21 AG025169 ,  Y1-AG-4553-01 ,  IAG BSR06-11 ,  OGHA 04-064 ) and from various national funding sources is gratefully acknowledged (see www.
Parasites from Cameroon (n = 436) are categorised into three groups of samples: (i) parasites from the Nkam valley (NKA07; n = 140), from individuals who had been exposed to only a single ivermectin treatment 80 days prior to sampling, i. e. , considered NLT  [37] , (ii) parasites from the Mbam valley (MBM07; n = 112), from people who had been exposed to multiple rounds of ivermectin treatment prior to sampling as described above  [37] , and (iii) parasites from individuals in the Mbam valley who were truly ivermectin naïve (MBM94; n = 184), sampled in 1994 prior to introduction of CDTI  [2]. Response phenotype data were not collected for the MBM94 parasites. In total, 184 ivermectin-naïve (with unknown phenotype), 225 GR, and 27 SOR parasites were genotyped. Worms from Ghana (n = 156) used for Sequenom genotyping were sampled from 6 communities with different ivermectin exposure histories (range: 2-17 treatments; Table G in S1 Text), and were composed of 105 GR and 43 SOR parasites.
DNA preparation and genome resequencing DNA extraction from worms from Cameroon was performed in the REFOTDE laboratory in Cameroon using an EZNA tissue DNA kit (Omega Bio-Tek Inc. , Norcross, GA, USA). DNA extraction from worms from Ghana was performed using the DNeasy Blood and Tissue kit (Qiagen, Toronto, ON, Canada) at McGill University in Montreal, Canada. The sequence data were generated from pools of worm DNA that were prepared by combining the DNA of individual worms that shared drug phenotype and geographic origin (i. e. NLT, GR or SOR from Ghana in 3 pools; NLT, GR and SOR from Cameroon in 3 pools; S1.
Read mapping and variant calling Reads from each pool were aligned to the draft genome sequence O_volvulus_Cameroon_v3 (WormBase Parasite;  [70] ) using BWA-MEM  [73] , after which reads were realigned around putative indels and duplicate reads (a PCR artifact that may skew variant read frequencies; see supplement of  [74]  for details) were removed using GATK (v3. 3-0)  [75]. Approximately 70% of the raw data were mapped, resulting in an average mapped coverage of 24. 47 ± 9. 07 per pool. Three 'pooled-sequencing' aware variant calling approaches were used to analyse the pooled mapping data: CRISP  [76] , FreeBayes (using the pooled-continuous parameter) (https:// github. com/ekg/freebayes) and PoPoolation2  [77] (S2 Table ). To ensure that comparisons could be made between all sequencing pools, the raw sequence nucleotide polymorphism (SNP) data were filtered using the following criteria: (i) the mapped read depth across a variant site was at least 8 reads but not greater than 3 SD from the genome-wide mean read depth for the given pool; (ii) there was no evidence of significant strand bias between forward and reverse reads (criterion applied to minimise SNPs caused by alignment artifacts, see supplement of  [74]  for details); (iii) variants were bi-allelic SNPs, and (iv) variants associated with reads mapped to Wolbachia or mtDNA sequences were removed.
Genome-wide analyses of differentiation To perform the genome-wide analyses of genetic differentiation of the sequencing pools, we used only variants found in the intersection of the three Pool-seq-aware SNP calling approaches described above for all following analyses. Given the statistical uncertainty associated with interpreting low coverage read frequencies for any given SNP locus, we focused on analyses of relative genetic diversity between groups by calculating F ST in 10-kb sliding windows across the genome, and F ST of gene features based on the O_volvulus_Cameroon_v3 genome build from WormBase Parasite (http://parasite. wormbase. org/; onchocerca_volvulus. PRJEB513. WBPS5. annotations). F ST values for each defined genomic window for both the genome-wide and genic datasets (10 kb or whole gene, respectively) were generated using PoPoolation2  [77]  fst-sliding.
Population-and ivermectin association analyses of individuallygenotyped worms Sequenom data were analysed using PLINK  [79]. Raw data were filtered based on allele frequency (loci with <0. 05 minor allele frequency were removed; PLINK-maf 0. 05), genotype quality (samples with <80% assay success rate were removed: PLINK-mind 0. 2; SNPs with <80% genotype call frequency were removed: PLINK-geno 0. 2). Filtered Sequenom data (130 SNPs in 446 samples [Table G in S1 Text; sample numbers in analysis are indicated in parentheses]; 81. 25% of total SNPs and 75. 33% of total samples, respectively) were analysed by multidimensional scaling to assess geographic vs. phenotypic influence on genetic diversity. Hardy-Weinberg equilibrium (HWE) was analysed using the same filtering conditions described above (PLINK-hardy), using the-keep function to analyse samples from each country separately.
e. , there were more loci or regions above significance thresholds in the Cameroonian pools. This difference is likely to be due to the unequal sample size between the two countries (Cameroon = 22 GR and 16 SOR worms; Ghana = 7 GR and 13 SOR worms); a greater proportion of total genetic diversity will be present in the Cameroon dataset as more worms are present, however, at the same time, the sequencing depth per Cameroon genome is lower than for the Ghana samples, and hence, will increase the stochastic variation in the Cameroon variant frequency. Although many single SNP loci from each country showed significant variation between SOR and GR pools when each country was analysed independently, only a single intergenic SNP (OM1b_7179218) was significantly different between pools and common to both countries (  ).
Acknowledgments We would like to thank all of the patients and collaborators from Cameroon and Ghana who participated in the studies, as well as the  Ministry of Public Health from Cameroon  and the  Ministry of Health from Ghana. We would like to acknowledge  Pierre Lepage ,  Rosalie Frechette ,  Audrey Ann Kustec ,  Patrice Charbonneau-Larose  and  Valerie Catudal  from the  Genome Quebec Innovation Centre ,  Andrew Robinson  and  Nathan Hall  from  La Trobe University  for computational support,  James Cotton  ( Wellcome Trust Sanger Institute ) and  Shannon Hedtke  ( La Trobe University ) for critical reading of the manuscript, and  Matthew Berriman  and  James Cotton  ( Wellcome Trust Sanger Institute ) for pre-publication access to O. volvulus genomic resources.
This investigation received financial support from  UNICEF/UNDP/World Bank/World Health Organization Special Programme for Research and Training in Tropical Diseases (WHO/ TDR) (Grant:  ANR-06-SEST-32 ); the  Wellcome Trust, UK  (Grant:  092677/Z/10/Z ); the  Natural Sciences and Engineering Research Council of Canada  (Grant:  RGPIN/2777-2012 ),  Fonds de Recherche du Quebec -Nature  et technologies (FQRNT),  Canadian Institutes of Health Research  (CIHR grants:  85021  and  94593 );  Centre of Host-Parasite interaction (CHPI), Quebec ; an  Early Career Development Fellowship  from  La Trobe University (SRD ); and computational resources from the  Victorian Life Sciences Computation Initiative. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The ferricenium/ferrocene redox couple was used as internal reference for the potential measurements. The temperature dependences of the magnetizations for powdered samples have been measured with a SQUID magnetometer (Quantum design MPMS-XL5; Quantum design Inc. , San Diego, CA, USA) operating between 2 and 300 K at a constant field of 2 kOe below 20 K and 10 kOe above. The experimental data have been corrected from the sample holder diamagnetism and the intrinsic diamagnetism estimated from the Pascal's tables  [54]. Melting points were determined in evacuated capillaries on a Kofler Bristoline melting point apparatus and were not corrected.
X-ray Crystal Structure Determination Measurements Single crystals of 2, 3•CH 2 Cl 2 and 5•0. 5CH 3 CN were grown as depicted in Section 2. 4. A well-shaped crystal of each compound was mounted on top of glass fibers in a random orientation. Intensity diffraction data were collected at 296 K on a Bruker D8 QUEST diffractometer (Bruker Co. , Billerica, MA, USA) equipped with a bidimensional CMOS Photon100 detector (Bruker Co. , Billerica, MA, USA), using graphite monochromated Mo Kα radiation (λ = 0. 71073 Å). The diffraction frames were integrated using the APEX2 package, and were corrected for absorptions with SADABS. The structures of 2 and 5•0. 5CH 3 CN were solved using OLEX2  [55] , with the SHELXS structure solution program using Direct Methods  [56]. The structure of 3•CH 2 Cl 2 was solved by Patterson methods  [57].
The structures of 2 and 5•0. 5CH 3 CN were refined with CGLS minimisation and that of 3•CH 2 Cl 2 with full-matrix least-squares methods based on F 2 (SHELXL-97)  [56, 57]. For the three compounds, non-hydrogen atoms were refined with anisotropic displacement parameters. All hydrogen atoms were included in their calculated positions, assigned fixed isotropic thermal parameters and constrained to ride on their parent atoms. A summary of the details about crystal data, collection parameters and refinement are documented in Table  4 , and additional crystallographic details are in the CIF files. ORTEP views were drawn using OLEX2 software  [55]. CIF files have been deposited at the Cambridge Crystallographic Data Center and were allocated the deposition numbers CCDC 1580128 for 2, 1580129 for 3, and 1580130 for 5.
Acknowledgments: Financial support from the  Fondo Nacional de Desarrollo Científico y Tecnológico [FONDECYT (Chile) , grant no. 1130105  ( David Carrillo ,  Carolina Manzur  and  Mauricio Fuentealba) , the  Vicerrectoría de Investigación y Estudios Avanzados ,  Pontificia Universidad Católica de Valparaíso ,.