dataesr/scientific-paragraphs-categorization huggingface dataset:
1 ). Here, we investigated the diversity and phylogeny of Ammotheidae by focusing on sea spiders collected in several poorly studied tropical areas (e. g. , Papua New Guinea, South Madagascar, Marquesas Islands) during the latest expeditions of the Muséum national d'Histoire naturelle of Paris (MNHN). We generated 104 sequences of the CO1 mitochondrial gene and 80 sequences of the 18S rRNA (18S) nuclear gene. These datasets were analysed to address the following three main questions: (1) Are Ammotheidae monophyletic? (2) Are the "big five" ammotheid genera  (Ammothea, Achelia Hodge, 1864 , Ammothella Verrill, 1900 , Cilunculus Loman, 1908  and Tanystylum) monophyletic? (3) How heterogeneous is the base composition in CO1 sequences of Ammotheidae?.
2010). DNA amplification were done using Hot start mix RTG Taq (GE Healthcare, Waukesha, WI, USA) in a 25 µl final volume containing between 1 and 5 µl of DNA and 1µl of each 10 mM primer. Initial denaturation was performed at 94°C for 4 min, then we applied 40 cycles of denaturation-hybridization of 30 s at 94°C, 30 s at hybridization temperature (50°C for CO1, 52°C for 18S), and 1 min at 72°C. Final elongation lasts 10 min at 72°C. Purification and cycle sequencing were performed by Eurofins (Munich, Germany) using the PCR primers detailed above. The sequences were cleaned using CodonCode Aligner v. 3. 7. 1 (CodonCode Corporation, Dedham, MA, USA) by comparing forward and reverse electrophoregrams. Nucleotide ambiguities were coded according to the IUPAC nomenclature. Cleaning was achieved after multiple DNA alignments using Se-Al v.
Phylogenetic analyses The 104 CO1 barcodes and 80 18S sequences generated for this study were compared to all sequences of Ammotheidae (48) and Aschorhynchidae (  19 ) downloaded from GenBank. For other pycnogonid families, we selected only species for which both CO1 and 18S sequences were available. Details on the 75 CO1 and 79 18S sequences from GenBank are provided in Appendix 1. The pycnogonid tree was rooted with 10 outgroup species, including two chelicerates (Uropygi: Mastigoproctus giganteus (Lucas, 1835); Xiphosura: Limulus polyphemus (Linnaeus, 1785)), six mandibulates (Stomatopoda: Squilla empusa Say, 1818; Branchiopoda: Triops longicaudatus LeConte, 1846; Chilopoda: Lithobius variegatus Leach, 1814 and L. forficatus (Linnaeus, 1785); Diplopoda: Thyropygus sp. and Orthoporus sp.
) and two onychophorans (Opisthopatus cinctipes Purcell, 1899 and Peripatoides novaezaelandiae (Hutton, 1876)). We avoided CO1 sequences characterized by an inverted bias in base composition (e. g. , scorpions, spiders, etc. ) in order to limit artefacts during phylogenetic reconstruction. This strategy was determined in agreement with previous studies showing that asymmetric mutational constraints occurred during the evolution of the mitochondrial genome of Chelicerata, including sea spiders  (Hassanin et al. 2005; Hassanin 2006; Arabi et al. 2010 Arabi et al. , 2012)). The 18S alignment was achieved using a first trial with CodonCode Aligner v. 3. 7. 1, and it was optimized manually on Se-Al v. 2. 0a11. Several regions with too many ambiguous positions for homology were removed from the analyses.
We analysed the 18S and CO1 (partitioned by codon positions or without partition) datasets separately, and the model (i. e. , GTR + G + I) was selected using the best AIC score calculated on jModelTest  (Posada 2008). For the reduced dataset of 135 taxa used for the concatenation of CO1 and 18S markers, we performed a partitioned analysis using a GTR for each marker.
Datasets The great extent of localities and depth range explored here allowed us to examine a large diversity of pycnogonids from 8 of the 11 families and from the seven main genera of Ammotheidae, except Ammothea (i. e. ,  Achelia, Ammothella, Cilunculus, Nymphopsis Haswell, 1884, Sericosura Fry & Hedgpeth, 1969 and Tanystylum). The presently monospecific Acheliana  Arnaud, 1971  is also sequenced for the first time with an undescribed species (in prep. ). With the sequences extracted from GenBank (see Appendix 1), we included one additional ammotheid genus (Ammothea) and two other families (Austrodecidae and Colossendeidae) in the analyses. We detected several potential errors in the GenBank sequences, involving substitutions in highly conserved regions (e. g. , Ammothea hilgendorfi DQ389936; Anoplodactylus batangensis DQ389918), as well as unexpected indels in the 18S stems, which are very constrained regions (see below) (e.
This explains why the CO1 gene did not provide robust support for most interfamilial relationships (Fig. 5 ). The sole exception concerns Callipallenidae and Nymphonidae, which are grouped together at the end of a very long branch in the CO1 and CO1 + 18S trees  (Figs 3, 5). This result clearly indicates that the divergence between Callipallenidae and Nymphonidae is more recent than that of other families. In agreement with this view, they share many morphological characters, including the structure of the 10-articled ovigers and the presence of well-developed chelae  (Bamber 2007b ). In addition, our datasets did not provide any evidence for their reciprocal monophyly. On the contrary, our analyses suggested that these two families are reciprocally polyphyletic (PP < 0. 92). However, we found very different polyphyletic patterns in the combined, CO1 and 18S analyses (Figs 3-5), which suggests that CO1 and/or 18S genes are not the best markers to resolve relationships at this level of the pycnogonid tree (Figs 3-4, Appendix 2).
Even with the 18S dataset, most basal relationships within Pycnogonida were not well-resolved, suggesting that most families of Pycnogonida have diverged rapidly from each other. As pointed out by  Charbonnier et al. (2007) , there is a substantial hiatus in the fossil record of Pycnogonida, between the Devonian (ca 400 Mya) and the Jurassic (ca 160 Mya), and the concomitant appearance of several different families during the Mesozoic is in agreement with a crown group radiation. To explain both the lack of resolution for interfamilial relationships and the long branch that separates outgroups from pycnogonids,  Arabi et al. (2010)  have also suggested that a very long period of time occurred between the origin of Pycnogonida, and the subsequent rapid diversification that led to extant families.
However, the paraphyly of Tanystylum was not highly supported by the 18S dataset, suggesting that the hypothesis of monophyly cannot be excluded. Although Achelia and Tanystylum share some superficial similarities, the latter genus shows a typical morphology characterized by palps with a reduced number of articles. Therefore, we consider that further studies are needed to decide whether a new genus should be described for members of the "Achelia sawayai group". The inclusive position of Acheliana within the genus Ammothea suggests that Acheliana should be synonymized with Ammothea. This result was partially perceived by  Arnaud (1971a) , who noted the close relationship between the two genera. However, the type species of Acheliana, A. tropicalis  Arnaud, 1971 , must be re-examined to provide a definitive conclusion.
From a morphological point of view, both latter species possess the typical characters of the genus Ammothella, but A. biunguiculata shows a very original profile with short chelifores and short abdomen (see  Dohrn 1881; Bouvier 1923; Hedgpeth 1941 ), and A. stauromata is easily recognizable, with its characteristic dorso-median tubercles (see Child 1982; Fig. 6A, C ). Therefore, both molecular and morphological data suggest that the taxonomic status of these two species must be revised. For A. biunguiculata, no taxonomic change can be proposed here because we do not have morphological material and the CO1 sequence is not available. For A. stauromata, however, we can describe a new genus, maybe provisional, as there is no evidence for a relationship with any other described genus. Thus, this taxon must receive due attention in future studies.
An underestimated biodiversity The CO1 data generated in this study on specimens collected during the recent MNHN expeditions indicate that the diversity of Ammotheidae was poorly represented in the nucleotide databases, such as GenBank and BOLD  (Ratnasingham & Hebert 2007). Indeed, all our new CO1 sequences show at least 7% of nucleotide divergence with the ca 430 pycnogonid sequences available in GenBank, and even up to 11% if the genus Sericosura is excluded from the comparisons. This means that none of the ammotheids analysed here can be identified at the species level using molecular barcoding. Actually, this result is not surprising if we consider that most of the pycnogonids previously registered in the GenBank and BOLD databases were collected along the coastlines of temperate South America (Chile and Argentina) and Antarctica (Fig.
grant TF-DeepEvo co-funded by the ANR and the Ministry of Science and Technology of Taiwan for supporting his visit to Germany. This work was supported by the project "Taxonomie moléculaire: DNA Barcode et Gestion durable des Collections", funded by the MNHN and the "Bibliothèque du Vivant" network funded by the CNRS, MNHN, INRA and CEA (Genoscope). CO1 and 18S sequences used for this study, including GenBank accession numbers.
Acknowledgements The studied specimens were collected during MNHN expeditions of the programs " Tropical Deep Sea Benthos " and " Planète Revisitée " (http://laplaneterevisitee. org). Deep-sea specimens were mainly collected during the many cruises organized by  P. Bouchet ,  S. Samadi  and L. C. of the  MNHN and Institut de Recherche pour le Developpement (IRD). Recent expeditions were funded by the  Total Foundation , the  Prince Albert II of Monaco Foundation , the  Sloan Foundation , the  Stavros Niarchos Foundation  and conducted by the  MNHN and Pro-Natura International. The KARUBENTHOS expedition was sponsored via European funding (FEDER), the  Port Autonome de la Guadeloupe (PAG). Specimens from hydrothermal vents were provided by the  IFREMER:BICOSE and FUTUNA 3 cruises  (financial support was provided by the  Eramet and Technip companies).
The authors declare no conflicts of interest. This work was funded by grants from the  Institut National de la Santé et de la Recherche Médicale (INSERM) ,  la Fédération Nationale pour l'Aide aux Insuffisants Rénaux (FNAIR)  et l' Agence Nationale de la Recherche (ANR). D. A. is supported by the  Centaure Network , and the  Fonds Emmanuel Boussard. Q. T. performed ELISA dosages. C. T. and L. M. managed the biocollection. M. R. performed analyses of the kidney biopsies. D. A. managed the cohort, designed the study, and provided critical advices. N. P.
∆CK2β-MCF10A Cells Have Reduced Expression of EMT miRNAs Since miRNAs regulate differentiation, cell renewal and invasion  [15] , we looked for potential changes in miRNA expression that might occur upon CK2β depletion. Using microarrays with the miRNA probe set (Agilent miRNA human V. 3), miRNA expression profiles were compared in ∆CK2βand Mock-MCF10A cells (Table  S1 ). These two cell lines were generated by shRNA-transduction as described in  [9] , in which less than 10% of the CK2β subunit remains in ∆CK2β-cells, whereas it was unchanged in Mock-cells (Figure  S1A, B ). We found that several microRNA families were significantly reduced in ∆CK2β-cells (accession number GSE102266, Figure  1A , green bars). The relative miRNA amounts were determined by RT-qPCR using TaqMan probes, including miR-200c, miR-205, miR-141, as well as miR 30b and miR-34b (Figure  1B ).
Transcriptomic Analysis EMT in epithelial cells has been shown to be associated with stem cell traits and chemo-resistance  [29]. Comparing ∆CK2βto Mock-MCF10A cells, we looked further for individual gene expression signatures, using a transcriptomic analysis according to published profiles. Agilent microarrays were performed in duplicates, as previously described  [9]  (accession number GSE102265) and correlations were done with gene set collections from MSigDB 3. 0, as described in Supplementary Figure  S1. With this approach, we found that gene signature of Mock-MCF10A cells was correlated with the epithelial profiles while the ∆CK2β-MCF10A signature matched with mesenchymal profiles described in the Charafe, Sarrio, Gotzmann, and Jechlinger collections  [30] [31] [32] [33]  (Figure  S2A ).
microRNA Profiling Total RNA was isolated using Trizol Reagent (Invitrogen, Carlsbad, CA, USA) and was submitted to ProfileXpert core facility (Lyon, France) for microRNA profiling. The samples were hybridized on human v. 3 miRNA Agilent array according to manufacturer instructions (Agilent protocol version 2. 2), and microarray data analysis was carried out using Feature Extraction software version 10. 7 (Agilent, Santa Clara, CA, USA). For better cross-array comparison, raw data were normalized with the Genespring software version 7. 3. 1 (Agilent), using two LabelingSpikes-InSignal (DMR_285 and DMR_31a) as internal standard. The threshold of detection was calculated using the normalized signal intensity of negative controls ±3 standard deviation. Spots with signal intensities below this threshold are referred to as (absent) with an arbitrary value of 0.
01*, and denoted 'A' in the Tables  S1  and S2. Quality of processing was evaluated by generating a scatter plot of 11 positive controls. Statistical comparison and filtering were performed using Genespring software 7. 3. 1 (Agilent). The average signal is averaged between two replicates, and log2 fold change is calculated between ∆CK2β and Mock conditions. The mean and standard deviation of the mean are then calculated to aggregate different probes signal of the same miRNA. Both mRNA and miRNA datasets are available under the accession number GSE102267.
SDS-PAGE was performed using pre-cast 4-12% gradient gel (Bio-Rad, Hercules, CA, USA). Separated proteins at 20 µg/lane were transferred to PVDF membranes (100 V for 60 min). Blotted membranes were blocked during 1 h at room temperature with saturation buffer (1% BSA in Tris Buffer Saline 10 mM, Tween 0. 1% (TBST)), and then incubated with primary antibody diluted in saturation buffer, for 2 h or overnight. After 3 washes with TBST, secondary antibodies were added for 1 h. Luminata Forte Western HRP substrate (Millipore, Billerica, MA, USA) was added and membranes were read with Fusion Fx7 (PerkinElmer, Waltham, MA, USA). Quantification was performed using ImageJ software.
Real-time PCR was carried out on C1000 Thermal cycler (CFX96 Real Time system, Bio-Rad) at 95 • C for 10 min, followed by 40 cycles of 95 • C for 15 s and 60 • C for 1 min. Data were analyzed with CFX Manager Software version V1. 5. 534. 0511 (Bio-Rad). The hsa-miR-720 was used as endogenous control for normalization. Normalized expression was calculated using the comparative CT method and fold changes were derived from the 2 -∆∆Ct values for each miRNA.
Gene Expression Microarray Analysis Single-sample GSEA (ssGSEA), an extension of Gene Set Enrichment Analysis (GSEA) calculates separate enrichment scores for each pairing of a sample and gene set. Each ssGSEA enrichment score represents the degree to which the genes in a particular gene set are coordinately up-or down-regulated within a sample. This analysis has been done with the GenePattern software  [53]. (http://software. broadinstitute. org/cancer/software/genepattern/modules/docs/ssGSEAProjection/4).
Acknowledgments: We thank  ProfileXpert (Bron ,  France ) for transcriptomic and miRnome analysis,  Alain Puisieux  for providing HMEC-hTERT cells,  Caroline Roelants  and  Manon Gervais  for qPCR studies,  Véronique Collins-Faure  for assistance for FACS and the animal unit staff at  Biosciences and Biotechnology Institute of Grenoble (BIG)  for animal husbandry. This research was supported by recurrent institutional funding from  INSERM, CEA, Ligue Nationale contre le Cancer  (accredited team  2010-2012 ) and  Ligue Comité de la Loire ,  University Grenoble Alpes , the  Espoir Foundation  and grants from the  French National Research Agency  ( PCV-08  CoCCINet).
Departures of methylation-estimated age from chronological age can be used to define intrinsic epigenetic age acceleration (IEAA) that measures cell-intrinsic ageing effects that are independent of chronological age and blood cell composition. A recent study suggests that IEAA can be used to predict lung cancer risk  [20]. However, it is not yet known whether IEAA lends itself for predicting breast cancer susceptibility in a prospective case-control study. To test this hypothesis, we analysed blood methylation data from incident breast cancer cases and matching controls of a large prospective study within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort.
White blood cell count estimates Quantile normalised data were used to infer blood cell proportions. We estimate blood cell counts using two different software tools. First, Houseman's estimation method  [27]  was used to estimate the proportions of CD8+ T cells, CD4+ T, natural killer, B cells, and granulocytes (also known as polymorphonuclear leucocytes). Second, the advanced analysis option of the epigenetic clock software  [4, 14]  was used to estimate the percentage of exhausted CD8+ T cells (defined as CD28-CD45RA-) and the number (count) of naïve CD8+ T cells (defined as CD45RA + CCR7+). We and others have shown that the estimated blood cell counts have moderately high correlations with corresponding flow cytometric measures  [27, 28]. For example, flow cytometric measurements correlate strongly with DNAm-based estimates: r = 0.
Funding This work was supported by grants from the  Institut National du Cancer (INCa, France)  ( 2012-070 ) to IR and ZH and the  European Commission (EC)   Seventh Framework Programme  ( FP7 Exposomics ) ( 308610-2 ) Translational Cancer Research (TRANSCAN) (TRANS201301184) Framework and the  Fondation Association pour la Recherche contre le Cancer (ARC, France)  to ZH. ZH was also supported by the  EC FP7 EurocanPlatform: A European Platform for Translational Cancer Research  (grant number:  260791 ). Funding for the work also comes from a grant from: " Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy " and the  Regional Government of Asturias. EPIC Greece  is supported by the  Hellenic Health Foundation. SH was supported by  NIH/ NIA   1U34AG051425-01. The funders of the study had no role in study design, data collection, data analysis, data interpretation or writing of the manuscript.
The work reported in this article was undertaken during the tenure of a Postdoctoral Fellowship (to SA) from the  International Agency for Research on Cancer , partially supported by the  EC FP7 Marie Curie Actions -People -Co -funding of regional, national and international programmes (COFUND). A. S. is supported by the PhD fellowship from the  Fonds National de la Recherche, Luxembourg  (AFR Code:  10100060 ). This study depended on the participation of the women in the EPIC cohort, to whom we are grateful.
Results and discussions As shown in fig. 2-A  & B for a global compression of 8% strain, the x and y components of the displacement, u x and u y (y is for the direction of the global compression, x is for the in-plane orthogonal direction) are measured with Using symmetries only 1/4 of the the particle is simulated. The material is supposed to be linear, elastic and isotropic for a global 8% compression. Fields are plotted on the deformed system (Eulerian). Results are qualitatively similar to what is found experimentally. Difference is explained because the system is at the limit of the small deformation hypothesis. an excellent smoothness and accuracy compared to what is classically done in the small deformation domain for disc compression  [23]. Nevertheless around the narrow particle boundary, data are noisy since correlation cells overlap both particle and unpatterned surrounding space: correlation information in the particle is mixed with surrounding black area where no correlation is possible.
Similarly, for ε xy , results qualitatively in agreement with Hertz's theory are observed since the shear strain is anti-symmetric with two opposite lobes going from the contact areas. Then, assuming a linear rheology, the stress field is computed from the measured strain field with a Young modulus of 2. 7MPa and a Poisson ratio of 0. 49 (not shown). In parallel to this, numerical simulations have been carried out, by means of the FE code Cast3M  [26]. Taking advantage of the symmetries, 1/4 of the particle is simulated in the same mechanical condition as the experiments except the material is considered as linear, elastic and isotropic for the full deformation range. As presented in fig. 3-A  & B the results are in qualitative agreement with what is measured experimentally but strain intensity do not seem to be the same.
Climatic, sociological and environmental conditions are known to affect the spatial distribution of malaria vectors and disease transmission. Intensive use of insecticides in the agricultural and public health sectors exerts a strong selective pressure on resistance genes in malaria vectors. Spatio-temporal models of favorable conditions for Anopheles species' presence were developed to estimate the probability of presence of malaria vectors and insecticide resistance in Lao PDR. These models were based on environmental and meteorological conditions, and demographic factors. GIS software was used to build and manage a spatial database with data collected from various geographic information providers. GIS was also used to build and run the models. Results showed that potential insecticide use and therefore the probability of resistance to insecticide is greater in the southwestern part of the country, specifically in Champasack province and where malaria incidence is already known to be high.
Data collection and geodatabase We used environmental and demographical data verified and managed by a geographic information system (GIS). Data on administrative borders (provinces, districts), village locations, demography, poverty, precipitation, temperature, relative humidity, land-use, hydrology, topography, malaria cases, and bed-net coverage were collected from various sources. Many of these factors potentially affect the presence of malaria vectors and development of insecticide resistance in mosquitoes. We only used already available environmental data, from local Lao administrative sources (for land-use, demography, and epidemiology) or international data providers (for climate, topography). The following data sets were acquired and used in the analysis: • The Lao Ministry of Agriculture and Forestry provided list and geographical coordinates of villages.
• Climate data such as monthly and annual temperature and rainfall averages were downloaded from WorldClim-Global Climate Data (http://worldclim. org). The raster data have a resolution of 1 km 2. • The Center for Malaria Parasitology and Entomology (CMPE) provided data on malaria cases and bed-net coverage by district. Data were integrated into a geodatabase as layers with attributes, each layer with detailed metadata. The GIS geodatabase contained administrative layers (locations of villages with information on the presence of health facilities, boundaries of villages with population, boundaries of districts and provinces), climate layers (temperature, precipitation, relative humidity), land-use (2002 and 2014), and epidemiological data on malaria. Bed-net coverage was given by district as number of people protected, percentage of people protected, number of bed-nets, and number of bed-net per person.