dataesr/scientific-paragraphs-categorization huggingface dataset:
5 A ). 338 differentially expressed genes (Table  S1 ; fold change ≥ 1. 5, P values ≤ 0. 05) specifically regulated by THPO were identified. Ingenuity pathway analysis revealed pathways related to IFN signaling and antiviral innate immunity as top significantly THPO-activated canonical pathways (Fig. 5 B ). More than 60% of the up-with-THPO gene list (Fig. 5  C and Table  S1 ) is composed of IFN-stimulated genes (ISGs) with characteristics of IFN type I response genes that are found in the Interferome database  (Rusinova et al. , 2013). Indeed, qRT-PCR assays showed that these genes are induced by IFN-α in LSK cells (Fig. S4 A ). qRT-PCR analyses on LSK-CD34 -Flk2 -HSCs stimulated in vitro with THPO confirmed the microarray results (Fig. 6 A ). Importantly, increased ISG expression was also detected in HSCs isolated 90 min after THPO injection in mice, showing that THPO could also induce an early IFN-like gene response in vivo (Fig.
5, Sca-PECy7, CD48-PB, and CD150-PE antibodies, and GFP was analyzed in the LSK-and HSC-gated populations. Alternatively, the presence of GFP was assessed by Taqmanbased qPCR assay using primers and probe spanning the GFP exon-exon junction  (Iijima et al. , 2013). In brief, 700-1,000 cells were FACS-sorted directly in 10 µl of prepGEM tissue extraction buffer (ZyGEM) and lysed according to the manufacturer's instructions. A nested PCR using GFP-specific primers was used to preamplify the L1 transgene, and the presence of the exonexon junction was then assessed by qPCR using primers and an exon-exon junction probe specific for GFP (Table  S1 ). Data were normalized to RNA 5S DNA. To test retrotransposition in vitro, 700 HSCs were isolated from L1-GFP mice immediately after TBI (2 Gy) or THPO injection and TBI treatment and cultured at 37°C in IMDM medium supplemented with 10% FBS, 1% penicillin-streptomycin, and 1 mM glutamine, and containing 25 ng/ml FLT3-L, 10 ng/ml IL-3, 25 ng/ml SCF, 25ng/ml THPO, 4U/ml EPO, and 50 µM β-mercaptoethanol supplemented, or not, with 25 ng/ml THPO.
Microarray analysis Triplicate samples of 10,000-15,000 LSK-CD34 -Flk2 -HSCs were sorted and incubated in vitro for 45 min in complete medium, with or without THPO before irradiation at 2 Gy. RNA was purified 45 min later using the RNeasy Plus micro kit (Qiagen) and were hybridized onto whole genome arrays (Affymetrix MouseGene2. 0ST). Raw data were normalized using the robust Multichip Algorithm (RMA) in Bioconductor R. Quality controls and statistics were performed using Partek GS. A classical ANO VA for each gene was performed and pairwise Tukey's ANO VA was applied to identify differentially expressed genes in THPO-treated and untreated samples. Functional enrichment analysis of differentially expressed genes was performed using ingenuity pathway analysis (Ingenuity Systems). All data have been submitted on GEO Omnibus site under the accession no.
GSE84195. Immunofluorescence 3,000-5,000 HSCs were cytospun on polylysine-coated glass slides, and IF was performed as previously described  (de Laval et al. , 2013). Monoclonal anti-γH2AX antibody (clone JBW301) was purchased from Millipore. The presence of the L1 ORF1 protein was assessed in HSCs using the rabbit anti-mouse L1 ORF1p antibody  (Martin and Branciforte, 1993; Malki et al. , 2014) , a gift of A. Bortvin (Carnegie Institution for Science, Baltimore, MA). The antibody was used at a concentration of 3 µg/ml. To test STAT1 activation, freshly sorted HSCs were stimulated for various times in vitro in the presence of THPO (100 ng/ml), cytospun as above and stained with antiphospho-Tyr(701)STAT1 antibody (clone 58D6 rabbit mAb; Cell Signaling Technologies). Detection was performed using Alexa Fluor 555-coupled antirabbit secondary antibody.
All slides were visualized using SPE confocal microscope (Leica). Pictures were analyzed using ImageJ software or CellProfiler. qPCR of genomic LINE-1 Quantification of genomic insertions of L1 was performed as described  (Coufal et al. , 2009; Muotri et al. , 2010). In brief, duplicate samples of 200 HSCs or LSK cells from WT or Mpl -/-mice were sorted directly in 10 µl of prepGEM Tissue DNA extraction buffer (ZyGEM) and lysed according to the manufacturer's instructions. L1 ORF2 was amplified in triplicate using either Taqman-or SYBR-Green-based qRT-PCR. Relative genomic ORF2 content was normalized to the nonmobile 5S ribosomal RNA genomic content as described  (Coufal et al. , 2009; Muotri et al. , 2010). Analysis of expressed L1 mRNAs RNA from HSCs and ESCs were extracted and subjected to RNase treatment as above.
Acknowledgments We thank  R. Dyunga  for technical assistance; SEV IL  Institut National de la Santé et de la Recherche Médicale  ( INS ERM )  UMS-33  and  Gustave Roussy  animal facilities; the  Imaging and Cytometry Platform of Gustave Roussy  for cell sorting and confocal analysis;  Dr. T. Okamura ,  Dr. Y. Ishizaka ,  Dr. M. Muller , and  Dr. T. Kolbe  for providing mice;  Dr. A. Bortvin  ( Carnegie Institution for Science, Baltimore, MA ) for the gift of anti-mORF1 antibody;  Dr. M. Goodhardt  ( INS ERM UMRS-1126 , Paris),  Dr. M. Gaudry  ( INS ERM U1170 ), and  Dr. G. Cristofari  ( IRC AN, Nice ) for helpful discussions and critical reading of the manuscript. This work was supported by  INS ERM  and grants from  Fondation pour la Recherche Médicale  (Equipe labellisée  FRM DEQ20150331743 ) and  Ligue Contre le Cancer  ( LNCC DM/ CB/003-14  and  LNCC DM/CB/103-14 ) to  F.
Murid rodents (Rodentia: Myomorpha: Muroidea: Muridae) represent the most diverse and abundant mammalian group. In this study, we reconstruct a dated phylogeny of the family using a multilocus dataset (six nuclear and nine mitochondrial gene fragments) encompassing 160 species representing 82 distinct murid genera from four extant subfamilies (Deomyinae, Gerbillinae, Lophiomyinae, and Murinae). In comparison with previous studies on murid or muroid rodents, our work stands out for the implementation of multiple fossil constraints within the Muridae thanks to a thorough review of the fossil record. Before being assigned to specific nodes of the phylogeny, all potential fossil constraints were carefully assessed; they were also subjected to several cross-validation analyses. The resulting phylogeny is consistent with previous phylogenetic studies on murids, and recovers the monophyly of all sampled murid subfamilies and tribes.
, , 2017;; Ndiaye et al. , 2016). Because of the high species richness of the family, determining the precise timing of its radiation is of particular paleobiogeographic interest. Several dated estimates for the age of Muridae are available owing to studies either focusing on the order Rodentia  (Adkins et al. , 2001 (Adkins et al. , , 2003;; Fabre et al. , 2012; Montgelard et al. , 2008) , on the superfamily Muroidea (Muroidea;  Schenk et al. , 2013 ; Steppan et al. , 2004)  or on various murid subsets (e. g. Bryja et al. , 2014; Chevret and Dobigny, 2005; Dobigny et al. , 2013; Fabre et al. , 2013; Pagès et al. , 2016; Rowe et al. , 2008 Rowe et al. , , 2011 Rowe et al. , , 2016b)). However, no clear consensus could be reached for the age of the family Muridae. Indeed, age estimates derived from all aforementioned studies are far from being congruent, likely because their datasets have not been designed for this particular purpose.
But our study stands out for rigorous evaluation of the fossil data for this highly diverse mammalian family. The present study has four main objectives: (i) to design a comprehensive multi-marker molecular dataset for the family Muridae, (ii) to review the murid fossil record in order to identify reliable and suitable primary fossil calibrations, (iii) to provide a reliable estimate of the timing of diversification of the family using multiple fossil calibrations, and (iv) to lean on the resulting dated phylogeny to reconstruct the biogeographic history of the family using up-to-date analytical approaches.
Taxon sampling For this study, new DNA sequences were generated for five murid species (Acomys cf. cineraceus, Acomys subspinosus (Waterhouse), Acomys wilsoni Thomas, Arvicanthis niloticus (Desmarest), Arvicanthis neumanni (Matschie) see Appendix A). Though we largely relied on GenBank data for this work, it is worth underlining that our research group generated thousands of murid sequences (all deposited in GenBank) in the past 15 years (we used some of these sequences for 44 species included in this study). In total, our dataset (Appendix A) encompasses 160 murid species representing 82 of the 151 known murid genera. All four extant subfamilies (if considering the Togo mouse from Leimacomyinae to be extinct) of Muridae are included. For the largest subfamily Murinae, we included representatives of all 10 tribes that have been defined by  Lecompte et al.
New sequences were deposited in GenBank under accession numbers KY634246 to KY634250. The newly generated sequences were further combined with data from GenBank. The resulting matrix (see Appendix A) encompasses the following six nuclear and nine mitochondrial gene fragments: 'acid phosphatase 5' (AP5), BRCA1, intronic portion of 'Peripheral benzodiazapine receptor variant' (BZRP), 'growth hormone receptor' (GHR), IRBP and RAG1, for the nuclear genes, and '12S ribosomal RNA' (12S), '16S ribosomal RNA' (16S), 'ATP synthase 8' (ATPase8), 'cytochrome c oxidase I' (COI), 'cytochrome oxidase II' (COII), 'cytochrome b' (Cytb), 'Aspartic acid transfer RNA' (tRNA-Asp), 'Lysine transfer RNA' (tRNA-Lys), 'Serine transfer RNA' (tRNA-Ser), for the mitochondrial genes. For nine taxa (Acomys cf. cineraceus, Acomys wilsoni Thomas, Aethomys chrysophilus (de Winton), Aethomys hindei (Thomas), Aethomys kaiseri (Noack), Aethomys silindensis Roberts, Arvicanthis nairobae J.
Phylogenetic analyses Phylogenetic analyses were conducted using both Bayesian inference (BI) and maximum likelihood (ML). Analyses were performed on the online computer cluster CIPRES Science Gateway  (Miller et al. , 2010; www. phylo. org) and on the high performance computing (HPC) cluster hosted in the Centre de Biologie pour la Gestion des Populations (CBGP) in Montferrier-sur-Lez, France. For both phylogenetic analytical approaches, we carried out partitioned analyses to improve phylogenetic accuracy  (Nylander et al. , 2004). The molecular dataset was divided a priori into 33 partitions: we used three partitions for each of the proteincoding genes (AP5, ATPase8, BRCA1, COI, COII, Cytb, GHR, IRBP and RAG1) and one partition for each of the rRNA-tRNA genes (12S, 16S, tRNA-Asp, tRNA-Lys and tRNA-Ser) as well as the BZRP intronic portion.
Bayesian inference analyses were carried out using MrBayes v3. 2. 6  (Ronquist et al. , 2012b). Two independent runs with four MCMC (one cold and three incrementally heated chains) were conducted: they ran for 50 million generations, with trees sampled every 1,000 generations. A conservative 25% burn-in was applied after checking for stability on the loglikelihood curves and the split-frequencies of the runs. Support of nodes for MrBayes analyses was provided by clade posterior probabilities (PP) as directly estimated from the majority-rule consensus topology. Following  Erixon et al. (2003) , nodes supported by PP ³ 0. 95 were considered strongly supported. Maximum likelihood analyses were performed using RAxML v8. 2. 8  (Stamatakis, 2014). Because this software does not allow simpler substitution models, we used three partitions with a General Time Reversible (GTR +Γ +I) model (see Table  2 ).
68 (B F >10,  Kass and Raftery, 1995). The final analysis (with nine verified fossil constraints and lognormal prior distribution for calibration constraints) was carried out by two independent runs each with 50 million generations and trees sampled every 5,000 generations. We used a conservative burnin of 12. 5 million generations per run. Post burn-in trees from both analyses were further combined using the LogCombiner module of BEAST. Convergence of runs was assessed graphically under Tracer v. 1. 6 and by examining the ESS of parameters.
Phylogeny of Muridae Our multilocus dataset representing all major lineages of the family Muridae is 10,482 bp long with 42. 5% missing data. Both BI and ML analyses yield similar topologies (see Fig. 1  for the topology inferred under BI, and Fig. S1  in Appendix D for the best-fit ML tree), as indicated by a high proportion of shared nodes (160 out of 162). BI and ML analyses differ only in the position of Pelomys fallax (Peters) and Zyzomys argurus (Thomas), but their placements are not significantly supported in either analysis. Clade support is moderate to high on average; if considering the number of nodes that are supported by PP ≥ 0. 95 or BV ≥ 70%, BI analyses yield a slightly more robust topology (135 well-supported nodes) compared to the ML tree (  122 well-supported nodes). Phylogenetic analyses confirm the monophyly of the family Muridae, of all its four constituent subfamilies, as well as of the previously defined tribes of the subfamily Murinae (Fig.
For an easy access to all corresponding fossil records, we have made data available on the Date-a-Clade Website (http://palaeo. gly. bris. ac. uk/fossilrecord2/dateaclade/index. html), Paleobiology Database (http://fossilworks. org/) and TimeTree Database (http://timetree. org). hamadryas hamadryas. Mol. Ecol. 13, 2819-2827.
46-14. 16 Ma). In Deomyinae and Gerbillinae, several lineages were able to colonize the Palearctic region from the Afrotropics (in our dataset, this concerns Acomys russatus (Wagner) and Gerbillus gerbillus Olivier). The subfamily Murinae originated in the Indomalayan region during the middle Miocene (median age of 14. 2 Ma; 95% HPD: 12. 70-16. 07 Ma); the corresponding basal split separated the Phloeomyini and all remaining murines ('core murines' sensu Steppan et al. 2005). The next major split occurred in the Indomalaya between Rattini and the remaining tribes of Murinae (median age of 12. 3 Ma; 95% HPD: 11. 28-13. 62 Ma), with an origin of Rattini estimated at 10. 4 Ma (95% HPD: 9. 23-11. 93 Ma). The ancestral area of Rattini was also inferred to be the Indomalaya; during the course of their diversification, a few taxa colonized Australasia (e.
Acknowledgments This study was supported by two projects of the  Czech Science Foundation , no. 15-20229S , the  Ministry of Culture of the Czech Republic  ( DKRVO 2017/15 ,  National Museum ,  00023272 ) and  JSPS KAKENHI   JP15H06884  ( Grant-in-Aid for Young Scientists Start-up ). Most analyses were run on CBGP HPC computational platform, while a minor part was performed on CIPRES Gateway. We thank  Alexandre Dehne Garcia  for his help on the HPC CBGP cluster and  Nick J. Matzke  for introduction into BioGeoBEARS analysis. We would also like to thank  Alisa Winkler  and  Christiane Denys  for discussion about some paleontological aspects, as well as  Arame Ndiaye  and  Pascal Chevret  for providing additional sequences. We are grateful to  Vincent Lazzari ,  Fredrick K. Manthi ,  Pierre Mein ,  Rajeev Pantanik  and  Sevket Sen  who provided permission for the reproduction of the figures in Appendix B.
The user's emotion in a tourist recommender system has been estimated using Emotion Generating Calculations and Mental State Transition Network. 15   so, a web chat room with emotions has been designed for perceiving e-learners' emotional states by means of interactive texts. And it recommends resources such as music and mottos to an e-learner when it detects negative emotional states. Meanwhile, it recommends emotion regulation cases to the e-learner's listeners and teachers. 16  In summary, the current recommendation systems are generally based on experimental data only without systematically exploiting professional knowledge and consumer's emotions in shopping scenarios. A so, it systematically lacks feedback processing in these systems. If the consumer is not satisfied with the recommendation result, there is no self-adjustment function.
Moreover, the existing recommendation systems are mostly closed, without considering the possibility of data and knowledge updating. Considering the above drawbacks, we propose a new recommendation system possessing the following advantages: i. It is a knowledge-based recommendation system. The rules of the knowledge base are extracted from four sensory experiments. ii. It can adjust the knowledge base automatically according to the consumers' feedback. The classical single recommendation process has been transformed into a cycle of recommendationevaluation -adjustment. iii. It is an open system so that the consumer can easily add or replace criteria and data. The knowledge base will evolve with new successful recommendation results. The originalities of the contribution are described below.
Body Data The Chinese Standard Human Body Database (female) is available for us. The statures of these samples are arranged from 145cm to 175 cm with a step length of 5 cm. According to this database, four standard body styles (Y, A, B, C) are defined from the difference of chest circumstance and waist circumstance. In this database, the shortest sample is 145cm and the tallest 175 cm and between them the other samples are arranged with an interval of 5 cm. Generally, a number of common rules for human body sizing can be extracted.
Fuzzy description of height Normally, we use two indices to describe a human body shape, namely height and fat-thin. However, how to evaluate tall-low and fat-thin is vague. It is for this reason that we use fuzzy sets to express human body shapes. Based on the garment designer's knowledge, tall-low can be expressed by b1, and fat-thin can be expressed by b1 & b4. According to the previous database of human body shapes, 160A is generally taken as the standard body shape for Chinese female population, and 145cm and 175cm can be considered as the lower and upper bounds of the whole population. We can describe the tall-low as five levels: X1: short, X2: a little short, X3: middle, X4: a little tall, X5: tall. The fuzzy function and rules are obtained by uniformly dividing the whole range of the body data in height into 5 classes or 5 fuzzy values, denoted as X1, X2, X3, X4, X5.
Virtual try-on and evaluation Having received the recommended product profile, we use the Clo3D garment CAD software for visualizing its fitting effects on the virtual body shape of the consumer. One example of virtual female jeans try-on is given in Figure  5. Based on the visual effects of the virtual product generated from the recommended profile in the CLO3D environment, the consumer gives her evaluation scores on the difference between the recommended and desired product profile. The evaluation procedure, including the steps of training, evaluation scale definition and adjustment of desired product profile, is described below.
In practice, reliable recommendation results depend on a big and comprehensive experimental dataset. Therefore, in the future, in order to obtain more generalized and concrete information about the knowledge, it is imperative to integrate more types of samples with more diversified garment styles and details into the system. ii. Self learning is realized by the current knowledge base, according to consumer feedback, the knowledge base can be automatically adjusted. However, the learning strategy of the knowledge base is still too simplified. In the future, more intelligent strategies can be introduced to make the knowledge base more accurate and intelligent. recommend the product with the highest similarity degree Calculating the similarity degree between the desired product profile and the existing products.
However, it is often criticized for overlooking the richness of human experience and behavior, and answering simple questions by drawing only on those variables that are easily quantifiable. Both types of research paper were systematically reviewed utilizing the databases PubMed, OvidSP, and Science Direct. Both PubMed and OvidSP have pros and cons, and so we used them both to maximize the breadth of our review into the medical science literature. PubMed is a government-sponsored system and is freely accessible by anyone who can access the Internet. Ovid is a privately owned system developed by Ovid Technologies. Ovid's primary advantage is that the user can search different databases in several disciplines using the same interface. PubMed is generally easier to search than Ovid, but it can return large numbers of irrelevant articles unless the user is familiar with the advanced search techniques of the system.
Ovid can be more difficult to learn, but complex and precise searching is easier to do on Ovid than on PubMed. It allows a greater level of control over a search which can result in retrieval of fewer irrelevant articles. One major advantage of PubMed is that it is readily updated not only with printed literature but also with literature that has been presented online in an early version before print publication by various journals. Because of these pros and cons  (Falagas et al. , 2008; Kelly and St Pierre-Hansen, 2008)  we wished to search both of these databases. Science Direct is provided by a vendor named Elsevier. Science Direct, as its name suggests, is a strong database for researching scientific topics; however, it also allows use to access social science topics, like psychology, which we felt was important in our context  (College, 2014).
Thus, we used these three databases in order to cast a wide net and capture what the existing research in medical science and psychology has to say about the factors that predict coping with chronic illness. The following Boolean search was conducted: In [Title/ Abstract], (Chronic illness OR chronic disease OR noncommunicable disease) AND (adjustment OR coping style OR acceptance OR appropriation OR attunement) AND (model OR framework OR assessment). We searched for original papers and review articles that had been peerreviewed. Articles selected for inclusion had to be published in either English or French and had to have passed ethics review at their institution, thereby conforming with standards outlined in the Declaration of Helsinki  (Rickham, 1964). Excluded from the review were papers describing work on genetic diseases, acute diseases, and child/adolescent populations.
Data Collection A research assistant having experience in qualitative interviews with low-SES persons will conduct individual 60-min semistructured face-to-face interviews at the location of each participant's choice. In line with sociological and information studies  [112, 113] , interviews will explore how participants experience, conceptualize, perceive, and understand aspects of N&G information and the IAM. The interview guide will consist of open-ended questions addressing the participants' routines in terms of internet use (both for their personal and children's development information needs), their experience in the use of IAM and N&G's information, the facilitating aspects of N&G and IAM, perceived barriers in their experience, and suggestions to improve the N&G website and IAM. On the basis of the theoretical model, the interview guide will be developed in a simplified language with input from team members and the steering committee.
Data Analysis Two principal investigators (PP and CL) have experience in qualitative research and 1 (CL) has experience in qualitative research with populations in situations of vulnerability. With the research assistant and a research trainee, they will read notes and interviews and meet regularly to build memos (meeting minutes with arguments for and against each analytical decision, as rigor is mainly based on researchers' reflexivity in this design  [110] ), case summaries, and themes (definitions and key examples) using hybrid deductive-inductive thematic analysis  [114, 115]. The research assistant will manage the analysis using specialized software (NVivo11). Themes will be derived from our theoretical model (Figure  2 ; deductive coding) and emerge from the data (inductive coding).