dataesr/scientific-paragraphs-categorization huggingface dataset:
Two samples (1 w-RHG and 1 f-AGR) were removed because they presented a clear excess of hemi-methylated sites. Accounting for age and heterogeneity in cell subtypes. To account for the potential confounding introduced by age and cellular heterogeneity in whole blood, we first estimated these variables in all samples. Ages were estimated from methylation data for all samples, with an elastic net regression model  32  , and the estimated ages were compared with the ages declared, when these were available (Supplementary Note 2). To account for cellular heterogeneity, we used a reference-based method in which the DNA methylation signature of each of the principal types of immune cells (granulocytes, monocytes, B cells, CD4 þ T cells, CD8 þ T cells and NK cells) was used to predict the proportions of these cell types in unfractionated whole blood  33.
Determination of differentially methylated sites. Sites differentially methylated between populations (DMS) were identified statistically, by fitting a linear regression model for each site (M-values Bpopulation þ sex þ age þ cell type proportions þ error), and applying empirical Bayes smoothing to the s. e. 's, with the R bioconductor limma package  62. Sites with a Benjamini and Hochberg adjusted Po0. 01 were considered to be differentially methylated. To define the amplitude of DMS, we used different criteria: a Benjamini and Hochberg adjusted P value o0. 01 and a difference in mean methylation level between the two populations of more than 2, 5 or 10%. For this analysis, methylation level was determined as the ratio of methylated probe intensity to overall intensity, the b-value  34. We extracted the overlaps between different DMS sets and calculated the P values measuring the probability of these overlaps being obtained by chance, using 10 7 resamplings.
Histone modification peak data for H3k4me1, H3K4me3, H3K9me3 and H3K27me3, which correspond to the histone marks for which data was available for PBMCs, were downloaded from the ENCODE website (http://genome. ucsc. edu/ENCODE/). A site was considered to colocalize with a histone modification mark if it falls into the region defined as a 'narrow peak' (FDR of 0. 01). TF-binding sites affinity scores for sequences of 30 bp around each methylation site were obtained using the TRAP software  63  and the position weight matrix of 85 human TFs from JASPAR  64. For each TF, a site was considered to have a high affinity if it fell into the top fifth percentile of the score distribution. P values for enrichment in genomic positions, histone marks or TF-binding sites among recent and ancient DMS were obtained using a w 2 -test.
We filtered out SNPs with an allele frequency below 10% in all populations. Age, sex and the proportions of the various cell types were used as covariates in the linear model. In addition, we included the first PC obtained from genotyping data as a covariate, to correct for varying degrees of AGR ancestry across individuals within RHG populations. We then estimated the genome-wide weight of each configuration (Supplementary Table  6 ) using eqtlbma_hm and the default grids provided by the eQtlBma package as a priori for the hierarchical model. The probability of a methylation site having no meQTL (p0) was estimated by the EBF method  67  , and various posterior probabilities were calculated with eqtlbma_avg_bfs. We then extracted all the methylation sites with at least one meQTL at an FDR of 0.
We filtered out SNPs with LD r 2 values 40. 8 in each pair of populations merged, for F ST , or in each population separately, for LSBL and iHS, using plink  69. Annotation using data from genome-wide association studies. For all sets of DMS genes and meQTLs, we explored their implication in human diseases and traits using hits of GWAS, obtained from the 02/06/2015 version of the NHGRI database, which we manually modified to include two recent GWAS of height  52  and age at menarche  51. Only GWAS signals with P values o5 Â 10 À 8 were considered. We used two approaches; a gene-based approach and a SNP-based approach. The gene-based approach relies on the simple fact that a DMS gene is the reported gene of a GWAS hit. A set of n DMS genes is considered enriched in GWAS genes if the proportion of DMS GWAS genes in this set is larger than in 95% of 10,000 randomly sampled sets of n genes.
Genes are randomly sampled from all genes that have at least one methylation probe in the Human-Methylation450 BeadChip, and are matched to the observed number of probes per gene observed in the tested set. We also tested if sets of DMS genes were enriched in genes associated to individual diseases/traits. P values were obtained by resampling. Only diseases/traits that were associated with more than five DMS genes were considered. The second SNP-based approach evaluates if meQTLs correspond to, or are in strong linkage disequilibrium (r 2 40. 8) with, SNPs reported as best GWAS hits. For each set of meQTLs, we first removed all SNPs in LD using plink ('--indeppairwise 50 5 0. 8')  69. We next retrieved SNPs in strong linkage disequilibrium with any of these SNPs, using the correlation coefficient implemented in plink calculated in our imputed genotyping data set.
Acknowledgements We thank  Vincent Colot ,  Etienne Danchin ,  Jean-Philippe Fortin ,  Tatiana Giraud ,  Aurelie Labbe, Guillaume Laval  and  Carla Saleh  for feedback on data analyses and reading of the manuscript. We also thank  Martin Sikora  and  Carlos Bustamante  for providing the variant calling of whole-genome sequencing data. We are grateful to all the study participants for their generous contributions of DNA. This study was funded by the  Institut Pasteur , the  CNRS , a  CNRS 'MIE' (Maladies Infectieuses et Environnement) Grant , and a  Foundation Simone & Cino del Duca Research Grant  (L. Q. -M. ), and the  Canadian Institute for Advanced Research (CIFAR)  (M. S. K. ). M. J. J. was supported by a  Mininig for Miracles post-doctoral fellowship  from the  Child and Family Research Institute.
Additional information Accession codes: The genotyping data generated in this study have been deposited in the European Genome-Phenome Archive under accession codes EGAS00001000605, EGAS00001000908 and EGAS00001001066. The DNA methylation data generated in this study have been deposited in the European Genome-Phenome Archive under accession code EGAS00001001066. Supplementary Information accompanies this paper at http://www. nature. com/ naturecommunications Competing financial interests: The authors declare no competing financial interests. Reprints and permission information is available online at http://npg. nature. com/ reprintsandpermissions/.
In this paper we present a system for automatic prediction of extraversion during the first thin slices of human-robot interaction (HRI). This work is based on the hypothesis that personality traits and attitude towards robot appear in the behavioural response of humans during HRI. We propose a set of four non-verbal movement features that characterize human behavior during interaction. We focus our study on predicting Extraversion using these features, extracted from a dataset consisting of 39 healthy adults interacting with the humanoid iCub. Our analysis shows that it is possible to predict to a good level (64%) the Extraversion of a human from a thin slice of interaction relying only on non-verbal movement features. Our results are comparable to the state-of-the-art obtained in HHI  [23].
Within EDHHI, the researchers investigated how the production of social signals during HRI is influenced by individual factors, such as personality traits and attitude towards robots. A number of face-to-face dyadic and tryadic interactions were realized in the experiments of this project, between ordinary people without prior experience with robots and the humanoid iCub. Exploiting the dataset collected in EDHHI, the goal of this paper is to investigate whether it is possible to predict the personality trait of extraversion from a set of non-verbal features extracted during a short interaction with the robot. Particularly, we take into account the first thin slices of an interaction (i. e. , the first minutes). Personality is generally addressed through the trait theory that individuates the factors able to catch stable individual characteristics underlying overt behaviour.
Hypothesis: The literature on personality traits and HRI shows that personality traits influence the production of verbal and non-verbal signals during HRI. Preliminary analysis on the EDHHI dataset reveal that extroversion and NARS influence the production of gaze and speech  [9]. Here, we contend that it is possible to predict the human's extraversion from the analysis of non-verbal features. Overview of the proposed system for automatic prediction of extroversion in HRI: The proposed system is sketched in Figure  1. We extracted a set of relevant non-verbal features from the depth image of a Kinect placed above the head of the iCub interacting face-to-face with adult participants to the EDHHI experiments. As it will be discussed in Section 3, the relevant features include quantity of movement, synchrony and the personal distance between human and robot (frequently studied in proxemics).
When the experimenter would detect a disengagement of the participant, a long pause or inactivity, she would invite the participant to withdraw from the robot and start preparing for executing the EDHHI experiments with iCub, which are out of the scope of this paper. Participants : 39 healthy adults without any prior experience with robots volunteered to participate to the experiments (11 male and 28 female, aged 37. 8y±15. 2y). They received an ID number to preserve the anonymity of the study. They signed an informed consent form to partake in the study and granted us the use of their recorded data and videos. Data Collection : The dataset from Project EDHHI includes the video stream collected by a Kinect RGB-D sensor (v. 1, 30fps) placed above the head of the robot in such a way to retrieve the body and face of the human interacting with the robot.
e. Θ(x) = 0 for x ≤ 0 ans Θ(x) = 1 for x ≥ 0) and n = 1,. , N. Then, a sliding window is applied to both Q and q (window size of 3s, step=1. 5s), and the count of how many times Q and q step up is done. Finally, the histograms of these countings are built according the obtained values in the ranges of [0, max no steps up] and [-1, 1], respectively. F4) Standard deviation of human-robot distance (STD-d): it is computed as the average of the pixels' values of the silhouette extracted from the depth image of the Kinect. Considering its position in the set-up, it is a good approximation of the distance between the two interactants. → The features F1-F4 are merged into a 72-dimensional vector. The resulting dataset included 39 instances, that is a feature vector for each participant.
Automated prediction of extraversion during HRI Features extracted from interactions with the iCub built the dataset for the prediction of Extraversion. The NEO-PIR scores were used as reference for labeling the personality trait of each subject. However, in the particular context of a first interaction with social robots, people's behavioural response can be not only determined by their personality, but also by their attitude towards robots and the context of the interaction. In particular, due to prior experiences with robotics, the attitude towards the robots plays a relevant role and can dramatically affect people's behaviour towards social robots. To catch this phenomenon, we seek a linear combination between the scores able to contextualise the personality information in the specific scenario of interaction with social robots.
This work addressed this point by relabeling the instances of the dataset using the NEO-PIR extraversion score toghether with NARS questionnaires results. More in the detail, a Principal Component Analysis (PCA) was carried out on a scores vector including: NEO-PIR, NARS-S1, NARS-S2, and NARS-S3 scores, respectively. NARS-S3 is an inverse scale, so its scores were reversed. For NEO-PIR, NARS-S1, NARS-S2, and N ARS -S3 scores, the PCA's eigenvalues were respectively 2. 17, 0. 85, 0. 56 and 0. 32, while the PCA's component load were 0. 32, -0. 56, -0. 57 and 0. 51. Only the first principal component was meaningful (eigenvalue greater than 1). The loadings reflected how the personality scores are captured by the first principal component. The values of the first component were quantized (High-Low) along their median and the final labels were obtained.
The dataset resulted in a 39 (instances) x 72 (features) matrix, that is there are more features than instances. For this reason, we decided to adopt for the  1. Average Percentage of Precision, Recall and F-score classification a Logistic Regression Classifier (LRC)  [16]  with penalty parameter C = 1 and L2 norm L2. The averaged performance of the trained classifier was assessed via a multiple-run k-fold stratified cross-validation. In this study, 10 run and 10 folds have been adopted. Table  1  summarises the performances of the LRC, according to the different subsets of used features. The table shows that the classification result relying on the Quantity of Movement alone on the standard deviation of the distance, is not able to overcome the chance level. However, classification results using also dominance and synchrony information overtake this level.
14 15  Within the framework of the PROLIFICA programme (Prevention of Liver Fibrosis and Cancer in Africa, funded by the European Commission FP7, P34114)  16  we identified a new simple laboratory index, the gamma-glutamyl transpeptidase (GGT)-to-platelet ratio (GPR), in a cohort of CHB carriers in The Gambia, West Africa and then assessed its diagnostic accuracy in two external validation sets from France  17  and Senegal. 15 e compared the diagnostic performance of GPR with that of Fibroscan, APRI and Fib-4 tests, using liver histology as a gold standard.
METHODS Patients From November 2011 to November 2013, all consecutive patients with positive hepatitis B surface antigen test (Determine, Alere, USA)  18  enrolled in the PROLIFICA programme in The Gambia underwent a standardised clinical examination including Fibroscan, abdominal ultrasound and blood tests: haematology (Medonic SE-12613, Boule Medical AB, Sweden); biochemistry (VITROS 350 analyser, Ortho, USA); hepatitis B e antigen (ETI-EBK Plus, Diasorin, Italy); antibody against HCV (anti-HCV) (AxSYM, Abbott, USA); antibody against hepatitis D virus (ETI-AB-DELTAK-2, Diasorin, Italy); antibody against HIV (anti-HIV) (Genscreen ULTRA, Biorad, USA); and HBV DNA using a quantitative in-house PCR (detection limit: 50 IU/mL). The data were prospectively collected using a specific case report form.
Validation sets Two external validation data sets of treatment-naïve monoinfected CHB carriers with viral load ≥2000 IU/mL and without excessive alcohol intake from France  17  and Senegal  15  were used to assess the performance of the novel index and other noninvasive markers. In the French cohort (n=138), only the patients with available data on GGT and platelet counts and a length of liver fragment >15 mm were included in the current analysis (n=63). Fibroscan was not performed in this cohort. In Senegal, only the patients with LSM ranging between 7 kPa and 13 kPa underwent LB following the scientific committee's recommendation, and the original analysis was restricted to patients with normal ALT (n=69). 15 In the current analysis, we also included patients who had elevated ALT, and thus we analysed 80 Senegalese patients.
Acknowledgements The authors thank the  MRC laboratories The Gambia unit , the local ministry of health and social welfare for supporting the project, all the study participants, the PROLIFICA team: in particular  Ignatius Baldeh ,  Famara Bojang ,  Amie Ceesay ,  Mavis Foster-Nyarko ,  Debbo Jallow ,  Abdulie Jatta ,  Adam Jeng ,  Sheriff Kolley ,  Yamundow Jallow Samba ,  Alagie Sanneh ,  Bakary, Sanneh ,  Demba Sonko ,  Lamin Bojang ,  Saydiba Tamba  and  Debbie Garside , the project manager of the PROLIFICA programme. The authors also thank  Professor Dominique Valla  for his precious recommendations on haemostatic precautions before liver biopsy in the local resource-constrained setting. The authors also thank  Dr Philippe Sultanik  for his contributions to statistical analysis and  Dr Patrick Ingiliz  for his constructive comments and suggestions.
Contributors All authors contributed to this study at different levels. All authors read and approved the final version. ML and YS designed the study and wrote the paper. ML and RN were locally responsible for the clinical aspects of the study. MK, JL and RG were in charge of the histopathological analysis. PS prepared the liver specimens with the support of MK and MTa; MV and PSM were in charge of the Senegalese cohort. VM was responsible for the French cohort, conceived the GPR and revised the manuscript. MTh revised the design of the study and the manuscript. YS was responsible for the statistical analysis. Funding The study in The Gambia is part of the  PROLIFICA  programme funded by the  European commission :  EC FP7 ,  P34114. The study in Senegal was funded by the  ANRS (National Institute of Research on AIDS and Viral Hepatitis), France.
Links between proteins highlight the evidence in the literature for each interaction. The schematic was constructed using the STRING database (http://string-db. org) (Szklarczyk et al. , 2015)  and GeneMANIA (http://www. genemania. org/) (Warde-Farley et al. , 2010). There is indirect molecular evidence linking RPGR function with vesicle trafficking, for example RPGR interactions with RAB8, whirlin and the cytoskeleton (see below), but since proposed ciliary gate proteins such as RPGR, RPGRIP1 and CEP290 are all required for opsin localisation to the OS it is currently more plausible that this defect is secondary to defective ciliary gate functions. Recent work has gone some way towards elucidating the RPGRIP1 interaction with RPGR by showing that the RPGRIP1 interaction domain of RPGR partially overlaps with the domain interacting with PDEd (PDE6D)  (W€ atzlich et al.
We set traps in trees and on the ground at night and checked them each morning for captured animals. We marked captured mouse lemurs by clipping the ears, and inserting microchips for easy identification using a transponder. We collected basic data (date, place of capture, sex, and basic morphometrics including body mass) for each captured individual. Animals were released at the location of their capture around sundown, the beginning of their active period. On a daily basis, total rainfall and minimum and maximum temperature at Parcel 1 were also recorded.
Statistical methods We used the chi-square functions in Graph Pad Prism and the Statistical Package for the Social Sciences (SPSS 22. 0) to ascertain the significance of differences in tick infestation rate by season, site and sex. A number of mouse lemur individuals were "trap happy" (captured multiple timesup to 43); some were heavily infested. Because these individuals become overrepresented when the sample comprises total captures and recaptures, comparisons by capture and recapture are useful only when looking at the overall infestation pattern across forest types and habitats. Comparisons by individual give a more accurate measure of infestation rates. For each statistical comparison, we indicate whether the test is based on number of captures or number of individuals in each test category.
Acknowledgments We wish to thank  I. A. Youssouf Jacky  and other members of E. Rasoazanabary's field team for help with collecting ecological and climate data. Support for fieldwork was provided by the  Wildlife Conservation Society ,  International Foundation for Science and the National Geographic Society. Financial support for ER was provided by the  Margot Marsh Biodiversity Foundation , the  American Society of Primatologists, Primate Conservation, Inc. ,  Wildlife Conservation Society , the  National Geographic Society , and the  Wenner-Gren Foundation for Anthropological Research. This research was conducted under IACUC Protocol No. 27-17-01 , and specimens were imported under CITES permit no. 08US158368.
047)  (Fig. 1 ) and in PBMC (p=0. 029) (data not shown). Finally, 2/15 patients did not perform two natalizumab infusions, due to a concomitant urinary infection by Escherichia coli and a clinically worsening of RRMS with a positive STRATIFY JCV ® at t3. Evaluation of JC viral load by Q-PCR in biological samples of 30 RRMS patients with the number of natalizumab infusions ranging from 4 to 12 (<12 months) In addition to the 15 patients described above, in this cohort were also enrolled another 15 patients with the mean number of infusions between 4 and 12 (<12 months). From these 15 patients, 18 whole blood samples in EDTA (4 obtained at t1, 4 at t2, and 10 at t3) and 14 urine samples (3 obtained at t1, 3 at t2, and 8 at t3) were collected and the JC viral load was assessed by q-PCR. Regarding the blood samples, the results showed that all 18 plasma samples were negative for viral DNA, whereas only 1/18 PBMC sample, obtained from a STRATIFY JCV ® -positive patient at t3, showed a JC viral load of 1.
From these 27 patients, 48 urine, 56 plasma, and 56 PBMC samples were collected. 8/48 urine samples (median JC viruria 5. 11 log10 gEq/mL; range 2. 94-6. 67), 9/56 plasma samples (median JC viremia 3. 01 log10 gEq/mL; range 1. 52-4. 72), and 6/56 PBMC samples (median copies number 2. 39 log10 gEq/10 6 cells; range 1. 48-4. 11) were positive for JCV DNA These data are summarized in Table  4.
We trained 64 of the VLSI output neurons of the network to become selective to one of two image classes, and the other 64 to become selective to the other class, via supervised learning protocol. The experimental protocol consists of showing a sequence of static images of objects from the Caltech 101 dataset coupled with a teacher signal to steer the activity of the output neurons. The DVS is put in front of a screen where the images are displayed. During the presentation, the images are flashed with a small jitter around the center of the visual field to simulate microsaccadic eye movements. The movement causes the DVS retina to continuously stream spike trains corresponding to the edges of the objects in the image. The spike trains are then routed to the STP synapse array, stimulating a population of neurons corresponding to the hidden layer of the neural network.
The activity in this layer is indeed the most important since it drives the plastic synapses that belong to the output layer neurons. After training, our classification system was able to respond selectively to natural images of cars and motorbikes taken from the Caltech 101 database. Although an extensive characterization of the system's ability to perform object recognition is out of the scope of this work, we draw the following conclusions from our experiment: • The choice of fixed, random projections from the input layer was surprisingly effective, though certainly not optimal for the task at hand. • A better solution would be to include an unsupervised learning stage in the training protocol to optimize the weights of the convolution layer as in traditional machine learning approaches  (LeCun et al.