dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , the network topology). Conceptually, some of this information can already be gleaned from visual examination of the network graph and it is recognized that often an appropriate visualization and layout of a network is a critical step in understanding  (Newman 2003). However, as the networks become larger and more complex, it will be increasingly necessary to use various graph theory-based tools and computational algorithms to identify important and informative topological features. For computational purposes, AOP networks can be represented in a variety of forms such as adjacency matrices or from-to matrices (See Supplementary Files). These can be readily processed using various software developed for network analyses (e. g. , Cytoscape, the graph, RBGL, and Rgraphviz packages in Bioconductor, etc.
Interactions among AOPs can take place at many different levels of biological organization. For example, at the cellular level there can be interactions among signaling pathways, receptor cross talk, assembly and regulation of transcription factor complexes, etc. Knowledge concerning many of these interactions has been collected and made accessible in several computational databases, such as the XTalkDB  (Sam et al. 2017). Moreover, studies that describe how to leverage this knowledge to address specific biomedical challenges have been published (e. g. ,  Jaeger et al. 2016). Another important example of pathway interactions involves crosstalk between nuclear receptors. Numerous examples in the context of endocrine disruption have recently been reviewed by  Kiyama (2016). Even when there are no directly shared KEs, multiple pressures on a biological system can lead to systemic impacts such as mitochondrial energy depletion, limiting enzyme depletion, changes in intra or extracellular matrix organization, etc.
5. 2. Important Considerations As is generally the case with AOPs, the ability to understand potential interactions among AOPs and predict their consequences depends on how comprehensively the AOPs cover the relevant biology. When large data gaps exist between MIEs and KEs, when the KERs are weak, or when AOPs relevant to the modes of action of concern are sparse, it is difficult to obtain a comprehensive picture of potential interactions among AOPs or to use AOP networks to understand observed patterns of response. This should always be considered as a limiting factor when assessing the predictive utility of an AOP network. On the other hand, when the pathway coverage is rich, the information can become overwhelming and may obscure rather than illuminate the critical path(s). This hearkens to the importance of scoping and problem formulation as well as being able to efficiently filter the overall AOP network represented in the AOP-Wiki to those AOPs most important to a given research or regulatory question  (Knapen et al.
Acknowledgments The authors thank the SETAC Pellston Workshop co-chairs,  C. LaLone  and  M. Hecker  for their coordination, organization, and guidance of the workshop. We acknowledge the other workshop participants (see LaLone and Hecker 2017) for their stimulating discussions and feedback and the respondents to the  Horizon Scanning  efforts for the charge questions and themes that informed our discussion. We gratefully acknowledge the  Society of Environmental Toxicology and Chemistry (SETAC) North America  staff, in particular  G. Schiefer ,  N. Mayo , and  T. Schlekat  who provided support to the workshop co-chairs, steering committee, and workshop participants before, during, and after the Pellston workshop. Funding for the workshop was provided by the  Society of Environmental Toxicology and Chemistry ,  United States Environmental Protection Agency ,  American Cleaning Institute, Cefic-LRI  ( European Chemical Industry Council Long-Range Research Initiative) ,  Chevron-Environmental, ECETOC (European Center for Ecotoxicology and Toxicology of Chemicals) ,  European Commission Joint Research Centre ,  European Crop Protection, ExxonMobil, Humane Society International, The Humane Society of the United States, Human Toxicology Project Consortium, Syngenta , and  Unilever.
Data analysis The effect of visuospatial cueing on the discrimination task (1 st task) was evaluated with the reaction time for correctly discriminated targets. However, this analysis included only trials in which the location of the target had been accurately determined according to the conscious detection task (2 nd task of the behavioral paradigm). This was done since no accurate discrimination could be reliably performed on somatosensory targets that were later in the trial reported as not consciously perceived (hence discriminated randomly during the 1 st task of the behavioral paradigm). In order to test this, we also calculated the reaction time for both valid (correctly predicted by a cue) and invalid (incorrectly localized by a cue) trials that were not consciously detected (where the somatosensory target was present but was not detected by participants) with paired t-test.
Reaction time (somatosensory discrimination task) Participants' mean reaction time for correctly detected target trials across conditions was 673 AE 73 ms (mean AE SD) (Table  1 ). The repeated measures ANOVA revealed a main effect of validity (F(1,14) ¼ 100. 22, p < 0. 001), indicating that, as expected, participants were faster in responding to validly cued as compared to invalidly cued trials (Fig. 2 ). Additionally, there was a significant main effect of session (F (1,14) Table  1. Reaction time (ms) for correct responses in the somatosensory discrimination task, and perceptual sensitivity (d 0 ) and response bias (beta) for the conscious somatosensory detection task (mean AE SD). Data are provided for each experimental cueing condition and for the two sessions of testing. Notice that reaction times for the somatosensory discrimination task (1 st task of the behavioral paradigm) were calculated only for somatosensory targets that were correctly detected on the somatosensory conscious detection task (2 nd task of the behavioral paradigm).
Funding statement Lorena Chanes was supported by the  Ecole des Neurosciences de Paris (ENP) and  Fundaci o "la Caixa ". Antoni Valero-Cabr  e is supported by research grants  IHU-A-ICM-Translationnel, Agence National de la Recherche (ANR ), projet G en erique "OSCILLOSCOPUS", the  PHRC Regional "NEGLECT"  and the  PHRC Regional "STIM-SD". Felipe Fregni  is supported by an  NIH  grant ( 1R01HD082302-01A1 ).
Data Analysis Participant's responses and EDA were analyzed using MATLAB R2015b software (MathWorks, Inc. , Natick, MA, United States) and statistical analysis was performed using R (version 3. 4. 1) and R Studio softwares (version 1. 0. 143). In the reachability judgment task, the dichotomic (yes-no) responses were recorded by the computer and the frequency of reachable responses was analyzed through a Space (peripersonal, extrapersonal) × Facial expression (angry, neutral, happy) ANOVA with repeated measures on both factors. The EDA was processed only for the PLDs presented in the peripersonal and extrapersonal spaces. Using the LEDALAB toolbox of MATLAB (version 349, Benedek and Kaernbach, 2010), the physiological signal was downsampled at 20 Hz and smoothed using the gauss-method with a 32 samples window.
We first decomposed the physiological signal into tonic and phasic components using continuous decomposition analysis, then we analyzed the average of the phasic activity over each epoch (CDA. SCR). The time window of interest was 0. 5 to 6 s after stimulus onset. Linear mixedeffect model was used to analyze the phasic activity (µS) as a function of Facial expressions (angry, happy, neutral), Space (peripersonal, extrapersonal), PLD Gender (male, female) and Participant Gender (male, female). This data analysis takes into account interpersonal variability as random variables (lme4 1. 1-13 package,  Bates et al. , 2015). According to the full model: PhasicActivity = (Facial expression * Space + Facial expression + Space + PLD gender + Participant gender + (1 | Participant)) (1) Reduced models (i.
Statistical analyses. Data were analyzed using SPSS Statistics (V24. 0) and significance was assumed at p<0. 05. All variables were checked for normality of distribution using Kolmogorov-Smirnov tests. All variables but age followed a non-normal distribution. For this reason, we performed nonparametric analyses. Mann-Whitney U tests were used for paired-group comparisons and the Kruskal-Wallis test were used for comparison of more than two groups, followed when applicable by Mann-Whitney U tests for post-hoc pairwise comparisons with correction for multiple comparisons. Association between categorization scores and other measures were examined using Spearman's correlation coefficients. Friedman tests were performed for repetitive measures analyses, followed by post-hoc Wilcoxon tests with correction for multiple comparisons if applicable.
Background: To date, no drugs have been approved for gambling disorder. Numerous publications have described the value of opioid antagonists. Indeed, the mesocorticolimbic dopaminergic pathway has been suggested as the underlying cause of reward-seeking behaviour, and it is modulated by the opioid system. Objective: This study aims to evaluate the relevance of opioid antagonists for treating GD. Method: A systematic literature review was conducted. A search of the PubMed electronic database, PsycINFO and the Cochrane Systematic Review Database without any limits was performed. Results: There is little information concerning the effects of opioid antagonists on GD. The total search with "nalmefene and gambling" without any limits revealed only 11 articles. The search with "naltrexone and gambling" without any limits generated 47 articles.
MATERIALS AND METHODS A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines  [23]. A search using MEDLINE, PubMed, PsycINFO, and the Cochrane Systematic Review       Database was performed without any limits. The search terms were a combination of medical subject heading (MeSH) terms and keywords, including "gambling and nalmefene" and "gambling and naltrexone". A manual search and screening of the bibliographies of selected articles was performed, in addition to the computerized search. The search strategy is summarized in Fig. (1). Extracted data included the following clinical and pharma-cological considerations: the drug used, study sample size, dosage and duration, inclusion criteria, primary outcome and tolerance.
Cognitive performance is highly heritable. However, little is known about common genetic influences on cognitive ability and brain activation when engaged in a cognitive task. The Human Connectome Project (HCP) offers a unique opportunity to study this shared genetic etiology with an extended pedigree of 785 individuals. To investigate this common genetic origin, we took advantage of the HCP dataset, which includes both language and mathematics activation tasks. Using the HCP multimodal parcellation, we identified areals in which inter-individual functional MRI (fMRI) activation variance was significantly explained by genetics. Then, we performed bivariate genetic analyses between the neural activations and behavioral scores, corresponding to the fMRI task accuracies, fluid intelligence, working memory and language performance.
This raises the question of their potential common genetic roots with cognitive ability assessed by tests. Imaging-genetics resources, such as the Human Connectome Project (HCP) provide an unprecedented opportunity to study the variability of behavioral endophenotypes along with behavioral tests in control subjects, as well as to determine their potential heritability or association with genetics. This dataset provides a unique opportunity to link in one study issues addressed by the genetics, neuroimaging and psychology communities because it contains: pedigree information, fMRI images and behavioral tests. There are existing bridges between these fields, but to the best of our knowledge data is rarely available at the same time. The rationale of this study is thus to look into the heritability of fMRI activations and behavioral scores separately and then to investigate the shared genetics roots between the two.
Following up on these ideas, we first proposed to study the genetic influence involved in fMRI activation differences among typically developed individuals. We used the pedigree data from the HCP language comprehension and verbal math fMRI tasks. These tasks recruit regions directly implicated in brain disorders, such as Broca's area in SLI  10  , the angular gyrus in developmental dyslexia  3  and the intraparietal sulcus in dyscalculia  4. A few studies have already attempted to estimate the narrow sense heritability of brain activations for various tasks. They notably include digit and n-back working memory  11, 12  , visual math subtraction  13  , and stimuli such as written words, faces and spoken language  14. However, these studies had relatively small sample sizes for reliably detecting heritability estimates ranging between 25 and 50%.
Material and Methods Subjects. This study utilized the dataset of the Human Connectome Project (HCP). The HCP scans and data were released in April 2017 (humanconnectome. org). The details of the release are available in the HCP reference manual. In this project, 1046 subjects aged between 22 and 37 years old (µ ± σ = 28. 8 ± 3. 7 years) completed the fMRI language task in the HCP S1200 release. To avoid population stratification, we only included the 785 Caucasian individuals (372/413 M/F) that were classified as race = "White" by the HCP; among these, the ethnicity of 69 was "Hispanic/Latino". This subgroup of the HCP contains 178 twin pairs (117 monozygotic twins (MZ) with 103 siblings and 61 dizygotic twins (DZ) with 61 siblings and 1 half sibling), 203 siblings, 1 half sibling and 60 unpaired individuals.
HCP task fMRI analysis. The analysis of fMRI data was carried out by the HCP consortium and we describe briefly their pipeline  15. The Story predictor covered the variable duration of a short story, question, and response period (~30 s). The Math predictor covered the duration of a set of math questions designed to roughly match the duration of the story blocks. The grayordinate data for individual task runs were processed in a level 1 analysis. Activity estimates were computed for the preprocessed functional time series from each run using a general linear model (GLM) implemented in FSL's FILM (FMRIB's Improved Linear Model with autocorrelation correction)  60. Predictors were convolved with a double gamma "canonical" hemodynamic response function  61  to generate the main model regressors.
Acknowledgements This work was supported by the  CEA DRF Impulsion program  ( SRAVS  project  2017 ,  P. I. Vincent Frouin ). Data were provided by the  Human Connectome Project ,  WU-Minn Consortium  (Principal Investigators:  David Van Essen  and  Kamil Ugurbil ;  1U54MH091657 ) funded by the 16  NIH Institutes  and Centers that support the  NIH Blueprint for Neuroscience Research ; and by the  McDonnell Center for Systems Neuroscience at Washington University.
Study population and data collection Our study sample comprised participants of the ongoing, internet-based, Students' Health Research Enterprise (i-Share) project, a prospective, population-based study of students in French-speaking universities and higher education institutions. Enrollment in the i-Share project started in 2013; to be eligible, a student had to be officially registered at a University or higher education institute, at least 18 years of age, able to read and understand French, and provide informed consent for participation. No compensation was given to students for completing the questionnaire on which the analyses of the current paper are based. The i-Share protocol was approved by the "Commission nationale de l'informatique et des libertés" (CNIL-National Commission of Informatics and Liberties), which ensures that data collection does not violate freedom, rights, or human privacy.
No information on ethnic or racial origin was collected in i-Share. Students were informed about the purpose and aims of the study through flyers, communications in classes, social media, and a newsletter (http://www. i-Share. fr). After formal pre-registration on the i-Share online portal, a change of password, and validation of the informed consent, students completed the self-administered baseline questionnaire. This questionnaire recorded information on the participant's health status, personal and family medical history, sociodemographic characteristics, and lifestyle habits. For this cross-sectional study, we acquired data from a large sample of students that had participated in the i-Share cohort study between February 2013 and January 2017. Students were eligible only when they completed all items in the questionnaire.
Furthermore, when we performed multiple imputation for non-response data and the sensitivity analysis, we found consistent results. However, there were also some limitations in this study. First, it was a cross-sectional analysis; therefore, we could not strictly separate the timing of exposure, outcome, and covariates. Moreover, no causality could be inferred between PPS and suicidal ideation. Second, only brief and succinct measures could be done in large sample studies and perceived parental support as well as suicidal ideation were assessed with only one item. This is a limitation that has to be taken into account when interpreting our results. However, the prevalence of suicidal thoughts found in our study fall within the range reported by the main studies on the subject  [4] [5] [6] 8]  which was somehow reassuring.
Third, the voluntary participation of students may have introduced a self-selection bias, although it is difficult to see how this potential bias could have influenced the observed associations. Fourth, the information was self-reported, which could lead to an information bias, particularly if participants under-reported the frequency of suicidal thoughts or the presence of a personal and/or family history, due to considerations of social acceptability. Again, this under-reporting would be expected to have a low impact on the associations observed. Fifth, there is an over-representation of women in our sample compared to the 56% of female students in France. However, we tested interactions with gender for the main analyses and none was significant. Further, in stratified analyses, aOR did not differ significantly between males and females and the confidence intervals were largely overlapping (data not shown).
Study selection Eligibility of retrieved studies was independently assessed by two reviewers (EB and PH) on the basis of title, abstract and full text reading if title and abstract were not informative enough. In the case of disagreement, it was a priori decided that a third reviewer (CF) would be consulted to decide whether or not the study should be included. The degree of agreement between the two reviewers was assessed through the Cohen's kappa coefficient and was considered as good if comprised between 0. 60 and 0. 74 and very good if equal or superior to 0. 75  [34, 35]. The software RStudio 1 version 0. 98. 1091 was used for this analysis.
Study selection Our search strategy in the four databases of interest resulted in the identification of 10,854 records. After the first round of screening, 125 records were assessed for eligibility on the basis of full-text reading. This resulted in the selection of 29 articles. The degree of agreement between the two reviewers was good (Cohen's kappa coefficient: 0. 66 [IC 95% : 0. 51-0. 81]). Hand search yielded 2 additional articles. After the whole process, 31 articles were selected for the review. Details of the selection process and reasons for exclusion are presented in Fig  1.
In order to have an exhaustive overview, we included studies whatever the year of publication. Studies included in this review were thus conducted over a large period of time 1988-2009 and published between 1990 and 2012. This point challenges the generalisability and the transferability of results: in particular, short duration BZDs and z-drugs were considered as safer in 3 studies, and the recommendation was thus to switch long duration BZDs to short duration drugs  [51, 54, 66]. Currently, considering new safety and epidemiological data, this practice is strongly discouraged, especially in the elderly  [4, 11, 17, 69]. Results considered as positive in these studies could thus no longer be considered to be so. Another element that could affect the transferability of interventions assessed in this review is the lack of economic data regarding the cost of the interventions.
CF received a grant from the  French National Agency for Medicines and Health Products Safety (Agence Nationale de Securitedu Medicament et des Produits de Sante) , grant number:  AAP-2016-005 , ansm. sante. fr. The funder had no role in study design, data collection and SHs. CF, GH, PH and EB received a grant from the  French National Agency for Medicines and Health Products Safety (Agence Nationale de Securitedu Medicament et des produits de sante)  for a previous study:  HYPAGE -Impact of the Decrease of Exposure to Hypnosedative Drugs in the Elderly Through the Discontinuation of Hospital-initiated Prescription  (grant number:  AAP-2013-095 ). The description of this study is available at ClinicalTrials. gov,identifier:  NCT02296957. The funder had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
These words were assigned to four experimental conditions according to the degree of orthographic overlap of bases and suffixes across languages: Cognate Base and Cognate Suffix (BCSC, e. g. , passage-passagem, in English and EP, respectively); Non-cognate Base and Cognate Suffix (BNCSC, brokerage-corretagem); Cognate Base and Non-cognate Suffix (BCSNC, e. g. , rarely-raramente); and Non-cognate Base and Non-cognate Suffix (BNCSNC, e. g. , blindly-cegamente). The cognateness degree of bases and suffixes were calculated using the NIM database  [70]. NIM computes the Normalized Levenshtein Distance (NLD), which ranges from 0 (not similar at all) to 1 (exactly the same). As expected, there were no differences in the mean of orthographic overlap of word bases between conditions BNCSC and BNCSNC (.
13 and. 18, respectively, p =. 46) or between conditions BCSNC and BCSC (. 79 and. 88, respectively, p =. 15). Likewise, there were no differences in the suffixes between conditions BNCSC and BCSC (. 55 and. 64, p =. 31) or in the suffixes between conditions BNCSNC and BCSNC (. 06 and. 12, p =. 19 ). Differences were only significant across conditions when they varied either in the type of cognate base or in the type of cognate suffix (all ps <. 001), as expected. For instance, the BCSC and BCSNC conditions vary in the suffix type and, thus, differences when comparing the cognateness degree of the suffixes were significant. In addition, when counting the number of times a given base appeared in the CELEX corpus (as in the N-Watch database,  [71] ), no differences were found across conditions (p =.
22). The same applies to suffixes (p =. 17). Experimental conditions were also matched in base frequency (p =. 39) and morphological family size (p =. 54). Fifty-six English derived words were used as primes and matched across conditions for logarithmic frequency per million words, length in number of letters, summed log bigram (SLBF), and number of orthographic neighbours (all ps >. 20; differences as a function of phonological neighbours were marginal [p =. 077]; see Table  1 ). These values were taken from the N-Watch database  [71]. As targets, we used the English bases of the English derived words (e. g. , brokerage-BRO-KER). Most of them were nouns that also function as verbs (61%). The mean cognateness degree of target words considered as a whole did not differ between conditions BCSNC and BCSC (cognate targets;.