dataesr/scientific-paragraphs-categorization huggingface dataset:
Untargeted Deconvolution of Biologically Active Compounds Using 1 H-NMR Data Both targeted and untargeted LC-MS data mining strategies enabled the correct identification of features of the bioactive compounds (4 1 -bromoflavone). Each of the 80 microfractions were furthermore analysed by 1 H-NMR to evaluate if a similar correlation experiment could unambiguously highlight characteristic NMR signals related to bioactive compounds. The area of each bucket over the microfractions was correlated with QR induction and cell viability using transformed biological data (log-and logit-transformed data for QR induction and cell viability evaluation, respectively)  [34]. Figure  4C  shows the correlation scatter plot from which the features of interest were selected. The correlation pattern generated using 1 H-NMR (Figure  4C ) was very similar to those observed using both NI and PI LC-MS data (Figure  4A, B ).
Targeted and Untargeted Peak Picking of LC-MS Data Native MassLynx data (Waters) were converted into netCDF (common data format) using DataBridge software (Waters). Automatic feature detection was performed between 0. 4 and 4. 5 min with MZmine 2 software  [37]  using parameters selected according to the TOFMS detector. For targeted approach m/z tolerance, noise level and retention time tolerances were set to 40 ppm, 0 count and 0. 3 min, respectively. For untargeted approaches the classical steps were used. Peaks with a width of at least 0. 03 s and an intensity greater than 100 counts (both NI and PI) were picked with a 20 ppm m/z tolerance and the generated peak lists were deconvoluted. Deisotope filtering was applied using the "isotopic peaks grouper" module with tolerance parameters adjusted to 0.
Statistical Correlation All calculations were performed using Excel (Microsoft, Redmond, WA, USA) as standard software. When mentioned, QR induction data were log-transformed and cell viability data were logit-transformed (Equation (  1 )). None of the LC-MS or 1 H-NMR data were normalized or transformed prior to the statistical analysis. Pearson correlations were calculated using the "=CORREL(array1, array2)" function from Excel between each features detected in each microfraction and the corresponding biological evaluation (array1 corresponding to the intensity values of one feature and array2 corresponding to the corrected bioassay value; both array need to be of the same length): Logit pxq " ln ˆx 1 ´x ˙(1).
Acknowledgments:  Philippe Eugster ,  Laurence Marcourt  and  Elisabeth Rivara-Minten  are acknowledged for their help in UHPLC-TOFMS analysis, NMR analysis and English revision of the manuscript, respectively. This methodological development for bioactivity profiling was supported by the  Swiss National Science Foundation   Sinergia Grant   CRSII3_127187. Author Contributions: S. B. and A. A. participated in the microfractionnation steps. S. B. and A. A. managed to the NMR and LC-MS experiments. A. N. and M. C. participated in the biological evaluation. S. B. , J. B. and S. R. participated in the data analysis. S. B. and J. L. W. managed the project. S. B. , A. A. and J. B. participated in manuscript writing. All authors read, arranged and corrected the manuscript. All authors read and approved the final manuscript.
Data Collection and Processing. Data collections were recorded at the synchrotron LURE Laboratory, Orsay, France, on beam line W32. A single crystal was used for two complete 360 ∘ rotations, one rapid at moderate resolution to get the strongest reflexions unsaturated and another slow rotation at the minimum distance to get accurate reflexions at the highest resolution. Due to geometry considerations of the experimental setup and the wavelength fixed at 0. 964 Å, the resolution at the edge of the MAR-345 image-plate detector was limited to 1. 0 Å but diffraction could be noted beyond this limit. The two data sets were processed, scaled, and merged with the CCP4 package of programs  [8]. The final set of unique reflexions was reformatted for compatibility with the SHELX suite of programs  [9].
Structure Determination and Refinements. The data set was used as a successful test case during the development of the SHELXD program  [10]. We started with the vancomycin aglycon  [11] , or better with the aglycon of balhimycin  [3] , as a rigid body seed (GROP instruction in SHELXD). This gave a sharp and contrasted solution with all the atoms of the aglycon and part of the sugars. The full structure was completely developed in a single run with the automated DIRDIF recycling procedure based on E-maps  [12]. Refinements were done with the SHELXL/H programs  [9]  with isotropic and then anisotropic thermal parameters for non-H atoms. Constraints on 1-2 and 1-3 distances as well as on planar rings were applied throughout but chiral volumes (zero and nonzero) were not constrained. During refinements, ten molecules of acetone were localized in addition to a number of water molecules with full or partial occupancies.
Acknowledgments We thank the  Ministère de l'Education Nationale, de la Recherche et de la Technologie , the  Centre National de la Recherche Scientifique (CNRS) , the  ANR  ( 12-BS07-0004-METALHEL-01 ), the  Région Bretagne  ( SAD CHIROPEM ) and the  LIA Rennes-Durham  for financial support. M. S. -H. is grateful for financial support from the  Ministry of Science and Higher Education in Poland  (' Outstanding Young Scientist' scholarship ). J. A. acknowledges the  National Science Foundation  ( CHE 1265833 ) for financial support and the  Center for Computational Research (CCR)  at the  University at Buffalo  for computational resources. G. M. thanks the  NIH, Minority Biomedical Research Support  (grant 1  SC3 GM089589-06 ) and the  Henry Dreyfus Teacher-Scholar Award  for financial support.
To get used to the procedure, participants read two practice texts with their respective recognition trials. Presentation was self-paced, pressing the spacebar to display the respective sections of each text. After each text but before the recognition task, three asterisks flashed in the middle of the screen for 500 ms, indicating the area where the target item would appear. Following their response, participants were given feedback for 1 second. Reading times for the target section were recorded as the time lapse (ms) from one spacebar press to the next. Recognition times were recorded as the time (ms) between the appearance of the target item and the yes/no button press. Materials presentation and data recording were performed using E-Prime 1. 2. After completing the first part of the experiment, participants took the Letter-Number Sequencing subtest of WAIS-III  (Wechsler, 2002)  as a distractor task.
Results We applied generalized mixed models using IBM SPSS 20. 0. When the outcome variables presented a binary structure (i. e. , accuracy in recognition and recall tasks), we used logistic mixed models with binomial distribution. For continuous outcome variables (i. e. , chronometric data), we preferred linear models with normal distribution. We performed analyses involving chronometric data after logarithmic transformations. To facilitate interpretation, however, we use original data in milliseconds when describing the distributions. In all cases, we entered Discrepancy (consistent, discrepant), Source Foregrounding (reinstatement, control) and their interaction as fixed factors into the model. All analyses included by-participant and by-item random intercepts. We applied the Satterthwaite correction to estimate the degrees of freedom when analyzing recognition times due to unequal number of observations.
Item recognition Before the inferential analyses, we examined the descriptive profile for the total trials (16 trials per subject, n = 640) by crossing our two factors (Source Foregrounding and Discrepancy). Descriptive data are presented in Table  2. The ability to recognize items was fairly high and above chance level in all conditions. Accuracy ranged from 76 % (Consistent-Reinstatement condition) to 89 % (Discrepant-Reinstatement condition), while mean recognition times for correct answers varied between 974 ms (Consistent Control) and 1,071 ms (Discrepant Reinstatement). This pattern suggests that participants performed the task in accordance to the instructions (precise and fast). A logistic mixed analysis on accuracy (correct response vs. error) revealed that Discrepancy significantly predicted response precision during the recognition task, F(1, 636) = 10.
First, with regard to recognition times, we also found that participants took longer to produce a response after reading discrepant rather than consistent stories. This time increase for discrepant sources was found both for correct answers and errors, thus ruling out the possibility of a speed-accuracy trade-off. One possible explanation is that discrepancies promoted distinctiveness in the mental representation. High distinctiveness would imply more separation, and therefore more components to consider when accessing the representation, thus taking more time to respond. Related research has already provided evidence in line with this idea. Kim and Millis (2006)  reported priming data from a recognition task in which participants read pairs of news stories and later had to recognize sentences they had read from sentences they had not read.
INTRODUCTION Motor imagery (MI) is the mental representation of an action without engaging its actual execution. MI practice (MIP) refers to the repetitive use of MI to improve performance  (Jackson et al. , 2001). MIP research usually combines psychological and neurophysiological approaches, and represents a relevant research topic for integrative neuroscience. There is now compelling evidence that MIP positively affects motor learning, with pioneering reports dating from the first half of the 20th century (e. g. ,  Sackett, 1934 Sackett, , 1935)). MIP has now multiple applications in both sport sciences and rehabilitation (for an overview, see  Guillot and Collet, 2008). Here, we will focus on the effects of MIP on performance in healthy individuals. Scanning the MEDLINE /Pubmed database (until June 2015) through the systematic crossover of the following terms: [''Motor imagery''/''Movement imagery''/''Mental rehearsal''/''Mental imagery''/''Mental practice''] by [''Performance''/''Learning''/''Sport''] yielded 188 studies (including 30, i.
Based on a sequence of foot movements learnt over the course of 1 week (5 MIP sessions), functional brain imaging data with positron emission tomography confirmed the main hypotheses. Increased contralateral orbitofrontal cortex and reduced ipsilateral cerebellum activations were recorded in the MIP group, but not in the control group. These brain changes corresponded to those elicited after physical practice of the same task, as reported in an earlier study  (Lafleur et al. , 2002). Findings of: (i) reinforcement of brain activity within motor system regions (i. e. , more intense and focused activations, sometimes with reduced recruitment of associative regions, Figure  3 ); and (ii) preservation of functional equivalence between MI and physical practice after motor learning (i. e. , learning-dependent changes being reflected in brain activations during both physical and MI) were later replicated in several experiments (e.
My company 21 In Table  2  the data about the type of services, chosen by Regus customers, are reported. Offices are chosen by 63% of the clients; secretary and internet (e. g. , wifi connection) are the most required services. The rent has been mainly paid by the client himself/herself (79%). The rent per day depends on the surface. But for an office it is 69 Euro for the whole day and 30 Euro per hour. In Table  3  the transport modes chosen by the clients to reach the Regus offices inside the Naples HSR station are reported. Not a high percentage of clients have chosen the HSR alternative (only 16%) but this behavior can be justified by the recent opening of the service, which is not yet deeply advertised and therefore known by the people. This transport alternative has not been chosen by those living in the city of Naples, 45%, and by those who have found it expensive (21%).
Parameter optimization and model comparison In a first analysis, we optimized model parameters by minimizing the negative log-likelihood of the data, given different parameter settings, using Matlab's fmincon function (ranges: 0<β<Infinite, and 0< BIC where compared between biased and unbiased models to verify that the utilization of the biased model is justified, even accounting for its extra-complexity. As an approximation of the model evidence, individual BICs were fed into the mbb-vb-toolbox  (39) , a procedure that estimates the expected frequencies and the exceedance probability for each model within a set of models, given the data gathered from all participants. Expected frequency is a quantification of the posterior probability of the model (denoted PP), i. e. the probability of the model generating the data obtained from any randomly selected participant.
Parameter recovery To validate our results, and more specifically to verify that valence-induced differences in learning rates reflect true differences in learning, as opposed to an artefact of the parameter optimization procedure, we checked the capacity of recovering the correct parameters in simulated datasets. To do so, we simulated performance on our behavioural task by virtual participants with different learning rates (Fig. S2  & Fig. S3 ). Concerning Experiment 1, we simulated unbiased ( ! ! =  ! ! ) and biased ( ! ! > 𝛼 ! ! ) participants. Concerning Experiment 2, we simulated unbiased (𝛼 ! ! = 𝛼 ! ! and 𝛼 ! ! = 𝛼 ! ! ), semi-biased (𝛼 ! ! > 𝛼 ! ! and 𝛼 ! ! = 𝛼 ! ! ) and biased (𝛼 ! ! > 𝛼 ! ! and 𝛼 ! ! > 𝛼 ! ! ) participants. We simulated N=100 virtual participants per set of parameter.
Financial disclosure SP and the study were supported by a  Marie Sklodowska-Curie Individual European Fellowship  ( PIEF-GA-2012 Grant 328822 ). SP is currently supported by an  ATIP-Avenir  grant. GL was supported by a  PHD fellowship of the Ministère de  l'enseignement supérieur et de la recherche. EJK is supported by a  Medical Research Council studentship. SJB is funded by a  Royal Society University Research Fellowship  and the  Jacobs foundation. The funders had no role in the conceptualization, design, data collection, analysis, decision to publish, or preparation of the manuscript.
Li et al. (143)  suggested that a p-value threshold of ~10 -7 should be used as the genome-wide significance criterion for the earlier commercially available genotyping arrays, but marginally more rigorous p-value thresholds ~5 × 10 -8 for more recent or merged commercially available genotyping arrays, ~10 -8 for all the most frequent SNPs in the 1000 Genomes Project dataset and ~5 × 10 -8 for the common SNPs only within genes. We identified 11 GWAS. Table  4  summarizes the main results. Among these studies, we found three studies that focused on treatment emergent or treatment worsening suicidal ideation in patients who were taking antidepressant drugs  (88) (89) (90)  and eight GWAS (144-151) that were focused on suicidality in behavioral domains (suicide attempts or completed suicides).
They used SNPs from three GWAS discovery studies of attempted suicides (BACCs, GSK-Munich, RADIANT), from one study of suicidal ideation (GENDEP) and from three studies of psychiatric disorders (PGC-MDD, PGC-BIP, and PGC-SCZ), in order to calculate polygenic scores for individual subjects in four validation datasets. They failed to detect any significant association evidence for any SNP in the GWAS or meta-analysis. Galfalvy et al. (149)  conducted a GWAS on completed suicides and patients having a history of attempted suicide with non-fatal outcome (n = 577) compared with psychiatric control and healthy volunteer groups (n = 1233). They used logistic regression to test association with genotype-SB. No SNP attained genome-wide significance in their study, but a number of SNPs in the ADAMTS14, STK3, TBX20, and PSME2 genes reached p < 1 × 10 -5.
The implication of their results is that no distinction can be made between these two groups in terms of a given CNV, and that should a given CNV be associated with attempted suicide in MD, it would probably be a common one. In other words, they failed to identify any CNVs that were not reported in the Database of Genomic Variants, which suggests that suicide attempt status is not influenced by any copy changes in the STAR*D sample. According to the authors, to be able to observe any effect of a common CNV, it would be necessary to increase sample sizes >20to 30-fold. Statistical limits are also an obstacle. For example, there is a technological limit because the level of detection of the size of a CNV is also a limit. Despite technological advances for association studies, only a comparatively small part of the heritability of the majority of complex traits has received an explanation and the variants which the studies concerned have identified are characterized by small effect sizes.
Equipment and stimuli Time estimation and production tasks were generated via the Microsoft PowerPoint software  (Wittmann et al. , 2015). The presentation was displayed on a 17 LCD color monitor. Participants viewed the display from a distance of 60 cm. During the timing tasks, digits were displayed, white on a black background, and were well-contrasted. Digits were 2. 4 • of visual angle high (1 cm corresponds to 1 • of visual angle at a distance of 57 cm). Digits were presented on the center of the screen inside a red-lined square that measured 4. 8 • of visual angle and they were displayed for the duration of 1 s. The keyboard was used when motor responses were required. Four verbal estimation tasks, followed by a subjective judgment of the passage of time, were administered. Following the estimation tasks, four production tasks were administered.
The first four babies registered with the pediatric service, after the beginning date of the study, were successfully enrolled in the study. All four cases were enrolled within a 30-day period between June and July 2014. The intervention was offered to whichever family members initially came to the pediatrician's office in order to register their newborn. Both parents (or primary caregivers) were then offered the 18-month long intervention. It was explained that the intervention would be intended to support the infant's physical as well as psychological and relational development with videofeedback as a specific tool. No family refused the intervention. Informed consent was obtained for filming infant-caregiver interaction and videofeedback sessions. Baseline data and medical histories of both parents and infant were obtained during the first visit.
However, to avoid the tipping-over instability of the wheelchair on the simulator, fastening straps have been used to hold the wheelchair in position. Regarding the data collection experiments, an electrical wheelchair is used, with the original embedded joystick as the main interface. To effectively evaluate the steering behaviours of the participants in relation to the general environment, the virtual worlds attempted as much as possible to represent the areas encountered frequently by the participants. Seven individuals participated in the data collection. A few of the data collection experiments are elaborated in Sections 3. 3-3. 5. The seven participants include a female and six males, aged between 26 and 74. Although none of the participants had a degenerative disability condition or tremors, three were regular wheelchair users while the rest had not used the wheelchair before.
The high value of constant  considered in the identification process thus represents the reduced influence of side risks on the participant's steering behaviour. Constant  on the other hand determines the magnitude of risk influence based on the presumed adaptation mechanism. Higher values imply that the magnitude of influence of the observed risk is considerably high within the close neighbourhood but negligible outside the neighbourhood. In parameter identification, navigation data exceeding 80,000 data sets per participant, obtained from the previous steering experiments, were collected and utilised. Out of the captured data, 85% are used in parameter identification, to ensure that the observed parameters represent well the natural behaviour of the participant, while only 15% are used in curve fitting validation.
Participants and Procedure A junior men's coxless pair aged 17 years with 10 years' experience in rowing participated in this study with the collaboration of their coach. The participants had no shared experience in rowing coxless pair together (i. e. , this was their first season rowing together). The data collection occurred at the very first step of a 1-month crew-training period before the national championship in which the pair were to perform together. Both participants were current members of the French Rowing Academy (Nantes, France). The "stroke rower" is seated on the closest seat to the stern of the boat (i. e. , he doesn't see his teammate; see Figure  1 ) and, as described in the rowing training theories  (Lippens, 2005) , he propels the boat and set the rhythm. The "bow rower" is seated on the first seat, near to the bow of the boat (i.
Data Collection Two distinct data sets were collected to account for the activity of the two rowers during the race. First, the phenomenological data were recovered through individual self-confrontation interviews  (Theureau, 2003)  with each rower. Second, the behavioral data FIGURE 1 | Bird's-eye view of a coxless pair. The onboard measurement system (Powerline, Peach Innovation) records the components of the forces applied by the rowers to the oarlock along the x-axis (in the direction of the boat's movement), the acceleration, the speed of the boat, and the angle formed by the oar with the y-axis (perpendicular to the boat's movement). were recovered using an automatic mechanical device during the race.
Phenomenological Data Collection The actors' phenomenology was the starting point for the descriptions of the actor/environment coupling. This was consistent with the enactivist view to social couplings and the claim that human activity displays autonomous characteristics that are not reducible to behavioral descriptions  (Varela et al. , 1991). The enactivist approach therefore devotes special attention to pre-reflective self-conscious phenomena, that is, the implicit ways in which a given actor experiences his/her ongoing activity. To capture actors' phenomenology through their prereflective self-consciousness embedded in the unfolding activity (i. e. , lived experience), our study included a methodology that used phenomenological forms of retrospective interviews. From this perspective, at each instant of the race under study, we used self-confrontation interview techniques to collect the phenomenological data that accounted for the pre-reflective selfconsciousness of the participants.
e. , what they were doing, feeling, thinking, perceiving; see  Theureau, 2003 , for further details). Based on this verbalization data set, we were able to further characterize how the participants experienced each stroke. Each interview was fully recorded using a video camera so we able transcribe the verbal data and synchronize the rower's verbalizations collected during the self-confrontation interview with the corresponding oar strokes. Each individual interview lasted 1 h.