dataesr/scientific-paragraphs-categorization huggingface dataset:
[33]  where both high and low frequency words were examined as well as nonwords, we examined whether the model would also show this pattern. The results showed that, just like the data, words were read faster than nonwords, t(85) = 14. 70, p,. 001, and high frequency words were read faster than low frequency words, t(44) = 3. 83, p,. 001 (High Frequency Words: 70. 7; Low Frequency Words: 80. 4; Nonwords derived from high frequency words: 120. 0; Nonwords derived from low frequency words: 125. 5) [2 words not in the database, 7 nonwords in the database].
In her first experiment, she examined stress regularity in both high and low frequency words and found that words with irregular stress were slower to read aloud than words with regular (i. e. , penultimate) stress, but that this was restricted to low frequency words. CDP++ showed the same pattern, with a main effect of Frequency, F(1, 101) = 75. 41, p,. 001, Stress Regularity, F(1, 101) = 6. 46, p,. 05, and an interaction between the two, F(1, 102) = 9. 31, p,. 005. Two t-tests showed that the difference between the high frequency words was not significant, t,1, but the difference between the low frequency words was, t(47) = 2. 88, p,. 01. (High Frequency Regular: 80. 6; High Frequency Irregular: 79. 9; Low Frequency Regular: 88. 7; Low Frequency Irregular: 96. 7). [9 words were not in the database, 1 outlier].
94, p,. 01, but not Stress Regularity, F(1, 42) = 1. 73, p =. 20, nor an interaction between the two, F,1 The successful simulation of both sets of results suggests that the seemingly inconsistent results between these two studies can potentially be explained once the properties of items are taken into account. ( Apart from stress consistency, Burani and Arduino  [18]  also looked at the overall number of words that share a particular letter sequence, which they called numerosity. They found that stress irregular words with a high numerosity were named faster than stress regular words with a low numerosity. CDP++ predicted a null effect with this dataset, t,1 (Regular words: 97. 2; Irregular words: 97. 6) [8 words were not in the database]. One final study looking at stress neighborhood was run by Sulpizio, Arduino, Paizi, and Burani  [35].
This caused the main ANOVA to fail to reach significance by items. The absolute size of the effect with the low frequency words was also smaller than the first experiment (11 ms vs. 24 ms). CDP++ predicted that there should not be a significant effect of consistency with either the high or low frequency items (both t's,1; High Frequency Consistent: 77. 4; High Frequency Inconsistent: 78. 0 [2 words not in the database]; Low Frequency Consistent: 86. 8; Low Frequency Inconsistent: 87. 2 [3 words not in the database]).
CDP++ displayed the same result, t(29) = 2. 40, p,. 05 (144. 5 vs. 158. 5 cycles) [1 nonword in lexicon]. Alternatively, when a similar manipulation was examined with words, skilled readers did not show any differences. CDP++ displayed this result also, t,1 (93. 1 vs. 92. 6 cycles) [44 4-syllable words not in the database], although there were a large number of items in the stimuli that it was not able to use. A very similar set of nonwords as Burani et al. [38]  was examined by Burani, Marcolini, and Stella  [39]. Similar results were also found, except that there was an exceptionally large error rate (21. 7%) on the morphologically simple nonwords, which was presumably due to the different composition of the lists the nonwords were part of in the two studies. CDP++ not surprisingly gave very similar results with these nonwords compared to the previous ones, t(28) = 2.
Unlike the data, however, the pseudohomophones were slower than the orthographically more normal nonwords, t(58) = 2. 29, p,. 05 (135. 5 vs. 126. 2 cycles) [49 Errors, 2 outliers, 9 nonwords in database]. Apart from just the error rates, these results should be taken with great caution because, unlike Perry et al. [5, 6] , we allowed the network to run even if a correspondence was very poorly learnt. That is, it triggered many ''dead nodes''  [5] , which we ignored (see  [41]  for evidence suggesting that nonwords with very strange spellings are not processed by the normal reading system and hence reasonable to ignore). This allowed slower responses in the pseudohomophone and matched nonword control condition that would typically be excluded, and hence is likely to be responsible at least in part for the difference in reaction times between the pseudohomophones and orthographically normal nonwords.
The Role of Sublexical and Lexical Process An important facet of the results that we have not explored is to what extent the lexical and sublexical parts of the model are responsible for the results. One way to examine this is to look at the performance of the model without sublexical or lexical input. This isolates the extent to which the results are simply caused by one route or the other. We first did this on the large database we first examined  [29] , removing all sublexical input. This caused the amount of explained variance to drop from 52. 3% to 49. 4% (note that just onsets alone account for 46. 4% of the variance and the correlations without onset coding are r =. 39 for the model with sublexical phonology and r =. 24 for the model without sublexical phonology). We then examined all of the stress consistency studies without sublexical phonology.
Finally, with the neighborhood effect, Pagliuca and Monaghan showed that their model was sensitive to this variable. However, they used a larger and currently untested stimuli set, and they also suggested that different versions of their network might be differentially sensitive to this. Obviously, a mega-study of Italian words would be useful for investigating these effects further. Apart from simulating data of normal readers, we also investigated data from acquired dyslexia. Whilst we did not try to model all of the patterns that exist, we did show that, with two very simple parameter changes, CDP++ can produce a stress dominance effect that is of a similar level to the group of patients that produced the largest effect in Colombo et al. [21]  -that is, it showed the most errors on low frequency words with subordinate stress.
Stress Consistent/Stress Regular: 92. 6; Stress Consistent/Stress Irregular: 97. 1; Stress Inconsistent/Stress Regular: 101. 8; Stress Inconsistent/Stress Irregular: 104. 8) [15 words were not in the database, many of which were 4 syllables long]. This pattern is therefore very similar to the one reported by Burani and Arduino.
This research was supported by grants from the  Australian Research Council  ( DP120100883 ) to CP and the  European Research Council  ( 210922-GENMOD ) to MZ. JZ was supported by the  Labex BLRI  ( ANR-11-LABX-0036 ) managed by the  French National Agency for Research (ANR) ,  Investments of the Future A*MIDEX  ( ANR-11-IDEX-0001-02 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
] [9] [10] [11]  In a pivotal European, Phase III, double-blind, randomized controlled trial in children and adolescents with ADHD (study SPD489-325; ClinicalTrials. gov identifier NCT00763971), LDX significantly improved ADHD symptoms compared with placebo, as assessed by the ADHD Rating Scale IV (ADHD-RS-IV) and Clinical Global Impressions-Improvement (CGI-I). 10 In addition to the placebo arm, this European study included osmotic-release oral system MPH (OROS-MPH) as an active reference treatment (as opposed to a direct comparator) to provide study validation and contextualize results. Whilst the study was not designed to support a formal statistical comparison of the two active treatments, a subsequent post hoc analysis indicated that LDX was significantly more effective than OROS-MPH at improving ADHD-RS-IV scores (effect size 0.
Subjects and procedures The data used here originated from three studies involving a total of 262 children aged 6-15 years who had been exposed to a DSM-IV A1 qualifying traumatic event. The first study involved children victims of an industrial disaster, the second, children requiring an orthopaedic/trauma intervention, and the third, children victims of a road accident. The French Consultative Committee for the Protection of Individuals in Biomedical Research of the South West (national ethics committee) approved all procedures of the preset study. For each participant, signed informed consent was obtained from the parents and the child.
Sample 1 The data used were obtained from a study carried out in 106 children aged 11-14 years  [45, 46]  in the aftermath of an explosion in a chemical factory on the 21 st September 2001 in Toulouse, France, which resulted in 30 deaths, 3000 injured, and 30000 damaged or destroyed homes. One year after the explosion, a cross-sectional study was conducted to evaluate the consequences on mental health in children educated in the 6 th grade in five middle schools situated in the disaster area. The children initially completed the Impact of Events Scale (IES)  [47] ; as a screening procedure. A total of 106 children were contacted for this study: the 50 who scored the lowest scores on the IES, and the 56 who scored the highest. The objective was to compare, children with clinically relevant PTSD symptoms (High IES score) to those with low PTSD symptoms (low IES score), on measures of Child's perception of family cohesion and adaptability, child's experience of the explosion, and parental characteristics  [48].
Statistical analyses Cronbach's alpha was used to assess internal consistency. We examined Test-retest reliability using intraclass correlation. Concurrent validity was determined using Spearman-correlation coefficient, since the data were not normally distributed. Factorial validity was assessed by principal component analysis (PCA) with varimax rotation. The Kaiser-Meyer-Olkin measure  [55] , an indicator of the appropriateness of the factor solution was computed. To verify the validity of the cut-off scores, a ROC curve was constructed which evaluated the sensitivity and specificity of each score compared to the diagnosis with the CAPS-CA. A total severity cut-off score of 40 points with the CAPS-CA was chosen as it corresponds to the cut-off for clinical diagnosis  [53]. Then we analyzed the ROC curve with the CPTS-RI cut-off.
Factor validity Two-hundred and six children provided data for the 20 items of the CPTS-RI. The case-items ratio was. 10, as recommended by Nunnally  [57]. The Bartlett test, which tests the null hypothesis according to which all correlations are equal to zero  [58] , showed that the factor model was appropriate (Bartlett test p,0. 001). The Kaiser-Meyer-Olkin measure  [55] , which is an indicator of the appropriateness of the factor solution, of 0. 89, revealed that all of the variables retained are a coherent group and constitute one or several adequate measures of the concept. The minimum value for appropriate factor analysis suggested to be 0. 6  [59]. We used principal component analysis (PCA) followed by varimax rotation and in keeping with the scale construct  [24]  set the model to three factors.
In each of them, the young girl, the object carried, the colors of the boxes and their places (right/left), the two female informants, their names and the voices associated with them were varied. The child could obtain a maximum score of 3 points (1 point for each story when the box linked to the more fluent statement was chosen). After the three stories, children listened to each statement again to evaluate their comprehension-this was done by asking them to repeat each statement. This step was used to ensure that children's choices were not linked to any difficulty of comprehension. All children succeeded at this comprehension test. All sentences were recorded and modified using Audacity software. The sentences were digitized (32-bit resolution) at a 44. 1-kHz sampling rate. For each story (N = 3), two different volunteers were asked to say the two sentences corresponding to the two locations in a neutral tone.
If you don't hear very clearly what they say, you have to press this button (the experimenter pointed to the button on the keyboard with the corresponding smiley). " Using the same headphones as in the conflicting sources task, children then heard a sentence (i. e. , " The dog went over there") and the experimenter asked: "Did you hear quite clearly what the girl said (the experimenter pointed to the corresponding button) or did you not hear very clearly what the girl said (the experimenter pointed to the corresponding button; order counterbalanced)?" These instances of instruction and feed-back were only used during the warm-up. The task was created using the software program Eprime to randomly present the sentences and to record which button was pressed by the child. For each item, 1 point was attributed to children when they pressed the button "heard quite clearly" and 0 when they pressed the button "not heard very clearly.
In this game, you have to answer as quickly as possible. " Children were then invited to place their index fingers on the two buttons. Using the same headphones as in Experiments 1 and 2, children then heard for instance, "The ball is in the orange box," when viewing a blue and an orange box (the sentences of Experiment 1 were used for this experiment). A warm-up (four items: two dysfluent, two fluent) was used to provide feed-back regarding children's accuracy and the need to respond as quickly as possible. During this warm-up, instructions were repeated if needed. The task was created using the software program E-prime to randomly present the sentences, to record which button was pressed and the reaction time linked to each response (measured in ms). Only reaction times linked to accurate responses were analyzed.
g. , syllables, words, and sentences in foreign languages) and non-speech (e. g. , laughs, sighs, coughs) vocalizations. Non-vocal sounds consist of industrial and environmental sounds. After scanning, participants were asked to categorize all voices in terms of their accent to make sure participants were able to recognize each accent. Each of the 3 digit strings of each of the 9 speakers were presented 3 times via Beyerdynamic headphones in a quiet room at an intensity of 70 dB SPL (C). Participants had to press one of the 3 buttons in response to each accent (a total of 81 trials). We used the Psychtoolbox3  (Brainard 1997; Pelli 1997)  for stimulus presentation in the fMRI and behavioral task based on MatlabR2007b (Mathworks, Inc. ).
All MRI data were analyzed using SPM8 (Wellcome Department of Imaging  Neuroscience, 1994 Neuroscience, -2007;;  http://www. fil. ion. ucl. ac. uk/spm). First, anatomical scans were AC-PC aligned with the re-orientation applied to all EPI scans done in the same session. Pre-processing of functional scans consisted of corrections for head motion (spatial realignment; trilinear interpolation) and scans were realigned to the first volume of the last functional scan (i. e. , the volume closest to the anatomical scan). Functional runs were then co-registered to their corresponding individual anatomical scans. Functional (3 mm isotropic voxels) and anatomical (1 mm isotropic voxels) data were transformed to Montreal Neurological Institute (MNI) space after segmentation of the anatomical scans. Normalized data were spatially smoothed by applying a Gaussian kernel of 8 mm full width at half maximum.
Viral vectors Recombinant adeno-associated viral (rAAV) vectors were produced using an AAV2 helper-free system (Agilent Technologies), as described in Darcq et al. [46]. The rAAV vectors expressed a shRNA sequence under the control of mU6 promoter, along with eGFP under the control of the CMV promoter. The shRNA sequence targeted at the Pdyn transcript (shPdyn, 59-GGTTGCTTTGGAAGAAGGCTACA-39) was selected using BLOCK-iT RNAi designer (Life Technologies, Grand Island, NY) and reduced expression of a rat prodynorphin-eGFP fusion in transfected COS-1 cells by 98% compared to a scramble shRNA sequence with no homology to any transcript (shScr, 59-GCGCTTAGCTGTAGGATTC-39), as analyzed by fluorescence-activated cell sorting (data not shown).
Data analysis Results of the FST, elevated plus maze and Pdyn densities were analyzed using t-test comparisons. Beam breaks in the locomotor tests were analyzed using area under the curve computation that was subjected to repeated-measures analysis of variance (ANOVA) or to one-way ANOVA on single test days. When applicable, AVOVA was followed by the Newman-Keuls multiple comparison post hoc test. In all cases, a normality test and equal variance test were performed before the ANOVA to ensure its validity.
A landmarked face mesh was applied to each frame of the sequence, which was then used as a basis for morphing in MATLAB (Mathworks). Face morphing consisted of morphing between angry dynamic facial expressions (one male and one female) and happy dynamic facial expressions (one male and one female) within gender. The resulting output was two different face continua (one per gender), each consisting of five within-modality stimuli, morphed between 90% anger and 90% happiness in 20% steps. Audio output from the audiovisual recordings was processed in Adobe Audition 2. 0 (Adobe Systems) and then morphed using the MATLABbased morphing algorithm STRAIGHT  (Kawahara, 2006). Vocal morphing ran in parallel with the facial morphing, in that the equivalent individual voices were also morphed between 90% anger and 90% happiness in 20% steps, resulting in two vocal continua, one per actor, each consisting of five vocal stimuli.
Continuous carryover experiment In the main experiment, stimuli were presented using the Psychtoolbox in MATLAB via electrostatic headphones (NordicNeuroLab) at a sound pressure level of 80 dB as measured using a Lutron Sl-4010 sound level meter. Before they were scanned, subjects were presented with sound samples to verify that the sound pressure level was comfortable and loud enough considering the scanner noise. Audiovisual movies were presented in two scanning runs (over 2 different days) while the blood oxygenation level-dependent (BOLD) signal was measured in the fMRI scanner. We used a continuous carryover experimental design  (Aguirre, 2007). This design allows for measurement of the direct effects (i. e. , that of face and voice emotion morph) and the repetition suppression effect, which can be observed in pairs of voices or faces (like in the typical fMRI adaptation experiments).
Imaging parameters Functional images covering the whole brain (32 slices; field of view, 210 ϫ 210 mm; voxel size, 3 ϫ 3 ϫ 3 mm) were acquired on a 3 T Tim Trio Scanner (Siemens) with a 12 channel head coil, using an echo planar imaging (EPI) sequence (interleaved; TR, 2 s; TE, 30 ms; flip angle, 80°) in both the carryover and localizer experiments. In total, we acquired 1560 EPI volumes for the carryover experiment, split into two scanning sessions consisting of 780 EPI volumes, and 336 EPI volumes were acquired for the multimodal localizer. For both the carryover experiment and experimental localizer, the first 4 s of the functional run consisted of "dummy" gradient and radio frequency pulses to allow for steady-state magnetization during which no stimuli were presented and no fMRI data were collected.
RETRO-PRIMING EXPERIMENT The psi task was a retroactive priming task devised by  Bem (2008) , as described further below. The computer used was a Dell Optiplex 745, running Windows XP. The program used for the psi task was designed by Daryl Bem at Cornell University with REAL basic. It was a slightly different version of the software than the one used by  Bem (2008) ; this version used pictures as primes instead of words. We used a Windows version of this software, using an algorithm to generate a random sequence of numbers. This psi task was a precognitive experiment in which the response time of participants was measured in order to see if they would be influenced by a prime (a picture) that they would see not before but after a word. Participants were shown a word on each of 64 trials and were asked to press one of two keys on the keyboard as quickly as they could, in order to indicate whether the word was pleasant or unpleasant.
Delivering a Personalized Physical Activity Program: A Design Process Despite an explosion of mobile phone applications concerning PA, few have been based on theoretically derived constructs in order to promote health behaviours and reduce sedentary behaviour  [19]. During the period from September 2013 -May 2014, the interdisciplinary team undertook initial app design, programming, and iterative user testing. Following these activities, initial app was developed and feedbacks from potential users were obtained. The Android smartphone platform was used. The smartphone battery life sufficiently to allow continuous accelerometer data capture throughout the day. The data collected via the smartphone's built-in accelerometer were transmitted to the project's local servers each evening for data storage and to allow researchers to monitor the quality of data while the design progressed.
Diagnosing The initial session is used to provide instruction on the general use of the App, and to collect data including age, size, weight and gender. Especially, attitudes towards PA and Physical Self-Esteem are diagnosed. The user has to answer the Physical-Self Inventory (PSI-6), a six-item questionnaire developed and validated by Ninot et al. ,  [18]. The PSI-6 is a short version of a previously validated questionnaire, the PSI-25, adapted from the Physical Self-Perception Profile  [18]. PSI-6 contains one item for global self-esteem (GSE), one item for physical self-worth (PSW), and one item for each of the four sub-domains identified by Fox and Corbin  [17] : physical condition (PC), sport competence (SC), attractive body (AB) and physical strength (PS). This questionnaire was proven to reproduce the factorial structure of the corresponding multi-items inventories  [18]  and to possess the same hierarchical properties.
Perspectives Benefits of Physical Activity are attempted for adults in the European Space. The C2S Project including a web resource, a ride for companies called "Challenging sedentary lifestyle", and the MOVE App, is a medical, technologic and scientific project. Thus, an empirical study (200 participants in experimental group vs 100 participants in control group) has been conducted to assess the effect of the program on DPA. Results could have important implications for advancing the field of PA sciences, and will be precious to involve the design of the App. Moreover collected data on daily PA or behaviour changes in a workplace setting will be stored and should be useful for health institutions.
SYSTEM USED FOR DUAL ELECTROPHYSIOLOGICAL DATA RECORDING We designed a system that allowed for simultaneous collection of electrophysiological data in two individuals. All hardware devices used in this study have been produced by "g. tec" (Graz, Austria). For each subject we recorded: -Respiration movement, using an elastic respiratory belt (g. RESP piezoelectric sensor). The belt was placed around participant's chest just below the axilla for transduction of rib cage circumference. -Pulse, using a photoplethysmographic sensor (g. PULSE). The light sensor was attached to the left index finger, facing volar surface on the distal phalange. -Electrodermal activity, using a galvanic skin conductance sensor (g. GSR). Two electrodes were placed on left middle and ring fingers, facing volar surface on the distal phalange.