dataesr/scientific-paragraphs-categorization huggingface dataset:
ICT tool to support designing According to the above premises, it appears quite clearly that there are just very few contributions which aim at using log files (or similar data sources) to infer what happened during design activities. In order to evaluate the viability of this approach and run a first estimation of the time and human resources involved, the authors opted to study collaborative design activities supported by an ICT tool that they are developing: the SPARK platform (SPARK project -http://spark-project. net -SPatial Augmented Reality as a Key for co-creativity). It exploits Spatial Augmented Reality (SAR, augmented reality displayed through projection) to support and foster collaborative creative thinking in the design process. The introduction of a mixed prototype -a tangible blank 3D shape and a virtual surface rendered on the shape by projection (Figure  1 ) -allows the participants to visualize a shared design representation of the solution and its evolution during the design process.
Metrics for the analysis of log file data The log file records every function the users carry out with the platform. This includes the changes made to the canvas that contains the textual/graphical contents to project (position, size, orientation, layer level for asset or group of assets), the background colour of the mixed prototype, as well as its spatial characteristics within the user interface. These data provide quantitative information about the outcomes of the session with an objective measurement of the whole process followed by the co-designers. Data are collected with time stamps, so that it is also possible to measure durations and get insights about the process and its progress. The data collected in the log files are to be processed to extract the indexes proposed in this paper and presented in Table  2.
ICED19 These pauses could be due to different reasons, which are not directly inferable from the log file data (from breaks to discuss the proposed solutions before applying new changes to the prototype, to simply coffee breaks). These curve also show that, despite the large variability of case studies, there are some evident regularities. Beyond minor fluctuations, most of the considered curves show a very similar angular coefficient in the timeframes where the curve is not flat. Samsonite could be an exception, but beyond more frequent breaks (higher presence of horizontal curve portions), the inclination of steep traits is very close to the other ones. These regularities are likely to be dependent on the technology, as it was the only factor the 5 sessions shared. Figure  3 : curves of cumulative time spent designing (x-axis) with regards to the number of design moves (y-axis).
Beyond the research questions addressed in this paper, the analysis of log file data also allows us to gather interesting and useful information about the ICT tool used to support the design activity. The 5 SPARK Project case studies used for this investigation were carried out under very similar conditions, except for the devices they used to communicate with the SPARK platform server and to interact with the other co-designers (a PC with mouse and keyboard, a tablet, a very large touchscreen). The processed data show that larger surfaces made the participants more capable of applying modification efficiently, with a smaller number of repeated moves to adjust what was not satisfactorily placed on the design canvas. In itself, despite the specificity of the technology used across the 5 case studies, this could be considered a conjecture this study allowed to formulate and that becomes interesting to verify with other ICT tools (e.
g. sketching tools, CAD/CAE systems…), which require interacting with a screen of various sizes. The analysis of the design process progress also showed a very interesting and unexpected result: independently from the design domain and from the duration of breaks, all the periods of activities where people interacted with the SPARK platform had approximately the same steepness. For what concerns the amount of human resources and time required to run this study on log files, it is worth to say that the direct comparison with design protocol analysis should consider that the starting point is extremely different in these two scenarios. Design protocol analysis requires multiple coders to go through the qualitative data contained in recordings, transcribe them into a protocol and label its segments.
Other SRs on the microdialysis technique addressed measurements of amino acids  [30] , acetylcholine  [31] , and adenosine  [32]. The research questions for this SR were to determine whether and how monoamine concentrations are influenced by 1) CRs; 2) naturally occurring sleep-wake stages, and 3) SD. We provide qualitative descriptions of the overall trends and quantitative comparison of monoamine levels between wake-sleep stages. In addition to our SR, we present data from an unpublished study of the medial prefrontal cortex (mPFC) before, during and after SD. These data are the first published on several monoamines during SD in the mPFC. Our microdialysis experiment was designed in line with our preceding behavioural work, showing that SD affects certain but not all mPFC related cognitive tasks in rats  [33, 34].
Data extraction and quality assessment Data were extracted on study design (e. g. independent or dependent groups), animal model (e. g. strain, sex), microdialysis technique (e. g. flow rate, perfusion medium) and outcome measurements (type of monoamine, concentration or % of baseline). From one publication, we extracted as much as possible without knowledge of Japanese  [37]. All other included publications were in English. Microdialysis experimental methods are heterogeneous due to the versatility of the technique. For example, separate experimental groups can be used for different interventions or brain regions, but within-subject crossover designs and simultaneous measurements in several brain regions are also common. When publications described separate experimental groups, the groups were treated as independent experiments, from hereon called "studies" (indicated by upper case letters in the reference_ ID, in contrast to publications from the same authors and same year, which are indicated by lower case letters).
If measurements were simultaneously performed in several brain regions within the same animals (k = 34 studies), the brain regions were treated as the experimental unit (instead of the animal), also called "studies". We only included "studies" meeting our inclusion criteria. For publications using seals (k = 4), each seal was treated as an independent observation, unless only pooled data were presented. This was deemed necessary because of the low number of animals included per study, and the high diversity in the probe numbers and locations per animal. Extracted data were tabulated in Excel. Outcomes were sorted by monoamine (dopamine, serotonin, noradrenaline, adrenaline, DOPAC, and 5-HIAA) and review question (CRs, sleep, and SD; results section 3. 3-3. 5). Study characteristics were tabulated per review question (appendix 1-3).
Network meta-analysis and meta-analysis Data were copied from texts and tables, or, if no numerical data were provided, extracted from figures with a graphical ruler (Universal graphic ruler, v3. 8. 6498). Concentrations were converted into nmol/L (nM) if necessary. In our protocol we only specified that a meta-analysis would be designed and performed if at least 2 included articles measured the same monoamine in the same condition. As sufficient data were available on serotonin and noradrenaline during the naturally occurring sleep stages (Slow Wave Sleep (SWS) and Rapid Eye Movement (REM) sleep) and wakefulness, we decided to conduct two network meta-analyses (NMAs). Five studies provided concentrations for SWS and wake only, because the REM sleep episodes were too short to collect a full dialysate sample, or because REM was not observed.
We used the netmeta function with random effect models and standardised mean differences. To verify the NMA results, we conducted four regular meta-analyses (MAs), using the metacont function and forest plots from the meta and metafor packages, hakn = TRUE. Experiment: Prefrontal cortex primary data before, during and after SD in rats Methods for this experiment have been described previously  [43]. Shortly, 11 Wistar rats were implanted with custom-made concentric microdialysis probes (4mm membrane length) in the medial prefrontal cortex (mPFC) at an angle of 12° (AP+3. 0mm; L ± 1. 8; V-5. 5; relative to Bregma). Rats recovered for approximately one-week postsurgery before the start of the experiment. They were connected to the microdialysis tubing and placed into separate compartments of SD devices.
After the experiment, fractions were stored at -80°C. The fractions used for monoamine measurements (20 µL) were transported on dry ice from Amsterdam to Beerse. Monoamines were determined by Janssen Pharmaceutica, Research & Development (Beerse, Belgium), department of Neuroscience Systems Biology. Their standard protocol comprises HPLC-FD following subsequent derivatization with benzylamine and 1,2-diphenylethylenediamine, as described before by Fujino et al  [44]. To prevent outliers affecting our results, we selected a non-parametric approach for data analysis. Monoamine median and interquartile ranges were calculated for each stage (light phase, dark phase, SD, and recovery) in Excel. Friedman's ANOVA's were performed in SPSS version 22 for each monoamine separately. If the Friedman's test was significant, post-hoc Wilcoxon tests were performed to compare baseline light with baseline dark, and SD and recovery with the corresponding baseline period.
Discussion This systematic review provides a full overview of the available evidence on monoamine levels in brain microdialysates in relation to CRs and sleep. To the best of our knowledge, this is the first systematic review on this subject. It includes all relevant studies retrieved by searches in two important databases. We are also the first to implement a network meta-analysis for the direct comparison of three sleep-wake stages. Other systematic reviews on sleep in animals have focussed on adenosine  [137]  and anxiety-related behaviour  [39]. In this review, we showed that monoamine levels fluctuate with CRs and naturally occurring sleep stages. In line with their function as "arousal" transmitters, they generally decrease from wakefulness to SWS, and further decrease from SWS to REM  [138].
Experimental Procedure Protocol was approved by the regional ethical committee named CENOXEMA (authorization on N/05-11-12/28/11-15). To evaluate the potential benefits of Gln or BCAA in AN, we separately performed distinct experiments following ABA protocol with Gln or BCAA supplementation. For this, 8-week-old C57BL/6 male mice (Janvier Labs, Le Genest-Saint-Isle, France) were placed in individual cages and fed with a standard diet. Mice were acclimatized during 5 days at 23 • C with a reversed 12 h light-dark cycle (dark phase: 10:00 to 22:00) and then randomized in two groups: ad libitum (CTRL) and activity-based anorexia (ABA). The ABA experimental procedure was the same as described in our previous studies  [14]. Briefly, ABA mice were placed in cages equipped with an activity wheel connected to Running Wheel ® software (Intellibio, Seichamps, France) while CTRL were placed in standard cages.
2% Tween 20 (TBST). Then, membranes were incubated overnight at 4 • C with primary antibody and washed three times for 5 min in TBST followed by incubation with appropriate horseradish peroxydase (HRP) conjugated secondary antibody (1:5000 dilution, Dako, Santa Clara, CA, USA) for 1 h at room temperature. Afterwards, blots were washed three times for 5 min in TBST and immunocomplexes were revealed by using the enhanced chemiluminescence (ECL) detection system (GE Healthcare, Orsay, France). Protein bands were scanned (ImageScanner III; GE Healthcare) and quantified using Image-Quant TL software (GE Healthcare). Protein levels were normalized with ß-actin (1:5000 dilution, A5441, mouse monoclonal antibody, Merck, Darmstadt, Germany).
Colonic Intracellular Signaling Pathways RayBio ® C-series Human and Mouse MAPK Pathway Phosphorylation Array C1 kits (RayBiotech, Tebu-bio, Le Perray en Yvelines, France) were used to measure the level of 17 proteins implicated in intracellular signaling. Briefly, nitrocellulose membranes were incubated for 2 h at room temperature in blocking buffer. Total colonic proteins (250 µg) were diluted in 5 mL of blocking buffer (50 µg/mL). Membranes were incubated for 4 h in diluted samples at room temperature. After washes, membranes were incubated overnight at 4 • C with detection antibody cocktail. After additional washes, HRP-Anti-Rabbit IgG (1:1000) was added for 2 h at room temperature. Chemiluminescence was detected on Amersham Hyperfilm ECL (GE Healthcare, Orsay, France). Spots were scanned (ImageScanner III; GE Healthcare) and quantified using Image-Quant TL software (GE Healthcare).
Statistical Analysis Data were analyzed with GraphPad Prism 5. 0 (GraphPad Software Inc. , San Diego, CA, USA) and expressed as mean ± standard error mean (SEM). Comparisons between two groups were performed using the unpaired two-tailed Student's t-test or non-parametric Mann-Whitney test, as appropriate. Concerning comparisons between three groups, differences were assessed using one-way ANOVA followed by Dunnet's multiple comparisons as a post hoc test; or two-way ANOVA followed by Bonferroni as a post hoc test. A value of p < 0. 05 was considered as significant.
Stimuli We used 32 original color photographs of full-frontal (16 female) faces without glasses, facial hair or make-up, and with neutral expression. Each picture was trimmed to remove the background, clothing, and the hairline and converted to grayscale. The resulting cropped faces were pasted onto a uniform grey background and Gaussian filtered using a Matlab program (similar to that used in Laprevote, Oliva, Delerue, Thomas, & Boucart, 2010). To avoid sharp edges around images, the background to filtered and non-filtered faces was then enlarged to fit the screen dimensions. Facial stimulus subtended a width of 6° of visual angle and a height of 9° of visual angle, eyes vertically adjusted to the center of the screen. Stimuli were presented in four spatial filtering conditions (non-filtered NF, low spatial frequency LSF, mid-spatial frequency MSF, and high spatial frequency HSF).
Based on previous studies (e. g. Goffaux et al. , 2003a; Goffaux et al. , 2003b; Holmes et al. , 2003; Tanskanen et al. , 2005) , LSF stimuli contained SFs below 8 cycles/image (c/img), HSF stimuli contained SFs above 32 c/img and the band-pass filtered MSF stimuli contained SFs above 8 c/img and below 16 c/img. Afterwards, images were normalized for contrast and luminance using the Spectrum, Histogram and Intensity Normalization and Equalization (SHINE) toolbox  (Willenbockel et al. , 2010)  with MATLAB (version R2009a, The Mathworks Inc. , 2009); see figure  1. This control was used to assess that only spatial frequencies would be responsible for the effects observed on variables.
Material and method Participants were seated in a light-and sound-attenuated room. EEG recording took place inside an electrically isolated Faraday cage. The experiment, created on E-prime 2. 0 (Psychology Software Tools, Pittsburgh, PA), was presented on a CRT computer screen with a refresh rate of 120 Hz. The definition was set to 1024*768 and the resolution was 72 dpi. In each trial, a central fixation cross was shown for 500 ms, followed by a blank screen for approximately 200 ms (randomized between 100 and 300 ms), and then by the test stimulus, which was displayed for 500 ms. The offset of the stimulus was followed by a permitted response time of up to 1500 ms. Each trial ended with an inter-trial interval of about 1750 ms (randomized between 1500 and 2000 ms) (see Figure  2 ). Participants had to categorize faces by gender, and gave their response by pressing one of two keys with their dominant hand.
Electrophysiological data processing An EEG was recorded from 64 electrodes according to the 10-10 classification system with a reference electrode placed on both earlobes and an earth electrode positioned on the C7 vertebra. Vertical and horizontal eye movements were monitored using four additional electrodes placed on the outer canthus of each eye and in the inferior and superior areas of the right orbit. Electrode impedance was maintained below 5 kΩ during the EEG recording. The EEG was amplified, digitized at a rate of 512 Hz, filtered and stored using Micromed™ software (Micromed S. p. A, Italy). EEG data were processed with Advanced Source Analysis ([ASA], version 4. 7. 3. 1, ANT Software, 2016) and custom-made routines in Matlab 7. 0 (R14 version, The Mathworks Inc. , 2004). After a 0.
Electrophysiological data Two clear visual ERP components were analyzed: the P1 (maximal at approximately 110 ms), and the N170 (maximal at approximately 160 ms). The amplitude values of these components were measured at different pairs of occipito-temporal electrodes in the left and right hemisphere where they were most prominent for both the P1 and the N170 (O1/O2, PO3/PO4, and PO7/PO8). Amplitudes were quantified for each condition as the mean voltage measured within 30 ms windows centered on the grand-average peak latencies of the components' maxima. Peak latencies for both components were extracted automatically at the maximum amplitude between 79 and 139 ms for the P1, and at the minimum value between 130 and 190 ms for the N170 at the same three pairs of occipito-temporal electrodes (O1/O2, PO3/PO4 and PO7/PO8) where these components were maximal (see for similar methodology, Barragan-Jason,  Cauchoix, & Barbeau, 2015; Caharel & Rossion, 2011; Cauchoix, Barragan-Jason, Serre, & Barbeau, 2014).
The mean amplitude and peak latency values of each component were then subjected to separate repeated-measures analysis of variances (ANOVA) with Filtering (NF, LSF, MSF, HSF), Hemisphere, and Electrodes as within-subject factors, using the Statistica (version 7. 0) software. Greenhouse-Geisser corrections  (Keselman & Rogan, 1980)  were applied with adjusted degrees of freedom, corresponding to the epsilon (ε) values. Post-hoc comparisons were performed with the Tukey test. Effect size was estimated with partial η 2. The alpha significance value used was 0. 05.
Procedure The ethics committees of the University of Teacher Education of the State of Vaud (Switzerland) and the University of Nice Sophia-Antipolis (France) approved the protocol design and the study. Data were collected over 6 months and participants were recruited in a social network or in gyms. Written informed consent was obtained from the participants prior to their participation (or their parents in the case of minors). Online survey completion did not exceed more than 20 min and responses to all questions were obligatory; there were no missing data. Participants were informed beforehand that the survey was not a test (i. e. , there were no right or wrong answers) and that all responses would be used for research purposes only. Participation was entirely voluntary and full confidentiality was guaranteed.
Data analysis This study included several types of analysis. First, confirmatory factor analyses (CFA) were performed to verify the validity of the scales. Second, a series of path analyses was performed to test the hypothetical model. The CFA series used bootstrapped maximum likelihood estimation with the AMOS 7. 0 program  [45]. The CFA of each subscale was examined with relative fit indices as recommended by Hu and Bentler (1999)  [46]  because the goodness-of-fit chi-square that compares the hypothesized model with the independent or "totally free" model is almost always significant, even for well-fitting models, making it an inadequate basis for model evaluation. Therefore, the Comparative Fit Index (CFI), the Tucker-Lewis Index (TLI), and the Root Mean Square Error of Approximation (RMSEA) were used to evaluate model fit because simulation studies have shown that these fit indices provide relatively consistent and stable assessments  [47].
A cutoff value of. 90 or above for the TLI and CFI is typically considered an acceptable criterion for model fit, although a value greater than. 95 is preferable  [46]. A critical value of. 08 or below for the RMSEA was considered satisfactory for good fit  [46]. The model was rejected if the probability value (p) was below. 05 [48]. Modification indices were used to flag fixed parameters in the model that would make a significant change in the goodness-of-fit chi-square value if freed, and the likelihood-ratio test based on the goodness-of-fit chi-square was used to identify misspecifications in the constrained models from the invariance analyses relative to the baseline model. The series of path analyses was performed using bootstrapped maximum likelihood estimation with the AMOS 7. 0 program  [45].
Third, the data were collected using a self-report survey and might have been influenced by social desirability. Fourth, the internal consistency of some measures was quite low and this might have been linked to our heterogeneous sample or to the psychometric quality concerning the controlled motivation scale, or the inconsistency between the external regulation and the introjected regulation. Moreover, our study was correlational and had the typical limitations of this type of design; it would thus be interesting to conduct a longitudinal study to more closely examine the causal relationships between variables. The motivational mechanisms underlying DM could be fruitfully deepened by complementary analyses of motivational profiles. These future investigations would help to develop programs to equip bodybuilders to avoid the risks associated with DM.
Methods The "inverse piano" setup Eight young, healthy subjects took part in the experiment (four males). They were righthanded, had no specialized hand training (such as playing musical instruments) and no injury to the hand. An "inverse piano" apparatus was used to record finger forces and produce perturbations. The apparatus had four force sensors placed on posts powered by linear motors, which could induce motion of the sensors along their vertical axes (for details see  Martin et al. , 2011). Force data were collected using PCB model 208C01 single-axis piezoelectric force transducers (PCB Piezotronics, Depew, NY). The signals from the transducers were sent to individual PCB 484B11 signal conditionersone conditioner per sensor -and then digitized at 1 kHz using a 16-bit National Instruments PCI-6052E analog-to-digital card (National Instruments Corp.
, Austin, TX). Each sensor was mounted on a Linmot PS01-23x80 linear actuator (Linmot, Spreitenbach, Switzerland). Each actuator could be moved independently of the others by means of a Linmot E400-AT four-channel servo drive. Data collection, visual feedback to the subject, and actuator control were all managed using a single program running in a National Instruments LabView environment. Visual feedback was provided by means of a 19" monitor placed 0. 8 m from the subject. The feedback cursor (a white dot) represented F along the vertical axis and M in a frontal plane computed with respect to a horizontal axis passing through the midpoint between the middle and ring fingers along the horizontal axis. Pronation efforts led to leftward deviation of the cursor. An initial target was placed on the screen (a white circle) corresponding to the total force of 10 N and zero total moment.