dataesr/scientific-paragraphs-categorization huggingface dataset:
Before starting the tasks, a brief tutorial session was conducted in both groups to introduce ECWP website and the task to be completed. Therefore, the lack of shopping experience with ECWP would not be a cause of stress in the experiment. Consequently, the stress of the stressed group comes from three sources: 1) time stress: trial experiment indicated that 15 minutes was barely enough to complete the task; 2) cognitive stress: preparing formal purchase application requires participants to be more prudent and attentive in their work; 3) psychological stress: the countdown clock further intensified the stress. Participants' behavioral data were collected by web cookie and java-based trackers. The data were processed by linear discriminant analysis method to construct and validate the mood recognition tool.
Analysis and Result of the First Stage The authors use the linear discriminant analysis (LDA) method to develop the mood recognition function. During the experiment, a dozen of mouse activity, keystroke, and clickstream data were collected by trackers and cookies. These data include session time, frequency of keyboard usage (defined by a five second delay between two keystrokes), frequency of keystroke, frequency of mouse click, frequency of mouse movement, frequency of mouse scrolling, traveling distance of mouse, timestamp for web page open, and timestamp for web page close. Then, these observations were converted into comparable variables. Among all the 100 cases collected by the experiment, 53 case were randomly selected as modeling cases (23 stressed and 30 relaxed), and 47 case were used as validating cases (27 stressed and 20 relaxed).
The syntax to crate discriminant model in SPSS is: Based on 53 selected cases, the discriminant model is: y = 0. 022dl_kystr -0. 046time_pg + 0. 414clic_min + 0. 001mvt_speed -6. 17 One-way ANOVA tests indicates that the null hypothesis can be rejected at 2% level. The withingroups correlation matrix shows that the largest correlation between predictors is 0. 241 (correlation between mvt_spd and clc_min). The p-value of Box's test of equality of covariance matrices is 0. 127. Wilk's lambda of the canonical discriminant function is 0. 509. And p-value is 0. 000, suggesting that the discriminant function is effective. The structure matrix (Figure  2 ) shows that dly_stk has the strongest discriminatory ability, followed by sty_pg, clc_min and mvt_spd. Classification result indicates that the 83% of the modeling cases and 87% of the testing cases can be correctly classified, suggesting that the model is stable and reliable.
Result of the Second Stage The dataset of the two-week live experiment consists of 52,896 sample users. The mood recognition tool finds that 27% of them experienced stress during a session. The statistics of the different groups are highlighted in Table  3. Click through rate and close rate are used to measure the performance of recommender system in different mood state (Figure  4 ). Click through rate measures how many displayed recommendations end up being clicked by ECWP users. It confirms that recommendations were more welcomed when users were in a relaxed mood, and users in a stressed mood showed little interest to recommendations. Close rate measures how often ECWP users reject a recommendation by removing it from the screen. It indicates that stressed users were more likely to reject recommendations actively than relaxed users.
ACKNOWLEDGEMENTS Firstly, we would like to express our gratitude to the  Science and Technology Commission of Shanghai Municipality (STCSM)  for financing this research (STCSM Project Number:  14DZ1101400   Personalized Recommendation Technology based Aeronautical Mobile Community Service Model Research and Application Demonstration ). Secondly, we would appreciate the support from our  French  research partner, who helped us find the experiment participants, shared with us their operation/business data, and provided us with the infrastructures to conduct the experiments. Finally, we would like to thank all the reviewers for their valuable comments, which had made this paper more insightful.
Results Ingredient selection. The objective of this trial was to assess whether the stimulation of TRPA1 and TRPM8 pathways could be as efficient as the stimulation of TRPV1 by capsaicin or capsiate  4  in impacting energy balance. Based on the literature, cinnamaldehyde was selected as the most representative TRPA1 agonist: since it activates specifically TRPA1 with an EC50 of 60 μ M  27. Its specificity on the human form of the receptor was confirmed in vitro (data not shown). To identify an efficient and specific agonist of hTRPM8, different ingredients with cooling properties were evaluated in vitro. Following these initial tests, a cooling flavor (CF) that specifically activates hTRPM8 with an EC50 of 0. 07% (Fig. 1 ) was selected. As a positive control, capsaicin was evaluated in parallel to cinnamaldehyde and CF in this trial  12.
Heart rate variability. For each treatment, participants went through two sessions A and B, where the administration and measurement protocol was the same but the recorded parameters were different (Fig. 2 ). ECG was logged during both sessions in order to evaluate ANS activity. Three parameters were calculated using power spectral analysis of the heart rate variability collected from ECG recordings in session A and B. These were: SNS index, PNS index and the VLF/TP ratio averaged over 10 minute intervals (see supplementary Tables S1 to S6 for data distribution). Sessions A and B were analyzed both separately and pooled together. Table  2  shows the statistical analysis of the comparison of AUCs of ANS parameters, each treatment being referenced to the placebo. AUCs of PNS index, SNS index and VLF/TP were compared between groups for each session A or B and the union of both sessions.
None of these parameters showed any consistent differences over both sessions A and B, except a reduced SNS index and VLF/TP induced by the CF compared to placebo statistically significant for session B only. The respective values were: -142. 944 ± 67. 928 (SE), p = 0. 0378 (unadjusted p-value) and -3. 072 ± 1. 235 (SE), p = 0. 0435 (adjusted p-value) (see complete analysis on Table  2 ). Blood pressure and heart rate. Mean arterial pressure, diastolic and systolic blood pressure (BP) were measured and calculated from the measures taken each 15 minutes during session B (time points: T0, T15, T30, T42, T57, T72, T87, T102, T117, T132) (see supplementary Tables S7-S9 for data distribution). Table  3  details the statistical analysis of the comparison of AUCs after ingestion until the end of the session of the different BP variables; each treatment being compared to the placebo.
5A ) and in Zone-12 (left cheek) immediately after intake of cinnamaldehyde (Fig. 5B ). Post-ingestion evaluation of the samples. Intensity and chemesthetic properties of products were evaluated after ingestion using a sensory questionnaire in each session. The answers were consistent over sessions and cinnamaldehyde, capsaicin and CF could be distinguished by their sensory properties (data not shown). At the concentrations tested, cinnamaldehyde was judged less intense than capsaicin (Fig. 6 ) and CF was judged 'not intense' to 'mildly intense' relative to the placebo (data not shown).
m. ). Furthermore, subjects were asked to refrain from spice consumption 2 days prior to each session. Each sample was evaluated in two different sessions A and B, with the same design but different data recording (Fig. 2A ). Each session started at 8:00 a. m. with a 30 minutes resting period and measurement preparation. During all the testing sessions subjects were seated comfortably. Room temperature was in the 20 °C to 22 °C range. Subjects were instructed to remain quiet and still, but they were allowed to watch TV. Parameters were recorded 30 minutes before and 90 minutes after ingestion of each sample (Fig. 2A ). The clinical trial was registered in Clinicaltrials. gov ID: NCT02193438. Treatment. All active TRP agonists were dissolved in 200 ml liquid tomato juice (Granini, Eckes-Granini, Henniez, Switzerland) at room temperature and served with a slice of bread.
The monitored signal was computed using the HRVLive! Software (Biocom Technologies, Poulsbo, WA, USA). Periodically fluctuating components included in the HRV and their amplitudes were calculated over time into a power spectral curve. To evaluate the activity of ANS, the analysis was performed at a very low frequency (VLF) range (0. 007-0. 04 Hz), low frequency (LF) range (0. 04-0. 15 Hz), high frequency (HF) range (0. 15-0. 4 Hz) and the total power (TP) (0. 007-0. 4 Hz). HF reflects parasympathetic (PNS) influence on heart beat; LF displays mainly sympathetic (SNS) influence and partly vagal activity  36. VLF reflects SNS activity related to energy metabolic regulation. VLFs are increased through thermogenic stimuli such as cold exposure and food or spicy food ingestion  5, 37. PO = (0. 8xbody weightx1000)/1440 NPVO 2 = VO 2 -POx1.
01031 NPVCO 2 = VCO 2 -POx0. 84361 FAT = (NVPO 2 -NVPCO 2 )/(2. 01494x(1-0. 707)) CHO = (NVPCO 2 -0. 707x NVPO 2 )/(0. 82821x(1-0. 707)) RQ = VCO 2 /VO 2 Indices of SNS and PNS activities were calculated as: SNS index = (VLF + LF)/HF PNS index = HF/TP VLF and VLF/TP are defined as the absolute and relative thermogenic SNS activities. Heart rate data (bpm) were also extracted from this recording and analyzed for the 15 minutes after ingestion. The ECG signal was recorded continuously. ANS activity was analyzed during the 30 minutes before sample ingestion and the 90 minutes post-sample ingestion. Blood pressure. Systolic (SP) and diastolic (DP) blood pressure were measured every 15 minutes before the ingestion (T0, T15, T30) and after (T42, T57, T72, T87, T102, T117, T132) by a nurse, using a standard blood pressure monitor (inflatable arm cuff) during same session as the indirect calorimetry measure.
Facial thermography. Infrared thermal imaging of the face was used as an exploratory, non-invasive recording method of sympathetic nervous system activity. Using a FLIR A325 infrared camera (FLIR Systems, Inc, Emitec AG, Rotkreuz, Switzerland) subjects were continuously recorded at a distance of ~1 meter. Thermographic imaging software, FLIR ResearchIR (FLIR Systems, Inc) was used to acquire images. The recording was performed by acquiring 1 image/second. Infrared Image analysis. Facial thermography data were extracted using in-house algorithms developed within the Matlab framework (The Mathworks Inc. , Natick, MA, USA). First, a graphical user interface (GUI) was created to help defining manually a temperature threshold highlighting primarily subject's faces. An alignment of the face was then computed and twelve regions of interest (ROIs) drawn manually using the GUI (Fig.
2B ). Face alignment/orientation correction was computed image by image using a binary mask based on manually set temperature thresholds. Holes in the binary mask were filled and the region with the greatest contiguous area was selected. The selected region centroid was determined using the built-in Matlab region properties tool (regionprops). With the same tool, an ellipse was fitted within the region to obtain the angular orientation of the ellipse's major axis compared to the x-axis. The original image was then shifted to center the region centroid at the origin (0,0) and rotated at an angle aligning the major axis of the fitted ellipse to the y-axis. Face alignments were computed for all images of the recorded session and average temperatures within ROIs extracted. Alignment performance was checked visually over the whole recorded session and discrepancies corrected by re-setting new regions and re-extracting the data in extreme cases (e.
Data management. A paper case report form (CRF) was used. All data required in the protocol and captured by the investigator were transcribed on the CRFs. All questionnaires were joined to the CRF. Indirect calorimetric data was automatically generated by the MAX-II program on an excel sheet. Heart rate power spectra analysis data (VLF, LF, HF, TP, HR for each second of recording) was exported into an excel file automatically by the HRV Live! program. Facial area temperatures were analyzed as described above and exported into an excel file. All generated data excel files were downloaded into the clinical data management system (CDMS, ClintrialTM).
Statistical analysis. For energy expenditure, fat and CHO oxidation, the raw data was first smoothed (using loess) then the time-standardized AUC were calculated as the integral over the time after ingestion until the end of the experimental session divided by the length of this time interval. All analyses were carried out using linear mixed-effects models with a random subject-wise intercept to take into account the correlation between repeated measures. For ANS variables, blood pressure and thermography the area under the curve (AUC) was computed after ingestion for each subject. AUCs were then compared between groups considering a linear mixed-effects model with the baseline measurement and the treatment group as fixed effects and the subject as random effect. For ANS parameters, the session was added as a fixed effect and an overall F-test was computed in order to compare the groups over the sessions.
P-values were adjusted for multiple comparisons according to Bonferroni. Additional analyses were performed to evaluate short term (within 15 minutes) effects of treatments on facial temperature and heart rate. To analyze fast changes, data were aggregated over 1 minute for before, right after ingestion (0-1 minute), after 5 minutes, after 10 minutes and after 15 minutes. Outliers were removed before aggregation. All analyses were carried out using linear mixed-effects models with a random subject-wise intercept to take into account the correlation between repeated measures. For facial temperature analysis, P-values were computed using a linear mixed-effects model while adjusted for multiplicity using Holm's method. Sample sensory evaluation and gut comfort evaluation. Pleasantness and intensity of the chemesthetic stimuli was evaluated during the ingestion period using a questionnaire.
Analysis of the DICOM images DICOM images were analyzed using OsiriX® software (Pixmeo SARL, Bernex, Switzerland) with 3D multiplanar reconstructions. The quality of the images obtained in the three groups was compared using 10 criteria, taking account of the bone visualization, the capacity to investigate the full range of motion, the visualization of the implants and the visualization of the soft tissues in contact with the implants. The four medical doctors evaluated all images blindly (two senior orthopaedic surgeons, one resident orthopaedic surgeon, and one rheumatologist who independently ranked each criterion from 0 to 5. Raw DICOM images were used to build threedimensional reconstructions of bone, implants and soft tissues, using Mimics® software (Materialize®, Leuven, Belgium). First, Mimics® automatically built the bone masks.
Second, to improve the accuracy of implant visualization, the Stereolithography (STL) files (3D Systems, Rock Hill, South-Carolina USA) provided by the manufacturer were matched with the postoperative reconstructions (Fig. 1 ). Third, the soft tissues were digitized by manual segmentation at each slice level (Fig. 2 ). Fourth, coordinates of digitized points were exported to spreadsheets and processed using Matlab® (MathWorks®, Natick, MA, USA), in order to analyze the position of all studied tissues during knee flexion and to quantify the potential displacements due to prosthetic implantation.
ACKNOWLEDGEMENTS pEU3-NIIb plasmid was kindly provided by  Yaeta Endo. We are grateful to  Hisaji Maki  and  Pablo Radicella  for discussion. Author contributions: I. K. and H. I. designed research; Y. Z. , T. M. , H. Y. , T. N. , M. F. , N. T. , K. I. , Y. F. and H. I. performed research; Y. Z. and I. K. wrote the paper.
Experimental procedure Stimuli and procedure. Stimuli consisted of 162 black and white photographs of natural scenes, all 640 x 480 pixels in size (with a visual angle of 31 x 24°), and were composed of 54 pleasant, 54 unpleasant and 54 neutral scenes. Visual scenes were in close-up and directly involved the participant (i. e. first-person perspective). Pictures were selected from several sources: the International Affective Picture System (IAPS;  [62] ), the Internet (no copyrighted material) and an in-house database. Unpleasant scenes included dangerous animals (sharks, snakes, spiders, etc. ), unsafe environments (tornadoes, fires, tsunamis, etc. ) and aggressive people (carrying weapons, with angry expressions, etc. ). Pleasant scenes included images showing safe, happy and friendly animals, idyllic landscapes (beaches, mountains, etc.
) as well as happy and friendly people. Neutral scenes included the same type of stimuli (animals, environments and people) in neutral situations. Stimuli were selected from an in-house database created for, and used in, a previous study  [1]  (stimuli pretested on 108 subjects  [1, 6] ), and were characterized by emotional valence, arousal level and tendency to action. Unpleasant stimuli (-6. 43 ± 1. 42; on a valence scale from 10-pleasant to -10-unpleasant with 0-absence of or weak valence) induced an arousal level of 5. 51 (± 0. 93; on a scale from 1-low arousal to 10-high arousal) and a tendency to avoid (-7. 27 ± 1. 18; on an action tendency scale from 10-approach to -10-avoid with 0-absence of or weak tendency to action). Pleasant stimuli (5. 11 ± 0. 74 on the valence scale) induced an arousal level of 4.
Each task involved three response choices corresponding to three response keys. In both tasks, the assignment of the three emotions (emotional task) or the three action tendencies (motivational task) to the three keys was indicated to participants before the experiment and counterbalanced between participants. Participants were familiarized with each task in advance outside the scanner using training trials which were different from those used in the experiment. Response accuracy and reaction time in milliseconds (RT, ms) were recorded for each trial. In both tasks, a response for a given scene was considered correct when the response (emotional valence or tendency to action) given by the participant was the same as the one given by participants in the database pre-test, i. e. when the response was congruent.
MRI acquisition Acquisition of data was performed using a whole-body 3-T MRI Philips scanner (Philips Medical Systems, Best, NL) with a 32-channel head coil at the Grenoble MRI facility IRMaGe in France. Functional T2-weighted images were acquired using a gradient echo-planar imaging sequence (repetition time (TR) = 2. 5s, time echo (TE) = 30 ms; descending acquisition; flip angle = 80°; field of view (FOV) = 220 x 216 mm2; acquisition matrix = 88 x 85 pixels; reconstruction matrix = 96 x 96 pixels; in-plane voxel size = 2. 89 x 2. 89 mm). Forty-four adjacent axial slices were acquired in descending mode (Slice thickness: 3. 25 mm, 0 mm gap), covering the entire brain. All scanning parameters were selected to optimize the quality of the BOLD signal while maintaining a sufficient number of slices to acquire whole-brain data.
In addition, for each participant, a rotation of approximately 15°was applied on the bi-commissural classical plane on the basis of an anatomical scan performed just before experiments, so that the new sectional plane connected the lower limits of the frontal lobe and of the pons. This plane was chosen to optimize the acquisition of data in temporal median regions and orbitofrontal regions. Finally, for each participant, a T1-weighted high-resolution three-dimensional anatomical volume was acquired (128 adjacent anatomical slices; ~1. 17mm thick each; FOV = 224 x 256 x 175 mm2; acquisition matrix = 192 x 132 x 128 pixels; reconstruction matrix = 288 x 288 x 128 pixels; voxel size = 1. 17 x 1. 94 x 1. 37 mm) and normalized to the MNI template. The orientation of anatomical slices was similar to that of the functional slices.
Data processing Whole-brain analysis. Data analysis was performed using the general linear model  [64]  for block designs in SPM8 (Wellcome Department of Imaging Neuroscience, London, UK, www. fil. ion. ucl. ac. uk/spm) implemented in Matlab 7 (Mathworks, Inc. , Sherborn, MA). Individual functional volumes were realigned, time-corrected, normalized to the MNI space and spatially smoothed by an 8-mm FWHM Gaussian kernel. Time series for each voxel were highpass filtered (1/128 Hz cutoff) to remove low-frequency noise and signal drift. On an individual level, statistical parametric maps were computed for several contrasts of interest. The first aim of the study was to identify and compare the cerebral regions involved in two distinct affective tasks. We therefore identified the cerebral regions specifically involved in the emotional task and the motivational task by contrasting one task to another ([ET > MT] and [MT > ET]).
Parameter estimates (% signal change relative to the global mean intensity of signal) of the affective appraisal experiment were extracted from these two ROIs for each participant using in-house software. The mean percentage of signal change was calculated for each condition (Tasks-Emotional, Motivational x Spatial Frequency-NF, LSF, HSF x Emotional Valence-Unpleasant, Pleasant, Neutral). These values were subjected to a repeated-measures ANOVA using Statistica 10. 0 software, with Task, Spatial Frequency, and Emotional Valence as within-subjects factors, with a statistical threshold of p < 0. 001. Stimuli used in the localizer experiment were different from the main experiment and included black and white photographs of 52 natural scenes and 52 facial expressions selected from a home database (partially published,  [6] ) and the KDEF database (Karolinska Directed Emotional Face set:  [76] ).
Funding: This study was funded by  Pôle Grenoble Cognition. (http://www. grenoblecognition. fr/index. php/animation/projets). Grant 1, AC. Grenoble MRI facility IRMaGe was partly funded by the  French program  " Investissement d'Avenir " run by the  'Agence Nationale pour la Recherche' ; grant ' Infrastructure d'avenir en Biologie Santé - ANR-11-INBS-0006. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Not all parameters could be measured for each eye or lens. Where two eyes were available from any donor, data were averaged for all statistical evaluations. Any value more than 2. 5 SD from the mean for that age, or its corresponding estimated value under the nonlinear regression analysis in cases of logistic model fitting (see later in the Methods section) was considered to be due to tissue damage and was eliminated from these calculations. This entailed no more than six values in any of the baboon and rhesus monkey data sets and 12 for the cynomolgus monkeys. All data are shown in the figures but those eliminated from the calculations are identified. Fourteen baboon lens wet weights were discarded because the ratio of dry weight/wet weight was more than 3 SD below the mean for that age, indicative of water uptake.
Data were fitted to the model (without logarithmic transformation) using a nonlinear regression technique to minimize the sum of square of residual. Nonlinear regression was implemented with Solver in a spreadsheet program (Excel; Microsoft Corp. ), setting D m and k as variables using the generalized reduced gradient method for solution (precision and convergence set to 10 À6 and 10 À5 , respectively). Data are presented in some figures according to the transformed logistic equation lnðDÞ ¼ lnðD m Þ À k=A; since this better illustrates the continued growth. For all analyses, the probability of a Type I error was well below 0. 01. Data were also tested against a model based on the twoparameter logistic equation with an added linear component of slope k 2 to identify possible nonasymptotic continuous growth: D ¼ D m e Àk=A þ k 2 A þ e: Adult body weights and life spans were obtained from the listing of Grzimek.