dataesr/scientific-paragraphs-categorization huggingface dataset:
Our studies were performed in accordance with the Declaration of Helsinki. All human participants provided informed written consent prior to the experiment according to institutional guidelines and regulations of the local research ethics committee. Stimuli. Stimuli consisted of faces adapted from the Radboud Faces Database 32 that varied in emotion (neutral, angry or fearful expressions). The strength of emotional expressions varied with 7 levels of anger and 7 levels of fear equalized in perceived emotional strength and a neutral condition (see Fig. 1a  for examples of stimuli). A complete description of the stimuli is provided in El Zein et al. , 2015  10. In the published EEG study, 36 faces (18 females) were used while in the current behavioral study, only 32 (16 females) out of these 36 faces were used only to reduce the length of the task that now included additional trials and a post-test that will be described in the experimental procedure below.
Behavioral data analyses. Post-test analyses. To check whether the post-test reflected experimentally induced face-emotion associations, a two-tailed student t-test was conducted on the mean ratings for faces belonging to Induced anger and those belonging to Induced fear (considering the associations for each participant, as they differed across participants). The area under the receiver operating curve (AUC ROC) was also calculated to assess if the ratings were significantly different from chance or reflected the experimental manipulation. Post-test ratings of neutral faces were employed to determine shared and idiosyncratic face-emotion associations. In practice, to label each face with respect to 'shared' and 'idiosyncratic' categories, we implemented a 'leave-one-out' procedure. First, we assessed whether the ratings provided by a participant for the different faces correlated positively with the mean ratings provided by all other participants for the same faces using the following linear regression: This analysis revealed a positive correlation coefficient β for all participants (T 30 = 9.
Shared associations ('shared anger'/'shared fear') or idiosyncratic associations ('idiosyncratic anger'/'idiosyncratic fear') or induced associations ('induced anger'/'induced fear') and emotion (anger/fear) were within-participant factors. Model selection. We performed model-guided analyses of the behavioural data to characterise the observed influence of face-emotion associations on emotion recognition accuracy. We used Bayesian model selection based on the model evidence (estimated by a 10-fold cross-validation estimation of model log-likelihood, which penalises implicitly for model complexity without relying on particular approximations such as the Bayesian Information Criterion or the Akaike Information Criterion). We applied both fixed-effects  35, 36  and random-effects statistics  37.
These models were used for effects related to shared, idiosyncratic and induced associations. After isolating winning models in each case, we used a full model that integrated all three types of associations changing the response bias. Where useful, Bayes factors were computed as means to test for a critical absence of effects observed, to distinguish between the lack of sensitivity of tests and genuine absence of difference  38. EEG analyses. EEG analyses were performed on a previous EEG study consisting of 24 participants that completed an emotion recognition task on morphed emotional expressions of anger and fear. EEG activity was recorded using an EEG cap of 63 sintered Ag/AgCl ring electrodes. Pre-processing of EEG data is described in details in El Zein et al. 10. The approach we use for analyzing EEG data consists in performing single-trial regressions against variables of interest, instead of averaging trials to compute evoked potentials.
Acknowledgements This work was supported by grants from the  French National Research Agency   ANR-11-EMCO-00902  (J. G. ),  ANR-14-CE13-0028  (V. W. ),  ANR-11-0001-02   PSL *,  ANR-10-LABX-0087 , the  Fondation Roger de Spoelberch and Inserm. We wish to thank  Lise Hobeika  and  Flavie Laroque  for piloting and helping in conducting the experiments, and  Rocco Mennella  and  Emilie Meaux  for helpful comments on the manuscript. We wish to thank  Amy Nelson  for proofreading the manuscript.
Task: Each of the nine stimuli was presented 10 times for identification. The stimuli were presented in a random order with a 1000 ms intertest interval. A Matlab ©  [27]  program (Version 6. 1) was used to deliver the stimuli and collect the responses. The stimuli were delivered by Samson Professional Studio © headphones in a quiet room. The responses were given by pressing a dedicated key on the keyboard as rapidly as possible. The participants were first familiarized with the task using the continuum endpoints. The same procedure was used for the children and the adults. The whole procedure took about 15 to 20 min. Participants: Nineteen French-speaking children with dyslexia (7 females; hereafter "dyslexic" DYS children) were recruited with the help of two speech therapists working in the Paris area.
The 12 DYS children (3 females) had much the same reading as the 12 CTL children (4 females), but they were older and exhibit a larger reading delay than the CTL. The reading delay was only significantly different from zero for the DYS children (95% C. I. limits: -2. 7, -3. 9 years; 0. 03, -0. 46 years; for the DYS and CTL, respectively). Data analysis: The boundary and the slope of the identification functions were assessed for each subject using Logistic Regression (SPSS 24 © [29], version 24. 0), with the identification response as the dependent variable and the stimulus as the independent variable. Discriminant Analysis (SPSS 24 © ), with drop-one-out cross-validation method was used to assess the reliability of the perceptual differences across individuals.
Acknowledgments: This work was supported by a public grant overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  (reference:  ANR-10-LABX-0083 ), and covered the costs to publish in open access. Many thanks to  Anne-Sophie Dalmas  and  Emmanuelle De Laubier  who helped to access the children with dyslexia and to the " Saint-Merri/Renard " and " Château des Rentiers " schools who gave us access to the control children. Many thanks also to  Cécile Houard  and two anonymous reviewers for their contributions to this paper.
Motion detection represents one of the critical tasks of the visual system and has motivated a large body of research. However, is remain unclear precisely why the response of retinal ganglion cells (RGCs) to simple artificial stimuli does not predict their response to complex naturalistic stimuli. To explore this topic, we use Motion Clouds (MC), which are synthetic textures that preserve properties of natural images and are merely parameterized, in particular by modulating the spatiotemporal spectrum complexity of the stimulus by adjusting the frequency bandwidths. By stimulating the retina of the diurnal rodent, Octodon degus with MC we show that the RGCs respond to increasingly complex stimuli by narrowing their adjustment curves in response to movement. At the level of the population, complex stimuli produce a sparser code while preserving movement information; therefore, the stimuli are encoded more efficiently.
In support of this, we showed that population sparseness increases for the naturalistic stimuli, so in this respect the retina is performing better under these types of stimulation, in the sense that a sparse code is more efficient from an information transmission point of view  57. To see if these changes in tuning curves have an effect on the coding of the motion signal, we implemented the stimulus reconstruction and speed decoding method. We chose speed as the relevant parameter for motion estimation, but we expect that with the proper adjustment, any property of the stimulus could be recovered, providing one is able to obtain a sufficiently good reconstruction  58. Another simplification that we took advantage of, is that our experimental design is based on the independent presentation of a single speed for each stimulus set (with slight variations around an average value for the MCs); this means that for the duration of each segment of stimulation, the whole population of cells is "seeing" the same speed.
Visual Stimulus Presentation Stimulus display was performed with a conventional DLP projector using custom optics to reduce and focus the image onto the photoreceptor layer, with size of a pixel of ≈4 µm, maintaining an average irradiance of 70 nW mm -2. Stimuli were generated using the code available at https://github. com/NeuralEnsemble/MotionClouds. Timing of the images was controlled using a custom-built software based on Psychtoolbox for MATLAB  66. Spike sorting analysis was performed using Spyking-Circus 67. Data was analyzed via redistributable Jupyter notebooks using SciPy  68  and running Python 3 kernels. All data fitting procedures were performed with LMFIT  69. Figures were generated with Matplotlib  70.
Note that the Motion Clouds software works in pixel units, so a proper scaling has to be applied. In our case, the conversion factor between pixels and mm is 240:1    The response to speeds for each spatial frequency was fitted to an skewed Gaussian. As expected, at higher spatial frequencies the preferred speed shifts towards lower values, together with a decrease in maximum response, however, this decrease becomes more pronounced when the complexity of the stimulus increases, to the point where the response to lower speeds becomes very similar for different spatial frequencies (no significant difference at speeds 0. 25 and 0. 5 mm s -1 (p > 0. 05 Wilcoxon signed-rank test). b Sparseness in the code, calculated as population sparseness (Equation  4 ), is a measure of how many cells are responding and their relative level of activity.
Acknowledgements Fondecyt  1150638  and  1140403  ,  CONICYT-Basal  Project  FB0008 , Millennium Institute  ICM-P09-022-F , L. U. P. was supported by  ANR  projects " BalaV1"  ( ANR-13-BSV4-0014-02 ) and " TRAJECTORY " ( ANR-15-CE37-0011 ). C. R. was supported by a  CONICYT Ph. D. scholarship.
Statistical Analysis Data are expressed as means +/-standard deviations. A Kolmogorov-Smirnov test allowed us to verify the normality of the distribution. Our data were not normally distributed, so to compare ASD and control groups we performed a Mann-Whitney U test (Statistica Software 8. 0, TIBCO, Tulsa, OK, USA). Relations between variables were obtained by Spearman rank order correlations (Statistica Software 8. 0). Significance was accepted when p < 0. 05 after adjustment by using the Bonferroni correction. 1. There was no statistical difference in anthropometric characteristics between the two groups (Table  1 ). Vineland scale results showed that children with ASD had lower scores than control children in all dimensions (Table  1 ), confirming the significant alterations of adaptive behavior in the ASD group.
In this work, we analyzed with an Optogait device (Microgate, Italia; data not shown) the first minute of walking on the treadmill, and no difference between our groups was found. Therefore, this factor cannot explain the VO 2 difference in this experimental context. However, this result is to be nuanced because on the treadmill, the walking pattern is no longer natural, and during the first minute there is an adaptation time that is possibly the same for all children with or without ASD. We also calculated the energy cost during this treadmill test, and the lack of significant difference between the two groups could not explain the VO 2 difference. It therefore seems essential to identify another reason to explain this difference. Motivational behavior could be one of the causes. If the child does not like to participate in physical activities, he will be less motivated to perform the exercise, and a child with ASD may be less likely to respond to social reinforcement or encouragement.
Annex A The data used was collected from 6 Dark Agouti male rats (Janvier Labs) aged of 10-15 weeks and weighing between 200-250 grams. After surgery for electrode implanting, they were kept in individual cages in a 12/12h (9am-9pm) light/dark cycle with ad libitum access to food and water. One week after surgery, the rats were introduced in their recording chamber and plugged for recording. The recording chamber consisted of a 60x60x60cm faradized box with removable container for the litter, so that the rats could be changed daily at 10 am without being unplugged. While in the recording chambers, the animals were exposed to a white noise of 70dB and were also provided with food and water ad libitum. The temperature of the chambers was regulated at 23˚C. Once the responses were stabilized, and after at least two days of habituation, baseline recordings, which we used for our analysis, took place during at least 24 hours.
ACKNOWLEDGEMENTS We thank the persons who commented to earlier versions of this document, especially the members of the  Public and Professional Policy Committee of ESHG and Ethics Committee of ESHRE  and  Erika Kleiderman ,  Bartha Maria Knoppers ,  Raul Piña-Aguilar ,  Alessandra Renieri ,  Gijs Santen  and  Kirmo Wartiovaara.
MeTHODS The present review summarizes clinical and preclinical data about causal relations between inflammation and neonatal brain injury, and recapitulates experimental evidences hypothesizing OTX as a novel anti-inflammatory and neuroprotective agent in the immature brain. A literature search was performed in December 2017-January 2018 using the PubMed library in English. No restriction of year and authors were applied and review papers were used as references only for the general concepts. The literature search relating to the pre-clinical studies was restricted to research conducted in rats and mice. Only papers that satisfied the following criteria were included: pertinence to the subject, presence of control groups, and clear descriptions of experimental procedures.
Genetic analyses Genetic analyses were conducted at the referral center for developmental problems. The pediatrician initially suspected Angelman syndrome due to the developmental delay and frequent smiling. However, analysis of chromosome 15 methylation invalidated this hypothesis and excluded 15q11. 2 microdeletion or uniparental chromosome 15 disomy. The aCGH was performed using a 4 × 180 K wholegenome oligonucleotide microarray following the manufacturer's protocol (Agilent Technologies, Santa Clara, CA, USA). The results were interpreted with Cytogenomics software v3. 0. 1. 1 (ADM2 method). A CNV was considered if at least three contiguous oligonucleotides presented an abnormal mean log ratio (> 0. 25 or ≤ 0. 25). This analysis identified a 1. 7 Mb interstitial de novo microdeletion in chromosomal region 2q13 (on the long arm of chromosome 2) between proximal chromosomal position 111,399,243 and distal position 113,102,535 (hg19/GRCh37).
Several additional clinical trials evaluating ld-IL-2 in multiple autoimmune and inflammatory diseases are ongoing, including TRANSREG (NCT01988506), which includes patients suffering from 1 of 11 selected autoimmune diseases. This study currently shows that ld-IL-2 treatment is well tolerated even in the long term  (106). Altogether, the safety of ld-IL-2, its effects on boosting Tregs and inhibiting Teff cells, and Treg insufficiency strongly support its clinical investigation in MDD (Figure  1 ). cONcLUsiON Regulatory T cells appear to link many biological abnormalities found in MDD, shedding light on how stress, inflammation, and neurobiological modifications can be linked to its pathophysiology. Tregs can also explain the associations between MDD and many inflammatory diseases related to Treg insufficiency, explaining the high rate of depression in these populations.
Microscopy, Cell Counting, and Image Analyses Images of brain sections were acquired with a Stereo Microscope Olympus SZX16 equipped with digital camera DP73, or a Zeiss Axio Imager Z2 microscope with structured illumination (ApoTome) equipped with Zeiss AxioCam MRm camera and processed using Axiovision software, or with a confocal laser scanning microscope Leica TCS SP5X equipped with a white light laser, a 405 nm diode for ultra-violet excitation, and 2 HyD detectors. For cell counting analyses on immunostained brain sections, at least three adjacent brain sections were analyzed throughout the entire z-dimension for each sample using confocal microscopy, according to previously reported procedures  (Cloarec et al. , 2016)  and as further detailed in Supplementary Materials. A phagocytic activation index (PAI) was defined as the ratio of Iba1 + Ed1 + cells to the total number of Iba1 + cells.
ImageJ software was used to quantify fluorescence areas on coronal brain sections selected according to their coordinates, as indicated in the rat brain atlas  (Khazipov et al. , 2015) , excluding the meninges. Percentage of fluorescence area of a given brain section was obtained by normalizing to the total area of this brain section. Whole-brain imaging after tissue clearing was performed using ImspectorPro software (LaVision BioTec) with a binocular stereomicroscope (MXV10, Olympus) equipped with a 2X objective (MVPLAPO, Olympus) used at magnifications 0. 8X. A laser (NKT Photonics SuperK extrem) with a 640/30 nm emision filtre and two cylindrical lenses was used to generate a light sheet. Samples were placed in an imaging chamber made of 100% quartz (LaVision BioTec) filled with DBE and illuminated from the side by the laser.
Images were acquired with an Andor NEO sCMOS camera (2,560 × 2,160 pixel size, LaVision BioTec). The Z-step size between each image was fixed at 3 µm. 3D (three-dimensional)-images, quantifications and movies were generated using Imaris x64 software  (version 8. 4. 1, Bitplane). Stack images were first converted to Imaris file (. ims) using ImarisFileConverter. 3D reconstruction of the sample was performed using "volume rendering. " Brain segmentation was performed using "surface" tool by creating a mask around each volume to remove meninges and peripheral tissue containing antibody aggregates, allowing GFP intensity quantification only in brain parenchyma. GFP mean intensity obtained was then normalized to the total volume of segmented brain. 3D pictures and movies were generated using the "snapshot" and "animation" tools.
Flow Cytometry Leukocytes from brains obtained from anesthetized P1 or P7 pups were isolated as previously described  (Cloarec et al. , 2016). Approximately, 1-3 × 10 6 leukocytes were incubated with Zombie NIR Fixable Viability kit (1:200; Biolegend) for 20 min at RT. Fc receptors were blocked using mouse anti-rat CD32 antibody (FcγII receptor, clone D34-485) for 10 min. at 4 • C to reduce nonspecific binding. Blocked cell samples were stained with antibodies against combinations of cell surface markers as previously described  (Cloarec et al. , 2016). An average of 1. 3 × 10 5 living singlet cells were analyzed per brain equivalent on a BD LSRFortessa cell cytometer and raw data were analyzed using FACSDiva V8. 0 software (BD Biosciences).
The group of animal was entered as a covariate and the time point (time when measures were repeated) was introduced as a random effect with autogressive covariance structure to account for the within-subjects correlations, assuming that two measurements timely close to each other are closely correlated, and less correlated when they get farther apart. As we are modeling binary data, we specified the binomial distribution and the logit function as the link one using the PROC GLIMMIX with sas 9. 4. Significance threshold was set at 0. 05 unless otherwise stated in the figure legends.
Left sides of the cohorts legend: univariate Cox analysis (A), mixed model for repeated data (B-D), or Chi2 test with Bonferroni correction (E,F). Right sides of the cohorts legend: odds ratio (OR) ± confidence intervals correspond to the risks in the untreated vs. their corresponding treated groups: postnatal mortality (A); failure at test (C,D); hindlimb paralysis (E); GTCS (F). ***p < 0. 001; **p < 0. 01; *p < 0. 05; ns, not significant, for all figure panels, except (E,F) where ***p < 0. 0002; **p < 0. 002. NA, not available. their backs onto their paws. A significant proportion of CMVinfected pups showed inability to right, as compared with MEMinjected pups, which all performed the test successfully (p < 10 -4 ; Figures 1C, 2; Supplementary Figure  2A ; Supplementary Table  1 ). In the cliff aversion test, pups were placed with their forepaws overhanging the edge of a board and the time required to turn away from the edge was recorded.
The table shows color-encoded results for each individual pup evaluated on a daily basis (P1 to P20 columns) for the phenotypic parameters as detailed below. Performances at the righting (R sub-column) and cliff aversion (C sub-column) reflexes were measured from P1 or from P4, respectively; performances to right (R) or to turn away from the cliff (C) were color-encoded following a binary rule (blue: success; red: failure). For righting reflex evaluation, rat pups were placed in a supine position, and the time required to flip to the prone position was measured. For the cliff aversion reflex, animals were placed with their forepaws overhanging the edge of a board; the time required to turn >90 • away from the edge was recorded. For both tests, a maximum observation time of 30 s. was used. Each pup was also monitored daily for the appearance of hindlimb paralysis (H sub-column) and of generalized tonic-clonic seizures (GTCS) (S sub-column); the data were color-encoded following a binary rule where blue reflected the absence of either event, whereas red indicated the occurrence of paralysis or seizure.
AUTHOR CONTRIBUTIONS RC and SB performed most experiments and data analyzes, with equal contribution. NT supervised, performed and validated auditory tests and participated in the overall strategy. FS performed most in utero injections. HL supervised and participated in flow cytometry. SC performed a subset of immunohistochemistry, confocal microscopy and image analyzes. MS participated in immunohistochemistry and pilot rescue experiments at the initial stages of the project. VP designed and performed most statistical analyzes. EBo performed and analyzed auditory tests. EP-P performed and analyzed qRT-PCR. EBu supervised in utero injections and performed a subset of them. FM headed the InMAGIC imaging platform and participated in images acquisition and analyzes. PG participated in the follow-up of the project and in the overall strategy.
FUNDING This work was supported by  INSERM  ( Institut National de la Santé et de la Recherche Médicale ), by  ANR  ( Agence Nationale de la Recherche)  grant  EPILAND  to  PS  ( ANR-2010-BLAN-1405 01 ), and by the  PACA (Provence-Alpes-Côte d'Azur) Regional Council  (CERVIR to PS, and IM3D3C grants). TS was supported by the  Deutsche Forschungsgemeinschaft  ( SFB796 ). This work was also carried out within the  FHU EPINEXT  thanks to the support of the  AMIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the  Investissements d'Avenir French Governement program  managed by the  French National Research Agency (ANR).
Procedure The experiment was conducted in a quiet room on a laptop computer (15. 4" LCD) using E-PRIME 11. 1. 4. 1 software. All sentences were recorded by a male native French speaker and digitized with Audacity™ GNU software. Participants were familiarized to the experiment with three practice trials (not included in the analysis). Each trial started with a fixation cross lasting 1000 ms in the center of the screen, followed by the presentation of the sentence through headphones. At the offset of the sentence the picture appeared and filled the screen until a response was given or until a time-off after 2800 ms. Participants were instructed to press the buttons of an external response box with their dominant hand, to answer whether the picture correctly represented the sentence (Yes or No response).