dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants A total sample of 33 children over a total population of 65 individuals with ASD diagnosis attending at Tours University Hospital Center (Tours, France) was selected. The subjects' selection is showed in Figure  1. The sample consisted of Caucasian children of European origin, as normative physical data have been well-defined in the Caucasian population  [9, 10]. The exclusion criteria regard non-Caucasian ASD children, ASD children with clinical diagnosis of a co-morbid psychiatric or medical condition such as epileptic seizures or frank neurological pathology (e. g. , cerebral-palsy, muscular dystrophy or evidence of other) and ASD children in pubertal age. The latter was an exclusion criterion since puberty is associated with a substantial decline in the number of dysfunctional clusters of MND.
Clinical data, including behavioral and cognitive evaluation, were collected from medical records, stored in a bio-clinical database. This was approved by CNIL, the French data protection authority. An extensive, multidisciplinary child neuro-psychiatric assessment was carried out for each subject. Assessment consisted of a preliminary developmental history, medical examination and neuropsychological assessment. Behavioral assessment was carried out using the Childhood Autism Rating Scale (CARS)  [20]. The CARS evaluates the severity of autistic behaviors in 14 functional areas by assigning a score from 1 to 4. An overall score is calculated by adding scores from all functional areas. It enables the assessment of severity of autistic symptoms, and the stratification of patients into three levels: "severely autistic" (score between 37 and 60), "mildly to moderately autistic" (score between 30 and 36.
Morphological Assessment For a detailed description of the morphological assessment protocol see Tripi et al. [11]  Cranio-facial examination of the ASD group employed a mixed approach of computerized photogrammetry and classic anthroposcopy. The examination was conducted by the same author (GT) in the same testing rooms. Photographs were obtained using a high-resolution digital camera. The Euclidean measurements, based on anthropometric landmarks and 2D photogrammetric examinations, were obtained using image analysis software (FlashCAD ® , Digitarch srl, Rome, Italy)  [6]. Standardized cranio-facial photographs were taken with the camera lens aligned with the subject's Frankfort horizontal plane  [22]. An internal measure of scale (adhesive paper sticker) was placed on the glabella landmark for the frontal view, and on the condylion of the mandible for the profile view.
Neurological Assessment All ASD children underwent a standardized and detailed neurological examination according to Touwen  [13]. This is a standardized and age-specific examination, which focuses on minor neurological dysfunctions. The following clusters were evaluated: posture and muscle tonus; reflexes; involuntary movements; coordination and balance; fine motor movements; associated movements; sensory functions and cranial nerve function. The assessment was conducted by the same author (GT), in the same testing rooms, over a duration of one hour. The assessment criteria used to identify MNDs was age specific; as developmental changes in the nervous system are known to induce changes in the expression and prevalence of MNDs. Literature data  [14]  indicate that the rate of MNDs at preschool age in the general population is relatively low, reaching its peak before the emergence of puberty.
Exome sequencing, targeted gene sequencing and variation analysis Genomic DNA from peripheral blood samples was extracted according to standard procedures. An exome capture kit (SeqCap EZ Exome probes; Roche-NimbleGen) was used to target all exons. Exons capture was followed by massive parallel 150pb paired-end sequencing (Illumina, San Diego, CA, USA). Read mapping and variant calling were performed following standard bioinformatics procedures  [5]. Filtering and prioritization of the variants were conducted using an in house interactive Paris Descartes bioinformatics platform pipeline based on the Ensembl database (release 67). Variants were filtered according to their frequency (1%) against the dbSNP (https://www. ncbi. nlm. nih. gov/projects/SNP/), 1000.
FUNDING/SUPPORT The study has been supported by a grant provided by France Parkinson. Thomas WIRTH was funded by a grant from the Revue Neurologique for this work. Abbreviations: ho: homozygous, htz: heterozygous, hmz: hemizygous, AR: autosomal recessive, AD: autosomal dominant, compound: compound heterozygous, ACMG : American College of Medical Genetics and Genomics, MIM mendelian Inheritance in Man, (#) reported likely pathogenic  [7] ; (##) reported pathogenic  [8].
CR acknowledges support by the French government, through the UCA JEDI Investments in the Future project of the National Research Agency (ANR) (ANR-15-IDEX-01). The experimental setup, shown schematically in Figure  S1 , consists of a microscope for visualization and positioning systems to control the two pipettes. All experiments were conducted in a darkened room. The white light illumination of the microscope (Nikon TE2000 ) was kept to a minimum and sent through a red long-pass filter (620 nm, Knight Optical, UK) to avoid disruption of the night phase of the dinoflagellates and to allow a greater dynamic range in capturing the bioluminescence. That background intensity was controlled in all experiments for uniformity. Pipettes were positioned with 3D micromanipulators (Patchstar, Scientifica).
FINITE ELEMENT COMPUTATIONS We performed experiments and counterpart numerical computations for 35 cells to estimate the force required for light production. The steady-state axisymmetric Navier-Stokes equations were solved numerically with the finite element software COMSOL [S1] to obtain the flow from a pipette impinging on a cell. Figure  S4a  shows a close up of the geometry employed. The geometry of the dinoflagellate is simplified to a sphere of radius R, positioned a distance H from the outlet of the pipette. The computational domain was chosen to be sufficiently large that the presence of the domain boundaries did not affect the calculations. The inner diameter of the micropipette nozzle was set at 25 µ m, with a flow rate Q = 1 ml/h, resulting in a fluid exit speed from the micropipette of V ∼ 1 m/s.
7 DMD-hPSC-derived CCs (DMD-CCs) and 18 WT-hPSC-derived CCs (WT-CCs) were analyzed. Cells arrhythmogenicity was tested using application of successive 10 s runs in the current clamp configuration defined by 5 brief (1 ms) depolarizing current injections at 2 Hz followed by a resting period. Data were analyzed using the Clampfit 10. 0 (Axon Instruments) software.
Statistical Analysis Statistical evaluation was carried out in GraphPad Prism 8. 3 software (GraphPad Software, Inc. , La Jolla, CA, USA). Available normality tests were performed for the obtained data, and Student's two-tailed t-test or ANOVA was used to assess statistically significant differences on normally distributed group pairs. Where normality tests failed, non-parametric tests were used. Individual statistical test used was specified in individual figure legends.
Objective: The increasing prevalence of obesity is expected to promote the demand for endocrine testing. To facilitate evidence guided testing, we aimed to assess the prevalence of endocrine disorders in patients with obesity. The review was carried out as part of the Endocrine Work-up for the Obesity Guideline of the European Society of Endocrinology. Design: Systematic review and meta-analysis of the literature. Methods: A search was performed in MEDLINE, EMBASE, Web of Science and COCHRANE Library for original articles assessing the prevalence of hypothyroidism, hypercortisolism, hypogonadism (males) or hyperandrogenism (females) in patients with obesity. Data were pooled in a random-effects logistic regression model and reported with 95% confidence intervals (95% CI). Results: Sixty-eight studies were included, concerning a total of 19.
Eligibility criteria Cross-sectional studies assessing the prevalence of endocrine disorders in obese patients were eligible for inclusion. Studies including patients with obesity (BMI >30 kg/m 2 ) or an increased waist circumference (determined by region-and ethnic-specific cut-off point values, see under 'Risk of bias assessment') were eligible; also studies with stratified data for the groups mentioned above were considered. The following endocrine conditions were considered: hypothyroidism, hypercortisolism, hypogonadism (males) and hyperandrogenism (females). With regard to hypothyroidism, a distinction was made between clinical (i. e. raised thyroid-stimulating hormone (TSH) and low free thyroid hormone levels) and subclinical hypothyroidism (i. e. raised TSH, normal free thyroid hormone levels) if possible.
Study characteristics Twenty-seven studies concerning (subclinical) hypothyroidism were included, published between 1987 and 2018. A total of 7446 patients with obesity were included, of whom at least 1288 males and 5256 females (3 studies did not differentiate gender). Mean age ranged from 35. 8 to 52. 7 years, mean BMI from 27. 2 to 53. 0 kg/m 2 , mean WC from 95. 7 to 137. 5 cm. Individual study characteristics are displayed in Supplementary data 2.
Study characteristics Twenty-two studies concerning hypercortisolism were included, published between 1996 and 2018 (Supplementary data 3). The 22 study groups contained a total of 5819 patients: 1888 males and 3931 females, with a mean age ranging from 37. 8 to 60. 9 years. Mean BMI ranged from 29. 8 to 51. 5 kg/m 2.
Study characteristics Eighteen included studies, published from 1994 to 2016, reported on prevalence of male hypogonadism (Supplementary data 4). They comprised a total of 4546 obese male patients, with a mean age ranging from 27. 9 to 61. 9 years. Mean BMI ranged from 24. 8 to 50. 3 kg/m 2. Six of included studies reported data on subgroups of patients with morbid/severe obesity, defined by BMI >40 kg/m 2.
Open-field test (OFT) Test takes advantage of the aversion of rodents to brightly lit areas  22. Each mouse is placed in the center of the OFT chamber (43 × 43 cm chamber) and allowed to explore for 30 min. Mice were monitored throughout each test session by infrared light beam activity monitor using actiMot2 Software (PhenoMaster Software, TSE). The overall motor activity was quantified as the total distance travelled (ambulation). Anxiety was quantified by measuring the time and distance spent in the center versus periphery of the open-field chamber. Behavior was scored using an infrared light beam activity monitor using actiMot2 Software (PhenoMaster Software, TSE) and it was statistically analyzed using Prism program.
Statistical analysis Data are reported as Box and whisker plots (mean, first and third quartiles, and maximum and minimum values). The number of independent data points (n) is indicated in the corresponding graphs or in the legends. For statistical analyses, p values were calculated by two-way ANOVA, one-way ANOVA with Tukey's multiple comparisons test or two-tailed unpaired Student's t test as indicated (Prism version 7, GraphPad Software). Differences were considered statistically significant when p values *(p < 0. 05), **(p < 0. 01), ***(p < 0. 001), and n. s. = not significant (p > 0. 05). For the analysis of human samples, means (±standard deviation or standard error of the mean) were compared with two-tailed unpaired Student's t test and Pearson's correlation coefficients with their 95% confidence interval were calculated.
Acknowledgements The authors thank  Maxime Perez  for establishing contacts among dispersed investigators. The authors thank the  CRC Core Facilities  for the technical support. G. K. is supported by the  Ligue contre le Cancer  (équipe labellisée);  Agence National de la Recherche (ANR)-Projets blancs ;  ANR  under the frame of E-Rare-2, the  ERA-Net for Research on Rare Diseases ;  AMMICa US23/CNRS UMS3655 ;  Association pour la recherche sur le cancer (ARC) ;  Association "Le Cancer du Sein , Parlons-en!";  Cancéropôle Ile-de-France ;  Chancelerie des universités de Paris  ( Legs Poix ),  Fondation pour la Recherche Médicale (FRM) ; a donation by Elior;  European Research Area Network on Cardiovascular Diseases (ERA-CVD, MINOTAUR) ;  Gustave Roussy Odyssea , the  European Union   Horizon 2020 Project Oncobiome ;  Fondation Carrefour ;  High-end Foreign Expert Program in China  ( GDW20171100085 ),  Institut National du Cancer (INCa) ;  Inserm (HTE) ;  Institut Universitaire de France ;  LeDucq Foundation ; the  LabEx Immuno-Oncology  ( ANR-18-IDEX-0001 ); the  RHU Torino Lumière ; the  Seerave Foundation ; the  SIRIC Stratified Oncology Cell DNA Repair and Tumor Immune Elimination (SOCRATE) ; and the  SIRIC Cancer Research and Personalized Medicine (CARPEM).
J. M. B-S. P. is supported by a  Ramon y Cajal  contract funded by the  Spanish Government. FACEGroups members  List of FondaMental Advanced Center of Expertise (FACE-BD ) collaborators  FACE-BD Clinical Coordinating Center  ( Fondation FondaMental );  B. Etain ,  C. Henry ,  E. Olié ,  M. Leboyer ,  E. Haffen  and PM Llorca;  FACE-BD Data Coordinating Center  ( Fondation FondaMental );  V. Barteau ,  S. Bensalem ,  O. Godin ,  H. Laouamri , and  K. Souryis ;  FACE-BD Clinical Sites  and Principal Collaborators in France;  AP-HP ,  DHU PePSY, Pôle de Psychiatrie et d'Addictologie des Hôpitaux Universitaires H Mondor, Créteil ;  S. Hotier ,  A. Pelletier ,  N. Drancourt ,  JP. Sanchez ,  E. Saliou ,  C. Hebbache ,  J. Petrucci ,  L. Willaume  and  E. Bourdin ;  AP-HP, GH Saint-Louis-Lariboisière-Fernand Widal, Pôle Neurosciences, Paris ;  F.
For the Backward Model, feature importance cannot be assessed as the X collinearity is never taken into account. We show in Figures 2 (bottom) and B. 5 (right, in Appendix) that our method outperforms baselines. Next, we apply our method to brain imaging data from the anonymized multimodal neuroimaging "Mother Of all Unification Studies" (MOUS) dataset  [27]. The dataset contains magnetoencephalography (MEG) recordings of 102 healthy native-Dutch adults who participated in a reading task. Twelve subjects were excluded from the analysis because of corrupted file headers. Subjects were exposed to a rapid serial visual presentation of Dutch words. The word lists consisted of 120 sentences, and scrambled lists of the same words. Each word was presented on the computer screen for 351ms on average (min: 300ms, max: 1400ms).
MEG preprocessing The raw MEG data was bandpass-filtered between 0. 1 and 40Hz using MNE-Python default parameters  [6, 7]. Specifically, we used a zero-phase finite impulse response filter (FIR) with a Hamming window and with transition bands of 0. 1Hz and 10Hz for the low and high cut-off frequencies. The raw data was then segmented 100ms before word onset and 1s after word onset (t = 0ms corresponds to word onset). Finally, each resulting segment was baseline-corrected between -100ms and 0ms, and decimated by 5 and thus led a sampling frequency of 240Hz. The average responses across words is displayed in Figure  3. For each subject and each time sample relative to word onset, we build an observation matrix Y ∈ R n×dy of n ≈ 2,700 words by d y = 301 MEG channels (273 magnetometers and 28 compensation channels).
(i. e. from 100 ms after stimulus onset) elicited by the retinotopically-tuned visual cortices (e. g. [24]. ). Second, 'Word Frequency' indexes how frequently each word appears in Dutch and was derived with the the Zipf logarithmic scale of  [31]  provided by the WordFreq package  [29]. Word Frequency is expected to specifically cause a variation in the late evoked MEG responses (i. e. from 400 ms), because it variably engages semantic processes in the temporal cortices  [18]. Third, 'Word Function' indicates whether each word is a content word (i. e. a noun, a verb, an adjective or an adverb) or a function word (i. e. a preposition, a conjunction, a determinant, a pronoun or a numeral), and was derived from Spacy's part of speech tagger  [11]. To our knowledge, this feature has not been thoroughly investigated with MEG.
Conclusion In this work, we proposed Back-to-Back (B2B) regression, a linear method to disentangle confounded factors from multidimensional observations. B2B repeatedly performs two successive multidimensional regressions on independent subsets of the data: the first regression is applied on the output domain (as in backward decoding), whereas the second regression is applied on the input domain (as in forward encoding). We provided a theoretical guarantee about the consistency of B2B, and compared it to several baselines in controlled synthetic experiments. We also applied B2B to a recent brain imaging dataset, analyzing the timing of brain responses and their connection to word features. We obtained results consistent with prior work in neuroscience literature, confirming the reliability of B2B for real data analysis.
Acknowledgements We are thankful to Gael Varoquaux and Alexandre Gramfort for the valuable feedback. This work was supported by ANR-17-EURE-0017 and the Fyssen Foundation to JRK. and when the models are tested on an these four variables as well as another 10 word-embedding features (bottom). These results illustrate that, unlike Regularized CCA, B2B remains robust even when the number of tested factors increases.
Afferent Activity In order to record the afferent activity from the tibialis anterior muscle, nerve was positioned on a monopolar tungsten electrode and immersed in paraffin oil. Nerve activity was recorded in reference to a nearby ground electrode implanted in a closed muscle, amplified (50 to 100 K) and filtered (30 Hz to 10 kHz) by a differential amplifier (P2MP ® SARL, Marseille, France). The afferent discharge was recorded (Biopac MP150) and fed into pulse window discriminators (P2MP ® SARL, Marseille, France), analyzing simultaneously afferent activity. The output of these discriminators provided noise-free tracings (discriminated units) that were counted by a data analysis system (Biopac AcqKnowledge software) at 1 s intervals (in Hz) and then displayed on a computer. The discriminated units were counted and recorded on separate tracings.
TBP (TATA box binding protein) was chosen as a housekeeping gene and its expression levels served as reference. The real time PCR reactions were carried out with 12. 5 ng of cDNA in a mix solution containing 1X iTaq™ Universal SYBR ® Green Supermix (Bio-Rad, Hercules, CA, USA) and 300 nM of forward and reverse primers. Oligonucleotides were designed from the nucleotide sequences described in GenBank: sry forward primer: Samples were amplified in triplicates. Mean Ct for each sample was calculated, and relative expression levels were determined according to the ∆∆Ct method: ∆Ct values represent the normalized levels of each target gene compared to the TBP control, and ∆∆Ct values were calculated by subtracting the mean ∆Ct of the control population to the sample ∆Ct. Relative expression levels were calculated using the 2 -∆∆Ct equation.
1% Luxol (Clinisciences, Nanterre, France) solution (in 95% ethanol, 5% glacial acid acetic). After two washings in 95% ethanol and distilled water, sections were differentiated 30 s first, in a 0. 05% Lithium carbonate solution followed by another differentiation step in 70% ethanol for 30 s. After washing in distilled water, sections were incubated in 95% ethanol for 5 min, in 100% ethanol twice for 5 min, and finally in Histo-Choice ® (Sigma Aldrich-Merck) twice for 5 min. Stained sections were examined using an Apotome microscope (Apotome v2, AxioObserver Z1, Zeiss) which was associated with high-resolution camera (Caméra CMOS Orca flash 4. 0 v2, Zeiss, Oberkochen, Germany). The slides were digitized and analyzed with ImageJ (NIH) software. Axon numbers, axon areas and myelin content were measured in each group of animals.
Statistics Results obtained from behavioral locomotor tests (PFI), electrophysiological recordings (EIF, KCl and lactate injection), muscle properties (weight/body weight ratio, contraction properties), and histological data were compared between all experimental groups. Data processing was performed using a software program (SigmaStat ® , San Jose, CA, USA). Normal data distribution was verified (Shapiro-Wilk test) and a two-way ANOVA (group factor × time factor) for repeated measures was used to compare groups with each other and over time for behavioral scores. One-way ANOVA (group factor) were used to determine the electrophysiological responses, and for the histological analysis. Then, statistics were completed with a multiple-comparison post-hoc test (Student-Newman-Keuls method). Data were expressed as mean±SEM.
iEEG recording and processing Intracranial data were acquired with an audio-video-EEG monitoring system, 12 patients with Neuralynx and 2 with Micromed with a sampling rate of 4000Hz and 1024Hz, respectively, and were then resampled to a common sampling rate of 1000Hz. Epochs were extracted from -200 to 1550 ms relative to the onset of the cue. To avoid artifacts, recording sites exceeding the threshold of ±300 µV in more than 5% of the epochs were excluded. All signals were re-referenced to their nearest neighbor on the same electrode (bipolar montage). In the following, we will refer to these bipolar montages as "electrodes". All data were visually inspected to discard any trial with epileptic or interictal activity. ERPs were obtained by averaging epochs for each condition. Data were filtered similarly to the scalp ERPs in Experiment 2a, with a 0.
Scalp and iEEG (Experiments 2a and 2b) We used two complementary methods to assess ERP effects of expectancy: a region of interest (ROI) analysis as well as a whole-sensors analysis (see Supplementary Material for details). In these two complementary analyses we used cluster permutation statistics (temporal clusters for the ROI approach and spatio-temporal clusters for the whole-sensors approach) enabling to assess the significance of observed effects and to correct for multiple comparisons  (Maris and Oostenveld, 2007). Data were analyzed using Fieldtrip toolbox and Matlab 2015b (The Mathworks, Inc. ).
A complementary analysis probing expectancy effects over the whole CNV time-window across all sensors confirmed the presence of a significant cluster for unmasked trials (p=0. 03 see Figure  S1a ), while no such cluster could be found for masked trials in spite of CNV topography (see Figure  S1b ). We then turned to source reconstruction analysis to estimate possible cortical sources of unmasked and masked scalp CNV components. The purpose of running a source localization analysis of scalp ERPs effects here was simply to provide an indicative estimation of these neural sources, in order to bridge this experiment with iEEG data of Experiment 2b that provides robust and direct measurements of some CNV neural sources. To do so, -and as described in the M&M section -, we estimated brain sources from the grand-average differences of masked and unmasked CNV conditions.
This discrepancy may result from insufficient coverage of these regions in the set of implanted patients, or alternatively it may point to the limits of source reconstruction. The absence of individual MRI anatomy and of a precise localization of each scalp EEG contact supports this second hypothesis. Concerning temporal cortices, the estimated sources were mostly located outside of hippocampal structures. We also note that a reanalysis of the current iEEG datasets focusing on high-frequency broadband activities (70-150Hz) could be complementary to confirm and expand our results. Similarly, reanalyzing our scalp ERP data while focusing on beta-band activities, that were low-pass filtered here, and that contribute to timing processes is a promising perspective openened by our present findings  (Kösem et al.