dataesr/scientific-paragraphs-categorization huggingface dataset:
1 or 5. 3)  72  , using the default 'recon-all' pipeline, which also incorporates renormalization. Parcellations of cortical grey matter regions and segmentations of subcortical structures were visually inspected following the standardized ENIGMA quality control protocol ((http://enigma. ini. usc. edu/protocols/imaging-protocols). Exclusions on the basis of this quality control resulted in the sample sizes mentioned above (see Data sets). In briefy, cortical segmentations were overlayed on the T1 image of each subject. Web pages were generated with snapshots from internal slices, as well as external views of the segmentation from different angles. All sites were provided with the manual on how to judge these images, including the most common segmentation errors. For subcortical structures, the protocol again consisted of visually checking the individual images, plotted from a set of internal slices.
Linear mixed effects random-intercept model mega-analysis. Model: linear mixed effects models were fitted separately for each cortical regional surface and thickness AI, as well as the total hemispheric surface area and mean thickness AI, and the subcortical volume AIs. This was accomplished by means of mega-analysis incorporating data from all 54 data sets, using the 'nlme' package in R  73. All models included the same fixedand random effects, and had the following formulation: AI ¼ diagnosis þ age þ sex þ randomð¼ datasetÞ where AI reflects the AI of a given brain structure, and diagnosis ('controls' (= reference), 'ASD'), sex ('males' (= reference), 'females') and data setwere coded as factor variables, with data set having 54 different categories. Age was coded as a numeric variable. The Maximum Likelihood method was used to fit the models.
Subjects were omitted if data were missing for any of the predictor variables (method = na. omit). The ggplot2 package in R was used to visualize residuals (Supplementary Figs. 3 4 5 ). Collinearity of predictor variables was assessed using the 'usdm' package in R (version 3. 5. 3. ). Significance. Significance was assessed based on the P values for the effects of diagnosis on AIs. The FDR  74  was estimated separately for the 35 cortical surface area AIs (i. e. , 34 regional AIs and one hemispheric total AI) and the 35 cortical thickness AIs, and again for the seven subcortical structures plus lateral ventricles, each time with a FDR threshold of 0. 05. Correlations between AI measures were calculated using Pearson's R and visualized using the 'corrplot' package in R (Supplementary Figs. 6 7 8 ).
The 95% confidence intervals for Cohen's d were calculated using 95% CI = d ± 1. 96 SE, with the standard error (SE) around Cohen's d calculated according to: SE ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n1 þ n2 n1 Ã n2 þ d 2 2 Ã ðn1 þ n2 À 2Þ s ð2Þ For visualization of cerebral cortical results, Cohen's d values were loaded into Matlab (version R2016a), and 3D images of left hemisphere inflated cortical and subcortical structures were obtained using FreeSurfer-derived ply files. Power analyses. As each linear model included multiple predictor variables, the power to detect an effect of diagnosis on AI could not be computed exactly, but we obtained an indication of the effect size that would be needed to provide 80% power, had we been using simple t tests and Bonferroni correction for multiple testing, using the 'pwr' command in R.
ADOS severity score. For each AI that showed a significant effect of diagnosis in the primary analysis, a within-case-only analysis was performed incorporating symptom severity based on ADOS score as a predictor variable for AI: AI = ADOS + age + sex + random (= data set). This was to understand whether the observed asymmetry changes in cases were dependent on ASD severity. ADOS scores were first adjusted using log 10 transformation to reduce skewing. Analysis of medication use. Data on medication use (i. e. , current use of psychiatric treatment drugs prescribed for ASD or comorbid psychiatric conditions) was available for 832 individuals with ASD, of which 214 were categorized as medication users. For each AI that showed a significant effect of diagnosis in the primary analysis, a linear mixed model analysis was performed within-cases only, AI = medication + age + sex + random (= data set).
Acknowledgements We thank the participants of all studies who have contributed data to the ENIGMA-ASD working group (http://enigma. ini. usc. edu/ongoing/enigma-asd-working-group/) 14. This research was funded by the  Max Planck Society (Germany). This study was further supported by the  ENIGMA Center for Worldwide Medicine, Imaging & Genomics  grant ( NIH U54 EB020403 ) to  Paul Thompson , and further supported by the  Innovative Medicines Initiative Joint Undertaking  under grant agreement number  115300  ( EU-AIMS)  and  777394  ( AIMS-2-TRIALS ), resources of which are composed of financial contribution from the  European Union 's  Seventh Framework Programme and Hor-izon2020  programs and the  European Federation of Pharmaceutical Industries and Associations (EFPIA)  companies' in-kind contribution.
Instruments Identification of patients' educative needs regarding cancer pain Educative needs were assessed using a 3-part form based on a questionnaire developed in patients with cancer from the Cancer Center Léon Bérard (Lyon, France) and adapted by our group to the field of pain. The original questionnaire was constructed on surveys conducted in 40 patients using a face-to-face structured interview and proofread by methodologists, health-professionals, patients, and social scientists. The 10-min questionnaire was pilot-tested with patients to ensure the clarity of the questions prior to the implementation of the trial. The first section gathered general data on the patients, their lifestyle, disease, pain and its treatment, general and sociodemographic information, degree of autonomy, presence of caregivers, type of hospitalization, geographic accessibility, cancer type and stage, treatment and the intensity, and type and treatment of their pain.
Statistical analyses The main judgment criterion was the mean of the 10-point scores of the 9 items from the second part of the questionnaire developed to identify patients' educative needs dealing with "How the patient feels his/her pain and pain impact on daily life". For each of these 9 items, 0 represents "not at all" and 10 represents "completely". For the statistical power computation, assuming a standard deviation (sd) of 2, we found that 62 assessable patients were required to estimate the 95% CI with a precision of 0. 5 points. We planned to enroll 75 consecutive patients to anticipate potential nonassessable patients. Statistical analyses were performed with a bilateral α risk of 5%. Exploratory data analysis provided frequencies and their 95% CI for qualitative variables and mean and SD for quantitative variables.
The local ethics committee approved the study protocol. This study has received ethical approval from the Comité de Protection des Personnes de Nord-Ouest III (April 2014), from the Comité Consultatif sur le Traitement de l'Information en matière de Recherche dans le domaine de la Santé (July 2014) and from the Commission nationale de l'informatique et des libertés (February 2015). The trial was conducted in accordance with the Declaration of Helsinki. All patients provided written informed consent. The trial is registered as ID-RCB 2014-A00239-38, clinical trial NCT02391740.
Acknowledgments The authors would like to thank the  teams of Argentan (C. Le Gal, C. Van Delook), Cherbourg (I. Lepleux) ,  Flers  ( F. Lecaer ,  A. L. Millet ), and  Saint -Lô  ( M. Feuillet ,  S. Gehanne ) for their participation in the study. They also thank the  TAKEDA Company  for its Grant that allowed the implementation of this study (Step 2). They are also grateful for the financial.
Here we apply the finite volume solution method described in  [25]. In practice, we pre-compute p(s k |µ k,i k , ϑ i k ) on a sufficiently fine grid of values for s k and µ k,i k , which is then used as a look-up table in the optimization procedure described below. For values of the mean input µ k,i k encountered during the inference procedure that are not on the grid we use linear interpolation. For additional details we refer to our Python implementation available at Github: https://github. com/neuromethods/inference for doubly stochastic IF models.
detrend from the Python package scipy) for these comparisons. The instantaneous phase of this time series, φ est (t), was then obtained using the Hilbert transform. Analogously, the instantaneous phase of the true signal, φ true (t), was calculated using the Hilbert transform of the sinusoidal input component. To visualize the oscillations in the true and inferred time series we used sin(φ true (t)) and sin(φ est (t)), respectively (Fig 4A  -C ). We next computed the synchrony index (SI)  [52]  as SI = 1 L L l=1 exp(i (φ true (t l ) -φ est (t l ))), (27) where t 1 ,. , t L are the time points of experimental measurements. The radius (or magnitude) of the SI measures the strength of locking and the angle indicates the average phase shift between the two oscillations. To measure the strength of locking of neuronal spiking to the (true) oscillatory signal we calculated the resultant vector length (RVL) of the circular mean of phase at spike times t 0 ,.
In-vivo data We applied our method to extracellular recordings from primary visual cortex of an anaesthetized monkey  [28, 53]. Extracellular potentials were recorded using an implanted multi-electrode array. After spike sorting, the data consisted of spike times of several single and multi-units. Following  [53] , we discarded units with signal-to-noise ratio < 2. 75 (average wave form amplitude divided by the standard deviation of the wave form noise) as multi-units. We further excluded units with average spike rate > 100 Hz and omitted unphysiologically short ISIs < 3 ms. This resulted in 26 single units that we used for fitting. To account for potential spike sorting errors in this dataset we replaced the factors of the conditional likelihood in Eq  (14)  by P error u(s k ) + (1 -P error )p(s k |µ k,i k , ϑ k ), where P error is the probability of a spike sorting error and u(s) is the density of a uniform distribution over a reasonably bounded interval.
Statistical analysis Data are given as the mean ± SEM, normalized to the untreated control group (=100%). Statistical analyses were performed using the Graph Pad Prism software. For statistical comparisons of more than two groups, One-way ANOVA was used, followed by Dunnett's multiple comparison tests versus the control. For statistical comparisons of two groups, Student unpaired t-test was used. The experimental data are evaluated using the GraphPad-Prism program (GraphPad-Prism, San Diego, CA, USA). p-values <0. 05 were considered statistically significant. The goodness of fits was estimated by the R-squared value (>0. 9) using Pearson correlation and linear regression analysis.
ACKNOWLEDGMENTS Parts of this were performed in the frame of a joint PhD thesis work (IL) co-supervised by  AGMN  and  AE between the University of Strasbourg (France)  and the  University of Basel  (Switzerland) that was part of the collaborative research program of the NeuroRhine Consortium that was funded by  INTER-REG IV Program (European Fund for Regional Development) in the Upper Rhine Region  and the Offensive Science Call 2012. Additional Research Funds were from the  Psychiatric University Clinics (UPK research Fonds)  and the  Swiss National Science Foundation  (SNF# 31003A 149728 , to AE). Authors' disclosures available online (https:// www. j-alz. com/manuscript-disclosures/19-0127r1).
The calculated structure of mdPrP (PDB ID: 6FNV) is composed of two distinct domains. The highly disordered N-terminal domain consists of residues from Gly 94 to Gly 122 , whereas the welldefined C-terminus domain is composed of residues from Ala 123 to Ala 233 and exhibits a backbone root-mean-square deviation (rmsd) of 0. 42 Å (Figure  3A  and Table  1 ). The C-terminus domain of mdPrP is characterized by a compact set of three α-helices and a short antiparallel β-sheet. The α1 helix is composed of residues from Asp 147 to Asn 156 and is followed by the 3 10 -helix turn from Met 157 to Arg 159. The geometry of α1 helix is classified as kinked according to the HELANAL web server. 46, 47 The α2 and α3 helices are longer than the α1 helix and are composed of residues from Gln 175 to Lys 197 and from Glu 203 to Gln 230 , respectively.
The geometries of α2 and α3 helices are linear and of curved type, respectively. The helices α2 and α3 form a twisted V-shaped skeleton that serves as a platform for anchoring the α1 helix and β-sheet. The antiparallel β-sheet is formed at the beginning of the Cterminus domain and consists of two β strands, β1 and β2, that are composed of residues from Met 132 to Leu 133 and from Tyr 165 to Tyr 166 , respectively. The structure of mdPrP is a None of the 20 structures exhibits distance violations over 0. 2 Å and torsion angle violation over 5°. b ensemble of structures was analyzed by PROCHECK-NMR  43  and WhatIF programs incorporated in ICING structure evaluation package  44  and PSVS. 45 tabilized by a disulfide bond between Cys 182 and Cys 217 that is located in the middle of α2 and α3 helices.
We compared our mdPrP structure with the structures of previously determined PrPs from white-tailed deer and Rocky mountain elk and observed several differences. For easier comparison of cervids' PrP structures, we unified the residue numbering based on the mdPrP amino acid sequence. Here, we have to mention that the wtdPrP structure was determined in complex with an antibody fragment POM1 at pH 6. 8; however, the PDB entry for wtdPrP includes residues from 128 to 228 (PDB ID: 4YXH). 36 The ePrP structure consists of residues from 124 to 234 (PDB ID: 1XYW) and was determined at pH 4. 5. 37 We used pH of 5. 5 for structure determination because the lower pH prevents aggregation and enables longevity of the prion protein samples that is necessary for structure determination by NMR spectroscopy.
Solvent accessibility analysis with the GETAREA program  49, 50  showed that the Tyr 131 residue is protected from exchange with solvents in all three structures (Figure  S4 ). However, the distance between Tyr 131 and Ile 185 in the α2 helix is 2 times longer in mdPrP and ePrP structures in comparison to the wtdPrP structure (Table  S2 ). In contrast, the distance between Leu 128 and Ile 185 is shorter in the mdPrP structure with respect to the distances in ePrP and wtdPrP structures (Table  S2 ). These observations indicate differences in interactions between the β1-α1-β2 and α2-α3 subdomains of the compared structures. It was previously proposed that different side-chain orientations of Tyr 131 play an important role in the interactions between these subdomains and furthermore could also affect the flexibility of the β2-α2 loop region.
42 NOE contacts were determined in 3D  15  N and  13  Cedited NOESY-HSQC experiments. Structure modeling of mdPrP was performed using the program CYANA 3. 1. 76 tructure refinement using the explicit solvent model was performed by the YASARA program. 77 An ensemble of 20 lowest energy structures of mdPrP was validated by the web server software ICING  44  and PSVS. 45 ackbone amide relaxation measurements including  15  N longitudinal (R 1 ), transverse (R 2 ), rotating frame (R 1ρ ) relaxation rates, and { 1 H}-15 N heteronuclear NOE were obtained at two different magnetic fields (14. 1 and 18. 8 T) at 298 K. 78  Residues Gln 95 , Thr 98 , Ser 100 , Asn 103 , Ser 106 , Met 137 , His 143 , Tyr 165 , Asp 170 , Gln 171 , Asn 174 , Asn 176 , His 180 , Cys 182 , Val 187 , Met 216 , and Ile 218 could not be analyzed because of the cross-peak overlap.
All recorded spectra were processed with NMRPipe software  79  and analyzed with CARA  80  and SPARKY software. 81 The prediction of backbone dihedral angles was made by the TALOS+ program. 82 Alignment was prepared using ClustalO. 83 An analysis was performed by the web server GETAREA. 49, 50 The potentials were calculated at an experimental pH of 5. 5 of mdPrP using PDB2PQR server,  84  APBS,  85  and PROPKA. 86, 87 ASSOCIATED CONTENT * S Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: 10. 1021/acsomega. 9b02824. 15 N amide backbone relaxation rates and hNOE of mdPrP; chemical shifts of H N , Cα, and Cβ atoms of mdPrP and ePrP structures; structural diversity of mdPrP, wtdPrP, and ePrP; and solvent accessibility of the selected amino acid residues of mdPrP, wtdPrP, and ePrP (PDF).
Distances between C Atoms of Selected Amino Acid Residues in Proximity of the α2-α3 Loop in mdPrP, wtdPrP, and ePrP Structures a Reported distances are average values obtained from the coordinates of the structural ensemble for mdPrP (PDB id 6FNV) and ePrP (PDB id 1XYW) that were determined by NMR and for wtdPrP (PDB id 4YXH) determined by X-ray crystallography. Standard deviations are reported for the ensemble of 20 lowest energy structures for mdPrP and ePrP. bLetters specify the position of distances in Figure5C.
Funding Funding was provided by the Slovenian NMR center through the Slovenian Research Agency grant no. P1-0242 and the CERIC-ERIC Consortium for access to experimental facilities. This work was also supported by a fellowship from the HEaD "Higher Education and Development" project (funded by the Friuli Venezia Giulia autonomous Region (Italy) through the Operational Program of the European Social Fund 2014/2020; grant no. FP1619889004) (to G. S. ).
Each IP was performed in triplicate and after raw data processing using MaxQuant software, we determined statistically significant interactors of DCC and Nrp1 compared to an IgG control pulldown using label-free (LFQ) intensities and Perseus software analysis (Figure  1A ). Gene-ontology (GO) enrichment analysis revealed that 'structural constituent of ribosomes' appeared as the most prominently enriched category in both DCC and Nrp1 pulldowns, indicating that both receptors can interact with ribosomal proteins (Figure  1B ). Specifically, 75 out of 79 ribosomal proteins (94. 9%) were detected in the DCC and Nrp1 pulldowns. Of these, 51 and 33 RPs were identified as statistically enriched interactors for Nrp1 and DCC, respectively, compared to IgG control pulldowns. There was no bias towards small or large ribosomal subunit proteins (Figure  1A , red dots).
After primary antibody incubation, dishes were washed twice for 5 minutes with 0. 002% Triton X-100 in PBS and incubated with anti-rabbit-PLUS (Sigma-Aldrich, DUO92002) and anti-mouse-MINUS (Sigma-Aldrich, DUO92004) PLA probes for 1 hour at 37°C, with ligase for 30 minutes at 37°C and with the polymerase mix with red fluorescence for 100-140 min at 37°C. The samples were subsequently mounted with the mounting medium (DUO82040, Duolink) and imaged using a Nikon Eclipse TE2000-U inverted microscope equipped with an EMCCD camera. The number of discrete fluorescent puncta from randomly selected isolated growth cones were counted using Volocity software (Perkin Elmer).
5% Triton-X-100, 0. 8M guanidine NaCl, 8U/ml Proteinase K in water) and incubated at 37°C for 1h. To expand the samples, digestion buffer was removed and gels were placed in water for several hours during which water was replaced every 30 min. Once gels detached from the glass dish, they were transferred to a bigger dish to allow expansion. For imaging, expanded gels were cut in pieces and transferred to poly-L-lysine coated glass bottom dishes. Imaging was performed using a 60x/1. 3 NA silicone oil objective lens on a Perkin Elmer Spinning Disk UltraVIEW ERS, Olympus IX81 inverted microscope and the Volocity software.
Quantification of Immunofluorescence For the quantification of fluorescence intensity, isolated growth cones were randomly selected with phase optics. For each experiment, the images were captured on the same day using the same gain and exposure settings and pixel saturation was avoided. Using Volocity software (Perkin Elmer), a region of interest (ROI) was defined by tracing the outline of each single growth cone using the phase image and the mean pixel intensity per unit area was measured in the fluorescent channel. The background fluorescence was measured in a ROI close to the growth cone that was free of debree or other axons and this was substracted from the mean fluorescence value of the growth cone.
Datadependent scans (Top 20) were employed to automatically isolate and generate fragment ions by higher energy collisional dissociation (HCD, NCE:25%) in the HCD collision cell and measurement of the resulting fragment ions was performed in the Orbitrap analyser, set at a resolution of 17500. Singly charged ions and ions with unassigned charge states were excluded from being selected for MS/MS and a dynamic exclusion window of 20 seconds was employed. Raw data were processed using Maxquant (version 1. 6. 1. 0) (Cox and Mann, 2008)  with default settings. MS/MS spectra were searched against the X. laevis protein sequences from Xenbase (xlaevisProtein. fasta). Enzyme specificity was set to trypsin/P, allowing a maximum of two missed cleavages. The minimal peptide length allowed was set to seven amino acids.
Label-free quantification (LFQ/iBAQ) analysis of proteomics data To identify significant interactors, t-test-based statistics were applied on label-free quantification (LFQ) intensity values were performed using Perseus software. Briefly, LFQ intensity values were logarithmized (log2) and missing values were imputed based on the normal distribution (width = 0. 3, shift = 1. 8). Significant interactors of DCC or Nrp1 pulldowns compared to IgG pulldowns were determined using a two-tailed t-test with correction for multiple testing using a permutation-based false discovery rate (FDR) method. To determine the stoichiometry of ribosomal proteins in 40S and 60S subunits, we compared the relative abundance of all detected ribosomal proteins as measured by iBAQ intensities. The obtained relative abundances were normalized to the mean of all iBAQ intensities for either 40S or 60S ribosomal proteins in each sample.
Caia Duncan ( Juan Mata lab, Unversity of Cambridge) ,  Katrin Mooslehner  and  Asha Dwivedy  for technical assistance and  Fabrice Richard  (PiCSL-FBI core facility, IBDM,  CNRS ,  Aix-Marseille University ; member of the  France-BioImaging national research infrastructure  ( ANR-10-INBS-04 )) for assistance with EM experiments. This work was supported by the  Netherlands Organization for Scientific Research  ( NWO Rubicon 019. 161LW. 033 )(M. K. ),  Wellcome Trust  Grants ( 085314/Z/08/Z  and  203249/Z/16/Z ) and  European Research Council   Advanced Grant  ( 322817 ) (C. E. H. ).
Cerebral networks in humans We then wondered how comparable mouse lemur and human brain networks are. To answer to this question, resting state fMRI data were recorded from 42 healthy humans ranging from 41 to 60 years old at 3. 0 Tesla using an interleaved 2D T2* SENSE EPI. Participants were asked to keep their eyes closed and relax without falling asleep during image acquisition. Human images were then processed with the same graph analysis and dictionary learning algorithms as mouse lemur images. Local functional regions were identified using a dictionary learning based on 35 components. Single components spread on bilateral structures were dissociated into two different regions (i. e. one in each hemisphere). Ultimately, the brain was partitioned into 56 local functional regions (55 cortical, 1 subcortical).