dataesr/scientific-paragraphs-categorization huggingface dataset:
In this regard, more studies are required, not only for identifying and avoiding any potential not yet described matrix effects on the CCS of analytes, but also for guaranteeing the reliability and precision of the CCS databases reported. Long-term and inter-laboratory assays are highly needed for achieving this purpose. High precision has been observed for the majority of CCS measurements of steroid ions as discussed above and shown by Figure  1C  for the deprotonated molecule of estradiol 17glucuronide. Thus, it provides confidence in the TW CCSN 2 values of the database for steroids. On the contrary, the precision related to the CCS measurement of other molecular ions can still be improved if the TW CCSN 2 values from the database are corrected by taking into account the additional measurements carried out through this work.
1, 1. 4 and 1. 8 Å 2 , respectively). For compounds presenting a CCS of 200 Å 2 , a difference of 1. 0 Å 2 involves a CCS deviation of ±0. 5%. As a consequence, CCS values reported by current and further databases have to be as precise as possible in order to narrow the thresholds applied to CCS measurements. It must be taken into account that small differences normally exist between the CCS of two molecules presenting equal or similar m/z, so a wide acceptance threshold could probably lead to wrong peak assignments either in targeted or non-targeted analysis. Furthermore, despite the influence of the matrix on the CCS of analytes seems to be negligible, their characterization in terms of CCS in a broad variety of matrices is required for improving the accuracy and confidence of the CCS values reported by databases.
Towards the implementation of IMS for the non-targeted analysis of steroid As discussed above, the integration of IMS in LC-MS systems increases detection sensitivity and selectivity as well as provides a novel parameter for compound identification (i. e. CCS). These advantages may enhance the performance characteristics of analytical methods intended for the targeted analysis of steroids but, undoubtedly, will improve non-targeted approaches outcome. In nontargeted methods, compound identification is usually carried out based on m/z spectra from public databases. However, compound identification cannot rely on Rt if standards are not available because LC methods can suffer slight variations within laboratories. Within this framework, CCS has huge potential for supporting compound identification.
Concluding remarks The recent commercialization of IMS-MS instruments is providing new opportunities to extend the current boundaries of targeted and non-targeted analysis. Nevertheless, more studies about the robustness and advantages provided by this technology are still required before it can be fully implemented in analytical laboratories, especially for those applications that can involve legal consequences. In this context, this work shows that CCS can be used with confidence for the identification of phase II steroid metabolites in addition to m/z and Rt. In general, this molecular characteristic has not shown to be influenced by urine matrix. If any effect was observed, it did not lead to CCS deviations above the threshold currently accepted for CCS measurements (i. e. CCS ± 2%). Considering TW CCSN 2 values from a published database, high accuracy was obtained for CCS measurements over time (i.
e. within four months), since ∆CCS < 1. 3% was observed in the majority of the cases. Based on our results, the feasibility of reducing the threshold from 2% to at least 1. 5% for CCS measurements increases. Obviously, this issue requires a deep discussion within the ion mobility community, and several aspects such as the related increase of false negative results should be considered before considering such parameter as additional new identification criteria, in the current context of Dec 2002/657/EU revision. On the contrary, keeping a wide threshold such as 2% can involve high number of false positive results since molecules with the same m/z normally present similar CCS. Normalized CCS databases are required for a deeper evaluation of the accuracy of CCS measurements that should lead to a consensus decision about reducing this threshold.
The CCS characterization of compounds in different matrices by different IMS technologies, and involving inter-laboratory studies, is view as the first step to create normalized CCS databases. Consequently, it brings new opportunities of collaboration within the ion mobility community and its related application areas. In our case, the validation of our CCS database for steroids by DTIMS and TIMS is within the framework of our current perspectives. In addition, the implementation of TWIMS in LC-MS workflows is a potential strategy to improve method sensitivity. The selection of the mobility region of targeted analytes reduces background noise, providing cleaned-up chromatograms and, consequently, greater S/N. In the case of phase II steroid metabolites in urine samples, sensitivity was improved between 2 and 7-fold.
Steroids in adult bovine urine samples Steroids in calve urine samples Steroid standards TW CCSN 2 values reported in the CCS database for steroids (in Hernández-Mesa et al. , Anal. Chem. 2018, 90, 4616-4625. Threshold of ±2% from the TW CCSN 2 reported in the CCS database for steroids.
[39]CSN 2 , accurate m/z and Rt of phase II steroid metabolites analyzed by LC-ESI-TWIMS-TOF-MS in urine samples. Notes:* TW CCSN 2 values of steroid standards that have been experimentally measured and reported for the first time. They are averaged values resulted from CCS measurements that were done within four months (n = 9). 1 TW CCSN 2 values previously reported by the CCS database for steroids[39]. 2Averaged TW CCSN 2 values of phase II steroid metabolites in urine samples (i. e. adult bovine and calve urines) within four months.
Acknowledgements J. M. thanks the  European Research Council (ERC)  (grant agreement n°  639170 ) and  ANR LabEx "Chemistry of Complex Systems " ( ANR-10-LABX-0026 CSC ). A. S. thanks the  EU  for a  H2020 Marie Skłodowska Curie Fellowship  ( 792101 ). P. C. thanks the  French government  for an MRT fellowship. CNR thanks  NSERC of Canada  for funding through the  Discovery Grants program  (application  418505-2012 ) and  Compute Canada  for supercomputing resources.
The absorption features are determined by the particular physical and chemical structure of the material. Thus, the variables characterizing light absorption features can be directly related to the chemistry and structure of the sample (Van der meer 2004). In spectroscopy, the partial least squares regression (PLSR) is a widely used method to develop predictive models for chemical constituents (which are optically active) of an investigated sample with spec-tral data, and is also very suitable for high-dimensional hyperspectral image data -when the number of samples is fewer than the number of investigated characteristics used as independent variables  (asner, martIn 2008). Carrots (Daucus carota var. sativa) are chosen as an object of investigations in our study. There have been few studies on the implementation of spectral approaches for quality estimation of carrots.
Computational Methods The non-natural β-amino acids, capped respectively with an acetyl (Ac) and an OMe group at the N-and C-termini, were designed using MOE (Molecular Operating Environment, 2013) and submitted to a "low mode" conformational search (MMFF94x force field, Born solvation, iteration limit = 40 000, MM iteration limit = 2,500, rejection limit = 500). The two lowest energy conformations having φ, θ, and ψ dihedrals corresponding to two different secondary structures, namely the extended one and a β-turn, were selected to derive partial charges with the R. E. D. III. 52 software  (Dupradeau et al. , 2010). Each geometry was optimized at the HF/6-31G(d) level, and two different spatial orientations were used to derive orientation-and conformation-independent RESP-A1 charges. The tetra-(m7 and m8) and hexapeptides (m11 and m12) of general formula 8 were built with the tleap module of Amber14  (Case et al.
, 2014)  by imposing an extended conformation (φ = ψ = ω = θ =180 • ), and solvated with an octahedral box of CHCl 3. All the systems were consequently submitted to a preliminary equilibration with the pmemd module of Amber14 package, using the ff14SB  (Maier et al. , 2015)  force field. In detail, the systems were relaxed by minimizing hydrogens and solvent (2,000 cycles of steepest descent and 5,000 cycles of conjugated gradient). The solvent box was equilibrated at 300 K by 100 ps of NVT and 100 ps of NPT simulation using a Langevin thermostat with a collision frequency of 2. 0 ps -1. Successively, a minimization of side chains and CHCl 3 with restraints on backbone atoms of 25 kcal/mol and a total minimization (2,500 cycles of steepest descent and 5,000 cycles of conjugated gradient) were performed.
2 and 0. 16, for the dihedral and potential factors, respectively. The aMD simulations of each peptide were run until convergence, for a total of 500 ns for the tetrapeptides m7 and m8, and 800 ns for hexapeptides m11 and m12, respectively. The root mean square displacement of backbone heavy atoms from the extended conformation was used as a metric of convergence (Figure  S1 ). All the analyses were conducted on the last 200 ns of trajectory. Cluster analyses were performed with the cpptraj module of Amber14 by sampling one of every 8 frames, using the average-linkage algorithm and requesting ten clusters; the pairwise mass-weighted RMSD on backbone Cα atoms was used as a metric. H-bonds were computed with cpptraj by setting a donor-acceptor distance threshold of 4. 0 Å and an angle cutoff of 110 •.
1 K. Samples were placed into 0. 5 mm-diameter Lindemann capillaries, which were rotating around their axes during data collection to reduce the effects of preferential orientation. The peak positions were determined after pseudo-Voigt fitting of the Bragg peaks. Lattice parameters were determined using DICVOL06. Rietveld refinement was carried out with the Materials Studio package  33  for the orientationally ordered C 60 molecule, whereas the FullProf Suite  34  was used to describe the orientationally disordered C 60 molecule with spherical harmonics. The CBrCl 2 H molecule was modelled in both cases as a rigid body (C-Cl: 1. 757 Å, C-Br: 1. 9296 Å, C-H: 1. 09 Å). Differential scanning calorimetry (DSC) and thermogravimetry (TG) analyses were conducted with a Q100 and a TA Q50 from TA Instruments, respectively.
37 or the structure of C 60 •CBrClH 2 , patterns were indexed with DICVOL06 leading to a monoclinic unit cell and a C2/c space group. Rietveld refinement in the Materials Studio package led to the lattice parameters a with the coordinates of its center of mass (0, 0. 13823Ĳ25), 0. 25). CBrCl 2 H is located on Wyckoff site 8f implying that the asymmetric unit must contain only half a solvent molecule with a final refined position for the central carbon atom at (0. 483(2), 0. 001(4), 0. 708(2)). The halogen atoms of this molecule are once again disordered with occupancies of 1/2 Cl : 1/2 Br,1/2 Cl : 1/2 Br, 1 Cl, indicating that one site possesses 50/50 disorder, while another site is fully occupied by a chlorine atom. Such behaviour has been proposed for the monoclinic structure of a polymorph of pure CBrCl 2 H named "mode 1".
Acknowledgements 1. The present study received financial support from the  Direction Générale de la Santé (DGS) , and the  Maison Dispersée de Santé (MDS). 2. The authors are grateful to  Dr Riff  for his availability and kind help, to transgender people who agreed to participate in the pretest and the study, as well as to the transgender associations who have all made contributions to this study ( Association Nationale Transgenre (ANT) ;  Association Beaumont Continental (ABC) ; C'est Pas Mon Genre; J'en suis -j'y reste;  En-Trans ;  Espace Santé Trans (EST). The authors would also like to thank  Patrice Desmons  ( CCOMS, France ) for his deep involvement in the elaboration of the protocol,  Silvia d'Oca  ( University of Cagliari, Italy ) for her help on additional questions analysis, and  Brian Hofer  for proofreading.
The oral productions of participants were recorded and manually and orthographically transcribed with the Child Language Data Exchange System (CHILDES;  MacWhinney, 2000)  using the embedded Computerised Language Analysis (CLAN) software programme and its CHAT transcription norms. Concerning transcription of the MBN and the PBN, inter-rater agreement was measured by comparing orthographical transcriptions from two experimenters (one by the author, AP and another by a psycholinguist who was not involved in the study and blinded to group allocation). More precisely, each experimenter first individually transcribed the discourses before comparing both sets of data and resolving any discrepancies until they reached 100% agreement, which was the case for less than 5 utterances. For the subsequent analyses, only AP annotated pauses and lexical content of all PBN and MBN.
Pause analysis Pause type and length were manually tagged with Praat software  (Boersma & Weenink, 2001)  using a 200 ms threshold. Various authors agree on the fact that a lower threshold is more likely to reflect respiratory features (e. g. Morel & Danon-Boileau, 1998 ) and a 200 to 250 ms threshold is the one most commonly found in the literature  (Zellner, 1994). This cut-off point of 200 ms was also chosen based on another study on pauses in AD  (Gayraud et al. , 2011). We included both silent and filled pauses in our analysis.
Neuroimaging data A high-resolution anatomical image, using a 3D T1-weighted sequence (plane resolution of 1 × 1 mm, slice thickness of 1 mm) was obtained. Grey matter density was assessed using a voxel-based morphometry method on Statistical Parametric Mapping version 12 (SPM 12, Wellcome Trust Centre for Neuroimaging). For each subject, the 3D T1 sequence was segmented to isolate grey matter and white matter partitions, modulated for deformation, normalized to the MNI (Montreal Neurological Institute) space, smoothed (8 × 8 × 8 mm) and pooled by group for statistical analysis. Correlations between grey matter density and pause frequency were assessed in the patient group using multiple regression (threshold for significance: p = 0. 001, uncorrected; cluster = 50 voxels).
Electroencephalography (EEG) Subjects were seated comfortably in an armchair in a soundproof room with low lighting, and watched a silent movie during EEG recording. Recording used the Neuroscan system (Synamps RT amplifier, Curry 7 and Neuroscan Stim² software) with 64 electrodes referenced on line to the nose according to the international 10-20 system. Impedance was <5 kΩ. Sampling frequency was 500 Hz with 0-200 Hz filtering. EEG data were analyzed on Matlab software (Mathworks, Natick, MA) via the EEGLAB interface  [26]. Filtering used a 0. 3-70 Hz band-pass. Recordings made during movement by the subject were identified manually for rejection; mean artifact rejection rate was <25% per subject per session. Supplementary artifacts due to eye movement or to the rehabilitation device were rejected by independent component analysis (ICA) on EEGLAB  [27].
The CROS system cannot stimulate the impaired ear, and BAHA could not do so sufficiently with the severe-to-profound hearing loss determined by the present inclusion criteria. Information transfer from the better side is artificial and insufficient to improve understanding (Figure  1 ). Comparison of efficacy between the three systems revealed a difference for SNENIC/CROS/BAHA, without selecting any one system. Meta-analyses of the HINT data  [4, 35]  showed better performance with CROS, than BAHA. The present results suggest better performance with CI than CROS or BAHA. Comparison between the 3 groups versus controls showed that performance for SIC/CROS/BAHANNE remained poor, highlighting the fact that a CI is not equivalent to a normalhearing ear. In S0N0, speech and noise arrive simultaneously in the two ears with no interaural differences in intensity or timing.
Participants Given the effect size reported in Experiment 1 (η 2 p = 0. 10), the required sample size for Experiment 2 was determine by conducting an a priori power analysis using G * Power software (version 3. 1;  Faul et al. , 2009). The analysis indicated that a minimum sample size of 14 participants was required in this study to detect a medium to large effect size with an adequate power (1 -ß > 0. 80) and an alpha of 0. 05. Following this, fifteen students from the University Paul Valery of Montpellier, France, participated (m age = 21. 7, s age = 3. 8, nine females and six males). All participants had normal or corrected-to-normal vision as indicated by self-report. They were a priori naïve to the purpose of the experiment and they did not participate in prior distance-perception experiments.
Each strength measure was recorded twice per subject and the highest achievable score, representing greatest strength, was used in analyses. Strength measures were z-scored and then averaged to create a single strength score for each subject that weighted each strength measure equally (following  [11, 51] ). Acoustic analysis. Vocal stimuli were analyzed using Praat 5. 3. 62 DSP package  [52]. Recordings were saved as WAV files at 44. 1 kHz sampling frequency and 16 bit amplitude resolution. We used a dedicated batch-processing script containing four distinct procedures to measure a variety of acoustic parameters that have been implemented as potential vocal indicators of formidability in humans or other mammals, including parameters related to voice pitch (measured as fundamental frequency, F0), amplitude and intensity, noise and perturbation, and formants.
To investigate whether strength and height were encoded in the acoustic structure of vocal stimuli, we computed stepwise linear regressions with acoustic variables as predictors, and either actual strength or actual height as outcome variables, split by sex, stimulus type (speech/ vocalization), and stimulus context (aggression/distress). Stepwise regressions were designed to test whether linear combinations of a wide set of acoustic characteristics could reliably predict physical formidability, and whether the structure of these models was consistent across stimulus types. To assess the individual contribution of each acoustic characteristic we computed zero-order correlations between each voice parameter and strength or height (reported in Supporting Information, S2 Text). The dataset for these analyses is also provided as Supporting Information (see S1 File).
Participants Participants from the USA were recruited from Amazon Mechanical Turk (see  [81]  for a review of the validity of this research method) to provide voice-based assessments of strength and height. All participants provided informed consent and completed the experiments online using a custom computer interface. They were compensated with $3. 50 USD. Ninety adults took part in Experiment 2 (48 females and 42 males, age = 33. 82 ± 9. 60) and 60 different adults took part in Experiment 3 (30 females and 30 males, age = 33. 80 ± 8. 98). Data from four participants in Experiment 2 and six participants in Experiment 3 who did not complete the experiment but rated more than half of the stimuli were included in analyses, as the exclusion of their responses did not change the overall pattern of results.
We inspected listeners' ratings and compared their reaction times against stimulus duration to ensure that they completed the experiments properly. Data from two participants who did not do so were removed (and are not reported in the participant statistics given above). Statistical analysis. In a series of linear mixed models, we first tested whether male vocalizers were stronger/taller than female vocalizers. Next, we tested the effects of vocalizer sex, listener sex, stimulus context, and stimulus type on attributed strength/height ratings. The third set of models added actual strength/height into the previous models to assess accuracy in listeners' strength and height estimates. As the strength and height distributions for males and females displayed little overlap, we split these models by vocalizer sex rather than including sex as a factor.
This work was supported by the  University of Sussex  through a scholarship to J. R. Author K. P. was supported by the  University of Lyon IDEXLYON  project as part of the " Programme Investissements d'Avenir"  ( ANR-16-IDEX-0005 ), and by the  European Commission  through a  Marie Skłodowska-Curie individual fellowship  ( H2020-MSCA-IF-2014-655859 ). The funders played no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Semantic dementia Patients with semantic dementia suffer from selective loss of semantic memory due to temporal lesions  (Galton et al. , 2001) , which can be demonstrated in language processing (e. g. , empty speech, loss of word meaning, semantic errors) and perception (e. g. , loss of knowledge about the identity or the function of objects, prosopagnosia;  Gorno-Tempini et al. , 2011; Neary et al. , 1998). At the same time, they experience a loss of semantic knowledge about tool function which has been related to impairments in Pantomime of Tool Use and Single Tool Use  (Hodges et al. , 2000; Moreaud et al. , 1998). Interestingly, predictions based on the technical-semantic hypothesis (i. e. , better performance in real tool use than in either pantomime of tool use or single tool use, see section 1.
Acknowledgments/Funding This work was supported by grants from  ANR (Agence Nationale pour la Recherche ;  Project Démences et Utilisation d'Outils/Dementia  and  Tool Use ,  N°ANR 2011 MALZ 006 03 ;  D. Le Gall ,  F. Osiurak ) and from the  Région Pays de la Loire  (Project  Outils et Vie Quotidienne/ Tool use and Daily Life Activities ,  2012-09689 OVQ ), and was performed within the framework of the  LABEX CORTEX  ( ANR-11-LABX-0042 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ; F. Osiurak) operated by the  French National Research Agency (ANR).
Latinus -10 fMRI acquisition Magnetic resonance data were acquired on a 1. 5T Siemens Magnetom (Siemens AG, Erlangen, Germany) scanner at the CERMEP (Imagerie du Vivant, Bron, F-69677, France) imaging facilities. Structural image acquisition consisted in a 3D anatomical T1-weighted sequence (TR=1970ms; TE=3. 9ms; inversion time=1100ms; FOV=256mm; matrix size: 256 2 voxel size: 1mm 3 ). Functional images were collected using a T2*-weighted gradient-echo planar imaging sequence (TR=2. 5s; TE=50ms; flip angle=90°; FOV=220mm; Matrix=64x64; in-plane resolution=3. 4x3. 4mm 2 ). The acquisition volume consisted of 29 interleaved axial slices with a 4mm slice thickness (no interslice gap); 500 volumes were acquired per participant.
The experimental part of this study was performed at CERMEP Imaging Facilities-Imagerie We are thankful to  Alix Thillay  for helpful comments on prior version of this manuscript. Development of the MacBrain Face Stimulus Set was overseen by  Nim Tottenham  and supported by the  John D. and Catherine T. MacArthur Foundation Research Network on Early Experience and Brain Development. Please contact Nim Tottenham at tott0006for more information concerning the stimulus set. This research was supported by grants from the  Agence Nationale de la Recherche  ( ANR-12-JSH2-0001 ), the  "Fondation Orange " and by the  Ministère des affaires sociales et de la santé, CHRU Bretonneau, Tours  ( PHRR05-FBB EPCA ).