dataesr/scientific-paragraphs-categorization huggingface dataset:
Continuum emission CORE observations towards VLA 3 at 1. 37 mm provides an insight to the chemical and physical structure of its envelope using continuum and line emission maps with an angular resolution of 0. 4 which corresponds to 1400 AU at 3. 33 kpc  (Gieser et al. 2019). In Figure  1 , we show the 843 µm continuum emission where we resolve the disk within this envelope with an angular resolution of 0. 2 arcseconds (∼700 AU). The 843 µm emis- Notes. Frequencies and upper energy levels are taken from the Cologne Database of Molecular Spectroscopy (CDMS,  Müller et al. 2005). For HCN;v 2 =1 4-3, l=1e and SO 2 12 4,8 -12 3,9 these data are taken from the Jet Propulsion Laboratory database  (Pickett et al. 1998 ). a The rms is calculated per velocity channel. For the vibrationally excited HCN lines the channel width is 0.
2007; Veach et al. 2013). For the further analysis we use the most prominent lines in our dataset: HCN (4-3), HCN (4-3) v 2 =1 (both l=1e and l=1f), HC 3 N (39-38), and SO 2 (12 4,8 -12 3,9 ). Figure  3  shows a closer look into the line emission towards the center of each core, averaged over a beam size. We note that among these transitions, the high signal-to-noise of the ro-vibrational HCN lines allowed for imaging with a higher velocity resolution of 0. 5 km/s in comparison to the rest of the lines presented in Figure  3 , which were imaged with a velocity resolution of 2 km/s. The l=1f transition of the vibrationally excited HCN is the strongest, reaching a peak brightness of 100 K towards core A, and 60 K towards core B. These values are much higher than the previously reported in the SMA observations of the same line of 30 K  (Benz et al.
In Figure  5 , we present the resulting intensity-weighted peak velocity map of the averaged HCN lines, v 2 =1, l=1e and v 2 =1, l=1f, and a position-velocity (PV) diagram obtained along the northeast-southwest velocity gradient. In order to estimate the kinematic mass of the protostar, we fit Keplerian rotation curves to the PV diagram using the publicly available python package KeplerFit 4  developed by  Bosco et al. (2019). KeplerFit uses a method developed by  Seifried et al. (2016)  which estimates the most extreme velocities at each radial position from the protostar, and then fits a Keplerian velocity profile to the extracted PV data points. In order to prevent fitting the unresolved emission which often results in flat velocity profiles, we refrain from fitting the velocities close to the protostar, hence, exclude the inner 700 AU (∼one beam size) from the fits.
Acknowledgements. We thank the anonymous referee for their time and valuable comments which improved this manuscript. This work is based on observations carried out under project number  L14AB  with the  IRAM NOEMA Interferometer. IRAM is supported by  INSU/CNRS (France) ,  MPG (Germany)  and  IGN (Spain). The authors thank  Alberto Sanna  for sharing the locations of the water masers. SSuri and HB acknowledge support from the  European Research Council  under the  Horizon 2020 Framework Program  via the  ERC Consolidator Grant   CSF-648405. ASM is supported by the  Collaborative Research Centre 956 , subproject  A6 , funded by the  Deutsche Forschungsgemeinschaft (DFG) , project ID 184018867. RK acknowledges financial support via the Emmy Noether and  Heisenberg Research Grants  funded by the  German Research Foundation (DFG)  under grant no.
KU 2849/3  and  2849/9. AP acknowledges financial support from the  UNAM-PAPIIT IN111421  grant, the  Sistema Nacional de Investigadores of CONACyT , and from the  CONACyT  project number  86372  of the ' Ciencia de Frontera 2019' program , entitled ' Citlalcóatl: A multiscale study at the new frontier of the formation and early evolution of stars and planetary systems ', México. DS acknowledges support by the  Deutsche Forschungsgemeinschaft  through  SPP 1833 : " Building a Habitable Earth " ( SE 1962/6-1 ). REP is supported by a  Discovery grant  from  NSERC.
Selection and investigation of 3D reconstructions of membrane proteins structures solved by cryo-EM in several amphipathic belts In order to discriminate whether there is an influence of the type of amphipathic belt on the 3D reconstructions used to image membrane proteins by cryo-EM, we have screened the whole Protein Data Bank to select for comparison the membrane proteins that have been solved only in multiple hydrophobic environments: nanodiscs, amphipols or detergents (Fig. 1A. and STable 1. ). The idea behind this selection is to keep the protein fold constant in order to normalize its influence on the reconstruction, and to be able to focus on the solvent belts alone. For small membrane proteins like the ones selected in this study, trans-membrane helices dominate the reconstructions  [23].
These reconstructions for proteins of similar folds will thus not be affected by protein's local structural changes, and warrants evaluating the effect of amphipathic belts on reconstructions. The identification of the belt is obvious to a trained eye, capable of detecting the trans-membrane parts of a protein in a structure. The hydrophobic belt is characterized by an expansion of lower-level density in the vicinity of the membrane region as a decrease in the map density. After the observation of map-density distributions of each structure of this dataset, an apparent feature was observable to identify the belt and is exemplified in Figure  1B. At high density levels, the very ordered parts of the structure are visible, on which reconstruction was anchored. Typically, trans-membrane helices are key features used in 3D reconstructions of membrane proteins and are visible at this level.
The further away from the protein, the less homogeneous the belt is during the 3D reconstruction across a particle dataset. We chose to measure distances at level 2 as it shows the full range of the belt (SFig. 3). Importantly, most, if not all, published cryo-EM maps have been subjected to masking, and/or sharpening, and reconstructions could have been created with symmetry enforced, leading to slight distortions of the belt (STable 1). All these parameters, along with the type of software used, will have influence on the reconstruction and the size of the belt. Nevertheless, the final reconstruction is the one under scrutiny and subjected to analysis and comparison by outside investigators. With these restrictions in mind, the current analysis is still worth to get a grasp of amphipathic belt sizes across various reconstructions.
Fig. 1. Visualization of the different types of hydrophobic belts surrounding membrane proteins. A/ General scheme of membrane protein purification from the membrane, kept in detergent (blue) or reconstituted in nanodisc (red) or amphipol (green), and imaged by cryo-EM to obtain a 3D reconstruction. The channel TRPV1 is used as an example of reconstruction (EMD-8117, PDB:5irx), with belts colored accordingly. B/ Typical map-density distribution and representative density levels of reconstructions, using the muscarinic acetylcholine receptor/G-protein complex as an example (EMD-20079, PDB:6oik). Level 0 corresponds to parts of the structure with strong density; the red bar shows the trans-membrane domain. Level 1 corresponds to the appearance of high density for the belt, depicted by the dotted arrow.
The TRPV family The Transient Receptor Potential Vanilloid (TRPV) family consists of six ion channels, varying in ion-selectivity according to the sub-family. Despite being functionally distinct, they share a highly conserved fold, being active as a tetramer formed around 6 trans-membrane helices per monomer  [24]. In the present dataset, we identified structures of TRPV1 in nanodiscs and amphipols, TRPV2 in nanodiscs, amphipols and detergents (LMNG and DMNG), TRPV3 in Nanodiscs, detergents and MSA-derived lipidic nanodiscs, and TRPV5 in nanodiscs and DMNG (STable 1. ). The fact that TRPV proteins share a conserved fold gives a unique opportunity to compare varying hydrophobic solvent belts. To these proteins, we also included structures of TRPV6 that were solved only in amphipols, benefiting from the fact that they share the same fold.
Similar average belts length across multiple protein types solved using different amphipathic solvents We have identified in the dataset multiple protein structures that have been solved only a few times in different hydrophobic belts. This limited number of structures prevents a statistical analysis on each protein. Instead, these proteins were evaluated in a group, thereby offering the opportunity to compare proteins with completely distinct folds, and originating from various sources and amphipathic environments (Fig. 4. , STable 1. , SFig. 8-13. ). Within each protein, the hydrophobic belt distances cluster rather well, showing a narrow distribution of distances, sampling apparently randomly across the distribution of all proteins shown in Fig. 2. Comparison of all these proteins reveals that their means group in similar ranges, with overlapping confidence interval of the mean, invoking a comparable hydrophobic belt around all these proteins.
Similar detergent belt reconstructions around GPCRs Twenty one unique structures of GPCR were found in the present database, belonging to the A, B, C or F classes (or G, R, F or S , respectively, according to the GRAFS nomenclature  [26] ), all solved in detergents. The vast majority used LMNG as a base, alone or in combination with other cholesterol-like detergents such as CHS, GDN or digitonin. All structures have been solved in complex with their cognate G proteins, and/or β-arrestin, in various flavors. Like for ABC transporters, all these GPCR structures share an overall fold that grants the direct comparison of their associated belts, with local differences between structures making it more worthwhile to analyze differences in the detergent belt measurements. The detergent belt distance distribution (Fig.
Different types of nanodiscs yield similar reconstructions; detergent belts are all of equivalent sizes. We checked whether a difference in distance distribution can be observed among the type of hydrophobic solvent. For instance, different flavors of Membrane Scaffold Proteins (MSP) are available to form nanodiscs, varying the length of a helical fragment within the MSP to make it longer or shorter  [10]. In the current dataset, proteins have been solved with 3 types of MSP, the short MSP1D1 and its longest version MSP1E3D1 comprising 3 helical insertion. MSP2N2 is formed by the fusion of two MSP1D1. Figure  6A  shows the distance distribution of the nanodisc belts sorted by nanodisc type, revealing that they are undistinguishable after reconstruction. Long and short distances of two types of nanodiscs formed by MSP1D1 and MSP2N2 follow the same distribution, with means equivalent to the mean obtained for all measurements in Fig.
The absence of a stable position makes it hard to visualize MSP positions by cryo-EM. We next explored if we could identify within a set of protein, or type of amphipathic compound, a combination that could influence the size of the solvent belt seen around membrane proteins. We could not establish any significant difference in the measurement distributions, all falling within the overall distribution described in Figure  2. Hereabouts, the incorporation of ABC transporters and GPCRs in this dataset yields an important viewpoint. From detergent quantification we know that the amount of detergent present around membrane proteins is directly proportional to the accessible hydrophobic area  [4]. The amount of detergent around ABC transporters (12 trans-membrane helices) is thus inherently larger than the one around GPCRs (7 transmembrane helices).
The higher the symmetry, the more pronounced the effect will be on reconstructions of amphipathic belts. Level 1 corresponds to the highest density, and represents the common minimum ordered layer, where the amphipathic compound is always present around the membrane protein. This layer concomitantly increases in size and decreases in density as it radiates away from the protein boundary, representing areas of space less and less populated by the solvent. This is influenced by the fluid properties of the solvent, as the sample is vitrified in liquid ethane. Each individual particle of a dataset represents a snapshot carrying its own belt-distribution. These observations reinforce the idea that the belt visualized by cryo-EM is a fraction of the volume occupied by the hydrophobic belt and represents the common minimum ordered solvent surrounding the protein.
Membrane proteins structure database extraction Based on the mpstruc database (https://blanco. biomol. uci. edu/mpstruc/) that lists all the membrane proteins of known 3D structure, we created a dataset containing only entries solved by Cryo-EM, as of January 17 th , 2020. We wrote a Bash shell script in order to automatically extract information from these entries. This allowed us to determine those which have been solved in multiple hydrophobic environments (nanodiscs, amphipols or detergents) and to sort them in distinct subsets. Then, for each entry, we extracted from the Electron Microscopy Data Bank (EMDB) the map-density distribution data in order to render graphs plotting the density distribution (i. e. the number of voxels as a function of the density). In order to create the graph of membrane structure determination by method over the years, a local copy of the RCSB Protein Data Bank (PDB) was parsed over each entry to extract the REVDAT record giving us their deposition years, as well as the EXPDATA record identifying the experimental technique used to solve the structure.
Measures of the solvent belt around the protein The measure of solvent belt thickness was performed in ChimeraX using the tool "tape" which is included in the software. The density map histogram was used to increase or decrease the contour information. Level 1 and 2 were manually chosen upon map and map-histogram inspection. At first, the density map showed the maximum of the solvent belt information (Level 2) and vertical lines were drawn to signal the limit the solvent belt. Then the density map contour was reduced in order to see clearly the protein density (Level 0). Horizontal lines were drawn to link the vertical lines and the protein density. The tape tool measured the distance (SFig. 2). This experiment was performed six times and in distinct positions of the solvent belt. Note that low-pass filtering of maps yields a clearer and sharper edge for the amphipathic belt, that could be useful for measurement.
A major drawback of these maps is the absence of noise that makes it impossible to distinguish when to stop the map contour level, and would result in inconsistent measurements across the dataset (SFig 3). All measurements were therefore conducted on non-filtered maps. All structures from which measurements were derived are listed in STable1 and measurements are presented in STable2. For example, 12 TRPV proteins in nanodiscs were included in the study, for which 3 measurements were carried out, making a total of 36 data points shown in Figure  3C.
These metrics are useful to interpret the data, but they are also skewed by the number of data points used. The larger the number, the better the definition of the distribution. Usually, 30 to 50 individual data points is considered a minimum for parametric statistics  [40]. Non-parametric statistics differentiate distributions based on ranks and are less sensitive to the amount of data points and to outliers. Both statistical methods were used on the data sets evaluated. In case of a difference in significance, non-parametric statistics were preferred and reported. Author contributions XR extracted the database. VZ measured all distances. VZ, AG and VC created the figures and analysed the data. All authors wrote the manuscript. Acknowledgments STable 1: List of proteins included in the study.
The model includes a systematic sequence of 8 phone decoders inside each syllable, as in  Ghitza (2011)  and  Yildiz et al. (2013) , together with a reset mechanism that automatically triggers an onset at the end of the last phone decoder (the 8th). This reset mechanism hence exploits predictions about the input spectrogram stored in memory, though no explicit fusion process exploiting temporal top-down timing predictions from higher linguistic levels. The model was tested on 220 sentences of the previously mentioned TIMIT database. It yielded only moderate syllabic detection accuracy, estimated at 53 %, by measuring the percentage of time when a given syllable labeled as such in the database corresponded to the theta-cycle detected by the model. Hence, altogether, the performance of these models seems rather limited.
COSMO-Onset is a Bayesian speech perception model associating a decoding module to decipher the spectrotemporal content of the speech input and a temporal control module enabling to control how the speech input is segmented into constituent linguistic units. The decoding module has a hierarchical structure similar to classical psycholinguistic models like TRACE  (McClelland and Elman, 1986) , with three layers of representations (acoustic features, syllable and word identity) usually considered in the context of isolated word recognition. The temporal control module associates a bottomup mechanism for syllabic onset detection with an original topdown mechanism for syllabic onset prediction, involving lexical temporal knowledge. The bottom-up onset detection process does not rely on oscillatory models as in  Ghitza (2011 ), Hyafil et al.
In the context of the COSMO-Onset model, we therefore use controlled coherence variables to modulate, over time, Step 1 is feature decoding, step 2 is syllable decoding, step 3 is word decoding; these steps are performed in a purely feedforward manner in the decoding module (i. e. , no lexical feedback to bias syllable or feature decoding). In the blue nodes, incoming input information is combined with information of the previous time-step. Step 4 is bottom-up onset detection from the input, step 5 is lexical, top-down onset prediction, step 6 is the combination of the results of steps 4 and 5 (applying either the AND or the OR operator). Finally, step 7 is a decision process on the combined onset probability; when an onset is detected, the states of decoding gates (open or closed) are changed.
Decoding Module The decoding module of the COSMO-Onset model is inspired both by the BRAID model (Bayesian model of Word Recognition with Attention, Interference and Dynamics) of visual word recognition  (Phénix, 2018; Ginestet et al. , 2019)  and by the classical, three-layer architecture of models of spoken word recognition, such as the TRACE model  (McClelland and Elman, 1986). It can also be construed as a hierarchical (multi-layered) dynamic Bayesian network  (Murphy, 2002) , with an external component to control information propagation. Three representation layers are featured in the model, with internal representation of phones, of syllables, and of words. Each is associated with a series of probabilistic dynamic models (i. e. , Markov-chain-like probabilistic terms, colloquially referred to as "decoders" above), which allow continuous accumulation of sensory evidence about the representation domain they consider.
Temporal Control Module The "bottom-up" portion of the temporal control module assesses the probability of syllabic onset events by relying on the temporal cues that can be extracted from the speech envelope (step 4, in Figure  2 ). This has been largely discussed in the literature and several models of syllable onset and boundary detection have been proposed  (Mermelstein, 1975; Ghitza, 2011; Hyafil et al. , 2015; Räsänen et al. , 2018; Hovsepyan et al. , 2020). These models process the speech envelope, either in search of rapid increases or decreases (troughs) in the energy of the speech envelope. In the COSMO-Onset model, syllable onset detection from the stimulus is based on tracking the rapid increase of energy in the speech envelope. If such an increase is detected for several successive time steps, and if the corresponding increase exceeds a given threshold, then the probability of an onset events gets high (terms of the form P(OBU t | L t j ), see Figure  1 ).
Column 2 provides the name of each word, corresponding to its phonological content. Column 3 provides the phonetic content of each word, that is, the sequence of acoustic phones. Column 4 provides the corresponding duration of the model input, in simulated time steps. Vowel and plosive phones in our simulations are acoustically represented as sets of pairs of formants (F1, F2) in Barks, a subjective perceptual scale  (Zwicker, 1961 ; see Figure  3 ). While it is classical to characterize vowels by their first two formants  (Fant, 1970) , it is less classical to use formant values for plosives (although, see  Schwartz et al. (2012)  for a characterization of plosives by formant values). More precisely, the formant values for the considered vowels are gathered from a dataset obtained using VLAM, the Variable Linear Articulatory Model  (Maeda, 1990; Boë and Maeda, 1998).
Relation With Classical Models in Psycholinguistics The decoding module in COSMO-Onset has a hierarchical structure similar to classical psycholinguistic models like TRACE  (McClelland and Elman, 1986) , though it replaces neural computations in TRACE by probabilistic computations. Still, a model like TRACE incorporates feedback from higher to lower levels in the decoding process, which means that not only the perception of phonemes influences the perception of words, but, conversely, word recognition influences phoneme identification, although the way this feedback process influences perception is still a matter of debate  (Norris et al. , 2016). We plan to include such feedback processes into the COSMO-Onset model, to provide top-down predictive processes at the decoding stage, in coherence with the predictive coding framework  (Friston, 2005; Friston and Kiebel, 2009).
This, combined with the architecture we proposed in the COSMO-Onset model, raises an interesting issue. Indeed, the temporal control module of COSMO-Onset can be seen as an "alternate" pathway of the decoding module for information propagation into the model. In other words, the COSMO-Onset model could yield specific predictions about the mathematical influence of syllabic parsing on lexical access; these could be compared with psycholinguistic data on spoken word recognition.
Assessment in Relation With Experimental Data on Speech Perception Finally, an important perspective concerns the ability of a model like COSMO-Onset to deal with specific experimental conditions in which the role of top-down processes in syllabic parsing and onset detection could be crucial. We have particularly in mind two types of experimental paradigms related to transformations of the temporal structure of speech. A first challenge concerns the processing of temporally compressed data. Ghitza and Greenberg (2009)  showed that speech perception resists rather well to temporal compression, up to a factor of 3, where intelligibility is degraded, and that the introduction of silences at specific moments provides the listener with "additional decoding time, " which restores intelligibility.