dataesr/scientific-paragraphs-categorization huggingface dataset:
( †) Taken either as the 1σ rms noise level at the location of the target line, or for line confusion limited spectra, the reported rms noise of the observations. References. Belloche et al. 2013). We used two publicly available datasets to cover both possible scenarios. The Prebiotic Interstellar Molecular Survey (PRIMOS) observations of the region cover the range from 1 to 50 GHz, and are most sensitive to cold, extended populations  (Neill et al. 2012) , whereas the IRAM 30 m survey of the region near 3 mm provides sensitivity to the warm, compact components  (Belloche et al. 2013 ). ASAI sources. The remaining sources are from the publicly available Astrochemical Surveys at IRAM (ASAI) Large A3, page 3 of 5 A&A 638, A3 (2020) Table  5. Upper limits to cyanoketene and the line parameters used to calculate them in each of the sets of observations.
The spectra from each dataset, along with a simulated line profile of cyanoketene at the established upper limit, are displayed in Fig. 2. From a chemical formation point of view, reactions resulting in addition of a CN group (-CN-addition reactions) involving either neutral or radical species are a viable pathway to the formation of larger cyanides  (Bennett et al. 2010; Garrod et al. 2017). Remijan et al. (2008)  presumed that a chemical formation route to cyanoformaldehyde (CNCHO) through -CN addition to H 2 CO might take place given the high abundances of H 2 CO and -CN in the gas phase. Current studies  (Das et al. 2013)  have followed up this supposition to measure not only the gas-phase and grain-surface reaction rates for this formation pathway but also possible destruction pathways of NCCHO.
[1] McGuire et al. (2018a); [2] Neill et al. (2012); [3] Belloche et al. (2013); [4] Melosso et al. (2018); [5] Cernicharo et al. (2018); [6] Higuchi et al. (2018); [7] McGuire et al. (2015); [8] Jørgensen et al. (2002); [9] Araki et al. (2017); [10] Hily-Blant et al. (2018); [11] Crapsi et al. (2005); [12] McGuire et al. (2018b); [13] Gratier et al. (2016); [14] Remijan et al. (2008); [15] McGuire et al. (2016). NGC 6334I are from ALMA projects #2015. A. 00022. T and #2017. 1. 00717. S, and were extracted from coordinates (J2000) α = 17:20:53. 374, δ = -35:46:58. 34. The dataset was imaged at a uniform angular resolution of 0. 26 × 0. 26 (McGuire et al. 2017).
Acknowledgements. The present investigations were supported by the  CNES  and the  CNRS program  " Physique et Chimie du Milieu Interstellaire " (PCMI). This manuscript makes use of the following ALMA data: ADS/JAO. ALMA# 2015. A. 00022. T and # 2017. 1. 00717. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada)  and  NSC and ASIAA (Taiwan)  and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ. The  National Radio Astronomy Observatory  is a facility of the National Science Foundation operated under cooperative agreement by  Associated Universities, Inc. The  Green Bank Observatory  is a facility of the National Science Foundation operated under cooperative agreement by  Associated Universities, Inc.
More than 75% of anaerobes isolated from selected clinical specimens belong to Gram-positive anaerobic cocci, non-spore-forming rods, Prevotella spp. , Porphyromonas spp. , Bacteroides fragilis group and Fusobacterium spp. [13]. The aim of this study was to determine the optimal growth media and conditions for the highest sensitivity and shortest detection time of medically important anaerobe bacteria by using microcalorimetry. The microcalorimetry data were compared with the results of conventional cultures. From this in vitro experimental work, we aimed to determine the optimal conditions for diagnosing infections caused by the tested microorganisms. The following representative anaerobes were investigated in this study: Cutibacterium acnes, Actinomyces odontolyticus, C. difficile, Parvimonas micra, Finegoldia magna, Bacteroides fragilis, and Fusobacterium nucleatum.
Typically, another 45 min must elapse before the signal from a specimen which is not currently producing or consuming heat returns to a value close to zero mW of heat flow. Heat flow rate data are then recorded continuously until the experimenter chooses to end an evaluation. In the study described here, heat flow from ampoules was recorded for up to several days. After the measuring phase the ampoules were removed from the microcalorimeter and the content of each ampoule was checked for the presence of microorganisms. Sterility of the negative controls was a basic requirement for the validity of an experiment. Time to detection (TTD) was defined as the time between the insertion of an ampoule into the calorimeter and the increase of the power-time curve above a certain threshold value. We determined this value at 10 mW above the lowest point of the curve immediately before the increase leading to the peak.
This result defines a potential (Figs. 5  and S10 ) properly describing the IBD control-active set, which reinforces the hypothesis exposed in the previous section. There, we claimed how therapeutic targets within the candidate derived from the proteomic coexpression dataset could be effective in the control of certain disease dynamics. The existence of this potential ultimately confirms the hypothesis by simply conducting further dynamical enquiries on it. In the next section, we will describe how we can infer IBD dynamics depending on the positions this potential assigns to each candidate protein in the manifolds. See SI text p 7-10 for further details on the methodological development. Therapeutic reconfiguration of the IBD complex space. Finally, we want to identify temporary actions in the expression of the selected therapeutic targets that potentially control the dynamics of IBD status.
The (  6 ) i. e. , proteins already described as IBD-related. For the sake of simplicity, we highlighted some candidates simply by its identifiers. See Fig. S0  for more details on networks. dx/dt = v dv/dt = F(x, v), F(x, v) ∼ 1 √ h Cxe -x 2 -η * v dynamics of UC and CD share a unique pattern, but involving a few different proteins in the reconfiguration of their systems. Consequently, our initial hypothesis would be proved by programming actions performed by the IBD potential on the promising therapeutic targets within the co-expression dataset. An experimental validation of this hypothesis by systematic screenings of our proteomic data would be unaffordable, enhancing the potential of our methodology. See SI text p 11-12 for further details on the methodology.
Materials and methods The calculations related to these sections were implemented using scripts based on R for weighted graphs analysis (wgcna package  30  ), in-house Matlab (2011a, The MathWorks Inc. , Natick, MA) functions and supported R  31  for the analysis of singularities on manifolds, and Python for nonlinear optimisation of control perturbations. WGCNA. We adopted the standard flow of WGCNA  11  to constructing the protein graphs of UC and CD, detecting protein modules in term of IBD status co-expression and detecting associations of modules to phenotype i. e. , control, active and quiescent disease with a soft-threshold, ξ , determined according to the scale-free topology criterion (SI Text). Gene ontology analyses coupled with bioinformatics approaches revealed drug targets and transcriptional regulators of immune modules predicted to favourably modulate status in IBD.
Acknowledgements We acknowledge the financial support by  Institut National de la Santé et de la Recherche Medicale (INSERM) ,  Inception IBD, Inserm-Transfert ,  Association Franois Aupetit (AFA) ,  Université Diderot Paris 7 , and the  Investissements d' Avenir programme   ANR-11-IDEX-0005-02  and  10-LABX-0017 ,  Sorbonne Paris Cité, Laboratoire d' excellence INFLAMEX.
Data availability The datasets and code used in this article have been uploaded to Figshare repository and can be found at https ://figsh are. com/s/5a75f e4825 8b1f1 c4f11 , https ://doi. org/10. 6084/m9. figsh are. 11672 391, and https ://doi. org/10. 6084/m9. figsh are. 11672 373 after the release date.
Generation of transmission spectra with Pytmosph3R Transmission spectra are computed using Pytmosph3R as described in  Caldas et al. (2019). Pytmosph3R is designed to simulate transmission spectra based on any 3D atmospheric structure, including outputs of a Global Circulation Model (GCM ). It produces transmittance maps at any wavelength that can later be spatially integrated to yield the transmission spectrum. The code can account for molecular opacities using the correlatedk method  (Fu & Liou 1992)  or monochromatic cross-sections calculated by ExoMol  (Yurchenko et al. 2011; Tennyson & Yurchenko 2012; Barton et al. 2013; Yurchenko et al. 2014; Barton et al. 2014). In this study, we only use the latter to ensure a complete compatibility with the TauREx retrieval code  (Waldmann et al. 2015b,a).
Retrieval model: TauREx The spectral retrievals has been computed with the TauREx retrieval code  (Waldmann et al. 2015b,a). TauREx consists in a Bayesian retrieval code which uses the ExoMol line lists  (Yurchenko et al. 2011; Tennyson & Yurchenko 2012; Barton et al. 2013; Yurchenko et al. 2014; Barton et al. 2014) , HITRAN  (Rothman et al. 2009; Gordon et al. 2013 ) and HITEMP  (Gordon et al. 2010). TauREx assumes a plane parallel exoplanetary atmosphere with a one-dimensional distribution elements and temperature-pressure profile. We assume the radius at the base of the model of Wasp-121b as the value for which the atmospheric pressure is 10 bar. We simulate the exoplanetary atmosphere assuming a pressure between 10 6 and 10 -4 Pascals. We consider an atmosphere dominated by Hydrogen and Helium, with a mean molecular weight of 2.
3 amu. We also suppose the presence of CO and H 2 O as trace gases. An important assumption is that TauREx does not take into account thermal dissociation of species and assumes the molecular abundances constant with the atmospheric pressure (see  Changeat et al. (2019)  for a detailed discussion on the effect of this assumption). We set up the prior range for the molecular volume mixing ratios of CO and H 2 O between 10 -12 and 10 -1 which allow us to explore a large range of solutions. Even if we never add clouds in our input simulation, we set up the prior range for clouds between 10 -12 and 10 -1 to allow the retrieval code to have more freedom. We also feel that this better simulates a real situation where clouds would be put in the retrieval without prior knowledge about their occurrence in the real planet.
The allowed range for the planetary radius and the atmospheric temperature (assumed constant in the vertical) are, respectively, 1. 3R Jup -4. 0R Jup and 400K -3600K, with R Jup the radius of Jupiter. The range for the temperature is linked to the minimal and maximal temperature in the GCM simulation of Wasp-121b as shown in Tab 1. As we simulate JWST observations, we set up the range of wavelength between 0. 6 to 10 µm. For the non-retrieved parameters such as masses and stellar radius, we use those describe in Tab 2. We note that the TauREx retrieval code is not designed to take into account the thermal dissociation of H 2. When H 2 thermal dissociation occurs, the He/H 2 ratio changes as a function of pressure and temperature. The mean atmospheric weight can also be lower than 2amu. In order to be consistent across all our sets of simulations, we used the same configuration file for our spectral retrievals and we left the He/H 2 ratio constant.
However, TauREx manages to find statistically significant solution in the ∇ -T case, but we keep in mind that this simulation is a conservative choice and we think that the truth remains in between ∇ -T and ∇ + T simulations. We note that the TauREx retrieval code is not designed to take into account the thermal dissociation of H 2. When H 2 dissociation occurs, the He/H 2 ratio changes as a function of pressure and temperature. The mean atmospheric weight can also be lower than 2amu in the atmospheric regions where H 2 dissociates. In order to be consistent with all the set of simulations, we used the same configuration file for our spectral retrievals and we left the He/H 2 ratio constant. By doing so we could estimate whether an atmosphere where H 2 dissociation does not occur can explain our input spectrum or not.
For instance, the TauREx retrieval code finds the planetary temperature according to the strength of the main spectral features. Since the amplitude of the spectral features is rather large because of the gas present in the hot day side of the planet, it is natural for a retrieval code to converge towards hotter temperatures. In case of H 2 O dissociation, the only relevant gas present in the hot day-side is the CO. The water absorption is due to the water present in the cold night side of the planet. Therefore, it is reasonable to retrieve a lower temperature in the case in which water dissociates in the day side. We demonstrated that a three-dimensional atmospheric structure induces spectral distortions impossible to explain with a 1D retrieval. We also demonstrate that the water features can be strongly reduced by the presence of molecular heterogeneities due to a large day/night temperature contrast.
Acknowledgements A. C. acknowledgess upport from  TUM  and  Cardiff University. R. B. acknowledges fundingf rom the European Union's  Horizon 2020 research and innovation programu  nder the  Marie Skłodowska-Curie  grant agreement n8 663830. S. T. a cknowledges  EPSRC  and  CDT in Catalysis for fundingu nder grant  agreement  n8 EP/L016443/1.
| MATERIAL S AND ME THODS Two independent surveys were undertaken using custom, Internet-based questionnaires provided in the local language. The surveys were designed by two healthcare consulting firms (survey 1, Decision Resources Group, Burlington, MA, USA; survey 2, Opinion Health, London, UK), and members of the public were recruited and screened to determine previous use of aesthetic medical procedures (survey 1; Appendix S1) or nonsurgical aesthetic treatments (survey 2; Appendix S2). Individuals were then invited to participate in the respective surveys. Responses to survey questionnaires were translated into English for review, validation, and analysis. All questionnaire data were anonymized. Given the observational, noninterventional nature of the surveys (and in line with local legislation), ethical approval was neither required nor sought.
, 2020)  and to investigate the co-variations in their brain activity related to their socio-behavioral interactions  (Dumas et al. , 2011; Babiloni and Astolfi, 2014; Czeszumski et al. , 2020). Hyperscanning has been used to study neural synchronization in a wide-range of social interaction contexts, ranging from parent-infant gaze communication  (Leong et al. , 2017)  to teacher-students classroom interactions  (Dikker et al. , 2017). However, there is currently no agreed-upon approach to carry out such 'inter-brain connectivity analysis', resulting in a scattered landscape of analysis techniques  (Burgess, 2013). To accommodate these growing demands of standardized analysis approaches in a fast-growing research field, we developed Hyperscanning Python Pipeline (HyPyP), a comprehensive and easy to understand open-source software package that allows (social) neuroscientists to carry-out and interpret a wide range of inter-brain connectivity analyses.
HyPyP can handle data from groups of two or more participants, with data collected either in a simultaneous hyperscanning recording context  (Dumas et al. , 2011)  or in a non-simultaneous setup  (Dmochowski et al. , 2014). HyPyP encourages the community to share their analyses script in open source and to provide supporting documentation for each analysis measure to motivate its use to probe specific psychologically relevant processes. As such, we hope to serve as a platform where consensus can be reached within the research community with respect to which analysis approach is best suited for which research context.
Software specifications The HyPyP library provides a suite of Python tools to manipulate hyperscanning data and inter-brain connectivity measures. Using a community-driven perspective, the code is open source, licensed under a three-clause Berkeley Software Distribution (BDS) license and editable at this address: https://github. com/GHFC/HyPyP. Running HyPyP requires Python 3. 7 (or higher) with major data science libraries, such as Scipy  (Virtanen et al. , 2020) , scikit-learn  (Pedregosa et al. , 2011 ), Pandas (McKinney, 2010)  and Matplotlib  (Hunter, 2007). HyPyP also takes advantage of other community-driven libraries, such as the MNE library  (Gramfort et al. , 2013)  for the handling of M/EEG signals and Autoreject  (Jas et al. , 2016)  for the pre-processing and rejection of artifacts.
Overview of the functionalities The HyPyP toolbox is designed to be integrated with MNE-Python  (Gramfort et al. , 2013) , a software package that enables comprehensive M/EEG data analysis at the intra-brain level. HyPyP implements these analyses at an inter-brain level (Figure  1 ). You can find the complete documentation with an application programming interface description on 'HyPyP Docs' at the link (https://hypyp. readthedocs. io). A detailed tutorial with a toy-dataset is also available on the Github page (http:// github. com/GHFC/HyPyP) and illustrates what the current version of HyPyP allows researchers in social neuroscience to do. It especially covers the following analysis steps for multi-person datasets (Figure  2 ): Load raw data. Raw data files for each condition and participant need to be converted into epochs with the appropriate MNE function before using HyPyP.
Epochs are one of the most common ways to analyze EEG signals; the MNE ecosystem has a dedicated object containing signals in a data array and all the associated parameters information-such as channel names, bad channels, frequency, sample frequency-in a metadata dictionary. Below, we illustrate how to load epochs from an example EEG dataset of two participants with the mne. read_epochs() function. # Loading data files epochs1 = mne. read_epochs(os. path. join('. ','data', ' 'participant1-epo. fif' '), preload=True) epochs2 = mne. read_epochs(os. path. join('. ','data', ' 'participant2-epo. fif' '), preload=True) Pre-process data. HyPyP includes tools to automatically preprocess data, but researchers are still strongly encouraged to manually inspect data and determine the most appropriate pre-processing strategy.
Users can also import already pre-processed data (from EEGLAB for exemple) and proceed directly to the analysis steps with HyPyP. [prep. ICA_fit, prep. ICA_choice_comp, prep. AR_local]  is an adaption of MNE-Python  (Gramfort et al. , 2013)  and Autoreject  (Jas et al. , 2016)  functions, taking epochs and returning them cleaned. This process involves rejecting bad epochs, rejecting or interpolating partially bad channels per participant, and then removing the same channels and the same epochs across participants. Thus, only channels and epochs that are 'good' for all the participants are preserved. Independent component analysis (ICA) removal is also matched between participants such that similar independent components (ICs) are rejected across participants. To improve decomposition quality  (Winkler et al.
, 2015) , the function filt removes slow drifts from raw data, then after epoching, ICA_fit prepares the signals by computing a global rejection threshold with Autoreject for bad channel rejection and pruning of highly artifacted epochs, and fits an ICA on the remaining set of epochs. ICA_choice_comp then plots ICs for each participant, lets the user choose the relevant component used as model for This is followed by a prompt asking which participant should be used as a template and which IC from this participant should be used as a template. Merge/split data. [utils. merge] takes epochs from each participant to align and merge them into a single data file (whether participant data were recorded in one file or in separate files). This is particularly important when users have loaded their data into MNE and just want to concatenate multiple participants in the same MNE structure.
For creating a hyper-dataset combining two recordings stored in epochs called epochs1 and epochs2, this is as simple as: hyper_epo = merge(epochs_S1=epochs1, epochs_S2=epochs2) [utils. merge] also takes previously pre-processed recordings: users can load data directly to visualize them (bad channels are still taken into account). Respectively, [utils. split] takes a single hyper-epoch with both participants' data merged and channel names indicating the participants 1 & 2 with "_1" and "_2", and split it into two single-participant epochs: epochs1, epochs2 = split(hyper_epo) Data analysis. [analyses. pow] computes Welch power spectral density (PSD) from pre-processed epochs. fmin and fmax set the minimum (fmin) and maximum (fmax) frequencies over which PSD is computed. The parameter n_fft states the length of the fast Fourier transform (FFT), and n_per_seg states the length of each Welch segment.
In the example below, circular correlation coefficient ('ccorr') is used. The results are then sliced to generate the inter-brain part of the matrix. This is exemplified below using Alpha_Low (frequencies); Cohens' D is computed for further analyses. This process can also be applied to intra-individual brain connectivity to support single participant analysis. In addition, the mode argument in the function can take different connectivity measurements (see section 'Inter-brain connectivity measures'). This generates connectivity matrices for each epoch (Figure  1B ). Similar to the inter-brain analyses, results are sliced to generate the intra-brain part of the matrix, exemplified here with Alpha_Low and Cohens' D. Statistics. [stats. statsCond] and [stats. statscluster] are adapted from MNE-Python statistical tests: a parametric t-test corrected for multiple comparisons and a non-parametric clusterlevel statistical permutation test using a pre-defined threshold (alpha), corrected with channel connectivity across space and frequencies (freq_list, ch_con_freq).
data_group = [np. array([psd1. psd, psd1. psd]), np. array  ([psd2. psd, psd2. psd])] statscluster=stats. statscluster(data=data_group, test=ind ttest, factor_level= None, ch_con_freq=scip. sparse. bsr_matrix(ch_con_freq), tail=0, n_permutations=5000, alpha=0. 05) The HyPyP non-parametric cluster-based permutations test can also be used to compare intra-brain connectivity values between participants. To that end, a matrix is created of a priori connectivity between channels across space and frequencies based on their position. con_matrixTuple = stats. con_matrix (epochs=preproc_S1, freqs_mean= np. arange[7. 5, 11] , draw=False) Note that for inter-brain connectivity measures, the resulting frequency bins are every integer frequency between fmin and fmax with a 1 Hz spectral resolution regardless of the data structure because the FFT window parameters are adaptive.
For ongoing cognitive processing, with a finer-grained account of neural content and timing, the phase synchrony measures might be more relevant. Following existing hyperscanning studies and functional connectivity network studies, HyPyP natively includes common measures of phase synchrony, power synchrony, coherence-based connectivity and directed measures of casualty or information transfer  (Astolfi et al. , 2007). We will continue adding and documenting measures on the HyPyP project page (https://pypi. org/project/HyPyP/), with the ultimate goal of formulating testable linking hypotheses between connectivity metrics and psychological processes, as such promoting consensus within the hyperscanning research field with regard to analysis choices. Here, we briefly describe the core connectivity measures that are implemented in HyPyP.