dataesr/scientific-paragraphs-categorization huggingface dataset:
Molecular cartography using two-dimensional (2D) representation of protein surfaces has been shown to be very promising for protein surface analysis. Here, we present SURFMAP, a free standalone and easy-to-use software that enables the fast and automated 2D projection of either predefined features of protein surface (i. e. , electrostatic potential, Kyte-Doolittle hydrophobicity, stickiness, and surface relief) or any descriptor encoded in the temperature factor column of a PDB file. SURFMAP uses a pseudo-cylindrical sinusoidal "equal-area" projection that has the advantage of preserving the area measures. It provides the user with (i) 2D maps that enable the easy and visual analysis of protein surface features of interest and (ii) maps in a text file format allowing the fast and straightforward quantitative comparison of 2D maps of homologous proteins.
Other methods with interesting outcomes in visualization and/or surface feature comparison  [16] [17] [18] [19] [20]  are restricted to a limited subset of surface descriptors or do not provide the tool to compute the corresponding 2D maps. Recently, Kontopoulos et al, developed Structuprint 21 , a program that enables the visualization of more than 300 protein surface features. They use the Miller cylindrical projection 22 , i. e. a projection that conserves the angles and the shapes of small surfaces but that induces an important overestimation of the area at the poles. This can be problematic since the different protein surface regions do not contribute equally when comparing different surface maps, and the assignment of surface regions either to the poles or equator results from the arbitrary orientation of the protein before projection.
Here, we present SURFMAP, a program that enables the projection of predefined protein surface features: (i. e. electrostatic potential, hydrophobicity, stickiness, surface topology), residues of interest, or any descriptor encoded in the temperature factor column of a PDB file. SURFMAP uses a pseudo-cylindrical sinusoidal equal-area projection that has the advantage of preserving the area measures at the cost of distorting shapes locally. Here, we aim to preserve the size of the regions of interest rather than providing a precise representation of their shapes. Indeed, we need for a method robust against local variations since (i) proteins are flexible objects and the shapes of surface features can vary locally, (ii) protein surfaces display many local irregularities that could introduce noise when comparing two homologous surfaces, and (iii), nowadays, we frequently use 3D models that can be more or less accurate depending on their similarity with the template used for building the model.
Usage: SURFMAP is a very easy-to-use program that only needs as input the pdb file(s) of the protein(s) that the user wants to study. One should notice that if the user aims to compare surface properties of different protein homologs, their 3D structures must be pre-aligned in order to produce comparable 2D maps that are in the same referential. SURFMAP enables the calculation of already encoded features such as electrostatic potential, Kyte-Doolittle or Wimley-White hydrophobicity, stickiness, CV, or can project any feature stored in the temperature factor column of the pdb file (Figure  2 ). SURFMAP is fast and takes on average about 3 seconds to generate a 2D map of a 200 residues globular protein. Electrostatic maps take a longer time (up to 30 seconds for a 200 residues globular protein), the limiting step being the computation of the electrostatic potential with APBS  27.
On the other hand, the surface region that corresponds to the blue binding site specific to the tetramers is conserved to a lesser extent in monomers forming tetramers, whereas it is not the case for the corresponding non-binding region in the proteins forming dimers. This reflects that this noninteracting region in proteins forming dimers is not subject to selection. Interestingly, the yellow binding site is not conserved in any of the proteins, including those able to form tetramers. To which extent this binding site is important for tetramerization deserves further investigation. In the last section, we show an example of a quantitative comparison of 2D maps calculated for 23 SODs that form either dimers or tetramers (see PDB codes in Figure  4 ). Therefore, we computed the Manhattan distance between each pair of SOD 2D stickiness maps.
CONCLUSION We presented SURFMAP, a program that enables the 2D projection of predefined protein surface descriptors or any surface feature stored in the temperature factor of a PDB file of interest. SURFMAP is a free standalone and easy-to-use software that runs on Windows, Linux, and macOS. It provides an easy and visual framework to analyze protein surface features, thereby enabling the user to (i) compare the distribution over the protein surface of different features or (ii) study the evolution of a specific feature(s) across homologous proteins. In addition, SURFMAP provides the map in a text file format allowing the fast and straightforward quantitative comparison of surface features of homologous proteins.
Data and Software Availability SURFMAP can be downloaded freely at https://github. com/i2bc/SURFMAP. SURFMAP can be installed manually or through a docker container. The docker container works on Linux, macOS, and Windows, and is the recommended way to use SURFMAP. The manual installation works only on Linux distributions. SURFMAP requires MSMS  28  , and APBS 27 for electrostatics calculation. Both are provided in the docker container, but if the user chose the manual installation, the latter must be downloaded and installed separately. for Thermostability. J. Mol. Biol. 1997, 270 (2), 259-274. https://doi. org/10. 1006/jmbi. 1997. 1105. (41) R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. 2020.
ASPICov was developed to provide a rapid, reliable and complete analysis of NGS SARS-Cov2 samples to the biologist. This broad application tool allows to process samples from either capture or amplicon strategy and Illumina or Ion Torrent technology. To ensure FAIR data analysis, this Nextflow pipeline follows nf-core guidelines and use Singularity containers. Pipeline is implemented and available at https://gitlab. com/vtilloy/aspicov.
). It is also important to consider NGS sequencing platform which will not provide the same sets of data and/or which are optimized for a particular strategy library kit. In order to cover a large range of sequencing technologies and handle all parameters of our analysis we developed ASPICov, a pipeline able to identify whole genome variations at the nucleotide or amino-acid level in samples using a reference sequence. This pipeline is a multistep Nextflow  [3]  pipeline able to process raw-reads sequences into usable information such as quality reports, VCF files, sequence consensus and plots (variants and coverage).
Implementation ASPICov workflow was created as a Nextflow pipeline following some of the nf-core standards requirements to setup a portable pipeline. Code wrapping the many tools used in ASPICov (see below) is written in bash and Python. Tools themselves have been integrated into readyto-use Singularity containers  [4]. Singularity definition files (used to build images) as well as binary images are all available for download (see below). ASPICov comes with a test data set. In such a way, users can validate the correct execution of ASPICov on their computing infrastructure after cloning the pipeline from its public Gitlab repository.
Pipeline steps and tools used The succession of genomic tools used (Fig  1 ) combined to an optimized computing configuration is a key for the robustness of the pipeline. To facilitate the use of ASPICov and make it highly reproducible, all tools are automatically installed via pre-built singularity images available from the National Oceanographic Data Center operated by Ifremer in France; member of the Research Data Alliance (https://rd-alliance. org) (ftp://ftp. ifremer. fr/ifremer/dataref/bioinfo/sebimer/tools/AspiCov/). These images are built from recipes available as part of the ASPICov source code (https://gitlab. com/vtilloy/ aspicov/-/tree/master/containers).
Input options ASPICov is designed to be used on Linux distribution and launched with a single command within a cluster job scheduler or locally. Project name, technology, method, path to data, references, Trimmomatic adapter and bedpe files information will be completed by users in a custom configuration file (supported by profile), according to standard Nextflow principles. To use this workflow on a computing cluster, it is necessary to provide an institute configuration file (using -c <institute_config_file>) in order to enable Singularity and to setup the appropriate execution settings for the environment.
Dataset used to design and to validate the pipeline Wuhan strain (NC_045512  [5]  was used as whole genome reference during pipeline validation. ASPICov has been optimized using a dataset from a single sample (Basa strain isolated from a patient with mild Covid disease at Limoges hospital) taken at different culture stages (P3, P4 and P7 passages), serially diluted (10 -1 , 10 -2 , 10 -3 , 10 -4 , 10 -5 , 10 -6 and 10 -7 ) and processed using Thermofisher and/or Swift Ampliseq protocols, Illumina (S1 Table ). We have thus determined a threshold corresponding to background noise: nucleotidic variants were considered as low quality if Phred score is below 200 or depth below 100 or allelic frequency below 0. 02. Mutation(s) not retained by filters are still available in VCF files tagged 'filter' whereas selected mutations are tagged 'filter-pass'.
Conclusions ASPICov pipeline is dedicated to detect and identify finely SARS-Cov2 mutations from a broad range of parameters (various samples, different sequencing approaches) with concrete applications in diagnostic and wastewater domains. In order to ensure FAIR data analysis, the workflow is built as a Nexflow pipeline, follows nf-core guidelines and use Singularity containers to wrap tool environments. Its efficiency and accuracy have been demonstrated. Due to detection of VOI/VOC and IonTorrent technology analysis, ASPICov is complementary to other pipeline such as viralrecon  [6]  and Farkas pipeline  [7]. Conception is different allowing to have an alternative and also a contribution to the diversity of tools for whole genome covid analysis. ASPICov is regularly updated on Gitlab for special variants according to WHO publications.
Acknowledgments CHU Limoges high security lab for providing SARS-Cov2 Basa prelevements. Margaux Gaschet  for help in librairies preparations. Marion Desdouits  for her support. The authors acknowledge the  Pole de Calcul et de Donnees Marines  (PCDM; https://wwz. ifremer. fr/en/ Research-Technology/Research-Infrastructures/Digital-infrastructures/Computation-Centre)  for providing DATARMOR supercomputer on which ASPICov has been developed and tested.
Background Sentiment expression and detection are crucial for effective and empathetic human-robot interaction. Previous work in this field often focuses on non-verbal emotion expression, such as facial expressions and gestures. Less is known about which specific prosodic speech elements are required in human-robot interaction. Our research question was: what prosodic elements are related to emotional speech in human-computer/robot interaction? Methods The scoping review was conducted in alignment with the Arksey and O'Malley methods. Literature was identified from the SCOPUS, IEEE Xplore, ACM Digital Library and PsycINFO databases in May 2021. After screening and de-duplication, data were extracted into an Excel coding sheet and summarised. Results Thirteen papers, published from 2012 to 2020 were included in the review.
Identifying Relevant Studies Four engineering and social science databases were searched on the 5th May 2021. These included SCOPUS, IEEE Xplore, ACM Digital Library and PsycINFO. Keywords (usually synonyms) were separated by the Boolean operators 'AND' and 'OR'. The search strategy included the following: (emotion OR sentiment) AND (speech OR verbal OR tone OR pitch) AND (expression OR identification) AND (experiment OR evaluation) AND robot. To focus the review on more recent and current state-ofthe-art methods, we decided to limit our search to the last 10 years, covering 2011 to 2021. Other limits included being published in English, focussing on humans (not animals) and the full-text being available. All items had to include an experiment/evaluation or research component, as well as focus on prosody, rather than semantics.
Selecting the Studies We created an Excel sheet, to document the searches. This document included the dates of each search, the databases searched and the literature identified through the searches. A two-step process helped to screen the literature for eligibility. This included first reading the abstracts and titles of the items, and removing those that did not meet the eligibility criteria. Duplicate studies between the searches were also removed in this step. The second step consisted of downloading and reading the full-text items and identifying reasons for exclusion. Items that passed the full-text screening stage were included in the review. The screening process was presented in a PRISMA diagram  [27].
Charting the Data and Collating, Summarizing, and Reporting the Results A second Excel sheet formed our coding framework, into which we extracted relevant data from each study. This included the following: title of the publication, first authors surname and publication date (year), publication type (journal or conference paper), study setting and country, robot/ system, purpose of the robot/system, speech detection/ expression, prosodic factors and description of them, study participants, evaluation/study method and outcome. Data from the coding sheet were summarised and presented in a manner which best answered the research question.
Results The database search yielded 1,889 results. Twenty-seven duplicates were removed, and another 1,806 were excluded during the first screening process. During the second screening, 43 studies were excluded with reasons. These included not being on speech (n = 12), not focusing on prosody (n = 11), not reporting on sentiment (n = 7) or experimental results (n = 7), not including anything on human-robot interaction (n = 3) and being published in languages other than English (n = 3). Consequently, a total of 13 publications were included in the review. The PRISMA diagram  [27]  in Fig. 1  demonstrates the search and screening process.
The pitch value 0 42-94 years spaces, was the default, while positive and negative num- com- bers represented higher and lower pitch respectively. mas and Neutral: Volume: 8/10, Rate: +1, Pitch: +0 full-stops) Excited: Volume: 10/10, Rate: +1, Pitch: +4 Sad: Volume: 8/10, Rate: -2, Pitch: -3 Sleeping: N/A Tired/confused: same as neutral Focused: Volume: 8/10, Rate: +0, Pitch: +0 Eyben; N/S System for agents Detect • Loudness Measured 5 affective dimensions: Experiment 2012 and robots emotion • F0 Activation; level of arousal/active engagement vs. Data from [28] envelope passiveness (i. e. , boredom). SEMAINE • Prob. of Valence: pleasant (positive) vs. unpleasant (nega- database voicing tive) emotions. (summary • Power Power: emotion is related to a feeling of power/con- N/S) spectrum trol vs.
Background: Most mass gathering events have been suspended due to the SARS-CoV-2 pandemic. However, with vaccination rollout, whether and how to organize some of these mass gathering events arises as part of the pandemic recovery discussions, and this calls for decision support tools. The Hajj, one of the world's largest religious gatherings, was substantively scaled down in 2020 and 2021 and it is still unclear how it will take place in 2022 and subsequent years. Simulating disease transmission dynamics during the Hajj season under different conditions can provide some insights for better decision-making. Most disease risk assessment models require data on the number and nature of possible close contacts between individuals. Methods: We sought to use integrated agent-based modeling and discrete events simulation techniques to capture risky contacts among the pilgrims and assess different scenarios in one of the Hajj major sites, namely Masjid-Al-Haram.
Simulation method In this study, we used a mix of agent-based modeling and discrete events simulation techniques. Agent-based models can be used to model the pedestrians' behaviors in crowds  [19]. To simulate the motion of pilgrims in Masjid-Al-Haram, we developed a multiscale simulation model in AnyLogic software (www. anylogic. com). AnyLogic is a multimethod simulation modeling platform that allows simulating pedestrian dynamics. Pedestrian movement in AnyLogic is modeled according to a social force model observing rules of physics. In the social forces model, the mass and dimension of each pedestrian, desired speed of the pedestrian in the absence of interactions, the direction of movement (toward attraction points), the repulsive force between pedestrians or pedestrians and obstacles such as walls or columns are formulated in the momentum equation.
Code walkthroughs and time measurements were used to verify the simulations. According to  [21] , the average speed of pilgrims was assumed to be 1. 1±0. 1 (m/s) in low-density areas. Since the movement of pilgrims was controlled by the embedded social forces method in AnyLogic's Pedestrian Library, the speed of pilgrims in high-density locations was adapted based on population density. Because of the randomness of movement, different time of entrances, different lengths of circulation on each circle and time of Tawaf, differences in approaching or departing direction of pilgrims that were entering or exiting Tawaf with who were circulating, different densities and consequently various speeds formed in Mata'af. In the model, we set the rate of entrance and path of pilgrims from the entrance gates to the Mata'af, and let pilgrims randomly uniformly were distributed over 100 circles around the Kaaba.
Pairwise distances between all individuals were calculated. Distances less than two meters were taken as a contact. For each number of contacts, the average number of individuals who had at least one contact, and the maximum number of contacts with one other individual were calculated. The generation of the pilgrims and the calculation of distances (New simulation run) were repeated until the simulated number of contacts remains approximately constant. The results of this simulation are estimates of contacts in an area in a time step of a dynamic movement. All calculations were done using a Python script. In the second method,  [23, 24] 's methodology was used to calculate the number of contacts. In this model, each pilgrim nds other pilgrims within a two-meter radius of himself/herself (close contact) at every time step.
In each area, when the total number of people was constant in time, i. e. in a steady-state condition, we calculated the number of contacts among the pilgrims and reported the average number of contacts per person in an hour. To nd the potential number of contacts during Tawaf and Sa'ay rituals, several scenarios were de ned considering the total number of pilgrims in each of these areas while observing social distancing measures. In this study, we report the results for a total population between 1,000 and 10,000 entering Masjid-Al-Haram through the entrance gates. Table  2  summarizes the number of contacts for different numbers of the total population in the Tawaf and Sa'ay rituals, respectively. A second-order polynomial trendline is tted to each dataset using the least-squares method.
Because many classical Be stars may owe their nature to mass and angular-momentum transfer in a close binary, the present masses, temperatures, and radii of their components are of high interest for comparison to stellar evolution models. Object κ Dra is a 61. 5 day single-lined binary with a B6 IIIe primary. With the CHARA Array instruments MIRC/MIRC-X and MYSTIC, we detected the secondary at (approximately photospheric) flux ratios of 1. 49% ± 0. 10% and 1. 63% ± 0. 09% in the H and K band, respectively. From a large and diverse optical spectroscopic database, only the radial velocity curve of the Be star could be extracted. However, employing the parallaxes from Hipparcos and Gaia, which agree within their nominal 1σ errors, we could derive the total mass and found component masses of 3.
Spectral Energy Distribution For the basic fitting of the spectral energy distribution (SED), we used the data set collected by  Klement et al. (2019)  and complemented it with flux-calibrated UV spectra from the IUE  (Boggess et al. 1978 ) and near-IR photometry measured by  Dougherty et al. (1991). A total of 32 IUE spectra were retrieved from the INES database. 13 They were selected to have been taken with the large aperture to ensure reliable flux calibration, but two clear outliers had to be discarded (SWP 29665 and LWP 08089). In fact, SWP 29665 appears to be a misidentified spectrum of the Be binary 59 Cygni because it has the same appearance as others of 59 Cygni, and a crosscorrelation function (CCF) analysis with a hot 45 kK template reveals the spectral signature of the hot companion at the predicted velocity  (Peters et al.
2013)  for the date of the observation. The other sources of SED data include the HPOL spectropolarimeter 14   (Davidson et al. 2014) , the Simbad database 15   (Wenger et al. 2000) , and the catalogs of the IR space missions IRAS 16   (Beichman et al. 1988; Joint Iras Science 1994) , WISE 17   (Wright et al. 2010; Cutri et al. 2021) , and AKARI/ IRC 18   (Ishihara et al. 2010a (Ishihara et al. , 2010b)).
5) stars with contrast ratios as high as 1:500. The MIRC-X twin instrument MYSTIC  (Setterholm et al. 2022 ) has operated at CHARA simultaneously with MIRC-X in the nearinfrared K band (with default R = 49 corresponding to an interferometric FoV of ∼65 mas) since 2021 August. Using different spectral modes, κ Dra was observed on 6 nights in 2012, 2013, and 2021 (Table  1 ). On a single night in 2021 (2021 December 18), both MIRC-X and MYSTIC data were obtained simultaneously. The star occupies a position in the sky for which six-telescope delay-line operation is not available at CHARA; thus, all of the data sets were obtained in five-telescope mode, i. e. , with 10 individual baselines (telescope pairs) and 10 closed triangles (of which only five are independent). Accordingly, each interferometric observation resulted in 10 sets of squared visibilities (VIS2) and 10 sets each of closure phases (CPs) and triple amplitudes (T3AMP).
The data were reduced using the dedicated pipelines for MIRC 19   (Monnier et al. 2007 ) and MIRC-X/MYSTIC 20  (pipeline version 1. 3. 5; Anugu et al. 2020). Some data sets required the removal of edge spectral channels due to a highly degraded signal-to-noise ratio (S/N). Calibrator observations were obtained before and/or after the science target and used to correct for atmospheric and instrumental effects in the raw measurements to obtain absolute-calibrated VIS2, CP, and T3AMP. The calibrators were selected using the SearchCal software 21   (Chelli et al. 2016 ) and are listed along with their uniform disk (UD) diameters in Table  2. The calibrator diameters were adopted from the JMMC catalog of stellar diameters  (Bourgés et al. 2014 (Bourgés et al. , 2017)). The absolute calibration of VIS2 and T3AMP is more susceptible to atmospheric turbulence than that of CP, as the latter is a differential measurement explicitly designed to cancel the turbulence-induced terms in the interferometric phase.