dataesr/scientific-paragraphs-categorization huggingface dataset:
Contents of the MGS website The full downloadable MGS collection includes 1517 gene sets: 1156 gene sets from the current work, which for completeness include 245 empty gene sets for which no significantly differentially expressed genes were identified; 122 gene sets derived from post-2005 studies of myoblasts and myotubes, referenced in our previous work  [29] ; 185 gene sets extracted from a previous meta-analysis of early muscle microarray data (pre-2005;  [47] ); and 54 gene sets identified by searching for muscle-relevant terms within the MSigDB collections (mostly comprising muscle-related pathways from Reactome or Biocarta databases and omitting any gene sets that could duplicate those created in the present analysis).
Analysis using the MGS in WebGestalt is achieved by selecting either hsapiens or mmusculus as the organism of interest, the Overrepresentation Enrichment Analysis (ORA) or Gene Set Enrichment Analysis (GSEA) as the method of interest, and selecting community-contributed functional database. The MGS collection is then selectable, and a list of genes can be uploaded for enrichment testing against the MGS. The. gmt format in which we provide the MGS gene set collection was originally developed for the GSEA tool  [12]  (http://software. broadinstitute. org/gsea/), and a large variety of gene set collections are available in this format for download from the Molecular Signatures Database (MSigDB  [19] ). MSigDB are now listing gene sets from community contributors, of which MGS is the first listed (http://software.
broadinstitute. org/gsea/ msigdb/contributed_genesets. jsp). Enrichment testing can be carried out on the MGS collection using the GSEA software by uploading the MGS. gmt file as a local gene matrix from within the GSEA software's gene set database selection dialogue. We note that it is also possible to concatenate. gmt files using a text editor or scripting language, and it can be of interest to compare, for instance, enrichment results of Gene Ontology terms against those of the MGS. To facilitate deeper functional interpretation of the results, the output from GSEA can be visually displayed using the Enrichment Map plugin  [48]  for Cytoscape, for example, to examine the overlap of enriched MGS with enriched GO terms, as we have done previously  [28, 30].
By comparison, it is possible to process very large numbers of microarray samples with only moderate computational demands. We welcome efforts to automate or semi-automate gene set extraction  [61] [62] [63] , which can help in this task, but we note that these require the use of expression matrices that have been supplied to GEO/ ArrayExpress by the team making the data submission and therefore depend on the data processing steps carried out by each team, whereas we chose to re-analyse raw probe intensities (. CEL files). We considered this to be of strong importance in order to standardize data processing and quality control. Interesting methods are also being developed for the extraction of gene sets from literature text mining  [64]. Gene sets for pre-2005 muscle studies are included in the full downloadable MGS collection.
We did not extend our analysis to proteomic datathis would be interesting in future work as protein levels more closely reflect the activities of biological pathways. It would also be interesting to systematically cross-relate gene expression to proteomic studies in order to identify which muscle transcripts consistently serve as faithful markers of the proteins that they encode. Future work could allow selection by keyword, or automated subsetting of the MGS, similarly to that facilitated by the MSigDB resource for its current collections, and perhaps by incorporation of the MGS into the MSigDB, if that becomes an option that MSigDB would provide to community contributors.
During our experiment, 3 participants suffered slightly from cybersickness and their session was cancelled instantaneously. Necessary assistance was provided to theses participants and we made sure that they felt healthy again before leaving. To balance our sample three other participants with same profile were later recruited. For the purpose of our experiment we created a VE from the architectural blueprints of Saint-Michel Notre Dame train C. We used the 3D software Unity3D® to design the train station. We applied the rules of interaction, immersion and interface  (Fuchs & Moreau, 2006)  to design our 3D environment. The detailed procedures for the construction of our virtual train station are illustrated in the Appendix section of this paper. Some pictures obtained from the virtual train station (left side) are compared to pictures taken from the same region in the real train station (right side), as shown in figure 1 below.
Physiological measure Electrodermal activity (EDA) was chosen as a physiological measure during the experiment. Endosomatic EDA was measured from the inner wrist skin of the participants using a sensor which quantified this activity for later analyses. In both virtual and real-life conditions of our experiment, an E4 wristband sensor Empatica® was used to monitor the EDA level. This wristband contained two electrodes on the sensor's band for EDA monitoring and has a permanent internal memory of 60 hours, with a synchronization resolution of 5 seconds. The data collected on the device can be streamed via bluetooth® on a smartphone. The sensor measured blood volume at a frequency of 64 Hz, with an electrodermal activity of 4Hz, at an acceleration of 32Hz and a skin temperature measured every 4 seconds.
EDA data were measured in micro Siemens (µs) at a frequency of 4Hz every 0. 25 second. Variables known to impact EDA recordings were supervised according to recommendations from previous studies  (Boucsein, 2012). Variations in temperature were measured during the experiment, since EDA level is affected by it. Room temperature for virtual reality experiment was monitored and maintained at 20°C using an air-conditioner, and the participants were asked to wear sport clothing, to be more comfortable and to prevent drastic rise in body temperature during the experiment due to sweating. The same condition was applied for reallife experimental condition, with the exception of the air-conditioned temperature. However, the average temperature of the air in both environments was 20. 3±0. 2°C with a nonsignificant difference between the two environmental conditions (p =.
The participants tested all the settings of the joystick: walk slowly, walk quickly, accelerate, decelerate, walk upstairs quickly and slowly. Afterwards, the participants were equipped with an E4 wristband sensor, and the EDA baseline was recorded for 10 minutes in the empty virtual train station. Once this phase ended, written and oral indications for the experiment were given to the participants in a story-telling format, as seen in section 2. 4. The virtual train station was then switched on and a trigger was activated on the EDA wristband to mark the start of the experiment, with a timer integrated in the app on which the data could be monitored online. During both the virtual and real-life experiments, any unexpected behavior or reaction was noted by the experimenter. The participants were not supposed to ask any question to the experimenter.
Data processing During this experiment, we evaluated cognitive load of participants through EDA analyses for physiological dimension, NASA-TLX for subjective dimension and memory binding score for behavioral dimension Data collected were analyzed for two factors: travelers' profile (regular or occasional) and environmental situation (virtual reality or real-life) using the statistical computing and graphics software, R. Descriptive analyses were first performed using the package tidyverse  (Wickham, 2017). The mean values and standard deviations for all collected data were hence obtained from these descriptive analyses. Normality and homoscedasticity tests were conducted using Car package  (Fox et al. , 2012). ANOVA was then calculated using the same package. Lsr  (Navarro, 2015)  for effect size calculation.
Electrodermal activity EDA data were uploaded in Matlab-based software Ledalab, designed to analyze skin conductance data  (Benedek & Kaernbach, 2010). The format of EDA E4 Empatica corresponds to "Text Type 1". The sampling frequency was 4Hz and was down-sampled to 1Hz, with threshold amplitude of 0. 01µs. The signal was first conditioned before storage. The uploaded data were preprocessed using low-pass Butterworth filter, down-sampled, cut, smoothened, and artifact-corrected. The data were then optimized by clicking on the corresponding button. A Continuous Decomposition Analysis (CDA) was then performed by decomposing the raw Skin Conductance (SC) data into continuous tonic and phasic activities providing an unbiased quantification of SCR amplitudes and number of SCR peaks.
Electrodermal activity Data obtained by continuous deconvolution analysis showed a mean SCR score of 0. 66μs at baseline for the four groups of travelers (occasional travelers in real-life condition, occasional travelers in virtual reality, regular travelers in real-life condition and regular travelers in virtual reality condition). This score increased exponentially during phase 1 to phase 3 and decreases from phase 3 to phase 4. The mean SCR scores of occasional travelers increased more than those of regular travelers at any moment. The results obtained are illustrated in figure  4  below. Since EDA was measured repeatedly throughout the experiment for the same participant, the independence of data was no more satisfied. A linear mixed model analysis was thus conducted on our two experimental factors (profile and condition), to test for random and fixed effects to account for spacing between each time points  (Krueger & Tian, 2004; Jaeger, 2008) , while taking time point (EDA_Phase) as error.
We present the first far infrared (FIR) dust emission polarization map covering the full extent Milky Way's Central molecular zone (CMZ). The data, obtained with the PILOT balloon-borne experiment, covers the Galactic Center region -2 • < < 2 • , -4 • < b < 3 • at a wavelength of 240 µm and an angular resolution 2. 2. From our measured dust polarization angles, we infer a magnetic field orientation projected onto the plane of the sky that is remarkably ordered over the full extent of the CMZ, with an average tilt angle of 22 • clockwise with respect to the Galactic plane. Our results confirm previous claims that the field traced by dust polarized emission is oriented nearly orthogonal to the field traced by GHz radio synchrotron emission in the Galactic Center region. The observed field structure is globally compatible with the latest Planck polarization data at 353 GHz and 217 GHz.
An additional difficulty comes from specific systematic effects which could be affecting data near the plane, such as band-pass mismatch and/or contamination by molecular lines (such as the CO lines) in the Planck photometric bands. In this context, the PILOT data bring useful information regarding both the structure of the magnetic field and the physics of dust. First, the angular resolution of the instrument (2. 2 ) is higher than that of the Planck data, which helps following the field structure into small and/or distant objects, while the large instantaneous field-of-view of the instrument allows mapping large regions, allowing to bridge high resolution ground-based observation which often suffer from a limited sky coverage. Second, the data obtained with the PILOT instrument operating at 240 µm will allow, in conjunction with polarization data obtained at longer wavelength, to constrain the polarization SED of thermal dust with a large lever arm, which is an important constraint for dust models.
Finally, the systematic effects affecting the PILOT data are likely to be of a different nature than that relevant for other instruments, which will help the analysis and interpretation of the polarized signals when co-analysis is possible. In this paper, we present the map of polarized dust emission in the CMZ obtained at 240 µm by the PILOT balloon-borne experiment. The PILOT observations cover a 4×6 degree field centred at (l, b) = (0, 0) (we refer to this observed field as "L0" in the rest of the article), a much wider field than all previous observations of polarized FIR emission in the GC region. The PILOT maps reveal the projected magnetic field structure across the whole CMZ with an angular resolution of 2 2, i. e. with 2. 3 times better linear resolution or 5 times more spatial information than the 353 GHz polarization data obtained by Planck.
The paper is organized as follows. In Sect. 2, we describe the observing strategy. The PILOT data processing is described in Sect. 3, including an overview of how we construct Stokes I, Q and U maps by using two map-making software packages (ROMA and SCANAMORPHOS) in Sect. 3. 5. In Sect. 4 we present the polarization results focusing on the polarization angles, we discuss the polarization maps in Sect. 4. 1, in Sect. 4. 2, we validate them by comparing the results of the two independent map-making procedures and in Sect. 4. 3 we compare our measurements with the ones obtained with the Planck satellite. We discuss the results in Sect. 5, focusing on the relevance of the CMZ in Sect. 5. 1, on the magnetic field orientation in Sect. 5. 2 and on the magnetic field strength in Sect. 5. 3. We investigate the relationship between the magnetic field structure in the CMZ measured by PILOT and recently proposed models for gas orbits in the Galactic Centre in Sect.
PILOT Observations PILOT observations of the GC region were obtained during the second flight of the PILOT balloon experiment, which took place from Alice Springs, Australia, as part of the 2017 French Space Agency (CNES) balloon campaign. A complete description of the PILOT instrument is presented in  Bernard et al. (2016) , while the performance of the PILOT instrument during the Alice Springs flight is described in  Mangilli et al. (2018). The data presented in this paper were obtained during four consecutive observing tiles while the target field was at elevations between 16. 5 and 56 •. The total duration of the observations was 32 min, including time for slewing and calibrations at the end of individual scans. The temperatures of the TRANS and REFLEX focal planes during the observations were stable, 304.
Pointing reconstruction The pointing of each detector can be computed given the Estadius stellar sensor information, the offset between the Estadius and PILOT optical axes and a description of the PILOT focal plane geometry. The Estadius stellar sensor  (Montel et al. 2015 ) data provides the pointing quaternion at a frequency of 4 Hz with an accuracy   (Cabral & Leedom 2000)  shows the orientation of the magnetic field projected on the plane, inferred from the measured dust polarization. Lower panel: Intensity as above, overlaid with total intensity contours at 0. 1, 0. 2, 0. 5, and 1 × 10 5 MJy/sr. The circles show the location of the 100-pc twisted ring as defined in  Molinari et al. (2011). The filled symbols are discussed in Sect. 5. 4. The names of key molecular regions are overlaid.
of 5 arcsec in both directions and 15 arcsec in field rotation. During the observations of the CMZ region presented here, the performances of the Estadius sensor were optimal. The offset between the Estadius and the PILOT optical axes was found to vary during flight, due to thermal and mechanical deformation of the instrument. This offset was monitored during the whole flight using bright sources. For that purpose, we used total intensity maps of individual observing tiles of the PILOT data obtained using the SCANAMORPHOS map-making algorithm (see Sect. 3. 5). These maps were obtained using coordinates computed with preliminary Estadius offsets derived during the flight from planet observations. These maps were correlated with Herschel maps of the same sky region at 250 µm for different assumed values of the Estadius offset.
The best Estadius offset for each observing tile was derived as the one providing the best Pearson correlation coefficient between the PILOT and Herschel data. Note that three different bright sources were used in the L0 observations to derive Estadius offsets. A preliminary version of the PILOT focal plane geometry was obtained during ground calibration of the instrument  (Bernard et al. 2016). Here, we used all parameters of this determination including the pixel size and array rotation values. However, we refined the detector array position offsets with respect to the PILOT focal plane center using in-flight measure-ments. For that purpose, we used a similar procedure as for deriving Estadius offsets but applied to maps obtained with data from individual arrays. The offset difference between the individual offsets and the Estadius offsets was adopted as the refined arrays offsets of the focal plane geometry.
SCANAMORPHOS The SCANAMORPHOS software was initially developed to process on-the-fly data from the PACS and SPIRE bolometer ar-rays on-board Herschel  (Roussel 2013). The software has subsequently been refined to cope with the dominant atmospheric emission in ground-based observations for different instrument architectures, such as ArTéMiS on APEX  (Roussel 2018 ) and NIKA2 on the IRAM 30-m telescope. It has also been adapted to process the polarization data of PILOT as outlined below. Its core principle is to exploit all the available redundancy in the observations, assuming only that the sky brightness is invariant in time and that the component of the low-frequency noise that is uncorrelated (termed individual drifts or flicker noise in the documents describing the code) has a symmetric probability distribution around zero, without necessarily being Gaussian.
e. assuming Q = U = 0). It is only when the major part of the average drift has been subtracted that we take polarization into account. For the observations discussed here, the data was taken in a way such that three observing tiles out of four were taken with roughly the same scan direction, the fourth tile being taken roughly in the orthogonal direction. We have explored the possibility of modifying the reference I, Q, U maps in the SCANAMORPHOS destriping module. These are normally com-Fig. 3 : PILOT maps of the I, Q, U parameters obtained towards the galactic center region, processed using the SCANAMORPHOS software. The maps are shown at the full resolution of the instrument of 2. 2 arcmin. The maps are in units of 10 3 MJy/sr. puted from the whole data at the previous iteration of the destriping, but it is possible to lower the weight of some tiles, or to provide independent maps from simulations , for example.
ROMA The ROMA code, described in de Gasperis et al. (  2016 ), was developed for cosmology experiments aiming at measuring the polarization of the Cosmic Microwave Background (CMB). For this reason it is optimized for noise dominated timelines. The code assumes stationnarity of the noise statistical properties, which is reasonably the case for the data of PILOT flight#2 (see  Mangilli et al. 2018). Under the assumption of a Gaussian and stationary noise, the ROMA code adopts a Fourier-based, preconditioned conjugate gradient iterative method to solve for the maximum-likelihood signal map yield by the Generalized Least Square (GLS) approach (see de  Gasperis et al. 2016, for details). In particular, given the pointing matrix A and the timeline vector D, the optimal GLS solution for the signal estimate S = ( I, Q, U) is: S = A T N -1 A -1 A T N -1 D, (6) where N ≡ n t n t is the noise covariance matrix in the time domain (n t indicates the instrumental noise at time t).
Notice that the matrix N is block diagonal only in case of no noise crosscorrelation. The PILOT timelines of the four galactic center region observations are provided to the code after gap-filling of calibration sequences at the ends of scans using a white noise realization with the white noise level of each detector. The glitches are also masked and the atmospheric contribution is removed as described in 3. 4. In order to ensure that the signal is noise dominated, we subtracted from the observed timeline a simulated timeline based on the Planck 353 GHz total intensity map extrapolated to the PILOT frequency. The subtracted component is assumed unpolarized and is therefore equal on the TRANS and REFLEX detectors apart for possible pointing mismatch. The difference timeline is processed through ROMA with 100 iterations that are enough to ensure a good convergence.
The map used for the signal subtraction is added back to the resulting total intensity map. The noise covariance matrix provided to the ROMA code was determined from the PILOT observations of the BICEP field obtained during the same flight  (Mangilli et al. 2018) , treated in the same way as the L0 data. The map resolution is set to N side = 2048 (1. 7 arcmin). The code also provides maps of the variances on the Stokes parameters as well as a map of the inverse conditioning matrix allowing to assess if a sufficient number of data taken at different HWP angles are available for a given pixel. The pixel inverse condition number (ICN) is defined as the ratio of the absolute values of the smallest and largest eigenvalue of the preconditioning matrix, and provides a good tracer of the errors in the map due to a non ideal angle coverage on the given pixel (a value of ICN = 0.
Comparison between map-making methods We take advantage of having two independent map-making methods to compare the polarization angles results obtained with the two pipelines. Fig. 8  shows the ψ map pixel histograms obtained with the SCANAMORPHOS software and the ROMA software in the signal dominated region defined by -1. 4 • < l < 1. 8 • and -0. 1 • < b < 0. 4 •. , ψ ROMA 0 = 18. 37 • ) , however the ROMA histogram shows a larger dispersion which is due to the poorer qual-Fig. 8 : Normalized histograms of the PILOT polarization angles obtained by analyzing the polarization angles map generated with the ROMA map making (black) and with the SCANAMORPHOS map making (red). ity of the ROMA polarization maps that show residual stripes, in particular in the Q map. As mentioned in Sec. 3. 5. 3, this is due to the fact that the ROMA map-making strongly depends on the accurate modeling of the detector noise and the noise covariance matrix for the moment does not include the off-diagonal terms linked to the correlated noise between the detectors.
In a simplified attempt to reproduce the low spatial frequency filtering of the signal in the JCMT data, we subtracted from the PILOT Q and U maps the average values measured in the region of   13  shows the histogram of the angles obtained from the two datasets. Although there is a significant dispersion and the statistics are low, the average of the angles are 17. 05 • and 16. 72 • for the PILOT and JCMT data respectively, and the corresponding average angle difference between the two datasets is 0. 54 •. Note that, if no background subtraction is applied to the PILOT data, the histogram is significantly skewed towards a non-zero angle difference value. This indicates that the somewhat large dispersion is unlikely to be due to noise in the PILOT or JCMT data, but is more probably due to inadequate filtering of the PILOT data.
Summary and conclusions In this paper, we have presented new measurements of the polarized dust emission at 240 µm in the Galactic center region. The data were obtained during 30 minutes of observations during the second flight of the PILOT balloon-borne experiment, which was launched from Alice Springs, Australia in April 2017. The observed region covers a wide field (-2 • < < 2 • , -4 • < b < 3 • ) at an angular resolution of 2. 2. This paper is the first PILOT paper to present scientific analysis of polarization measurements towards astronomical targets. To validate these measurements and assess the overall quality of our data processing pipeline, we present polarization maps from the PILOT data using two independent map-making methods (SCANAMORPHOS and ROMA). The two pipelines generate similar I, Q and U maps, and yield a similar distribution of polarization angles across our observed field.