dataesr/scientific-paragraphs-categorization huggingface dataset:
Definitions • "Functionnectome" (with an uppercase "F") refers to the overall method and the program applying the method. "functionnectome" (with a lowercase "f") refers to the 4D volume resulting from the application of the Functionnectome. • "Voxel-wise" analysis refers to the projection of functional signals onto the white matter as this is done at the voxel level. "Region-wise" analysis runs on predefined parcels of brain regions, grouping voxels together and reducing the number of steps to the order of hundreds at most, though at the cost of precision. Requirements (Read this before starting!) 1. The Functionnectome runs on Python 3. 6 (or higher). In addition to the basic Python distributions, four python libraries are needed: Numpy, Pandas, Nibabel and H5py. If you install the Functionnectome toolbox using pip (see the "Installation" section), they will be installed automatically.
Overview In the GUI, the analysis parameters can be set and the input and the output can be defined. The Functionnectome analysis can be launched from the GUI once the parameters are set. The settings can be saved and loaded for future analysis. • Input: 4D volume, NifTI o Sum of all probability maps used for a given subject (depends on the mask) o Used during the processing, might be useful for alternative processing *Unique ID: If all input files are in the same folder, the program uses the filenames (without the extension). If the files are in different folders with similar paths (cf. bullet 4 in Requirements), the ID is the name of the folder (e. g. it will be "subject01" in the example given above with "/myHome/subject01/data/mydata. nii") A quick guide to run an analysis: 1. Select the input 4D files by clicking on either "Choose files" buttons (depending on your data storage organisation).
• NbCore: Number of cores (CPU) used to run the computation. o For example, running the Functionnectome in voxel-wise analysis, using a graymatter mask containing 180,000 voxels, and with an fMRI scan of 1,050 volumes, on a machine with 16 cores, gives: • 0. 004 x 180,000 x 1,050 / 16 = 47,250 sec » 13 hours o Using the region-wise analysis drastically cuts the time needed, even using a less powerful machine (at the cost of some precision and sensitivity in the final results). Indeed, with the same input fMRI volume, using a segmentation of 438 brain areas (as is the priors), with a 4-core machine, we get: • 0. 004 x 438 x 1,050 / 4 = 460 sec » 8 min 4. Choose the file type of the priors. If the priors of the software are to be used they are made available as HDF5 files. Where users wish to use their own templates we recommend using the same file type.
Monitoring fungal growth Spores (typically 10 6 in 30 µl) were added to each well of 12-well plates containing 700 µl of NGM agar supplemented with 0 mM, 1 mM, or 2 mM CoCl 2 and incubated at 25°C. Fungal growth was recorded using an Zeiss Axio Observer Z1 microscope equipped with Definite Focus, a motorised stage, a PeCon GmbH incubation chamber and a Hamamatsu C11440-42U30 camera. All images from a stack were aligned using the ImageJ macro Align_Slice (https://github. com/landinig/IJ-Align_Slice/) to compensate for any image drift. Spore and hyphae length were measured using the ImageJ toolbox HyphaTracker v1. 0  (Brunk et al. , 2018; Schindelin et al. , 2012). The thresholds to convert images into binary data were set automatically, the minimal area was set to 5 pixels and the last frame was selected to be the reference.
Swe2 gene set A number of proteins predicted for D. coniospora ARSEF 6962  (Zhang et al. , 2016) , referred to here as Dan2, were absent from the original Swe2 protein set as judged by the results of reciprocal BLASTP searches. These included members of highly repeated families, for example integrases and transposases, as well as a few with atypical structures (KYK54054. 1/KYK54053. 1/KYK56388. 1/KYK54065. 1)) that we did not attempt to curate. The remaining Dan2 protein sequences were aligned by TBLASTN against the Swe2 genome. Those matching an existing predicted Swe2 protein-coding gene were considered likely to reflect events of gene expansion in Dan2 and not pursued further, with the exception of genes encoding proteins with an enterotoxin alpha domain (PFAM: PF01375), which was the subject of manual annotation  (Zhang et al.
, 2021). Then Dan2 protein sequences that aligned with predicted coding sequence from Swe2 with an identity greater than 90% and for which the main exon was more than 80% of the predicted coding sequence for the gene were manually curated and added to the set of predicted protein-coding genes in Swe2. When polishing of the Swe2 genome removed one or more N from a predicted protein-coding gene, the RNAseq reads (SRR1930119, SRR1930124) and/or BLASTX alignment at NCBI against nr/nt databases were used to validate the corrected sequence.
Completion of the Swe proteome The absence of MAT1-2-1 from the predicted gene set for Swe2 suggested that there might be other lacunae in gene prediction. We therefore conducted an all-against-all comparison between the protein-coding genes of Dan2 and Swe2 (Genbank GCA_001625195. 1 and GCA_001618945. 1, respectively). This identified 298 genes present in Dan2 for which there was no equivalent prediction for Swe2. Detailed sequence searches in the Swe2 genome (see Methods) revealed plausible sequences for 39 of them, and these were added to the Swe2 gene set. This set was then used to predict genes in the Swe1 and Swe3 genomes, using a sequential approach (see Methods). Direct searches of the remaining 259 Dan2 genes against the Swe1 genome failed to identify any further previously unpredicted genes.
5 ). The remaining 3 proteins have poor or non-existent annotation. Genes g7143. t1/ODA77116. 1 and g3072. t1/ODA80305. 1 that acquired non-synonymous mutations at different steps of culture (Table  2 ) correspond to hypothetical proteins of unknown function, found in a phylogenetically restricted range of fungi. Still more extreme is the case of fg4743. t1/ODA79924. 1. Despite using sensitive search tools (including HHblits  (Steinegger et al. , 2019)  against the UniRef100 database), we were unable to find any plausible homologues in any other species. Indeed, there is no equivalent of fg4743. t1 even in the genomes of the 2 available D. coniospora Dan strains. The gene was predicted ab initio with the software tool Augustus  (Lebrigand et al. , 2016) , but has only very marginal RNAseq read support, with 11 and 16 reads in samples from mycelia and spores, respectively, not covering all of the predicted coding sequence and with none of the reads confirming the predicted intron-exon boundaries (Supp.
Acknowledgements We thank  Marie-Anne Félix  for insightful comments,  Philippe Fort  for helpful discussion concerning Ras GAP function and phylogeny, and the CIML imaging (ImagImm) and bioinformatics platforms. University to the CIML, and the  Agence Nationale de la Recherche program  grant ( ANR-16-CE15-0001-01 ), and " Investissements d'Avenir "  ANR-11-LABX-0054  ( Labex INFORM ),  ANR-16-CONV-0001 , and  ANR-11-IDEX-0001-02 , and from the  Excellence Initiative of Aix-Marseille University  -A*MIDEX.
Introduction In the quest for a quantum advantage, situating quantum algorithms and technologies with respect to classical approaches for solving NP-hard problems is a key question. Seminal works from Shor and Grover were a major breakthrough in this perspective in the early developments of quantum computing. Based on the theoretical model of quantum computation, they developed quantum algorithms that provide respectively an exponential speed-up for integer factorization  [1] , and a quadratic speed-up on exhaustive search in an unstructured database  [2] , a principle applicable to any NP problem providing at most such a speed-up in terms of query complexity  [3, 4]. However, implementing these algorithms requires fault-tolerant quantum machines handling a large number of qubits, which have yet to be built.
Acknowledgments We thank  Thomas Ayral ,  Antoine Browaeys ,  Thierry Lahaye  and  Christophe Jurczak  for discussions. This work was developped as a collaboration within the  European Commission  in the  Horizon 2020 FET-Quantum Flagship project PASQuanS  ( 817482 ). It was granted access to the HPC resources of CINES under the allocation 2019-A0070911024 made by  GENCI. It was also supported by  EDF R&D , the  Research and Development Division of Electricité de France  and by  Loria , the  Lorraine Research Laboratory in Computer Science and its Applications University of Lorraine, France. WL was supported by the  Austrian Science Fund (FWF)  through a  START grant  under Project No. Y1067-N27  and the  SFB BeyondC  Project No. F7108-N38 , the  Hauser-Raspe foundation. This material is based upon work supported by the  Defense Advanced Research Projects Agency (DARPA)  under Contract No.
The Emirates Mars Mission (EMM) -Hope Probe -was developed to understand Mars atmospheric circulation, dynamics, and processes through characterization of the Mars atmosphere layers and its interconnections enabled by a unique high-altitude (19,970 km periapse and 42,650 km apoapse) low inclination orbit that will offer an unprecedented local and seasonal time coverage over most of the planet. EMM has three scientific objectives to (A) characterize the state of the Martian lower atmosphere on global scales and its geographic, diurnal and seasonal variability, (B) correlate rates of thermal and photochemical atmospheric escape with conditions in the collisional Martian atmosphere, and (C) characterize the spatial structure and variability of key constituents in the Martian exosphere. The EMM data products include a variety of spectral and imaging data from three scientific instruments measuring Mars at visible, ultraviolet, and infrared wavelengths and contemporaneously and globally sampled on both diurnal and seasonal timescale.
Key Global Circulation Models and Tools The scientific analyses needed to address EMM science objectives from the mission data sets require the use of global circulation models. Specifically, we will employ the LMD-MGCM to infer unobservable quantities (at least by EMM) such as winds or values at local times not observed, to compare with derived or retrieved quantities, or to understand the behavior exhibited by the data. EMM will also utilize an advanced visualization tool for the three instruments' data, called JMARS (http://jmars. asu. edu), to visualize and cross-link datasets of disparate temporal and spatial scales ultimately permitting advanced data discovery and analysis. Sections 3. 1 and 3. 2 describe LMD-MGCM model and JMARS respectively.
The Java Mission-Planning and Analysis for Remote Sensing (JMARS) The Java Mission-Planning and Analysis for Remote Sensing (JMARS) is an advanced javabased software package developed by Arizona State University's Mars Space Facility, to provide scientists, instrument team members, and the general public a mission planning and scientific data-analysis tool that can be used to study different planetary systems  (Christensen et al. 2009). JMARS graphical user interface provides access to many Mars related scientific products, such as, image footprints, rasters, local mosaics, vector-oriented data, numerical and graphical maps, all of which are derived from instruments of different National Aeronautics and Space Administration's (NASA) missions, including Viking 1 and 2 Orbiters, MGS, 2001 Mars Odyssey, MEX, and MRO  (Dickenshied et al.
2014; Christensen et al. 2009 ) as well as a host of derived data products. In addition, other functionality such as running models such as the KRC thermal model  (Kieffer 2013)  or querying complex database related dataset such as the LMD-MGCM can interface with measured data and provide preliminary mechanisms to make scientific interpretations. Using JMARS, multiple datasets of interest can be queried based on user desired observational parameters, data quality, geographical location and time period, among a host of other parameters. These data can then either be visualized in context with one another, or cross-linked with intersecting products from other modules or instruments from different missions for the same study scenario  (Dickenshied et al. 2014). Science data returned from the EMM's three instruments (EXI, EMIRS and EMUS) once in the science orbit, will be hosted in JMARS software package, whereby data can be displayed, analyzed independently, compared across datasets, and validated.
Analysis performed using JMARS on the measurements derived from EXI and EMIRS for the lower atmosphere, and from EMUS for the upper atmosphere includes, but are not limited to, identifying possible patterns and behaviors in the global snapshot of the atmosphere, comparing products of similar data quantities, conducting spatial and temporal comparisons with data from other missions or modules, correlating conditions in the lower atmosphere with those in the upper atmosphere, and analyzing responses of selected episodic events. Figure  5  illustrates EMIRS observation footprints mapped in 2D and in 3D using JMARS.
Objective A: Characterize the State of the Martian Lower Atmosphere on Global Scales and Its Geographic, Diurnal and Seasonal Variability EMM Objective A focuses on characterizing the state of the lower Martian atmosphere and the processes that are driving the global circulation to improve our understanding of the energy balance in the current Martian climate. Understanding the energy balance will help in identifying the sources and sinks of energy globally and how the lower atmosphere responds to solar forcing diurnally and seasonally. To meet objective A science, we plan to (A1) merge EMIRS and EXI observations into a combined multi-dimensional snapshot of the global atmosphere, (A2) compare products of similar data quantities between EXI and EMIRS, (A3) conduct spatial and temporal comparisons to LMD-MGCM and other observations or spacecraft datasets, and ultimately (A4) produce a reference climatology using meteorological data assimilation techniques.
A1: Merge Observations into a Combined Multi-Dimensional Snapshot of the Global Atmosphere The data from the EMIRS and EXI instruments will help in understanding the energy balance in the current Martian climate and how the lower atmosphere responds to solar forcing diurnally and seasonally. Individual observation sets from EMIRS and EXI will be used to retrieve temperature, dust, water ice, ozone, and water vapor in the lower atmosphere independently and then will be deployed on JMARS, for visualization, to create a multidimensional snapshot of the global atmosphere every ∼10 days. The 10-day time interval will provide maps that can be accessed through queries, and which will have adequate spatial and diurnal coverage to understand the general behavior and patterns of the retrieved values.
A3: Conduct Spatial and Temporal Comparisons to GCM and Other Observations or Spacecraft Datasets Conducting spatial and temporal comparisons between EMIRS and EXI's retrievals and other data from previous missions or models are of tremendous benefit. It (1) furthers our knowledge in processing EMM observations prior and during the science mission; it (2) helps interpolating and interpreting EMM data by placing them in similar context and state of other data sets; it (3) aids in identifying parameters, within an observation, that require new modeled physical processes to precisely replicate its observed behavior and enhance the accuracy of developed retrieval algorithms; and it (4) facilitates new scientific discoveries that could be made when explaining unexpected behavior and inconsistency between the model and the observed data.
The data retrieved from EMIRS and EXI including atmospheric and surface temperatures, water ice optical depths, dust optical depth, and water vapor and ozone column abundances, will be compared with the LMD-MGCM output that will be binned and sampled similar to EMM mission's observation taking into consideration diurnal sampling as well. Also, derived products such as the water ice aerosol particle size, explained in Sect. 4. 2, will be compared to LMD-MGCM results with proper geometric-averaging treatment of the latter's results to account for the column-integrated nature of the aerosol retrievals. Retrieved constituents will also be compared, when possible, to previous missions' retrievals, such as MGS, 2001 Mars Odyssey, MEX, MRO, and TGO, to gain similar insight. The JMARS visualization and mission planning tool will also be used for sampling different temporal and spatial scales and for mapping activities based on EMIRS and EXI concept of operations.
A4: Produce a Reference Climatology Using Meteorological Data Assimilation Techniques Both surface and atmosphere properties are expected to vary diurnally, spatially, and seasonally. However, our current understanding is limited by the relative lack of local time coverage in existing observational datasets which is critical as diurnal variations in Mars' atmosphere are considerable (e. g. Navarro et al. 2017; Smith et al. 2017). Data assimilation techniques will be used to produce a comprehensive dataset of Martian atmospheric circulation (so-called "maps without gaps"), which can be used to study Mars' climatology, atmospheric events, and other phenomena  (Lewis et al. 1996; Navarro et al. 2017). Using these techniques, we will optimally combine observational data from EMIRS and EXI with numerical (theoretical) models contained in the LMD-MGCM  (Forget et al.
B1: Correlate Conditions in the Lower Atmosphere with Those in the Upper Atmosphere Upper atmosphere variables that EMM will measure (e. g. exobase hydrogen or oxygen density or temperature, or thermospheric mixing ratios) may increase or decrease in response Fig. 6  Schematic illustrating data assimilation using EMM observations. To produce an atmospheric temperature "analysis" in a particular atmospheric column, first we make several forecasts of the temperature in the column using the LMD-MGCM; the spread of values over the forecast ensemble members sets the uncertainty in the prediction. Observations from EMM (in this case a vertical temperature profile retrieved from EMIRS spectra) are then combined with the forecast using data assimilation to produce the analysis. (Topography is from MGS-MOLA; the vertical distance in the atmospheric profile is greatly exaggerated) to changes in the lower atmosphere.
Observations from Mars Year (MY) 28  (Heavens et al. 2018; Clarke 2018; Fedorova et al. 2018 ) and MY 34  (Aoki et al. 2019; Fedorova et al. 2020)  global dust storms provided evidence of enhanced water vapor transport to the middle atmosphere leading to an increase rate of water loss from the Martian atmosphere. Using the LMD-MGCM, the behavior of dust storm events can be studied by providing the theoretical expectations necessary to link between dust storms and atmospheric conditions up to the exobase. Data from EMIRS measurements of temperature and dust optical depth will support this analysis.
This impacted the abundance of hydrogen by decreasing the amount by almost 25% and increasing the escape rate through the increase in temperature in the exosphere  (Mayyasi et al. 2018). Our primary source of data on coronal mass ejections will be magnetic field data from MAVEN and suprathermal (i. e. ∼10 eV to 20 keV) ion and electron data from both MAVEN and Mars Express, both of which orbit Mars in elliptical orbits which take them into the upstream solar wind and thus allow them to detect CMEs. Solar energetic particles (SEPs) are defined as high-energy (typically >20 keV) charged particles (ions and electrons) accelerated by solar flares or interplanetary shocks  (Richardson 2004). SEPs are known to cause heating, ionization and chemical changes in terrestrial planet atmospheres  (Seppälä et al.
2009; Khayat et al. 2019). We plan to monitor the atmospheric changes in response to polar cap variability to work on understanding the connection, if any, between it and atmospheric escape. EMIRS surface temperature data and EXI visible images will be used to study polar cap surface temperature and albedo variation on a sub-seasonal timescale. EMIRS determines the surface temperature of Mars by calculating the brightness temperature at wavenumber 1300 cm -1 , where the atmosphere of Mars is almost transparent and the radiance is not absorbed by the atmospheric gases and aerosols. The LMD-MGCM will also provide the theoretical expectations needed for polar cap spatial and temporal variability.
EMIRS surface temperature data and EXI visible images from the red band will be used to measure surface temperature over the full diurnal cycle and capture albedo variations on a seasonal timescale. Analysis of this data will allow estimates of deposition and removal of dust and how it affects the surface temperature and albedo of Mars. The KRC thermal model in JAMRS will be used to compute diurnal temperatures on Mars over the full seasonal range; the KRC thermal model uses thermal wavelengths to determine the radiative effect of a dusty atmosphere and compute globally the annual average surface temperature and albedo  (Kieffer 2013).
Objective C: Characterize the Spatial Structure and Variability of Key Constituents in the Martian Exosphere EMM Objective C focuses on characterizing the spatial structure and variability of the Martian exosphere, the collisionless upper region of the atmosphere where escape to space can be most directly characterized. Specifically, the EMUS instrument measures hydrogen and oxygen through observations of solar resonant fluorescence that are then converted from brightnesses to geophysical densities as part of the EMUS data processing. Objective C is fulfilled through analysis of these density data products, producing new information on the processes that govern exospheric dynamics and escape. We identified three key analyses, described in the following sections, to be performed in achieving this objective: (C1) comparing the EMUS derived density structures to model predictions; (C2) deriving hydrogen and oxygen escape rates and variability from derived density profiles; and (C3) comparing EMUS derived escape rates to model predictions.
To thoroughly interpret EMUS datasets, it is vital to compare them with outputs from numerical atmospheric models to understand these variations. For example, running various eddy diffusion coefficients through numerical models to analyze how they affect hydrogen distribution in the thermosphere and exosphere, then matching them with EMUS datasets to pinpoint the eddy diffusion rate from real data-ultimately providing us with new knowledge of the Martian atmosphere's physics. The focus of this analysis is to identify key parameters that we predict will drive spatial and temporal variability to EMUS observed constituents and then testing them through numerical models. This will help us recognize the magnitude and span in which these parameters have an effect on EMUS observed quantities and their variations.
Conclusion The EMM/Hope probe, arrived at Mars on February 9, 2021 and will study the Martian atmosphere during a primary mission of one Martian year. With its unprecedented orbit, EMM's observations will reveal atmospheric behavior and connections to form a new and global perspective. Here, we summarized and identified the strategy put forward by the EMM science team to achieve the scientific goals and objectives set for the mission. The basic strategy utilizes EMM data, including its unique combination of temporal and spatial coverage, to test our current understanding of Martian atmosphere, including that obtained from global circulation models and previous scientific missions to Mars. The strategy can be adapted to any unanticipated changes in instrument or spacecraft performance as the science phase data arrives from the EMIRS, EXI, and EMUS scientific instruments.