dataesr/scientific-paragraphs-categorization huggingface dataset:
Most non-coding RNA genes are annotated by aligning by asterisks: � P-values < 0. 05; �� P-values < 0. 01 and ��� P-values < 0. 001 (one-tailed t-test). The red horizontal line indicates 100% splicing efficiency (i. e. splicing efficiency when transfecting WT RNU4ATAC). https://doi. org/10. 1371/journal. pone. 0235655. g005 genomic sequences against entries of the Rfam database (the database listing RNA families), thus numbering starts from the first nucleotide of the RNA. Yet for unknown reasons, Homo sapiens RNU4ATAC nucleotidic coordinates are inconsistent and erroneous in Rfam, Ensembl, and gnomAD websites with a +1 or +2 shifting [chr2:122,288,457-122,288,583 or chr2:122,288,458-122,288,584 instead of chr2:122,288,456-122,288,585 (Genome build GRCh37/hg19); chr2:121,530,881-121,531,007 instead of chr2:121,530,880-121,531,009 (GRCh38/hg38)].
On the other hand, the seven RNU4ATAC variants never associated with a phenotype did not impact the splicing efficiency in our functional test, even those four predicted by the RNAstructure software to have a strong impact in regions important for splicing, among which three had also a high CADD score. This absence of effect could derive, again, from the fact that we test the splicing of only one U12-type intron while we showed that the level of minor intron retention of the 699 human genes containing a U12-type intron varies depending on the gene, the cell-type and the RNU4ATAC genotype of TALS patients  [9]. Therefore, a negative result obtained with our cellular assay may not necessarily mean that the tested variant has no effect on minor splicing. Adding up to the previously discussed limitations of U4atac/U6atac secondary structure predictions, the poor concordance of these predictions and cellular test results could also be due to the fact that an important parameter, i.
e. the stability of U4atac/U6atac di-snRNP, is not taken into account by the RNAstructure software. Stability is nevertheless a very important factor, certainly explaining the strong effect of the n. 66G>C variant located just 3' to Stem I. Indeed, the presence of this variant lengthens and therefore stabilises Stem I, which most probably impairs the U4atac ejection needed for the activation of U6atac  [36]. In this work, we 1) provided all the available relevant information needed for RNU4ATAC sequence variant classification in clinical molecular diagnostic, 2) evaluated for the first time the utility in the clinical setting of a structural and of the CADD prediction tools, and 3) presented a cellular assay that functionally assesses the variants identified in patients. This functional test allows to move variants from the "probably pathogenic" to the "pathogenic" class, leading to greater confidence in patient reporting and clinical management.
This work was supported by  CNRS ,  Inserm , UniversiteParis 7 and UniversiteLyon 1 through recurrent funding, the  ANR Aster  (no. ANR-16-CE23-0001 ) and  U4ATAC-BRAIN  (no. ANR-18CE12-0007-01 ) grants and an  Inserm/ Hospices Civils de Lyon  grant to P. E. (Contrat d' Interface pour Hospitaliers). A. C. was supported by a grant from  Inria  ( These Inria-Inserm "Medecine Numerique" -2016 ) and C. B. P. by a grant from the  Fondation pour la Recherche Medicale  to P. E. (Financement d'un ingenieur -ING20160435660). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
II. THE PROJECT The GPU4S (GPU for Space) project, funded by the European Space Agency (ESA) aims to explore the suitability of embedded GPUs for space covering both the software and hardware perspectives. The project is coordinated by the Barcelona Supercomputing Center (BSC), a leading institution in code parallelisation and optimisation, parallel programming models, critical systems and in particular embedded GPUs as well as an NVIDIA Center of Excellence. In addition it is home to two supercomputers in the actual TOP500 list, Marenostrum and Marenostrum P9 CTE. The latter is based on GPUs and it is currently in the 7th position of the most energy efficient supercomputers (Green500, November 2019). Airbus Defence and Space (ADS) in Toulouse, formerly Astrium, is Airbus' aerospace division, a world-leading primary spacecraft hardware and software supplier.
While OpenCL support is not provided on these low-end devices, it is possible to program efficient solutions for general purpose computations employing general purpose computations over embedded graphics  [15]   [16]. One of the main issues of both high and low-end embedded GPUs is the lack of public domain architectural designs, which prevents researchers to certainly know the full execution pipeline of a device under a specific program execution, which also does not allow observability for space qualification. Vendors only supply high-level implementation details and all of the development tools are proprietary and close source. The only GPU with open specifications to date is Broadcom's VideoCore IV, but the limited number of development tools available work at the assembly level. This results in low productivity and high complexity, while no debugging or profiling methods are available.
A. 3. Machine Learning and other Accelerators In the previous section, we identified the increased autonomy as an emerging on-board requirement, which requires neural network processing to be implemented. These algorithms are very computationally demanding and GPUs have been identified as suitable architectures for their acceleration. However, almost all embedded GPU design companies have introduced their own machine learning accelerators together with custom software stacks, which can provide higher performance neural network processing, mainly inference, in a more energy efficient way. Similarly, they introduce other special purpose accelerators eg. for vision workloads. For example, the Project Trillium from ARM includes the Machine Learning Processor, ARM's Object Detection processor, as well as ARM's NN (Neural Network) software.
Moreover, ARM's neural network software allows interoperability of machine learning operations on the CPU, GPU or the specific accelerators that can be found in their SoC. Similarly, Imagination Techologies provide Series2NX, a family of neural network accelerators with variable low arithmetic precision from 16 bit down to 4 bits and a software stack with various machine learning frameworks. Think Silicon offers the NEMA xNN power efficient inference accelerator which features 8 bit operations as well as approximate computations. Finally, Nvidia includes in its Xavier SoC Tensor Cores, which is a matrix multiplication accelerator for machine learning workloads, NVDLA Nvidia's Deep Learning Accelerator which is released as open source IP and can accelerate various deep learning algorithms, as well as the PVA (Programmable Vision Accelerator) an image processing VLIW-based (Very Long Instruction Word) accelerator.
Alternatively, in the event that a new, more efficient image compression algorithm is developed, a hardware accelerator can be repurposed to support it, thereby decreasing the size of the data consequently reducing the time for downlink communications. For the aforementioned explanations, both of these potential approaches have their unique strengths, and are thus included in our study. This category can be subdivided in two levels: B. 1. Soft GPU cores Soft embedded GPU cores are implemented in RTL (Register Transfer Level) by using a hardware-description language such as VHDL or Verilog. The design is then synthesized into the FPGA, which can be used seamlessly by the software, either through graphics or compute APIs. Certain GPU design companies offer evaluation solutions for specific FPGA devices, such as Think Silicon with its highend NEMA GPU and the low-end Think2.
High-level synthesis in OpenCL makes the development and debugging effort significantly easier versus a hardware description language. However, the performance of the generated circuits from the high-level synthesis needs to be evaluated. In addition, existing space-qualified FPGAs, like the V5QV from Xilinx, are not supported for high-level synthesis. Lastly, we have found that existing HLS tools cannot obtain the same OpenCL code and execute it unchanged on an FPGA, because they need extra code for interfacing between the host and accelerator sides. Moreover, the kernel code needs to undergo major modifications and annotations, so that the generated hardware is efficient, to a degree that will not have anything in common with the original OpenCL code. Because of this, the conclusion is that further research on this path is required, but this is beyond the scope of our study, which focuses solely on embedded GPUs, and is more amenable to a future project related to the design of ASIC hardware for the space.
V. PRELIMINARY GPU EVALUATION RESULTS Based on the above presented surveys on space applications and existing embedded GPUs, we have narrowed down the  GPUs to be evaluated. For cost and effort reasons the selection of the platforms to be evaluated experimentally had to be limited to 3 COTS GPUs, with additional GPU platforms to be evaluated using their published results. The initial selection included 3 embedded GPUs, two European which can be used either as IP or COTS and the latest non-European GPU which has the highest theoretical performance among all GPUs in the embedded market. In particular, the selected GPUs are: the latest ARM Mali GPU G72 found in HiSilicon's HiKey 970, the Imagination Technologies PowerVR Series 6 (GX6650) GPU found in the Renesas RCAR H3 automotive board compatible with ASIL-B functional safety level and the latest embedded NVIDIA platform Xavier which is designed for autonomous driving and certifiable up to the highest automotive safety level, ASIL-D.
All platforms have a 10W TDP performance mode which has been determined as an upper limit for onboard systems based on our analysis and two of them, the RCAR H3 and NVIDIA Xavier are automotive-grade products, designed to comply with functional safety certification, therefore they include hardware reliability features. Unfortunately the RCAR H3 production has been canceled by Renesas after its selection, which has not permitted the procurement of this device for benchmarking. For this reason, we have replaced it by another COTS GPU from NVIDIA, the Jetson TX2, which has a product variant specifically designed for industrial applications. Based on the space application survey we have designed a GPU benchmark suite for space, which consists of kernels extracted from several space domains. Initially we planned to use Brook Auto  [16]  which can facilitate the certification/qualification of the GPU software and allows the programmability of low-end GPUs, however since the selection of the GPU platforms included only the latest high-end GPUs we have used their corresponding native programming APIs, OpenCL and CUDA.
More information about the designed benchmark suite is going to be provided in a future publication which is currently under peer review, and the source code will be released as open source after the end of the project. In Figure  3  and Figure  4  we present some preliminary results for various implementations of two widely used kernels in space algorithms, the matrix-matrix multiplication and the Fast Fourier Transform (FFT). In the matrix multiplication benchmark (Figure  3 ) we notice that for small sizes the 3 GPUs have similar performance, however when the size of the input is increased, the NVIDIA GPUs outperform the ARM one. Although this is partially due to the fact that there is no ARM optimised library for this operation, a comparison of the same hand-written and optimised implementation in all platforms shows the same trend.
Regarding COTS GPUs we propose the use of a solution that we previously developed for the automotive sector, based on dual-modular softwarebased diverse redundancy  [27]. This solution exploits the parallelism of the GPU hardware in order to execute two copies of the same GPU kernel, while ensuring that they are executed on different hardware resources and with a time slack between them, so that a soft error cannot be manifested in both copies in the same way. The comparison of the results of the two copies is performed by a safety microcontroller, such the one already included in automotive-grade platforms like Xavier. For the GPU IP solutions, since their hardware design can be modified, we propose a hardware solution, again from our previous work in the automotive domain  [28]. This solution applies the same concept with  [27] , however in this case it is the hardware which guarantees the spatial and temporal diversity.
Finally, another challenge we had to cope with was the budget limitation, for a project that we extended well beyond its narrow initial scope. Planned as a pilot project with an initial budget of 150K euros, the number of possible GPUs to be purchased and experimentally evaluated was limited to 3. Moreover, this further constrained the effort that could be devoted to conduct the various surveys including communication and legal agreements with vendors, platform setting up, code development and optimisation in order to perform a fair comparison and a demonstrator, as well the preliminary reliability assessment and definition of the adoption roadmap. Despite this limitation, we managed to extend the project outcome significantly beyond its limited scope, for example covering much more hardware devices that the four classes of embedded GPUs we identified in our taxonomy: highlevel synthesis with OpenCL on FPGAs and machine learning accelerators.
ACKNOWLEDGMENTS This work has received funding from the the  European Space Agency (ESA)  under the GPU4S (GPU for Space) Project, answer to the  ESA   ITT AO/1-9010/17/NL/AF  tender with title " Low Power GPU Solutions For High Performance On-Board Data Processing " and from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 772773 ). This work has also been partially supported by the  Spanish Ministry of Economy and Competitiveness (MINECO)  under grant  TIN2015-65316-P  and the  HiPEAC Network of Excellence. MINECO partially supported  Leonidas Kosmidis  under  Juan de la Cierva Formación postdoctoral fellowship  ( FJCI-2017-34095 ) and  Jaume Abella  under  Ramon y Cajal postdoctoral fellowship  ( RYC-2013-14717 ).
Based on a fixed power budget , SoftCast scales the magnitude of the DCT coefficients to offer a better protection against channel noise. Hadamard Transform is then applied on the scaled coefficients to provide packet loss resilience. By mixing the data, it ensures that each packet contains approximately the same amount of information. Finally, the obtained coefficients are directly mapped in pairs (I and Q planes in the Orthogonal Frequency Division Multiplexing technology) and transmitted without any coding step in a pseudo-analog manner referred as Raw-OFDM  [13]. In addition, metadata are transmitted to the receiver to be able to recover the video signal. For each GoP, they represent a small amount of three datasets: • The mean of each chunk, denoted by with = (1, 2, … , ); • The variance/energy of each chunk, noted ; • A bitmap which indicates the positions of the discarded chunks in the GoP.
To ensure a correct decoding process, they are transmitted in a robust way after entropy coding (e. g. by using Binary Phase-Shift Keying modulation  [14]  and Huffman coding). We note that the added green block in Fig. 1  induces an additional small dataset corresponding to the average value of each frame in the GoP (8 bits per frame before Huffman coding  [11] ). At the receiver side, a Linear Least Square Error (LLSE) decoder is used to get the best estimation of received values. The decoded values are then reassembled to form frames that are passed through an inverse 3D-DCT process. In case of bandwidth-constrained environments, the discarded chunks at the transmitter side are replaced by null values. Following the original works  [8] , linear video coding has gathered a significant interest from the research community  [13] [14] [15] [16] [17].
400∼469 denoted by Parkrun*). Finally, the resulting AGCC GoP-size decomposition is also given in brackets in Table  3. As observed, both cuts and temporal fluctuations are perfectly detected: For each subsequence, the optimal GoP-sizes are found and the last one is adjusted to avoid the ghost effect. The proposed methods are compared with the classical SoftCast scheme assuming three standards and fixed GoPsizes (8, 16 and 32 frames)  [8, 13, 14]. • As explained by Xiong et al. [13]  in Section 2, under the same channel conditions, the received quality directly depends on the data activity (see eq. (  3 )). Therefore, one video containing high temporal fluctuations (e. g. , Husky) is more difficult to transmit than a video with low temporal fluctuations (e. g. , Akiyo) resulting in a lower reconstructed quality;.
Combining the information provided by MUSE on the condition of the ionised ISM with these multiwavelength datasets, enables us to study the entire star formation cycle from parsec to galactic scales and from the very early phases of star formation to the creation of HII-regions and a warm ionised medium. We present recent MUSE AO observations of the nearby spiral galaxy NGC 7793. We use the extensive spectral information provided by MUSE to study the ionisation state of the gas: we identify the boundaries of HII regions and then investigate the properties of the DIG, in an attempt to shed some light on the origin of this diffuse component and on the process of stellar feedback. In a followup paper (hereafter Paper II; Della Bruna et al. , in prep. ), we will combine the MUSE results on the ionised gas with the stellar and cluster population studied within the HST-LEGUS programme and the dense gas properties provided by the ALMA-LEGUS data, to provide a full picture of the star formation cycle at scales of ∼10 pc.
Data description Our target is NGC 7793, a flocculent spiral galaxy (SAd) at a distance 3. 4 Mpc  (Zgirski et al. 2017) , located in the Sculptor group. Its main properties are summarised in Table  1. NGC 7793 is part of the sample of galaxies studied by the Hubble Space Telescope (HST) Treasury programme LEGUS 1  (Calzetti et al. 2015) ; the dataset is described in  Grasha et al. (2018). Recent ALMA data in the CO (J = 2-1) transition provide us with information about the giant molecular cloud (GMC) population at a 0. 85 resolution, and detection limits corresponding to a minimum cloud mass of 10 4 M  (Grasha et al. 2018). A threecolour image from HST and the coverage of the ALMA dataset are shown in Fig. 1. Within the field of view (FoV) of these two datasets, we have obtained IFU spectroscopy with the MUSE instrument at the ESO Very Large Telescope (VLT) observatory, as part of the Adaptive Optics Science Verification run 2.
Instrumental signature, flux calibration, and data alignment The data have been reduced using the MUSE ESO pipeline  (Weilbacher et al. 2014, v2. 2). We follow the first conventional reduction steps: we combine flat and bias frames into a master frame, we perform wavelength calibration and compute the line spread function (LSF) from the arc-lamps frames. We use a precomputed geometry table to determine the location of the IFU slices on the FoV and we perform illumination correction combining twilight flats. Using the pipeline recipe muse_scibasic, we then combine all this information to remove the instrument signature from science exposures, sky frames and standard star. We perform flux calibration using the standard star and astrometry calibration using the astrometric catalogue distributed with the pipeline.
We then perform spectral fitting using pPXF  (Cappellari & Emsellem 2004; Cappellari 2017 ). This software models a galaxy spectrum as G mod (x) = T (x) * L(cx), where x = ln λ, T are template spectra and L is the line of sight velocity distribution (LOSVD) of the stars. The underlying assumption is that any difference between the galaxy spectrum and the templates is purely due to broadening by the LOSVD. The template resolution is matched to the instrumental one by convolving the templates with a kernel κ defined by LSF instrument (x, λ) = LSF templates (x, λ) * κ(x, λ). The best fitting template is determined by maxmizing a penalised likelihood function, with a penalty function describing the deviation of the LOSVD from a pure Gaussian shape. We assume We run pPXF on the Voronoi tessellated datacube, masking all the relevant emission lines in the MUSE range, as well as sky subtraction residuals.
HII regions selection In the literature a variety of approaches are used to separate HII regions from DIG, relying mainly on the properties of the Hα emission. Widely used are hiiphot, a method proposed by  Thilker et al. (2000) , based on the gradient of the Hα A134, page 7 of 21 surface brightness, and the selection criterion proposed by  Wang et al. (1998) , which defines the boundary between DIG and HII regions according to the ratio between the local and the mean (at half-light ratio) Hα luminosity. We adopt a slightly different approach, and base our selection on the Hα surface brightness and on characteristic emission line ratios, similarly to what presented by  Kreckel et al. (2016). We start by selecting Hα bright regions using the Python package astrodendro 4. This tool allows to organise data into a hierarchical tree structure.
10 ), of which seven were never catalogued before. Bibby & Crowther (2010)  previously identified three candidate WR in the MUSE FoV; we confirm two of these (see last two columns of Table  2 ). We do not observe any HeII emission at the location of the third candidate (ID 20 in Bibby & Crowther 2010). Wofford et al. (2020)  select candidate luminous blue variable (LBV) stars combining the LEGUS-HST data with the MUSE dataset presented in this work and independently identify four of the candidates WRs presented here. We see that one of the Hα bright regions flagged in Sect. 6. 1 due to its high [OIII]5007/Hα ratio is associated with a WR star. WR, SNR, and PNe candidates are listed in Table  2. Spectra of all the candidates can be found in Figs. A. 1-A. 3). We do not identify the origin of the remaining regions highlighted in Fig.
Acknowledgements. The authors thank the anonymous referee for the helpful comments and constructive remarks on this manuscript. This work is based on observations collected at the  European Southern Observatory  under  ESO  programme  60. A-9188 (A). A. A. acknowledges the support of the  Swedish Research Council, Vetenskapsrådet , and the  Swedish National Space Agency (SNSA). GB acknowledges financial support from  DGAPA-UNAM  through  PAPIIT  project  IG100319. MF acknowledges support by the  Science and Technology Facilities Council  [grant number  ST/P000541/1 ]. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  757535 ). AW acknowledges financial support from  DGAPA-UNAM  through  PAPIIT  project  IA105018.
Predicting fluorescently labelled cellular structures from brightfield images is a recent application of convolutional neural networks and has already proven a valuable tool in biological imaging studies. These methods reduce the need for time-consuming manual annotations for supervised datasets, potentially reduce the costs in high-throughput imaging screens, and open up a number of potentially novel analyses. However as with any prediction there can be sources of error and uncertainty. Here we present BFNet, a method to visualise the uncertainty in predicted images by applying Monte Carlo dropout during inference and calculating per-pixel variance as an uncertainty map. Our method demonstrates the ability to highlight regions of an image where prediction is difficult or impossible due to imaging artefacts such as occlusions or out-of-focus images, as well as more general uncertainty when a trained model is applied to new data from different imaging of experimental settings.