dataesr/scientific-paragraphs-categorization huggingface dataset:
Clustering analyses were done with the se-qMINER software  (39) , and clustering normalization was done with the K-Means linear option. Venn diagrams were generated with Venny  (40)  or InteractiVenn  (41). Additional binding site analyses were performed using the MEME Suite  (42). De novo identified motifs were referred to as follow: R = purine (G or A); Y = pyrimidine (T or C). Further bioinformatics analyses were performed with bedtools (https://bedtools. readthedocs. io/en/latest/index. html)  (43) : fasta sequences were obtained from bed files with GetFastaBed, location intersections were performed with Intersect interval and Multiple intersect, and gene centric analyses of peak distribution were performed with WindowBed.
8x AMPure XP beads, DNA was sequenced with a HiSeq 4000 (Illumina) as single-end 50 bp reads. Sequences were trimmed to remove primer and bait fragment sequence with the sabre tool (https://github. com/najoshi/sabre), mapped to the genome with Bowtie and converted to restriction fragment space as described  (47). Interactions were called on single 4C experiments with peakC (48), using a sliding window of 21 fragments.
Microscale thermophoresis Microscale thermophoresis (MST) was performed as described  (50). The apparent Kd value of the GR DBD bound to DNA was measured using a Monolith NT 115 from Nan-oTemper Technologies GmbH. Recombinant purified GR DBD was labelled with the fluorescent dye NT-647 (Nan-oTemper Technologies) using the Monolith NTTM Protein Labelling kit (amine reactive). The labelling procedure and the subsequent removal of free dye were performed within 1 hour. Unlabeled DNA was serially diluted from a concentration of 100 M to 1 nM in the presence of 1667 nM labelled receptor, and loaded into Premium capillaries (Nan-oTemper Technologies reference MOK025). Measurements were performed at 20 • C in 10 mM Hepes KOH pH 7. 4, 150 mM KOH, 3 mM MgAc, 1 mM TCEP, and 1% BSA, at 50% LED power and 20% IR-laser power.
Taking advantage of available Myod1 ChIP-seq datasets in myotubes (57-59), we found that half of the muscle-specific enhancers were bound by Myod1 in myotubes (Figure  1I  and J ). In contrast, prostate-specific enhancers were mainly associated with ERG and SIX2 motifs, whereas PPAR (NR1C) and RXR (NR2B1) motifs were found at enhancers identified in adipose tissue, and ERRa (NR3B1) and THRb (NR1A2) were predominant in liver (Supplementary Figure  S1I ). Thus, most active enhancers in skeletal muscle encompass binding sites for transcription factors distinct from those identified in prostate, adipose tissue and liver.
GR and Myod1 cooperate to regulate gene transcription in myofibers Since our data revealed that GR and Myod1 are among the major transcription factors associated with enhancers in skeletal muscles, we questioned whether these factors are functionally intertwined. Motif analyses of GR cistrome revealed that Myod1 E-boxes (5 -CAGCTG-3 ) are present in more than 20% of GR-bound enhancers (Figure  5A ). Moreover, seqMINER analysis of Myod1 ChIP-seq datasets in myotubes and of GR cistrome in skeletal muscles indicated that Myod1 is bound at one-third of GR peaks (cluster 2, Figure  5B  and C ). The absence of H3K4me3 associated with the presence of H3K27ac at these shared sites suggested that they are selectively located at enhancers (Fig-   (B, C ) Tag density map of H3K27ac, H3K4me1, H3K4me3 and GR in skeletal muscles, and Myod1 in C2C12 myotubes, ± 5 kb from the GR peak center (B) and corresponding average tag density profiles of cluster 2 (C).
(D) Overlap between genes bound by GR in skeletal muscle and by Myod1 in C2C12 myotubes obtained from 3 merged datasets, and genes that are down regulated in GR (i)skm-/-mice. (E) Western blot analysis of skeletal muscle nuclear extracts immunoprecipitated with anti-GR or anti-Myod1 antibodies. Membranes were decorated with anti-GR and anti-Myod1 antibodies. rIgG served as a control for immunoprecipitation. Non-immunoprecipitated extracts (10% input) were also analyzed. (F) ChIP-qPCR analysis performed at indicated locations with anti-Myod1 antibodies or rIgG in skeletal muscle of wild-type mice. (G, H) ChIP-qPCR analysis performed at indicated locations with anti-GR (G) or anti-Myod1 (H) antibodies in C2C12 myotubes transfected with siRNA directed against Myod1 (siMyod1) (G), GR (siGR) (H) or scrambled siRNA (siCtrl).
Of note, GR protein levels and recruitment to the enhancer GRE of Eif4ebp2 were not affected under these conditions (Figure  7F  and Supplementary Figure  S7E ). Additionally, co- immunoprecipitation experiments revealed that Nrf1 and GR interact in muscle nuclear extracts (Figure  7G ). Moreover, Nrf1, GR or Myod1 silencing decreased the transcript levels of the GR target genes Eif4ebp2 and Pik3r1 by 50% (Supplementary Figure  S7F ). However, none of these knock-downs affected the expression of Cytc and Uqcrc1, that were shown to be up-regulated upon NRF1 overexpression  (68) , or that of various other various nuclear encoded mitochondrial proteins (Supplementary Figure  S7E  and F ). Of note, basal and maximal respirations were lower in Nrf1-and Myod1-silenced myotubes than in control cells, in agreement with previous reports (69) but were not significantly altered by GR loss (Supplementary Figure  S7G-H ).
ACKNOWLEDGEMENTS We are grateful to  Jean-Marc Bornert ,  Laetitia Paulen ,  Régis Lutzing  and  Isabelle Hazemann  for providing excellent technical assistance. We thank the IGBMC animal house facility, the  Structural Biology , the cell culture, the  Mouse Clinical Institute (ICS, Illkirch, France) , and the GenomEast platform, a member of the ' France Génomique' consortium  ( ANR-10-INBS-0009 ). Author contributions: D. M. generated the original hypothesis. D. D. , D. R. , V. U. -P. , K. B. R. , K. G. and G. L. performed molecular and histological characterizations of mice. S. J. , M. P. and D. R. performed ChIP-seq experiments. S. A. J. performed microarray experiments. A. -I. R. and D. D. performed bioinformatics analyses. D. R. performed cell culture experiments. I. M. L. B. , B. P. K.
and V. D. -S. performed biophysical analyses. N. K. and T. S. performed 4C-seq experiments and analyses. A. F. performed in situ muscle strength measurements. A. -I. R, D. R. , D. D. , I. M. L. B. and D. M. took primary responsibility for data analysis and writing the manuscript. All authors edited the manuscript. FUNDING INSERM; CNRS; Unistra; IGBMC;  Agence Nationale de la Recherche  [ ANR-10-BLAN-1108 ,  AndroGluco ;  ANR-16-CE11-0009 ,  AR2GR ]; AFM strategic programme 15352;  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  [starting grant 678624 -CHROMTOPOLOGY];  ATIP-Avenir program  (to T. S. ); INSERM young researcher grant (to D. D. );  ANR-10-LABX-0030-INRT , a  French State  fund managed by the  ANR  under the  frame program Investissements d'Avenir  [ ANR-10-IDEX-0002-02 ];  French Infrastructure for Integrated Structural Biology FRISBI  [ ANR-10-INSB-05-01 ] and Instruct-ERIC; A.
-I. R. is an  IGBMC International PhD Program  fellow supported by  LabEx INRT  funds; D. R. and V. D. -S. were supported by  ANR-16-CE11-0009 ,  AR2GR , V. U. -P. by the  Ministère de l'Enseignement et de la Recherche  and by the  Fondation pour la Recherche Médicale  (FRM); D. D. by the  French Muscular Dystrophy Association  ( AFM-Téléthon ); FRM and the  Association pour la Recherche à l'IGBMC (ARI) ; K. G. by  ARI. Funding for open access charge:  Agence Nationale de la Recherche.
DATA AVAILABILITY ChIP-seq (GSE14251) and (GSE164543), 4C-seq (GSE142516), ATAC-seq (GSE151790), and RNA-seq (GSE151791) data reported in this study are available at GEO database at https://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?acc=GSE142796, under the accession number GSE142796. Microarray data are available at GEO database at https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc= GSE144228, under the accession number GSE144228. Myod1 ChIP-seq datasets were obtained from GSE21621, GSE49313, and GSE56077. H3K27ac, H3K4me1, and H3K4me3 datasets from prostate, adipose tissue, and liver were obtained from GSE47194, GSE58491, and GSE77625, respectively. The following link was generated to visualise our datasets in UCSC: https://genome. ucsc. edu/s/annabelo23/ mm10 GSE142518 UCSC.
Quantum Lookups Constructing the initial database from our offline queries requires a QROM 4  circuit. We do not assume special, cheap QROM operations (i. e. , the QRAM model), but rather give the cost in terms of a Clifford+T simulation of QROM. With no depth restriction, the cheapest (in total operation count) is due to Babbush et al. [2]. Berry et al. [4]  give a version that is cheaper in T-operations and smoothly parallelizes, but since we have no need to parallelize and consider the full operation count, we use only the Babbush et al. QROM circuit.
We then estimated the cost of solving an m × n linear system, using costs from subsection 5. 1. Once we found the optimal m, we used Q# to get an exact cost of solving the linear system. The code for this estimation is available at https://github. com/sam-jaques/offline-quantum-period-finding/. Our results are in Table  2  and Table  3. We include results for Shor's algorithm to attack RSA-2048 and an exhaustive quantum key search on AES-128 for comparison. Exhaustive Key Search. We also estimated the cost of performing an exhaustive quantum key search on the ciphers, summarized in Table  4. The circuits for these are slightly different, as we need to attack the full encryption, rather than just the permutation. Chaskey and Elephant modify the key slightly before using it. Elephant transforms the key from 128 bits to the block size, so it is much more efficient to modify the key as part of the search oracle and search a 128-bit space, rather than search a key space as large as the full block size.
Part 1: Defining the kinetic classes Defining the kinetic classes first requires a dataset of healthy volunteers (training dataset, generally N > 10) acquired with the same tracer and, ideally, with the same experimental protocol and scanner as the PET images to which the SVCA will be applied (testing dataset). Good quality anatomical MRI images are also necessary to define tissue masks. The data processing pipeline performed for each subject of the training dataset is summarized in Fig. 1. The dynamic PET images of each participant must first be normalized in order to enhance the dissimilarity of the tracer kinetics between tissue types and to reduce intra-subject variability. PET image normalization is done by subtracting the mean of all voxels within the PET frame from each voxel of that frame and dividing the result by the standard deviation of all voxels within the PET frame.
The final shape of the blood activity curve should be visualized to check that it exhibits the expected behaviour of a sharp peak at early frame times followed by a fast washout, typical of PET blood input functions. 4. High-binding grey matter can be derived from the pathological grey matter segmented in a set of participants with known neuroinflammatory processes. Alternatively, the high-binding grey matter can be derived from the thalamic region in healthy volunteers, as this is the region with the highest expression of TSPO in the healthy brain  [23, 28]. The final kinetic classes are obtained by averaging all the normalized time-activity curves across participants. Alternatively, the median can be used, which may be more appropriate when handling data outliers. As an additional note, in neuroinflammation PET studies with secondgeneration TSPO tracers, the Ala147Thr genetic polymorphism modulates the tracer-to-target affinity.
Quality control It is important to run quality control steps throughout the SVCA to ensure that the kinetic classes, selected reference region voxels and final SVCA reference region time-activity curve exhibit their expected behaviour. First, the normalized time-activity curves for the training dataset should exhibit similar behaviour (i. e. time-activity curve shape) within each tissue type. Plotting the normalized time-activity curves of the individual tissue classes against each other is important for identifying tracer kinetic heterogeneity and investigating outliers, both of which must be done before calculating the summary statistic to be used as the kinetic class. Once the SVCA reference region has been extracted, the selection of voxels should be visualized to ensure that a suitable number of voxels are present and that these are generally evenly distributed across the healthy grey matter.
Validation SVCA must be validated before it can be applied for the first time to a dataset using a new PET tracer. Proper validation would ensure that the reference region extracted with SVCA lacks specific/displaceable binding. In this context, a blocking study could be conducted by applying the SVCA to the baseline scan to extract a candidate region and then by testing whether the distribution volume (V T ) of this region is affected by the blocking agent  [30]. However, given the background level of TSPO expression present in the brain of even healthy volunteers, some degree of specific binding will inevitably be present in the extracted region  [31, 32]. Nonetheless, a blocking study would still be useful because it would allow the extent of TSPO expression in the extracted region to be quantified.
The authors also reported significant correlations between the BP ND of the thalamus (estimated with SRTM2) calculated using either the SVCA or cerebellum references, albeit without reporting the magnitude of the correlation. In a more extensive evaluation of SVCA for 18 F-DPA714, 14 healthy volunteers (seven HABs and seven MABs) underwent a 90-min 18 F-DPA714 PET exam  [23]. Three participants (two HABs and one MAB) had a second PET scan around 1 week later, and 10 participants (seven HABs and three MABs) had data collected with an arterial input function. Four kinetic classes were defined: blood pool, white matter, low specific binding (corresponding to the cerebellum) and high specific binding (corresponding to the thalami). The four classes were derived from the ensemble of the population (SVCA_ALL) and for HABs and MABs separately (SVCA_HAB/MAB).
Portability of kinetic classes The portability of the kinetic classes to data acquired from different imaging sites and with different experimental designs has not yet been systematically investigated. In  [38] , the 11C-(R)-PK11195 BP ND estimates obtained with different sets of kinetic classes, derived from different sites, were highly correlated but different in terms of absolute value. This indicates that the use of kinetic classes derived from different imaging protocols will probably introduce a bias in the estimates, which may not be a problem if the bias is constant for all participants. In a non-TSPO brain PET study with 11 C-PIB  [47] , the kinetic classes were defined from a dataset of healthy individuals and patients with Alzheimer's disease acquired with ECAT EXACT HR+ (CTI/ Siemens)  [29]  and then applied to an independent dataset of healthy volunteers and patients with multiple sclerosis acquired with a Siemens HRRT.
The results supported the kinetic class portability, although performance was dependent on the PET reconstruction settings. Our recommendation in this case is to re-define the kinetic classes using site-specific control data collected independently + low variability of the distribution volume ratio (DVR) estimates + high sensitivity to clinical abnormalities differences in the time-activity curves of the extracted reference region between genotype subgroups  [24]  from but with the same PET imaging protocol as the study of interest. If the dataset of healthy volunteers is part of the study, the reference region in these participants should be obtained using a leave-one-out approach: for each participant in the healthy volunteer group, SVCA should be applied using the kinetic classes defined from all the other healthy volunteers except the one to which the method is applied.
Most of the software is developed in C++ and runs on an on-board PC, with the exception of the gain tuning algorithm which runs in MAT-LAB/Simulink on a ground PC. Most of the onboard software are open source, and can be found at https://git. openrobots. org/ projects/telekyb3, while the OPTIM-tune software is available at https://github. com/AppliedControlTechniques/Optim-tune. A simulation and an experimental campaign have been carried out, in which the task has been to tune the controller gains, for the selected values of f xy and different Q ratios. The interested reader is referred to the multimedia attachment of this letter for the experiments' videos. In all the experiments and simulations, the lateral acceleration of the reference trajectory has been pushed up to 1. 5 ms -2 , jerk to 10 ms -3 , lateral velocity to 2 ms -1 , to bring the platform to its lateral motion limits.
This work was partially funded by the  European Robotics Research Infrastructure Network   H2020 INFRAIA TERRINet (2017-2020 ) under Grant Agreement no. 730994  during translational infrastructure access in TERRINet; by  Poznan University of Technology  under grant no. 214/SBAD/0220 ; and by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement ID: 871479 AERIAL-CORE.
However, we did not consider the duck movements as a time varying predictor, which could have impacted our ability to detect duck movements as a predictor of viral transmission between départements. This hypothesis could be tested in the future with duck movement data collected over time (time-dependent variable). Alternatively, our results could also mean that the virus did not spread through duck movements in the long-distance, but by some other mechanisms. Two major contenders for alternative mechanisms of viral spread are humans and farm equipment as fomites and wild birds as carriers. We did not find any significant association between human population density-which acted as a proxy for human activity-and viral spread. This suggested that human populations, which were not specifically involved in the duck-producing industry, did not play a major role in spreading the virus.
Genomic data, bioinformatics and phylogenetic methods 196 HPAI H5N8 viruses were isolated amongst the 484 farms that experienced an outbreak between November 2016 and March 2017. The viral genomes were sequenced (one sequence per farm) at the French national reference laboratory for avian influenza (ANSES-Ploufragan, France). Each sequence was geocoded and associated with the département where the outbreak was located (Tarn, Aveyron, Lot-et-Garonne, Gers, Landes, Pyrénées-Atlantiques and Hautes-Pyrénées) along with the collection date. The whole concatenated virus genomic sequences were aligned using the MUSCLE multiple sequence alignment algorithm  [27]  implemented in MegaX software with default parameters  [28]. Influenza viral genome is particularly prone to frequent genomic reassortments  [29] , which, if undetected, may provide spurious genomic signals.
Hence, we also considered human population per département as a separate predictor. These timeindependent variables were computed from the French Directorate-General for Food (DGAl) and CIFOG databases  [35]. We also considered the total number of infected farms in a département to be a potential predictor of viral spread as départements with high number of cases could represent a major source of virus for other departments. Also, as the first case was detected in Tarn, we considered a potential predictor that represented Tarn to the most likely source of viral spread for all départements. Finally, two time-independent spatial variables were considered as previous studies identified spatial patterns in the epidemic spread  [1, 35]. We tested the effect of the Euclidean distance between départements centroids as well as the border sharing between départements on the viral spread between departments.
Livestock density plays an important role in viral transmission and hence could be linked to Ne  [42]. We used the densities of duck and chicken farms per département as potential predictors. Humans, as fomites, can also influence Ne and hence human population density was also used as a predictor. Both these predictors were timeindependent. Also, all non-binary variables were log transformed and standardised so that their means and standard deviations equalled 0 and 1, respectively. The modelling exercises and the Markov chain Monte Carlo (MCMC) simulations were conducted in BEAST v2. 6. 3  [43]. For posterior sampling, we used the coupled MCMC package  [44]  with 100 million iterations. The convergence and mixing of the MCMC chains were checked visually in Tracer v1. 7. 1  [45]. An analysis was considered successful if the effective sample size of each of the parameters was at least 200.
2), substantial (3. 2 > K < 10), strong (10 > K < 100) or decisive (K > 100). In the final model, we included only the predictors with at least substantially confident result. These statistical analyses were conducted in R v4. 0. 3  [48]. All the data, including genetic data accession numbers, xml files and R scripts, are available in the following repository, https://github. com/dc27708/H5N8_MASCOT.
Mechatronic and soft robotics are taking inspiration from the animal kingdom to create new high-performance robots. Here, we focused on marine biomimetic research and used innovative bibliographic statistics tools, to highlight established and emerging knowledge domains. A total of 6980 scientific publications retrieved from the Scopus database , evidencing a sharp research increase in [2003][2004]. Clustering analysis of countries collaborations showed two major Asian-North America and European clusters. Three significant areas appeared: (i) energy provision, whose advancement mainly relies on microbial fuel cells, (ii) biomaterials for not yet fully operational soft-robotic solutions; and finally (iii), design and control, chiefly oriented to locomotor designs. In this scenario, marine biomimicking robotics still lacks solutions for the long-lasting energy provision, which presently hinders operation autonomy.
Database Search The Scopus database was consulted on 27 August 2020 and used to retrieve bibliographic records related to biomimicking for 1950-2020. The search was restricted to scientific publications written in English, avoiding biomedicine and agricultural oriented results. To identify relevant publications, the following keywords were used in the combined fields of title, abstract and keywords (per publication): TITLE-ABS-KEY (bioinspired OR bioinspiration OR "biologically-inspired" OR biophysics OR bionics OR biomimicking OR biomimics OR biomimetics OR biomimic OR biomimetic OR bio-mimicking OR bioinspired OR "microbial fuel cell" OR "bio fuel cell" OR bioenergy OR bio-energy) AND (marine OR sea OR underwater OR water OR ocean OR exocean OR exo-ocean OR astrobiology OR moon OR moons OR "planets" OR deep-sea OR deepsea OR acid-minedrainage) AND (design OR cybernetics OR robot OR mechatronic OR vehicle OR engineering OR mechanical OR technologies OR materials) AND NOT (agricultural OR agriculture OR tumour OR orthopaedic OR fouling OR pharmacology OR medicine OR tumor OR antibiotics OR lubricants OR lubricant OR dentist OR dentistry OR dental OR dentin OR surgical OR bone OR surgery OR disease OR clinic OR clinical OR "biomedical device" OR biomedicine OR solution OR endoprosthesis OR trauma OR orthopaedic OR surgery OR "in vitro" OR drug OR anticoagulant OR inflammatory OR "oil spill" OR saliva OR salivary OR therapy OR healing OR wound OR europium OR urban OR megacity OR megacities OR child OR children OR dietary OR nurse OR "medical imaging" OR tomography OR "magnetic resonance" OR x-ray OR nutrition OR pregnancy OR "body water" OR nursing OR infancy OR "comparative study") AND PUBYEAR >1950.