dataesr/scientific-paragraphs-categorization huggingface dataset:
They involve gradually degrading a sample of interest (i. e. , an image) with subtle Gaussian noise until the sample becomes a random Gaussian field, learning the noising process in terms of a score function, and then, by inversion from a Gaussian noise realization, generate a meaningful sample  [287]. Specifically, the inverse process uses the gradient of the log-density of the prior (the score) which is approximated with a NN trained for score matching, and generates an image according to the a-priori probability distribution of the training dataset. DMs can be used to solve inverse problems by adding a data fidelity gradient descent step in the inverse diffusion, or by using the pseudo a-posteriori probability distribution conditioned to the observed data, which work in an unsupervised manner.
Such an idea was discussed as the first use case in a recent perspective article  [289]. Along that direction, virtual twins of physical PCCT scanner models can scan patient avatars to produce simulated data. Along a complementary direction, a limited number of real PCCT scans can be used to train a generative model for realistic image augmentation. For example, it was recently shown that the diffusion model can be used to synthesize realistic data with suppressed privacy leakage  [290]. This will facilitate federated learning at the level of datasets. AI model Development: When sufficiently informative PCCT data are available, more advanced AI models should be developed to address current weaknesses of deep reconstruction networks in the CT field. The well-known problems of deep networks include stability, generalizability, uncertainty, interpretability, fairness, and more.
DL dictionary learning This work did not involve human subjects or animals in its research. This work was supported by the  French National Research Agency (ANR)  under grant No  ANR-20-CE45-0020 , and the  National Institutes of Health (NIH)  under grant Nos  R01EB026646 ,  R01CA233888 ,  R01CA237267 ,  R01HL151561 ,  R42GM142394 ,  R21CA264772 ,  R01EB031102 ,  R01EB032716 , and  R21EB027346.
This work aims to predict the microstructure of recrystallized medium and high-entropy alloys (MEAs and HEAs) with a face-centered cubic structure, in particular the density of annealing twins and their thickness. Eight MEAs and five HEAs from the Cr-Mn-Fe-Co-Ni system are considered, which have been cast, homogenized, cold-worked and recrystallized to obtain different grain sizes. This work thus provides a database that could be used for data mining to take twin boundary engineering for alloy development to the next level. Since the stacking fault energy is known to strongly affect recrystallized microstructures, the latter was determined at 293 K using the weak beam dark-field technique and compared with ab initio simulations, which additionally allowed to calculate its temperature dependence.
Since these models have only been applied to a limited number of pure metals and binary alloys so far, it is unclear, whether they can be used to predict the distribution and thickness of annealing twins in more complex solid solutions such as medium and high-entropy alloys (MEAs and HEAs). The present work therefore aims to fill this gap. In addition, the microstructural and physical parameters, as well as the methods used to obtain them, are not systematically reported in this field of research, which hinders the comparability of the data, and more importantly, prevents data mining to take twin boundary engineering in alloy development to the next level  [14, 15]. Therefore, the second objective of the present study is to provide a comprehensive database of annealing twin microstructures in single-phase FCC recrystallized MEAs and HEAs from the Cr-Mn-Fe-Co-Ni system.
In addition to the aforementioned alloys, MEAs and HEAs, which we had previously investigated using the same processing route and characterization methods, were considered here, i. e. , MnFeNi  [16, 18] , CrCoNi  [19, 20] , CrFeNi  [21, 22] , and five Cr x Mn 20 Fe 20 Co 20 Ni 40-x HEAs with x = 14, 18, 20, 22, and 26 at. %  [23] , which are labelled Cr 14 , Cr 18 , Cr 20 , Cr 22 , and Cr 26 , respectively. As this database includes eight MEAs and five HEAs with a broad range of properties (shear modulus, SFE and lattice parameter), it may be used to determine how these affect the recrystallized microstructure of FCC materials. After recrystallization, specimens were prepared for metallographic examinations following Ref. [19]. Texture analyses were performed along the longitudinal axis of the swaged rods using electron backscatter diffraction (EBSD) in a Quanta FEI 650 ESEM equipped with a Hikari XP camera (EDAX, AMETEK).
Acknowledgments M. S. acknowledges funding through a  Feodor-Lynen Scholarship , granted by the  Alexander von Humboldt  foundation. G. L. acknowledges funding from the  German Research Foundation (DFG)  through project  B8  of the  SFB/TR 103. A. F. and F. K. acknowledge funding from the  Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO)  [ VIDI  Grant No. 15707 ]. F. K. acknowledges the  DFG  via the  Priority Program SPP 2006  " Compositionally Complex Alloys -High Entropy Alloys ". Part of the calculations were conducted on the Dutch national einfrastructure with the support of  SURF Cooperative.
The Navier-Stokes equations can be solved numerically with physically consistent accuracy in space and time using DNS (e. g. , Moin and Mahesh  [9] ). Despite spanning a wide range of lengths and time scales, the entire flow fields of key idealized flows (e. g. , HIT, (Buaria et al. [10] ; Cao et al. [11] ; Ishihara et al. [12] ; Iyer et al. [13] [14] [15] ; Kaneda et al. [16] ; Yeung et al. [17] )) can be obtained. Researchers can extract desired information about turbulent structures and statistics by analyzing datasets harvested from the flow fields.
6 × 10 5 or Taylor-based Reynolds number of Re λ of ∼1000. Several major computational and experimental studies are consistent with this criterion, where the values of Reynolds number are (i) 3 × 10 8 (out-scale Reynolds) for the tidal channel  [27] ; (ii) Re λ ∼ 1300 from the DNS by Yeung and Ravikumar  [6] ; (iii) Re λ ∼ 3. 2 × 10 3 (return channel) or 2 × 10 3 (mixing layer) from very large wind tunnel experiments by Praskovsky et al. [28]  (iv) Re λ ∼ 1450 from another very large wind tunnel experiment by Saddoughi and Veeravalli  [29]. Using several experimental and simulation datasets, Figure  4. 6 of  [30]  shows that Re λ > 5 × 10 3 is sufficient for forced turbulence to recover the Kolmogorov 4/5 law. However, this figure also indicates that an order of magnitude higher Re λ is needed for decaying turbulence, which might be sensitive to initial conditions, to approach the 4/5 scaling.
ACKNOWLEDGEMENTS The research leading to these results has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement number  674990  (EXCILIGHT). This work was performed using HPC resources from the " Mésocentre" computing center of CentraleSupélec and École Normale Supérieure Paris-Saclay  supported by  CNRS  and  Région Île-de-France  (http://mesocentre. centralesupelec. fr/).
The spread of such resistance is of concern because coselection promotes the acquisition of antibiotic resistance  [10, 39]. Although the gene encoding the LysR-type regulator is present on PAGI-2, it was considered to be part of Module B, since we demonstrated that it was part of the pcoA2 operon. As ICEs are modular and cargo genes can be exchanged, we assessed how the two cargo modules are distributed in bacterial genomes by performing blast searches against the NCBI database (Fig  6B ). Modules A and B from ICE1_IHMA are both present in several bacteria from various genera. The most frequently represented genus was Pseudomonas, and the corresponding predominant species are aeruginosa and paraeruginosa. The two modules were also identified in other genera with distinct distributions, for example in a few Achromobacter and Stenotrophomonas genomes, whereas only Module A was identified in some Comamonas genomes.
aeruginosa and P. paraeruginosa, with only two strains of Pseudomonas not belonging to these species. We then examined the distribution and conservation of the lysR-cusS region over a database of 494 complete P. aeruginosa and P. paraeruginosa genomes. The clades were named based on the approach described by Freschi et al. [40]  from 1 to 5, and, as proposed by Rudra et al. [25] , outlier group 3 was (re)classified as P. paraeruginosa (P. p). Bioinformatics analysis revealed the locus to be present in several strains of P. aeruginosa clades 1 and 2, and (as expected) in the IHMA87 strain, which is classed in clade 3 (Fig  6C ). The locus was not detected in the few clade 4 and 5 strains for which genomes are available (www. pseudomonas. com;  [27] ). The locus was present as part of a putative ICE in over 80% of genomes.
Bioinformatics analyses ICEs were identified and delineated using the web-based tool ICEfinder  [65]. ICEs were typed using ConjScan  [66]  on the Pasteur Galaxy platform  [67]. Sequences were aligned and visualized with clinker  [68]. Blast analyses were performed with BLASTn  [69]. Complete genome sequences of P. aeruginosa (n= 487) and P. paraeruginosa (n=7) were downloaded from the Pseudomonas Genome Database  [27]. Genomes were annotated with Prokka  [70] , and core genomes were determined with Roary  [71]  before alignment with MAFFT using the default parameters  [72]  on the Galaxy server  [73]. The resulting alignment was used to build a maximum-likelihood phylogenetic tree with FASTTREE  [74]  on the Galaxy server. The phylogenetic tree was finally visualized and annotated using iTOL  [75].
Acknowledgments We thank  Eric Faudry  for valuable advice on protein purification and  Emmanuel Thévenon  for giving us access to the ImageQuant imager. P. aeruginosa IHMA87 was obtained from the  International Health Management Association, USA. This work was supported by  GRAL , funded through the  University Grenoble, Alpes graduate school (Ecoles Universitaires de Recherche)   CBH-EUR-GS  ( ANR-17-EURE-0003 ). We further acknowledge support from  CNRS ,  CEA  and  Grenoble Alpes University. Victor Simon 's Ph. D. fellowship was funded by the  French Ministry of Education and Research.
Statistical analysis Descriptive analyses were conducted of the main demographic and exposure characteristics of the study subjects, by case/control status and by country. The odds ratio (OR) and 95% confidence interval (CI) were calculated using conditional multiple logistic regression analysis, with covariates of country, maternal educational level, for the risk of developing a brain tumor as a function of cumulative mobile phone use time and cumulative estimated RF energy (J/kg). The exposure variables (cumulative call time and RF CSE at the different locations in the brain) were categorized in tertiles on the basis of the distribution of these variables among the controls at 1 year before the reference date. Analyses are shown of the data from Korea and Japan together as numbers of cases were too low for meaningful comparisons of risk between countries.
A c c e p t e d V e r s i o n 14 In this study of Korean and Japanese young people, we observed a slight elevated risk of all brain tumor among the heaviest users of mobile phones. The confidence interval was large, however, and there was little evidence of a dose response relationship. ORs by level of RF energy were close to 1. 0, again with no evidence of an association with dose. Analyses were performed by merging the Korean and Japanese data because the statistical power was insufficient unless the inclusion criteria were expanded to include cases with midbrain tumors, which were excluded from the MOBI-Kids international study, with relaxed residential matching criterion for controls. The greatest strength of this study was our assessment of RF exposure from mobile phone use. The RF exposure algorithm was modified to consider the specific types of phones used in Korea and Japan, using modified phone-type classifications corresponding to the situations in Korea and Japan.
Conclusions Despite the aforementioned limitations such as the small study sample size, our findings suggested that mobile phone use does not greatly impact the development of brain tumors. However, because we combining evaluated young people in both Korea and Japan who were geographically and genetically similar, small numbers and large confidence intervals might preclude any clear conclusions. Patricia de Llobet (ISGlobal) for assistance in coordinating the mobile phone database. We would also like to thank all members of the Exposure Assessment Subcommittee.
This work was supported by the Japanese Ministry of Internal Affairs and Communications [grant number JPMI10001]. This work was supported by the ICT R&D program (2017-0-00961 and 2019-0-00102) of MSIT/IITP, Korea. The international MOBI-Kids study (including development of the study protocol and questionnaire, data management, and validation) was supported by the European Community's Seventh Framework Programme [grant numbers 226873 and 603794] and Spanish Ministry of Science and Innovation (MINECO). ISGlobal was supported by the Spanish Ministry of Science, Innovation and Universities through the "Centro de Excelencia Severo Ochoa 2019-2023" Program [grant number CEX2018-000806-S]; the Generalitat de Catalunya through the CERCA Program; and the Secretariat of Universities and Research of the Department of Business and Knowledge of the Generalitat of Catalonia through AGAUR (the Catalan Agency for Management of University and Research Grants) (Project #2017 SGR 1487).
Acknowledgments: We would like to thank all participants and their relatives who participated in the  MOBI-Kids  study for their time and collaboration. We acknowledge all of the field work coordinators, research assistants, interviewers in all the study centers, and all the hospital services for their cooperation and collaboration in the Korean and Japanese centers. We would also like to thank  M Matsushita  and  M Ikuyo  for data management of the Japanese mobile phone database;  Carmen Cabas  and  Alex Albert  ( ISGlobal, Barcelona ) for development of the online questionnaire and maintenance and validation of the database,  Laurel Kincl  and  Gemma Castaño-Vinyals  ( ISGlobal ) for coordinating the international study including interviewer training, and.
Characterizations The density of the different bulk samples was determined through the Archimedean principle using a Kern balance. This balance enables weighing solids in air (m air ) as well as in a solvent (in our case, ethanol m ethanol ). If the density of the buoyancy medium is known (ρ 0 ), the density of the solid (ρ) is calculated as follows: were collected from 10 • to 80 • (2θ) with a 0. 012 • step size and an acquisition time of 5 s/step. Variable temperature (VT-PXRD) data were collected under air using an XRK900 Anton Paar furnace. The powder sample was placed into a corundum sample holder. Data were collected from 10 • to 80 • (2θ) with a 0. 024 • step size and an acquisition time of 1 s/step. Structure refinements were performed from powder diffraction data using the Fullprof academic software which enables the use of the anisotropic broadening function  [30].
Raman spectra were recorded using an In Via Reflex Renishaw micro-Raman spectrometer at 532 nm. A grating of 2400 gr/mm was used, along with an objective x100 while using a laser power of 3 mW at the sample to avoid any damage of the sample. Electron backscatter diffraction (EBSD) maps were recorded on both anti-glass and ceramic materials using an OIM™ TSL/EDAX system mounted on a FEG-SEM (Zeiss SUPRA 55 VP) microscope in order to confirm the crystalline nature of the samples and to visualize the microstructure, grain size as well as the crystal orientation using OIM 8. The data were acquired at a high voltage of 20 kV in VP mode. The pixels whose confidence index is under 0. 1 were removed from the maps. The nanostructure of the samples was characterized by Transmission Electron Microscopy (TEM) micrographs, Scanning Transmission Electron Microscopy -High Angle Annular Dark Field (STEM-HAADF) imaging and EDS elemental analyses.
2. Its zero-error classical capacity C 0 (W BAC ) is not yet characterized. A lot of work has been done in nding outer and inner bounds on this region  [23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]. To date, the best lower bound on the sum-rate capacity is log 2 (240/6) 1. 3178  [32]. Thanks to Proposition 4. 1, we were able to compute the regions C NS 0,≤n (W ) for n going up to 7, which led to Figure  4. The code can be found on GitHub. It uses Mosek linear programming solver  [34]. Note that the linear program from Theorem 3. 10 has still a large number of variables and constraints although polynomial in n. capacity region C(W BAC ), whereas the grey dashed curve shows the best known inner bound border on the zero-error classical capacity region C 0 (W BAC ), made from results by [32, 28, 27]; see [32] for a description of this border.
Acknowledgements We would like to thank  Mario Berta  and  Andreas Winter  for discussions about the use of non-signaling correlations for multiple access channels as well as the anonymous referees for their suggestions that improved the paper. This work is funded by the  European Research Council  ( ERC Grant AlgoQIP , Agreement No. 851716 ). We also acknowledge funding from the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No  101017733  within the  QuantERA II Programme.
coli  18. Based on the demonstration that S. cerevisiae coq4 mutants failed to assemble complex Q and did not synthesize CoQ, COQ4 was proposed to fulfill a structural role in the organization of complex Q 18. However, the biosynthetic complex is destabilized in most yeast strains deleted for coq genes, and CoQ, or at least late biosynthetic intermediates like demethoxy-CoQ, seem important for complex Q stability  19. COQ4 proteins display a conserved motif, HDxxH(x)11E, which has been proposed to be a zinc-binding domain  18. Consistent with this hypothesis, the aspartic acid and glutamic acid residues of the HDxxH(x)11E motif coordinate two magnesium ions in the crystal structure of a cyanobacterial homologue of COQ4, Alr8543 from Nostoc sp. PCC 7120 (PDB: 6E12) (Figure  S1A ), which has been used in the past to model eukaryotic COQ4  20.
ACKNOWLEDGEMENTS This work was supported by grants from the  ANR  to FP ( ANR-19-CE44-0014O2-TABOO ), from the  NIH  to DJP ( R35GM131795 ), from  Telethon Italy  ( GGP13222 ), from  Fondazione IRP Città della speranza  (to LS), from  Fondazione CARIPARO  ( 20/19 FCR ) to ET, and from  European Regional Development Fund (FEDER)/Andalusian Regional Government  ( FEDER operational program   2014-2020 ;  P18-RT-4572 ).
Data-dependent acquisition (DDA) was performed for the top 20 precursor ions with the charge state of 2-6 and an isolated width of 2. Intensity threshold was 5E3. Dynamic exclusion was 30 s with the exclusion of isotopes. Other settings for DDA include Orbitrap resolution of 15000 and HCD collision energy of 30%. Raw files were analyzed by SequestHT Search Engine incorporated in Proteome Discoverer v. 2. 5. 0. 400 software against human databases downloaded from Uniprot. Label-free quantification was enabled in the searches. The resulting data was analyzed by Perseus v 1. 6. 15. 0 software 45. Seahorse AnalysisHEK293 WT and ΔCOQ4 cells were split, and 4 x 10 5 cells were plated per well in 6-well plates and allowed to grow overnight. On day two, cells were transiently transfected with a mix of 2 µg plasmid (pcDNA3.
TERACUBE will operate at submillimetre (sub-mm) wavelengths, providing unique data on planetary atmosphere dynamics, composition and temperature. As a very good example, the TERACUBE science case applied to Venus atmosphere variability is complementary to the proposed ESA M5 mission ENVISION to study Venus atmosphere and geology. The high spectral resolution (e. g. 100 KHz) and sensitivity of such a heterodyne receiver will allow the spatial and temporal mapping of i) Doppler lineshifts winds, ii) abundance of minor species (e. g. CO(5-4) at 576 GHz, H 2 O 110-101 at 557 GHz) down to a few ppb, and iii) atmospheric temperature, in the altitude range of 70-120 km on Venus. In order for CubeSats scientific payloads to evolve towards the most compact architectures possible while integrating THz instruments, it is essential to consider the receiver and the antenna as a single unit, whereas until now the approach has been conservative and consisted of studying them separately.
This analysis is performed on the 11 heated/cooled sections configuration; the power is provided as a 10 W step and it is kept constant for 10 min. Before the heat power is supplied to the evaporator sections, the liquid plugs position/length is measured. The camera is slid along the rail and 12 picture per tube are taken (an example is Fig. 2b ); once the images are acquired, the power supply is switched on and the reference time is set to t = 0s. A direct visual analysis of these images is conducted to identify the coordinates of the menisci and the precise locations of the heated/cooled sections. This data are the input of a Matlab® script which correlates the positions of the menisci and heated/cooled sections, providing the global phase distribution. T C* +/ -2 • C.
Acknowledgments The Authors would like to express their deepest gratitude and appreciation to the  M. Sc. student  Mattia Capuani , whose dedication and meticulousness in conducting the experiments gave an invaluable contribution to the realization of this research paper. The present work is funded by  GRANT AWARD FA9550-19-S-0003-EUROPEAN OFFICE OF AEROSPACE RESEARCH   AND DEVELOP-MENT -Air Force Office of Scientific Research (USA)  -Id. number  WS00317867  -Pulsating heat pipes understanding and prediction. The views expressed are those of the authors and do not necessarily reflect the official policy or position of the  Department of the Air Force , the  Department of Defense , or the  U. S. government.
These training data consisting of 232 data sets in total was used for the generation of a deep learning based autocontouring model and validated on 20 unseen T2w-MRIs. For quantitative evaluation the validation set was contoured by a radiation oncologist as gold standard contours (GSC) and compared in MATLAB to the automatic contours (AIC). For the evaluation, dice similarity coefficients (DSC), and 95% Hausdorff distances (95% HD), added path length (APL) and surface DSC (sDSC) were calculated in a caudal-cranial window of AE 4 cm with respect to the prostate ends. For qualitative evaluation, five radiation oncologists scored the AIC on the possible usage within an online adaptive workflow as follows: (1) no modifications needed, (2) minor adjustments needed, (3) major adjustments/ multiple minor adjustments needed, (4) not usable.
For model testing, data of 20 patients treated at the MRI-Linac between March 2020 and July 2021 were randomly selected. Patient characteristics for this cohort are given in Table  1. For every patient, one 2 min T2-weighted MRI acquired during fractionated MRgRT was randomly chosen for evaluation (cf. Table  S1 ). All patients included in this study were treated under a phase 2 feasibility trial (NCT04172753). The study was approved by the institutional review board of the medical faculty Tübingen (IRB 659/2017B01) and all patients have given written informed consent.