dataesr/scientific-paragraphs-categorization huggingface dataset:
e. repulsive) contact occurs between the sulfonamide oxygen and the carbonyl oxygen of Glu166 backbone. Additionally, the tetrahydroquinoline scaffold was not fully exploring S2 subpocket boundaries. As reported by Cannalire, et al. 63  and Zhang, et al. 8  , the volume of the S2 subpocket in SARS-CoVs M pro is highly similar to that of the MERS-CoV homologue. However, the volume of S2 in SAR-CoVs M pro (252 Å 3 ) is significantly larger than in other CoVs' homologues of the α-genus, such as the HCoV-NL63 M pro (45 Å 3 ) 8,63. Therefore, exploiting this knowledge might be key to design specific inhibitors of CoVs M pro. In order to refine the available X-ray structural model and to gather more structural insights (e. g. protein flexibility and binding pocket rearrangements  12, 13  ) to guide the design of better binders of the subpocket S2, we ran all-atom molecular dynamics simulations using the AMOEBA polarizable force field  [29] [30] [31] [32]  on M pro (PDB code: 7L11) in complex with x0195 (PDB code: 5R81).
QUB-00006 was generated based on the structure and position of x0195 in the co-crystal (5R81), then placed in the receptor structure (M pro dimer with the PDB code: 71LL); next, the complex M pro -QUB-00006 was equilibrated using MD simulations (see SI, section 1 for the detailed protocol), followed by free energy calculations. To explore the potential of our computational platform in designing new binders with no or few experimental data such as ligand-M pro co-crystal structures, we leveraged all-atom molecular dynamics simulations on QUB-00006 complexed with M pro. The aim of this approach is to gather insights on the binding conformation of the newly in silico designed ligand, assess pocket fitness, and evaluate its binding affinity using ABFE calculations. The initial molecular conformation is mostly anchored at the binding pocket, with the α, α-difluoro-methyl group attached to the tetrahydroquinoline core fully occupying the buried part of the S2 subpocket, which is composed by the side chains of Met49   and B ) the relaxed structure of x0195 (in purple), obtained after the equilibration step, interacting with the amino acid residues of the substrate binding site.
,  68  where they found that a total of 1200 and 626 fragments from the Cambridge Structural Database (CSD) and Protein Data Bank (PDB), respectively, have close intermolecular S-O contacts (with a distance of 3. 52 Å or less). Another study analyzing the protein structures deposited in the Protein Data Bank reports 1133 interactions between His and halogen atoms found in 3833 PDB entries with one or more halogenated ligands co-crystallized with a protein  69. Moreover, the strong S-F interaction identified during the simulations is in good agreement with experimentally observed distances for fluorine-sulfur contacts in crystal structures (2. 8-3. 4 Å)  70. It is worth noting that such interactions involving sulfur and halogen atoms are usually better captured with polarizable models than with their classical counterparts  [71] [72] [73].
This specification is executable in Maude  [6] , and the computation of metrics is achieved by using Maude's rewriting tools. The second approach applies at runtime or online, and works by instrumenting an existing platform for executable BPMN (Activiti  [2]  in this work). In this case, access to a database is used for storing information related to the execution of the process. This information is particularly useful for computing the process execution time, resource usage, and costs. This approach is also dynamic in the sense that the number of replicas for each resource is not defined once and for all, but can be updated by using the metrics computed during the process execution. In particular, a strategy that relies on the resource usage values for dynamically updating the number of replicas of each resource is presented.
Dynamic Quantitative Analysis In this section, we will show how an existing platform (Activiti  [2]  in this work) can be instrumented to extract the required information from its database and compute properties periodically during the process execution. We will also show how we can develop dynamic resource allocation strategies for varying the number of resource replicas at runtime and thus impact the results of these properties. Note that in this section, we do not have any restrictions on the BPMN syntax, we just need BPMN processes to be executable. Moreover, there is no need to have probabilities associated to split exclusive and inclusive gateways, since we have real data-based conditions.
g. , tasks) executed by this instance. Monitoring techniques for BPMN executed using Activiti mostly aim at analysing the information stored in a database, and extracting the information required for computing the properties of interest (such as AET and resource usage percentage). Figure  6  gives an overview of this data extraction. We first need to retrieve the information regarding task execution and completion. This is what we can see in Figure  6  (top right, (a)). For each task, we also extract the corresponding process execution instance and the times of beginning and end. This information is useful for determining which resources were in use and for what amount of time. Second, we retrieve execution traces for each process instance as shown in Figure  6  (bottom right, (b)). An execution trace corresponds to a list of tasks executed by this specific instance.
When the period completes, the data extraction is triggered. Then, we extract the required information from these data to compute the properties presented in Section 3. 3 on execution times and resource usage. As an example, to compute the resource usage percentage per resource replica, we analyse the tasks executed during the last period of time. For these tasks, we look at the resources associated with each task and sum up the durations each resource was active during that period. Then, we divide this total time by the number of replica and compute a percentage out of these numbers by using the time of activity for each replica out of the time of the period. As we will see below, the results are represented using curves that show the different property values (e. g. , average execution time) along time.
The proposal here takes a different approach since the idea is to compare the possibility to make the decision at design time or at runtime, with static or dynamic allocation of resources. This work is part of a long term project with the goal of developing different tools for the analysis of BPMN processes. [18, 19]  present an approach transforming BPMN into the input language of the CADP model checker, thus allowing the automated ver-ification of functional properties and the comparison of BPMN processes. In  [12] , basic BPMN processes were specified. This work provides operations for the estimation of execution times, and uses model-checking techniques to verify reachability problems and LTL properties. In  [9] , a model similar to the current one was proposed and was used for stochastic analysis using the statistical model checker PVeStA  [3].
Acknowledgements. This work was supported by the  Région Auvergne-Rhône-Alpes  within the  "Pack Ambition Recherche" programme. The first author was partially supported by projects  UMA18-FEDERJA-180  ( J. Andaluca /FEDER)  and  PGC2018-094905-B-I00  ( Spanish MINECO/FEDER). The third author was in part supported by the  ECOS-NORD  project  FACTS  ( C19M03 ).
Liu); however, only one line was detected at 89. 7 GHz. As the line might   2018 ) catalog, while the remaining IDs are from the radio catalog of  Smolčić et al. (2017)  and correspond to ID+2E7 in the deblended FIR/(sub)mm catalog  (Jin et al. 2018). CO(4-3) [CI](1-0) CO(5-4) H 2 O(1 1,0 -1 0,1 ) Fig. 2. NOEMA and ALMA (ID9316) 3mm spectra. Detected lines are highlighted in green with their names labeled. The red line shows a power-law fit to the continuum that increases with frequency ν 3. 7. have been CO(6-5) at z = 6. 7, a NOEMA 1mm observation (ID: W18EY, PI: D. Liu) was carried out to search for the [CII]158 um line, but no line was detected. In the winter of 2019, this galaxy was observed with a NOEMA 3mm setup (ID: W19DQ, PI: D. Liu). The NOEMA data were reduced and calibrated using IRAM GILDAS software packages.
ALMA The above-mentioned four NOEMA sources in COSMOS have public imaging data in the ALMA archive at 345 GHz (ID: 2016. 1. 00463. S, PI: Y. Matsuda), in which two of them are also observed at 210 GHz (ID: 2016. 1. 00279. S; PI: I. Oteo). The four galaxies are solidly detected in the dust continuum and their fluxes were accurately measured (Table  3 ). No serendipitous line was found in the ALMA data cubes. ID9316 was observed with five ALMA setups at 3mm, continuously covering frequencies from 89. 5 to 103. 5 GHz. We processed each setup by reproducing the observatory calibration with their custom-made script based on the Common Astronomy Software Application package  (CASA; McMullin et al. 2007). We then exported the data into uvfits format to generate uv tables to perform further analysis with the IRAM GILDAS working on the uv space (visibility).
The E(B -V) values indicate high attenuation, that is, 2 < A V < 3, if adopting the Milky Way reddening parameter R V = 3. 1 and 3 < A V < 4. 6 if adopting R V = 5 for dense dust, which is in line with the optically thick dust suggested by FIR data (Sect. 4). The age is not reliably constrained because of the high attenuation. Note that we are fitting stellar and dust SEDs separately rather than using an energy balance between optical and FIR. This is because dust and stellar components are often found to be spatially unassociated in high-z dusty galaxies, e. g. ,  Franco et al. (2018) ,  Hodge et al. (2019). Literature work has shown that in this type of galaxy the star formation from optical emission is just a tiny fraction of the total SFR  (Puglisi et al. 2017; Calabrò et al. 2018). Therefore, there is no reason to assume that the dust and stellar are well mixed.
Acknowledgements. This work is based on observations carried out under projects number  S18DI ,  W20DM ,  W17EK ,  W18EY  and  W19DQ  with the IRAM Interferometer NOEMA. IRAM is supported by  INSU/CNRS (France) ,  MPG (Germany)  and  IGN (Spain). This paper makes use of the following ALMA data:  ADS/JAO. ALMA 2013. 1. 00118. S,  2016. 1. 00279. S,  2016. 1. 00463. S,  2016. 1. 00478. S and  2018. 1. 00874. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA) , and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO , and  NAOJ. The  Cosmic Dawn Center (DAWN)  is funded by the  Danish National Research Foundation  under grant No.
140. G. E. M. , S. J. and D. B. S. acknowledge the  Villum Fonden  research grants  37440 ,  13160  and the  Cosmic Dawn Center of Excellence  funded by the  Danish National Research Foundation  under the grant No. 140. S. J. is supported by the  European Union 's  Horizon 2021 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement No  101060888. J. R. W. acknowledges support from the  European Research Council (ERC)   Consolidator Grant funding scheme  (project  ConTExt , grant No. 648179 ). F. V. acknowledges support from the  Carlsberg Foundation   Research Grant   CF18-0388  " Galaxies: Rise and Death ". Y. G. acknowledges the  National Key R&D Program of China  No. 2017YFA0402704  and  National Natural Science Foundation of China  grant Nos. 11861131007  and  12033004.
Q. S. G. is supported by the  National Key Research and Development Program of China  (No. 2017YFA0402703 ), and by the  National Natural Science Foundation of China  (No. 11733002 ,  12121003 ,  12192220  and  12192222 ), and by the science research grants from the  China Manned Space  Project with No. CMS-CSST-2021-A05.
Methods: We defined our experimental framework using the National Electrical Manufacturers Association NU-2 2018 Image Quality standard, but replaced the standard sphere set with more appropriate sizes (4, 5, 6, 8, 10 and 13 mm) better suited to current PET scanner performance. Four experiments, with different spheres-tobackground ratios (2:1, 4:1, 6:1 and 8:1), were performed. An additional dataset was acquired with a radioactive background but no activity within the spheres (water only) to establish a baseline. Then, we artificially simulated radioactive spheres to reproduce other experiments using synthetic data inserted into the original sinogram. Images were reconstructed following standard guidelines using ordered subset expectation maximization algorithm along with a Bayesian penalized likelihood algorithm.
We first visually compared experimental and simulated images. Afterward, we measured the activity concentration values into the spheres to calculate the mean and maximum recovery coefficients (RC mean and RC max ) which we used in a quantitative analysis. Results: No significant visual differences were identified between experimental and simulated series. Mann-Whitney U tests comparing simulated and experimental distributions showed no statistical differences for both RC mean (P value = 0. 611) and RC max (P value = 0. 720). Spearman tests revealed high correlation for RC mean (ρ = 0. 974, P value < 0. 001) and RC max (ρ = 0. 974, P value < 0. 001) between both datasets. From Bland-Altman plots, we highlighted slight shifts in RC mean and RC max of, respectively, 2. 1 ± 16. 9% and 3. 3 ± 22.
In medical imaging, the SimSET package uses Monte Carlo techniques to model the physical processes and instrumentation, in particular in PET imaging  [10]. A major limitation in particle-tracking simulation is the significant computation time which limits the generation of large datasets. Analytical simulation is another method that models the average probability of photon interactions instead of individual photon tracking. Therefore, it significantly improves computational cost and can generate large datasets promptly  [10] [11] [12] [13] [14] [15]. By combining physical (phantom and patient) data with fast analytical simulation, we could quickly generate significant sets of imaging configurations to explore for instance lesion detection  [14, 15]. Hence, it would make it possible to generate a large number of different datasets from a single physical sample  [13] [14] [15].
The data insertion simulation method consists in embedding synthetic information with known characteristics such as location, volume, shape and activity into pre-acquired data using system modeling. Technically, the data insertion simulation method relies on the forward projection through the scanner model of the synthetic spheres into simulated sinogram. It is then summed to the original sinogram to obtain new raw data for the reconstruction of simulated datasets. The purpose of such simulation method is to link practicality and ground truth. In order to design a hybrid method, Juma  [10]  used and compared two simulation techniques, SimSET and a forward projector, that generates lesion sinogram estimating events from an estimated activity map. He considered the analytical simulation as the most appropriate method, taking into account simulation times, technical limitations related to file formats and the opportunity to easily simulate time-of-flight (TOF).
In a recent study, Gabrani-Juma et al. [14]  contrasted results obtained with analytical simulation using a digital phantom to NEMA image quality (IQ) results and observed that the simulated spheres showed a systematic overestimation of about 20% compared to the physical spheres for sizes smaller than 22 mm in diameter. However, the digital phantom did not have the same geometry and volume as the physical NEMA body phantom. Hence, it is difficult to conclude whether this oversight came from the simulation or structural differences of the study object. We have similar analytical simulation and a reconstruction toolbox for remote reconstruction provided by the manufacturer through a research collaboration. Our final goal is to use it on clinical data to create a scalable ground truth responding to the clinical need.
PET-CT system All the experiments were performed on the Discovery MI 5-ring positron emission tomography-computed tomography (PET-CT) digital system (General Electric Healthcare, Chicago, IL, USA). This PET-CT device is combining TOF, high resolution and high sensitivity, which improve overall image quality by reducing the noise in the reconstructed images and enhance lesion detection  [16]. There are several reconstruction algorithms available to produce images from the acquired raw data such as the common ordered subset expectation maximization (OSEM). A more recent Bayesian penalized likelihood (BPL) algorithm, gives access to a regularization parameter β that allows to reduce image noise through each iteration  [17, 18]. Results from the NEMA NU2-2012 standard performance tests for this configuration of the device have been published  [19].
Simulation method The simulation method consists of inserting synthetic information with known characteristics, such as location, volume and activity, into pre-acquired raw data using system modeling. Finally, modified raw data are reconstructed into simulated axial images  [13] [14] [15]. Offline data insertion and image reconstruction were achieved using a reconstruction research toolbox (Duetto v02. 13, General Electric Healthcare, Chicago, IL, USA). Beforehand, we exported the raw data and computed tomography attenuation correction (CTAC) images from the scanner to a dedicated high-performance research workstation Z8 (Hewlett-Packard, Palo Alto, CA, USA) where modeling, simulation and reconstruction were executed using MATLAB R2018b (The MathWorks Inc. , Natick, MA, USA).
Modeling First, we determined the spatial coordinates of the center of each physical sphere from the CT images. We modeled the spheres according to the internal diameter of the physical set using a coded MATLAB function. Spatial coordinates and internal diameter were used as input data in the function as a complement to the exam dimensions, the voxel dimensions and a factor value for upsampling. Due to the limitation of a finite sampled voxel size, a binary mask with the current image resolution would be insufficient to represent a real-world sphere. In addition, the modeling step presents a constraint we have to consider for the data insertion and reconstruction afterward. The synthetic information inserted into the pre-acquired data should have commonalities in terms of reconstruction and acquisition parameters, such as matrix size or slice thickness.
Data insertion Once these preliminary steps have been completed, we conducted the generation of modified raw data using specific functions provided by the manufacturer. The insertion process relies on the forward projection through the scanner model of the synthetic  During this process, scanner and phantoms effects (geometric efficiency, detector efficiency variations, resolution and attenuation) are applied using raw data and CTAC images from the original acquisition  [14, 15]. Poisson noise realization is applied to estimate noise of the inserted data. It is based on the counts per second per unit volume of inserted spheres in the image domain prior to the forward projection. Ultimately, original and simulated sinograms were summed to obtain new raw data used for the reconstruction of the simulated datasets.
Images and data analysis A statistical data analysis was conducted in order to assess the similarity between the simulated and the experimental data considered as the reference. We first assessed the visual equivalence of the simulated images compared to the experimental results. For each set of acquisitions, we had three sets of images, equivalent in terms of total counts, which we averaged to obtain a single frame. Visual inspection was performed on the central slice allowing to get all the spheres in the same plane. Afterward, we also performed a quantitative analysis using the image interpretation software PETVCAR ® on the AWServer client console (General Electric Healthcare, Chicago, IL, USA). To perform this analysis, we placed, for each set of images, 6 spherical VOIs centered on each sphere.
As the background activity might vary from one experiment to another, even if the SBR was the same, we performed this calculation in order to normalize the different datasets and avoid discrepancies between reiterations (Table  1 ). Simulated and experimental averaged recovery coefficient (RC) were compared considering entire datasets and reconstruction  (2)  RC mean/max = AC mean/max (kBq/mL) AC theoretical (kBq/mL) algorithms datasets. First, we carried out a nonparametric Mann-Whitney test to highlight any differences in RC mean and RC max distributions between the experimental and simulated data. Then, we performed a Spearman test  [20]  to estimate their crossed correlation. Moreover, we verified the normality of the distribution of differences between experimental and simulated quantitative data using D' Agostino-Pearson test  [21] , in order to further refine the statistical analysis creating Bland-Altman plots  [22, 23].
The Mann-Whitney test showed no statistical differences between experimental and simulated RC values (both RC mean and RC max ). This was also confirmed when we studied the impact of the reconstruction algorithm on RC values, despite a higher value for the BPL algorithm. Spearman tests revealed a strong correlation for all datasets (ρ > 0. 950). From Bland-Altman plots, we determined the mean differences and agreement intervals (within 95% of the differences). Considering all data, we obtained results for RC mean and RC max of 2. 1 ± 16. 9% and 3. 3 ± 22. 3%, respectively. This analysis showed a small difference between OSEM and BPL for both RC mean (respectively, 4. 4 ± 14. 5% vs. -0. 32 ± 18. 1%) and RC max (5. 9 ± 22. 2% vs. 0. 77 ± 21. 6%). All the results of the statistical analysis are presented in Table  6.
Based on real images and using a model of the scanner, the method generated images whose visual rendering and visualization of objects are similar to experimental images. Discrepancies observed in the visual comparison occurred for target presenting challenging size and contrast in terms of detection for the device. It is the limit of the method which shows slight deviation from the physical images when the limits in performance of the system are reached. Nevertheless, given this specificity and weakness of these differences, we considered them negligible for our study and the final clinical implementation and assessed the equivalence between the two datasets. From the quantitative analyses, we were able to verify that the experimental and simulated data were comparable, correlated, with differences normally distributed.
We showed that we were able to simulate realistic visual and quantitative results compared to experimental data even under challenging situations such as small and low contrast targets. Based on computer programs developed by the PET manufacturer, the method uses the same processes available on the physical systems. In contrast to some simulation studies  [11] [12] [13] , the method offers all available features of the physical system like PSF or TOF implementation. Files generated during the initial acquisition and reconstruction are used to generate new datasets inserting virtual information to obtain a practical render of the exam. These synthetic datasets are useful for qualitative and quantitative assessment of system performance as they combine real backgrounds with inserted objects of known size, activity and location.