dataesr/scientific-paragraphs-categorization huggingface dataset:
Database A dataset of six patients from Hospital de la Santa Creu i Sant Pau (Barcelona) who underwent a LAAO were selected for the study. All patients had an implanted Amulet device (Abbott Vascular, Santa Clara, CA, USA), with sizes adapted to the different LA and LAA anatomies, as shown in Table  2. The selection of patients was based on the availability of a complete CT study at follow-up including the whole atrium anatomy and an echocardiography (transesophageal, TTE) study with mitral flow analysis. All patients had nonvalvular atrial fibrillation, with four of them being permanent and the remaining two paroxysmal (see Table  2 ). One of them (Pat 2) also presented severe mitral regurgitation. Three patients (Pat 4-6) had a history of DRT after LAAO. The study protocol was approved by the local ethical committee of the hospital, and patient written consents were obtained.
CT images were acquired at least twice, between months 1-3 and months 3-6 after LAAO implantation, respectively. Prospective cardiac-gated computed tomography angiography was performed with a Phillips Brilliance iCT scanner. A volumetric scan from heart to diaphragm (14-16 cm) was acquired. Cardiac phase reconstruction was performed at 30-40% of the RR interval. Initial digital image post-processing and reconstruction were achieved with the Brilliance Workstation™ to assess LAAO device positioning and presence/location of DRT. DRT was defined as a CT hypodensity at the left atrial extremity of the device: (1) not explained by imaging artefacts; (2) inconsistent with normal healing; (3) visible in multiple CT planes; and (4) in contact with the device. Patient data were anonymised prior to any computational processing.
3D Model Construction For each selected patient, the anatomy of the left atrium (LA) was extracted out of the clinical computed tomography (CT) images, by using the open-source software Slicer (4. 10. 1 (http://www. slicer. org/)) and a semi-automatic region-growing technique. A binary mask representing the LA was obtained, and a surface mesh was created with the classical marching cubes algorithm, followed by a smoothing process to correct irregularities generated by the segmentation process. A Taubin filter smoothing was applied (λ = 0. 5 and µ = -0. 53) [35] , followed by manual removal of self-intersecting faces and nonmanifold edges wherever necessary. The software used to reconstruct the models were Meshlab 2016. 12 (http://www. meshlab. net/), and Meshmixer 3. 5 (http://www. meshmixer.
com). The pulmonary veins could be reconstructed directly from the CT-based segmentation. To consistently define the inlets of the computational models for all 3D models, we followed a common criterion to determine the PV length, defining a cut just before the first branch coming out from the LA. In order to solve the fluid domain inside the LA, a volumetric mesh was generated. The Delaunay algorithm available in the open-source software Gmsh (4. 0. 4 (http://gmsh. info/)) was used for this purpose, creating a tetrahedral mesh with a maximum of 5 × 10 5 elements due to the limitations of the academic license of ANSYS Fluent 19. 2 (Ansys, Inc. , Canonsburg, PA, USA (https://www. ansys. com/products/fluids/ansys-fluent)), which was the software used to run the fluid simulations. Additionally, one patient was selected for the mesh convergence study, creating meshes of 1 × 10 5 , 2 × 10 5 , 3 × 10 5 , 4 × 10 5 , and 5 × 10 5 elements.
Scenario 2: Generic Patient Pressure Wave as Inlet, Imaging-Based Personalised Outlet Velocities, and Rigid Wall The second scenario replaced the constant inlet pressure in Scenario 1 with the available pressure waves in the pulmonary veins of an AF patient. For each simulated case, this AF pressure wave was personalised, as it was adapted to the cardiac rhythm of each patient, derived from the ECG. A smoothing filter was also applied to the pressure wave, using MATLAB R2018a (Mathworks, Natick, MS, USA), to remove the noise in the signal. Similarly to Scenario 1, patient-specific velocities were set up as outlet boundary conditions and the LA wall was assumed as rigid. 2. 3. 3. Scenario 3: Generic Patient Pressure Wave as Inlet, Imaging-Based Personalised Outlet Velocities, and Dynamic Mesh Wall Deformation Scenario 3 incorporated deformation of the LA wall to study the differences with respect to the rigid wall assumption in the previous scenarios.
A MV annulus ring displacement function measured by Veronesi et al. [37]  was used, assuming similar longitudinal behaviour in nonvalvular AF and healthy subjects (i. e. , from 8 to 10 mm), as shown in  [38]. Finally, the displacements of the LA wall results from the Laplace equation: ∇ • (γ∇ -→ u ) = 0, (1) where -→ u is the velocity field of the mesh displacement. γ is the diffusion coefficient that controls the propagation (i. e. , interpolation) of the boundary displacements away from the application points as follows: γ = 1 d α , ( 2 ) where d is a normalised boundary distance and α is a diffusion parameter introduced by the user. α was set to 0, which yielded a uniform diffusion of the boundary motion throughout the mesh. The resulting dynamic mesh displacements were also synchronised to each patient, based on the cardiac rhythm extracted from each individual ECG and using linear interpolation functions available in MATLAB R2018a (Mathworks,Natick, MS, USA), as described above for the pressure waves.
The authors compared five different combinations of velocity and pressure boundary conditions in the inlets and outlets, including some equivalent to the scenarios in our work. They found practically negligible differences in the simulations with different boundary conditions, in disagreement with our results, which could be explained by the idealized LA geometry and lack of LA wall movement in their in vitro setup. In agreement with our findings, García-Villalba et al. [30]  recently demonstrated the benefit of in silico haemodynamic indices for assessing the risk of LAA thrombogenesis in a database of six patients, having better patient stratification, based on residence times and kinetic energy, when using moving-wall simulations compared to rigid LA walls. In an elegant way, the authors set up flow boundary conditions at the PV/MV based on volume changes of the LA and the left ventricle extracted from available dynamic CT scans and applying mass conservation.
Limitations and Future Work The current study presents some limitations that should be acknowledged. First, the mesh and cardiac cycle convergence analysis should be performed with more elements and beats, respectively, for better identifying the necessary spatio-temporal resolution to get accurate and stable simulation results. However, using meshes with 5 × 10 5 elements and 3 heart beats represented an acceptable simplification with reasonable computational times that was useful enough to identify the most appropriate boundary conditions. It could also be interesting to explore the use of mixed meshes including prisms close to the LA wall for a more accurate model of local haemodynamics. The limited number of processed cases was due to the challenge in obtaining patient datasets including post-procedural CT scans, covering the whole LA.
In most hospitals, LAAO patients are followed up with echocardiography or CT images, which only include part of the LA to minimise radiation. Additionally, in the analysed cases, there was a large variation in the CT and echocardiography acquisition times at follow-up for the different patients. Ideally, both imaging modalities should be acquired during the same day to better synchronise LA geometry (and deformation, if available) with echocardiographic measurements. Acquisition of MV velocities with echocardiography is straightforward, thus being well suited for outlet boundary conditions. On the other hand, we could not reliably measure velocities in all pulmonary veins (issues with right and left superior PV) for the whole database since the visual window was not good enough, thus limiting their use at the inlets (i.
The overlap between the plain voice paradigm and the applicative paradigm (compare again Table  1  and Table 2 ) often poses an analytical challenge. We explicate this challenge in the following with the two verbs tutung 'burn' and pio' 'twist'. The dataset in (  16 ) shows these two verbs marked by the (undergoer voice) realis prefix ni-only: nitutung in (16a), and nipio' in (16b). (16) a. lemba itu i-teleb=na injan valley dist rls. uv-clear. uv1=3sg. gen after tooka=mo i-teleb ni-tutung=mo finish=cpl rls. uv-Clear. uv1 rls. uv-burn. uv1-cpl 'he cleared the valley (of the bushes), after clearing (it), (he) burned it' [podok_langgat 076ff] b. Lima=ku ni-pio i inang=ku. hand=1sg. gen rls. uv-twist. appl1 hon mother=1sg. gen 'My mother twisted my hand. ' We have already seen in example (13) that tutung 'burn' is a transitive base that can be used in transitive contexts without any applicative marking (cf.
Non-Realis Realis Actor Voice mo-/mog-/moN-no-/nog-/noN- Undergoer Voice 1 Ø ni- Undergoer Voice 2 -i ni-+ -an Locative Voice po-/poN-/pog-+ -i ni-+ po-/poN-/pog-+ -an The one exception is the uv2 suffix -i in non-realis mood. However, the lack of a suffix of this shape is a somewhat idiosyncratic property of Tagalog. Most other closely related Meso-Philippine languages such as Cebuano, Bikol, Waray-Waray, etc. include such a suffix in their voice paradigms, usually in the so-called subjunctive mood, which has been lost in Tagalog. Table  10  illustrates this with Cebuano data.
Conveyance Voice i-gi-i- The most conspicuous difference between the Philippine and the Totoli paradigms is the lack of a conveyance voice, which is marked by a prefix while the other undergoer voices are marked by suffixes. There is also no voice formative in the Totoli paradigm that specifically signals patient voice, corresponding to Tagalog -in and Cebuano -un. Before further discussing similarities and differences between Philippinetype languages and Totoli, it will be useful to take a brief look at a more typical non-Philippine-type symmetrical-voice language, using Madurese as our example. In Madurese, there is a simple actor voice vs. undergoer voice distinction, both marked by prefixes, as seen in example (  19 ). (19) Madurese a. Ale' noro' Ebu. yngr. sibling av. follow mother 'Little Brother followed Mother.
Philippine-type languages only mark voice. Most non-Philippine-type languages mark voice and applicatives, but by two clearly distinct set of formatives (prefixes and suffixes). In Totoli, however, the same formatives may sometimes mark an applicative, and sometimes a voice. The above comparisons also make it clear that in all three languages -Totoli, Tagalog and Madurese -we are basically dealing with the same formatives, except for Madurese -agi. It is thus tempting to speculate about which kind of changes may have lead from one system to the other. Before doing so however, a caveat is in order. Following the pioneering work of  Wolff (1973) , it has been widely assumed that Proto-Austronesian clause structure and verb morphology looked very much like they do in current Meso-Philippine languages (see Ross 2009 for some recent modifications).
Such forms do not occur in Philippine-type languages. They may have provided a major stepping stone for forms with an actor voice prefix and the former undergoer voice suffixes -i and -an such as manakei and manakean in (22) above. The development of the forms with pronominal prefixes themselves, however, is complex and cannot be further expounded here  (Wolff 1996 and Himmelmann 1996 , 2020  provide further details and discussion). In the current context, the role played by transitivity and the basic valency profile in the development of the Totoli system is of particular interest. Transitivity distinctions do not play a major role in the grammar of Philippine-type voice alternations and there are reasons to assume that the lexical bases of verbal items are essentially intransitive (see  Kaufman 2009  on the so-called nominalist hypothesis for Tagalog and other Philippine languages).
The emergence of a new valency profile in these languages would further correlate with a change in function of the former undergoer voice suffixes -i and -an (for the latter, such a change is in fact rarely attested). These fully lose their function as markers of different types of undergoer voices and only occur in their applicative function. To emphasize the point made above once again: while this scenario may have some plausibility, it is highly speculative and very sketchy. There are very many details that need to be worked out in much more detail. Among many other points, we would need proper basic valency profiles for all the languages mentioned here (including Tagalog and Madurese) as well as for a few additional representatives of the two basic types, i. e. Philippine vs. non-Philippine type.
1 The research for this article was carried out within the  Collaborative Research Centre SFB1252 Prominence in Language (Project-ID 281511265 ) funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) at the University of Cologne  -support we very gratefully acknowledge. Sonja Riesberg  also gratefully acknowledges funding and support from the  Australian Research Council  as a postdoc in the  Centre of Excellence for the Dynamics of Language. Nikolaus Himmelmann  and  Sonja Riesberg  are especially thankful to the  Volkswagen Foundation  for long-term support and funding of their research and fieldwork in Tolitoli. A very special thanks to all Totoli speakers who helped us understand the 'mysteries' of Totoli voice and.
Besides, torque and slip limiters were considered to address the physical saturation problem in electric motors and wheel slip constraints in the control design. Energy management using LPV control technique is also an appealing idea in electric vehicles. Waleed et al. mainly studied the energy management of electric vehicles with variable power source-fuel cell, battery and ultra-capacitor  [165] [166] [167]. An H 1 gain-scheduled controller associated to weighting functions was proposed to regulate the fuel cell current and the converter voltage, and to further achieve the optimal power output of the electrical motors. Additionally, the weighting functions in LPV control were selected through a genetic algorithm. Different driving cycles con- ducted in MATLAB/SIMULINK showed the good performance of designed controller.
For instance, Wang et al. put forward a discrete-time LPV controller based on LFT to minimize the energy consumption in a parallel HEV  [173]. The LPV control scheduled the gain with varying parameters, and split the usage of engine and battery successfully. Nevertheless, a two-layer LPV control method was developed for energy management of an HEV  [174]. The authors aimed at determining the optimal control actions under inputs and states constraints and time-varying parameters. Model predictive control with LPV model was designed in  [175]  to improve the fuel economy of HEVs. The vehicle speed was considered as the varying parameter in LPV model. The effectiveness of the developed controller was verified with a MATLAB/SIMULINK simulation platform. Torque ripple reduction is another control issue of HEVs.
The principle of an SCR system is illustrated in Fig. 9. As reported in  [183] , considering the kinetic dynamics, the three-state SCR model can be established. Note that there are strong nonlinearities involved in this SCR model. Moan et al. proposed an adaptive LPV control strategy to simultaneously minimize the emission and the ammonia slip in urea-SCR systems  [184]. In this work, a three-state nonlinear model with time-varying parameters was utilized. To improve the performance of the LPV controller, the authors proposed a quasi-LPV model to represent the complex nonlinear dynamics of urea-SCR systems. Moreover, the technique of principal component analysis (SPCA) was employed to reduce the complexity of the LPV model and the computational load for micro-control implementation. At last, performance comparisons between controllers based on a low-order model and a high-order model were conducted on GET-Power and MATLAB/SIMULINK platforms  [185, 186].
Acknowledgements This work was supported in part by  Vehicle Power Specific Research Project of Fundamental Product Innovation Program  under Grant NO. JCCPCX201704 ，the  French Ministry of Higher Education and Research ; in part by the  National Center for Scientific Research (CNRS) ; in part by the  Nord-Pas-de-Calais Region  under Project  ELSAT 2020 ; in part by  National Natural Science Foundation of China  under grant  61703111 ; in part by the  Guangdong Natural Science Foundation  for  Distinguished Young Scholar  under grant  2019B151502026 ; in part by the  Local Innovative and Research Teams Projects of Guangdong Special Support Program  under grant  2019BT02X353.
The worldwide CoVid-19 pandemic has led to an unprecedented push across the whole of the scientific community to develop a potent antiviral drug and vaccine as soon as possible. Existing academic, governmental and industrial institutions and companies have engaged in large-scale screening of existing drugs, in vitro, in vivo and in silico. Here, we are using in silico modelling of possible SARS-CoV-2 drug targets, as deposited on the Protein Databank (PDB), and ascertain their dynamics, flexibility and rigidity. For example, for the SARS-CoV-2 spike protein-using its complete homotrimer configuration with 2905 residues-our method identifies a large-scale opening and closing of the S1 subunit through movement of the S B domain. We compute the full structural information of this process, allowing for docking studies with possible drug structures.
Results Protein selection. We have downloaded protein structure files as deposited on the Protein Data Bank  35  , including all PDB codes that came up when using "SARS-CoV-2" and "Covid-19" as search terms, as well as minor variations in spelling. In our first search of April 18th, 2020, this resulted in 133 protein structures. Further searches on May 20th and 29th, and August 25th, 2020 gave a total of 287 structures. Many of the structures found, as outlined above, have been deposited on the PDB in dimer or trimer forms. Hence one has the choice to study the rigidity and motion of the monomer or the dimer/trimer. Clearly, the computational effort for a dimer/ trimer structure is much larger than for a monomer since, in addition to the intra-monomer bond network, inter-monomer bonds also need to be taken into account.
Furthermore, it is not necessarily clear whether the possible motion of a monomer or dimer should be computed to have the most biological relevance. We have calculated the motion of full dimer and trimer structures only for a few selected and biologically most relevant such structures while the default results concentrated on the monomers. Nevertheless, we wish to emphasize that when results for a certain monomer exist, it should nearly always be possible to also obtain their motion in dimer/trimer configuration. For some protein structures, we have found that steric clashes were present in the PDB structures that made a flexibility and sometimes even just the rigidity analysis impossible. Usually, this is due to a low crystal resolution. A list of all current protein PDB IDs included in this work is given in Table  S1.
Rigidity and flexibility. In Figs. 1  and 2  we show examples of different rigidity patterns that emerge from the First analysis. In line with previous studies comparing these rigidity patterns across various protein families  19  , we find that they can be classified into about four types. In Fig. 1a  we see that for the crystal structure of SARS-CoV-2 nucleocapsid protein N-terminal RNA binding domain (PDB: 6m3m), the largest rigid cluster in the pristine structure, i. e. at E cut = 0 , largely remains rigid through the dilution process of consecutively low- ering E cut values. When bonds are opened the rigid cluster shrinks but the newly independent parts are flexible and not part of any new large rigid cluster themselves. Only very small parts of the protein chain break to form their own independent rigid structures before at a certain E cut the whole protein is essentially flexible.
We shall denote such a behaviour as brick-like. In contrast, in Fig. 1b  we observe that for chain A of the co-factor complex of NSP7 and the C-terminal domain of NSP8, which forms part of the RNA synthesis machinery from SARS CoV-2 (PDB: 6wiq), already the crystal structure has fallen into three independent rigid structures. Opening bonds, we find that the largest rigid cluster breaks into 2 rigid structures. Then these now four domains retain their character upon opening more and more bonds until they simply dissolve into full flexibility (We use the term domain here to denote large regions of a protein connected in the same rigidity cluster. Usually, domains in a biological sense are also detected as domains in a rigidity sense as used here  32. However, this relationship is not necessarily always the case.
). While brick-and domain-like behaviour is only found occasionally, two further types of rigidity dissolution are more prevalent. In Fig. 2a  we see that for, e. g. the monomer of the crystal structure of the COVID-19 main protease (M pro ) in apo form (PDB: 6m03), the rigid cluster dominating the crystal structure quickly falls apart upon change in E cut with five newly formed independent rigid clusters emerging towards the C-terminal of the ). Different rigid clusters of the polypeptide chain appear as coloured blocks along the protein chain with each C α labelled from its N-terminal at 1 to its C-terminal. The three columns at the left of each cluster decomposition denote the index of the hydrogen bond to have been opened last (with 117 and 124 to total number of H bonds in (a) and (b), respectively), the energy cutoff E cut (E) and the mean number of bonded neighbours r (mean coordination) at that value of E cut.
The label A indicates that protein chain A is being considered from the structure. We emphasize that the E cut scale is not linear since new rows are only added when the rigidity of a structure has changed. When the value of E cut decreases (the downward direction points towards larger, negative E cut magnitudes), rigid clusters break up and flexibility increases. The colour coding shows which atoms belong to which rigid cluster. Flexible regions appear as thin black lines. gives results for chain A of a complex resulting from the reaction between the SARS-CoV main protease and a carbamate (PDB: 6y7m). Distinct rigid clusters of the polypeptide chain appear as coloured blocks along the protein chain with each C α labelled from its N-terminal at 1 (left edge) to its C-terminal (right edge).
The coloring and choice of blocks follows the same logic as detailed in Fig. 1  except that the left most data columns are not shown for clarity. The energy cutoff E cut decreases in the downward direction towards larger, more negative E cut magnitudes; rigid clusters break up and more of the chain becomes flexible. protein chain. These new clusters remain stable to the opening of further bonds, even when the remnants of the original rigid cluster has become fully flexible. Such a behaviour relies on a certain critical E cut value to domi- nate the rigidity dissolution and is reminiscent of so-called first-order phase transitions in statistical physics. Hence this rigidity-type is usually denoted as 1st order  19. The behaviour seen in 2b for the crystal structure of the complex resulting from the reaction between the SARS-CoV main protease and tert-butyl (1-((S)-3-cyclohexyl-1-(((S)-4-(cyclopropylamino)-3,4-dioxo-1-((S)-2-oxopyrrolidin-3-yl)butan-2-yl)amino)-1-oxopropan-2-yl)-2-oxo-1,2-dihydropyridin-3-yl)carbamate (PDB: 6y7m) is markedly different.