dataesr/scientific-paragraphs-categorization huggingface dataset:
Performance without APS Figure  7  shows the simulation results of SOLTICE software observed in Paraview when no APS is applied. The maximum flux density on the tubes is 2. 2 MW/m 2 and the corresponding total absorbed power is 3. 9 MW. Despite a high optical efficiency in this case (80. 8%), the results are not acceptable for operating the receiver. Figure  8  shows that the maximum tubes temperature (1827 • C) is much higher than the melting point of SS 310S, due to a maximum flux density of 2. 2 MW/m 2. Furthermore, the particle temperature in the 40 tubes is very heterogeneous, with a much higher particle temperature in the middle tubes. These results prove the need to apply an APS in order to preserve the solar receiver, extend its lifetime and obtain more uniform particle temperature in all the 40 tubes.
The model presented in this paper will be compared with experimental results. A scanning bar travelling at the aperture of the receiver will allow comparison of the simulated and experimental flux distributions with the aiming point strategy. Uncertainties with the optical simulations can be due to the actual tracking error and beam quality of the heliostats. As for the thermal model, it will be confronted to temperature profiles measured with thermocouple data and a thermographic camera installed on a drone. Future studies should include thermo-mechanical stresses on the tubes, accounting for the flux variations on the receiver during the day, the start-up and shut-down, the thermal shock due to cloud appearance and other fluctuating parameters all along the year such as the DNI or the ambient temperature.
The structure of CbA5H air shows localized structural differences with anaerobically purified CpI (PDB ID: 4XDC)  9  (Supplementary Figure  3 ). The peptide-loop that spans T365, S366, and C367 in CbA5H (hereafter "TSC-loop", corresponding to T297, S298, and C299 in CpI) is shifted from the conformation observed in the structures of CpI, DdH, or HydA1 (Fig. 2e  and Supplementary Figure  5 ). The bulky side chain of the strictly conserved tryptophan 371 (W303 in CpI), adjacent to the TSCloop, exhibits a different conformation in CbA5H air , hinging away from the loop. The alpha helix carrying this residue is slightly shifted away from the H-cluster. Consequently, the orientation of the residue of the conserved cysteine C367 in CbA5H air is different from that of the corresponding side chain in standard [FeFe]-hydrogenases (Fig.
196 V, all at 5 °C) for CbA5H WT and variants. Rate constants were obtained by analyzing kinetic data of anaerobic inactivation such as those shown in Fig. 4a  with the "AAI" model (Supplementary Fig. 14  shows the dependence of k react on E). The CbA5H WT reference for the kinetic parameters of the variant proteins, measured at pH7 is marked in bold. The % accuracy is our estimate of the maximal error for the determination of each rate constant. under oxidizing conditions, which increases in the order WT < L364F ≈ P386L ≈ A561F < L364F-A561F < C367D. k 1 (s -1 ) k -1 (s -1 ) k inact (s -1 ) k react (s - Monitoring the IR-signatures of the H-cluster confirms that the residual O 2 -resistance of the variants results from the formation of a residual fraction of H inact (Fig. 3f  and Supplementary Figure  13 ).
However, a manually trimmed model of CpI (4XDC), in which the non-conserved part was removed based on a sequence alignment, yielded a solution with TFZ (translation function Z) and LLG (log likelihood gain) scores of 14. 7 and 137, respectively. Subsequently, Phenix Autobuild was used to generate the model  47. The first round of Autobuild resulted in an improved model with an R free of 0. 38. The model was then further optimized, using alternative rounds of interactive building in Coot  48  and automatic refinement in Phenix. Group occupancy was refined for the iron sulfur clusters. The final model and structure factor were deposited at RCSB protein data bank (PDB ID: 6TTL). Statistics for data collection and refinement are summarized in Supplementary Tables 1, 2. To verify placement of critical parts of the model, simulated annealing omit maps were calculated in Phenix after removing the relevant parts from the input model.
456 V vs SHE, pH 8. 5: -0. 546 V, pH 10: -0. 636 V) to ensure complete activation of the enzyme film. The intermediate steps at oxidative potential were of 100 mV amplitude. Before analyzing the H 2 -oxidation current, we subtracted the background capacitive current recorded in a control experiment without enzyme. Chronoamperometry in Fig. 3. Experiments were carried out in an anaerobic chamber (Coy Laboratory Products, USA) under an atmosphere of 98% N 2 and 2% H 2. A PalmSens potentiostat (PalmSens 4) was used, controlled by the PalmSens software PSTrace 5. 2. The gastight electrochemical cell was water jacketed to control the temperature and a PGE rotating disc electrode was used as a working electrode and controlled by a rotator (Autolab). The reference electrode (Ag/AgCl, 3. 5 M KCl) was kept in a non-isothermal side arm, being connected to the main cell compartment by a Luggin capillary.
Acknowledgements We thank the staff at beamline  ID29 (ESRF, Grenoble)  for technical support during X-ray data collection. M. W. , J. D. , and T. H. gratefully acknowledge financial support from the  Volkswagen-Stiftung (Design of [FeS]-cluster containing Metallo-DNAzymes (Az 93412 )), from the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany´s Excellence Strategy -EXC 2033 -Project number  390677874  and the  DIP Programme  ( LU 315/17-1 ). T. H. and E. H. also received funding within the  DFG Research Training Group GRK 2341 : ( Microbial Substrate Conversion ). The French authors are supported by the  CNRS ,  Aix Marseille Université ,  Agence Nationale de la Recherche  and the  Excellence Initiative of Aix-Marseille University  -A*MIDEX, a  French "Investissements d'Avenir" programme  ( ANR-12-BS08-0014 ,  ANR-14-CE05-0010 ,  ANR-11-IDEX-0001-02 ).
Data availability The coordinates and structure factors of CbA5H air have been deposited in protein data bank (PDB) under PDB-ID: 6TTL All data are available in the main text or the supplementary materials. Further data supporting findings of this study are available from the corresponding authors upon reasonable request. Further publicly available datasets used or indicated in this study comprise PDB-ID: 4XDC, PDB-ID: 3LX4 and PDB-ID: 1HFE. Source data are provided with this paper.
17 17 Fig. 2 X-ray structure of CbA5H WT crystallized under aerobic conditions (CbA5H air ). a Cartoon structure of CbA5H air (PDB ID: 6TTL, chain A; see Supplementary Figure 2 for the homodimer). H-and F-domain are colored in yellow, the N-terminal SLBB domain (soluble ligand binding β-grasp) 51 is presented in green. b Anomalous electron density map, depicting the positions and distances between iron-sulfur clusters, including the H-cluster (stick model), the two F-clusters (FS4A and FS4B) and the additional N-terminal cluster within the SLBB domain (FS4*) located more than 20 Å away from any other cluster. c Potential cluster coordination site within the SLBB domain, consisting of 3 cysteine and one histidine ligand. d, e CbA5H air exhibits a characteristic structural displacement of the peptide loop containing C367 (TSC-loop) nearby the [2Fe] H -cluster.
We used two sets of approximate values for atomistic radii; the default values distributed with most GROMACS versions,  73  and a more recent set from Rowland et al. 74  These results are reported in the Supporting Information, section 3. Partial charges of choline, tetrabutylammonium, and urea were derived using the CHelpG scheme,  75  computed with Gaussian16 software. 76 ree Energy Estimates and Partition Coefficients. Assessing the correctness of nonbonded parameters, modeled via the Lennard-Jones and the Coulomb interactions, for individual CG models was performed by comparing experimentally and theoretically derived water-octanol partition coefficients (log P) to values computed for our models by thermodynamic integration (TI). The TI routine is described in full detail in the Supporting Information, section 4.
Thermal Expansion Coefficient. We estimated α, the thermal expansion coefficient of each DES studied, and compared the results against values reported in the literature wherever available. α is given by α ρ = - ∂ ∂ i k j j j j y { z z z z T (ln ) p (1) where ρ is the density of the evaluated system, T is temperature, and p is pressure. In order to evaluate α, each DES was simulated three times as either dry (χ H 2 O = 0) or wet (χ H 2 O = 0. 1) within a temperature range of 288-338 K. The range was scanned in intervals of 5 K. In the case of DL-menthol-acetic acid DES, the upper limit of the temperature range was extended from 338 to 353 K due to the available experimental data of densities at higher temperatures. Radial Distribution Functions. Liquid structures of CG DES systems were studied by comparing them against their atomistic counterparts by evaluating the radial distribution functions of key component pairs: hydrogen bond acceptor-(HBA-) HBD, HBAwater, and HBA-counterion.
For each system, we computed the length of the micelle, the radius of gyration and moments of inertia, and the asphericity. We also report the number of micelles present in the system in the last 1 μs of the simulation. All numerical data are reported in the Supporting Information, section 9. The results show that our models reproduce the phenomenon reported by Arnold et al. : the formation of cylinder-like SDS micelles in the DES and spherical SDS micelles in water. The behavior in water and DES is illustrated in Figure  9. As a final test, one of the elongated micelles was removed from the DES phase and solvated with water in order to study whether it would remain stable in the aqueous phase. As is evident from the inset in Figure  9 , the micelle rapidly disintegrates and begins to form spherical micelles.
DNA sequencing and sequence data analysis The DNA samples were purified, amplified and sequenced by the INRA Transfert Environnement (Narbonne, France) sequencing platform. The bacterial biodiversity was examined by sequencing the 16S rDNA (v3-v5 variable region) using primers F-5'-GTGYCAGCMGCCGCGGTA-3' and R-5'-CCCCGYCAATTCMTTTRAGT-3'. The primers used to amplify the specific fungal barcode ITS2 were w404 5'-CTTTCCCTACACGACGCTCTTCCGATCTTGTGARTCATCGAATCTTTG-3' and w40 5'-GGAGTTCAGACGTGTGCTCTTCCGATCTTCCTCCGCTTATTGATATGC-3'. For the DNA amplification by PCR, the mixture contained 5 μL of DNA template, 4 μL of dNTPs (25 mM), 1 μL of each primer (100 ng/μL), and 0. 5 μL of Taq polymerase in a total volume of 50 μL. The PCR cycle was 2 min at 95°C (denaturation step), 1 min at 56°C or 65°C for ITS2 or 16S rDNA amplification respectively (annealing step) and 1 min at 72°C (elongation step).
The two genetic targets were sequenced using paired-end sequencing with Miseq technology (Illumina, San Diego, USA). Sequence analysis was performed according to Mothur's SOP  (Kozich et al. , 2013). Simply put, the reads were trimmed; barcodes, primers and homopolymers longer than 8 pb were removed using  Mothur (v. 1. 37. 4 ) software  (Schloss et al. , 2009). Chimera were detected and removed using UNCHIME  (Edgar et al. , 2011). After alignment, the reads with 100% identity were clustered into a single sequence. The bacterial reads were assigned by the Wang method  (Wang et al. , 2007)  that was executed in Mothur with SILVA database. Operational taxonomic units (OTUs) were defined using the traditional method. The ITS2 reads were processed in the same way as bacterial sequences. Fungal OTUs were assigned at the genus level using the massBLASTer algorithm in the UNITE database (https://unite.
Diversity analysis Alpha diversity, which estimates the diversity within a sample, was calculated according to the Shannon index and Specific Richness (SR) to measure variations in the first sorting cabin, the sorting cabin, and the shredder. To assess the statistical differences, Mann-Whitney and Kruskal-Wallis tests were performed using Statgraphics Centurion XVI software (Statistical Graphics Corp. ,USA). The Sorensen-Dice coefficient (QS)  (Tuomisto, 2010)  was calculated to compare the beta diversity between measurements and the similarity in composition between the bioaerosols emitted inside the plant and in the IR. The more the microbial compositions between two samples have in common, the closer the index value is to 1 (identical composition). This index was calculated using the Mothur software for bacterial and fungal biodiversity, over a period of four months: January (winter), April (spring), August (summer) and October (fall).
Bayesian networks analysis Bayesian networks were used to identify the similarities between measurements  (Pearl and Russell, 2002). The software used for this technique is BayesiaLab (www. bayesia. com). For each measurement, the biodiversity data is summarized into the presence (value 1) or absence (value 0) of the genus. Each measurement is then a vector whereby each coordinate symbolizes the presence or absence of a specific genus (Table  S1 ). The data table used displays measurements as columns and genera as rows. Based on this data, the idea is to automatically build a network that highlights the statistical correlations between the measurements, based on the frequency of shared genus between samples. Each measurement becomes a node and whenever a statistical correlation is strong enough (p value < 0.
General sequencing data A total of 63 samples (87% success rate) and 68 samples (94% success rate) were successfully sequenced for bacterial and fungal biodiversity, respectively. Samples with DNA concentrations lower than 3 ng. µL -  were not sequenced (winter samplings or IR). The number of reads (a read is a sequence analyzed by the sequencing machine) after sequencing varied extensively depending on the location and on the month of sampling. The median of the reads was approximately 25,000 except for 16S rDNA reads for the IR, which contained less reads than the other sampling locations (Table  1 ). Two samples with a very low number of bacterial reads were not considered.
Indeed, stationary sampling does not take into account the movement of workers during their shift, nor the professional gestures that can affect exposure. Finally, the HTS technologies do not allow the identification at the species level due to the short reads provided by Illumina sequencing, the sequencing depth, the sequenced regions and the errors of sequence annotations in databases  (Nilsson et al. , 2006; Yamamoto and Bibby, 2014a). The genus level does not allow for a precise risk assessment. Indeed, a genus can gather a large number of species (e. g. more than a hundred for the genus Bacillus) and within a genus, only some species can be pathogenic  (Kwok et al. , 1999). Nevertheless, the HTS constitutes a powerful screening and characterization tool at the genus level, enabling to focus on potential pathogenic species, in a second and more targeted process.
3) Data Analysis: The high number of microelectrodes available with this TFT platform results in the acquisition of a considerable amount of data that cannot be processed using available software of the MCS acquisition system. Moreover, the scanning process of the gate lines using the JAPASTIM control card is very specific to our system. Therefore, our TFT system needed the development of a specific algorithm that includes special functions adapted to the TFT platform functioning and that can also be applied to larger MEAs. A spike sorting algorithm on MATLAB was thus developed in our lab to process data from cardiomyocyte electrical activity measured through the TFT platform  [20]. The different steps of this algorithm consist of extracting the information for each microelectrode in each drain line.
A. Electrophysiology With TFTs In this study, we describe the results of in vitro measurements of cardiomyocyte extracellular potential with a new MEA using the TFT technology, which shows the possibility to use our TFT platform for in vitro electrophysiological investigations. Our results demonstrated that beating cardiomyocytes can be cultured and their electrical activity captured with our TFT platform. Cell contraction was observed with an inverted microscope, while the cell-extracellular potentials were recorded with the TFT platform and MCS equipment. A photomicrograph of the cardiomyocyte culture and the corresponding measurements of their extracellular electrical activity are shown in Figs. 7  and 8. A 2-D reconstruction of the average peak-to-peak (pk-pk) amplitude recorded on each microelectrode was also realized after filtering and data processing with our spike sorting algorithm.
The high density of microelectrodes on the TFT substrate provides a clear advantage over conventional MEAs. Moreover, the transparency of the TFT array allowed the simultaneous observation of cell contraction with inverted microscope, which is more difficult with CMOS technology. Cardiomyocytes grown on our TFT platform adhere tightly to the substrate and contract isometrically, avoiding the motion artifacts that might cause deterioration in the SNR. This allows recordings over many days, facilitating, for example, investigations of long-term drug effects. Raw data from Fig. 8(b). were filtered with a bandstop resonator filter at 50 Hz and Q-factor of 10, combined with a high-pass filter with a cutoff frequency of 200 Hz. Depending on the microelectrode, these two filters provided a noise attenuation of 6-13 dB, compared to an attenuation of 0.
Primers design Primers were designed based on benA sequences downloaded on GenBank ® /NCBI and aligned on ClustalW2 ® software (Conway Institute UCD, Dublin, Ireland)  (Rodríguez et al. , 2015). In order to ensure the specificity of primers (Table  2 ), the expected PCR products were compared using BLAST (Basic Local Alignment Search Tool, NCBI) to the sequences of the CoreNucleotide, dbEST and dbGSS databases. The expected PCR products all had a query coverage and percent identity of 100% for the corresponding species and no other species were identified in the request. For the standard range, the reaction mixture contained: 10 µL of SYBR™ Select Master Mix 2X, 1 µL of forward primer, 1 µL of reverse primer, 2 µL of pure genomic DNA (gDNA) of each Penicillium species reference (standard range : 0.
Statistical analysis Descriptive statistics were calculated to provide information about the sample and paired ttest was also used to compare the two methods. Bland-Altman plots were used for descriptive evaluation of the concordance of the results from two methods. This was performed by plotting the difference for analysed variables between two methods against the mean results. Only results at p < 0. 05 were considered statistically significant. All statistical analyses were realized using SAS system version 9. 4 (SAS Institute Inc. Cary, NC, USA).
The Message Passing Interface (MPI) library is commonly used to communicate among cores irrespectively whether they share memory or sit at a di↵erent node. A simulation program is launched by a number of MPI processes that is usually equal to the total number of 1 https://www. archer2. ac. uk/about/hardware. html cores considered: a practice used in this work. The MPI rank is frequently used as shorthand for the MPI process ID. A good multi-physics example is CFD modelling of gas turbine combustion. The flow-field in a combustor is highly turbulent, chemically reacting and consists of multiple fluid phases and has significant e↵ects from radiative heat transfer. Liquid fuelled engines work by using a fuel injector to create a spray of fuel droplets, whilst the aerodynamic design creates a high swirl to provide a stable combustion zone for gaseous fuel generated from evaporation.
50 GHz. The C++ code related to this analysis can be found in: https://github. com/akjt/SharedMemoryParallelisation. git. It is clear that with more ranks involved the communication cost rises since the single rank needs to receive data from up to 39 ranks. Also, the speed-up using the SHM method seems to be consistently the same for all message sizes peaking at 3x faster for the case with 40 ranks.
Atomising spray in a diesel engine configuration ṁd = ⇡ D 2 d 4 ⇢ d u d (24) The computational domain has been discretized with 200 ⇥ 96 ⇥ 96 finite volume cells and a time-step of t = 15. 0 µs has been employed. Fig. 9  shows the jet penetration for all three cases including the experimental results obtained by  Hiroyasu and Kadota (1974)  and the numerical findings available by  Apte et al. (2003a). The comparison between the base line code and ATEL is almost identical. There two elements which inevitably will result in small deviations. Firstly, in the base-line code the spray part is advanced in time using the flow information from the previous time-step, while the flow step was based on spray information obtained from the same time-step. In ATEL, both the spray and the flow advance in time using the results from the start of the time-step.
Although this source of deviation is small, it is observable when a one-to-one comparison is made. Secondly, the stochastic breakup model employed in this case-study relies on a random number generator. While each MPI rank has its own private stream, altering the number of ranks will ultimately cause di↵erent random number sequences. In comparison to the experimental results, it is evident that both the base line code and ATEL fail to capture the penetration depth during the start but do so at a later stage. Collision and coalescence are not incorpo- rated in the spray model, which is most likely the reason for this observation, as the droplets at the early stage are clustered together  (Apte et al. , 2003a). N All performance test cases have been conducted on a node with 2 processors of Intel(R) Xeon(R) Gold 6148 CPU @ 2.
1). Table  7  shows the parallel scaling figures using the baseline code, while Table  8  shows the performance data for the ATEL framework. The figures associated to CPUh covers the entire simulation including pre-and postprocessing, while the flow and spray CPUh are the costs of their respective time-marching, which in turn is 95% of the total computational cost. Comparing the baseline performance to those presented in Table  8 , the ATEL framework improves the parallel e ciency by 21 percentage point and a total cost reduction of 735 CPUh. For a smaller number of ranks the baseline code obtains higher parallel e ciencies that can be attributed to the relative even load distribution of droplets among the decreasing number of blocks, and therefore more particles per block. Additionally, the flow-to-spray cost ratio is larger due to the increased number of cells per block.
Acknowledgements This work is financially supported by  Rolls-Royce  and the  EPSRC (Engineering and Physical Sciences Research Council )  through the  Centre for Doctoral Training in Gas Turbine Aerodynamics ( grant ref. EP/L015943/1 ). The authors are also grateful to  Dr. Christopher Goddard  from  Rolls-Royce  for providing the GENRIG test case and  Dr. Ruud Eggels  from  Rolls-Royce Deutschland  for valuable discussions.
Synchrotron Radiation (LNLS, Project  D04B-XAFS2-20180311 ),  Campinas, SP, Brazil. We also thank the  Laboratory of Structural Characterization (LCE/DEMa/UFSCar)  for the TEM facilities. The authors acknowledge the financial support of the Brazilian research funding institution  FAPESP  (under grants No. 2017/12437-5 ,  2019/18082-2 ,  2018/18208-0  and  2013/07296-2 ),  CNPq  (under grants No. 405140/2018-5 ,  426511/2018-2 ,  308706/2018-8 , and  311463/2017-7 ) and  CAPES.