dataesr/scientific-paragraphs-categorization huggingface dataset:
Small-angle neutron scattering (SANS) SANS measurements were carried out on the D11 instrument at the Institut Laue-Langevin (ILL) in Grenoble. 24 Experiments were performed with a neutron wavelength of 6 Å and a path length of 2 mm at three sample-to-detector distances (SD): 1. 5 m, 8 m and 39 m, covering the Q-range: 0. 002-0. 42 Å À1. Absolute intensities were obtained with reference to the secondary calibration standard, H 2 O (1 mm path length), which has a differential scattering cross-section of 0. 983 cm À1 on the D11 instrument at l = 6. 0 Å. The raw data are available in the ILL data repository. 25 For each measurement, the 2D detector data were integrated over the azimuthal angle (0-2p), and the D 2 O background was subtracted. Curves from the same sample at different SD were then combined and rebinned (the protocol for the rebinning algorithm is given in the ESI †).
Light scattering The effects observed by small-angle scattering were investigated in further detail using light scattering methods. The DLS data are presented in three forms: the first-order field correlation function, an inverse Laplace transformation of the correlation function and the parameters resulting from fits of the correlation function to a bimodal exponential model of the form: g (1) (t,Q) =A exp(ÀD 1 Q 2 t) + (1 À A)exp(ÀD 2 Q 2 t) (2) where A and (1 À A) are the relative intensity contributions from components 1 and 2 respectively. The weight fractions of each component can then be obtained by taking into account the respective volumes of the scatterers. 29 The first-order field correlation function provides a clear and objective indication with regards to the presence and relative intensity of different modes in the sample.
Salt effects. As observed in the SANS data, both in this work (Fig. S5 , ESI †) and previously,  21  (data reproduced in Fig. S9b , ESI †) the presence of NaCl, appears to suppress the presence of larger aggregates at high pH. The abundance of sodium present in the as received surfactant, was quantified by conducting inductively coupled plasma mass spectrometry measurements (ICP-MS) and was found to be 0. 8 ppm. This equates to a concentration in solution of 1 mM and a molar ratio of approx. 21 : 1, surfactant : salt. Light scattering measurements, taken at total salt concentrations of 6 mM and 21 mM § (i. e. molar ratios of approx. 4 : 1 and 1 : 1, surfactant : salt, respectively), are shown in Fig. 6. It can be seen that even at low concentrations, the presence of additional NaCl completely suppresses the appearance of larger structures at high pH and nullifies the addition-rate effect.
Acknowledgements The authors gratefully acknowledge the  ILL  for the allocated beamtime and the  Partnership for Soft Condensed Matter (PSCM)  for the use of their instruments. D. W. H. was supported by  BMBF  grant [ 05K16KT1 ] and L. C. acknowledges the  TU-Berlin  and the  ILL  for postdoctoral funding through a three-year cooperation agreement.
1f ), while cBbRAGL lacking its corresponding NBD* domain (which has limited sequence similarity to NBD but, like NBD  17  , forms a dimer in solution (Extended Data Fig. 1c, d )) retains substantial activity (Fig. 1e ). In addition, while the C-terminal tail (CTT) of RAG1 is dispensable for activity  2  , the C-terminal tail of BbRAG1L (CTT*; Fig. 1c ) is important for BbRAGL cleavage activity  10  (Fig. 1e ). Hence, RAG1 and BbRAG1L have evolved opposite dependencies on the N-and C-terminal portions of their core regions. RAG and BbRAGL exhibit a third important difference: while both are active transposases in vitro  10, 18, 19  , only BbRAGL exhibits substantial transposition activity in cells  10, [12] [13] [14] [15] [16]  (Fig. 1g ). We note that BbRAGL activity is being assessed in a heterologous (mammalian) cell context.
Cryo-EM data acquisition. 3 μl of purified complex was applied to freshly glow discharged C-flat™ 400 mesh, R2/1 and R1. 2/1. 3 holey grids for intact DNA and nicked DNA complexes, respectively. Grids were blotted for 4 seconds in 100% humidity and plunge frozen in liquid ethane using a Vitrobot Mark 3 (FEI Company). A Titan Krios electron microscope (Janelia Research Campus, HHMI) operated at 300 kV, with a spherical aberration corrector and a Gatan Image Filter (slit width of ~20 eV), was used to acquire images with a K2 Summit direct electron detector (Gatan) in super-resolution mode. The image stacks were collected at a nominal magnification of 81,000X, corresponding to 0. 675 Å per super-resolution pixel, at a dose rate of ~10. 2 electron per physical pixel per second. The total exposure was 80 and 54 electrons per Å 2 , fractionated into 50 and 40 frames, for intact DNA complex and nicked DNA complex, respectively.
Modeling and refinement. An initial model was obtained by structural profiling of cBbRAG1L and BbRAG2L sequence propensities as previously described  [44] [45] [46]. In brief, separate models of cBbRAG1L (aa 473-1110) and BbRAG2L were built accounting for accessibility, charge, hydropathy, consensus secondary structure, consensus intrinsic disorder profiles and fold recognition assessment using the Discovery Studio software suite 3. 0 (Accelrys). The models were refined by remote homology techniques starting from mouse RAG1 structural templates in PDB: 3GNA  17  for NBD* of BbRAG1L and PDB: 4WWX  20  for cBbRAG1L and BbRAG2L. To eliminate steric conflicts and further minimize energy, these models were iteratively refined until convergence by repeated cycles of Generalized Born simulated annealing molecular dynamics for implicit solvent using NAMD 2.
3 Å) by Molecular Dynamics Flexible Fitting (MDFF)  49. The flexibly fitted model was able to account for most density, except that BbRAG1L loops 640-650, 704-720, 732-740, 1046-1053 and BbRAG2L loops 11-22, 34-49, 67-74, 85-108, 179-190 300-314, which were adjusted and rebuilt in COOT 0. 8  50. The density for loop 603-630 in BbRAG1L was insufficient for model building. An all-alanine chain was built to fit the density for the C-terminal tail (CTT*) of BbRAG1L (1105-1125). The DNA chains from the previously published model (PDB: 3JBY)  21  were fit into the map and then changed to the correct DNA sequence in COOT 0. 8. The model was adjusted in COOT 0. 8 manually with iterative cycles of automatic rebuilding using Rosetta FastRelax protocol  51. The model was further refined using the phenix.
real_space_refine module in PHENIX with secondary structure restraints and Ramachandran restraints  52. The final model was validated using MolProbity  53  and EMringer  54  (Extended Data Table  1 ). All molecular representations were generated in PyMol (http://www. pymol. org) and Chimera  55. In vitro transposition assay. The in vitro intermolecular transposition reaction (Extended Data Fig. 7b ) was performed as described under in vitro DNA cleavage. The 12/23RSS substrate was replaced by 10 nM linear donor fragment with tetracycline resistant marker and 10 nM pECFP-1 target plasmid. The final concentrations of RAG protein, Mg 2+ , and DTT were 50 nM, 1. 5 mM, and 2 mM respectively. After proteinase K digestion, DNA was ethanol precipitated. 50 ng DNA was transformed into electrocompetent MC1061 bacterial cells and spread on plates containing kanamycin or tetracycline/kanamycin/streptomycin.
High throughput sequencing and data analysis. High-throughput sequencing was performed on an Illumina NextSeq 500. Cutadapt version 1. 16 was used to identify barcodes with the adapter-matching error rate set to allow one mismatch in a 7-8 bp barcode. 95. 9% of reads were successfully matched to a barcode and unmatched reads were discarded. Next, cutadapt was used to trim the barcode sequences, primer sequences, and 12RSS and 23RSS sequences. The resulting trimmed sequencing data contain only vector sequence or genomic DNA sequence from transposition events and other random integration events. Overall, 60. 0% of barcoded sequencing reads contained identifiable RSS sequences and other reads were discarded. Trimmed sequences were aligned to human genome GRCh38 using Bowtie2 (version 2. 2. 9) using "very sensitive" end-to-end alignment mode.
Gene, exon, and transcription start site (TSS) definitions were downloaded from Ensembl gene v93, dataset Human genes (GRCh38. p12). Active TSSs and active genes/exons were defined based on H3K4me3 (experiment ENCSR000DTU) and H3K36me3 (experiment ENCSR910LIE) ChIP-seq datasets, respectively, from HEK293 cells from ENCODE (https://www. encodeproject. org).
Data availability statement. The model of the cBbRAGL-nicked 3'TIR synaptic complex has been deposited in the Protein Data Bank with accession code PDB: 6B40. The cryo-EM maps of cBbRAGL in complex with intact or nicked 3'TIRs have been deposited in EMDataBank with accession codes EMD-7043, 7044, 7045, and 7046. High-throughput DNA sequence data to identify transposition events in the human genome have been deposited in the NCBI Sequence Read Archive with accession codes SRR8430227-SRR8430233 (Project PRJNA514369). e, (Left) Fourier shell correlation (FSC) curves of the half maps from gold standard refinements of cBbRAGL-nicked 3'TIR complex with no symmetry applied (blue), cBbRAGL-intact 3'TIR complex with no symmetry applied (red), and with C2 symmetry applied (green). (Right) FSC curves of the gold standard refinement of cBbRAGL-nicked 3'TIR complex with C2 symmetry applied (blue) and of the C2 symmetrized map and model (green).
Cryo-EM data collection, refinement and validation statistics Summary of relevant parameters used during cryo-EM data collection and processing. Refinement and validation statistics are provided for the molecular model of the BbRAGL-3'TIR synaptic complex with nicked DNA with C2 symmetry. band. Cleavage products were resolved on acrylamide gels and are indicated schematically (circles indicating hairpin ends). For gel source data, see Supplementary Figure  1. e, f, Cleavage of substrates containing two or one TIRs or RSSs as indicated above the lanes, by cBbRAGL, cRAG, or complexes in which cBbRAG1L/cRAG1 lack the indicated domain. Black asterisks mark the two single cleavage products. Reaction time of 60 min was used here and in other cleavage reactions unless otherwise specified. g, Transposition frequency measured in HEK293T cells using the assay of Extended Data Fig.
ACKNOWLEDGMENTS The authors thank  Y. Kong  for computational analysis of genome transposition data,  W. Eliason  for assistance with  SEC-MALS ,  C. Akey  for help with freezing of the grids for BbRAGL-intact 3'TIR,  R. Huang  and  H. Chuan  for help with cryo-EM data collection,  J. Wang  for advice and insight in structural analysis,  E. Hendrickson  for HCT116 cells, XEGEN for NBD phylogenetic sequence analysis,  Z. Chou ,  X. Liu ,  H. Zhang  for an insertion-site mapping script, and  M. Ciubotaru  for advice, and members of the  Schatz lab  for comments on the manuscript. This work was supported in part by  R01 AI32524  and  R01 AI137079  (D. G. S. ),  R01 AI102778  (Y. X. ),  2013CB917800  from the  Ministry of Science and Technology of China  and  91231206  from the  National Natural Science Foundation of China  (A.
The methodology is tested through the simulation of the process with BatchColumn® software. As physicochemical properties of the aroma compounds and the vapour -liquid equilibrium (VLE) of all involved mixtures are required for simulation study, group contribution methods are used to predict missing properties such as vapour pressure, vaporisation enthalpy and liquid and vapour heat capacities. For VLE calculation, the modified UNIFAC group contribution method is considered and new binary interaction parameters for the main groups COO and CH2O are regressed from experimental data. Simulation results are in good agreement with experiments carried out in a fully automated batch distillation column at 15 kPa and help optimise the industrial process operation. The proposed methodology can be applied for the design of other solvent recovery process in fragrance industry.
Several studies have demonstrated that commercial simulation programs relate with a reasonable accuracy the operating variables of the distillation process and the key organoleptic properties of the alcoholic beverage products. ASPEN Plus, ChemCAD and ProSim Plus are the most used commercial process simulators for wine and must distillation because they perform rigorous mass and energy balance calculations  (Decloux et al. , 2005; Batista and Meirelles, 2011; Valderrama et al. , 2012). Their strength lies in their accurate computation of the phase equilibria of the mixtures under a wide-ranging of operating conditions thanks to their extensive databases of physicochemical properties and thermodynamic models. Decloux et al. (2005)  validated the modelling with ProSim Plus software of a continuous industrial plant to produce neutral spirits including seven interconnected distillation columns where they considered seven representative congeners (acetaldehyde, ethyl acetate, acetal, methanol, 2-butanol, 1-propanol and allyl alcohol).
In addition to water and ethanol, all binary liquid -vapour equilibria (VLE) including the congeners, were assessed by using UNIFAC model. An optimisation study allowed the understanding of the congener's distribution along the distillation column with the operating conditions in order to maximise the ethanol recovery yield and the quality of the neutral spirit. Batista and Meirelles (2011)  simulated the continuous cachaça production with ASPEN Plus software by considering 10 congeners (methanol, isopropanol, isobutanol, 1-propanol, isoamyl alcohol, ethyl acetate, acetaldehyde, acetone, acetic acid, CO 2 ) into the feeding of the mixture ethanol -water. NRTL thermodynamic model was selected for computing the VLE where the interaction parameters of the 66 related binary mixtures were adjusted from experimental data or from UNIFAC predicted values.
the assumption of ideal vapour phase. The studies aforementioned on spirits production by batch distillation process have been mainly performed by in-house software and rare papers have been devoted to simulation studies using commercial software. In  Valderrama et al. (2012) , the module CC-Batch of ChemCAD was used for simulation studies of a bi-distillation process for whisky production. Similarly,  Decloux et al. (2014)  demonstrated the suitability of using the BatchColumn® software (Prosim S. A) for simulating the batch distillation process of an orange bitter distillate product. The raw material was the alcoholic extract of bitter orange peels macerated during three weeks. In addition to water and ethanol, four other aroma molecules defining the quality of the bitter were selected such as two monoterpene hydrocarbons, α-pinene and dlimonene, and two oxygenated hydrocarbons linalool and linalool oxide.
Therefore, optimal operation can be investigated in order to increase both solvent recovery yield and purity by minimising the content of aroma compounds. However, unlike to the alcohol beverage production where most of congeners can be found in the database of the commercial simulators, the most significant aroma molecules found in the perfume and fragance industry are missing or their properties are partially described in experimental databases. Consequently, VLE predictions are incomplete, mass and energy balances are not calculated and the process cannot be simulated by commercial process software. This paper aims to simulate dimethyl carbonate solvent recovery from an aroma mixture by a batch distillation software and to validate it with experimental measurements. We address several challenges.
The last challenge refers to the validation of the batch distillation process simulations at 15 kPa through the application of BatchColumn® software (Prosim S. A). Experimental measurements are carried out to validate the models used for predicting the physicochemical properties of aroma compounds and the thermodynamic method. The paper is organised as follow. First, the methodology is presented and the computeraided methods are described along with experimental data needed to develop them. Next, each step of the methodology is discussed through the separation of dimethyl carbonate from a mixture of aroma compounds by batch distillation under reduced pressure. Finally, Simulation results are presented and compared with experimental runs that are carried in a fully automated laboratory batch distillation column.
2. Methodology for solvent recovery from a mixture with aroma compounds Fig. 1  shows the main steps of the methodology for the design and analysis of the solvent recovery process from aroma compounds mixture in the perfume and cosmetic industry. Step 1 -Process Definition: It comprises the selection of the operating conditions as well as the list of typical aroma compounds. The working region of the separation process is defined by the total pressure or temperature condition. Step 2 -Database Conception Step 2. 1 -Pure component properties: A model describing the variation of properties with temperature has to be set in order to carry out the simulation studies in a commercial process simulator. For distillation processes, vaporisation enthalpy, heat capacity and vapour pressure properties are needed.
First, a predictive method based on group contribution (GC) is chosen for missing property values. Both group selection and their contribution values must be carefully selected. Suitability of the GC method can be assessed by comparison of the predicted values with available experimental data. In the next step, the temperature dependence of the property is regressed upon a mathematical formulation that be chosen among those available in DIPPR database. Step 2. 2 -Vapour -liquid equilibrium of mixtures Reliable computation of phase equilibrium data is crucial for simulation studies on distillation processes. It is a complex task because aroma compounds are mainly multi-cyclic molecules often including shared oxygen atoms. The resulting complex intermolecular interactions in the liquid phase (such as van der Waals interactions and hydrogen bonds) imply the use of an excess Gibbs free energy model (activity coefficient model), such as predictive group contribution methods.
If high average relative error (ARD) is obtained from the fitting of the interaction parameters between UNIFAC main groups, the molecular decomposition of the aroma compound can be revised as well as the accuracy of the experimental data considered in both, the training and validation sets. Other predictive methods can be tested if the average relative errors remain unacceptably high. Step 3 -Process Simulation: A proper process simulator is selected allowing the implementation of pure component and mixture databases and the further study of the design operating variables on the solvent recovery process. For the sake of simplicity, commonly assumptions related to the hydrodynamic, heat transfer loss and separation efficiency are retained and they have to be validated in the experimental prototype.
Step 4 -Experimental validation: Experimental runs are carried out in an adequate equipment satisfying the main assumptions in the previous process simulation study. The average relative deviation (ARD) or the maximum absolute deviation (MAD) between the experimental data and the simulated results will check the suitability of the models implemented in the database for predicting the pure component properties and the phase equilibrium data of the mixtures in step 2. Step 5 -Sensitivity analysis and optimisation If good agreement exists between the simulation and the experimental results, further simulation studies can be carried out in order to identify the key operating parameters and the computation of their optimal values satisfying one or several objective functions.
Pure component properties database Correlation models of the vapour pressure, the vaporisation enthalpy and the liquid and the vapour heat capacities are required for all components listed in Table  1 , DMC and the aroma compounds α-pinene, eucalyptol, linalool, cis-3-hexenol, fenchone and benzyl acetate. Values for DMC, α-pinene and benzyl acetate are provided by the DIPPR database available in BatchColumn® software  (Prosim, 2017a). However, eucalyptol, linalool, cis-3-hexenol and fenchone are missing compounds in the DIPPR database. Therefore, they are introduced in the database thanks to the Simulis®Thermodynamics property server interface  (Prosim, 2017b)  and property values are predicted by using group contribution methods implemented in the computational tool IBBS  (Heintz et al. , 2014).
1993 ). Hence, the most appropriate UNIFAC main group is CyCH2O which involves three different subgroups in order to characterise the cyclic oxygen atom in tetrahydrofuran (THF), 1,3 dioxane and trioxane as it is displayed in Table  4 , cy(CH2-O-CH2), cy[(CH2)O(CH2) 1/2 ] and cy[(CH2) 1/2 O(CH2) 1/2 ], respectively. to Table  3 , we found three alternative ways for decomposing the whole molecular structure of eucalyptol by using separately each main chemical groups, trioxane(I) and tetrahydrofuran (II) from Table  4. Decomposition (III) consider the cyclic ether group like a linear CH2O chemical group in modified UNIFAC method as it is implemented in Simulis®Thermodynamics software (ProSim, 2017b): (I) 1-cy[(CH2) 1/2 O(CH2) 1/2 ] + 1-cyC + 1-cyCH + 4-CyCH2 + 3-CH3; (II) 1-cy(CH2-O-CH2) + 1-cyCH + 4-CyCH2 + 3-CH3; (III) 1-CH2O + 1-cyC + 1-cyCH + 4-CyCH2 + 3-CH3.
12 (N. A. in Table  5 ) can be computed from available experimental VLE data of binary mixtures including one component that contains carbonate group COO(41) in the chemical structure while the second component has a cyclic oxygen atom cyCH2O(43) or a linear ether group CH2O(13) according to Table  3. Table  5. Modified UNIFAC parameters in Simulis®Thermodynamics software (1 (S 9,G ) ; 2(S 9,X. V 9,G ) and 3(S 9,G ; V 9,G ; W 9,G ))    2001 ), 3 Francesconi R. (1997), 4 Ma et al. (2004)   The three constants S +. F ; V +. F ; W +. F of the group interaction parameters of Modified UNIFAC method were determined from the experimental data reported in  Main Group S 9,X (K) The interaction parameters S 9,G ; V 9,G ; W 9,G were then used for computing the binary VLE data between DMC and 1,2-epoxybutane which was selected for the validation set.
Experimental VLE data were measured at three temperatures 288. 15 K, 298. 15 K and 313. 15 K  (Francesconi and Comelli, 1996). Modified UNIFAC decomposition of 1,2-epoxybutane was defined as 1CH3+1CH2+1CH+1CH2O. Fig. 3  displays the boiling and the dew temperature curves versus the molar fraction of DMC as well as the values of relative errors ∆P and ∆y in the Eq. (  13 ) showing the satisfactory agreement of the predicted results with the experimental points. Therefore, these parameters can be used for predicting the VLE of binary mixtures including DMC and especially for the mixture DMC -eucalyptol. The suitability of the Modified UNIFAC method for predicting binary VLE of the aroma molecules was also studied by comparing experimental data with predicted results. Table  8  displays the average relative deviation of the temperature (or pressure) and the vapour composition of the binary mixtures including α-pinene, fenchone, eucalyptol or linalool with linear alcohols, alkanes, alkenes and cyclic compounds.
Some experimental VLE data of binary mixtures including only aroma compounds were also considered. The experimental set contains VLE data under a great variability of temperature and pressure conditions. Most of data were measured at reduced total pressure and low temperature. Overall, very good agreement occurs between the experimental data and the predicted results from Modified UNIFAC model. These results indicate the suitability of applying the Modified UNIFAC model for predicting binary VLE of mixtures including aroma compounds. Groups CH3 C=C AcCH AcCH2 OH CH2CO CCOO CH2O COO CyCH2 CyCH2O CH3(1) 0 2 2 2 3 2 3 2 2 3 3 C=C(2) 2 0 2 2 3 2 2 2 2 2 3 AcCH(3) 2 2 0 2 3 3 2 2 2 3 2 AcCH2(4) 2 2 2 0 3 3 3 2 2 2 2 OH(5) 3 3 3 3 0 3 3 3 2 3 3 CH2CO(9) 2 2 3 3 3 0 2 3 1 2 2 CCOO(11) 3 2 2 3 3 2 0 3 2 3 1 CH2O(13) 2 2 2 3 3 3 3 0 N.