dataesr/scientific-paragraphs-categorization huggingface dataset:
At the end of the incubation, samples were rinsed with ammonium formate, cryofixed with a Leica EM GP Automatic Plunge Freezer (ESRF) and stored in liquid nitrogen until the analysis. The distribution of silver and physiological elements was studied by nano-XRF at ID16a at the ESRF (41) under cryogenic conditions. The beam was focused to 26 × 42 nm 2 (vertical × horizontal) using a pair of Kirkpatrick-Baez mirrors. The fluorescence signal emitted from each sample pixel was recorded by a multielement silicon drift detector (SGX Sensortech Ltd. ) and fitted with the PyMCA software to generate elemental maps  (42). On the same beamline, 2D phase contrast holographic images  (43)  and 3D tomographic data sets were acquired on selected murine fibroblast cells exposed to 33-or 90-nm-diameter AgNWs.
Detectors included a Si 3 N 7 diode for I 0 and a silicon drift detector (X-flash Bruker) for the emitted X-ray fluorescence. From rectangular regions of interest, Ag L III edge spectra were generated for each pixel by stacking 2D images acquired at energy steps across the absorption edge. The XANES were aligned using the simpleElastix image registration library (45) and extracted using PyMCA. Bulk XANES spectra of reference compounds and pristine and aged AgNWs were recorded in the unfocused mode under the same conditions and fitted to the experimental data using a least-squares algorithm. Synchrotron experiments were performed on beamlines ID16A and ID21 at the ESRF, Grenoble, France, under User Proposal LS-2549. We thank Dr. Alejandro Quieruga (LBNL) for discussions on wire mechanics and Dr.
Charlet group of the Safe(r) Ecodesign Research and Education Applied to Nanomaterial Development (SERENADE) LabEx (S. G. L. , D. T. , A. -E. P. d. R. , M. V. , M. Safi, J. -P. S. , C. C. , L. C. , B. G. ) and the Nanowire Intelligent Re-Design and Recycling for Environmental Safety (NANOWIR2ES) project (S. G. L. , B. O. -S. , D. A. , C. V. , B. G. , L. C. , A. H. ). The SERENADE LabEx project received funding from Excellence Initiative of Aix-Marseille University, a French "Investissements d'Avenir" program. The NANOWIR2ES project received funding from the European Union European Research Area Network Safe Implementation of Innovative Nanoscience and Nanotechnology program and the US Consumer Product Safety Commission. The University of Grenoble-Alpes cofunded (with SERENADE) the Institut des Sciences de la Terre biology lab and an Invited Professor position for B.
Experimental Experiments were performed on a Philips CM300 ST TEM operated at 300 kV and equipped with a motorized compustage. Electron beam precession was generated by a Nanomegas Spinningstar device. Samples were prepared by grinding a small amount of powder in an agate mortar in absolute ethanol. A drop of the suspension was deposited on a carbon-coated copper grid. The grids were mounted on a standard single tilt sample holder that had been mechanically thinned, in order to increase the accessible tilt range from about [-25° ; +25°] to about [-55° ; +55°]. Electron diffraction patterns were collected on a TemCam F416 TVIPS, which is a rapid, large field of view, high-resolution and high dynamic range CMOS camera (4kx4k, 16 bits). The camera control software EMMenu on a standard PC communicates with the TEM through a DB9 serial connection.
The "serial acquisition" module included in EMMenu was used to pilot the sample tilt, the blanking of the beam and the recording of the electron diffraction patterns. Selected Area Electron Diffraction (SAED) patterns were obtained using a relatively large SAED aperture that selected a sample area of 2 µm in diameter. For the tomography a step size of 1° and a precession angle of 1. 25° were chosen in order to cover the reciprocal space with a reasonable number of frames without losing information on the reciprocal lattice. A smaller angular step size causes a progressive angle deviation due to the limited precision of the compustage movements. On the contrary, a too large angle step leads to data sets of bad quality, as the reflections are not positioned precisely enough in the reciprocal space.
Set-up: 1. Align the TEM, especially the eucentric height 2. Align the precession electron diffraction 3. Reduce the beam intensity as much as possible while still being able to see the crystals. 4. Go to a square of the grid that was not irradiated before. 5. Find a promising crystal of the appropriate size. In our case the data acquisition part was automated using the "Series acquisition" option included in the EMMenu TVIPS software. However, for any TEM that can be controlled by a computer it is sufficient to write a small program that follows the steps described above.
Test structure 1: Sr5CuGe9O24 The quality of the obtained data was first tested on the structure of Sr 5 CuGe 9 O 24 that was recently solved  [19]. The structure of this phase is monoclinic, space group P2/c (n°13), cell parameters a = 11. 8817(3) Å, b = 8. 1928(2) Å, c = 10. 3237(2) Å,  = 101. 597(1)° and contains 9 independent cation positions and 13 independent oxygen positions (fig. 3 ). The low symmetry and high number of independent atoms makes it a good test structure for our method. The crystal chosen for this analysis was of dimensions of approximately 1 µm*0. 5 µm. A dataset of 100 frames was recorded with an exposure time for each frame of 0. 5 s. The complete data set yielded the structural model that was subsequently refined  [19]. Reduced data sets were obtained from the complete data set by only retaining a certain number of frames symmetrically around the neutral position of the goniometer.
Each data set was treated with the PETS program  [17]  in the JANA2006 package  [18]. The diffracted intensities were extracted by PETS. Then the unit cell parameters and the space group were determined with JANA2006. Figure  4  shows the relevant planes of the reciprocal lattice as reconstructed by JANA2006. No systematic extinctions are observed except for the [h0l] plane where all reflections with l = 2n + 1 are very weak and can be attributed to multiple diffraction. The corresponding extinction condition h0l: l = 2n corresponds to two possible space groups Pc and P2/c. The structure solution was attempted with both space groups using the SIR2014 program  [20]. The P2/c space group yielded the better results and was therefore retained for the structure of Sr 5 CuGe 9 O 24. 1  summarizes the results of the structure solutions for the different data sets obtained for a resolution of 0.
Reducing further the number of frames to 30 (735 independent reflections, completeness of 26 %) yields a structural model that is slightly less complete (8 cation positions out of 9 and 9 oxygen positions out of 13) and less accurate. For all these datasets, the chemical species were easily identified considering the peak height in the electron density map obtained by SIR2014  [20]. Peak height values could be divided into several groups corresponding to the different chemical species. In each case the highest peaks correspond to Sr being the heaviest atoms in the structure, followed by Ge and then Cu as the lightest cation. Finally the oxygen positions were possibly followed by spurious peaks. The peaks from the different groups are separated by significant drops (table  2 ) in height from the lowest peak in one group to the highest peak in the next group.
Test structure 2: manganese formiate The low dose electron diffraction tomography allows reducing the electron dose the sample suffers during the experiment to the absolutely minimum quantity since the sample is irradiated exclusively during the data acquisition. The method is therefore suitable for data acquisition from beam sensitive materials such as metal organic frameworks (MOFs). Manganese formiate ([Mn(HCOO) 2 (H 2 O) 2 ] ∞ ; a = 8. 9049 Å, b = 7. 4793 Å, c = 9. 8307 Å, β = 97. 9542°) is a beam sensitive MOF of low symmetry (space group P2 1 /c (n°14)) containing essentially light atoms and a total of 10 independent positions of non-hydrogen atoms  [21]. Prior to the data acquisition we tested the lifetime of a crystal in the electron beam in the experimental conditions by irradiating a different crystal and following the evolution of the diffraction pattern.
Noticeable changes in the intensities were observed after 15 s to 20 s of irradiation. As a result we chose to perform a data acquisition of 60 frames with an exposure time of 0. 2 s for each frame. The resulting total dose for the acquisition of the data set in these conditions was 1. 08 e -/Å². The crystal chosen for this analysis was approximately 0. 7 µm*0. 9 µm in size. As before, the data set was treated with the PETS program  [17]  for the extraction of the intensities. A total of 1203 (out of 1877 possible) independent reflections up to a resolution of 0. 7 Å was observed, which corresponds to a completeness of 64 %. The JANA2006 package  [18]  was used for the determination of the unit cell parameters and the space group. Figure  5  shows the relevant planes of the reciprocal lattice where the extinction conditions are compatible with the known space group P2 1 /c.
Unfortunately, the orientation of the crystal was such that the b*-axis was in the missing cone and we could not obtain any information about the possible extinctions of 0k0 reflections. From the observed extinctions (h0l: l = 2n), three space groups are possible: Pc, P2/c and P2 1 /c. The structure solution was then attempted by the superflip program integrated in JANA2006 for all space groups and the best result was obtained for space group P2 1 /c. The structures obtained in the Pc and P2/c space groups were physically not acceptable because of the interatomic distances between the atomic positions. Figure  5 : Cuts from the reciprocal space of manganese formiate, the extinction for all h0l with l = 2n + 1 is clearly visible. The solution obtained from superflip contained all non-hydrogen atoms of the structure, but the program proposed more atom positions than could be real and the attribution between O and C to the different positions was not always correct (fig.
Acknowledgements The authors acknowledge the nancial support from  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brazil (CAPES) -Finance Code 001 ,  CNPq/MCT ,  FAPITEC ,  Nanofoton Network ,  INCT-FOT ÔNICA ,  FAPESP 2016/20886-1  and  2018/11283-7 ,  Army Research Laboratory   W911NF-17-1-0123  and  Air Force Office of Scientic Research  ( FA9550-12-1-0028 ).
Data were acquired in the positive mode and calibration was performed using a calibrating solution of ESI Tune Mix (Agilent) in CH 3 CN/H 2 O (95/5, v/v). The system was controlled with the software package MicrOTOF Control 2. 2 and data were processed with DataAnalysis 3. 4. Identification of different species, for example different protein oligomerization states or DNA-binding species, are based on the fact that a species can be detected, if and only if several consecutive charge states forming the charge state envelope of the species in question are present on the mass spectrum.
Protein crystallization, data collection and structure determination For all proteins, crystallization screenings were performed using 96-well plates in a sitting drop setup using a Freedom EVO 100 pipetting robot (Tecan). For the SeMet-substituted A70M I84M mutant of DdrO, screening was done by mixing 0. 5 l of protein at 10 mg/ml in 20 mM Tris-HCl pH 7. 4, 280 mM NaCl with 0. 5 l of reservoir solution from various commercial screens. Hits (spherulites) were initially obtained in position G2 of the JCSG-Plus screen (0. 1 M Bis-Tris pH 5. 5, 2 M NaCl) and were used as a seed in the subsequent optimisation conditions. Diffracting crystals were obtained by mixing 0. 5 l of 10 mg/ml of protein with an equal volume of reservoir solution containing 0. 1 M Bis-Tris propane pH 7, 3. For cryoprotection, all crystals were transferred in a drop made of mother liquor supplemented with 20-30% (w/v) glycerol before flash freezing directly in liquid nitrogen.
X-ray data were collected at different beam-lines at the ESRF (Grenoble) and data integration was performed with Mosflm from the CCP4 program suite  (26). The structure of the full-length protein was solved using the SeMetsubstituted mutant of the protein (A70SeM I84SeM) and the SAD phasing method (using Crank2 from the CCP4 suite). The structures of native DdrO, NTD and CTD were subsequently solved by molecular replacement (using Molrep from the CCP4 suite). All structures were refined with Refmac5 and phenix. refine with structure validations performed using Procheck from the CCP4 suite or MolProbity within the Phenix suite  (27, 28). In the case of TLS refinement, TLS groups were defined as per the TLS Motion Determination homepage (http://skuld. bmsc. washington. edu/ ~tlsmd/). CCP4mg software was also used to generate figures  (26).
HTH XRE-type DNA-binding domains are present in numerous transcriptional regulators, for example in B. subtilis prophage PBSX repressor protein Xre, in regulatory proteins Cro and C1 of several bacteriophages, and in the biofilm formation regulator protein SinR of B. subtilis. No similarity to a domain in the conserved domain database was found for the C-terminal region of DdrO, but this region does contain the site where DdrO is cleaved by metalloprotease IrrE. Using a bacterial two-hybrid system, we previously showed  (12)  and confirmed here (Figure  1B ) that DdrO interacts with itself, indicating that DdrO forms at least dimers. To determine the oligomeric state of DdrO in solution, SEC-MALLS experiments were performed. At low DdrO concentration, monomeric DdrO and a complex with a MW of 27.
Overall crystal structure of DdrO In line with the conserved domain search for DdrO, the structure prediction tool Phyre2  (31)  showed an N-terminal DNA-binding domain of the XRE family with good confidence, but no 3D structure was predicted for the Cterminal region of DdrO. To understand the structural basis for DdrO dimerization and function, the DdrO structure was determined using X-ray crystallography. The struc-   tion of SeMet DdrO for X-ray structure determination by the SAD method. The 3D structure of native DdrO was subsequently solved by molecular replacement. SeMet and native DdrO structure were solved at 3. 1 and 2. 84 Å resolution, respectively. Statistics on data collections and structure refinements are presented in Table  2  and the composite omit map for native DdrO in Supplementary Figure  S8.
Moreover, SinR NTD dimer structure and interface are very similar in DNA-bound SinR and isolated SinR structures (rms deviation 1. 28 Å; PDB codes 3ZKC and 3QQ6, respectively)  (33, 34). The DdrO NTD dimer structure superposes well with that of DNA-bound SinR (rms deviation 1. 87 Å) (Figure  5 , Supplementary Figure  S10 ). However, subtle differences are observed. The NTD-NTD interface area of DdrO is 313 Å2 and shows only one reciprocal hydrogen bond between the side chain of H59 and the main carbonyl atom of V65 indicating a weak interaction (Supplementary Figure  S10 ). According to the PISA webserver this interface has a Complex Formation Significance Score (CSS) of 0, suggesting that it is not a dimeric interface, in accordance with SEC-MALLS and BACTH results. The SinR NTD interface is also relatively small (520 Å2 ) and is formed exclusively by the ␣3-␣4 loop and the N-terminal half of ␣4 showing four hydrogen bonds (between T40-Q45, N41-Q45, N41-S43 and P42-I44) (34) (Supplementary Figure  S10 ).
All CTD residues highlighted in this paragraph are highly conserved in deinococcal DdrO proteins (Supplementary Figure  S1 ). Prediction of protein binding energy using PRODIGY  (35)  indicates a dissociation constant of 6 × 10 -6 M for the full-length DdrO dimer interacting through its CTD (as in Figure  4C ) and of 2. 6 × 10 -4 M after cleavage, supporting the data obtained in solution. To investigate the importance of the ionic interactions involved in CTD oligomerization as observed in the crystal structures, various point mutants were constructed (Supplementary Figure  S12 ). The DdrO mutants were produced, purified and first analysed in vitro by size exclusion chromatography (SEC). Almost all these DdrO mutants show less efficient oligomerization compared to wild-type DdrO (Supplementary Figure  S13 ), with the strongest effect observed for the D96R mutant (Figure  7A ), validating the involvement of the reciprocal ionic interactions in the dimerization/tetramerization state of DdrO.
Surprisingly, DdrO dimerization via its CTD was not considered and investigated in Lu et al. (42) , although the same CTD-CTD interfaces and DdrO polymer as found for D. deserti DdrO are present in their crystal structure (PDB code 6JQ1). Our work includes a domain dissection approach and a number of in vitro, in vivo and in silico experimental data that support the model that DdrO dimerization is mediated by its CTD and that cleavage by IrrE prevents this dimerization mode. The 3D structures of Deinococcus IrrE and DdrO are the only structures of widespread COG2856/XRE protein pairs that have been experimentally solved. Also, protease activity has been demonstrated for only two COG2856 domain-containing proteins, IrrE and B. subtilis ImmA  (12, 19). ImmA and IrrE share less than 15% sequence identity, mainly located around the peptidase signature HEXXH (Supplementary Figure  S18 ).
In order to examine the structural characteristics of Zr films, the Bragg Brentano configuration X-ray diffraction (BRUKER D8) with the Co (λ=1. 78897 Å) source at a current of 40 mA and a voltage of 35 kV was used. Diffractograms were collected under air flow in the 20-80° angle range at a scan rate (2θ) of 0. 1° s -1. Film morphology was observed on the top surface and brittle fracture cross section by using the JEOL JSM-7800F field emission gun scanning electron microscope (FEG-SEM). The nanohardness and effective Young's modulus of the films were carried out by using the Nanoindentation Tester (applied load is 5 mN) from CSM Instruments, equipped with a Berkovich diamond indenter tip at room temperature. The nanoindentation data were the average of twelve measurements performed for each sample and determined based on the Oliver-Pharr model  [30].
In this paper, we elaborated the nickel-based alloy type Hastelloy G30 and we analyzed it by techniques of characterization in order to interpret the results obtained through a computer simulation. The calculation of the thermo-physical properties of the alloy and the simulation of filling and solidification of the casting was performed by the software ProCAST. The validation of the numerical results was done by the following experimental techniques: optical microscopy and hardness test. In the center of the elaborate piece, the experimental results showed the appearance of porosity and leading to embrittlement of the alloy. According to the numerical simulation, this is caused by a fraction of the liquid that remains trapped in this area.
Numerical results of filling and solidification of Hastelloy G30 The Figure  1  shows the filling system, the riser and the alloy mesh generated by Mesh-Cast (ProCAST software package). The filling system is lateral, constituting of a sprue, a runner and a gate. The riser is located at the top of the casting. We performed a tetrahedral mesh, with a distance between two nodes of 2 mm which allowed to have a finite element model consisting of 181 200 nodes and 860 250 elements. We chose a mesh density in order to have precise results, and to reduce the computation time as much as possible. Figure  1  : Mesh of the filling system, the riser and the alloy. The figure  2  shows the distribution of the temperature of the plate during the cooling at ambient temperature by the air. According to this figure, the lines of the contours of the temperature field appear in the form of concentrated circles.
Antibacterial Activities Antibacterial assay was performed against both gram-positive and gram-negative bacterial strains (Figure  5 ). All of the NPs showed considerable antibacterial potency against the tested strains (S. aureus, B. subtilis, K. pneumonia, P. aeruginosa and E. coli). 1 , expressed as mean ± standard deviation of the original data. Samples showing the zone of inhibition, ≥12 mm, to be considered as significantly active. The data obtained was further processed to appraise MIC through broth micro dilution method. Among all of the tested strains, S. aureus was found to be the most susceptible bacterial strain with maximum zone of inhibition against S-ZNPs (20 ± 0. 98 mm) with a MIC of ≥100 µg/mL. C-ZNPs proved most potent against K. pneumoniae (17 ± 1. 11 mm) having a MIC of ≥100 µg/mL.
These results are in the range of previous work dealing with the antibacterial activity of biosynthesized ZnO NPs  [47] [48] [49] [50] [51]. 1 , expressed as mean ± standard deviation of the original data. Samples showing the zone of inhibition, ≥12 mm, to be considered as significantly active. The data obtained was further processed to appraise MIC through broth micro dilution method. Among all of the tested strains, S. aureus was found to be the most susceptible bacterial strain with maximum zone of inhibition against S-ZNPs (20 ± 0. 98 mm) with a MIC of ≥100 µg/mL. C-ZNPs proved most potent against K. pneumoniae (17 ± 1. 11 mm) having a MIC of ≥100 µg/mL. B. subtilis and E. coli showed maximum susceptibility towards W-ZNPs (9 ± 0. 60 mm and 10 ± 0. 75 mm, respectively. P-ZNPs proved to be most potent against P.
Machine learning (ML) has become a crucial component of early drug discovery. This research area has been fueled by two main factors. The first is the fast-growing availability of relevant experimental data. Examples of such datasets are those containing the bioactivities of molecules of known chemical structure against a non-molecular target (e. g. , a cancer cell line), binding affinities of such molecules against a molecular target (e. g. , a particular kinase validated for a specific cancer type) or the X-ray crystal structures of a molecular target. This factor has been boosted by the development of community resources, such as ChEMBL [1], PubChem [2], NCI-60 [3], or PDBbind  [4] , that curate and facilitate re-using these datasets for predictive modelling. The second factor is the easy access to high-quality and well-documented implementations of a range of ML algorithms, including those of recent advances such as XGBoost [5], deep learning [6], or conformal prediction  [7].
Moreover, the micro voids could affect the fatigue life of a structural component  [4, 5]. In the LCM process, voids appear due to faulty injection schemes (such as incorrect placement of injection ports and air vents), or faulty injection and vent pressures, and finally wrong filling simulations. The quality of LCM-processed components depends not only on the preform and resin system, but also on the filling process itself. Parameters influencing filling behavior are the mold geometry, resin viscosity, mold temperature, placement of the injection ports, and the most critical parameter being the permeability evaluation of the reinforcement. Software such as PAM-RTM [6], LIMS  [7]  and Polyworx  [8]  one to predict the filling behavior and to obtain the filling times and flow front shapes. This software requires some inputs: 1.