dataesr/scientific-paragraphs-categorization huggingface dataset:
This methodology of estimation of the intensity of singular stress field was used to predict the failure of single lap joint  [3]  cylinder butt joint  [4]  and also double lap joint  [5]. Other authors proposed design rules based on analytical models and testing of custom specimens to take into account the strong multi-axial loading at the joint  [6]. Dragoni et al. present useful software to help the engineer in the everyday design of bonded joints based on this method  [7]. Finite Element Analysis (FEA) is widely used to design structures. In order to cope with singular stress fields, Point Stress criterion could be performed  [8, 9]. Simplicity is one of the main assets of this method. However, it requires a substantial materials database and the results are closely related to the mesh size.
A modification of the coupled criterion is proposed in this paper to take into account the loading rate of a bonded structure. The coupled criterion requires a smaller material database. In this paper, the commercial adhesive EA 9395 is used. Tests were performed with three different loading rates by using the modified Arcan device. Failure envelopes were built and exhibited an influence of the loading rate on the failure strength of the bonded joint. The material toughness in modes I and II were calculated by previous authors by using DCB  [12]  and ENF tests  [13]. In this paper, the fracture toughness is presumed to be independent of the loading rate. In order to validate the failure load prediction obtained with the coupled criterion, a tubular sample composed of two co-axial substrates bonded by adhesive layers is also used in this study.
DC load dSPACE Wind tunnel Prior to investigating different power control strategies, several tests are carried out within the wind tunnel by changing the load and the wind speed. The voltage, the current and the electrical power in the DC bus are then measured. In addition, the mechanical speed (Ω) of the PMSG is observed and measured in order to not exceed the maximum value recommended for this WT. Collected experimental data are given in Figure  4. As can be seen in Figure  4a ,c, all power curves reach a maximum point and then decrease as v WT and Ω rise. Additionally, it is noticed that each value of v WT corresponds to one value of p WT. However, one value of i WT is attributed to two values of p WT (Figure  4b ). Thus, v WT is used as the controlled parameter. Appl. Sci. 2020, 10, x FOR PEER REVIEW 7 of 23 power in the DC bus are then measured.
Performance of Proposed Power Control Strategies In order to demonstrate the effectiveness and applicability of the proposed power control strategies, algorithms were performed using MATLAB Simulink. At first, the simulations were carried out to investigate the performances of the WT under varying step sizes for each algorithm. Due to the constraints linked to the characteristics of the WT studied in this work, two points, LPP1 and LPP2, allow for reaching the same power for a specific wind velocity. They are examined in Figure  8. Thus, the wind speed is fixed at 6. 5 m/s and the limited power (P lim ) at 50 W, which is under the maximal power provided in this case. After studying the performances of each proposed control strategy, a comparison of the results with a specific step size is carried out.
Performance of Proposed Power Control Strategies In order to demonstrate the effectiveness and applicability of the proposed power control strategies, algorithms were performed using MATLAB Simulink. At first, the simulations were carried out to investigate the performances of the WT under varying step sizes for each algorithm. Due to the constraints linked to the characteristics of the WT studied in this work, two points, LPP1 and LPP2, allow for reaching the same power for a specific wind velocity. They are examined in Figure  8. Thus, the wind speed is fixed at 6. 5 m/s and the limited power ( lim P ) at 50 W, which is under the maximal power provided in this case. After studying the performances of each proposed control strategy, a comparison of the results with a specific step size is carried out.
Comparison of the Power Control Methods In order to compare the performances of the proposed power control methods previously mentioned, the voltage perturbation step size of 4 V is selected. Figure  15a ,b depict the effectiveness and the system response for LPP1 and LPP2. It can be observed that the response timeliness is identical whatever the algorithm method used. In contrast, the oscillation state differs according to the power control method. The FL method's power control has better dynamic performances with more steady oscillations compared to the fixed step size and Newton's methods. Figure  16a ,b represent the descriptive statistical analysis (called a boxplot) of these three selected methods. The boxplot is a standardized way of displaying the dataset based on a five-number summary i.
The boxplot is a standardized way of displaying the dataset based on a five-number summary i. e. , the minimum, the maximum, the sample median, the first quartile (25th percentile) and the third quartile (75th percentile). The results clearly show a huge power variation of the fixed step size and Newton's methods compared to the FL technique, despite having roughly the same mean and median (around 49-50 W) (Table  2 ). The variability could be calculated by measuring the interquartile range (IQR) which means the difference between the third and first quartiles (the distance covering 50% of the data). Regarding LPP1 (Figure  16a ), the IQR values are 29, 11 and 3 W for the fixed step size, Newton's and FL methods, respectively, whereas for LPP2 (Figure  16b ), the IQR values are 32, 12 and 5 W for the fixed step size, Newton's and FL methods, respectively.
coli strains: RKCK16 and RKCK19 were used, respectively. Cells were grown to OD 600 0. 3-0. 6, a 500 µl aliquot was pelleted and resuspended in 40 µl M9-glucose supplemented with 30 µg ml -1 kanamycin. 7. 2 µl of resuspended cells was loaded onto a 1% agarose pad using PBS as the diluent. TIRFM acquisition. Live cells were imaged using an Oxford NanoImager (ONI) superresolution microscope equipped with four laser lines (405/473/561/640 nm) and ×100 oil immersion objective (Olympus 1. 49 NA). Pal-mCherry fluorescence images were acquired by scanning a 50 µm × 80 µm area with a 561 nm laser (0. 2 mW) set at 49°incidence angle (100 ms exposition), resulting in a 512 × 1024 pixel image. For PAmCherry experiments, fluorophores were first activated by 1 s exposure to a 405 nm laser. Images were recorded by NimOS software associated with the ONI instrument.
Image stacks of several μm thickness were taken with 0. 125 μm z-steps and 15 images (three angles and five phases per angle) per z-section and a 3D-structured illumination with stripe separation of 213 nm. Laser power was set at 10% to scan the sample over 4 µm thickness (32 slices) without too much photobleaching. Image stacks were reconstructed using Deltavision softWoRx 6. 1. 1 software with a Wiener filter of 0. 002 using wavelength-specific experimentally determined OTF functions (see ref. 37  ). Cells were automatically aligned using ImageJ software so the fluorescent ring appears as a vertical bar from the lateral projection of 3D-SIM imaging. Distribution of fluorescence intensity along the x-axis of bacteria was determined using a custom script implemented in MATLAB (version 2012a, MathWorks) (see ref.
40  ). In addition, with noise reduction through application of the built-in MATLAB medfilt2 median filter, raw images were initially thresholded over twofold the background intensity (optimised to provide a binary image highlighting specific signal from the septation ring). Intensity along the selected bacteria was measured 11 times between the bacterial end-points, each at a uniformly spaced offset from the bacterial long axis. The mean profile was calculated and normalized to the range 0-100 for comparison between bacteria. Diameter measurements correspond to the height of ring, calculated automatically through ImageJ software from each reconstructed and filtered image. A total of 30 cells were analysed per condition, in triplicate. All data were plotted in GraphPad software (Prism V). Image data analysis.
All images were analysed using ImageJ software. Fluorescence distribution along x-axis of cells was determined by the plot profile function with a line width of 4 points for TIRFM images and 40 or 80 for FRAP images. Values were then normalized to 0-100 for comparison between cells in Excel. FRAP values were normalized using FRAPnorm plugin  37. For all experiments, ~30 cells were analysed from at least three independent repeats per condition. All data were plotted in GraphPad Prism 8 software. SPT data analysis. The ImageJ plugin ThunderSTORM 41 was used to localise single particles. The PSF Integrated Gaussian method in ThunderSTORM was used for sub-pixel localisation, to remove aberrations, localisations with photon offsets of <1 and sigma values <1 were filtered out. For fields of view that displayed significant XY drift, this was corrected using ThunderSTORM cross-correlation drift correction with five bins at a magnification of 5.
Including the first post-bleach frame, we fit the model output to the data from six frames each separated in time by 2-min intervals (the final frame is 10 min post-bleach). We used the pdepe solver in Matlab to numerically solve the equation for c and the function immse to calculate the mean square error between the simulated and experimental data (the value was multiplied by 100,000 to avoid numerical issue with small numbers). The fitting was performed by the patternsearch function of the Global Optimization toolbox. The initial guess was a diffusion constant of 10 -3 μm 2 s -1 , converted into units of pixels, the length unit on which the solver was run. Our results were not sensitive to this choice. Applying this procedure, we obtain a spatially varying effective diffusion constant for each cell.
The swarmplots in Fig. 3f  show the median of the profile for dividing and non-dividing cells while those in Supplementary Fig. 5b  also include the mutant strains. These plots were created using the web application https://www. estimationstats. com as described in Ho et al. 45. Each cell has a different length and so to combine the data from all cells (for a given mutant/dividing state), we re-express the effective diffusion constant in terms of the relative, rather than absolute, position in the cell, using interpolation (via interp1) to obtain this profile at the same 51 relative positions (0, 0. 02, …, 1) for each cell. We then combine the spatial profiles from all cells (Fig. 3g  and Supplementary Figs. 5d  and 6c ). Shown is the median with its bootstrapped 95% confidence interval, found using the Matlab function bootci.
Blots were washed as described above and detection was carried out using Amersham ECL Western Blotting Select Detection Reagent (GE Lifesciences #RPN2235), according to the manufacturer's instructions in GBOX-CHEMI-XRQ. Images were recorded using GeneSys software. Protein expression and purification. The C-terminal domain of Pseudomonas aeruginosa TolA (TolA  ) and its derivatives were expressed as fusion proteins with an N-terminal, His-tagged Im9 in E. coli BL21 (DE3) cells after induction with 1 mM IPTG 37 °C. After 4 h growth, cells were harvested by centrifugation (4000 × g, 4 °C) and resuspended in binding buffer (50 mM Tris, 1 mM MgCl 2 , 2 mM imidazole, 1 mM PMSF (Sigma), 300 mM NaCl, pH 7. 5, with protease inhibitor cocktail (Roche cOmplete Easypack tablets)). Cells were lysed by sonication on ice (Misonix Sonicator 4000).
NMR data were acquired using either GE/Omega software using pulse sequences written in-house, or Topspin software and pulse sequences in the Topspin libraries from Bruker Biospin. All data processing was conducted using NMRPipe  49. Processed spectra were visualized and assigned using the CCPN software suite  50. All NMR data were collected at 20 °C. Resonance assignments for TolA 224-347 bound to TolB  [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34]  were obtained using standard 2D and 3D double and triple resonance data including  15  N-HSQC,  13  C-HSQC,  15  N-edited TOCSY-HSQC, HNCO, HNCACO, HNCA, CBCANH, CBCA(CO)NH, HBHA(CBCACO)NH, H(CCCO)NH, (H)CC(CO)NH and HCCH-TOCSY. The NMR samples contained 0. 6 mM 15N or 13C/15N-labelled TolA 224-347 and 3 mM unlabelled TolB  [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34].
Resonance assignments for the TolB  [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34]  peptide in complex with TolA 224-347 were obtained using 2D versions of some of the experiments listed above using a sample containing 0. 2 mM 13 C/ 15 Nlabelled TolB  [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34]  and 1. 1 mM unlabelled TolA. Details of the assignments are included in BMRB deposition 27397. Residual dipolar couplings (RDCs) for  15  N-TolA 224-347 bound to TolB 22-34 were measured using a sample aligned in 5% w/v C 12 E 6 PEG/n-hexanol and the IPAP NMR experiment  51, 52. Values for the axial and rhombic components of the alignment tensor were estimated initially from the shape of the distribution of RDC values and then refined by fitting using in-house software and the previously determined X-ray structure of free TolA (PDB: 1LR0) and the RDC values for regions of secondary structure not involved in peptide binding; D a /R values of 13.
Backbone ϕ and ψ torsion angles for TolA  and TolB  [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34]  were estimated from Cα, Cβ, C′, N and Hα chemical shifts using the program DANGLE  58. Hydroge  bond restraints were based on slowly exchanging amides identified in  15  N-HSQC spectra collected in D 2 O and observed NOEs characteristic of regular secondary structure. A square well potential and a force constant of 0. 5 were used for the RDC restraints (SANI terms) with experimental error for the RDCs set to 2 Hz. In th  final ARIA iteration, 600 structures were calculated; the 20 lowest energy structures were selected for a final round of water refinement. The family of structures was then validated using PROCHECK-NMR. Isothermal titration calorimetry. All ITC experiments were performed using the C-terminal domain of E.
coli TolA (TolA 224-347 ). TolA was dialysed into 50 mM Hepes buffer, 50 mM NaCl at pH 7. 5 overnight at 4 °C and precipitates removed by centrifugation (10,000 × g, 10 min). The concentration was adjusted to 150 μM. TolA was loaded into the cell and synthetic TolB peptide (residues 22-33; 2 mM) loaded into the syringe of an iTC200 microcalorimeter (Microcal/GE Healthcare). Titration consisted of 20 injections (1 × 0. 4 μl, 19 × 2 μl) measured at 25 °C with an interval of 150 s between injections, and a stirring speed of 1000 rpm. Heats of dilutions were measured by injecting syringe samples into buffer under identical titration conditions and subtracted from each data set. Data were analysed using Origin 7. 0 software, and fitted to a single site binding model.
Fluorescence anisotropy. Fluorescence anisotropy experiments were used to observe the change in overall anisotropy of P. aeruginosa TolB  [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34]  fluorescein isothiocyanate (FITC) in complex with the C-terminal domain of TolA. A titration curve was recorded using 40 μl fractions in a 96-well, black absorbent, 100 μl plate, n = 3. Data analysis was performed in SigmaPlot 12. 0, using a non-linear regression dynamic curve fit using the quadratic equation: f ¼ ðA T þ B T þ k ± p ððA T þ B T þ kÞ 2 À 4A T B T ÞÞ=2; where f = concentration of TolA-TolB, A T = Total concentration of TolA, B T = Total concentration of TolB, and k = K d. Experiments were carried out using either a CLARIOstar plate reader or a Fluoromax-4 spectrofluorimeter (Horiba JobinYvon).
FITC was excited at 470 nm and emission was monitored above 515 nm using cutoff filters at both emission channels in the T-mode. Monochromator entrance and exit slits were set to 2 mm. A total of 4000-10,000 data points were collected for each reaction. At least four anisotropy traces were collected for each reaction and averaged for each time-dependence fit. All anisotropy traces were fit to a single exponential rate equation. Each reaction concentration was collected in triplicate and validated using recombinant protein from different protein purifications. SigmaPlot was used for linear regression analysis to fit the dependence of k obs on the varied concentration of TolA. Titrations were performed at increasing concentration of protein relative to a constant concentration of peptide at 1 μM.
Experiments were typically performed with 4-10-s data collection periods. Assessing the stability of the E. coli OM. The stability of the E. coli outer membrane was assessed for tol phenotype and impaired growth in the presence of SDS, for engineered strains and plasmid-transformed strains. Overnight cultures were used to inoculate 5 ml LB cultures with appropriate antibiotic and subsequently brought to log phase (OD 600 ~0. 4). Aliquots of these cultures were then spotted at regular intervals on SDS-containing plates (0. 4 and 2%) in serial dilutions of spotted cell densities, from 0. 02 to 0. 000002 using the initial OD 600 measurement. Plates were then incubated at 37 °C overnight. Images were recorded in GBOX-CHEMI-XRQ, using GeneSys software. Cultures unable to grow in the presence of 2% SDS are deemed to have destabilized outer membranes.
Growth comparison of mCherry/PAmCherry-Pal strains. Overnight cultures grown in M9/glucose with appropriate antibiotics were diluted in fresh medium to OD 0. 04 and grown at 37 °C. Culture density was measured every hour using Biochrom WPA CO8000 Cell Density Meter which was then plotted as a function of time. Steered molecular dynamics simulations of the TolB-Pal complex. The structure of the E. coli TolB-Pal complex was obtained from the Protein Data Bank (PDB: 2W8B)  18. The disordered N-terminus of Pal, not present in the crystal structure, was modelled using Modeller 9. 19  59  and attached to the tripalmitoyl-Sglyceryl-cysteine residue as previously described  60. The tripalmitoyl-S-glycerylcysteine residue was then inserted into the inner leaflet of an E. coli outer membrane model  61.
Acknowledgements C. K. acknowledges financial support for this work from the  European Research Council  (advanced grant  742555 ; OMPorg) and the  Wellcome Trust  (Collaborative Award  201505/Z/16/Z ). P. G. I. acknowledges studentship funding from the  Medical Research Council UK. We thank  Eoin Cassels  for preliminary ITC data on the E. coli TolA-TolB complex,  Greg Papadakos  ( Roslin Foundation ) for construction of TolB inter-domain mutants and  David Staunton  ( Oxford Molecular Biophysics Suite ) for access to biophysical instrumentation. We are indebted to  Dominika Gruszka  ( Crick Institute ),  Nathalie Reichmann  (Oxford),  Nick Housden  (Oxford) and Melissa Webby (Oxford) for helpful comments on the manuscript and for useful discussions throughout this work and to  Jolyon Claridge  and  Jason Schnell  ( Oxford ) for assistance with production of isotopically labelled TolB peptide.
Early studies provided few, if any, detections of galaxies in close proximity of DLAs (e. g. Møller & Warren 1993; Steidel et al. 1994; Lowenthal et al. 1995; Djorgovski et al. 1996; Bunker et al. 1999; Fynbo et al. 2000; Bouché et al. 2001; Grove et al. 2009 ). Significant progress on connecting strong Lyα absorbers to the galaxy population was made with the introduction of integral field spectrograph (IFS) instruments. In particular, SINFONI on the Very Large Telescope (VLT) provided some of the first significant datasets based on blind searches for Hα emission in the observed near infrared (i. e. 0. 7 z 2) from faint galaxies within a relatively small field of view around background quasars (e. g. Bouché et al. 2007; Péroux et al. 2011). These surveys probe the absorber environment up to ≈ 50 kpc, focusing on small scale associations and so effectively probing the halos of the closest galaxies in absorption.
17+115352. 4 at z ≈ 5. 26, within which we detect multiple strong H i absorption line systems. In Section 2, we present the details of the observations and data reduction. Section 3 presents the search for galaxies within the MUSE cube and Section 4 presents the analysis of the absorption line systems, including associated metal transitions. Section 5 introduces the simulation data used in this study. In Section 6, we present the results of combining the two datasets, whilst Section 7 provides our summary and conclusions. Throughout this paper, we assume a cosmology defined by the parameters in Planck  Collaboration et al. (2016, i. e. H 0 = 67. 7 km s -1 Mpc -1 , Ω m = 0. 307 and Ω Λ = 0. 693) and express magnitudes in the AB system. Unless stated otherwise, distances are given in the proper coordinate frame.
X-Shooter data The background quasar spectrum (Fig. 2 ) comes from a recent dataset obtained at the VLT using X-shooter spectrograph  (Vernet et al. 2011)  to observe 41 z > 5 bright quasar spectra (PID 98. A-0111 and 100. A-0243; PI: Rafelski,  Becker et al. 2019 ). These quasars were identified via WISE IR color selection and confirmed with low resolution spectroscopy  (Wang et al. 2016; Yang et al. 2016 Yang et al. , 2017)). X-shooter provides moderate resolution spectroscopy across three wavelength ranges: UVB (300-550 nm), VIS (550-1020 nm), and NIR (1020-2480 nm), although the UVB arm contains no to little flux for quasars at this redshift. The data typically have a signal to noise S/N > 10 per spectral bin and we use slits of 0. 9 in the optical and 0. 6 in the NIR to achieve a resolution of R ∼ 8000 -9000, sufficient to measure Lyα and accurate metallicities  (Rafelski et al.
Continuum-detected galaxies For continuum-detected galaxies, we run SExtractor  (Bertin & Arnouts 1996)  on the deep white-light image, folding in the propagated pixel variance and masking regions where the number of exposures falls below 6 (i. e. the region outside the dashed yellow bounding line in Fig. 1 ). Only sources above 5 times the propagated error and with minimum area of 10 pixels are marked as detected. For each detected source, we reconstruct a 1D spectrum using all pixels within the segmentation mask, also transforming the wavelength to vacuum. We then measure redshifts using the Marz redshifting software  (Hinton et al. 2016 ), which we customize 1  with high-resolution synthetic templates for passive and starforming galaxies at z < 2. Following automatic template fitting, individual sources are inspected and classified by two authors (MFu and MFo) in four classes (4, secure redshift with multiple features; 3, good redshift with single but unambiguous feature; 2, possible redshift, based on a single feature; 1, unknown redshift).
(2001)  composite to fit the observed quasar continuum at λ rest ≈ 1200 -1400 Å. To allow for a tilt in the template, we apply a power-law factor to the composite at λ rest < 1215 Å of the form (1215. 67 -λ rest ) α , finding a slope of α = 0. 045 by normalising to the peak flux at the Lyβ emission wavelength in the quasar spectrum. We then use this template as a guide when fitting the continuum at λ rest < 1215. 67 Å using lt continuumfit. Using this estimated continuum model, we make an initial census of strong Lyα absorption (in the range bounded by the intrinsic quasar Lyα and Lyβ emission) and metal absorption systems along the sightline using pyigm guesses (part of the pyigm suite of codes 3 ). We include all systems where we observe either damping wings in the H i absorption 2 https://github.