dataesr/scientific-paragraphs-categorization huggingface dataset:
Reads were then aligned to the E. coli BW25113 reference genome (NCBI accession number CP009273) with BWA-MEM version 0. 7. 17-r1188  43. The output SAM alignment files were converted to BAM files and sorted using SAMtools version 1. 1 view with default parameters  44. SNPs and In/Dels were called using GATK HaplotypeCaller Version 4. 2. 0. 0  45. Identified variants were finally annotated using snpEff version 4. 3  46. Construction of E. coli BW25113 acrB chromosomal point mutants. To generate unmarked single point mutant clones, an E. coli BW25113 ΔacrB mutant was complemented (chromosomal reconstitution) using lambda-Red recombination with either wildtype acrB, acrB S450P, or acrB A446P. Briefly, unmarked BW25113 ΔacrB was generated from E. coli ΔacrB (JW0451-2, ΔacrB747::kan,) strain by removal of the FRT-Kan-FRT cassette using the FLP recombinase following transformation with pFLP2  47.
Undissolved material was removed as described before and 30 µL of supernatant were transferred into 250 µL real-time PCR tubes. Samples were heated from 25 °C to 95 °C (1 °C increase every 20 s) while recording CMP-fluorescence (λ ex 380 ± 20 nm / λ em 460 ± 20 nm) in a Qiagen Rotor-Gene Q PCR machine. For automated data analysis (Rotor-Gene Q software) fluorescence intensities (F) were derived to the temperature (T) and maxima of dF/dT over T were given as melting temperatures (T m ). T m values were calculated from five independent TSA measurements (n = 5) with the centre of the error bars as the mean value. Site-directed mutagenesis. Mutagenesis was performed using either ExSite TM (Stratagene) method with 5'-phosphorylated primers or QuikChange TM (Agilent) method. All primers used in this study are listed in (Table  S12 ).
The reservoir contained 50 mM ADA pH 6. 6, 5% Glycerol, 8-9% PEG4000 and 110-220 mM ammonium sulfate. Crystals were harvested after 2 months and cryo-protected by serial transfer of crystals into reservoir supplemented with increasing of glycerol concentration to 28% before flash-cooling in liquid nitrogen. Diffraction data collection and refinement. Data were collected on beamline PROXIMA 2 A (Soleil Synchrotron, Saint Aubin, France) using an Eiger detector (Dectris), respectively, indexed and integrated with XDS  52. All the structural models were iteratively built-in COOT  53  with a structural model (PDB ID: 5JMN)  54  and refined with REFMAC5 in CCP4i v7  55. The structure was validated with MolProbity v4. 5  56. Polder electron density maps were calculated by phenix. polder  57. All figures were generated by Pymol (Schrödinger LLC).
Drug agar plate assay. A colony of E. coli BW25113 ΔacrB harbouring pET24acrB-His ecoding wild-type (WT) or variant AcrB were grown overnight in LB medium containing 50 μg/mL kanamycin at 37 °C. Dilution of the cultures to OD 600 10 -1 -10 -6 was prepared and 4 μL of each diluted culture were spotted on LB agar plates containing 50 μg/mL kanamycin, supplemented with AcrB drugs (12. 5 μg/mL dicloxacillin, 12. 5 μg/mL fusidic acid, 12 μg/mL linezolid, or 80 μg/mL TPP + ). Plates were incubated at 37 °C for 14-16 h. Agar plates were imaged by ImageQuant TL (GE Healthcare BioSciences AB, Uppsala, Sweden). The intensity of the cell growth (Table  S10 ) was quantified by ImageJ 1. 52o software. The cell growth of WT at first dilution is set to 1. The intensity of cell growth at each dilution higher than 10% of cell growth of WT at first dilution is considered as cell growth.
Unmodified western blots are shown in Fig. S8. Molecular dynamics simulations. All-atom molecular dynamics (MD) simulations of AcrB embedded in a model phospholipid bilayer and a 0. 1 M aqueous NaCl solution containing 100 molecules of BDM88832 (8) were performed with the AMBER18 package  58. 100 molecules were used to increase the probability to detect uptake events. Protomer-specific protonation states were adopted for AcrB following the previous works  13, 59, 60  : residues E346 and D924 were protonated only in L and T protomers, while residues D407, D408, and D566 were protonated only in the O protomer. The topology and the initial coordinate files (the experimental structure of AcrB with PDB ID 4DX7 was used as starting conformation) were created using the LEaP module of the AMBER18 package.
The protein was embedded in a mixed bilayer patch composed of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine (POPE) and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoglycerol (POPG) in a 2:1 ratio, for a total of ~650 lipid molecules symmetrically distributed across the two bilayer leaflets. The AMBER force field protein. ff14SB  61 as used to represent the protein, the lipid17 (http://ambermd. org/GetAmber. php) parameters were used for the phospholipids, the TIP3P model was employed for water  62  , and ions parameters were taken from ref. 61. The General Amber Force-Field (GAFF)  63  was used to parametrize BDM88832 (8), using the antechamber tool of AMBER and atomic charges derived an electrostatic potential distribution calculated with Gaussian16 64 as in ref. 65. Each system was first subjected to a multi-step structural relaxation via a combination of steepest descent (5000 steps) and conjugate gradient (up to 25000 steps) methods using the pmemd program implemented in AMBER18, as described previously  29, 33, 38, 59, 60, [66] [67] [68].
Finally, five MD simulations of 2 μs each were performed under an isothermal-isobaric ensemble. A time step of 2 fs was used for all runs before production, while the latter runs were carried out with a time step of 4 fs after hydrogen mass repartitioning  69. R-H bonds lengths were constrained using the SHAKE algorithm, and coordinates were saved every 500 ps. The Particle mesh Ewald algorithm was used to evaluate longrange electrostatic forces with a non-bonded cut-off of 9 Å. Post-processing of the MD trajectories. MD trajectories were analyzed using either in-house tcl and bash scripts or the cpptraj tool of AMBER18. Hydration of the TM region of the protein (including the residues lining the binding sites of BDM88832 (8) and BDM88855 (  9 )) was estimated by calculating the average density values along the cumulative trajectory, using the grid tool of AMBER18.
Acknowledgements We thank  Rudy Antoine  for bioinformatics support to confirm genetic variants from whole-genome sequencing data,  Valérie Landry  for cytotoxicity experiments,  Virginie Meurillon  and  Andrea Bosin  for technical assistance and  Paolo Ruggerone  for useful suggestions and fruitful discussions. This research was financially supported by l' Agence Nationale de la Recherche  ( ANR, France)  in partnership with the  Bundesministerium für Bildung und Forschung (BMBF, Germany)  (program  EFFORT ,  ANR-19-AMRB-0007  (RCH, MF),  BMBF-16GW0236K (KMP) ). GM and AVV received support from the  National Institutes of Allergy and Infectious Diseases  project number  AI136799. Research was further supported by  Feder  ( 12001407  ( D-AL) Equipex Imaginex BioMed ),  ATIP-Avenir ,  Institut National de la Santé et de la Recherche Médicale ,  Centre National de la Recherche Scientifique ,  Université de Lille, Institut Pasteur de Lille ,  Région Hauts-de-France.
The NMR facilities were funded by the  Région Nord-Pas de Calais (France) , the  Ministère de la Jeunesse, de l'Education Nationale et de la Recherche (MJENR) , the  Fonds Européens de Développement Régional (FEDER)  and the  Université de Lille. This research used the Savio computational cluster resource provided by the  Berkeley Research Computing program  at the  University of California, Berkeley  (supported by the  UC Berkeley Chancellor ,  Vice Chancellor  for Research, and Chief Information Officer). We acknowledge  SOLEIL Synchrotron in Saint Aubin, France , for provision of synchrotron radiation facilities and we would like to thank  Dr. William Shepard  and  Dr. Serena Sirigu  for assistance in using beamline Proxima 2 A (Proposal Number: 20190173 and 20180035). The synchrotron trips to SOLEIL Synchrotron were partly supported by iNEXT, under PID:  7108 , funded by the  Horizon 2020  programme of the  European Union.
Data availability Atomic coordinates and structure factors reported in this paper have been deposited in the Protein Data Bank under accession numbers, 7OUK (BDM88855 inhibitor bound to the transmembrane domain of AcrB), 7OUL (BDM88832 inhibitor bound to the transmembrane domain of AcrB-R971A), 7OUM (BDM88855 inhibitor bound to the transmembrane domain of AcrB-R971A). Atomic coordinates that were used and support the findings of this study are available in the Protein Data Bank under accession numbers 4DX5, 4DX7, 4U96, and 5JMN. Whole-genome sequencing data (fastq files) for parental and BDM73185 resistant E. coli isolates have been deposited at NCBI (BioProject ID: PRJNA764862). Source data are provided with this paper.
Using these criteria, we identified three hotspots, located on chromosome (chr) 7, chr13 and chr17, respectively (Figure  1A ). To identify the aspects of mitochondrial metabolism regulated by these loci, we constructed co-expression protein networks using weighted gene co-expression network analysis (WGCNA)  (29)  and identified five modules (Figure  1B ). Eigengenes, representing the first principal component of two of these modules (Brown and Green), mapped to the same regions as the chr13 and chr17 loci, respectively. DAVID enrichment analyses  (30)  revealed that the chr13 locus (26 proteins; Figure  2A  and Table  S2 ) that overlapped with Brown module (72 proteins, 96% overlap) was highly enriched for mitochondrial complex-I proteins (24 proteins, P = 2. 2E-61), and the chr17 locus (22 proteins; Figure  3A  and Table  S3 ) that overlapped with Green module (44 proteins, 73% overlap) was highly enriched for mitochondrial ribonucleoprotein complex proteins (17 proteins, P = 3.
LC-MS analysis of CoQ CoQ8, 9 and 10 levels were analyzed using a TSQ Altis triple quadrupole mass spectrometer (ThermoFisher) coupled to a Vanquish LC system (ThermoFisher). 15   CoQ9H 2 and CoQ10H 2 were detected in any of the samples, though some bleed through into these channels was detected for the oxidized analytes, though these are resolved from the reduced standard peaks (as reported previously). Statistical analyses were performed using Prism v9. 4. 0 (GraphPad Software, Inc. , La Jolla, CA, USA). Errors bars plotted on graphs are presented as the mean ± SEM unless reported otherwise. The critical significance value (α) was set at 0. 05, and if the P values were less than α, we reported that the observed differences were statistically significant. Correlations were calculated using biweight midcorrelation using the bicorAndPvalue function of the WGCNA package  (29).
Wind farm flow control (WFFC) is a topic of interest at several research institutes, industry and certification agencies world-wide. For reliable performance assessment of the technology, the efficiency and the capability of the models applied to WFFC should be carefully evaluated. To address that, FarmConners consortium has launched a common benchmark for code comparison under controlled operation to demonstrate its potential benefits such as increased power production. The benchmark builds on available data sets from previous field campaigns, wind tunnel experiments and high-fidelity simulations. Within that database, 4 blind tests are defined and 13 participants in total have submitted results for the analysis of single and multiple wake under WFFC. Some participants took part in several blind tests and some participants have implemented several models.
A number of control-oriented models with different levels of complexity have been proposed in literature to implement those control strategies but uncertainty remains high and a systematic validation and comparison under different control settings have been lacking. In order to assess the performance of the WFFC technology, the capabilities of WFFC models should be evaluated. Accordingly, FarmConners consortium  (FarmConners, 2019)  has launched a common benchmark for code comparison; where highfidelity simulation results, wind tunnel experiments and field data measured at a full-scale wind farm are brought together. This unique database combines the efforts of the connected WFFC projects of different sizes all over Europe and consists of 4 blind tests in total: 1) SMARTEOLE Sole du Moulin Vieux (SMV) field measurement campaign  (Duc et al.
High-fidelity simulations being a key enabler for the industrial implementation of WFFC, the subset of the extensive CL-Windcon LES database used for the FarmConners benchmark is detailed in Section 4. The participating models for the CL-Windcon LES blind tests are then evaluated for 3 and 9-turbine wind farm configurations, with 5 and 7-diameters (D) spacing, respectively. Similarly, Section 5 utilises up to 8-turbine subsets of 32-turbine layout of reference wind farm developed under TotalControl project and compares the participating model results with the reference LES database under upstream wake steering. Here in this article, we present the results from all the blind tests together to facilitate cross-comparison of the model performances and validation/reference data set characteristics.
P4 Wind speed and turbulence intensity: The wind speed was defined using nacelle anemometers at each wind turbine. The turbulence intensity was calculated using the mean and standard deviation of the anemometer data for each wind turbine. Wind Direction: It has been assumed that the average of the directions indicated by turbines' wind vanes was a good estimate of the free wind. The multiple wake simulations used the average of all wind turbines measurements, and the single wake simulations averaged SMV5 and SMV6 direction data. Heterogeneous flow: The non-homogeneous flow field in the steady simulation was obtained through speed factors between turbines in free sectors (V g -velocity factor, T I g -turbulence intensity factor). The factors were obtained using the wind turbines anemometer measurements, and they were applied regardless of the wind direction.
Preparation of the calibration data set: The tuning of the engineering model was performed using the provided calibration data set. As FLORIS is a steady state model, some of the SCADA data were discarded by looking at the nacelle position measurements, with the aim of only using data characterised by fairly steady and uniform inflow and operating conditions. Specifically, a 10-min SCADA data point was discarded if: -The variation of the nacelle orientation of any turbine between two consecutive timestamps exceeds 20 • , so as to discard data measured under strongly-varying wind direction. -The deviation of the nacelle orientation of any turbine from the average pointing direction of the entire wind farm exceeds 20 • , so as to discard data measured under highly non-homogeneous wind direction.
(2019). It is noted that the SCADA wind speed standard deviation data has not been used to obtain any turbine-specific turbulence intensity correction across the site, as was done for the wind speeds. Air density -Historical atmospheric data have been obtained from ERA5 and NEWA reanalysis data sets at the closest available nodes to the site. The long-term averaged air density at the site was found to be 1. 23 kg/m 3 , with values ranging between 1. 13 and 1. 35 kg/m 3. These values have been used to correct the power curve in the simulations (see below). Wind shear -The LiDAR measurements were provided at different heights and for a period of about 8 months. The two measurements closest to the low tip and high tip of the rotor (40 and 120 m, respectively) have been used to estimate the average shear profile across the turbine rotor.
Validation Data pre-processing The wind farm field data blind tests are prepared as Single and Multiple wakes, where the upstream turbine SMV6 was steered with -13. 3 • mean with respect to the incoming south-westerly wind. The results are evaluated in terms of balanced energy gains, as defined in  Fleming et al. (2019). Since the experiments were not realised in the form of a toggle test, the baseline case is taken by considering two 2-months periods before and after the field tests, as shown in Figure  10. The wind speed and direction reference signals for computing the energy ratios come from the Windcube v2. Due to the proximity of this sensor with the controlled turbine, the atmospheric conditions measured should be very similar to the ones faced by SMV6. The reference power signal is issued from SMV7 wind turbine, which is the only remaining upstream turbine for the southerly wind sector.
7 m/s, turbulence intensity of 5. 39%, and roughness length of 0. 001 m was selected (referred as A4 in the original database (CL-Windcon, 2019)). CL-Windcon LES blind test is among the most diverse and comprehensive in terms of the wake steering control strategies applied within the FarmConners benchmark. The yaw misalignment was varied in the range of ±30 • for the first and/or the second row of turbines, see Table  8  for particular control settings among 3WT and 9WT scenarios. The direction for the yaw convention, as well as the distribution of the control set-points among the turbines are illustrated per investigated case in the presentation of the results later in Sections 4. 2 and 4. 3. Targeted test cases explore single full wake, as well as multiple wake under wake steering WFFC. Available data covers power production, hub-height and/or rotor effective wind speeds at the turbines, as well as several structural loading variables namely flap-wise root bending moment, total shaft bending moment and total tower bottom bending moment.
Multiple Wake Results Similar to the single wake results, the mean power estimated by the participating models are compared with the CL-Windcon LES blind test database for the multiple wake cases. Accordingly, the power difference per turbine and the wind farm level power gain via equations 7 and 8 are presented on Figures 26 and 27     8. For the investigated configuration, Figure  26  indicates that the power gain is mainly driven by the control setting applied at the upstream turbine, WT1, where the misalignment of WT2 has relatively lower impact. This is in line with other LES studies that investigate multi-turbine wake steering (e. g. Archer and Vasel-Be-Hagh, 2019). Especially with the lateral spacing of WT3, all the control cases where WT1 is misaligned indicate positive power gain up to more than 20% in CL-Windcon LES runs.
9 power adjustment up to yaw angles of between 20 and 30 degrees. Beyond ensuring that the statistical properties of the wind field are matched (the same turbulence and mean wind speed), no specific calibration to match to the TotalControl LES data was performed. Note that the new wake model is intended to form part of a future publication. It should also be noted that the controller used for the wind turbine differs from the DTU controller typically used for this turbine. Compilation issues prevented the DTU controller from being used and so the turbine control strategy and basic controller detailed in Recalde-Camacho et al. (  2020 ) is used instead.
Figure30shows the wake loss reduction for both WT32-WT28 and WT29-WT25 turbine pairs with -20 • and -30 • upstream misalignment (counter-clockwise), respectively. The time-series on the left is illustrated at the frequency of the wind speed signals for all the (quasi-) dynamic models, including the validation data set TotalControl LES. Notably high fluctuations are seen for both of the control settings in the validation data set, which are very well captured by P8 with 5-sec resolution. P20 is also in relatively good agreement, where the observed dynamics in the validation data set is closely followed. For P10, the rotor effective wind speed is used throughout the model, resulting in much lower fluctuations compared to other dynamic models due to spatial averaging of the point-wise wind speed variation.
Acknowledgements. The FarmConners benchmark is organised and conducted under the  FarmConners  project, funded by the  European Union 's  Horizon 2020 research and innovation programme  with grant agreement No  857844. 62 https://doi. org/10. 5194/wes-2022-5Preprint. Discussion started: 15 February 2022 c Author(s)  2022. CC BY 4. 0 License.
Code availability. The notebooks for the blind tests results, including data snippets, can be achieved via the public repository of FarmConners benchmark  (Göçmen et al. , 2021). Data availability. All the data used in FarmConners benchmark blind tests can potentially be made available for non-commercial purposes. Please contact us (per blind test data set) using the details provided under the FarmConners benchmark wiki page https://farmconners. readthedocs. io/en/latest/contact_us. html.
CONCLUSION Here in this article we present the results of the FarmConners benchmark for code comparison under controlled operation. The benchmark brought together 4 data sets generated under several European WFFC projects: 1) SMV wind farm field data, 2) CL-Windcon wind tunnel experiments, 3) CL-Windcon LES and 4) TotalControl LES databases. Although the original benchmark included more control strategies (i. e. axial induction) and quantities of interest (i. e. load channels), the analysis presented here is limited to wind speed and power behaviour under wake steering WFFC. The results from 13 participants and 16 participating models in total are then presented separately under these 4 blind tests. The highlights of the blind test exercises are summarised individually in their corresponding sections through the article.
A compilation of the observations/reference simulations and participating model trends for the overall benchmark is listed below. Customisable WFFC-oriented models The overwhelming majority of the participating models in FarmConners benchmark are parametric, typically modular frameworks; indicating their popularity within the field. Using similar approaches to resolve the wake behaviour, the main difference among them is the calibration procedure. Accordingly, the importance of variety in terms of control set-points in the calibration dataset is underlined in all the blind tests. Similarly, a clear description of the calibration procedure with a list of parameters when disseminating the results is crucial for reproducible and credible assessment of the potential gains via WFFC. Beyond flow modelling FarmConners benchmark also highlights the importance of turbine representation and controller implementation in realisable power gains via wake steering WFFC.
Protein Identification and Label Free Quantification MS raw files were analyzed using Mascot DAEMON version 2. 3. 2. The C. canephora reference protein sequence database was used for peptide-to-spectra matching for both genotypes (downloaded from NCBI the 1 July 2019). This database contained 25,574 protein entries totaling 10,251,572 amino acid residues  [51]. The following parameters were used in the search: trypsin as enzyme, maximum of two missed cleavages, mass tolerances of 5 ppm on the precursor ion and 0. 02 Da on the MS/MS, fixed modification of carboxyamidomethylated cysteine, and oxidized methionine and NQ deamidation as variable modifications. All peptide matches with a MASCOT peptide score below a p-value of 0. 05 were filtered and assigned to a protein according to the principle of parsimony.