dataesr/scientific-paragraphs-categorization huggingface dataset:
The Ekolongouma (EKG) logger locations have the most significant pixel-wise correlation and model validation statistics (see Table  2 ). The water loggers provide us with point information, whereas the PALSAR-2 data and the derived (modelled and interpolated) water levels are indicative of the average water level evolution within a 1-hectare area. Due to differences in the topography, the logger water levels may not be indicative of the wider pixel average water levels. We can see an example of this by comparing the time series of the two Ekolongouma water loggers, which are separated by only 1. 7 km, but measure considerably different water levels (Figure  14 ). In this case, the modelled data more closely represents the actual water levels recorded by the EKG03 water logger. It is possible that the EKG02 logger was positioned in a hollow (a micro-topographical feature at a lower level than the surrounding terrain) which is, therefore, less likely to be representative of the water levels corresponding with the wider 1-hectare pixel area.
Supporting Model Calibration and Validation Lee et al. [15]  produced water level maps for the central Congo Basin which corresponded with the availability of ALOS PALSAR satellite imagery. They suggested that these maps could be valuable for both validating and calibrating hydrodynamic models' representations of variations in water level and surface water extent. Our interpolation of the ALOS-2 PALSAR-2 HH backscatter data using the net water input provides the first high temporal (daily) resolution map of water level evolution over a region of the Cuvette Central peatlands, and these maps could be used within the development and assessment of hydrological models. Additionally, extending our study over a longer time frame would enable a more accurate assessment of spatiotemporal water level variations with net water input and a more accurate derivation of pixel-wise relationships.
5% (Figure  6d ) of peatland pixels within our study region have had their daily water levels modelled using the rainfed algorithm. In flood-prone regions, the higher temporal resolution of Sentinel-1 C band data could be used in conjunction with the ALOS-2 PALSAR-2 data to better capture shorter-range temporal changes in flooding. This would be especially applicable along the edges of rivers where there is less dense swamp vegetation limiting the shorter wavelength C-band signal from resolving changes in water level over the peat surface. Additionally, flood models that are based on river hydrodynamics would be useful for infilling or complementing the water level maps in floodplain areas.
Microtopography Considerations The landscape is characterised by spatial variations in the distribution of vegetation and root systems, resulting in a series of tree roots/hummocks (dryer elevated regions) with depressions (hollows) in between where water pooling can occur. These features can vary over spatial scales of less than 2 m. However, the SAR data we used is at 100 × 100 m resolution after speckle filtering, which is not high enough to capture such micro-topographical features and may, therefore, underestimate the highest flood levels while overestimating the lowest ones. Additionally, the water-logger depth measurements used to validate the SAR data may have been taken in hollows, on hummocks or in an area that is not representative of the average elevation of the surrounding area.
However, the recently launched Surface Water and Ocean Topography (SWOT) mission  [56, 57]  will help to address this gap. The SWOT mission uses interferometric altimetry at Ka-band to measure both land and ocean water surface topography at high spatial resolution and its measurements are available as gridded global datasets  [56]. In future studies, these data could be used to validate SAR-derived water level estimates, particularly over some of the less vegetated, floodplain regions of the Cuvette Centrale.
Conclusions We have presented a method for combining the daily pattern of net water input with high-resolution L-band SAR data to estimate daily, 100 m resolution time series of water levels across a largely rainfed region of the Cuvette Centrale tropical peatland complex. We have also introduced a technique for distinguishing between rainfed and flood-prone areas. We show that 50% of the region is directly rainfed, while 22. 5% is partially rainfed. The remaining 27. 5% is located in floodplains or locations that receive significant groundwater input and have a low correlation with direct rainfall. With the recent open release of ALOS-2 PALSAR-2 level 2. 2 ScanSAR data, the methods we describe can be adapted and applied across the entire peatland complex from 2014 to the present, allowing for higher-resolution estimates of water levels, inundation extents and surface water storage evolution.
This effectively acted to rotate the pattern of each image's backscatter values across longitude, using the longitude with the lowest incident angle as the pivot point. The eastto-west backscatter patterns in the resulting images are noticeably more balanced; 5. The original resolution of the ALOS-2 PALSAR-2 scenes was 25 m but the effective resolution was reduced following the application of the speckle filter. We geocoded the scenes within the co-registered stack to 100 m resolution using the SAR-Mosaic function with a bilinear resampling option. The final geocoded product was in the WGS84 projection; 6. To express the data in units of decibels (dB), we applied the Sigma nought (σ 0 ) backscatter calculation across each image within the co-registered stack, using the following equation: σ 0 = 10 * log 10 DN 2 + calibration f actor (A1) where DN is the digital number value of the amplitude, or DN 2 is that of the intensity, and the calibration factor specific to ALOS-2 PALSAR-2 level 1.
Acknowledgments: We would like to thank  Helen Plante  at  Leeds University  for providing excellent administrative and translation assistance related to this research. We also thank all the software developers of open-source Python packages that enabled us to carry out this research efficiently, to the developers of openly available meteorological and terrain data sets used within this study and to  ESA  and  NASA  who have provided their satellite data under open-source terms for production of the altimetry virtual stations we used.
The system was fully controlled using XCalibur software (v3. 0. 63; Thermo Fisher Scientific, Waltham, MA, USA). The UPLC system was equipped with a Symmetry C 18 precolumn (300 Å, 20 × 0. 18 mm, 5 µm particle size, Waters, Milford, MA, USA) and an ACQUITY UPLC ® BEH C 18 separation column (130 Å, 75 µm × 200 mm, 1. 7 µm particle size, Waters). The solvent system consisted of formic acid 0. 1% in water (solvent A) and 0. 1% formic acid in acetonitrile (solvent B). Peptides (500 ng for each sample) were first trapped for 3 min at 5 µL/min with 99% A and 1% B. Elution was carried out at 60 • C at a flow rate of 350 nL/min, using a linear gradient from 2 to 35% B in 126 min, then 70% B in 4 min and equilibration was carried out for 14 min at 2% B. To minimize carry-over, a column wash (50% acetonitrile for 20 min) was performed between each sample in addition to a solvent blank.
Proteomics Data Processing Proteome Discoverer 2. 5 (Thermo Fisher Scientific, Waltham, MA, USA) was used for bottom-up proteomics sequencing and to quantify the proteins based on the intensities of the precursor ions of unique and razor peptides. A processing workflow and a consensus workflow were used to characterize and validate/quantify the proteins in each analyzed sample, respectively. A Fasta file protein database containing entries from NCBI gathered 1,532,988 sequences of Hymenoptera, 20,214 protein sequences from Aethina tumida, 14,703 sequences from Tropilaelaps species, 59,574 sequences from Varroa species, 7578 entries from bee viruses, 32,221 entries from Crithidia and Lotmaria species, and 115,707 entries from mites. The trypsin enzyme, a precursor mass tolerance of 20 ppm, a fragment mass tolerance of 0.
5 Da, and the chemical modifications (Oxidation/+15. 995 Da as dynamic on methionine and tryptophan residues and pyridylethyl/+105. 058 Da as static on cysteine) were selected for subsequent data processing. The datasets were aligned to extract for each individual file, the LC-MS/MS mapping features. The settings were as follows: A maximum retention time (RT) shift of 10 min. , a mass tolerance of 10 ppm, and coarse parameter tuning were set. Protein annotations were successfully validated when the q-value scores were calculated below the false discovery rate (0. 05).
Statistical Analyses Results were analyzed using R. 4. 0. 4  [124]  and graphs were generated using the ggplot2 package  [125]. When mortality on day 7 and 14 was compared, binary data (survival 0 or 1) were analyzed using generalized linear models (GLM) with binomial distribution. The feeding success after 24 h was analyzed following the same process. Survival curves were fitted to the data to follow mortality over time. Data were analyzed using the survfit package in R and log rank tests. If significance was reached, pairwise comparisons with Benjamini-Hochberg corrections were further computed.
In reconstructions without enforced symmetry, the -helices forming the coiled-coil are not equivalent in either dataset, as they exit the OM plane in different orientations (Figure  1f ). The reasons for these structural differences are not obvious. We speculated that intra-or inter-protomer hydrogen bonds at the stalk-barrel interface could be responsible for the subtle conformational differences. In the E. coli VpOmpM1 structure, the sidechain of R113 in one protomer interacts with the sidechain of N155 (part of the periplasmic end of the 2 strand) of another protomer. This interaction is observed only in two inter-protomer interfaces, but not the third one (Figure  1g ). A similar interaction was observed in the native VpOmpM1 structure: at two inter-protomer interfaces, R113 interacts with the carbonyl oxygen of G115 and the sidechain of N116 (both part of the -barrel end of the stalk) (Figure  1h ).
Another possibility is that the flexible interface between the stalk and the -barrels imparts favourable mechanical properties to the cell envelope, as having a somewhat flexible PG-OM tether would allow the OM to deform slightly when external mechanical forces are applied. Using an integrative approach, we have shown that VpOmpM1 might exist in multiple conformations, despite the fact we did not observe any particle populations with a compact stalk in our cryo-EM datasets. Unravelling the roles of both potential states of the stalk is a challenging problem that will likely have to be resolved using in vivo studies. We expect that the extended stalk state observed in cryo-EM reconstructions is the PG-binding state based on structural comparisons with SCWP-binding SLH domains from monoderm bacteria.
The 145,518 particle stack was subjected to heterogeneous refinements against the C1 map from non-uniform refinement and three decoy templates, or against the C3 map and the same three decoy templates. Particles were re-extracted in 600 pixel boxes with a pixel size of 0. 574 Å. The final particle stacks (96,280 particles for the C1 map; 119,001 particles for the C3 map) were subjected to non-uniform refinement either with C1 or C3 symmetry enforced. The native VpOmpM1 dataset was processed similarly, except that two rounds of 2D classification were performed, four classes were used to make the ab initio map, four decoy templates were used in heterogeneous refinement, and the pixel size remained 1. 148 Å throughout. The final B-factor-sharpened maps from non-uniform refinement were used to build the models.
Crystal structure determination The purified VpOmpM1 stalk construct was concentrated to ~20 mg/ml. Sitting drop vapour diffusion crystallisation screens were set up using a Mosquito robot (SPT Labtech). Crystals grew in 0. 1 M citric acid pH 3. 5, 2. 0 M ammonium sulphate at 20˚C. Crystals were cryo-protected in mother liquor supplemented with ~20% PEG400 and flash-cooled in liquid nitrogen. Diffraction data were collected at the synchrotron beamline I03 at Diamond Light Source (UK) at a temperature of 100 K (Supplementary Table  2 ). The dataset was processed with XIA2  74  , scaled with Aimless 75 , and the space group was confirmed with Pointless 76. Data quality was evaluated in Xtriage  73. Arcimboldo  77  , part of the CCP4i2 suite  78  , was used for ab initio phasing. The initial model was extended by Buccaneer and subjected to cycles of manual building and refinement in Phenix  73  (Supplementary Table  2 ).
For each protein model, the β-barrel domain of the VpOmpM1 trimer was embedded in a model Escherichia coli outer membrane using the CHARMM-GUI Membrane Builder module  80. The inner leaflet consisted of 90% 1-palmitoyl-2-oleoyl-sn-glycero-3phosphoethanolamine (POPE), 5% 1-palmitoyl-2-oleoyl-sn-glycero-3phosphoglycerol (POPG), and 5% 1',3'-bis[1-palmitoyl-2-oleoyl-sn-glycero-3phospho]glycerol (cardiolipin), and the outer leaflet consisted of 100% LPS (R1 core, 5 x O1 O-antigen units). This asymmetric bilayer system was solvated in 150 mM KCl, with additional calcium ions to neutralise the LPS headgroups (~115 mM in each system). Atomistic Molecular Dynamics Simulations. All simulations used the CHARMM36m forcefield  81  with the TIP3P water model  82. Simulations were carried out using the GROMACS simulation package (version 2021.
0. 3-0. 5 l of diluted protein was added to the cis (ground side) chamber, and diluted with electrophysiology buffer as required to promote protein insertion into the bilayer. Protein insertion events were detected as sudden jumps in current in constant-voltage mode. All measurements were carried out using Ag/AgCl pellet electrodes attached to an Axopatch 200B amplifier headstage, and a Digidata 1440A digitiser. The cuvette and headstage were enclosed in a custom-made Faraday shield during recording. Clampex software was used for recording. Clampfit software was used to analyse data.
Acknowledgements We thank the  University of York (UK) electron cryo-microscopy facility  for access to instrumentation and support. We acknowledge the  Diamond Light Source  for I03 beamline access (proposal mx-24948) and support. We thank  Arnaud Baslé  for collecting X-ray data, maintaining the  Newcastle Structural Biology Laboratory , and computing support. We thank  David Staunton  ( University of Oxford ) for carrying out the dynamic scanning calorimetry experiments. This work was supported by a  Wellcome Trust Investigator award  ( 214222/Z/18/Z ) to B. v. d. B. , providing salary support to A. S. and Y. Z. C. B. , S. G. , J. W. , and R. E. S. were supported by funding from the  French National Research Agency (ANR)  (no. Fir-OM ANR-16-CE12-0010 ), the  Institut Pasteur Programmes Transversaux de Recherche  (no.
PTR 39-16 ), and the  French government Investissement d'Avenir Program ,  Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases  (grant no. ANR-10-LABX-62-IBEID ). We acknowledge the use of the  IRIDIS High Performance Computing Facility , and associated support services at the  University of Southampton , in the completion of this work. K. E. N. was supported by a  Ph. D. Studentship  from the  Engineering  and.
The Gaia-ESO Survey is a public spectroscopic survey that targeted ≳10 5 stars covering all major components of the Milky Way from the end of 2011 to 2018, delivering its final public release in May 2022. Unlike other spectroscopic surveys, Gaia-ESO is the only survey that observed stars across all spectral types with dedicated, specialised analyses: from O (T eff ∼ 30 000-52 000 K) all the way to K-M (≳3500 K). The physics throughout these stellar regimes varies significantly, which has previously prohibited any detailed comparisons between stars of significantly different types. In the final data release (internal data release 6) of the Gaia-ESO Survey, we provide the final database containing a large number of products, such as radial velocities, stellar parameters and elemental abundances, rotational velocity, and also, for example, activity and accretion indicators in young stars and membership probability in star clusters for more than 114 000 stars.
Introduction The launch of the European Space Agency's astrometric Gaia mission in 2013 (e. g. Perryman et al. 2001; Gaia Collaboration 2016 , 2018 , 2021)  prompted a new wave of Galactic studies. Gaia is delivering precise distances, kinematics, photometry, and spectrophotometry for more than 1. 5 billion stars, as well as radial velocities and chemical abundances for the brighter stars in the sample (see further Gaia Collaboration 2021;  Recio-Blanco et al. 2023). A variety of ground-based spectroscopic surveys have been carried out since 2010 to collect complementary stellar parameters, elemental abundances, and radial velocities, which, when combined with Gaia astrometry, have the power to revolutionise our view of the Milky Way. Spectroscopy breaks the degeneracy between foreground extinction and stellar temperature to which the Gaia Blue Photometer and Red Photometer Prism (BP/RP) spectrophotometry data alone are susceptible  (Bailer-Jones 2011).
2014; Heiter et al. 2015b Heiter et al. , 2021)). The Gaia-ESO Survey has a unique analysis structure  (Gilmore et al. 2022; Randich et al. 2022 ). While the model atmospheres and the atomic/molecular data are fixed, the data are analysed by a multitude of different analysis teams hereafter referred to as 'nodes'. Each node runs a pipeline that generally employs a different method from the other nodes, and is executed by experienced spectroscopists that are familiar with the pipeline. In this regard, the Gaia-ESO Survey has a unique advantage over all other spectroscopic surveys in that almost every spectroscopic analysis method ever considered is included in the survey, allowing us to make the first objective comparison between analysis methods, characterise the level of systematic error present in stellar spectroscopy, characterise the random and systematic uncertainty contributions for all measurements, and provide a robust ensemble measurement of stellar parameters and elemental abundances for the survey.
Within GES, a major focus has been placed on producing stellar parameters that are both internally self-consistent and externally calibrated with respect to a well-determined calibration sample of benchmark stars  (Jofré et al. 2014; Blanco-Cuaresma et al. 2014 ). The GES spectra not only cover a wide range of stellar populations (and therefore parameter space) and are analysed with a variety of pipelines, but they are also taken with a variety of instrumental configurations designed to cover the characteristic spectral features of each stellar spectral type. The effort to transpose such an inhomogeneous set of data and results onto a single, self-consistent scale is not trivial. Essential to this process is the availability of a comprehensive set of calibrators across the H-R diagram. These calibrators include globular and open clusters spanning a wide range in metallicity, as well as the Gaia Benchmark Stars.
2004). GES observing blocks are split into two or more exposures and individual spectra are stacked to produce nightly stacked spectra for each field. When all observations from a particular field are complete (certain fields are repeated across nights with arbitrary separation), all spectra for an object are stacked to produce a final stacked spectrum known as a 'singlespec' for each object or 'CNAME' (the GES object name based on its coordinates, equivalent to the ESO 'OBJECT'). Where spectra are available in the ESO archive for the GES calibrators and objects in cluster fields in the GES instrumental setups, these have been retrieved, reduced with the GES pipelines, and added to the GES dataset. Radial velocities (RVs) are determined for all individual and stacked spectra  (Sacco et al.
2014; Gilmore et al. 2022). GES internal Data Release (iDR) cycles consist of the following general procedure, which is illustrated in Figs. 1  and 2. In Fig. 1 , the general flow is described: targets are selected under the three programmes (Open Clusters, Milky Way, Calibrations), which are observed as necessary using UVES and GIRAFFE (see  Randich et al. 2022; Gilmore et al. 2022; Pancino et al. 2017a , for the target selection in the three categories, respectively); raw spectra from a selected time period are reduced and released to the spectral analysis teams from the operational database at CASU in a standard FITS format  (Wells et al. 1981)  with radial velocities and useful ancillary information such as observing parameters and photometry attached in FITS extensions (the spectral metadata).
Further details are provided in  Gilmore et al. (2022). There are four spectral-analysis WGs dedicated to the analysis of different samples of stars within the GES consortium. Multiple analysis nodes operate their specific pipelines on the data within A129, page 4 of 33  Hourihane, A. , et al. : A&A proofs,   4. WG13: This WG analyses the UVES and GIRAFFE spectra of stars in young clusters containing OBA-type stars. For UVES, the setup is U520, while for GIRAFFE, HR3, HR4, HR5B, HR6, HR9B, and HR14A are used. 5. WG14: This WG works to identify and characterise outlier stars from the whole survey. These are stars presenting peculiarities that endanger the determination of their stellar parameters and abundances by standard routines. The stars are tagged using a specially developed library of flags to allow filtering of the dataset during the homogenisation and scientific analyses.
The characteristics of each setup in UVES 3  and GIRAFFE 4  in which targets were observed for Gaia-ESO and the working groups that analysed each setup are given in Table  1. Each WG lead collates the results from their nodes and performs an initial homogenisation to put the parameters on a consistent scale for the WG. The interim results including node results files and WG-recommended results are delivered to the CASU operational database pending final homogenisation and delivery to the internal GES Science Archive at WFAU. The final homogenised results form the basis of the public Gaia-ESO catalogues delivered to the ESO archive. A detailed description of the contents of each internal Data Release and each public data release via ESO is contained in  Randich et al. (2022). GES makes use of the set of calibrators selected by the Calibration and Standards Working Group (WG5), which is responsible for the observational calibration strategy for GES  (Pancino et al.
2014 ) by various spectral analysis methods  (Jofré et al. 2014 (Jofré et al. , 2015)). This leads to a sample of 34 stars with accurate temperatures -with a precision of about 100 K -, surface gravities, and abundances with precisions of 0. 1 dex. The latest catalogue can be found in  Jofré et al. (2018). One of the main motivations to assemble the Gaia Benchmark Star dataset was the calibration of GES. To this end, substantial spectral analysis of the sample has been performed in the manner of GES -namely, using a combination of analysis methods (and adopting many of the GES WG11 methods), using the same line list and atmosphere models, and looking for new candidates to match the needs of GES. The original set of Gaia benchmarks was expanded for the final release of GES to include stars at lower metallicity and at higher and lower temperatures (warm and cool benchmarks) to better cover the parameter space of the different science samples (which are described in e.