dataesr/scientific-paragraphs-categorization huggingface dataset:
gov/. January SST data from 2013 to 2020 were acquired by MODIS-Aqua and downloaded from NASA ocean color website https://oceancolor. gsfc. nasa. gov/. SST data have a spatial resolution of 4 × 4 km in the equatorial region. Finally, we extracted January SST and sea ice concentrations in the Palmer grid from lines 0 to 900 and stations 0 to 220 (Figs. S1  and S2 ). Underway O 2 /Ar-NCP measurements. The O 2 concentration in the mixed layer is influenced by physical and biological processes. Using Ar, an inert gas with similar solubility properties as O 2 , we decomposed total O 2 into physical and biological components. Seawater O 2 /Ar ratios were measured underway from the ship's flow-through system, using an equilibrator inlet mass spectrometer.
Grid staƟon (km) Grid line (km) Fig. 5  An ice-associated plankton assemblage (MET module from WGCNA analysis). a The MET subnetwork presents a diverse group of correlated ASVs or nodes, with each node colored by its centrality (i. e. , darker color for higher centrality). The hub nodes of the network, i. e. , the top-10 ASVs with the highest connectivity or the central members for the network, were identified at the genus level as Thalassiosira, Odontella, Porosira, Actinocyclus, Proboscia, Chaetoceros, and Gyrodinium. b ASVs with higher module membership, i. e. , higher intramodular connectivity, are more correlated with volumetric NCP (R = 0. 7, two-sided t-test p = 9. 6 × 10 (EIMS)  44. Biological O 2 supersaturation was estimated as ΔðO 2 =ArÞ ¼ ðO 2 =ArÞsample ðO 2 =ArÞsat À 1 ! 100% High-resolution NCP in units of mmol O 2 m -2 day -1 , was then derived from Δ (O 2 /Ar) and NCEP reanalysis winds as previously described in  45  , except for a modification to the gas exchange weighting following  46.
rRNA gene amplicon libraries were constructed using dual indexed 18S rRNA gene V4 primer set  16  , EukF (5′-CCAGCASCYGCGGTAATTCC-3′) and EukR (5′-ACTTTCGTTCTTGAT-3′). For each sample, PCR amplifications were conducted in triplicates with one blank as a control for contamination. The PCR reactions followed a 30-cycle program with annealing temperature at 57 °C. The resulting PCR products were purified using QIAquick PCR Purification Kit (Qiagen), and were pooled in equimolar concentration to 10 ng/µl approximately. The pooled amplicon libraries were sequenced at Duke Center for Genomic and Computational Biology in three MiSeq 300PE runs. Sequence processing. Paired-end reads with dual indices were assembled using VSEARCH v2. 3. 4 (Rognes et al. 2016) following the algorithm described in  49.
The merged reads were (i) demultiplexed in QIIME 1  50  ; (ii) trimmed to remove Illumina adapters, primers, and barcodes, using BBDuk (v38. 29) (http://jgi. doe. gov/ data-and-tools/bb-tools/); and (iii) processed following the DADA2 pipeline (version 1. 10. 1) to infer ASVs  51. Quality filtering and denoising with chimeras removal were performed using the incorporated functions in the DADA2 package. In total, 2480 ASVs were identified from the five-year amplicon dataset. The sequence counts per sample (after quality filtering) were reported in Supplementary Data 1, with median = 67,547 reads per sample. ASVs were then classified by the 'assignTaxnonomy' function in DADA2 following the naïve Bayesian classifier method  52  , using a DADA2 formatted Silva 132 reference database (DOI 10. 5281/zenodo.
1172783)  53. The resulted classification for each ASV is presented in Supplementary Data 3. Alpha diversity. After discarding two samples with the lowest counts 2016S33 and 1016S34, the libraries (n = 117) were rarified to even depth. Alpha diversity indices Chao1 and Shannon (H′) were calculated for each sample using R package Phyloseq v1. 26. 1  54. Pielou's evenness was calculated as J = H′/ln(S), where S is the total number of ASVs observed in a rarified sample. Canonical correspondence analysis (CCA). CCA was conducted to investigate the relationships between community composition changes and environmental constrains  55  using R package vegan (v2. 5-4)  56. In total, 14 environmental variables were initially examined, including XgridCal, YgridCal, PAR, Salinity, SST, Chl, mixed layer depth, volumetric NCP, biological oxygen supersaturation, PO 4 , SiO 4 , N+N, fsim, and fmet.
Bacterial production (BP) and primary production (PP) were not included in this analysis due to a large number of missing values. After a stepwise variable selection based on Akaike Information Criterion (permutation = 1000 per step), the constrained community CCA was conducted with selected environmental variables and the results were presented in Fig. 3. Construct statistical models for NCP. Below we describe a three-step procedure: (i) ASV counts were normalized to generate QMP; (ii) in order to reduce the model dimension, a WGCNA was applied to QMP to generate modules or bio-assemblages, and (iii) the resulting WGCNA modules and environmental variables were fed to the genetic programming (GP) algorithm to construct predictive models for NCP. Quantitative microbiome profiling (QMP). All samples from the year 2014 and four samples from the year 2013 (2013SA, 2013SB, 2013SC, and 2013SD) were processed first, and 0.
As a complementary analysis, we recalculated QMP (QMP_recal) using the empirical HPLC-CHEMTAX normalization for samples from 2013 to 2016 (no HPCL data for the year 2012). The resulted QMP_recal is highly similar to the internal standard method QMP (y = 0. 99x, R 2 = 0. 87), except for one sample 2015S13. Weighted gene correlation network analysis. ASVs which were not observed more than three times in at least 20% of the samples were removed from the count table. WGCNA was conducted to identify inter-connected plankton bio-assemblages (modules) and correlate them with environmental variables using R package WGCNA v 1. 66  27. The calculated QMP for 112 samples were included in one WGCNA run with all samples considered independent of each other. The QMP matrix was log-transformed. The detailed R codes for each step of this analysis are presented as a supplementary file.
Soft thresholding power was set at 4, which was the minimum value for the scale-free topology fit reaching R 2 = 0. 9. In module identification using dynamic tree cut, the minimum module size was set at 10 in order to generate medium to large modules. The analysis resulted in 12 WGCNA modules from thousands of ASVs, thereby significantly reducing the number of input variables for the NCP model. The co-occurrence network of each module was visualized using an open-source tool Cytoscape (v3. 7. 0). Genetic programming to build NCP models. Based on community structure (modules) and abiotic environmental variables, GP was used to construct statistical models predicting volumetric NCP. GP is a machine-learning approach based on evolutionary computation and it has been successfully used to construct a NCP algorithm based on satellite observations in a previous study  31.
In this study, the input factors for GP are, (i) the eigenvalues for 12 WGCNA modules, representing the biological/community factors with resolution at ASV level, and (ii) a list of physical factors, including MLD, PAR, PAR_mld, Salinity, SST, f sim , f met , X grid, and Y grid, which may directly or indirectly influence plankton physiology. The combined dataset (n = 112) was randomly split into even training and validation datasets. GP was then conducted using Eureqa (v1. 24. 0) following the recommendations by  57. The candidate solutions with varying complexity were ranked by mean squared error (Table  S2 ). In order to reduce the risk of overfitting, the complexity of the candidate solutions was kept to a minimum.
Acknowledgements We thank the scientists, scientific support personnel, the crew on  R/V Laurence M. Gould , and the  PAL-LTER team  for their assistance in sample collection and underway measurements. We are grateful to  Hans Gabathuler  for instrument support and  Naomi Shelton  for field sampling. This work is supported by  NSF   OPP-1643534  to N. C. ,  NSF   OPP-1341479  to A. M. , and  NSF   PLR-1440435  to H. D. and O. S. (Palmer LTER). M. M. was supported by the  UK Natural Environment Research Council  via the  BAS Polar Oceans program.
Data availability DNA sequencing data generated in this study have been deposited in the National Center for Biotechnology Information (NCBI) under accession number PRJNA508517. Palmer LTER data are available through Datazoo (http://pal. lternet. edu/data). Silva 132 reference database used for taxonomy classification was downloaded from (https://doi. org/10. 5281/ zenodo. 1172783). Source data are provided with this paper.
This allowed us to derive the orbital elements of Kleopatra's two moons. Results. Using both archival astrometric data and new VLT/SPHERE observations (ESO LP 199. C-0074), we were able to identify the true periods of the moons, P 1 = (1. 822359 ± 0. 004156) d, P 2 = (2. 745820 ± 0. 004820) d. They orbit very close to the 3:2 meanmotion resonance, but their osculating eccentricities are too small compared to other perturbations (multipole, mutual), so that regular librations of the critical argument are not present. The resulting mass of Kleopatra, m 1 = (1. 49 ± 0. 16) • 10 -12 M ⊙ or 2. 97 • 10 18 kg, is significantly lower than previously thought. An implication explained in the accompanying paper (Marchis et al. )  is that (216) Kleopatra is a critically rotating body.
Adaptive-optics observations For fitting the orbits of Kleopatra moons, we used three astrometric datasets denoted as DESCAMPS (from 2008;  Descamps et al. 2011) , and SPHERE2017, SPHERE2018, which were obtained with the VLT/SPHERE instrument  (Beuzit et al. 2019)  in the framework of the ESO Large Programme (199. C-0074; PI P. Vernazza). A detailed description of all adaptive-optics observations, their observational circumstances, reductions, and resulting astrometric positions is included in the accompanying paper by Marchis et al. (see Tabs. 2 and 3 therein), because most of it shared with the analysis of Kleopatra's shape. Altogether, the number of measurements is 15 and 18 for the absolute astrometry of the inner and the outer moon, respectively. For testing purposes, we also used measurements on individual close-in-time images, which are much more numerous (45 plus 45).
N-body model For orbital simulations, we use the Xitau program 1 , originally developed for stellar applications  (Brož 2017; Nemravová et al. 2016). It is a full N-body model, based on the Bulirsch-Stoer numerical integrator from the SWIFT package (Levison & Duncan 1 http://sirrah. troja. mff. cuni. cz/ ˜mira/xitau/ 1994), accounting for mutual interactions of all bodies. For our purposes, it was necessary to modify it in several ways. Namely, we implemented: (i) a fitting of relative astrometry, (ii) angular velocities, (iii) adaptive-optics silhouettes of the primary, (iv) variable distance, (v) variable geometry (u, v, w), (vi) bruteforce algorithm, (vii) multipole development (up to the order ℓ = 10; see Section 3. 2), and (viii) external tide (see Section 3. 3). Consequently, for a comparison of observations of Kleopatra and its moons with our model, we can use the metric: χ 2 = w sky χ 2 sky + w sky2 χ 2 sky2 + w sky3 χ 2 sky3 + w ao χ 2 ao , (1) χ 2 sky = N bod j=1 N sky i=1        (∆u ji ) 2 σ 2 sky major ji + (∆v ji ) 2 σ 2 sky minor ji        , (2) (∆u ji , ∆v ji ) = R -φ ellipse - π 2 × ( u ) ′ ji -u ji v ′ ji -v ji , (3) χ 2 sky2 = N sky2 i=1        (∆u i ) 2 σ 2 sky major i + (∆v i ) 2 σ 2 sky minor i        , (4) χ 2 sky3 = N sky3 i=1        (∆u i ) 2 σ 2 sky major i + (∆v i ) 2 σ 2 sky minor i        , (5) χ 2 ao = N ao i=1 360 k=1 (u ′ ik -u ik ) 2 + (v ′ ik -v ik ) 2 σ 2 ao i , (6) where the index i corresponds to observational data, j individual bodies, k angular steps of silhouette data, ′ synthetic data interpolated to the times of observations t i (including the light-time effect).
Brute-force vs. multipole In order to account not only for J 2 but for a gravitational acceleration by an arbitrary shape of the central body, we implemented a brute-force algorithm in Xitau. Hereinafter, we assumed a constant density within the body. The respective volumetric integral: f bf (r) = -Gρ V r -r ′ |r -r ′ | 3 dV ′ (7) was approximated by a direct sum over 24 099 tetrahedra, obtained by a Delaunay triangulation of the ADAM shape model, using the Tetgen program  (Si 2006). The shape was also shifted to the centre of mass and rotated so that the principal axes of the inertia tensor correspond to the reference axes. Although the computation is slow (24 099 interactions instead of 1), it can be used as a verification of fast algorithms. As far as 'fast' is concerned, we also implemented a multipole development of the gravitational field up to the order ℓ = 10, according to  Burša et al.
In this work, C ℓm , S ℓm coefficients were not fitted, but kept constant. In principle, it is possible to fit all of them (with a dedicated version of Xitau), but it turned out that for almost circular/equatorial orbits (and sparse astrometric datasets) it is not possible to distinguish between individual multipoles, which makes the problem degenerate. In order to understand which multipoles are important, we estimated χ 2 's for different multipole degrees (up to some ℓ, see Fig. 1 ). We used an already converged model for ℓ = 10, without re-convergence, though. It is clear that the model is very sensitive up to ℓ = 6. It may be the case that changing other model parameters (especially P 1 , P 2 ) might improve the fits for ℓ < 6. Degrees ℓ > 6 seem to be insignificant for our analysis. Fig.
2. Periodograms for P 2 computed separately for three datasets (DESCAMPS, SPHERE2017, SPHERE2018). The χ 2 = χ 2 sky value was optimized for the first dataset and then only P 2 was varied. We show the old incorrect period (dotted line), together with an expected spacing between local minima given by the timespan ∆P = P 2 /(t 2t 1 ), and the new correct one (gray line). The shift of P 2 for SPHERE2017 and an increased χ 2 for SPHERE2018 were present due to incorrect identification of the two moons. It was corrected after computing the periodograms and before fitting the orbits.
Fitting of individual seasons Free parameters of our model are as follows: masses m 1 , m 2 , m 3 , osculating orbital elements of the two orbits P 1 , log e 1 , i 1 , Ω 1 , ̟ 1 , λ 1 , P 2 , log e 2 , i 2 , Ω 2 , ̟ 2 , λ 2 at a given epoch T 0 , and the rotation pole orientation l pole , b pole , i. e. , 17 parameters in total. With Xitau, we can fit any or all of them with the simplex algorithm  (Nelder & Mead 1965). Initial values (P's, m's) were taken from  Descamps et al. (2011). All e's, i's were "zero" at t = T 0 , but they are free to evolve. As a first step, we tried to fit individual datasets. Regarding DESCAMPS, we immediately reproduced their Fig. 2 , including the suspicious outlier (bottom left). In fact, it fits on the other side of orbit, but its error in true longitude is ∼ 90 • ! It is an important observation.
Fitting of DESCAMPS + SPHERE As a next step, we fitted all datasets together. This required not only a substantially longer time span (3780 d vs 40 d), but also a 2-dimensional periodogram with a fine spacing, ∆P ≃ P 2 /(t 2t 1 ) ≃ 10 -3 d. We simply cannot use 1-dimensional periodograms for P 1 and P 2 , because the moons are interacting. If we change P 1 (only), χ 2 for P 2 also changes (albeit more slowly). The only way how to find a joint minimum is to try all combinations. Given the period uncertainties are at least several 10 -2 d, this represents about 10 3 combinations. For each of the (initial) values, we performed 50 iterations by simplex (with both P 1 and P 2 free). 2 We verified that this was enough to reach a local minimum. This way, we can be sure that we did not miss a global minimum.
Best-fit and alternative model Let us finally present the best-fit model, with χ 2 = χ 2 sky + χ 2 sky2 + 0. 3 χ 2 ao = 368. Its parameters are summarized in Tab. 3 and the results in Figs. 4, 5, 6. The orbits can be perhaps seen more clearly if we plot the three datasets separately (Figs. 9, 10). To emphasize the orbital elements are not constants in our dynamical model, we demonstrate it in accompanying Fig. 11. The oscillations of a, e, i for the inner moon reach 6 km, 0. 04, 0. 5 • , 2 1 iteration takes ∼ 10 min, in total 1 week on 70 CPUs Fig. 4. Best-fit model with χ 2 = χ 2 sky + χ 2 sky2 + 0. 3 χ 2 ao = 368. Top: The orbits of Kleopatra's moons plotted in the (u, v) coordinates (blue, green lines), observed absolute astrometry (SKY; black circles), and residua (red, orange lines for bodies 2, 3, i.
e. , inner and outer satellites). Kleopatra's shape model for one of the epochs is overplotted in gray. The axes are scaled in km; with a variable viewing geometry, but without a variable distance. The mean semimajor axes of orbits are: a 1 499 km, a 2 655 km. Bottom: The residua of (u, v) in arcsec for the epochs of three datasets (DESCAMPS, SPHERE2017, SPHERE2018). The uncertainties of astrometric observations were approximately 0. 01 arcsec. respectively. The inclinations with respect to Kleopatra's equator are close to zero. The dominant short-periodic terms are directly related to the ∼ 5. 4-hour rotation of Kleopatra. The longer 100 and 270-day periods of inclinations correspond to the nodal precession if the reference plane is the equator. The RMS residua of absolute astrometric measurements are approximately 17 mas (or 23 mas for relative), which should be compared to the assumed uncertainties of 10 mas.
The resonant angle is defined as: σ = 3λ 2 -2λ 1 -̟ 1 , (19) or alternatively ̟ 2 instead of ̟ 1. The stable configuration is expected when conjunctions occur in the apocentre of the outer moon (or the pericentre of the inner moon). On the other hand, sky values for a range of periods P 1 , P 2 and optimized models. Every black cross denotes a local minimum (i. e. , not a simple χ 2 map). All datasets (DESCAMPS, SPHERE2017, SPHERE2018) were used together, consequently, the spacing between local minima is very fine. The global minimum is denoted by a red circle. The dashed line indicates the exact 3:2 period ratio. it's not a circular restricted three-body problem: (i) the moons have comparable masses, (ii) the central body is irregular which induces perturbations on the synodic rotation time scale (sideric P = 0.
224386 d). According to our tests with bodies purposely placed in the exact resonance, or offset in the longitude so that the libration amplitude is ∼ 90 • , regular librations are notable only if the initial (osculating) eccentricities e 1 , e 2 10 -2 (cf. Fig. 11 ). In the current best-fit configuration, they ain't. In the future, it is important to better constrain the masses of moons. This task would require an extended astrometric dataset compared to what is available at the moment. If their low densities are confirmed, the interpretation would be that regolith making up both Kleopatra and the moons is relatively 'fine' (with block sizes smaller than the moon diameters) and it is more compressed in Kleopatra and less compressed in the moons. On contrary, if densities are high the interpretation would be the opposite: 'coarse' regolith in Kleopatra and monolithic material in the moons.
Acknowledgements. We thank an anonymous referee for valuable comments. This work has been supported by the  Czech Science Foundation  through grant  21-11058S  ( M. Brož ,  D. Vokrouhlický ),  20-08218S  ( J. Hanuš ,  J. Ďurech ), and by the  Charles University Research program  No. UNCE/SCI/023. This material is partially based upon work supported by the  National Science Foundation  under Grant No. 1743015. P. Vernazza ,  A. Drouard ,  M. Ferrais  and B. Carry were supported by  CNRS/INSU/PNP. M. M. was supported by the  National Aeronautics and Space Administration  under grant No. 80NSSC18K0849  issued through the  Planetary Astronomy Program. The work of TSR was carried out through grant  APOSTD/2019/046  by  Generalitat Valenciana (Spain). This work was supported by the  MINECO (Spanish Ministry of Economy ) through grant  RTI2018-095076-B-C21  ( MINECO/FEDER, UE).
The research leading to these results has received funding from the  ARC  grant for Concerted Research Actions, financed by the  Wallonia-Brussels Federation. TRAPPIST is a project funded by the  Belgian Fonds (National) de la Recherche Scientifique (F. R. S. -FNRS)  under grant  FRFC 2. 5. 594. 09. F. TRAPPIST-North  is a project funded by the  University of Liège , and performed in collaboration with  Cadi Ayyad University of Marrakesh. E. Jehin  is a FNRS Senior Research Associate. The data presented herein were obtained partially at the W. M. Keck Observatory, which is operated as a scientific partnership among the  California Institute of Technology , the  University of California  and the  National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of the  W.
Many software solutions are available for proteomics and glycomics studies, but none are ideal for the structural analysis of peptidoglycan (PG), the essential and major component of bacterial cell envelopes. It icomprises glycan chains and peptide stems, both containing unusual amino acids and sugars. This has forced the field to rely on manual analysis approaches, which are time-consuming, labour-intensive, and prone to error. The lack of automated tools has hampered the ability to perform high-throughput analyses and prevented the adoption of a standard methodology. Here, we describe a novel tool called PGFinder for the analysis of PG structure and demonstrate that it represents a powerful tool to quantify PG fragments and discover novel structural features. Our analysis workflow, which relies on open-access tools, is a breakthrough towards a consistent and reproducible analysis of bacterial PGs.
A transformative step for the characterisation of disaccharide peptides has been the use of reversed-phase HPLC (rp-HPLC) and mass spectrometry (MS) towards the end of the 1990s  (Garcia-Bustos et al. , 1988; Glauner, 1988; Glauner et al. , 1988; Martin et al. , 1987). Combining muropeptides separation by rp-HPLC and MS characterisation has hinted at a more complex structure than previously reported. Despite tremendous advances in both rp-HPLC-MS instrumentation and software development for the automated analysis of large datasets, 'peptidoglycomics' is still in its infancy. The experimental strategy to analyse PG structure has barely changed over the past 30 years. Even though rp-HPLC-MS has been routinely used over the past decade, the analysis of MS data remains a black box. Except for a recent study describing the PG structure of Pseudomonas aeruginosa  (Anderson et al.
, 2020b) , no information is available in the literature about the strategy used to identify muropeptides in rp-HPLC-MS datasets. This task relies on searching a subset of expected structures, but the complexity of both the search space and search process is often not described. We previously provided the proof of concept that shotgun proteomics tools can be used for the automated and unbiased analysis of PG structure  (Bern et al. , 2017). The analysis of Clostridioides (previously Clostridium) difficile PG led to the identification of many muropeptides never reported before. This work also demonstrated that PG analysis could be carried out with relatively high throughput, opening the possibility to analyse large numbers of samples (such as clinical or environmental isolates) using a minimal amount of material (typically microgram amounts).