dataesr/scientific-paragraphs-categorization huggingface dataset:
Where count data were reported for subspecies, these were aggregated to species level and any records of hybrid species or specifying genus only were removed. The longitude and latitude of each survey site (just the first site of each NA-BBS survey route) were also provided by NA-BBS and PECBMS. Not all sites were surveyed in every year and only sites surveyed at least three times during the defined time period were included in analyses. Note that similar results were found when restricting data to sites surveyed in at least 10 years during the defined period. Sound recordings. Sound files for all species detected on NA-BBS and PECBMS surveys were downloaded from Xeno Canto, an online database of sound recordings of wild birds from around the world (http://www. xeno-canto. org). Specifically, we identified all files longer than 30 s, with associated metadata categorising them as high quality (category "A") and as either "song", "call" or "drumming" types; sound files whose type category including the term "wingbeat", "flap", "begging", "alarm" or "night" types were excluded.
In total, count data were available for 202,737 sites and 620 species in North America, with a mean ± SE of 15. 62 ± 0. 6 sound files available per species. For Europe, count data were available for 16,524 sites and 447 species, with 21. 05 ± 0. 9 sound files per species. Soundscape reconstruction. This is described in detail in the main text. Soundscape characteristics. Four acoustic indices were used to explore changes in the acoustic properties of reconstructed soundscapes. The Acoustic Diversity Index (ADI) uses the Shannon-Wiener index to estimate acoustic diversity, dividing spectrograms into frequency bands and calculating the proportion of each band occupied by sounds above a set amplitude threshold  30. Higher values represent a more even distribution of sound across frequencies and are associated with increased species richness.
In all analyses, separate models were constructed for North American (204,813 sites on 4197 routes spanning 22 years) and European data (16,524 sites spanning 21 years), and for each acoustic index in turn. To explore large-scale temporal trends while accounting for any geographic differences in acoustic characteristics, we fitted Gaussian General Linear Mixed Models (GLMMs) via the R package 'lme4'  65. Standardised annual site-level values for each acoustic index were fitted as the response variable, with latitude, longitude and year (continuous) as fixed effects. To account for non-independence of soundscapes from the same site, random effects of site and year were included in all models, along with route and state (North America models, Eq. (1a)) or country (Europe models, Eq. (1b)). To assess the importance of fixed effects, we performed a likelihood ratio test by comparing models with and without a particular term, reporting the χ 2 value and associated significance.
Spatial autocorrelation of modelled residuals was examined by Moran's I, separately for each year, using the package 'ape' 66. While significant spatial autocorrelation was found, the sizes of the estimates were negligible (Supplementary Table  6 ) and therefore this is subsequently ignored. To explicitly explore how temporal trends in the acoustic properties of reconstructed soundscapes varied geographically, we refitted the models described above, including latitude*year and longitude*year interaction terms. To visualise the large-scale annual variation in acoustic properties we refitted these models with year included as a categorial rather than a continuous variable, with predictions from these models providing continent-level annual estimates for each acoustic index (Fig. 3 ). To explore the relationships between site-level trends in each acoustic index, we fitted GLMs with the standardised annual values for each index as the response variable and year (continuous) as the explanatory variable (Eq.
Acknowledgements We thank the thousands of volunteer citizen-scientists who contributed to the long-term bird-monitoring programmes in North America and Europe, the institutions that manage these programmes, and those funding these activities. The  Norwegian Environment Agency  finances the Norwegian common breeding bird monitoring and the  Swedish Bird Survey  is supported by the  Swedish Environmental Protection Agency  and carried out in collaboration with all 21 regional county boards. It acts within the framework of the strategic research environment Biodiversity and  Ecosystem Services  in a Changing Climate (BECC). J. R. was supported by  Charles University in Prague  (project no. PRIMUS/17/SCI/16 ). We are also grateful to the many sound recorders that have submitted files to the  Xeno Canto collection  (www.
Data availability The North American bird monitoring data are available directly from U. S. Geological Survey (https://www. pwrc. usgs. gov/bbs/) and the European bird monitoring are available, on request, from PECBMS (https://pecbms. info/). Sound recordings were downloaded from Xeno Canto (http://www. xeno-canto. org). Acoustic indices for soundscapes constructed from simulated communities, site-level acoustic index data for reconstructed soundscapes for NA-BBS and PECBMS sites, and source data for all figures are available from the Open Science Framework under accession code: https://osf. io/ jyuxk/ (ref. 67 ).
coli ACS for the activation of glycolate (82 M -1 s -1 ; deacetylated enzyme)  8. Based on a homology model created with ACS of S. enterica (Protein Data Bank (PDB) ID: 2P2B; 62% sequence identity)  12, 13  , we identified Val379 as a target for directed mutagenesis to open up the active site for accommodation of the slightly larger glycolate (Supplementary Fig. 1 ). A substitution of Val379 by alanine (Val379Ala) had previously been reported to enhance the activity of the enzyme with the slightly larger propionate in S. enterica ACS  12. We tested three different Val379 substitutions (Supplementary Table  1 ), isolating the variant Val379Ala (glycolyl-CoA synthetase (GCS)), which showed an improved apparent K m for glycolate (13 ± 3 mM) and a catalytic efficiency of 853 M -1 s -1 (Table  1 ).
Next, we sought to alter the substrate preference of MePCC. Such engineering of biotin-dependent acyl-CoA carboxylases had only been attempted in a few studies  [15] [16] [17]. For structure-guided rational design, we built a structural model of MePCC, for which we obtained a 3. 48-Å cryo-electron microscopy (cryo-EM) dataset (Fig. 2 , Supplementary Fig. 2  and Supplementary Table  2 ). We then targeted several residues in the first and second shell of the active site of the carboxyltransferase subunit, which form the substrate binding pocket. The selected residues were assumed to be involved in direct or indirect binding of the natural substrate propionyl-CoA, which differs in the Cα position from glycolyl-CoA by a hydroxyl group (Fig. 2 , Supplementary Fig. 3  and Supplementary Table  1 ).
The glycerate concentration of the samples was measured using derivatization following analysis via UPLC-MS/MS (for detailed UPLC-MS/MS methods, see the Supplementary Methods). Cryo-EM sample preparation and data collection. For cryo-EM sample preparation, 4. 0 µl of the purified complex (1 mg ml -1 ) containing 1 mM β,γ-imidoadenosine 5′-triphosphate, 2 mM MgCl 2 and 4 mM glycolyl-CoA was applied to glow-discharged Quantifoil 2/1 grids, blotted for 3. 5 s with force 4 in a Vitrobot Mark IV (Thermo Fisher Scientific) at 100% humidity and 4 °C and then plunge frozen in liquid ethane that had been cooled by liquid nitrogen. For MePCC, cryo-EM data were collected on an FEI Glacios transmission electron microscope operated at 200 keV using the SerialEM software package  49. A total of 2,637 video frames were recorded at a calibrated pixel size of 1.
18 Å using a magnification of 36. 000×. The total dose was 50. 2 e -Å -2 , distributed over 40 frames. Micrographs were recorded in a defocus range of -0. 5 to -3. 5 μm. All subsequent imaging processing steps were performed in the cryoSPARC software package. The dose-fractionated videos were gain-normalized, aligned and dose-weighted using the patch motion algorithm. Defocus values were estimated using the CTFFIND4 implementation, and 168,488 particles were automatically chosen using a reference-free blob picker. Particle sorting and reference-free two-dimensional classification were performed to remove non-particle candidates and damaged particles. Ab initio model generation using the stochastic gradient descent algorithm was used to prevent any model bias using a completely data-driven starting model.
The particles were further classified using heterogeneous refinement into three classes. The best-aligning class was subsequently subjected to three-dimensional refinement yielding 3. 67 Å global resolution and a B factor of -99 Å 2. After per-particle defocus and higher-order contrast transfer function (CTF) correction in the Refinement_New algorithm, the resolution improved to 3. 48 Å with a B factor of -86. 8 Å 2. For GCC M5, cryo-EM data were acquired with an FEI Titan Krios transmission electron microscope using SerialEM software  49. Video frames were recorded at a nominal magnification of 105,000× (calibrated physical pixel size: 0. 8512 Å px -1 ) using a K3 direct electron detector (Gatan) in correlated double sampling mode and a GIF quantum energy filter (Gatan) at a 20-eV slit width.
The total electron dose of ~55 e -Å -2 was distributed over 35 frames. Micrographs were recorded in a defocus range of -0. 5 to -2. 7 μm. Cryo-EM micrographs were processed on the fly using the Focus software package  50  if they passed the selection criteria (iciness < 1. 05; drift 0. 4 Å < x < 70 Å; defocus 0. 5 µm < x < 5. 5 µm; estimated CTF resolution < 5 Å). Micrograph frames were aligned using MotionCor2 (ref. 51 ) and the CTF was determined using GCTF  52. Using Gautomatch (http://www. mrc-lmb. cam. ac. uk/kzhang/), 5,398,283 particles were picked template free on 20,057 acquired micrographs. Candidate particles were extracted with a pixel box size of 340 using RELION 3. 1 (ref. 53 ) and cleaned using reference-free two-dimensional classification. A total of 2,863,953 particles were imported into cryoSPARC  54  , used for ab initio construction of initial models and subjected to multiple rounds of heterogeneous refinement to obtain the best-aligning 2,181,317 particles.
Additionally, in the density maps, coordinated water molecules were visible (Supplementary Fig. 12 ). Structural modelling and analysis. Homology modelling was performed using SWISS-MODEL  13. As a template for homology modelling of EryACS1, the structure of an ACS mutant of S. enterica 12 (PDB ID 2P2B; 62% amino acid identity) was used, which had an adenosine-5′-monophosphate-propyl ester (propyl-AMP) bound-an analogue of the reaction intermediate propionyl-AMP. Based on the position of the propyl-AMP in the S. enterica ACS, a glycolyl-AMP was modelled into the active site of the EryACS1 homology model. For initial homology modelling of the β subunit of MePCC, the structure of the β subunit of PCC from Ruegeria pomeroyi (PDB ID 3N6R; 72% amino acid identity)  19  was used. Initial cryo-EM map fitting was performed in UCSF Chimera version 1.
14 (ref. 55 ) using homology models based on PDB ID 3N6R  19. Automatic refinement of the structure was done using phenix. real_space_refine of the PHENIX 1. 17. 1-3660 suite  56. Manual refinements and water picking were performed with COOT 0. 8. 9. 2 (ref. 57 ). All structural depictions were created using PyMOL (the PyMOL Molecular Graphics System; version 1. 8; Schrödinger). Modelling of propionyl-CoA and glycolyl-CoA into the active sites of the MePCC and GCC, respectively, was based on the position of methylmalonyl-CoA in the structure of a methylmalonyl-CoA carboxytransferase from Propionibacterium freudenreichii  58  (PDB ID 1ON3; 52% amino acid identity). Manual fitting and adjustments of the CoA thioesters reflecting differences in active-site architectures were done with COOT and PyMOL.
The full code and the list of reactions of each model can be found at https://github. com/he-hai/PubSuppl within the 2020_TaCo directory. Pathway thermodynamics analysis. MDF analysis  22  was applied to evaluate the thermodynamics feasibility of the tartronyl-CoA module. The Python packages equilibrator_api, equilibrator_assets and equilibrator_pathway were used for the analysis. The change in Gibbs energy of the reactions was estimated using the component contribution method  22. CO 2 was considered as the substrate for the carboxylation reactions as its concentration is pH independent, unlike that of bicarbonate, thus simplifying the calculations. Metabolite concentrations were constrained to the range 1 µM to 10 mM, as descripted previously  22. The pH was assumed to be 7. 0, the ionic strength was assumed to be 0.
Acknowledgements We dedicate this publication to our colleague and friend  Arren Bar-Even , who was at the very heart of this project. We thank  I. A. Berg  and  D. S. Tawfik  for fruitful discussions throughout the project. J. M. S. and S. K. S. are grateful to  E. Conti  for scientific independence and great mentorship. This work was funded by the  Max Planck Society (MaxSynBio)  and  FET-Open  Grant  686330  ( Future Agriculture ). The work conducted by the  US Department of Energy Joint Genome Institute  (a  DOE Office of Science User Facility)  was supported under contract number  DE-AC02-05CH11231. J. -C. B. acknowledges financial support from the  Région Aquitaine  and from the  French Government 'Investments for the Future '.
Data availability The structural data that support the findings of this study have been deposited in the PDB with the accession codes 6YBP (MePCC wild type) and 6YBQ (GCC M5), and in the Electron Microscopy Data Bank with the accession codes EMD-10770 (MePCC wild type) and EMD-10771 (GCC M5). Other publicly available PDB structures used in this study are 2P2B, 3N6R and 1ON3. Other data supporting the findings of this study are available in the paper and Supplementary Information, or from the corresponding author upon reasonable request.
It can be estimated whether the receiver follows directly or inversely the activity of the sender, what was applied in computational models to distinguish two circuits with different mechanisms (excitation and inhibition) but identical GC values  [26]. Baccalá and Sameshima introduced a new approach, termed Partial Directed Coherence (PDC)  [27] , to estimate a frequency-domain representation of the direct GC between two nodes, i. e. , controlling the influence of other variables included in the model. However, a common limitation to all these approaches is that they are restricted to linear interactions. Alternatively, methodologies based on Information Theory, as Transfer Entropy (TE)  [28] , estimate the connectivity using conditional entropies, allowing the identification of linear and nonlinear interactions, with the counterpart that they require larger datasets and cannot provide a spectral decomposition.
Animals and Surgery Five adult male Long-Evans rats (250-300 g) were each implanted with a 32 channels silicon probe (Neuronexus Technologies, MI, USA) across the dorsal hippocampus (data are available at http://dx. doi. org/10. 20350/digitalCSIC/12537 (accessed on 19 July 2021)). Data from the same subjects have been used in a previous study  [7]. An Ag/AgCl wire (World Precision Instruments, Sarasota, FL, USA) electrode was placed in contact with the skin on the sides of the surgery area and used as ground. The data were acquired at 5 kHz, with an analog high-pass filter at 0. 5 Hz. After digitalization, we low-pass filtered the signals at 300 Hz, removed the line noise at 50 Hz and its first harmonic with Notch filters and down-sampled the signals at 2. 5 kHz. At the end of the experiments, animals were perfused with 4% paraformaldehyde, and the final position of the electrodes confirmed histologically.
Data Acquisition We carried out a test of mismatch novelty in all subjects. First, we did a habituation process with two sessions per day during 11 days before the surgery and 8 days after recovery. Each session consisted in 10 min freely exploring an open field (plexiglass sandbox of 50 × 50 cm, opened at the top and with three visual cues in three of the walls). The 9th day after recovery, we performed the "novelty" test. For 10 min, the subjects were introduced in a "novelty chamber" consisting of a transparent methacrylate box inside the familiar environment, with a square base 35 cm wide and 40 cm high, and with sandpaper on the floor to provide a noticeable tactile stimulus. This corresponded to the "novelty session". Immediately after that, the animals were left in the original open field for another 10 min, which was considered as the control condition.
Independent Component Analysis An extensive description of the methodology applied on the present dataset can be found in  [7]. Briefly, the aim of independent component analysis (ICA) is to separate N statistically independent sources that are mixed on M sensors (where N ≤ M). As the distributions of the sources are unknown, it performs a blind separation of patterns. Moreover, it assumes that the sources have different spatial distributions, they are spatially immobile, and their time-courses are independent, although it is robust to even high levels of source correlation  [31]. Each recorded LFP u m (t) is then modeled as the sum of N sources s n (t) multiplied by a constant weight factor V mn : u m (t) = N ∑ n=1 V mn s n (t), m = 1, 2,. , M (1) where V mn is the mixing matrix or topography and represents the voltage contribution of the source n to the sensor m.
Power Analysis of Time Series As ICA does not ensure the correct polarity and amplitude of each generator  [49] , we normalized the power of the IC-LFPs, imposing to each dataset (i. e. , animal) an averaged mean value of zero and a standard deviation equal to the unit. This way, we increased the similarities inter-subject and facilitated their comparison. We used the multitaper method  [50]  to compute the power spectra of the IC-LFPs. We selected all the continuous data for each session. Based on previous studies  [7]  we defined the following frequency bands: theta (6-10 Hz), slow gamma (30-60 Hz), medium gamma (60-100 Hz), and fast gamma (100-150 Hz). The power of each band was computed as the mean value of the power spectrum at that frequency band.
Knowing the ice thickness distribution of a glacier is of fundamental importance for a number of applications, ranging from the planning of glaciological fieldwork to the assessments of future sea-level change. Across spatial scales, however, this knowledge is limited by the paucity and discrete character of available thickness observations. To obtain a spatially coherent distribution of the glacier ice thickness, interpolation or numerical models have to be used. Whilst the first phase of the Ice Thickness Models Intercomparison eXperiment (ITMIX) focused on approaches that estimate such spatial information from characteristics of the glacier surface alone, ITMIX2 sought insights for the capability of the models to extract information from a limited number of thickness observations. The analyses were designed around 23 test cases comprising both real-world and synthetic glaciers, with each test case comprising a set of 16 different experiments mimicking possible scenarios of data availability.
INTRODUCTION The ice thickness distribution of a glacier is one of its fundamental properties. By defining the glacier's morphology and total volume, ice thickness controls the ice dynamics, defines the amount of water stored, and determines the glacier's lifetime in a changing climate. Knowing the ice thickness distribution is, thus, not only necessary for most glaciological investigations, but is also paramount for assessing long-term glacier changes, hydrological impacts, or contributions to sea-level change  (IPCC, 2020). In the past decades, a number of initiatives have been ongoing to better characterize the thickness of Earth's ice masses. With Bedmap  (Lythe et al. , 2001) , Bedmap2  (Fretwell et al. , 2013) , the datasets by  Bamber et al. (2003)  and  Bamber et al. (2013)  or BedMachine  (Morlighem et al.
, 2017 (Morlighem et al. , , 2020)) , standard ice thickness products had been established for Antarctica and Greenland, and similar datasets now exist also for glaciers and ice caps around the globe  (Huss and Farinotti, 2012; Farinotti et al. , 2019). The advances have been spurred by both the increased capability of measuring glacier ice thickness at large scales and the development of models inferring thickness from characteristics of the surface. To be efficient, large-scale ice thickness mapping requires airborne platforms. Whilst such platforms have been used for surveying ice sheets and other large, cold ice masses for almost 70 years (for reviews, see, e. g. ,  Plewes and Hubbard, 2001; Schroeder et al. , 2020) , airborne systems capable of operating in mountain environments have emerged only more recently  (Blindow et al.
, 2012; Rutishauser et al. , 2016; Zamora et al. , 2017; Langhammer et al. , 2019b; Pritchard et al. , 2020). Data of such ice thickness surveys outside the ice sheets have been collected in the Glacier Thickness database (GlaThiDa)  (Gärtner-Roer et al. , 2014) , now at its third release  (Welty et al. , 2020). Hosted and curated by the World Glacier Monitoring Service, the database now collects a total of nearly four million airborne and groundbased point observations. Still, GlaThiDa v3 only covers about 1,100 glaciers, corresponding to ∼6% of the glacierized surface outside the ice sheets  (RGI Consortium, 2017). The relative data sparseness requires the use of model-based interpolation approaches to derive glacier-wide ice thickness distributions from discrete observations (e. g. ,  Farinotti et al.
Here, we present the results of ITMIX2, the second phase of ITMIX, which aimed at comparing the capability of individual models to extract information from limited subsets of ice thickness observations. With a set of dedicated experiments, ITMIX2 also investigated whether the spatial distribution of these observations has a discernible influence on the model results, possibly leading to recommendations for the configuration of future data acquisitions. ITMIX2 was based on an updated set of both real-world and synthetic test cases addressed in ITMIX1, and includes glaciers and ice caps in different climatic regimes for which information on both surface characteristics and ice thickness is available. The general idea was to perform a set of experiments in which different subsets of the thickness observations are available for model calibration, and in which the ice thickness of the remaining profiles had to be estimated.
ITMIXSETUP ITMIX2 built upon the dataset used in ITMIX1. Individual test cases and specific additions to this dataset are described hereafter (Section 2. 1). The experimental design of ITMIX2 included 16 experiments per test case, aimed at mimicking different possible layouts for the ice thickness data available for model calibration (Section 2. 2). A description on how ITMIX2 was organized from the practical side, including the terms for ITMIX2 admission, is given in Section 2. 3.
These data were retrieved from a Glaciers are sorted alphabetically, with synthetic cases at the end of the list. "Pr. " is the priority by which each glacier was asked to be considered (cf. Section 2. 3), with "1" indicating compulsory cases. "Type" follows the GLIMS classification guidance by  Rau et al. (2005)  (SB, simple basin; CB, compound basin; mtn. : mountain). "A" and "cs" are the glacier area and horizontal resolution of the provided gridded datasets, respectively. "SMB," "dh/dt," and "vel. " indicate whether gridded information on surface mass balance, rate of ice thickness change, and ice flow velocity at the surface were provided (x) or not (-). For velocity, "p" indicates that only punctual information from repeated stake positions was available. Npts is the number of available point ice thickness measurements after gridding.