dataesr/scientific-paragraphs-categorization huggingface dataset:
See Table  S6  for lists of genome IDs. Amino acid sequences of 37 conserved housekeeping genes were automatically identified, aligned, and concatenated using Phylosift  [58]. All phylogenetic reconstruction was performed using MrBayes v3. 2. 6  [59]  to estimate consensus phylogenies and clade posterior probability support values. Sequence substitution was modeled according to a WAG substation model with gamma-distributed rate variation between sites. Trees were visualized and formatted using iTol  [60]. The sequence cluster tree was built with NCBI's Conserved Domain Database tool (CDD)  [21, 22]. This tool uses reverse position-specific BLAST, a method that compares query sequences to databases of position-specific score matrices and obtains E-values, such as in PSI-BLAST  [21, 22]. WebLogo3 was used to plot the amino acid distribution at each position of the GH motif  [52].
2 M MgCl 2 , 10% glycerol, and then flash-frozen in liquid nitrogen. X-ray diffraction data were collected at the European Synchrotron Radiation Facility (ESRF, Grenoble, France) on the ID30a1 (MASSIF-1) beamline  [67, 68]. Diffraction data were indexed and scaled using the XDS program suite  [69]. SpmX-Mur-Ae crystals belong to the trigonal space group P3 2 21, with unit cell dimensions of 100. 44 × 100. 44 × 96. 62 Å and three molecules per asymmetric unit. Phase determination was carried out by the molecular replacement method with PHASER  [70] , using as a search model the structure of the phage P22 lysozyme (PDB entry 2ANX)) to 1. 9 Å resolution (Rcryst 21. 1%, Rfree 25. 5%,) (Table  S2 ). The molecular replacement solution model was rebuilt de novo using PHENIX  [71]  to prevent bias from the model.
The structure of SpmX-Mur-Ae was completed by cycles of manual building with COOT  [72]  and addition of water molecules with ARP/wARP  [73]. Several cycles of manual building and refinement with REFMAC  [74] , as implemented in the CCP4 program suite, were performed until R work and R free converged  [75]. Stereochemical verification was performed with PROCHECK  [76]. The secondary structure assignment was verified with DSSP  [77] , with all residues within most favorable or allowed regions of the Ramachandran plot. Figures were generated with PyMol (http://www. pymol. org). Coordinates of the final refined model were deposited at the Protein Data Bank (PDB, http://www. rcsb. org) and were assigned PDB entry code 6H9D. The data collection and refinement statistics are summarized in Table  S2.
The data points plotted in Figure  4A  represent measurements of P22-lyso and SpmX-Mur-Cc activity over several days. Lines are drawn to help guide the eye toward basic trends and do not reflect line fitting. Data points are from various days and sacculi preparations, but are normalized to parallel reactions with HEWL on the same day and with the same sacculi as each data point. Fluorescent microscopy. -Quantification of stalk morphotypes and stalks with multiple foci was done by hand using tools in Fiji. Quantification of fluorescence data was achieved using MicrobeJ  [82]. Mean stalk intensity was measured in A. biprosthecum cells by using Fiji to draw line ROIs that did not overlap with the focus at the base of the stalk, measuring mean fluorescence along the ROI. Figures and statistics were performed using GraphPad Prism version 8.
Acknowledgements We thank members of the  Brun, Vernet , and  Dessen  laboratories for support, advice and encouragement. Many thanks to  Breah LaSarre ,  Farrah Bashey-Visser ,  Jay Lennon ,  Daniel Schwartz , and  Ernesto Vargas  for critical manuscript reading and editing. Enthusiastic thanks for critical preprint review by the journal clubs of the  (Pamela) Brown Lab (University of Missouri ) and the  Süel Lab (UCSD). We thank  David Flot  ( ESRF, beamline ID30a1 ) for support in data collection. The  NIH  supported this work with grants  2R01GM051986  and  R35GM122556  (to Y. V. B. ), and  NRSA F32GM112362  (to A. M. R. ). A  Fulbright US Research Scholar Award  supported the work of Y. V. B. at the  Institut de Biologie Structurale in Grenoble. Y. V. B is supported by a  Canada 150 Research Chair in Bacterial Cell Biology.
This work used the platforms of the Grenoble Instruct-ERIC Center (ISBG :  UMS 3518 CNRS-CEA-UGA-EMBL ) with support from  FRISBI  ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology  (PSB).
We can find an explicit approximation for this line (by fitting a two-degree polynomial on the level-line, using MATLAB's methods) which is given by: log 10 B crit = 0. 085(log 10 A) 2 + 0. 707 log 10 A + 0. 125. (4) This means that for a given level of adaptation A, the population can only invade the whole space if the heterogeneity is smaller than B crit , otherwise it will suffer local extinctions, being restricted to a limited range or disappearing altogether. Solving the equation for A, we can interpret this result the other way round: for a given level of spatial heterogeneity B, the population will be able to invade only if its adaptation level is greater than the solution A *. Unfortunately, the method we used does not provide us with an explanation or an intuition as to why the regime change occurs on this line, but it is nevertheless an improvement of the condition found by  Kirkpatrick and Barton (1997) , B crit = √ 2A, or equivalently, log 10 B crit = log 10 A + 0.
We build upon the analysis of a (hypothetical) flood-alleviation scheme in  Bokhove et al. (2018a) , in which more available physical and economic data is incorporated. The paper outline is as follows. In Section 2, FEV and available flood-storage volume are revisited to allow use in subsequent sections. In Section 3, FEVs are calculated for the River Calder and the River Brague floods of 2015. Subsequently, several flood-mitigation measures proposed within these river catchments are analysed in Section 4, with conclusions drawn in Section 5.
River Brague 2015 flood Following the River Brague 2015 flood, the regional authority, assisted by a panel of experts, performed a comprehensive appraisal of the resulting hazards and damages (Préfécture des Alpes-Maritimes, 2015). Field measurements were conducted to compute peak discharges based on topographical sections, flood levels, and river-bed roughnesses. The peak discharge at the Biot station was estimated to reach 240 m 3 /s within the envelope 185-295 m 3 /s (Lebouc & Payrastre, 2017), higher than the 1∶100-year-return-period peak discharge of 200 m 3 /s (Préfécture des  Alpes-Maritimes, 2015). A further study using radar rainfall data and a hydrological model of the catchment yielded hydrographs of the event  (Lindénia, 2016). Downstream tributaries also experienced major events, aggravating flooding in Antibes.
ACKNOWLEDGEMENTS We acknowledge the  EA  for providing data of the River Calder and the  Syndicat Intercommunal de l' Amélioration de la Qualité des Eaux de la Brague et de ses Affluents (SIAQUEBA) ,  Brague basin agency , and the  Direction Départementale des Territoires et de la Mer des Alpes Maritimes  ( DDTM06 ) for reports on the Brague case. This article contains public-sector information licensed under the Open Government Licence v3. 0. This work grew out of  UK-EPSRC's Living with Environmental Change network ''Maths Foresees '' (Grant  EP/M008525/1 ) and  Professor Sarah Dance 's  Data Science Fellowship ''Data Assimilation for the REsilient city'' (Grant   EP/P002331/1 ). The work of G. P. and J. -M. T. was funded by the  H2020  project  NAIAD  (Grant  730497 ) from the  European Union 's  Horizon 2020 research and innovation programme.
79 2. 3. Experimental set-up 80 About 18 L of digested sludge were poured into a laboratory-scale PP tank placed under a fume 81 hood and continuously stirred with an overhead plastic propeller at 30 rpm (Figure  S1 ) in order to 82 control experimental conditions. Stirring allows optimizing air transfer within the digestate and 83 therefore represents a "worst case scenario" compared to unstirred real scale tanks. A Tinytag 84 data logger (TG-4100, Gemini Data Loggers, UK) was used to record the temperature in the 85 sample every 10 min. The surface of the sample was exposed to air to promote oxidation of the 86 sample during 10 weeks. The surface to volume ratio varied from 0. 39 dm -1 (7. 1 dm 2 :18 L) to 87 0. 51 dm -1 (7. 1 dm 2 :14 L) during the experiment because of multiple sample collection (see 88 below).
If broadly true, this would hamper the creation of a genome-based prokaryotic virus taxonomy 18. Fortunately, explorations of viral sequence space are revealing structure  19, 20  and population genetic support for a biological species definition, driven primarily by vertical evolution 21 , and new hypotheses to explain variable evolution among prokaryotic viruses 22. Such findings, alongside rapidly expanding viral genome databases, allowed the International Committee on Taxonomy of Viruses (ICTV) to present a consensus statement suggesting a shift from the 'traditional' classification criteria 23 (e. g. virion morphology, single/multiple gene phylogenies) towards a genome-centered, and perhaps one-day, largely automated, viral taxonomy 24. This shift is particularly.
critical given the modern pace of viral discovery in which, as of November 2018, hundreds of thousands of metagenome-derived viral genomes and large genome fragments (735,112 at IMG/VR  25  , with a total of 110,384 'species' or viral OTUs) now dwarf the 34,091 available from prokaryotic virus sequences in the NCBI GenBank database  26. Together with the recently proposed 'minimum information about uncultivated virus genomes' (MIUViGs) community guidelines  27  , evaluation of approaches to establish a scalable, genome-based viral taxonomy is needed to enable a universal classification framework. The ability to classify thousands of microbial UViGs is invaluable for foundational hypothesis testing anywhere microbes, and their viruses, might matter. With microbiome and phage therapy research exploding, this includes ecosystems such as industrial bioreactors and the human gut and lung, as well as the oceans, the deep-sea floor, and soils.
0 created artifactual VCs of both undersampled genomes and highly overlapped regions of viral sequence space  30  , and also lacked several key, community-desired features including per-VC confidence metrics, a metric for establishing hierarchical taxonomy, and scalability. Here we introduce and evaluate vConTACT v2. 0 (hereafter v2. 0), which significantly updates the network analytics and feature set of the original program. We apply this program to (i) establish a centralized, 'living' taxonomic reference network as a foundational community resource and (ii) demonstrate that v2. 0 is robust and scalable to modern datasets.
1b ): (i) VCs that wrongly clustered genomes with no close relatives (termed 'outlier genomes' from 'undersampled VCs'), (ii) a given VC that contained multiple ICTV genera represented by virus genomes that shared many genes and/or modules with other VCs (termed 'overlapping VCs'), and (iii) VCs that encompassed multiple ICTV genera sharing many genes and/or gene modules across genomes within the VC, and within subsets of the genomes in the VC (termed 'structured VCs'). While a significant advance, v1. 0 struggled to adequately differentiate between these issues. Further, v1. 0 lacked several key features to enable broader adoption and utility as described above. To address these issues in v2. 0, we evaluated and ultimately implemented a new clustering algorithm, established confidence scores and distance-based taxon separation for hierarchical taxonomy, and optimized and evaluated scalability and robustness to a large-scale viral metagenomic dataset.
vConTACT v2. 0 is scalable to modern virome datasets Beyond accurate classification, a major bottleneck regarding automated taxonomic assignments is the ability to scalably and robustly integrate large sets of newly discovered virus genomes. To evaluate this, we added 15,280 curated viral genomes and large genome fragments (≥ 10 kb) from the Global Ocean Virome (GOV) dataset  41  to our reference network in successive 10% increments (i. e. , 0%, 10%, …, 100% of the total dataset), totalling 16,960 sequences (Fig. 4a ). Through this process, we evaluated 2 types of network changes to assess robustness. First, we checked whether the incremental addition of GOV data to the network would lead to changes in node connections, as estimated by the 'change centrality' metrics (CC, values range from 0-1 with 0 indicating no change and 1 indicating complete change, see Fig.
4b ). Second, we evaluated the concordance between v2. 0 clustering and ICTV genera using the same performance metrics as above (Sn, Acc & PPV, see Fig. 4c ). As seen in Fig. 4b , a large fraction of the data initially experiences a moderate change (CC = 0. 4), but the whole dataset eventually stabilized, as CC values for most of the data ranged from 0 to 0. 1. A similar trend was observed for accuracy (Acc, Fig. 4c ). This indicated that, not only can v2. 0 scalably handle thousands of new input sequences, but our original reference network clustering is robust to large-scale data additions such that the underlying reference network remains consistent with established ICTV genera. Outside of better sampled genera or VCs, we also examined whether GOV data may partially resolve ICTV outlier and singleton genomes as a proxy for assessing the taxonomic ramifications of so much new data.
We reasoned that more data might create new connections to singletons such that outliers may become better connected to new or existing VCs. We found that, of 38 single-member VCs of singleton and outlier genomes (Supplementary Fig. 6 ), three Mycobacterium phage VCs were improved, while two other Mycobacterium virus genomes were merged into larger heterogeneous VCs now composed of six ICTV genera, which did not constitute an improvement. This implies that even though the GOV data derive from an environment very different to that of most of the reference taxa (oceans vs soils and humans), these environmental data could help improve some taxon assignments of isolate genomes. Separately, looking at the overall taxonomic impact, we observed that 919 new VCs were created with the full GOV dataset (15, 280 total contigs).
Given the strong concordance of the 'known' VCs to ICTV genera, we posit that these new VCs represent 919 new viral genera that are not represented among the 264 ICTV genera already known from RefSeq genomes. If true, this demonstrates the power of integrating viral sequence datasets from under-explored environments to better map viral sequence space. While enticing, we agree with the recent consensus statement that any taxonomic reference network be constrained to complete genomes  24  , and that large genome fragments commonly derived from metagenome-based studies be utilized in a relevant manner to address questions specific to that study.
Community availability and future needs The utility of v2. 0 depends upon its expert evaluation and community availability. In close discussion with members of the ICTV Bacterial and Archaeal Viruses Subcommittee, we made the resulting optimized tool available in two ways. First, the source code is available through Bitbucket (https://bitbucket. org/MAVERICLab/vcontact2) as a downloadable python package. V2. 0 is a highly scalable tool, with memory and CPU requirements concomitant with the amount of sequences processed. The largest memory requirements are by the all-versus-all protein comparisons used to build the protein clusters. Overall, there is a strong linear (R 2 = 0. 99, see Supplementary Fig. 7 ) correlation between number of sequences and runtimes. For example, running the full virus dataset RefSeq with Diamond (for the protein comparisons) would take ~10 minutes on a regular laptop, while a GOV-sized dataset would run for several hours.
Second, CL1-based VC generation may require manual parameter optimization if datasets of an extreme, and probably unlikely, nature are encountered. Such datasets would have to be dominated by overlapping genomes (i. e. , those that share a large proportion of genes), and though such genomes exist (e. g. , the HGCF Mycobacterium phages), they are currently rare among cultured isolates and their frequency in nature remains unknown. Notably, at least for the oceans, such highly recombinogenic viruses are likely uncommon as the incremental addition of GOV data resulted in a stable network and separate analyses of a more recent version of this GOV dataset found ocean viral populations to be quite structured  59. However, in case such datasets dominated by overlapping genomes are encountered, we have added an auto-optimization option for determining the optimal distance for hierarchical decomposition of structured VCs in v2.
2 ). In parallel, the ICTV taxonomy (ICTV Master Species List v1. 3, as of February, 2018) was retrieved from the ICTV homepage (https://talk. ictvonline. org/files/master-species-lists/). ICTV-classifications were available for a subset of genomes at each taxonomic rank, and the final dataset included: 884 viruses from two orders, 974 viruses from 23 families, 363 viruses from 28 subfamilies, and 940 viruses from 264 genera. To maintain hierarchical ranks of taxonomy, we manually incorporated 2016 and 2017 ICTV updates  49, 64, 65  to NCBI taxonomy when ICTV taxonomy was absent.
e. isolated protein with no relatives). Calculating genome similarity between viruses. The resulting output was parsed in the form of a matrix comprised of genomes, PCs and singleton proteins (i. e. , 2,304 × 52,138 matrix) (Supplementary Table  1 ). We then determined the similarities between genomes by calculating a one-tailed P value of observing at least c PCs in common between each pair of genomes, based on the following hypergeometric equation as per Lima-Mendez et al 29 : ( ≥ ) = ∑ ( , ) (1) in which c is the number of PCs in common; a and b are the numbers of PCs and singletons in genomes A and B, respectively; and n is the total number of PCs and singletons in the dataset. The hypergeometric formula calculates the probability of sharing a number of common PCs between two genomes at or above the number (c) under the null hypothesis that the observed result is likely to occur by chance.
Data sets. Full-length viral genomes were obtained from the National Center for Biotechnology Information (NCBI) viral reference dataset 26, 63  ('ViralRefSeq', version 85, as of January, 2018), downloaded from NCBI's viral genome page (https://www. ncbi. nlm. nih. gov/genome/viruses/) and eukaryotic viruses were removed. The resulting file contained a total of 2,304 RefSeq viral genomes including 2,213 bacterial viruses and 91 archaeal viruses (Supplementary Table.
ACKNOWLEDGEMENTS. We thank  Laura Bollinger ,  Gareth Trubl , and  Igor Tolstoy  for their comments on improving the manuscript, as well as  Wesley Zhi-Qiang You  for helping push the network analytics. High performance computational support was provided as an award from the  Ohio Supercomputer Center  to MBS. Funding was provided in part by the  Department of Energy's Genome Sciences Program Soil Microbiome Scientific Focus Area  award (# SCW1632 ) to  Lawrence Livermore National Laboratory ; an  NSF   Biological Oceanography award  (OCE# 1536989 ), and a  Gordon and Betty Moore Foundation   Investigator Award  (# 3790 ) to MBS. Funding was provided to JRB by the  Intramural Research Program  of the  NIH, National Library of Medicine. The work conducted by the  U. S. Department of Energy Joint Genome Institute  is supported by the  Office of Science of the U.
TCCON (Total Carbon Column Observing Network)   column-averaged dry air mole fraction of CH 4 (X CH4 ) measurements have been widely used to validate satellite observations and to estimate model simulations. The GGG2014 code is the standard TCCON retrieval software performing a profile scaling retrieval. In order to obtain several vertical information in addition to total column, in this study, the SFIT4 retrieval code is applied to retrieve CH 4 mole fraction vertical profile using TCCON spectra (SFIT4TCCON) at six sites (Ny-Ålesund, Sodankylä, Bialystok, Bremen, Orléans and St Denis) during the time period of 2016 -2017. The retrieval strategy of SFIT4TCCON is investigated. The degree of freedom for signal of the SFIT4TCCON retrieval is about 2. 4, with two distinct species of information in the troposphere and in the stratosphere.
The SFIT4 code is based on the optimal estimation method  (Rodgers, 2000) , which is an updated version of SFIT2  (Pougatchev et al. , 1995)  and commonly used in the Network for the Detection of Atmospheric Composition Change -the Infrared Working Group (NDACC-IRWG)  (De Mazière et al. , 2018). The TCCON sites and the SFIT4TCCON retrieval strategy are introduced in the next section. The motivation behind this study is to retrieve vertical information of CH 4 from TCCON spectra. In section 3 the difference in X CH4 retrieved using the SFIT4TCCON to the X CH4 retrieved from the standard TCCON retrievals is investigated. The tropospheric and the stratospheric X CH4 retrieved using the SFIT4TCCON retrievals are compared with other available datasets, such as ground-based in situ measurements, Atmospheric Chemistry Experiment -Fourier Transform Spectrometer (ACE-FTS) satellite observations, and TCCON proxy data.
Error Total column Troposphere (0-16. 5 km) Stratosphere (  16. 5-50 km) 3 Results and discussions The retrieval strategy listed in Table  2  is applied for all six sites. In this section, the data quality of the SFIT4TCCON retrievals is evaluated with TCCON standard retrievals, ground-based in situ measurements, ACE-FTS satellite remote sensing observations, TCCON proxy X CH4 data and AirCore measurements.
ACE-FTS satellite observations The comparison results between the SFIT4TCCON stratospheric X CH4 and the ACE-FTS satellite observations are discussed in this section. The vertical range from the tropopause height up to 50 km is treated as the stratosphere in this study. ACE-FTS satellite monitors the atmospheric CH 4 concentration mainly in the stratosphere since 2004 in the solar occultation mode  (Bernath et al. , 2005). The latest level 2 version 3. 6 data with data quality flag equal to 0 (without any known issue) are selected from ACE/SCIAST dataset  (Sheese et al. , 2015). The ACE-FTS CH 4 profile is retrieved on target altitudes with a vertical resolution of 3-4 km, and then it is interpolated onto a 1 km grid. To our knowledge, there is no validation report for the version 3. 6 CH 4 data yet, but the older version v2.
TCCON proxy data In this section, the tropospheric and stratospheric X CH4 from SFIT4TCCON retrievals are compared with the results derived from the N 2 O and HF proxy methods at the six TCCON sites. We refer to  Wang et al. (2014)  and  Saad et al. (2014)  for the details of computing the tropospheric and stratospheric X CH4 by the proxy retrieval method using N 2 O and HF. Figure  9  shows the time series of the tropospheric and stratospheric X CH4 from the TCCON proxy N 2 O and HF method. First, the tropospheric and stratospheric X CH4 from the N 2 O and HF proxy methods are close to each other. However, a slight seasonal and site dependent bias is observed. The tropospheric X CH4 from N 2 O and HF proxy method are very close to each other for St Denis. While for the other five sites, the tropospheric X CH4 from the N 2 O method is larger by about 15-20 ppb than the HF method.
The bias in the tropospheric X CH4 between the N 2 O and HF methods are in a good agreement with the Figure  7  in  Wang et al. (2014). As the TCCON N 2 O retrievals are corrected to the WMO scale  (Wunch et al. , 2015)  while HF retrievals has not been validated, the systematic bias is probably due to the uncertainty of the X HF product. Second, at St Denis (a moist site), the TCCON HF retrievals are strongly affected by H 2 O so that the TCCON proxy method tropospheric and the stratospheric X CH4 data using HF have many outliers.