dataesr/scientific-paragraphs-categorization huggingface dataset:
S11  to S13 at http://thethrashlab. com/publications came from these annotations and inspection with IMG's analysis tools, including Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway assignments and transporter predictions. Transporters highlighted for dissolved organic matter (DOM) uptake were identified based on information at the Transporter Classification Database  (92). Carbohydrate-active enzymes (CAZymes) were predicted using the same routines as those in operation for the updates of the carbohydrate-active enzymes database (http://www. cazy. org) (93). RPKM abundance of taxa and genes. Abundance of taxa within the sample was quantified by evaluating mapped reads using RPKM (reads per kilobase per million) normalization  (94)  according to A ij ϭ (N ij /L i ) ϫ (1/T j ), where A ij is the abundance of bin i in sample j, N ij is the number of reads that map to bin from sample j, L i is the length of bin i in kilobases, and T j is the total number of reads in sample j divided by 10 6.
A total of 3,840 genes recruited reads in all remaining samples. All calculations are available in Table  S1  at http://thethrashlab. com/publications or the R markdown document Per. gene. RPKM. Rmd in supplemental information. Table  S1  at http://thethrashlab. com/publications includes only analyzed data for the uncultivated bins reported in this study. Note that RPKM values indicate abundance measurements across a small number of samples. While we can evaluate the relative expression of genes for those samples, our data set lacks sufficient power to evaluate estimates of significance in differential expression. nrfA sequence assessment. Initial annotation of our bins identified putative homologs to the nrfAH genes associated with dissimilatory nitrite reduction to ammonia. Since nrfA-type nitrite reductases can be misannotated due to homology with other nitrite reductases, annotation for these genes was curated with phylogenetic analysis using known nrfA genes (38) obtained via A.
Welsh (personal communication). Alignment, culling, and inference were completed with the FT_pipe script. The tree was rooted on the designated outgroup octaheme nitrite reductase sequence from Thioalkalivibrio nitratireducens ONR. Node labels were constructed with the newick utilities (90) script nw_rename. Visualization of the alignment (Fig. S8B  and C ) to confirm the presence of the first CXXCK/CXXCH and highly conserved KXQH/KXRH catalytic site was completed with the MSAViewer (96) online using the unculled nrfA alignment as input. SFam homology searches. To identify group-specific expansions in particular gene families, we performed a homology search of all predicted protein-coding sequences in each bin against the Sifted Families (SFam) database (41) using hmmsearch (HMMER 3. 1b  [97] ) with default settings except for the utilization of 16 CPUs per search.
Accession numbers. Sequence read data are available at the NCBI SRA repository under the BioSample accession numbers SAMN05791315 to SAMN05791320 (DNA) and SAMN05791321 to SAMN05791326 (RNA). Additional supplemental information. Table  S1 , scripts, workflows, and key files, including fasta files for each tree, are available at the Thrash Lab website: http://thethrashlab. com/publications.
ACKNOWLEDGMENTS We thank the crew of the  R/V Pelican ,  Allana Welsh  and  Mostafa Elshahed  for providing fasta files for nrfA and WS3 phylogenetic comparisons, and  Mostafa Elshahed  for helpful comments regarding WS3 phylogeny. Portions of this research were conducted with high-performance computing resources provided by  Louisiana State University  (http://www. hpc. lsu. edu). Funding for this work was provided to J. C. T. through the  Oak Ridge Associated Universities Ralph E. Powe Junior Faculty Enhancement Award  and the  Department of Biological Sciences of Louisiana State University. A portion of the funding for this work was provided by a  Planning Grant award  to O. U. M. from  Florida State University. Funding for the research vessel and collection of oceanographic data was provided by the  National Oceanic & Atmospheric Administration ,  Center for Sponsored Coastal Ocean Research  award  NA09NOS4780204  to N.
MTBD, microtubule binding domain. (B) The motor domains from the phi-particle and the crystal structure of cytoplasmic dynein-2 bound to ADP. vanadate (ADP. Vi, PDB: 4RH7). Coiled-coil helices 1 and 2 (CC1 and CC2) control microtubule affinity. Both motors display a bent linker and stalks that have low microtubule affinity due to the bulge in CC2. (C) AAA1 nucleotide binding sites are similar in ADP. Vi-bound dynein-2 and the ADP-bound dynein-1 phi-particle. The main catalytic residues are labeled: WB, Walker B; SI, Sensor-1; RF, Arginine finger. See also Figure  S4. the two microtubule binding domains (MTBDs) from binding simultaneously  (Torisawa et al. , 2014). Here, we show dimerization has a more fundamental effect on microtubule affinity by locking the motors into a weak affinity state.
STAR+METHODS This study EMDB: EMD-3698; PDB: 5NUG. Human cytoplasmic dynein-1 tail in the twisted state (8. 4A ˚). This study EMDB: EMD-3703; PDB: 5NVS. Human cytoplasmic dynein-1 tail in the parallel state (12A ˚). This study EMDB: EMD-3704. Full length human cytoplasmic dynein-1 in the phi-particle conformation (15A ˚). This study EMDB: EMD-3705; PDB: 5NVU. Human cytoplasmic dynein-1 bound to dynactin and an N-terminal construct of BICD2 (8. 7A ˚). This study EMDB: EMD-3706; PDB: 5NW4. Locally refined human cytoplasmic dynein-1 tail bound to dynactin and an N-terminal construct of BICD2 (12. 4 A ˚).
These particles were classified into 10 distinct 2D class averages in RELION-1. 4 (Scheres, 2012)  and then used as templates to pick all particles from 3000 negative stain micrographs in Gautomatch. Performing several cycles of 2D classification ensured that each class contained only relatively similar particles. All subsequent processing was performed in RELION-1. 4 (Scheres, 2012)  unless otherwise stated. For 3D classification of the phi-particle, a map of dynactin was low pass filtered to 100A ˚and used as an initial model. This worked well because dynactin and the phi-particle share a thin, elongated shape of similar size. A 30A ˚reconstruction of phi-particle was generated after several iterative classifications. This map was then used as an initial model for 3D refinement and classification of the cryo-EM dataset.
Cryoelectron microscopy In order to obtain a sufficient proportion of phi-particles on cryo-EM grids, freshly purified dynein in GF150, at a concentration of $0. 15mg/ml, was used directly after SEC. Quantifoil R1. 2/1. 3 Au grids were prepared by coating with homemade thin carbon layers. These were made in an Edward carbon evaporator (Auto 306) on mica to a thickness of between $50 nm. Carbon-coated grids were plasma cleaned for 25 s using a 9:1 mixture of Ar and O 2 gas. 3 mL of the sample was incubated on the grid for 30 s and then blotted, using a Vitrobot IV (FEI), for 2-5 s at 100% humidity and 4 C. All grids were stored in liquid nitrogen dewars before loading to the electron microscope. Screening of cryo-EM conditions was performed on a 120kV FEI Spirit T12 microscope. Initial datasets were collected on FEI 300kV Polara microscope with Falcon III detector.
We obtained preliminary 2D class averages and confirmed the possibility of successful 3D reconstruction with these datasets. Due to the flexibility, heterogeneity and severe orientation preference of the phi-particle, large datasets were collected to obtain sufficient views with relative stable conformations. Despite the large size of full-length dynein, contrast was limited by its thin elongated shape and an additional carbon support which was required to prevent the complex from falling apart. We therefore only selected holes that allowed us to obtain both sufficient contrast and intact particles. Datasets were collected on FEI 300kV Titan Krios microscopes equipped with Falcon II or K2 summit detectors over 8 separate sessions. A summary of the imaging conditions of all the datasets are listed in Table  S1.
Structure of the dynein phi particle To begin processing the cryo-EM dataset, all electron micrograph movies were aligned by Motioncorr  (Li et al. , 2013). Only movies with relatively small and smooth movements for all frames were used for further analysis. Any movies with abnormal normalization or background, heavy contamination, thick ice, low contrast or large drift were discarded. CTF parameters of the averaged movie frames from Motioncorr were automatically determined by Gctf  (Zhang, 2016). The equiphase averaging (EPA) method in Gctf was used to estimate the maximum information limit for each micrograph. Only micrographs which contained CTF information higher than 4A ˚were used in subsequent processing. We also only used micrographs with a defocus range of 1. 5-5. 0 mm for Falcon II datasets or 1.
0-3. 5 mm for K2 datasets. Automatic particle picking was performed using Gautomatch, on phase-flipped micrographs generated by Gctf. The templates for picking were generated in RELION from 2D class averages of negative stain EM images of the phi particle. Gautomatch was run using a low threshold (0. 1 cross-correlation) to pick as many particles as possible. The output is a list of particle coordinates with the associated cross-correlation coefficient. Micrographs from each dataset were then empirically split into 3-5 groups according to the contrast observed. The correlation cut-off parameter was refined for each group of micrographs to optimize particle selection. Selection of good particles was performed on output coordinates from Gautomatch using the script box_filter. com (http://www.
mrc-lmb. cam. ac. uk/kzhang/useful_tools/scripts/). We then used RELION to extract the particles from the original micrographs rather than the phase-flipped images. These selected particles were subsequently split into sub-datasets ($50,000 particles per sub-dataset, total particle numbers for each dataset are given in Table  S1 ) for 2D analysis in RELION. Initial 2D classification of each sub-dataset into $20 classes could not sufficiently separate all of the distinct conformations and orientations of the phi-particle, due to its extreme flexibility. To improve the quality of the 2D averages, sub-classification was performed on each class that contained good averages. Sub-classification allowed us to further separate the smaller conformational changes within each major class. In addition to the initial 2D classification a subsequent 3-6 cycles of 2D sub-classification was required to remove most bad particles.
cam. ac. uk). The weighted and averaged particles were used for all subsequent processing. No further processing required the movie stacks. 3D classification and refinement of the full-length dynein led to a 15A ˚reconstruction. It was not possible to further improve the maps because of the continuous relative rotation between motor domain and tail. We therefore solved the structure of the motor domain and tail separately and used the 15A ˚map as a reference to assemble the two parts together. We re-centered and re-extracted particles so that they contained only the motor domain or tail region, using a box size of 400 for motor domains and 288 for the tail. We processed these datasets separately in the following 2D and 3D classification and refinement steps. 2D classification was performed to select high quality motor domains.
2D and 3D classification were performed after masking out the motor domains which are extremely flexible. We obtained an 8. 7A ˚density map of the DDB which closely resembled the TDB structure but contains significant additional density for the HC and LIC. In order to resolve the dynein tail within DDB further, we subtracted the density of dynactin from each particle  (Bai et al. , 2015)  using the previously determined structure of dynactin (PDB: 5ADX) as a mask. Using the subtracted map for further 2D and 3D analysis significantly improved the density in the dynein tail region. We resolved the nearly complete tail in DDB to 12A ˚and can rigid body fit HC helix bundles (except bundle 9) into to the density map.
Model building and refinement A de novo model of the 3. 8A ˚motor domains was manually built using COOT  (Emsley et al. , 2010). The model was refined using REFMAC  (Brown et al. , 2015). Statistics of the model building and refinement are summarized in Table  S1. Helical bundles 2-8 in the phi-dynein tail were manually built by fitting poly-alanine a helices into density using COOT. Helical bundle 1 and the N-terminal domain were obtained from a previous 5A ˚crystal structure (PDB: 5AFR) of the N terminus of the dynein tail  (Urnavicius et al. , 2015)  and fit into density. A homology model of helical bundle 9 was built using the Phyre2 server  (Kelley et al. , 2015). Homology models for the human dynein intermediate chain WD40 domain and human dynein light intermediate chain were also obtained from Phyre2.
These models were fit into tail of phi-particle as rigid bodies using UCSF chimera  (Pettersen et al. , 2004). A de novo model was manually built into the 3. 8A ˚map of the phi-dynein motor domains using COOT  (Emsley et al. , 2010). The model was refined using REFMAC  (Brown et al. , 2015). Statistics of the model building and refinement are summarized in Table  S1. A model for the dynein tail was assembled using the 8. 4A ˚map of the phi-dynein tail. Helical bundles 2-8 in the dynein tail were manually built by fitting poly-alanine a helices into density using COOT. Helical bundle 1 and the N-terminal domain were obtained from a previous 5A ˚crystal structure (PDB: 5AFR) of the N terminus of the dynein tail  (Urnavicius et al. , 2015)  and fit into density. A homology model of helical bundle 9 was built using the Phyre2 server  (Kelley et al.
, 2015). Homology models for the human dynein intermediate chain WD40 domain (based on PDB: 1ERJ) and human dynein light intermediate chain (based on PDB: 4W7G) were also obtained from Phyre2. These models were fit into tail of phi-particle as rigid bodies using UCSF chimera  (Pettersen et al. , 2004). Helix bundles 1-4 in DDB were directly built into the 8. 7 A ˚density map while dimerization domains of the HC, ICs and Robl were unambiguously fit into the density as rigid bodies. Helix bundles 5-8 and LICs were fit into the 12 A ˚density map as rigid bodies. The position of helix bundle 9 was estimated according to the position of bundle 8 and also the characteristic curvature of the neck region. This was further conformed from locally refined 2D averages.
Ratio of phi-and open-dynein In order to determine the ratio of phi and open-dynein in wild-type dynein (wtDyn) and mutant dynein (mtDyn) both samples were treated exactly the same during expression, purification and preparation of negative stain grids, as described above. Micrographs were randomly collected in different squares of the negative stain grids. Three rounds of 2D classification on each dataset were performed to remove bad particles. The high quality particles for both dynein datasets were then sub-classified into 100 sub-classes. In total, 4793 particles for wtDyn and 11819 particles for mtDyn were used in the classification and following quantification. Particles in these sub-classes were then assigned to one of 3 sub-datasets corresponding to either phi, open or ambiguous conformations.
Ambiguous conformations refer to those particles that could not be easily assigned to phi or open conformations. Additional 2D classification on these sub-datasets was performed to make sure each sub-dataset only contains phi, open or ambiguous conformations. Any misassigned particles were moved to the correct sub-dataset. Manual inspection of the particles within each class confirmed that the different conformations had been grouped correctly. Averaged particle numbers of each class were calculated on 25 randomly selected micrographs for both wtDyn and mtDyn. After selection, the micrographs were removed from the dataset in order to avoid repetitive selection. This process was repeated 9 times and the mean and SEM were calculated.
Distance between motor domains The distance between motor domains in wtDyn or mtDyn was directly measured from the 2D averages of negative stain data for open dynein. For DDB-bound dynein, dynactin density was subtracted from the images for focused 2D classification as described above. 90248 particles of wtDyn and 29733 particles for mtDyn were classified into 100 2D classes and 10086 DDB particles into 50 2D classes. The coordinates of the centers of each motor domain in each class were manually recorded. These values were then used to calculate the distance between the two motor domains in each class. The distance measurements were split into groups with 2. 5nm interval each. The number of particles was summed for each distance group to estimate the distance distribution.
Motor domain orientation In order to clearly determine the orientation of individual motor domains, small masks were generated according to the mass center of each motor domain in each class for local 2D refinement. This allowed us to obtain much better density of individual motor domains such that their orientations could be determined. Most motor domain particles were successfully assigned into two major classes ('inverted' or 'parallel') based on their stalk and linker orientations. Motor domains that could not be resolved after focused classification were defined as in an ambiguous orientation. Total particle numbers for each of the three major classes were summed to estimate the distribution of motor orientations in both open dynein (wtDyn and mtDyn) and DDB datasets. We confirmed that the distribution of motor orientations was similar in negative stain and vitreous ice.
Dynein microtubule affinity and processivity All movie analysis was performed in FIJI  (Schindelin et al. , 2012). For kinetic analysis of dynein binding, 5-10 microtubules from each slide were selected at random. Kymographs along each microtubule were generated, and the number and duration of binding events greater than 2 frames (0. 25 s) long was counted manually. For quantification of the microtubule binding rate and dwell time of single dynein complexes, 3 movies were collected (3 repeats over two different days) at two dynein concentrations (0. 29nM and 0. 58nM). The microtubule binding rate was calculated as the average number of binding events per second, per micrometer of microtubule, per nM dynein for each microtubule. Dwell time data was plotted as a histogram, with the bin size corresponding to one frame, and was fit to a two-phase exponential decay model (R 2 > 0.
Quantification of SEC traces Analytical SEC was performed to separate the components formed when dynein, dynactin and BICD2N were mixed at a ratio of 1:2:20. To quantify the proportion of protein in the dynein-dynacin-BICD2N complex, fractions containing all components were identified by SDS-PAGE followed by SYPRO-Ruby staining as described above. The data from traces were plotted in GraphPad Prism and normalized to the baseline A280 reading at 5mL. The section of the trace corresponding to the DDB was extracted and the area under the curve (AUC) was calculated in GraphPad Prism. The DDB:total-protein ratio was calculated by dividing this by the AUC of the whole trace. This was repeated four times using three different preparations of the components. Statistical significance was determined by an unpaired, two-tailed t test.
Immunofluorescence data quantification To quantify the GFP (Dynein) intensity at centrosomes, images of G2 cells with well-separated centrosomes (as visualized by PCNT staining) were acquired (at least 14 cells per condition and per experiment, N = 3 experiments, n > 87 centrosomes per condition). A SUM projection (16-bit in TIFF format) of the 11 slices surrounding each centrosome was created in FIJI and used for quantification. GFP fluorescence intensity at the centrosome was measured in a 12x12 pixels ROI centered on the centrosome (as visualized by PCNT staining). GFP cytoplasmic background was defined as the average fluorescence intensity measured from 3 ROIs (12x12 pixels) placed in the cytoplasm and subtracted from the GFP fluorescence intensity at centrosome. To quantify the GFP-Dynein and p150 Glued intensities at spindle poles, images of whole metaphase cells were acquired (at least 19 cells per condition and per experiment, N = 3 experiments, n > 120 spindle poles per condition).
A SUM projection (16-bit in TIFF format) of the 11 slices surrounding each spindle pole was created in FIJI, and used for quantification. GFP and p150 Glued fluorescence intensities were measured in a unique region of interest (ROI) of 12x12 pixels placed at the spindle pole, and were divided by the corresponding cytoplasmic background, as defined by the median fluorescence intensity from 3 ROIs (12x12 pixels) placed in the cytoplasm. GFP and p150 Glued intensity data were analyzed using the R software. For each experiment, datasets were assessed for normality and homogeneity of the variances using a Shapiro-Wilk and a Bartlett tests, respectively. In all analyzed datasets, either normality or homogeneity of the variances was not reached and the datasets were analyzed using non-parametrical statistics.
ACKNOWLEDGMENTS We thank  S. Scheres  for cryo-EM advice;  S. Chen ,  G. McMullan ,  C. Savva ,  J. Grimmett , and  T. Darling  for technical support at the  MRC-LMB ;  D. Clare  ( Diamond Light Source, UK electron Bio-Imaging Center :  EM13361 ),  T. Boesen ,  J. Song ,  M. Dong  ( Aarhus University ), and  J. Novacek  ( CEITEC ) for help with data collection;  S. Maffini  ( MPI-Dortmund ) for help with live-cell microscopy;  S. Bullock ,  D. Barford ,  C. Lau , and  J. Carter  for comments on the manuscript. This work was funded by grants from the  Wellcome Trust  ( WT100387 ) and the  Medical Research Council, UK  ( MC_UP_A025_1011 ) to A. P. C. and the  Worldwide Cancer Research  project ( 16-0093 ) to A. W. B.
Workshop format Over a hundred experts from different professional backgrounds (science, policy & practice) and different fields of expertise contributed to the workshop. They included natural scientists, animal and human health scientists as well as social scientists, policy representatives from national governments and the EU, and experts working in Europe, but also in other regions in the world. For an overview of the various fields of expertise represented in the workshop, please see the participants list  [5]. The workshop program featured a combination of specific topics and generic integrative sessions. PowerPoint presentations of the sessions, containing dedicated references can be reached through the Sessions webpage of the workshop (http://www. biodiversity. be/health/82). In the topical sessions, participants exchanged experiences and views from their fields and projects, whilst exposing the arguments for and possible ways to apply the One Health perspective in their areas of expertise.
This not only constrains decision-making and good resource allocation, but also the innovation of data collection protocols and the development of databases to capture and quantify the value of interdisciplinary approaches. Evaluations should provide information for the contracting institutions on the strengths and weaknesses of their OH initiatives and the impact they achieve, and help OH implementers to assess whether they are making progress towards achievement of the stated goals. Furthermore, there was a call to introduce OH concepts in primary, secondary and tertiary education with the aim to raise awareness and create a natural understanding of systems and their interlinked nature. To enhance the evaluation of OH, it was perceived to be important to create further evaluation capacity by providing training on evaluation of OH/EH and to build stronger links with the community to be able to benefit from community knowledge, approaches and experience.