dataesr/scientific-paragraphs-categorization huggingface dataset:
The open-source pipeline for oligotyping is available from http://oligotyping. org. This method has been used previously to identify Gardnerella distributions in vaginal samples  (Eren et al. , 2011) , Nitrospira specificity in sponges  (Reveillaud et al. , 2014) , and Blautia specificity in animal hosts  (Eren et al. , 2014). In order to get the best possible insights into Vibrio oligotype distributions across habitat types, we grouped samples into three broad analysis groupings; substrate (host) associated habitats only, substrate habitats along with their surrounding water samples, and environmental and substrate associated habitats (Table  2 ). First, we analyzed only substrate-associated samples (fish, sponge and our abiotic substrate-plastic marine debris). We subsampled these datasets to the median Vibrio sequence count of 30,000 prior to analysis in order to minimize the range in initial sequencing depth between samples, which can otherwise reduce the entropy value of discriminating points in datasets with much lower sequence counts.
We then processed them through the oligotyping pipeline, as described in  Eren et al. (2013a). To establish entropy components that fully decomposed our sequence data, we started with the strongest two components, then manually chose the next component that best removed remaining entropy in the resulting oligotypes, and re-ran the analysis. This iterative process yielded a final 12 entropy points that fully decomposed our sequences. We allowed for oligotypes to occur in only a single sample (-s 1) but discarded them if they did not represent at least 0. 5% of the relative abundance of that sample (-a 0. 5). Not all samples contained 30,000 sequences, and sequencing depth ranged from 83 to 30,000 after rarefaction (Table  2 ). Global alignment prior to oligotyping for short Illumina reads is unnecessary, as positional shifts in sequencing reads due to natural indels will produce entropy peaks at the position of insertion or deletion (and subsequent positions), and the decomposition of the dataset based on any of these peaks will eventually result in the same oligotypes as if they would have been previously aligned.
VISUALIZATIONS AND STATISTICAL ANALYSES OF OLIGOTYPE DISTRIBUTIONS To visualize Vibrio community similarity between samples and habitats we constructed Nonmetric Multidimensional Scaling (NMDS) plots as part of our oligotyping pipelines. We included the covariance ellipsoids calculated as part of the oligotyping pipeline on these plots to visualize the spread of a given habitat's community variance. Importantly, covariance ellipsoids delineate the total high-dimensional space, not only the two axes shown in the NMDS plot. Statistical analyses of Vibrio oligotype distributions between and within habitat types followed a 4-step analysis using the ecological statistics packages PrimerE v. 6 and the R package Vegan  (Oksanen et al. , 2012). First, we normalized an oligotype matrix, which consisted of samples across rows and oligotypes down columns (Supplementary Data Sheet 2), by percent per sample (i.
SIMPER decomposes average Bray-Curtis similarities between all pairwise habitat comparisons into percentage contributions of each oligotype. To gain insight into the taxonomy and ecology of our oligotype sequences, we isolated the representative sequence from the 17 most abundant oligotypes outlined above. We then used MEGABLAST to query these sequences against National Center for Biotechnology Information's (NCBI) nr database in June 2014 (nr = non-redundant amalgamation of GenBank, RefSeq, EMBL, DDBJ and PDB databases). We kept only 100% matches across the entire 60 bp query, and extracted the "isolation source" and "host" feature using Geneious (v. 6. 1) annotation tables. We also extracted the most abundant 2 or 3 taxonomies from perfect hits, not including "uncultured bacterium" (Table  3 ).
We created a PhyML tree of existing full-length Vibrio 16S rRNA gene sequences downloaded from type strains in the SILVA ARB v5. 1 database and then added our oligotype sequences to this tree using the Maximum Parsimony feature in ARB across the V6 region only (using a V6 "filter" in ARB). Our rationale for building this tree was not to reconstruct phylogenetic relationships between Vibrio species but rather to make some inference about the habitats from which closely related Vibrio 16S rRNA gene sequences have been isolated. To this end, we binned the isolation source and host annotations of both our oligotype MEGABLAST hits, and our ARB isolates, into five habitat categories. These were "Marine Host," "Seawater," "Other Marine," "Terrestrial," and "Freshwater. " Marine host included all sequences isolated from the skin or innards of a host in seawater (e.
g. , tunicates, fish, crustaceans, sponges and sea cucumbers). "Other Marine" included sediment, biofilms and algae, or other marine plant associated sources. "Terrestrial" included any sample taken from the terrestrial environment, or from a terrestrial host (e. g. , humans, birds, and plants), and freshwater included hits isolated from a freshwater environment, or freshwater host (e. g. , freshwater shrimp). We color-coded the proportion of each category and displayed them at the terminal nodes of each oligotype sequence in our PhyML reference tree. Lastly, to visualize the isolation source of the ARB isolate reference sequences, we colorcoded the nodes of the tree according to the isolation source of each reference sequence at the terminal node of that branch. We used the online software Interactive Tree of Life (iTOL)  (Letunic and Bork, 2011)  to visualize the phylogenetic tree.
"WITHIN-HABITAT VARIANCE" OF VIBRIO COMMUNITIES To determine differences in habitat specificity, we calculated the median Vibrio community variance across all datasets within the same habitat (its "within-habitat variance"), and compared that across habitats. This allowed us to determine if some habitats contained a specific Vibrio community, or if Vibrio communities varied widely even within the same habitat. To calculate the median variance of all datasets in a habitat, we normalized our oligotype abundance matrixes by the maximum value of each oligotype, then calculated Bray-Curtis community similarity between all pairwise comparisons using vegdist{vegan} function in R  (Oksanen et al. , 2012). We then calculated each habitat's multidimensional "centroid" using the median value of each sample within a habitat across all principal components.
PHYLOGENETIC AND METADATA ANALYSIS OF ABUNDANT OLIGOTYPES Our phylogenetic analysis of ARB reference sequences revealed no 16S rRNA gene phylogeny-habitat relationship. This is evidenced by the spread of reference sequences from the same isolation source across the phylogenetic tree (Figure  7 ). Our 17 abundant oligotypes were also not monophyletic according to the habitat in which they were most abundant (e. g. , sponges or fish). For example, oligotype sequences dominant in FreshwaterFish and Freshwater (Oligotype 2, 4, and 8) were found to branch in different parts of our phylogenetic tree (Figure  7 ). MEGABLAST queries of abundant oligotypes returned on average 120 perfect matches from the NCBI nr database, although variance in this number was high (ranging from 0 to 1000). No oligotype with more than two perfect NCBI hits came from only one source, and any oligotype with more than 50 perfect NCBI hits was isolated from at least three different sources.
Oligotype 5, which was the most abundant across our habitats (Figure  6 ), also had the maximum number of allowable perfect hits (1000). Which habitat a query sequence came from was an extremely poor predictor for the isolation source of its NCBI hits, although Oligotypes 4 and 8, which contributed to FreshwaterFish had previously been isolated from freshwater environments (Table  3 ), and both matched V. cholerae, found in brackish water, while abundant oligotypes in marine organisms (e. g. , 1 and 6), did not return any previous isolations from freshwater environments. Surprisingly, Oligotype 2, which contributed most to freshwater environments, had no perfect hits from freshwater sources. The majority of isolation sources from all our NCBI hits (76%) were marine hosts, although we note the potential for database biases.
DISCUSSION Our results represent the first attempt to use subtle nucleotide variation at a single, 60 bp gene marker to make sense of community level patterns across habitats within the genus Vibrio. Although our results are not the first to explore Vibrio community patterns across diverse habitats  (Hunt et al. , 2008; Preheim et al. , 2011a; Szabo et al. , 2013) , we use preexisting sequence data to extend conclusions made by previous authors to a broader survey of unexplored habitats and provide novel insights into substrate-associated communities and their surrounding water. The breadth and scope of our analyses, 211 samples representing seven unique habitats, revealed or supported several interesting patterns suggestive of two broad hypotheses regarding different aspects of Vibrio ecology and life history.
First, Vibrio contains many generalist taxa, each adapted to a wide range of animal hosts. Second, our data suggest even these "host-adapted" vibrios occur as members of free-living communities facilitating long distance dispersal to disparate hosts. We suggest both of these characteristics, combined with previous understanding of rapid growth rates  (McDonough et al. , 2013; Skorupski and Taylor, 2013)  fit the description of an "r-strategist" life history. Several patterns within our data support these suggestions. Fish acclimated to marine salinities share highly similar Vibrio communities with geographically and phylogenetically distinct sponges (Figure  1, Table 4 ). Both marine acclimated fish and sponge communities were typified by a strong dominance of Oligotypes 1, 4, and 6, all of which were above 15% mean relative abundance in both "Sponge" and "MarineFish" samples.
We confirmed this result with significant ANOSIM groupings of MarineFish and Sponge datasets pooled together, at the exclusion of Plastic samples (data not shown). We found groupings were again characterized by high abundance of Oligotypes 1, 4, and 6 on biotic substrates, and Oligotype 5, 3, and 12 on Plastic. This suggests that vibrios behave differently with respect to adaptation to and colonization of biotic vs. abiotic substrates. Furthermore, the finding that Vibrio oligotypes associated with plastics overlap with those in the surrounding seawater suggests that recruitment may take place far from the origins of the plastic marine debris itself, typically thought to be land-based. Explaining the similarity between Vibrio communities in fish and sponges is challenging, but overlap in habitat geography during sampling, or overlap of laboratory sample preparation in space or time can be ruled out.
Sponges were collected by SCUBA and Remote Operated Vehicle (ROV) deep-sea dives from the Northeast Atlantic and Mediterranean Sea  (Reveillaud et al. , 2014)  from 1981 to 2011, while all fish were collected from experimental aquaria filled with sterilized water and Instant Ocean® salt mix in a Woods Hole, MA laboratory in 2013  (Schmidt et al. , submitted; Supplementary Data Sheet 1). Furthermore, although sequencing was conducted on the same Illumina HiSeq instrument, run dates were several months apart, and sample storage occurred in different freezers, making contamination between projects unlikely. In addition to community level patterns outlined above, our MEGABLAST analysis of the representative sequences from oligotypes most representative of our sponge and fish samples revealed that each was previously isolated from a variety of marine hosts including crabs, jellyfish, sea squirts, corals, fish, clams, and sea cucumbers (Table  3 , Table  S2 ).
ADVANTAGES AND LIMITATIONS OF OLIGOTYPING ANALYSIS FOR VIBRIO By separating regions of a marker gene that contain biologically meaningful variation from stochastic error, oligotyping allows single nucleotide differences across short marker genes to identify potentially ecologically meaningful patterns with extremely small amounts of information  (Eren et al. , 2013a). This technique provides substantial benefits, avoiding the need for lengthy and expensive culturing and sequencing protocols, and allows researchers to tap into massive existing databases to ask novel ecological questions at high-throughput levels across global scales. We note, however, that some serious limitations do exist for these type of data. 16S rRNA gene sequences can be nearly identical across multiple Vibrio species  (Gomez-Gil et al.
CONCLUSIONS Our analysis combines a novel bioinformatics technique with large quantities of Vibrio 16S rRNA gene sequence data to reveal patterns of Vibrio ecology across a wide range of environmental, host, and abiotic substrate-associated habitats. Despite the drawbacks for phylogenetic and taxonomic inference of using a single, short rRNA gene sequence, our analyses show strong convergence between host-associated communities, despite wide geographic and phylogenetic distance between them. We also show a surprising overlap, and a lack of significant divisions, between Vibrio communities in hosts and those found in their surrounding aquatic environments. Our results further support that Vibrio, as a genus, is largely populated by generalist r-strategist species, capable of long distance dispersal, a wide breadth of growth requirements, and rapid growth rates  (Szabo et al.
ACKNOWLEDGMENTS We would like to thank the  VAMPS bioinformatics team , principally  Andy Voorhis  and  Anna Shipunova. We also thank  Joseph Migliore  with the  Rhode Island Department of Environmental Management  for assistance collecting samples from  Mt. Hope Bay. Open  ocean plastic samples were collected by students and staff at the  SEA Education Association/SEA Semester in Woods Hole. This work was supported by an  NSF   Collaborative grant  to  Erik Zettler  ( OCE-1155379 ),  Tracy J. Mincer  ( OCE-1155671 ) and  Linda A. Amaral-Zettler  ( OCE-1155571 ),  NSF   TUES grant  to  Erik Zettler  and  Linda A. Amaral-Zettler  ( DUE-1043468 ). Additional support came from the  Woods Hole Center for Oceans and Human Health  from the  National Institutes of Health and National Science Foundation  ( NIH/NIEHS 1 P50 ES012742-01  and  NSF/OCE 0430724 -J: Linda A.
055 (SE 0. 023) Northeastern Atlantic Marine Reveillaud et al. , 2014 with sponge JCR_SPO_Bv6 Sponge-associated 49 0. 09 (SE 0. 014) Northeastern Atlantic Marine Reveillaud et al. , 2014 VTS_MIC_Bv6 Aquarium water - 31 0. 016 (SE 0. 0037) MBL, Woods Hole, USA Mixed SRP047374 (but see associated with fish Supplementary Data Sheet 1) VTS_MIC_Bv6 Fish-associated 20 0. 3 (SE 0. 039) MBL, Woods Hole, USA Mixed SRP047374 (but see Supplementary Data Sheet 1) The mean Vibrio relative abundance across all samples in a given project is shown with standard error. For sequences first published by this study the accession numbers for that project's NCBI Sequence Read Archive (SRA) BioProject is given. Frontiers in Microbiology| Systems Microbiology November 2014 | Volume 5 | Article 563 | 2.
, 2011) , where the target neuron is segmented in compartments and the stimulation potential fieldseparately calculated with the FEM-is applied at each compartment location. For this purpose, the NEURON software  (Hines and Carnevale, 1997)  provides a very suitable environment to perform these cable-equation-based calculations. In the following, this will be referred to as the hybrid FEM-cable-equation approach (see Figure  1A ). The above procedure, where the neuron response computation is performed after that of the extracellular potential field, suits for most configurations, but relies on two approximations: (1) The membrane and extracellular potential are uniform over each compartment (in fact, the neurons thickness is not modeled in this framework); and (2) The extracellular potential field is not affected by the presence of the neuron (in fact, the potential field is calculated in a separate model that does not include any microscopic description of the tissue).
However, it has been argued that these two assumptions are not valid when target neurons are close to the stimulation electrode  (Schnabel and Struijk, 2001). Thus, in such cases, other approaches are required to calculate the neural response to the stimulation. In a recent study  (Moulin et al. , 2008) , we have developed a 3D FEM based on a thin-film approximation, which allows the calculation of the neuron response in the same framework as that used for the computation of the extracellular potential field. Here, we extend this approach to the case of neural stimulation and show that it can reveal important membrane polarization at a sub-compartment level. In the following, this will be referred to as the whole FEM approach (see Figure  1B ). This article is organized as follows: In the first part of the Methods section, we show how to build a FEM to compute the potential field using the software Comsol Multiphysics (COMSOL AB, Stockholm, Sweden) and put special emphasis on the description of the electrical properties of the stimulation and ground electrodes.
In the second part, we explain how to implement both the hybrid and whole FEM approaches, using NEURON and the Comsol environment, respectively. In the Results section, we illustrate the usefulness of both approaches for the computation of neural response to extracellular stimuli. In particular, we show that the hybrid FEM-cable-equation approximation can be used in the case of thin fibers and/or dendritic structures, while the whole FEM model allows detailing the precise response of the different parts of the neural membrane in thick regions of the cell such as the cell body. The code corresponding to this example is made available as a supplementary download for both the hybrid and the whole-FEM approaches.
HYBRID APPROACH STEP 1: COMPUTATION OF THE EXTRACELLULAR POTENTIAL FIELD USING A FINITE ELEMENT MODEL (FEM) The following procedure describes the different steps to build a realistic finite element model for the calculation of the potential field generated by a current-controlled stimulation. The only requirement is the software Comsol Multiphysics  (version 3. 5). This procedure computes the potential field generated in the absence of the neuron. This field is thus supposed to be identical if the neuron were present. The different steps are the followings: 1. 1 Run the Comsol Multiphysics software. From the "Model Navigator" window, select a 3D model and, in the proposed list, choose Electromagnetics > Conductive Media DC. 1. 2 Build the domain geometry, by combining the predefined elementary shapes (e.
HYBRID APPROACH STEP 2: CALCULATION OF THE NEURON RESPONSE USING NEURON The following procedure describes how to simulate the response of a neuron to an extracellular potential field previously calculated and interpolated at the center of each compartment. The model is created in the free software NEURON (version 7. 2 or later) and requires only the knowledge of the distribution of the extracellular potential along the neuron, saved previously in a text file for instance. For this procedure, we do not use the NEURON's graphical interface, but only a. hoc script gathering all the following instructions. 2. 1 Create the neuron morphology in 3D. For that purpose, create each compartment using the pt3dadd function so as to keep the 3 dimensional organization of the neuron. Use small-enough compartments to get accurate computation.
3. 3. From the multiphysics section, select the 1st DC model. Set the geometry #1 active and the geometry #2 inactive in this domain. Then, select the 2nd DC model. Set the geometry #2 active and the geometry #1 inactive in this domain. In each case, define the value of σ. For the neuron geometry, σ is equal to the inverse of Ra expressed in Ohm. m. 3. 4. For each geometry, define the initial value (at t = 0) of the variable. In domain #1, set V-0 (the default value). In domain #2, set Vint to the neuron resting potential (this corresponds to step 2. 7 in the Neuron simulation). 3. 5. Define the element type depending on the expected precision and smoothness of the solution to the problem, and taking into account computation time and computer memory issues. In the current simulation paradigm, the extracellular potential is not interpolated so as to be used in another software (such as NEURON), but the extracellular and intracellular potentials (V and Vint) are computed simultaneously.
THE MIRROR RESPONSE OF A FIBER WITH THE HYBRID APPROACH Figure  3  shows a model of a compartmentalized fiber stimulated by an extracellular potential field. The computations were done in the Neuron environment. We considered a 2-μm-diameter-large and 260-μm-long passive uniform fiber (Figure  3A ), which was segmented in 1-μm-long compartments to ensure accurate computation of its response to the field  (Joucla and Yvert, 2009b). The electrical parameters were set to the following values: Ra = 100 Ohm. cm, c m = 1 μF/cm 2 , g l = 10 -4 mS/cm 2 , E l = -65 mV, while the ionic currents (I i ) were set to 0. The electrical potential field created in the neural tissue by a -1 μA cathodic ground surface stimulation (Figure  3B , modeled in the Comsol Multiphysics software, with g GS = 500 S/m 2 ) was interpolated at the locations of the fiber compartments, passing above the stimulation electrode at z = 50 μm (Figure  3C -top).
Once a correct model of the extracellular potential field has been determined, the response of a modeled neuron has to be calculated. For that purpose, a cable equation, relating the membrane potential to the extracellular potential, is classically solved. This cable equation (see  Joucla and Yvert, 2009b  for instance) is discretized in space using finite differences, leading to a system of N linearly dependent first-order differential equations (N corresponding to the number of compartments in the neuron geometry). This system of equations can be solved in a homemade simulation program or in a dedicated environment, such as the GENESIS  (Bower and Beeman, 1998)  or NEURON software packages  (Hines and Carnevale, 1997). We used the latter, a tool of choice for subcellular to network simulations (according to the NEURON website, more than 1176 publications have been using NEURON as of January 29, 2012).
NEURON models can be built either graphically or using the associated script language. We used scripts, which allowed an easy implementation of the extracellular stimulation protocol, using the extracellular process. We note that, although the source term of the cable equation is the spatial second derivative of the extracellular potential along the neuron geometry, the extracellular process works by defining only the electrical potential at the location of each compartment, the approximated spatial second derivative being calculated internally by NEURON based on the 3D neuron geometry. This allows a very easy implementation of extracellular stimulation paradigms, since the only mandatory step is to interpolate the extracellular potential at the corresponding coordinates in the FEM model. Regarding practical implementation of an extracellular process, it should be noticed that the cable equation should be time-discretized using the first-order backward Euler scheme and not the second-order Crank-Nicholson scheme, since the latter is prone to oscillations and large errors  (Carnevale and Hines, 2006).