dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2018; Oleson et al. , 2007a; CB & LDI, unpublished data). Lewis et al. (2018)  reported the production of D-Calls from tagged female and male blue whales, with D-Calls being produced mainly during surface behaviors. The communicative function of D-Calls and the interpretation of their presence in remote sensing data, however, remain widely unresolved, due to the different behavioral contexts in which D-Calls have been recorded. Here, we report three encounters with blue whale trios from two different populations in a putative reproductive context on their respective feeding grounds. During these three events, data from concurrent passive acoustic and visual observations were collected. Joint visual-acoustic data are essential for understanding the communicative function and behavioral context of vocalizations (e.
, 2016 for a record from the Indian Ocean). This behavior can either end with the intruding male leaving the pair or the primary escort being replaced by the intruding male. The latter displacement of the primary escort has been very rarely observed and most often the initial female-male pairing is maintained (RS, unpublished data). In all three observed cases reported here, the intruding individual (male, in the two cases where sex was known) did not displace the primary escort. Although actual mating was not observed during the three encounters described here, the observations are in line with early whaling data  (Lockyer, 1984; Mackintosh & Wheeler, 1929) , which state that conceptions are not seasonally or regionally restricted. One type of vocalization, the D-Call, was consistently recorded during all three escort events reported here.
ACKNOWLEDGMENTS ES thanks  Prof. Dr. Per J. Palsbøll  for the supervision of the initial Master research project, the  Marco Polo fund , and the  University Groningen  for covering travel expenses. We thank the  Melimoyu Ecosystem Research Institute, SNP Patagonia Sur , and the company  Teledyne Reson  for partially funding the acoustic data collection in southern Chile. RHG is thankful to  WWF-Germany/Chile  for partially funding fieldwork through grants to  Centro Ballena Azul. CLB thanks the team of the  Mingan Island Cetacean Study  for their logistical support of boats and lodging, access to the North Atlantic blue whale database, and field assistance;  Yvon Bélanger  for opening his home to her and RS's field crews; for financial support from the  National Science Foundation  ( Graduate Fellowship ),  National Defense Industrial Association, American Museum of Natural History  ( Lerner Gray Fund for Marine Research Grant ),  Penn State Applied Research Laboratory , and private donors Jeff and Lynn Kraus; and graduate advisors at  Penn State University David L.
Amplicon Library and Sequencing Prior to Ion Torrent PGM sequencing, the amplicons were assessed for fragment concentration using an Ion Library Quantitation Kit (Life Technologies TM , United States), then concentrations were adjusted to 26 pM. Amplicons were attached to Ion Sphere Particles using Ion PGM Template OT2 400 kit (Life Technologies TM , United States). Sequencing was performed with Ion Xpress Barcode Adapters (1-16 Kit; Life Technologies TM ) and a 318 TM chip (Life Technologies TM ) on an Ion Torrent Personal Genome Machine (Life Technologies TM ). Sequences were binned by sample and quality filtered within the PGM software (Torrent Suite TM software life Technology) to remove polyclonal and low-quality reads. Fastq files for each sample were exported for the subsequent bioinformatics analysis.
High-Throughput Sequence Analysis The quality and number of reads for each sample were assessed using FASTQC v0. 11. 4 1. Raw sequences were filtered by quality using scripts from FASTXToolkit 2. Only reads where at least 80% of the sequence had a minimum acceptable Phred quality score of > 20 were retained. Reads which passed all quality control steps were concatenated into a single FASTA file for subsequent processing. Filtered quality sequences were analyzed using the Quantitative Insights Into Microbial Ecology (QIIME) software version 1. 8. 0  (Caporaso et al. , 2010b). Sequences were clustered in OTUs using a 97% sequence similarity threshold using open-reference OTU picking approach with USEARCH pipeline version 6. 1  (Edgar, 2010). This pipeline involves clustering, chimera checking, and quality filtering.
The taxonomy was assigned using RDP classifier  (Wang et al. , 2007)  and Greengenes database gg_13_8_otus  (DeSantis et al. , 2006). The OTUs representative sequences were aligned using Pynast  (Caporaso et al. , 2010a)  with a minimum sequence length threshold of 150 bp. A phylogenetic tree was constructed with FastTree  (Price et al. , 2010)  Finally, in order to reduce artifactual sequence, the resulting OTU table was filtered at 0. 005% to remove singletons (OTUs represented by only a single sequence) and reduce spurious OTUs  (Navas-Molina et al. , 2013; Flynn et al. , 2015). In addition, reads belonging to Streptophyta were removed from the dataset and not included in the analysis as members assigned to this taxon were considered to be contamination of chloroplast from diet and water, and not a part of the gut microbiota  (Rud et al.
Statistical Analysis Statistical comparisons in all the analyses were conducted between the control and probiotic groups at the same sampling point, except in the LEfSe analysis where the effect of the SWT was evaluated comparing control groups between freshwater and seawater. To investigate experimental group differences between bacterial communities (beta diversity), the software package PRIMER-E v. 6 PERMANOVA+ was used (PRIMER-E, Plymouth, United Kingdom)  (Clarke and Gorley, 2006). Beta diversity was calculated in QIIME based on weighted and unweighted UniFrac metrics. Dissimilarity matrixes from UniFrac were imported to PRIMER-E to evaluate significant differences between groups by permutational multivariate analysis of variance (PERMANOVA). To assess alpha and beta diversity indexes of mucosa samples, a linear mixed model was applied using water habitat and diet as fixed factors, and tank as random factor.
To detect the impact of SWT on the potential differences of OTUs with LEfSe microbiota data from the control group was used. Survival curves from the in vivo viral challenge were calculated by the Kaplan-Meier method and compared by log rank (Mantel-Cox) pairwise comparison testing for equality of survival pattern between groups using pooled replicates. Statistical analyses were performed using SPSS version 11. 0 (SPSS, Inc. , Chicago, IL, United States) with significance determined at P < 0. 05. The results of all other analysis (i. e. , alpha diversity, gene expression, and histological evaluation) were analyzed using JMP Pro 14. 3 (SAS Institute, Inc. , Cary, NC, United States). The data was checked for normality (Shapiro-Wilk test) and homogeneity of variance  (Tap et al. , 2009). Data fulfilling parametric test assumptions was analyzed either by t-test or two-ways ANOVA (followed by Tukey HSD).
DATA REDUCTION We analysed new and archival ALMA observations of CO line emission in central cluster galaxies to investigate emerging trends in the molecular gas structure and kinematics. Targets were principally selected from single object studies in the literature (see Table  1 ) with the addition of new ALMA observations of the central galaxy in Abell 2052 and archival observations of NGC 708 at the centre of A262. This produced a total sample of 12 central cluster galaxies. The sample spans a wide range in molecular gas mass (10 7 -10 11 M ), X-ray cavity power (10 42 -10 46 erg s -1 ), star formation rate (a few to ∼ 500 M yr -1 ) and redshift (up to 0. 596). However, predominantly bright, gas-rich systems were preferentially, and necessarily, selected as early ALMA targets. They are therefore over-represented in our sample.
This sample is neither complete nor unbiased and we are careful to consider this throughout our analysis. For each target, we selected CO line observations from the ALMA archive as detailed in Table  1. The central galaxies were observed with the ALMA 12 m array at frequencies corresponding to the CO(1-0), CO(2-1) or CO(3-2) rotational transition lines with additional spectral windows used to image the sub-mm continuum emission. Each dataset consists of a single pointing centred on the galaxy nucleus and covers the ionized gas peak and the brightest filaments. The ALMA field of view ranges between ∼ 1 arcmin in band 3 at ∼ 100 GHz and ∼ 20 arcsec in band 7 at ∼ 300 GHz. The spatial resolution and sensitivity vary significantly from the earliest Cycle 0 observations (eg. A1664 and A1835) with ∼ 15 antennas and baselines up to 400 m to the latest datasets (eg.
RXCJ1504. 1-0248) with > 40 antennas and baselines up to a few km. This variation is reflected in the synthesized beam size and rms of the final data cube as detailed in Table  2. Each ALMA dataset was calibrated using the required version of CASA  (McMullin et al. 2007) , which ranged from version 3. 3. 0 for the Early Science data to version 4. 7. 0 for the latest datasets. Datasets taken in the early ALMA cycles were manually calibrated with tailored data reduction scripts generated by ALMA staff while later datasets were reduced by the automated ALMA science pipeline. Several datasets did require additional flagging and other modifications, for example to the phase centre (eg. A1835) and total flux calibration (eg. Phoenix) and to limit the impact of poor phase solutions (eg. 2A0335+096,  Vantyghem et al.
2016). Continuum-subtracted data cubes were generated using the CASA tasks UVCONTSUB and CLEAN. Different weightings were tested for each dataset to determine the optimum for imaging. Briggs weighting with a robust parameter of 2 (close to natural weighting) was favoured for targets with extended filaments to provide the highest signal-to-noise in these structures. Otherwise, Briggs weighting with a robust parameter of 0 was used to produce a good compromise between spatial resolution and sensitivity. We note that ALMA's good uv coverage ensured that images generated with a range of weightings did not show any major differences. The rms in each final datacube was compared with and found to be close to the corresponding theoretical rms, which is dependent on the array configuration, integration time, frequency and atmospheric conditions.
For the four remaining targets, PKS 0745, 2A 0335, RXJ 0821 and RXCJ 1504, the CO(3-2) cube was convolved with an appropriate 2D Gaussian so that the resulting synthesized beam matched that of the CO(1-0) observation. The integrated intensities (in K km s -1 ) for key molecular structures were then determined by extracting spectra from the same spatial regions in the CO(1-0) and CO(3-2) datasets and fitting a single Gaussian model. The measured line ratios are detailed in Table  3. The CO(3-2)/CO(1-0) line ratio is consistent with 0. 8 for the vast majority of the regions and targets analysed. This is expected for a predominantly optically thick medium and in agreement with measurements of the global line ratios in earlier single dish observations  (Edge 2001; Salomé & Combes 2003). There is also no clear spatial variation in the line ratio for the extended filaments compared to the central molecular gas peaks.
ACKNOWLEDGEMENTS We thank the reviewer for helpful comments that improved the paper. HRR acknowledges support from an  STFC   Ernest Rutherford Fellowship  and an  Anne McLaren Fellowship. BRM acknowledges support from the  Natural Sciences and Engineering Council of Canada  and the  Canadian Space Agency Space Science Enhancement Program. ACF acknowledges support from  ERC  Advanced Grant Feedback 340442. ACE acknowledges support from  STFC  grant  ST/P000541/1. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  NSC  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ. The scientific results reported in this article are also based on data obtained from the  Chandra Data Archive.
puteoserpentis Cosel, Métivier & Hashimoto, 1994  [23, 34, 35]. However, the MAR Branchipolynoe seepensis and the Gulf of Mexico B. seepensis are likely separate species, based on three reasons. The prime reason is that the stated genetic distance for cytochrome c oxidase subunit I (COI) exceeds 5% between specimens from the two localities  [23] , though unfortunately the COI sequences are not available on GenBank. Second, the Gulf of Mexico and MAR localities are thousands of kilometers apart. Finally, one group (the true B. seepensis) is found at methane seeps, while the other (MAR group) is from hydrothermal vents  [23]. In this paper, we present molecular sequence data from samples of Branchipolynoe from a series of Pacific Costa Rican methane seeps that have been explored in recent years  [36, 37] , as well as from West Pacific hydrothermal vents (North Fiji and Lau Basins and Vanuatu).
Phylogenetic Analysis and Species Delimitation A maximum likelihood (ML) phylogeny was generated for unique COI sequences from specimens acquired for this study, along with unique available Branchipolynoe COI sequences on GenBank. Additional B. symmytilida COI sequences were sourced from Hurtado et al. [24]  and Plouviez et al. [45]. Sequences referred to as B. pettiboneae (GenBank Accession ITS = KY753840, Mitochondrial = KY753825, MG799393, MK694794-799) were sourced from Zhang et al. [3]  and Van Audenhaege et al. [46]. The sequences MK694794-799 are arguably not of B. pettiboneae and instead belong to one of the new species described here, B. tjiasmantoi n. sp. Sequences of B. longqiensis (AF39996, KY211993, KY753847, KY753826) were acquired from several sources  [3, 29, 30]. Maximum parsimony (MP) and ML phylogenies were also generated for a concatenated COI, 16S, and ITS datasets for a cutdown selection of terminals representing each Branchipolynoe species.
Aligned sequences for COI, 16S, and ITS were concatenated using SequenceMatrix v 1. 8  [47]. Branchinotogluma japonicus was used as the outgroup for all the analyses based on previous phylogenetic results  [3] , and COI, 16S, and ITS1+5. 5SrDNA+ITS2 were used for this species (GenBank Accession KY753824, KY753841). ML analyses were performed using RAxML v. 8. 2. 10  [48, 49]  on the COI data and the concatenated data partitioned by gene, using the model GTR + G. Support was assessed via a minimum of 1000 thorough bootstrap replicates. A Bayesian inference (BI) analysis of the concatenated data partitioned by gene was also conducted using MrBayes v. 3. 2. 6  [50]. Two independent Markov chain Monte Carlo (MCMC) searches were run for 20,000,000 generations. A burn-in of 0. 1 was applied to each search, and the combined results were checked for stationarity.
The majority-rule consensus tree was annotated with posterior probability node values. The MP analysis of concatenated data was conducted using PAUP* v. 4. 0a161  [51] , using heuristic searches with the tree-bisection-reconnection branch-swapping algorithm and 1000 random addition replicates. Support values were determined using 100 jackknife replicates each with 100 random addition searches, heuristic search with tree-bisection-reconnection, and character deletion set at 37%. To find the most suitable models for the BI analysis and for assessing genetic distances among and within Branchipolynoe species, jModelTest v. 2. 1. 4 [52, 53]  was implemented on the separate COI, 16S, and ITS datasets using the Akaike information criterion (AIC). The models selected were GTR + I + G for COI and 16S and HKY+G for ITS.
The uncorrected and model-corrected COI and ITS pairwise distances were generated using PAUP* v. 4. 0a161. In addition to using the phylogenetic results and comparisons of genetic distance, we also used automated barcode gap definition (ABGD)  [54]  to assess species delineation for B. halliseyae n. sp. and B. kajsae n. sp. Settings used were Pmin 0. 001 Pmax 0. 1 with 10 steps, relative gap width 1. 5, and 20 bins for distance distribution and the Kimura 2 (K80) model with a transition/transversion ratio (from jModeltest) of 2. 4 for the COI dataset.
halliseyae n. sp. and B. kajsae n. sp. ; they again formed reciprocally monophyletic sister groups (Figure  2a ). Supporting the phylogenetic analyses of the COI data and concatenated COI, 16S, and ITS data, the COI distances between Branchipolynoe halliseyae n. sp. and B. kajsae n. sp. showed a clear 'barcode' gap of several percent (Figure  2b ). Furthermore, a median-joining haplotype network for all COI sequences for these two species showed a minimum of 18 base changes between them (Figure  2c ). The COI distances between B. seepensis and B. kajsae n. sp. and B. halliseyae n. sp. were 7. 2% and 8%, respectively (Table  2 ). For the new West Pacific species B. tjiasmantoi n. sp. , there was a COI distance of at least 13. 6% to each of the four new Costa Rican species, as well as to B. seepensis and B.
symmytilida. It was approximately 9% to 10% divergent from the Japanese B. pettiboneae and also the Indian Ocean B. longqiensis (Table  2 ). Five COI sequences available on GenBank, MK694794-799, and identified in  [46]  as B. pettiboneae from vents in the Manus Basin (Papua New Guinea), all fell within the clade identified here as Branchipolynoe tjiasmantoi n. sp. They were all at least 9% divergent from other GenBank B. pettiboneae sequences. One of the sequences, MK694796, was the same haplotype as specimens from sites further east (Figure  5e ), while the others were more divergent (up to 3. 2%) from other Branchipolynoe tjiasmantoi n. sp. haplotypes (Table  2 ) but were regarded here as the same species. The Costa Rican samples fell into two areas of the COI tree (Figure  1 ): Branchipolynoe halliseyae n.
sp. and B. kajsae n. sp. formed a clade (weakly supported) that was a well-supported sister group to the Gulf of Mexico B. seepensis, while B. eliseae n. sp. and B. meridae n. sp. formed a clade (weakly supported) that was sister group to the EPR B. symmytilida. The West Pacific and Indian Ocean species, including the new species B. tjiasmantoi n. sp. , formed a well-supported clade that was a sister group to the B. halliseyae n. sp. + B. kajsae n. sp. + B. seepensis clade (Figure  1 ). The ML phylogeny (likelihood score of -8344. 808), based on the combined COI, 16S, and ITS data for an individual from each species, showed (Figure  3 ) largely the same topology as the COI data alone (Figure  1 ). The only difference was that B. eliseae n. sp. was a sister group to B. symmytilida, though this was poorly supported, and the MP analysis of this concatenated dataset (not shown) showed the same topology as Figure  1 , with B.
eliseae n. sp. and B. meridae n. sp. as a clade. There was a single most parsimonious tree, with a length of 1103 steps. The ML, BI, and MP support values were high (Figure  3 ), with the exception of the ML and BI support of 64% and 0. 6 posterior probability, respectively, for the clade of B. eliseae n. sp. and B. symmytilida. Also, the sister group relationship between B. pettiboneae and B. longqiensis (77/. 99/65) was not highly supported. supported, and the MP analysis of this concatenated dataset (not shown) showed the same topology as Figure  1 , with B. eliseae n. sp. and B. meridae n. sp. as a clade. There was a single most parsimonious tree, with a length of 1103 steps. The ML, BI, and MP support values were high (Figure  3 ), with the exception of the ML and BI support of 64% and 0.
Acknowledgments: The authors are grateful to  Chief Scientists Erik Cordes ,  Lisa Levin  and  Bob Vrijenhoek , the captains and crews of the  R/V Atlantis  and  R/V Melville  and the crew and pilots of the  HOV Alvin and ROV Jason II  for crucial assistance in specimen collection on cruises to  Costa Rica  seeps and West Pacific vents. On the  Costa Rica cruises ,  Danwei Huang  and  Ben Moran  were very helpful is gathering the Branchipolynoe specimens. Thanks also to  Robert Vrijenhoek  ( MBARI ) for inviting GWR on the cruise to various West Pacific hydrothermal vent localities. We are very grateful to  Verena Tunnicliffe  for providing specimens of Branchipolynoe tjiasmantoi n. sp. from  Tonga and Vanuatu. We also thank  Phil Zerofski  and  Sea Camp  and  Wewin Tjiasmanto  for support for  SIO  and the  SIO Collections.
Resistive index (RI) measurements, study protocol and data collections RI measurements Bedside Doppler ultrasonography was performed with a 7. 5 MHz transducer (Compact Xtreme CX50, Philips Medical Systems, Bothell, WA, USA), using a previously described RI measurements technique  [7, 24]. Briefly, sonography and color Doppler mode were used to localize one of the kidneys and interlobar arteries. Pulse-wave Doppler was used to measure blood velocities in the interlobar arteries. Peak systolic and end-diastolic velocities were measured on 5 consecutive pulses. RI was calculated as follows: RI = (peak systolic velocity-end-diastolic velocity)/peak systolic velocity. The RI values presented here correspond to the average of the 5 measures performed on the 5 consecutive pulses. All RI measurements were performed by intensivists experienced in kidney Doppler ultrasonography and certified in echocardiography.
Parameters collected or calculated at each RI measurement On each RI measurement, the following data were recorded: HR, systolic, diastolic and mean arterial pressures, norepinephrine dose, AKIN score based on SCr and urine output  [25]  and the day of ICU hospitalization on the day of RI measurement (with D1 corresponding to the admission day to ICU). In patients with severe AKI defined as AKI AKIN 2 or 3 on D3, "recovery of severe AKI" or "no recovery" was noted for each RI measured thereafter. This item was evaluated post hoc according to urine output and SCr by an adjudication committee (N. L and P. A) blinded to RI values.
Other data The following data were recorded: age, past medical history of diabetes mellitus and chronic hypertension, treatment at admission, baseline SCr (lowest value in the 3 months preceding admission, if no SCr was available, the baseline value was estimated using the MDRD formula, assuming a creatinine clearance of 75 ml/ min/1. 73 m 2 ), infection site, nosocomial or communityacquired infection, responsible microorganism(s), ICU admission weight, arterial lactate concentration, Sequential Organ Failure Assessment (SOFA) score and Simplified Acute Physiologic Score II (SAPS II) at D1  [26, 27].
Statistical analysis Data are presented as median [interquartile range] or number (percentage). The study population was divided into 2 groups according to the AKIN classification at D3. For the patients who died before D3, the AKIN score on the date of expiry was used. The AKIN2-3 group was composed of patients with severe AKI (AKIN 2 or 3) at D3, and the patients without AKIN criteria or with AKIN 1 at D3 made up the AKIN0-1 group. The 2 groups of patients were compared using Mann-Whitney U test or Fisher's exact test as appropriate for variables at inclusion. RI values at D1 were compared according to AKIN classification at D3 using Kruskal-Wallis test. We studied the impact on RI of MAP, PP, severe AKI, recovery of severe AKI (for RI measured after D3), time since ICU admission on the day of RI measurement, past medical history of diabetes and/or hypertension.