dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, note that the bat activity we used is not an abundance, but a metric more sensitive to habitat quality: for example, one bat foraging all night within a sampled site will produce a huge value of bat activity instead of some bats crossing the sampled site. For the first step, echolocation calls were detected and classified to the most accurate taxonomic level using the TADARIDA software  (Bas, Bas, & Julien, 2017)  which allows a confidence index to be assigned to each classification of call. For the second step, all echolocation calls were checked using BatSound© software except the most represented species, Pipistrellus pipistrellus, for which we only checked 20% from the 0. 5 confidence index due to a high quantity of calls (see Table  S4. 2 for more details on the identification procedure).
To avoid overparameterization due to a limited dataset (species, Nyctalus spp. genus and richness: n = 64; Pipistrellus spp. genus including the three Pipistrellus species: n = 192), we chose to build models including six degrees of freedom (df). We performed a hierarchical partitioning (R package hier. part) to identify the first three covariates (3 df) having the best conjoint contributions, in order to implement them with the farming system variable (3 df) in full models (Table  S4. 3). According to the sampling design (i. e. , simultaneous recordings of bat activity among four farming systems on the same night), we included the date in the models as a random effect with the aim being to check for internight variations. For the Pipistrellus spp. model, we added a second random effect on the three species composing the genus, in order to take into account activity variations among species.
July and S4. 1), as well as correlations between distances to boundaries and roads and between distances to wetlands and hedgerows (Table  S4. 5). To take into account these correlated covariates, we did not simultaneously include them in the modeling procedure. In a second step, we checked there were no multicollinearity problems performing variance inflation factors (VIF) using the corvif function (R package AED;  Zuur, Ieno, & Elphick, 2010)  on each full model. All variables showed a VIF value <2, meaning there was no striking evidence of multicollinearity  (Chatterjee & Hadi, 2006). We generated, based on full models (Table  S4. 3), a set of candidate models containing all possible variable combinations ranked by corrected Akaike Information Criterion (AICc) using the dredge function, but not simultaneously including correlated covariates.
For each set of candidate models, we did multimodel inference averaging on a delta AICc <2 using the model. avg function to obtain an averaged regression coefficient for each fixed effect (R package MuMIn;  Barton, 2015; Table S4. 6 ). We used the allEffects function (R package effects) to get a predicted activity of bat species from the best models in Figure  2. We did not detect spatial autocorrelation on residuals of each best model using dnearneigh and sp. correlogram functions associated to Moran's I tests (R package spatial; Moran, 1950; Table  S4. 7), as well as any obvious problem in the overdispersion ratio (0. 8-1. 4; Table  2 ) on the best models. Relative variance explained by each fixed effect (pseudo R²) was calculated from generalized linear models, because it is not covered by recent computing methods for GLMMs using a negative binomial distribution.
| Mechanism hypotheses, limitations, and perspectives We hypothesized that resource limitation drives the foraging selection of generalist bat species within studied fields and could explain T A B L E 2 Description of the dataset for each response variable from the 64 sites, the number of bat passes, occurrences (% of sites for which species were recorded), the best models from the multimodel inference procedure, and the response variable distribution selected (NB: negative binomial; θ: overdispersion ratio). Full models are shown in Table  S4. 3. the results depending on the diet, composed of arthropods for European species  (Vaughan, 1997). Because the fields are all wheat, other influences such as structural heterogeneity did not seem to be important. For a given farming system, diversity of taxa (Arachnida, Coleoptera, and Diptera) are less abundant in tillage than in conservation tillage  (Holland & Reynolds, 2003; Rodríguez et al.
ACKNOWLEDGMENTS This work was supported by DIM ASTREA grants from  Region Ile-de-France. We sincerely thank Agrosolutions for their technical support on farming practices and the  University of Paris-Sud  for funding field study fees. This cooperation with Agrosolutions, which is the agrienvironmental expert consulting subsidiary of the InVivo agricultural cooperative group, did not influence sampling design, analyses, and conclusions. We also especially thank the farmers who agreed to participate in the study.
The board will also continue to increase international awareness at medical schools and research institutes. Additionally, a grant from the WMF will be used to fund a new WikiJournal of Medicine "Best article Prize" to be awarded to authors of articles of exceptional scientific content, readability and value to the general public, as voted by an external judging panel. Articles of any format submitted by September 2017 are eligible for a competition with prizes of US $200, $100, and $50. This continued outreach should bring the publication rate up for the journal to be eligible for MEDLINE/PubMed indexing in 2017. Inclusion in these databases will in turn increase the credibility of the journal, and attract further article submissions. The high exposure that WikiJournal of Medicine articles have through Wikipedia will likely lead to good citation rates.
Our analysis indicates that marine cyanophages are already well represented in genome databases and that one of the EVGs likely represents a new cyanophage lineage. Several EVGs encode many enzymes that appear to function for an efficient utilization of iron-sulfur clusters or to enhance host survival. This suggests that there is a selection pressure on these marine viruses to accumulate genes for specific viral propagation strategies. Finally, we revealed that EVGs contribute to a 4-fold increase in the recruitment of photic-zone viromes compared with the use of current reference viral genomes. IMPORTANCE Viruses are diverse and play significant ecological roles in marine ecosystems. However, our knowledge of genome-level diversity in viruses is biased toward those isolated from few culturable hosts.
been a powerful approach used to clarify the biology of viruses, including their infection strategies as well as their ecological significance (4-7). However, the diversity of viral genomes is still underrepresented in publically available genome databases  (8, 9). For example, SAR11 (Pelagibacterales) and SAR116 are major marine prokaryotic components, but only four and one phage genomes have been sequenced for these bacteria, respectively  (10, 11). Cyanophages, for which about 100 genomes have already been characterized, are the sole exception. To address the issue of the paucity of viral genomic data, Roux et al. analyzed publicly available prokaryotic genome sequence data to mine marine and nonmarine viral genomes that have been sequenced along with the genomes of their hosts  (12). They identified 12,498 viral DNA sequences (either long fragments or whole circular genomes) representing 264 predicted new genera.
The following five most abundant gOTUs represented Ͼ98% of the total cyanophage content: (i) G386, including T4-like myoviruses (35. 1%); (ii) G14, including podoviruses (33. 7%); (iii) G234, including a siphovirus and dwarf myoviruses (23. 4%); (iv)    2  and S1B  for the list of genomes). Thus, marine cyanophage genomes are well represented in the current databases. (v) Diverse marine Bacteroidetes phages. Bacteroidetes is one of the most abundant bacterial phyla in the oceans (e. g. , 30% of the bacterioplankton during phytoplankton blooms)  (64). Members of this phylum are involved in the decomposition and remineralization of phytoplankton biomass  (65). A recent study revealed that an algal bloom is followed by the presence of a rapid succession of diverse Flavobacteriaceae bacteria  (64).
Environmental viral genomes as a reference during marine virome analyses. We mapped protein sequences and raw reads from independently generated photic virome data (i. e. , the Pacific Ocean viromes [POV]) (86) on the RVGs and EVGs. The RVG set recruited 4. 70% of the POV proteins, while the EVG/RVG union set recruited 22. 6% of the proteins (i. e. , a 4. 8-fold increase; Fig. 8A ). At the nucleotide sequence level, the RVG set recruited 1. 02% of the POV reads, while the EVG/RVG union set recruited 4. 20% of the reads (i. e. , a 4. 1-fold increase; Fig. 8B ). Thus, the EVGs serve as an effective additional reference viral genome data set for exploring viromes from photic oceans.
g. , nucleotides) but also diverse metabolic and cellular processes. Phylogenetic trees. Multiple sequences were aligned using the MAFFT program (version 7. 245)  (104) , with the FFT-NS-2 mode and a maximum of 1,000 iterations (--retree 2, --maxiterate 1000). Conserved positions in the alignments were selected with the trimAl program (version 1. 3) (105). Maximum likelihood trees with 100 bootstrap replicates were calculated with RAxML (version 8. 2. 4) (106) using the fast bootstrapping mode, and models were selected by the use of ProteinModelSelection. pl (i. e. , LGF for DNA polymerase B and LG for chaperonins, IscU, and ATC). Recruitment of Pacific Ocean virome sequences. Reads (3. 68 M sequences) and proteins (2. 78 M sequences) of 16 photic POV samples were downloaded from iMicrobe (http://data.
imicrobe. us). These sequences were mapped on EVGs and RVGs using BLASTn (for reads; E value, Ͻ1eϪ3) and tBLASTn (for proteins; E value, Ͻ1eϪ3) if the alignment revealed Ն60% identity and covered Ն80% of the query sequence. Accession number(s). Read and assembled sequences obtained from OBV were deposited at DNA Data Bank of Japan (DDBJ) under accession numbers DRR053207 to DRR053215 and SAMD00045684 to SAMD00045692. The sequence data for the OBV project are accessible under DDBJ BioProject accession number PRJDB4437. Sequences and additional data are available at ftp://ftp. genome. jp/pub/db/community/EVG2017.
ACKNOWLEDGMENTS We thank the  Tara Oceans consortium , people, and sponsors who supported the  Tara Oceans expedition  (http://www. embl. de/tara-oceans/)for making the data accessible. Computational work was completed at the  Supercomputer System, Institute for Chemical Research, Kyoto University. This work was supported by the  Canon Foundation  (no. 203143100025 ),  JSPS/ KAKENHI  (no. 26430184  and  16KT0020 ),  Scientific Research on Innovative Areas  from the  Ministry of Education, Culture, Science, Sports and Technology (MEXT) of Japan  (no. 16H06429 ,  16K21723 , and  16H06437 ), and the  Collaborative Research Program of the Institute for Chemical Research, Kyoto University  (no. 2016-28 ). P. H. was supported by the  OCEANOMICS "Investissements d'Avenir" program  of the  French Government  (no.
ANR-11-BTBR-0008 ). M. B. S. was supported by  Gordon and Betty Moore Foundation  grants (no. 3790  and  GBMF2631 ), and S. R. was partially supported by the  University of Arizona Technology and Research Initiative Fund  through a grant from the  Water, Environmental , and  Energy Solutions Initiative  and the  Ecosystem Genomics Institute  to  M. B. S. This  is contribution number 51 of the Tara Oceans Expedition  2009 -2012.
If W is the unit square, this gives C(x, y) ≈ ρ k∈Z d ϕ(k)e 2πik. (x-y) , for any x, y ∈ W , where ϕ denotes the Fourier transform of C 0 and k. (xy) is the inner product between k and (x-y). The case of a general rectangular set W can easily been deduced, see  [24]. This approximation turns out to be quite accurate when ρ is not too small, see  [24]  for some justifications and a simulation study. Several functions are available in the spatstat library  [3]  of R  [32]  to manipulate DPP models on a bounded subset of R 2. They allow to define parametric models of homogeneous and inhomogeneous DPPs, to simulate them (using the spectral approximation presented above and the Gram-Schmidt algorithm described in Section 2. 2), and to fit them to a real dataset.
More precisely, when J < 1, X is attractive, otherwise X is repulsive. The equality J ≡ 1 characterizes the PPP, where there is no interaction between the particles. For the case of the β-GPP, we get from  [13]  the following proposition. Proposition 5. 1. The J-function of the β-GPP with intensity γ π is given for any r ∈ R + by: J(r) = 1 1 -β + βe -γ β r 2. Note that for any β this J-function is bigger than one, which confirms that the β-GPP is a repulsive point process. When β tends to 0, this expression tends to 1, which corresponds to the J-function of a PPP. This J-function allows to validate the β-GPP as a distribution model of the repartition of the base stations for each operator and each technology. We use the spatstat package to obtain an estimate of the J-function from the raw data.
Afterward, samples were sequentially incubated in MF-20 mouse antibody (Developmental Studies Hybridoma Bank, University of Iowa, USA) and antimouse Cy3conjugated secondary antibody (Jackson Immunoresearch) with 4,6diamidino-2-phenylindole (DAPI, Sigma) for 1 h each. Samples were imaged at 5 randomly chosen positions maintaining the acquisition settings. Myodifferentiation was then scored by the percentage of positive sarcomeric myosin cells using the CellC image analysis software (n ≥ 15 images per condition). 29 Myotube thickness and fusion index (nuclei per myotube) were quantified manually using ImageJ (n ≥ 100 myotubes). 2. 7. Immunofluorescence. hMSCs were fixed in 4% Formaldehyde (Sigma) with 2% Sucrose (VWR) and permeabilized for 5 min at room temperature using 0. 5% Triton X-100 (Sigma) in 20 mM HEPES buffer (Sigma) supplemented with 0.
DISCUSSION This study describes a robust and versatile technology to investigate the relationship between cells and their environment in more relevant physiological conditions. Since 2D cultures are far from the in vivo environment, SW-like cultures were engineered to recapitulate 3D environments by providing both ventral and dorsal cell interactions. Several systems that stimulate dorsal interactions have been used before to provide. hMSCs differentiation after 14 days of SW culture in growth media. (A) Differentiation was assessed by qPCR. Data were first normalized to GAPDH gene expression and then to the 2D condition level that was assigned a value of 1 (red dashed line). Not detected markers were assigned a value of 0. # designates markers detected for SW but not for 2D (and thus unable to normalize), which were assigned an arbitrary value of 25 to stress the high expression observed.
In this work, we present a dataset, which provides information on the structural diversity of some endemic tropical species in Madagascar. The data were from CIRAD xylotheque (since 1937), and were also collected during various fieldworks (since 1964). The field notes and photographs were provided by French botanists; particularly by Francis Hallé. The dataset covers 250 plant species with anatomical, morphological, and architectural traits indexed from digitized wood slides and fieldwork documents. The digitized wood slides were constituted by the transverse, tangential, and radial sections with three optical magnifications. The main specific anatomical traits can be found within the digitized area. Information on morphological and architectural traits were indexed from digitized field drawings including notes and photographs.
Value of the data The dataset consolidates anatomical, morphological, and architectural traits of plant species from different sources (xylotheque, field notes, and photographs). The described traits of tropical plant species can be useful to understand the biogeographical variation within species and genera in plant anatomy regarding the ontogeny and structure of sampled plants. To understand the diversity of wood characteristics and technological behaviors that directly governed the choice of tropical timber use. To understand the relationship between tropical wood structure and certain physical, mechanical, chemical, and biological properties of the material.
Data Madagascar is an important insular hotspot for biodiversity conservation  [11]. More than 80% of the currently known flora species are endemic to the island  [4]. Endemic tropical species in Madagascar are well known for its important value in ecology and economy but are predicted to face mass extinction in the near future because of global warming and deforestation  [2, 3]. Identification of Malagasy vascular plant species was documented and can be accessed via the website Tropicos (Madagascar catalogue, http://www. tropicos. org/Project/Madagascar). The architectural analysis on tropical species as described by  [1, 7, 8]  emphasize on the dynamics of growth and structure of a plant species in the competitive nature of the forest, thereby conforming to its architectural model. Recent studies of plant architecture may not only provide complimentary information for species-level identification purposes, but also for understanding plant structure evolution within a clade  [10]  and their ecological strategies  [5, 6].
Together with stem anatomy, architectural variations involving the branching process, rhythmicity, and orientation can be comparatively analyzed to study the evolution of growth in both temporal and spatial contexts  [9]. This approach can also provide guidelines for tree selection and management in agroforestry practices  [12]. The dataset gathers anatomical, morphological, and architectural traits of endemic plant species. The aims is to promote and to make more accessible for scientific and public use a specific part of the CIRAD wood collection and unpublished morpho-architectural data from Madagascar. More than 1000 microscopic anatomical wood slides, exclusively available in this open access dataset, were sampled since the early 20th century and compiled with more than 500 digitized field notes such as botanical illustrations and photographs.
Microscopic structure of wood For each species, three microscope slides were prepared consisting of transverse, tangential, and radial sections (Fig. 1 ). The slides were then digitized using a camera Olympus DP71 mounted onto a microscope Olympus BX60. Each slide was digitized with three optical magnifications: ×40, ×100, and×200. The images (1600×1200 pixels) were then processed by Archimed Microvision® software and stored in 24-bit RGB TIFF. Within each image, the main specific anatomical traits can be found, and these traits were indexed following the List of Microscopic Features for Hardwood Identification from the IAWA (http://iawa-website. org/).
Morphology and architecture of plant structure Information on morphological and architectural traits were indexed from illustrations of digitized field notes (300 dpi 24-bit RGB JPEG) (Fig. 2 ) and scanned photographs of 24×36 mm format (600 dpi, 24-bit RGB JPEG) from various researches in Madagascar, for extended visual information. The traits were indexed following the modalities as reviewed in  [1, 7, 8]. Both anatomical and morphological traits may not apply to all of the species available in the dataset.
A neural network-based method (CANYON: CArbonate system and Nutrients concentration from hYdrological properties and Oxygen using a Neural-network) was developed to estimate water-column (i. e. , from surface to 8,000 m depth) biogeochemically relevant variables in the Global Ocean. These are the concentrations of three nutrients [nitrate (NO 3 -), phosphate (PO 4 3-), and silicate (Si(OH) 4 )] and four carbonate system parameters [total alkalinity (A T ), dissolved inorganic carbon (C T ), pH (pH T ), and partial pressure of CO 2 (pCO 2 )], which are estimated from concurrent in situ measurements of temperature, salinity, hydrostatic pressure, and oxygen (O 2 ) together with sampling latitude, longitude, and date. Seven neural-networks were developed using the GLODAPv2 database, which is largely representative of the diversity of open-ocean conditions, hence making CANYON potentially applicable to most oceanic environments.
For each variable, CANYON was trained using 80 % randomly chosen data from the whole database (after eight 10 • × 10 • zones removed providing an "independent dataset" for additional validation), the remaining 20 % data were used for the neural-network test of validation. Overall, CANYON retrieved the variables with high accuracies (RMSE): 1. 04 µmol kg -1 (NO 3 -), 0. 074 µmol kg -1 (PO 4 3-), 3. 2 µmol kg -1 (Si(OH) 4 ), 0. 020 (pH T ), 9 µmol kg -1 (A T ), 11 µmol kg -1 (C T ) and 7. 6 % (pCO 2 ) (30 µatm at 400 µatm). This was confirmed for the eight independent zones not included in the training process. CANYON was also applied to the Hawaiian Time Series site to produce a 22 years long simulated time series for the above seven variables. Comparison of modeled and measured data was also very satisfactory (RMSE in the order of magnitude of RMSE from validation test).
g. , Global Ocean Ship-based Hydrographic Investigations Panel, GO-SHIP program;  Talley et al. , 2016) , or conducting continuous measurements of surface-water properties. These approaches have been and still remain essential as their estimates generally have the highest accuracies. Such measurements have been assembled into global databases (e. g. , GLODAPv2;  Key et al. , 2015; Olsen et al. , 2016) , which are a key resource for making budgets of chemical elements, directly from available measurements  (Takahashi et al. , 2009)  or indirectly through specific innovative methods  (Landschützer et al. , 2013 (Landschützer et al. , , 2014 (Landschützer et al. , , 2016)) , conducting climate change research  (Le Quéré et al. , 2015)  and biogeochemical modeling (e. g. , use of data for model initialization and/or validation;  Doney et al.
, 2015 (Sauzède et al. , , 2016)). The present study takes advantage of the simultaneous release of the GLODAPv2 database  (Olsen et al. , 2016)  and the planning of the BGC-Argo program  (Johnson and Claustre, 2016). The two observational systems and resulting databases are highly complementary, and new approaches can be developed to synergistically use their respective strengths, i. e. , measurement accuracy for GLODAPv2, and spatio-temporal coverage for BGC-Argo, with a specific emphasis on O 2 measurements. We thus focus in this study on the development of global neural network-based transfer functions using O 2 as a primary input to retrieve nutrient concentrations and carbonate system parameters in the water column down to 8,000 m. Hereinafter, we refer to our method as CANYON, for CArbonate system and Nutrients concentration from hYdrological properties and Oxygen using a Neural network.
The GLODAPv2 Database The Global Ocean Data Analysis Project version 2 (GLODAPv2) was an effort from the international community to consolidate all data from ocean bottle samples collected as part of many oceanic cruises  (Olsen et al. , 2016). The GLODAPv2 database (available at http://cdiac. ornl. gov/oceans/GLODAPv2/) provides a single high-quality internally consistent global data product that contains CO 2 -relevant ocean interior measurements from ship-based surveys. The GLODAPv2 database includes samples of core variables such as salinity, oxygen, macronutrients, and seawater CO 2 chemistry from 724 oceanic cruises. In this study, we focused on seven variables representative of the macronutrients and of the seawater carbonate system: nitrate (NO 3 -), phosphate (PO 4 3-), silicate (Si(OH) 4 ), pH on the total scale (pH T ), total alkalinity (A T ), total dissolved inorganic carbon (C T ), and partial pressure of CO 2 (pCO 2 ).