dataesr/scientific-paragraphs-categorization huggingface dataset:
The selection of vegetation indices gives interesting results and better averages than the use of unreduced hyperspectral datasets. The MNF's ability to reduce noise while minimizing signal loss explains this level of performance unlike the ACP. Feature selection methods remain less accurate than feature extraction ones. While considering the ANOVA and correlation matrix selection method, low results can be explained by empirically choosing the correlation threshold. Oldeland et al. [54]  used a threshold of r = 0. 75 without any explanations. Another selection method could be tested such as the Kruskal Wallis algorithm which iteratively tests the best combination of uncorrelated indices  [37]. Moreover, these authors have shown that not only vegetation indices can be used, but also various derivatives (1st order or 2nd order) of (part of) the spectral signature.
Towards an Urban Sampling Strategy for Mapping Urban Tree Diversity? This study shows that the accuracy of urban tree diversity classification strongly depends on the field dataset which in turn depends on the defined nomenclature. Considering the producer's and user's accuracy of tree classification (c. f. Supplementary Table  S2 ), it is advisable to define in advance an appropriate nomenclature based on a discriminating analysis of species and families classes. Indeed, some family classes can be better separated thanks to their spectral signature than two different species, or inversely. For instance, Tilia tomentosa (Silver linden) and Tilia disticum (Linden) species are well distinguished while belonging to the same family. Spectra variability can vary according to many factors inherited from urban morphology and urban tree management: street width and orientation, the shading effects of buildings or nearby species, tree age and size [57,58], etc.
Increasing the -folds in the learning process can help to minimize the influence of the unbalanced sampling by preventing from overfitting. Although further studies are needed to define an optimal sampling strategy for urban areas, we assume this strategy must be adapted to the pursued objective: Do we want to have a good map of all the species/families in a considered city? Do we want to map the dominant/specific species? While the in situ dataset should be well defined at the beginning of the study, other techniques may also be of interest to evaluate. For example, object-based image analysis (OBIA) which aim is to segment an image based not only on averaged or pixel majority spectral signatures but also on their spatial arrangement  [64] [65] [66] [67] , can be helpful in minimizing neighboring misclassifications by reducing pixel-level noise while combining spectrally similar pixels.
Conclusions This research aimed to assess the ability of VHSR hyperspectral satellite data in order to discriminate against urban tree diversity. The influence of hyperspectral techniques (dimension reduction and classification methods as well as learning data and techniques) on the classification accuracy was also evaluated on the case study of Toulouse, based on two sublevels of taxonomic nomenclature (species and families) with a minimal number of individual samples per class (4 to 10). All datasets are available upon request for the moment and will be freely available once published. This study highlighted the great potential of VHRS (4 m) hyperspectral images (192 spectral bands). Combined with MNF dimension reduction method, the SVM classifier and a 10-folds learning method with 10 individuals per class, the discrimination of 14 species reached an overall accuracy of 78.
Supplementary Materials: The following are available online at http://www. mdpi. com/2072-4292/11/11/1269/s1, Figure  S1 : Results of ANOVA for the selected Vegetation indices applied on hyperspectral datasets; Table  S2 : Confusion matrices for the SVM classification applied with 10-Folds method on (top) reduced image HySpex at 2 m by MNF and (down) reduced image HYPXIM 4 m by MNF; Table  S3 : Tables showing the Producer's and User's Accuracies (PA and UA) obtained from the MNF-reduced image datasets, with RF and SVM classifiers trained with 2-folds and 10-folds for the 14 species training dataset, on the one hand, and the 12 families on the other hand. Funding: This research was funded by the French National Science foundation project HYEP (Hyperspectral imagery for environmental Urban planning).
Urban tree diversity classification results obtained using the 2-folds learning method and applied on reduced MNF image datasets, with SVM and RF classifiers according to subsets exhibiting at least 10 individuals per class. For the two considered classifiers, results obtained from the MNF image (with AOA = 55. 09% and 55. 91% for RF and SVM respectively-Table4) datasets are always greater than those obtained from other reduced/unreduced image datasets. VI by ANOVA has an AOA of 45. 83% (AOA = 49. 65% and 49. 65% for RF and SVM respectively) and VI by PCA 40. 97% (AOA = 39. 48% and 42. 46% for RF and SVM respectively). PCA and unreduced image datasets show similar AOA (38. 50% and 38. 60% respectively).
| Whole-genome sequencing We quantified sample libraries with KAPA library quantification kit (#7960298001, Roche) twice with two independent dilutions at 1:10,000 and 1:20,000. We calculated the average concentration of undiluted libraries and normalized them to 2 nM each then pooled them for sequencing step. We generated high-coverage genomic data for the 11 M. incognita France. We performed two runs to balance the read's representation among the isolates and obtain homogeneity of coverage for the different samples (Table  S2 ).
| Variant detection We trimmed and cleaned the reads from each library with cutadapt  (Martin, 2011)  to remove adapter sequences and bases of a quality inferior to 20. We mapped the clean reads to the M. incognita reference genome  (Blanc-Mathieu et al. , 2017) , using the BWA-MEM software package  (Li, 2013). This reference genome is described as triploid with three equally highly diverged A, B and C genome copies as a result of hybridization events. Most of the triplicated regions have been correctly separated during genome assembly, according to genome assembly size (183. 53 Mb) that is in the range of the estimated total DNA content in cells via flow cytometry (189 ± 15 Mb)  (Blanc-Mathieu et al. , 2017) ). Hence, the genome was considered in this analysis as mostly haploid. However, the distribution of per-base coverage on the genome assembly presented a 2-peaks distribution with a second minor peak at ~twice the coverage of the main peak (Figure  S2 ).
| Test for association between biological traits and genetic clusters We used Fisher's exact test in R to assess whether there was a significant association between the SNV-based clusters and the host races or the crop species from which the isolates were originally collected. We also conducted an isolation-by-distance (IBD) analysis using the adegenet R package  (Jombart & Ahmed, 2011)  to check how well the genetic distances correlate with geographical distances between the sampling points of the isolates. Geographical distances were calculated from exact sampling locations, when available, or centre points if the region was known but not the exact sampling location. Sample R3-4 was excluded from this analysis since it was a mix of samples pooled together from different geographical locations.
This work was supported by the Agence Nationale de la Recherche programme (grant number ANR-13-JSV7-0006-ASEXEVOL), the Insitut National de la Recherche Agronomique programme (grant number Accueil de Chercheur Etranger SPE 2016). GK has received the support of the EU in the framework of the Marie-Curie FP7 COFUND People Programme, through the award of an AgreenSkills + fellowship (under grant number 609398), EM received a post doc fellowship from the Consórcio Pesquisa Café, and EVSA received funding from Embrapa for sequencing at UCA Genomix platform.
Toullec and Moriceau show for the first time that transparent exopolymer particles (TEP) selectively increase biogenic silica dissolution from fossil diatoms compared to fresh diatoms. In line with results by  Akagi (2013)  they suggest that diatom excretion of TEP may accelerate the dissolution of refractory silica and provide an alternative source of DSi when limitations arise. Alvarez et al. extracted data from over 320,000 records of Porifera from the Global Biodiversity Information Facility (GBIF) to describe the overall distribution of sponge orders and families along DSi gradients and depth. They conclude that the use of sponge taxa assemblages as a proxy for DSi concentration or depth must be treated with caution as these animals are adapted to a great range of DSi conditions, as well as other unspecified variables that are also related to depth.
ACKNOWLEDGMENTS This research topic results from a collaborative effort initiated during the first international conference SILICAMICS, held in the Aber Wrac'h near Brest (France). This conference, that had the ambitious objective to better understand silicification processes and the role of silicifiers in the marine ecosystems, has been supported by  Labex-Mer, EuroMarine , the  University of Western Brittany , and Local Authorities from the région Bretagne.
However, even though various data are available in the literature, they remain fragmentary and not sufficient, in our opinion, to draw definitive general conclusions for an optimal protracted chelation therapy. In this context, the aim of the present work was to better understand the influence of important therapeutic variables on the overall efficacy of a repeated DTPA chelation treatment. This new useful information may help to design better treatment schedules that can be both efficient and the least burdensome as possible for the patient. Thus, our experimental work design featured: (1) all DTPA treatment schedules commenced several days or weeks following contamination in order to consider only Pu firmly-fixed in tissues and not the available pool of circulating Pu just after contamination; (2) the various DTPA treatment regimens tested used comparable cumulated total dose to assess only the effects of the distribution and/or the frequency of multiple injections as therapeutic variables; (3) the effects of the variables on the efficacy were evaluated in both systemic tissues and lungs.
Assessment of offspring emotional reactivity Fish propensity to express fear-related behaviour (e. g. , emotional reactivity) was evaluated individually in a novel-tank test (social isolation in a novel tank) at 75-76 dpf (Fig. 1 ). The novel tank (30 × 19 × 16 cm) was supplied with 12 • C flow-though recycled water. Fifteen fish per female, randomly netted either at the top or the bottom of the water column, were observed. The treatment order was randomly chosen. Behavioural responses were video-recorded for 30 min. We analysed the first (0-5 min) and the last (25-30 min) 5-min intervals with EthovisionXT software (Noldus, Netherland). The following behavioural parameters were calculated for each individual: total distance travelled (cm), maximum swimming velocity (cm/s), angular velocity ( • /s) (i.
Gene expression profiling was conducted using an Agilent 8×60 K microarray (GPL24910) as previously described  (Zarski et al. , 2017). Samples were randomly distributed on the microarray for hybridization. The data were processed with GeneSpring software (Agilent v. 14. 5) using gMedianSignal values. After data processing, one sample from the hatching/17 • C group, which behaved differently from other samples, even after normalization, was removed from subsequent analysis. Corresponding data were deposited in Gene Expression Omnibus (GEO) database under the reference GSE113377.
Statistics Due to low number of simultaneously ovulating females, percentage mortalities and malformations were compared between treatments using nonparametric Mann-Whitney tests (R, Mann-Whitney-Wilcoxon non-paired tests). Fish length was analysed after taking into account the temperature as a fixed factor (two levels: 12 • C and 17 • C) and the females as a random factor. A generalized linear mixed model (GLMM) was fitted using the nlme package in R 3. 3. 1 (R Core Team, 2016), and by assuming a normal distribution. Significance of the random effect was checked using the 95% confidence interval of the variance, 0 being excluded of the interval in case of significance. The analyses of the novel tank test consisted in testing the effect of the temperature, the effect of the interval (two levels: 0-5 min and 25-30 min) and their interaction on each dependant variable.
Correlations between all parameters were evaluated using a Pearson test in R software. The analyses of the spatial learning test consisted in testing the effects of the temperature, the trial (considered as a covariable), and their interaction on each dependant variable. We fitted a GLMM model (using the nlme package) assuming first-order autocorrelation and specified ''individual'' nested under ''female'' as random effect factors to account for female effect and repeated observations of individual fish. For all models, if there were non-significant effects on factors or interactions, stepwise backward eliminations were performed to sequentially simplify the full model. The models were validated using analysis of residuals (normality assessment). All the models and their respective results are presented in Table  1.
We compared the proportions of fish making the right choice in less than 900 s in at least four out of the last seven trials by a chi-square test. Table  1  Statistical models. Statistical models used for biometric data (variables: fish length and K-factor) and behavioural data obtained from the novel-tank test (variables: distance travelled, maximum velocity, angular velocity, and thigmotaxis) and the spatial learning test (variables: latency to leave the start-box, i. e. , latency SB, and latency to make the right choice, i. e. , latency RC). All analyses were performed with R software. Differences were found to be significant when P < 0. 05 and tendencies were considered for 0. 05 < P < 0. 1. For microarray analysis, gene expression data was scale normalized and log(2) transformed before statistical analysis.
Funding This work was supported by the  French National Research Agency (ANR)  under grant agreements  ANR-13-BSV7-0002  PReSTO'Cog to  Violaine Colson  and  ANR-13-BSV7-0015   Maternal Legacy  to  Julien Bobe. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Testate Amoebae Subsamples were extracted from the different peat sections, following the procedure described by Booth et al. [18]. The material was washed through a sieve of 250 µm mesh size, and the filtrate was used for microscopic analyses. Testate amoebae were identified and counted using an inverted microscope at 200-400× magnification (OLYMPUS IX71), following Uthermöhl's method  [18]. At least 150 specimens were counted per sample, and were identified to the species level whenever possible  [18]. A depth of water table (DWT) was inferred from testate amoeba using the European calibration data set  [19]. The testate amoeba diagram (Figure  3 ) was drawn using the rioja R package  [20]. Zone delineation was achieved by constrained hierarchical clustering, with clusters constrained by sample order (depth).
Palynology A continuous sampling (2. 5 cm thick) totalizing 109 samples was made. Peat samples were processed for pollen analysis using standard techniques  [22]. They were treated with HCl, NaOH, HF, ZnCl 2 , and acetolysis. A minimum of 400 pollen grains from terrestrial plants was counted in each sample to ensure statistical significance. Cyperaceae, hygrophilous plant, and spores were excluded from the pollen sum. Pollen grains were identified with the aid of a reference collection of modern pollen types, pollen keys, and photographs  [23] [24] [25]. The pollen diagram (Figure  4 ) was drawn using TILIA and TGView software  [26]. Pollen zones were delimited with the help of CONISS  [27].
Conclusions This study shows the very good complementarity of pollen and testate amoebae analyses (Figure  5 ). With the help of a robust age-depth model, it is therefore possible to reconstruct climate and local human impact on vegetation and peatland development accurately. Climate seems to be the main driver of observed changes until 2600 cal BP, after which deforestation seems to be the main driver of observed changes. Large peatlands are known to record a quite large pollen rain signal. However, the presence of trees around the peatland could have created a filter for regional pollen rain. Thus, the vegetation history inferred from the pollen data seems very local, and human activities beyond the trees are weakly recorded. The results show four successive phases, corresponding to major Holocene climatic oscillations and to anthropogenic impact as follows: (1) The early-middle Holocene (9000 to 5800 cal BP): peatland formation probably began 9000 years ago, first by filling a basin in which water accumulated.
Bacterial Toxin-Antitoxin systems (TAS) are involved in key biological functions including plasmid maintenance, defense against phages, persistence and virulence. They are found in nearly all phyla and classified into 6 different types based on the mode of inactivation of the toxin, with the type II TAS being the best characterized so far. We have herein developed a new in silico discovery pipeline named TASmania, which mines the >41K assemblies of the EnsemblBacteria database for known and uncharacterized protein components of type I to IV TAS loci. Our pipeline annotates the proteins based on a list of curated HMMs, which leads to >2. 10 6 loci candidates, including orphan toxins and antitoxins, and organises the candidates in pseudo-operon structures in order to identify new TAS candidates based on a guilt-by-association strategy.
In addition, we classify the two-component TAS with an unsupervised method on top of the pseudo-operon (pop) gene structures, leading to 1567 "popTA" models offering a more robust classification of the TAs families. These results give valuable clues in understanding the toxin/antitoxin modular structures and the TAS phylum specificities. Preliminary in vivo work confirmed six putative new hits in Mycobacterium tuberculosis as promising candidates. The TASmania database is available on the following server https://shiny. bioinformatics. unibe. ch/apps/tasmania/.
Alternative structures have been mentioned by van Melderen and her group, which highlights the existence of orphan TA loci  [38]. So far, TAS screening approaches usually skip the multigene TA systems, despite known tripartite TAS  [29, [39] [40] [41]  and TAS modules inserted within operons  [7, 42]. Validated and predicted TAS are collected in the TADB2 database  [43]. TADB2 focuses mainly on type II TAS that were mined from the literature (N = 105 TA loci) and from previous published screens (N = 6088 TA loci) extracted from 870 bacteria and archaea genomes. The 6088 TA loci were predicted using Blastp on 126 genomes  [37]  or PSI-Blast searches with validated literature datasets  [44]. A few of them were additionally combined with known operon structure obtained from STRING  [45]. TADB2 also includes a search tool called TAFinder (http://202.
120. 12. 133/TAfinder/index. php) combining homologous search and operon structure module filters  [43]. TAFinder uses Blastp searches with the TADB2 dataset and HMM searches with 108 Toxin HMMs and 201 Antitoxin HMMs to select the TA loci. These loci are then filtered using protein size (by default >30aa and <300aa) and intergenic distance (by default from -20nt to +150nt). TADB2 and TAFinder are very stringent in their criteria to minimize false positives. Our primary goal is to provide the microbiology community with a largely extended database of the type I to type IV (and potentially type V to VI as side hits) toxin and antitoxin loci. We also propose an objective annotation of the TA independently of the cognate components. With the current nomenclature based on the identification of the toxin cognate, the antitoxin would "inherit" the toxin family name.
EnsemblBacteria assemblies The EnsemblBacteria database (Rel. 33 Nov. 2016) contains N = 41'610 genomic assemblies that correspond to N = 23'921 unique taxonomic identifiers (taxonomy ids), indicating a high degree of redundancy in the assemblies. At least one hit was found for N = 40'993 assemblies present at least one hit with the TASmania HMM scan, of which N = 22'950 correspond to unique taxonomy ids. A closer look at the taxonomy ids shows that 40% of the genomic assemblies belong to the Proteobacteria phylum and 34% to the Firmicutes phylum, these two groups making up three quarters of the database (S1 Fig). The Actinobacteria and Bacteroidetes phyla represent 12% and 3% of the assemblies, respectively. The remaining 11% of the assemblies correspond to N = 72 other phyla and/or unclassified bacteria.
TASmania hits global statistics After scanning EnsemblBacteria with the HMM profiles, we obtain N = 1'155'070 putative toxin gene hits, corresponding to N = 228'074 unique toxin protein sequences; and N = 1'283'761 putative antitoxin genes hits, corresponding to N = 270'733 unique antitoxin protein sequences. In total, the putative toxin or antitoxin hits correspond to N = 2'298'903 unique pseudo-operons containing TA modules (including redundant ones). A phylogenetic analysis of the TA hits distribution shows that Cyanobacteria are very TA-rich and are the most common phylum in the top 200 most TA-enriched genomes (S2 Fig). Our method does not use a protein length filtering, thus allowing for discovery. The protein length distribution of the putative toxin and antitoxin hits confirms previous results  [46] , as shown in Fig 2.
TASmania performance We compared TASmania putative TAS hits with the ones proposed by TAfinder. Since we cannot download the entire datasets from this webtool, we used a few reference model strains as a proof of principle: Mycobacterium tuberculosis H37Rv (M. tuberculosis), Mycobacterium smegmatis MC 2 155 (M. smegmatis), Caulobacter crescentus CB15 (C. crescentus) and Staphylococcus aureus NCTC8325 (S. aureus). The putative hits were manually downloaded from these  ). Looking closely at the TAfinder hits missed by TASmania, the module Rv2653c/Rv2654c in M. tuberculosis H37Rv seems to encode prophage proteins, with no IPR annotation, hence their absence from TASmania (S4 Table ). This module could be a real TAS and if this. These TASmania-specific TA hits correspond mostly to: i) type I or type IV systems; ii) orphan loci; iii) guilt-by-association "x" loci iv) unusual combinations ("TT", "AA").
Rv0078A is also flagged as a toxin via its IPR annotation (IPR014942 AbiEii toxin type IV). This unexpected predicted "TT" pair could be the signature of a new family of TAS, with Rv0078B being a potential example of a TAS cognate that "switched" function  [4]. T52 hits like Rv0078B are found in diverse pseudo-operons structures, although T52 should in theory be a toxin of type I and therefore rather appears in pseudo-operons looking like orphans ("T"). M. tuberculosis presents only a single pseudo-operon with T52 hit, while it is absent from M. smegmatis and appears in N = 34 different loci in Thalassomonas actiniarum. In the latter, T52 hits are all orphan toxins, suggesting that, in this species at least, T52 looks more like a classical SymE-like toxin type I (the antitoxin cognate being a ncRNA, it cannot be annotated currently by TASmania).