dataesr/scientific-paragraphs-categorization huggingface dataset:
The Substitution Model was set accordingly to the jMODELTEST result for the COI dataset, and the Tree Model was set to a Yule and a birth-death model in different analyses, each analysis consisting of a 50 million generation run sampled every 1000 generations. Second, we used the whole molecular dataset (seven genes) and the only unambiguous hydroporine fossil known to calibrate the tree: †Calicovatellus petrodytes Miller and Lubkin from the mid-Miocene (see Appendix 3 for more details). Since we included the two extant genera of the tribe Vatellini in the dataset (see Miller 2005 for a revision), but not all the species of the genera, and since the fossil is likely to be sister to the extant genera, we chose in a conservative way to place the calibration point on the stem of the tribe Vatellini based on the "apomorphy-based method" described in  Sauquet et al.
0). The tree root was constrained with the same prior as in the first calibration set, and likewise, both Yule and birth-death Tree Models were used in different analyses. The run settings were selected to be the same as the first set of calibration as well, with the best partitioning scheme recovered in phlogenetic analyses. Third, we carried out a set of analyses based on the molecular matrix of the COI gene and the fossil data to calculate the rate of evolution within the radiation of Australasian Hydroporini. This set of analyses was performed as a cross-validation to check the applicability of the interval of substitution rates (0. 0145-0. 0195 subs/s/Myr/l) on our data. All settings were the same as the one used to calibrate the MRBAYES topology using the full dataset and the fossil of †Calicovatellus petrodytes.
We also conducted different analyses using both Yule and birth-death Models. For all analyses, the best BI topology was used to perform dating analyses in order to optimize the search of optimal ages through a minimization of parameter space to explore. The convergence of the runs was investigated using ESS, a conservative burn-in of 25% applied after checking the log-likelihood curves and the different runs merged using LOGCOMBINER 1. 7. 4  (Drummond et al. 2012b ). The maximum credibility tree, median ages and their 95% highest posterior density (HPD) were generated afterwards under TREEANNOTATOR 1. 7. 4  (Drummond et al. 2012b ). The best analysis was selected based on BF estimates derived from marginal likelihoods of the runs using both the fossil and the CO1 dataset.
ANCESTRAL RANGE RECONSTRUCTION We used the likelihood model Dispersal-Extinction-Cladogenesis (DEC,  Ree et al. 2005   The biogeographic regions used in this study were modified from  Unmack (2001)  to account for present distribution patterns of Australasian Hydroporini  (Watts 1997 (Watts , 2002;; Hendrich 2003 Hendrich , 2008;; Hendrich and Fery 2008; Hendrich and Watts 2009; Hawlitschek et al. 2011 Hawlitschek et al. , 2012 ; and our field notes) (Fig. 1 ). The seven selected areas yielded a set of 2 7 = 128 theoretically possible ranges from which we excluded those relying on biological implausibility such as widely disjoint areas. We computed a matrix in which each species was coded as present or absent for each area considered in the analyses (Appendix 1). The choice of temporal constraints and dispersal rates between the discrete distribution ranges was based on paleogeographic and paleoclimatic data  (Drexel et al.
Diversification Analyses The tempo and mode of diversification were investigated using birth-death methods. We used all chronograms inferred with fossil or rate calibrations for the Australasian Hydroporini, Paroster and Sternopriscus. Diversification analyses were performed under the R 2. 15 software implementing the ape (  (Paradis et al. , 2004) ), diversitree ((FitzJohn, 2012)), picante  ((Kembel et al. , 2010) ), and TreePar (  (Stadler, 2011) ) packages. Time-dependent analyses. -First, we used the TreePar approach  (Stadler 2011)  with the "bd. shifts. optim" function allowing the estimation of discrete changes in speciation and extinction rates in possibly incompletely sampled phylogenies. It estimates the maximum likelihood speciation and extinction rates together with the shift times t = (t 1 ,t 2 ,.
Trait-dependent analyses We assessed the impact of living in subterranean areas (calcrete aquifers) vs. living in epigean areas (surface habitats) by testing the hypothesis of higher diversification rates due to aridification of Australia in the Miocene and the extensive loss of mesic habitats. We used the Binary State Speciation Extinction model (BiSSE;  Maddison et al. [2007] ) implemented in the diversitree package (FitzJohn 2012). We built the likelihood function using "make. bisse," that is then optimized by maximum likelihood using "find. mle. " Different models were run to test whether speciation, extinction, or transition rates were independent or constrained by the trait. Eight models were built with an increasing complexity starting from the simplest model with no difference in speciation, extinction and transition rates for all character states (three parameters) to the most complex model with speciation, extinction, and transition rates varying independently in each of the character states (six parameters).
2012). We investigated some of these potential sources of errors using the most complete molecular dataset ever assembled for a group of Dytiscidae including seven gene fragments, around 80% of the described species, all the extant genera and four tribes representing a large part of the subfamily diversity  (Ribera et al. 2008). Molecular dating was based on one of the best preserved specimens of the beetle fossil record that was not preserved in amber  (Miller and Lubkin 2001) , and we carried out a series of cross-validations to test the robustness of the age estimates using different settings, priors and calibrations (e. g. , fossil-based or substitution rate-based dating, Yule model or birth-death process, different distribution laws for the calibrate node prior). Despite the well-known pitfalls of single calibration  (Ho and Phillips 2009) , our analyses show a close correspondence between the ages of all calibration schemes with overlapping confidence intervals and highly similar median ages whatever the priors or settings we used (Table  3 ).
All analyses conducted using the BEAST and multiple different priors recovered an Oligocene origin of the group (Fig. 5 ). In addition, the parallel analysis of crossvalidation carried out with the COI dataset and the fossil resulted in rates of evolution congruent with the interval (0. 0145-0. 0195 subs/s/Myr/l) used to calibrate the tree. Overall, our divergence time estimates are in agreement with the results of  Leys et al. (2003)  who found a 21. 5 Maorigin for the Australian Hydroporini, an age moderately younger than the median ages we find in this study with different sets of calibration. The ages recovered in  Leys et al. (2003, ≈17 Ma)  and in  Leijs et al. (2012, ≈14 Ma)  for the Paroster radiation also appear in accordance with our results.
2013). Furthermore, our analyses accounted for missing taxon sampling using taxonomic knowledge about the extant diversity of clades. The completeness of DNA matrices is another issue that may bias the inference of diversification patterns. In our study, the overall gene coverage within the group was sufficient for a good phylogenetic resolution and consistent Bayesian-relaxed-clock estimates. In the case of Paroster, however, the unknown extant diversity of subterranean species  (Guzik et al. 2011 ) combined with a reduced nuclear dataset compared to other included genera may underestimate the diversification rates, though we believe this is unlikely to change the overall pattern obtained. The results recovered here highlight important differences in diversity dynamics for closely related genera that evolved in different freshwater ecosystems in Australia.
ACKNOWLEDGMENTS We want to thank the editor-in-chief  Frank Anderson , associate editor  Brian Wiegmann  as well as two anonymous reviewers,  Gael J. Kergoat  and  Ignacio Ribera  for fruitful comments on previous versions of this manuscript. FLC wishes to thank  Hélène Morlon  for supervision and advice. The  Department of Environment and Conservation in Western Australia  is acknowledged for giving permission to conduct scientific research in Western Australia (Permit numbers:  SF 003017  and  NE 002348 ), and the  Parks and Wildlife Commission of the Northern Territory  is thanked for giving permission to conduct scientific research in the Northern Territory (Permit Number:  23929  and  RK-400/ RK-660 ). The  Department of Environment and Conservation in New South Wales  (Scientific License No.
FUNDING This work, as well as ongoing research on the Australian predaceous water beetle fauna was supported by a grant from the  German Science Foundation (DFG)  grants  BA2152/6-1 , 7-1 and 11-1. FLC was funded by the  CNRS  and an  ANR  grant  ECOEVOBIO-CHEX2011  awarded to his advisor,  Hélène Morlon.
Outputs were pooled and resulting dataset were curated for protein multiple occurrences. The sequences were aligned using ClustalW  [38]  followed by manual curation. The sequence alignment was generated by Gblocks  [39]  and unambiguously aligned amino acid sites were retained for phylogeny inference using the maximum likelihood method implemented in PhyML  [40]. Analyses were engendred under the LG model of amino acid replacement  [41]  with a gamma distribution of evolutionary rates across sites  [42]. Internal branch supports were evaluated using the approximate Likelihood Ratio Test  [43].
Phylogeny of Ail, PagC and OmpX-related proteins among Bacteria Ail, PagC and OmpX homologs were searched in genome databases (see Material and Methods). Phylogenetic analysis (Fig. 1A ) clearly separated the 89 proteins from 53 bacterial species into three well-supported groups. The Ail group recovered the y1324 canonical Ail protein described in Y. pestis KIM  [4]. The PagC group contained the STM3031 PagC protein from Salmonella Typhimurum  [54]. Finally, the OmpX group was characterized by several proteins annotated OmpX, such as Ent638_1301 from Enterobacter sp. 638. Interestingly, the protein y1682 of Y. pestis KIM usually named Ail clusters with the OmpX group  [4]. Our phylogenetic analysis clearly indicates that all the Photorhabdus proteins clustered within the Ail group (plu2480 and plu2481 from P.
coli XL1Blue was controlled by real-time PCR (data not shown). We first evaluated the human serum resistance of the E. coli XL1Blue strains harboring the different derivative plasmids (Fig. 3 ). The positive control, E. coli XL1Blue expressing the ail gene from Y. pestis strain KIM, y1324 (ail Yp ), showed an elevated serum resistance, similar to the one observed in all strains when the complement in the serum was heated-inactivated. Moreover, we observed intermediate serum resistance with the ail2 Pl and pagC Pl genes whereas the resistance with the ail1 Pl , ail Pa and pagC Pa genes was weak. In order to assess if the association of E. coli XL1Blue to eukaryotic cells was affected by expression of Photorhabdus ail or pagC genes, association assays of mammals cells (CHO) and insect cells (Sl2b) were performed.
luminescens TT01  [36]. Interestingly, another gene encoding an outer membrane protein, the ompF-like gene, probably underwent a tandem duplication  [62]. Characteristic features of an ancient whole-genome duplication were also detected in the P. luminescens TT01 genome  [63]. In addition, a 275-kilobase single block duplication, with cryptic phenotypic consequences, was observed in phenotypic variants of P. luminescens  [64]. ail1 Pl expression is PhoP-dependent. Total RNA from phoP mutant or TT01 wild-type strain of Photorhabdus luminescens was used for RT-qPCR analysis with internal primers specific for the indicated genes. mRNA levels were normalized against those of a reference gene (gyrB). Data are presented as a ratio of values for phoP mutant and TT01 wild-type strain. A ratio of 1 indicates no difference in expression level between both strains.
Qualitative comparisons with moth trajectories With Fig. 6  we aim at a qualitative comparison between our strategy driven trajectories and behavioral data provided by the Kanzaki-Takahashi Laboratory  [36]. There, a silkmoth (Bombyx mori) walking in a wind tunnel tries to locate a pheromone source (relatively high pheromone concentration) located 60 cm away from the starting position  [8, 36, 37]. Large parts of the trajectories are estimated to lay inside the odor plume. We show three arbitrarily selected behavioral search paths and the corresponding track-angle distribution. These are much broader than our histograms (Fig. 4C , 4D and 5C) but there is a central (surge) peak, as well as a second peak due to zigzagging, though here occurring at 100u and non-symmetric (probably due to the low number of samples).
Electrophysiological recordings and their analysis Neurons from the macroglomerular complex (MGC) were recorded from male Agrotis ipsilon Hufnagel during pheromone stimulation of the antennae. The pheromone stimulus is a blend of three components (ratio 4:1:4): (Z)-7-dodecenyl acetate, (Z)-9tetradecenyl acetate and (Z)-11-hexadecenyl acetate. The stimulation lasted 200 ms. There are 4 to 10 trials for each neuron. Extracellular recordings were performed by inserting two glass electrodes filled with Tucson ringer into the MGC. After amplification the signal was band-pass filtered (0. 3 to 5 kHz) and sampled at 16 kHz. Spike sorting (R-package SpikeOMatic) yielded single neuron signals. For more details see  [38, 39]. We here analyzed the responses of 8 neurons that exhibited clear multiphasic responses (Fig.
We used three different doses: minimum = 0. 1 mg/ml, medium = 0. 3 mg/ml and maximum = 1 mg/ml of main pheromone component ((Z)-7-dodecenyl acetate). The plume contour (indicated by black dashed lines in Fig. 4A, 4B  and 5A ) is defined as the parabolic region where 90% of all pheromone detections occurred. For trials that lasted longer than 3 min, the filter paper was replaced every trial, otherwise, we used one filter paper for two consecutive trials. The cyborg trajectories were recorded using path integration provided by the odometry tracking module of the Khepera III Toolbox (http://en. wikibooks. org/wiki/Category:Khepera_III_Toolbox). We employed three reactive search strategies and one cognitive strategy, namely infotaxis  [6]. Whenever the moth detected an odor patch the corresponding peak in the EAG activated the neuron simulator resulting in a multiphasic response as described above.
The condition for a detection event in the simulated signal were three consecutive ISIsv70 ms followed by an inhibitory phase (ISI §350 ms). This signal controlled the resulting robotic movements. We only exploited the On response and the inhibitory phase of the simulated neuronal signal. Onset and duration of all movement sequences subsequent to the surge were predefined or determined by a new detection event. The movements of our cyborg are clearly less precise than, for example, movements in computer simulations. This is due, for instance, to packet loss in the WIFI connection, odometry errors (i. e. , imprecision in the path integration), and friction issues. All movement related data reported are thus to be considered approximate. Reactive search strategies. After each detection, the cyborg received a surge command leading to a straight upwind movement (approximately 5 cm).
In addition to global Internet search, a total of four international databases were screened for journal articles, books, patents, conferences, and symposia proceedings in the field of food science, food industry, life science, and biomedical information (FSTA®, BIOSIS® Preview, Medline®, Foodline®). As this article aims at providing basis for discussion about the real use of these methods by dairy industry laboratories, current lacks, and future possibilities to facilitate the transfer from research knowledge to food industry applications, only references published between 2000 and 2013 were considered. Only representative publications for each technique and type of application were retained, and priority was given to reviews discussing about specific techniques and their applications. Methods for strain identification or characterization were excluded.
CONCLUSION Table  1  summarizes the current uses and lacks of the main analytical methods in dairy microbiology. Indeed, many databases are now available and provide a deeper understanding of the physiology and metabolic characteristics of strains. Still underexploited, they could help developing routine methods to evaluate LAB and probiotics physiological states. For instance, the current biological and physiological knowledge about fermenting bacteria should help developing chromogenic media based on biochemical reactions specific for intrinsic strain properties. These chromogenic tools are very straightforward, more specific than classical agar media, and very relevant for use in microbiological controls performed by the dairy industry. Promising FC methods were described for analyses of mixed cultures in milk, yogurt, and other fermented milk products.
Gene families Principal component analysis (PCA), based on 73 and 12 families of carbohydrate active enzymes (CAZys,  [16] ) and auxiliary activities (AAs),  [30] ), respectively, clustered P. gigantea with other efficient lignin degraders (  [39] , Figures  3A  and S2 ). Gene numbers were extracted from 21 fungal genomes and excluded genes encoding putative GMC oxidases such as methanol oxidase, alcohol oxidase and glucose oxidase (Dataset S1). Highest contribution of PC1 (50% of variance separating white-rot and brown-rot fungi) and PC2 (13. 0% of variance)) values were those genes associated with degradation of plant cell wall polysaccharides and lignin, respectively (Text S1). Hierarchical clustering analysis with this dataset also categorized P. gigantea into a clade of white-rot fungi that included the polypore P.
The major hemicellulose of conifer is galactoglucomannan (  [40] , Figure  1 ) but, in the case of mannan degradation, no significant increase in genes encoding GH2 b-mannosidase, GH5_7 endo-mannanase and GH27 agalactosidase was observed relative to other wood decay fungi (Figure  4 ). Similarly, no significant differences in the number of genes involved in arabinoglucuronoxylan hydrolysis were identified, except for two transcriptionally convergent GH11 genes present in P. gigantea (Text S1). Encoding putative endo-1,4-bxylanases, wood decay fungi typically harbor one or no GH11 genes. Auricularia delicata is another exception with three of these endoxylanases. Also unusual among white-rot fungi, none of the P. gigantea protein models were assigned to GH95 (Dataset S1). This family includes 1,2-a-fucosidases that hydrolyze the a-Fuc-1,2-Gal linkages in plant xyloglucans.
However, P. gigantea was grouped near B. adusta and P. carnosa. These associations seem in line with the preferential colonization of softwood substrates by P. carnosa  [49]  and with the efficient degradation of conifer extractives by B. adusta culture supernatants  [50]. The highest contribution to PC1 (26. 0% variance) and PC2 (6. 8% variance) were aldehyde dehydrogenase and long chain fatty acid CoA ligase, respectively (Figures  3A  and S3 , Text S1). Also potentially involved in intracellular lipid metabolism, CYP52 and CYP505 clans of cytochrome P450s are associated with degradation of fatty acids and alkanes. Relative to other white-rot fungi, P. gigantea had a slightly greater number of CYP52encoding genes whereas CYP505 gene numbers were similar (Figure  4 ; Dataset S1; Figures  S31, S32 ; Tables  S13-S15 ).
P. gigantea also diverges from other Agaricomycetes with respect to genes encoding proteins that are more distantly connected to lignocellulose degradation, including hydrophobins (Figures  S33  and S34 ; Tables  S17-S19 ), transporters (Table  S20 ) and non laccase MCOs (Figure  S20 ). Detailed analyses are provided for CAZys (Tables S7-S10; Figures  S22-S30 ; Dataset S1), peroxidases (Figures  S8-S13 ), auxiliary proteins, cytochrome P450s (Figures  S31-S32 ; Table  S13 -S15), potential regulatory genes (Figures  S4-S7 ; Tables S3, S11-S12) and genes involved in secondary metabolite synthesis (Table  S16 ).
Differential gene expression of P. gigantea in response to substrate Transcript levels were determined in cultures in which the sole carbon source was glucose (Glc), freshly harvested loblolly pine wood (Pinus taeda; LP) extracted with acetone (ELP), or freshly harvested but not extracted loblolly pine wood (NELP) (Text S1). GC-MS analysis  [51]  identified the major extract components as resin acids (46%), triglycerides (13%) and fatty acids (11%) (Text S1; Figure  S35 ; Table  S21 ). Excluding genes with relatively low transcript levels (RPKM values ,10) in LP-containing media, transcripts of 187 genes were increased. 2-fold (p,0. 05) in NELP or ELP relative to Glc. Of those Glc-derived transcripts with RPKM values. 10, 146 genes had higher transcripts in Glc relative to NELP or ELP (Figure  5 ; Dataset S2).
Mass spectrometry (nanoLC-MS/MS) identified extracellular peptides corresponding to a total of 319 gene products in NELP and ELP cultures (Dataset S2). Most proteins were observed in both NELP and ELP culture filtrates, which contained 294 and 268 proteins, respectively. Approximate protein abundance, expressed as the exponentially modified protein abundance index (emPAI)  [52] , varied substantially within samples. As expected, gene products with predicted secretion signals and high transcript levels were often detected. Other detected proteins (e. g. MOX model Phlgi1_120749;  [53] ) may be loosely associated with cell walls and/or secreted via 'non-classical' mechanisms (  [54] ; www. cbs. dtu. dk/services/SecretomeP). Still other peptides correspond to true intracellular proteins released by cell lysis, e.
g. ribosomal proteins (Dataset S2). Glycoside hydrolase gene expression was heavily influenced by media composition with transcripts corresponding to 76 genes increasing. 2-fold in NELP-or ELP-containing media relative to glucose medium (Figure  6 ). Some of these genes were highly expressed with RPKM values well over 100. For example, transcript and peptide levels matching GH7 cellobiohydrolase (CBH1; model Phlgi1_34136) were among the ten most highly expressed genes (Table  1 ). Indicative of a complete cellulolytic system, this CBH1 was accompanied by upregulated transcripts   S1 ). GMC oxidoreductases methanol oxidase, glucose oxidase and aryl alcohol oxidase were excluded because confident functional assignments could not be made and/or their inclusion did not contribute to separation of white-and brown-rot species.
(B) PCA of 21 fungi using genes encoding 14 enzymes involved in lipid metabolism (KEGG reference pathway 00071, Dataset S1). There is no significant segregation of white-rot and brown-rot fungi although P. gigantea was positioned in the third quadrant with B. adusta and P. carnosa. Symbols for white rot and brown rot fungi appear in blue and red, respectively. Tremella mesenterica is a mycoparasite. For raw data and contributions of the top 20 families see Dataset S1, Text S1 and Figures  S2  and S3. doi:10. 1371/journal. pgen. 1004759. g003 and extracellular proteins corresponding to another CBH1 (Phlgi1_13298), a GH6 family member CBH2 (Phlgi1_17701) and GH5_5 b-1,4 endoglucanases (EGs; Phlgi1_86144, Phlgi1_84111), all of which feature a family 1 carbohydrate binding module (CBM1). Also highly expressed were putative bglucosidases (Phlgi1_127564, Phlgi1_18210) and a GH12 (Phlgi1_34479).