dataesr/scientific-paragraphs-categorization huggingface dataset:
3). Analyses were performed with the LIMMA package implemented in R/Bioconductor48. The tiling array and whole-genome data sets were analysed separately. Expression values were extracted and quantile-normalised, log 2 -transformed, quality controlled and analysed for differences in expression between individuals and wing regions. P-values were adjusted for multiple hypotheses testing using the False Discovery Rate (FDR) method 49. We detected isoform-specific expression differences between Hm aglaope/amaryllis and Hm rosina/melpomene using RT-PCR and qRT-PCR on RNA extracted from developing hindwing tissue (further details in Supplementary Information). Previously published RNAseq data was also used to assess gene expression differences between Hm aglaope and amaryllis18 (further details in Supplementary Information).
They were then aligned to the target regions using the fosmid sequences from known races45 with sequence from the Yb BAC walk12 used to fill any gaps. Alignments were performed with BWA v0. 5. 642 and converted to pileup format using Samtools v0. 1. 12 before being filtered based on quality (≥20) and coverage (≥10). BWA alignment parameters were as follows: fraction of missing alignments given 2% uniform base error rate (aln -n) 0. 01; maximum number of gap opens (aln -o) 2; maximum number of gap extensions (alne) 12; disallow long deletion within 12 bp towards the 3'-end (aln -d); number of first subsequences to take as seed (aln -l) 100. We then calculated coverage and minor allele frequencies for each race and the difference between these using custom scripts in R50. 1048-62H20, accession FP565804) were then aligned to the BAC and genome scaffold18 references using the MAFFT alignment plugin of Geneious v.
Secondary PCR of these products was then performed using a primer in exon 2 (HM25_long_F2, Supplementary Table  2 ) and the nested universal primer A. Other isoforms were detected by RT-PCR using primers within exons 2 and 9 (gene25_for_full1 and gene25_rev_ex3). We identified isoforms from 5' RACE and RT-PCR products by cutting individual bands from agarose gels and if necessary by cloning products before Sanger sequencing. Cloning of products was performed using TOPO TA (Invitrogen) or pGEM-T (Promega) cloning kits. Sanger sequencing was performed using BigDye terminator v3. 1 (Applied Biosystems) run on an ABI13730 capillary sequencer. Primers fzl_ex1a_F1 and fzl_ex4_R1 were used to confirm expression of the furthest 5' UTR. For isoforms that appeared to show some degree of race specificity we designed isoform specific PCR primers spanning specific exon junctions (Extended Data Fig  2, 4 , Supplementary Table  2 ) and used these to either qualitatively (RT-PCR) or quantitatively (qRT-PCR) assess differences in expression between races.
RNAseq analysis of Hm amaryllis/aglaope RNA-seq data for hind-wings from three developmental stages had previously been obtained for two individuals of each race at each stage (12 individuals in total) and used in the annotation of the Hm genome18 (deposited in ENA under study accessions ERP000993 and PRJEB7951). Four samples were multiplexed on each sequencing lane with the fifth instar larval and day 2 pupal samples sequenced on a GAIIx sequencer and the day 3 pupal wings sequenced on a Hiseq 2000 sequencer. Two methods were used for alignment of reads to the reference genome and inferring read counts, Stampy43 and RSEM (RNAseq by Expectation Maximisation)54. In addition we used two different R/Bioconductor packages for estimation of differential gene expression, DESeq55 and BaySeq 56. Read bases with quality scores < 20 were trimmed with FASTX-Toolkit (http://hannonlab.
cshl. edu/fastx_toolkit/index. html). Stampy was run with default parameters except for mean insert size, which was set to 500, SD 100 and substitution rate, which was set to 0. 01. Alignments were filtered to exclude reads with mapping quality <30 and sorted using Samtools57. We used the HT seq-count script in with HTseq58 to infer counts per gene from the BAM files. RSEM54 was run with default parameters to infer a transcriptome and then map RNAseq reads against this using Bowtie37 as an aligner. This was run with default parameters except maximum number of mismatches, which was set to 3.
Annotation and alignment of fizzy family proteins In the arthropod genomes, some fizzy family proteins were found to be poorly annotated based on alignments to other family members. In these cases annotations were improved using well annotated proteins from other species as references in the program Exonerate41 and the outputs were manually curated. Specifically, the annotation of B. mori fzr was extended based on alignment of D. plexippus fzr; the annotation of B. mori fzy was altered based on alignment of Drosophila melanogaster and D. plexippus fzy; H. melpomene fzy was identified as part of the annotated gene HMEL017486 on scaffold HE671623 (Hmel v1. 1) based on alignment of D. plexippus fzy; the Apis mellifera fzr annotation was altered based on alignment of D. melanogaster fzr; the annotation of Acyrthosiphon pisum fzr was altered based on alignment of D.
Acknowledgements We thank  Christopher Saski ,  Clemson University , for assembly of the  He BACs. Moises Abanto  and  Adriana Tapia  assisted with raising butterflies. Thanks to  Mathieu Chouteau ,  Jake Morris  and  Kanchon Dasmahapatra  for providing larvae for in situ hybridisations. Anna Morrison ,  Robert Tetley ,  Sarah Carl  and  Hanna Wegener  assisted with lab work at the  University of Cambridge. Simon Baxter made the Hm fosmid libraries. We thank the  governments of Colombia, Ecuador, Panama and Peru  for permission to collect butterflies. This work was funded by a  Leverhulme Trust  award and  BBSRC grant  ( H01439X/1 ) to CDJ, NSF grants ( DEB 1257689, IOS 1052541 ) to WOM, an  ERC  starting grant to MJ and a  French National Agency for Research (ANR)  grant to  VL  ( ANR-13-JSV7-0003-01 ).
data generated for this study are available from ENA (http://www. ebi. ac. uk/ena) under study accession PRJEB8011 and PRJEB12740 (see Supplementary Table  1  for previously published data accessions). The updated Cr contig is deposited in Genbank with accession KC469893. The assembled Hm fosmid sequences are deposited in Genbank with accessions KU514430-KU514438. The microarray data are deposited in GEO with accessions GSM1563402-GSM1563497. Reprints and permissions information is available at www. nature. com/.
Coimmunoprecipitation experiments HEK 293T cells were transfected in the presence of Lipofectamine 2000 (Invitrogen). One of the following combinations of constructs were used for transfection: (1) a cDNA encoding the GFP-tagged myosin IIIa tail alone (aa 1,107-1,621; accession number NM_148413); (2) a cDNA encoding the GFP-tagged myosin IIIa tail with a cDNA encoding the Flag-tagged MORN4 fragment (aa 1-146; accession number NM_198108); or (3) a cDNA encoding the GFP-tagged myosin IIIa tail with a cDNA encoding the Flag-tagged spectrin βII-R16 fragment, used as a negative control (C-terminal region of spectrin βII starting from the spectrin repeat 16; aa 1,914-2,364; accession number NP_008877. 1). Protein extracts were incubated at 4°C for 2 h with a mouse monoclonal antibody directed against FlagM2 (Sigma-Aldrich) and then for 30 min with protein G-coated µMACS magnetic MicroBeads (Miltenyi Biotec).
Acknowledgments We thank  Jean-Pierre Hardelin  for critical reading of the manuscript. This research was funded by the  European Research Council  (advanced grant " Hair bundle ,"  ERC-2011-AdG294570 ), the  Agence Nationale de la Recherche  ( ANR ; grant  EAR MEC ,  ANR-11-BSV5-0011 ),  ANR program  " Investissements d'Avenir " (grant  ANR-10-LABX-65 ),  Errera Hoechstetter , and the  Foundation BNP Paribas. The authors declare no competing financial interests. Submitted: 2 September 2015 Accepted: 7 December 2015.
, 2015; Honeybee Genome Sequencing Consortium, 2006; International Aphid Genomics Consortium, 2010). Taste receptors with seven transmembrane domains have been reported for these three species using algorithms for computational searching based on structural features and on genomic databases  (Clyne et al. , 2000; Kim et al. , 2000). In Drosophila, 68 GRs have been identified, and for most of them, the respective patterns of expression in taste neurons of larvae, adult legs, and proboscis are presently known  (Scott et al. , 2001; Robertson et al. , 2003; Chyb, 2004). In aphids, 77 taste receptors have been found using sequence homology  (Smadja et al. , 2009). For the honeybee, only ten GRs have been described  (Robertson and Wanner, 2006). Here, we report the expression of GRs identified by PCR analysis in the wing of these three species, suggesting the universality of this sensory modality.
The dose response with three concentrations of glucose and denatonium is presented in an additional figure (Figure  S1B ). The statistical analysis of the fluorescence intensity induced by tastants on 50 sensilla at the same location is reported in the insets of Figure  3. Transcripts of Taste Receptor Genes Are Detected in the Drosophila Wing Specific sequences of identified chemosensory receptors in insects for which genome databases are available were used to design primers for RT-PCR analysis. Our first goal was to detect the presence of RNAs coding GR proteins in the wings of three phylogenetically divergent insects, D. melanogaster, A. mellifera, and A. pisum, which were used as key models for the Diptera, Hymenoptera, and Hemiptera orders, respectively. The gene organization (introns/exons) of the tested GRs is presented in the Supplemental Results, along with the sequences and locations of primers.
(B) Flies were placed in the chamber, and the accumulation in the cylinder was measured 2 days after the start of the experiment. Gray bars represent the mean (±SEM, n = 10) percentage of flies that entered the small cylinder. Experiments were conducted with (on) or without (off) the pulverized sugar solution. A significant difference as determined using a Tukey non-parametric test (***p < 0. 001) indicates that Poxn W flies diverged from the three control genotypes. (C) A kinetic study was performed between 0 and 48 hr for the four genotypes (data represent the mean ± SE, n = 10, Tukey non-parametric test: ***p < 0. 001, **p < 0. 01 for Poxn W versus CS flies). (D) The four strains were tested for their accumulation in the cylinder, as stimulated by the nebulization of three substances (100 mM glucose, 10 mM quinine/ 10 mM denatonium, and water control).
The behavioral data obtained using this approach suggest that these wing sensory organs are required for the exploration and subsequent aggregation on food sources. The orientation/exploration response could rely on a sophisticated coincident system, such as the wing margin nerve integrating the inputs of both mechano-and chemosensory neurons. Although the two types of neurons are spatially separated along the wing margin, they may burst in synchrony following mutual stimulation by air friction and chemical capture. The absence of the vibratory component in the dissected wing may partly affect the calcium diffusion in the wing axons after chemo-stimulation, leading to an underestimation of its effects compared with the diffusion in a flapping wing. We hypothesize that the air vortex created by wing vibrations during flight and/or grooming facilitates the access of hydrosoluble molecules inside the gustatory wing hairs.
ACKNOWLEDGMENTS We are very grateful to  Gilbert Engler  for assistance in confocal microscopy and fruitful discussions. Angela Algeri  is greatly thanked for her critical reading of the manuscript, and H. R. thanks  Laury Arthaud  and  Sophie Tares-Amichot  for technical help. The  Drosophila Bloomington Stock Center  is acknowledged for fly stocks. This work was supported by the  ANR ''blanc'', acronym: '' Gustaile '', and by the  French Government (National Research Agency, ANR)  through the  LABEX SIGNALIFE program  (reference #  ANR-11-LABX-0028-01 ). Received: August 18, 2015 Revised: February 16, 2016 Accepted: April 5, 2016 Published: May 5, 2016.
, 2007) , bottlenose dolphins in particular possess 23 G protein-coupled OR genes that are not pseudogenized, thus potentially functional (SEVENS database of G-protein coupled receptor genes; available at: http://sevens. cbrc. jp/search. php?db=ttru&level=4). Similarly, taste receptor genes were found to be mostly pseudogenized in Odontoceti: in bottlenose dolphins sweet, umami, bitter and sour taste receptor genes were found to be inactivated, whereas salty taste receptor genes are intact and potentially functional  (Jiang et al. , 2013; Feng et al. , 2014; Kishida et al. , 2015). Finally, go/nogo behavioral tests with trained bottlenose dolphins showed that they can perceive sour, bitter and salty tastes nearly as well as humans  (Nachtigall and Hall, 1984; Friedl et al. , 1990; Kuznetzov, 1990).
Data Collection The same fish were presented to the group of dolphins on three consecutive days (thus producing an increasingly intense odor of rotten fish) with two sessions per day with the largest possible time interval between experimental and feeding session. Three days made up for an experimental section. Three sections were done in total with a new mixture of fish each time, leading to 18 sessions in total over 9 days (Table  1 ). During a section, the fish was stored at room temperature to facilitate the change in odor composition. This change was also detectable for humans who could discriminate between the fish and the control barrel based on their smell only. During the experimental sessions, that lasted 10 min, each barrel was videorecorded with a Sony HDR-XR155 video camera and neither the experimenter nor another person was close to the pool.
Data analysis Statistical analyses were run using R software (version 2. 15. 0, R Development Core Team, www. r-project. org). We calculated a Respiration Bias Index (RBI) using the following formula: (#Respi fish -#Respi control ) (#Respi fish +#Respi control ) , resulting in RBI values ranging from +1 to -1. Thus, positive RBI values indicate a bias in respiration activity toward the fish barrel (i. e. , dolphins breathe more often in the area around the barrel containing fish compared to the control barrel). Accordingly, negative RBI values indicate a bias in respiration activity toward the control barrel (i. e. , dolphins breathe more often in the area around the control barrel compared to the barrel containing fish). This kind of index is common for example in primate laterality studies  (Hopkins, 1999).
Data Collection Experimental sessions were performed 1-5 times per day between the feeding sessions (with the largest possible time interval between experimental and feeding session) and lasted on average 8 ± 2 min. Two experimental sessions were separated by at least 60 min. During one experimental session one single flavor was tested. The four stimuli were tested consecutively, meaning we completed all sessions for a given flavor before testing a new flavor: first herring, then salmon, followed by shrimp and last control (the order of the four stimuli was chosen randomly). All dolphins were together in the pool and when they saw the experimenter coming, they immediately and spontaneously approached her standing at the side of the pool. The dolphins were free to participate, meaning that they received ice cubes only when begging (i.
Data Analysis Statistic calculations were run using R software (version 2. 15. 0, R Development Core Team, www. r-project. org). As we predicted that all dolphins would react differently to fish (salmon/herring) vs. non-fish (shrimp/control) items or to familiar (herring/control) vs. unfamiliar (salmon/shrimp) food, we compared begging latencies between different flavors by using a Wald test on a Linear Mixed Model, considering the individual as random factor (R-package: lme4). Data have been log-transformed prior to analyses in order to homogenize the variances. Pairwise comparisons were performed with the contrasts method (correction for multiple testing: false discovery rate; R-package: doBy). As dolphins were unrestrained in this experiment, number of ice cubes received differed between individuals and between different tastes.
Another possibility relates to choices based on taste, odor, or flavor differences. In line with this, salmon and herring, the flavors eliciting longer latencies in our study, are about 1. 5-2. 5 times more energetic than shrimps (National Nutrient Database for Standard Reference, US National Agricultural Library; available at: http://ndb. nal. usda. gov/). Learned flavor preference may also underlie the choice of dolphins for some foods over others. In the wild, the diet of bottlenose dolphins is primarily composed of fish (94. 2% of stomach contents in stranded dolphins), whereas crustaceans are only occasional prey (2. 0%;  Spitz et al. , 2006). This might be caused, aside from differences in pelagic vs. benthic preys, by a spontaneous or learned preference for the flavor of fish. Finally, chemoreceptive abilities may be useful during sociosexual interactions.
FUNDING This work was supported by the  Centre National de la Recherche Scientifique  ( Groupement de Recherche 2822 ), by the  Agence Nationale de la Recherche  (grant ORILANG to AL) and the  Institut Universitaire de France. Further, it was funded by the  Association Nationale de la Recherche et de la Technologie  ( CIFRE grant  # 2010/0471  to DK). No funding source played a role study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Θ =0 • Θ =22. 5 • Θ =45 • 0 500 Time(ms) Trials Θ =67. 5 • Θ =90 • Θ =112. 5 • Θ =135 • 0 500 Θ =157. 5 • Θ =180 • Θ =202. 5 • Θ =225 • 0 500 Θ =247. 5 • Θ =270 • Θ =292. 5 • Θ =315 • 0 500 Θ =337. The paper is organized as follows. First, we describe the Negative-Binomial encoding model (NBM) and two possible implementations of over-dispersion sources. Second, we propose a statistical test for evaluating whether the observed over-dispersion is significantly better described by the Negative-Binomial model than the Poisson model. To assess the generality of this model, we then apply this test to three data sets of spiking responses in different animals, different states and different visual stimuli: in the lateral geniculate nucleus (LGN) neurons of anaesthetized mice presented with drifting gratings, in the primary visual cortex (V1) of awake macaque monkeys viewing oriented moving bars and in the middle temporal cortex (MT) of anaesthetized macaque monkeys presented with drifting gratings.
Thus, this analysis provides a formal test for both hypotheses (PM and NBM) given the observed variability in the spiking. We first compared the power of this test to the chi-square test for the general NBM hypothesis and for the specific PM hypothesis on surrogate data of spike counts. Figure  4  shows that the Fano Gamma test did better than a chi-square goodness of fit test at a significance level of 0. 025. This latter test has a larger probability to make a type II error for both distributions (dashed red lines in Figure  4. C and dashed blue lines in Figure  4. D). Over-dispersion evidence from neurophysiological data We applied these tests to evaluate evidence against 2  the Poisson model on three data sets. The first data set results from recording signals from mouse LGN neurons (72 cells) in response to moving gratings.
The Fano Gamma test shows, as do the other tests, that the evidence of over-dispersion is only significant in MT (with a rate of 64%) among cell/condition pairs, compared to LGN and V1 (with rates of 19% and 26%, respectively). Similarly, it shows that evidence against the NBM is significantly lower in the MT dataset (15%) compared to LGN and V1 data sets (52% and 41%, respectively). For these particular datasets where the number of trials is limited (mostly less than 20 trials per condition), we have shown that in the LGN and V1 data sets, the high values of Fano factor observed could be simply related to the sampling of the stochastic process itself. Indeed, looking only at the FF values induces a false belief that these datasets exhibit a significant degree of over-dispersion, while the FG test reliably rejects this hypothesis.
The χ 2 test shows slightly more evidence against the Poisson model than the FG test which could be explained by categorization issues. It shows also very strong evidence against the NBM model known to be suitable for over dispersed data. Finally, we have shown that the situation is different for the MT data set where we could indeed show evidence of a higher variability than expected from a Poisson model and less evidence against a Negative Binomial model. It is worth mentioning that our statistical test (as validated on surrogate data) allows us to make this conclusion, while other tests fail to do so. Since we mostly observe over-dispersion in the MT data set, we propose to compare the performance of an NBM-based to a PM-based direction decoder applied to this data set.
The results show that there is more evidence of over-dispersion in MT and that the proportion of cells showing over-dispersion seems to increase along the visual hierarchy. (B) The percentage of observations with evidence against PM and NBM are given with respect to three criteria: the Fano factor test discards the observations with a Fano factor value higher or lower than its formal expected value (equal to one for the Poisson model), the χ 2 test and the F G test with a significance level of 0. 025. The bar plots correspond to the percentage of observations with evidence against the NBM (left) and the PM (right). The tests were applied to 3 data sets (from the left to the right, respectively): LGN cells (mouse), V1 cells (awake macaque monkey) and MT cells (anaesthetized macaque monkeys).
The results show that the Fano factor values are misleading when the number of trials is not taken into account. Looking only at the FF gives strong evidence against PM and NBM that is not confirmed by the other tests. The χ 2 test shows slightly more evidence against the Poisson model than the FG test which could be explained by categorization issues. It shows also very strong evidence against the NBM model known to be suitable for over dispersed data. In summary, the FG test gives more coherent results: weak evidence of over-dispersion in LGN and V1 data sets and low evidence against the NBM in MT data set. coded variable (direction θ). This decoding approach allows us to infer the stimulus (the hidden variable θ) given a particular neuronal response (an observed spike raster Y). The decoding algorithm consists of maximizing the posterior probability P (θ|Y ) function of the estimated direction θ given a distribution hypothesis.
These correspond to the mean spike count (the mean parameter of the Negative-Binomial model) with respect to direction. Thus, we defined for the MT data set a generative tuning function as von Mises functions peaking on the preferred direction. The parameters corresponding to the scaling, maximum and minimum values (κ o , κ d , R 0 , R 0 +R m ) are free to vary. The main tuning function is given by: f M T (θ) = R 0 + R m • e κ d •cos(θ-θ0) -e -κ d e κ d -e -κ d (10) Figure  6. B shows the estimated tunings of f for 16 different cells. Then, we should consider the estimation of inverse-dispersion parameter φ. We tested two hypotheses. First, having no prior on the tuning of φ as a function of the stimuli direction, we supposed that this parameter is stimulus independent (constant chosen as the average value of its estimates over the different conditions) and varying between cells.