dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis 4. 4. 1. Amino acids Among the 22 amino acids we listed in our external standards, 21 were correctly detected and quantified (i. e. cysteine did not provide reliable measurements, as this amino acid is unstable and is quickly transformed into cystine during derivatization). Significance of amino acid fingerprintings among species and rearing conditions was assessed using multivariate discriminant analyses and MANOVAs. Because false discovery rate can be induced by multiple comparisons, we used a Benjamini-Hochberg procedure to adjust all P-values. Comparisons of the amino acid fingerprints of each experimental modality showed significant differences in the concentration of at least one amino acid between the two species, their ageing and their rearing conditions. Of particular interest, both species and especially M form, accumulated phenylalanine, tryptophan, tyrosine, and valine 1 h after emergence (Fig.
4A  and B ). S form specimens did not show fingerprint variation according to the rearing conditions 1 h after emergence, but significant changes were observed 24 h after emergence, with proline being accumulated under dry conditions (Fig. 4B ). For details about data interpretation and conclusion, please report to Hidalgo et al. [1]. 4. 4. 2. Sugars Among the six sugars listed in our reference standard, only galactose and glucose were reliably quantified in our mosquito samples. Indeed, arabinose, fructose, ribose and trehalose were below the quantification limit of the equipment used. Significance of the galactose and glucose concentrations variation among species and rearing conditions was assessed using Kruskal-Wallis tests, followed by a Bonferroni procedure resulting in a decreased threshold of significance from P-value o0.
Acknowledgements These assays were supported by the  Agence Nationale de la Recherche  through grant  ANR-08-MIEN-006  to FS. The authors would like to thank  Moundai Tchonfienet ,  Sougrinoma Zoungrana  and  Boubakar Nikiema  ( IRSS, Bobo-Dioulasso ) for their help in mosquito rearing and sample preparation. Authors would like to thank the  PRIDE team  for their support.
05). b Protein accession numbers from the National Center for Biotechnology Information non-redundant (NCBInr) database. c Positive values mean more expressed in "rs" (rainy season) conditions whereas negative values mean over expressed in "ods" (onset of the dry season) conditions. d Protein functions are checked using http://www. uniprot. org. K. Hidalgo et al. / Data in Brief 5 (2015) 255-268.
0 × 10 -5. The allele specificity was assessed by mapping the pooled ZFP57/KAP1 reads against the B6 genome (NCBI37/mm9) and the JF1 genome (built using a Python custom script that takes into consideration the SNP data set from NIG Mouse Genome database (34)) individually and not allowing mismatches (Bowtie option -v 0). Duplicate reads and reads overlapping insertion or deletion were removed. We defined as informative reads (IR) those overlapping SNPs in both genomes and allele specific reads ASR B6 and ASR JF1 the reads matching the SNPs in the B6 and JF1 genomes, respectively. To take into consideration the mapping bias, the same pool of reads were processed in the same manner but allowing up to three mismatches (Bowtie option -v 3) to define the informative control reads (IR C ) and the allele-specific control reads (ASR C B6 , ASR C JF1 ).
Also the motifs of the 20 known ICRs were generally not affected by SNPs. At only one ICR (Peg13), one of the five motifs was mutated on the JF1 allele, but this did not abolish ZFP57 binding (data not shown). We then analysed in further details the only locus (Ston2) in which a SNP disrupted the TGCCGC motif in the binding-permissive allele (see above). In this case, a G > C mutation in one of two closely spaced (2-3 bp) motifs changed their relative orientation. Intriguingly, the JF1 allele with the motifs in inverted orientation was associated with binding while the B6 allele with the motifs in the same orientation was not (Figure  2E ). We then tested in vitro the binding of ZFP57 to these alternative sequences. Specifically, the protein fragment containing the DNA binding domain of ZFP57 was produced in E.
ACKNOWLEDGEMENTS The authors would like to thank  Dirk Sch  übeler and  Ann Dean  for plasmids, cells and protocol for RMCE,  Tuncay Baubec  for advise on RMCE,  Genesia Manganelli  for teaching ESC culturing,  Stefano Pepe  for advise on nucleofection and  Basilia Acurzio  for assistance on DNA cloning. H. K. and S. L. were recipient of  Marie Curie  fellowships from the  EU-FP7 -ITN INGENIUM.
VOL. 65 For extinct species, we inferred body mass using previously established inference models taken from the literature and from skeletal measurements of fossil material. In several cases, inferences were taken directly from the literature. The body mass was thus inferred or compiled for 553 extinct mammal species ranging in geological age from the Late Jurassic (Oxfordian) to the Pleistocene, but most paleontological data date from the Cenozoic. The compilation also includes the geological age (online Supplementary Data File S1, available on Dryad at http://dx. doi. org/10. 5061/dryad. 4nd2j), determined as accurately as possible. Typically, this is at a lower level than epoch, and using the land mammal ages. The compilation also includes the phylogenetic position (online Supplementary Data File S2, available on Dryad at http://dx.
doi. org/10. 5061/dryad. 4nd2j), for which we tried to select the most consensual interpretation from the recent literature, although this exercise is necessarily a bit subjective. When the phylogeny is not consensual or poorly resolved, we inserted polytomies to reflect this uncertainty. The tips of the branches were adjusted to reflect the geological age of the fossils of each taxon. The length of the branches subtending the extinct taxa is unknown because the age of the last common ancestor of various extinct or extinct and extant taxa is unknown. However, given the richness of the mammalian fossil record, it is reasonable to assume that most branches were fairly short (except when other fossils imply long ghost lineages). Thus, we adjusted the terminal branches subtending extinct species to occupy at least 1 myr and the internal branches to occupy at least 0.
ML centralized the data and built the timetree (with some help from FJ) according to these data, and in consultation with the paleomammalogists. These body mass estimates of extinct species and the phylogenetic tree we used are available at http://dx. doi. org/10. 5061/dryad. 4nd2j. Even though we have estimates of the body masses of very many extant and extinct species, there remain many clades of which we do not have body mass estimates of extinct species. In addition, the fossils for which we have body size estimates will generally not be the direct ancestors of present-day species. However, we do have prior hypotheses also about the body masses of the unobserved ancestral and present-day species. Typically, we assume that the body mass of an ancestral species very approximately equals the body mass of the present-day species that descended from it.
(2012). For the species that were measured, we can plot estimated body size a against measured body size m, which shows that this procedure yields quite accurate predictions (m pred = 0. 96m obs +0. 22, r 2 = 0. 95; Supplementary Fig. S1 , available on Dryad at http://dx. doi. org/10. 5061/dryad. 4nd2j). Evidently, the discrepancy between m and a is smaller for species with measured close relatives, and worse for species that appear isolated on the phylogeny. This leads to the question of what is an appropriate variance v for the priors on ancestral body masses. To infer appropriate v for every node in the tree, we made use of the concept of Fisher information: the second derivative of the likelihood function at its maximum. At the maximum of the likelihood function, its first derivatives are of course zero.
In reality, there will be less information available about X, because m Y itself is not known without error (Supplementary Fig. S2 , available on Dryad at http://dx. doi. org/10. 5061/dryad. 4nd2j). Let v Y denote the prior variance around m Y. Then, the information about X provided by Y is: 1/(v Y +s 2 t XY ). In other words, Y provides more information about X if it is known with little error (small v Y ), if the rate of evolution is low (small s 2 ), and if the branch between the species is short (small t XY ). Fisher information has the attractive property of being additive for independent observations. If X has another descendant Z, then the information about X provided by Z is 1/(v Z +s 2 t XZ ), and independent of the information provided by Y. Therefore, the Fisher information about X provided by its ancestor W and two descendants Y and Z is 1 /(v W +s 2 t WX )+1/(v Y + s 2 t XY )+1/(v Z +s 2 t XZ ).
Therefore, early species have a far greater effect on the prior distribution of the ancestral body size of the deepest nodes than present-day species have  (Ané 2008). The first influence is on the prior means: if fossils tend to have small body sizes, this markedly decreases the prior expectation on the mean body sizes of nearby ancestral nodes (Supplementary Fig. S2 , available on Dryad at http://dx. doi. org/10. 5061/dryad. 4nd2j). The second effect is on the variance around the mean, which becomes much smaller for deep nodes, due to shorter branches between these nodes and extinct lineages than between deep nodes and present-day species (Supplementary Fig. S2 , available on Dryad at http://dx. doi. org/10. 5061/dryad. 4nd2j). Thus, our approach illustrates how fossil and present-day information can be integrated without assuming that the ancient species represent direct ancestors of present-day species.
To be able to investigate the early population history and migratory patterns of taurine cattle in detail, the present model is now conditioned on a larger ancient (n = 193, including the Iranian samples) and modern (n = 597) mtDNA dataset that widely covers the area of the Neolithic westwards expansion from the 7th millennium BCE onwards. The focus of the study is on the time period when cattle were first introduced to Europe, thereby allowing us to address the following questions: i) Is the scenario of a single and severe domestication bottleneck in the Near East still supported when adding the much larger dataset from western Anatolia and Europe? ii) Did cattle reach Europe in a single dispersal process or is there evidence for multiple introductions or continuous gene-flow between regions? iii) How much of the genetic diversity from the Near East was introduced to the European continent? iv) Did the spread of cattle coincide with the spread of the Neolithic culture? and v) Are there any signs of admixture with female aurochs along the expansion route of domestic cattle?.
Methods Material 150 samples of prehistoric domestic cattle from 24 archaeological sites were taken to analyse a 434 bp long mitochondrial d-loop fragment (for detailed information on the archaeological sites and sample age see Additional file 1. The majority of investigated individuals (113) come from Western Anatolia and Southeastern Europe, i. e. a region defined as an "interim zone"  [5] , pointing to its bridging position between the "Neolithic core zone" and its European fringe. A further 22 samples from Southern France and Southern Italy represent the first domesticated cattle to reach Europe on the "Mediterranean route" of the Neolithic expansion. Additionally, new prehistoric samples from Germany (4), Northern and Western France  (11)  and Syria (1), plus 80 previously published sequences mainly from Central and Western Europe and Iran were used for population genetic analyses (  [23] [24] [25] [26] [27] [28]  and GenBank: KC172647 -KC172649).
A total of 597 modern d-loop sequences of 240 bp length were collected from previously published studies  [29, 30]. They each provide representative sets of sequences that match the European, Anatolian and Near Eastern study area of the present paper, thereby also covering areas which are underrepresented in the aDNA dataset, e. g. Italy and the Iberian Peninsula. For a complete list of GenBank accession numbers of previously published sequences see Additional file 2.
Descriptive and summary statistics All new and previously published ancient DNA sequences were subdivided into the following geographical groups: Iran/Syria, Western Anatolia, Southeastern Europe, Southeastern Central Europe, Italy, Southern France, Central/Western Europe, and Spain. These groups were further subdivided into chronological subgroups reflecting up to four different Neolithic and post-Neolithic periods per region (see Additional file 4 for detailed information on the groupings). Modern sequences were grouped according to their country of origin (also see Additional file 2). For statistical analyses, all ancient sequences were cut to a 399 bp fragment to match the fragment sizes of previously published ancient sequences (positions 15,914-16,312 according to reference sequence GenBank V00654).
From the split time until 5,000 years BCE migration between the two populations is allowed at per generation rate M E ('early migration'), after which it is changed to rate M L ('late migration'). Prior values for N D are drawn uniformly from the range 1 -1,000, P uniformly from the range 0 -1, and both migration parameters uniformly from the range 0 -0. 01. The mutation rate is fixed at 45% per million years, the posterior modal value previously estimated by Bollongino et al. [23]. We used the above-mentioned 597 previously published modern sequences of 240 bp length (positions 16,023-16,262 according to reference sequence GenBank V00654) and cut the ancient sequences accordingly. The resulting 790 sequences were grouped into 4 sample groups: ancient Near Eastern and Anatolian (n = 24), ancient European (n = 169), modern Near Eastern and Anatolian (n = 100) and modern European (n = 497).
Results Out of 150 newly analysed bones and teeth from prehistoric domesticated cattle, 113 yielded replicable and highly reliable mitochondrial HVR1 sequences, constituting a success rate of 75. 3%. The sequences have been deposited in GenBank [GenBank: KF307209 to KF307322]). None of the blank controls contained amplifiable amounts of bovine DNA (for further detailed discussion of the validity of the ancient DNA data see Additional file 6). The successfully analysed samples come from Bosnia-Herzegovina (3 of 5), Bulgaria (52 of 68), France (15 of 19), Germany (4 of 4), Italy (5 of 14), Romania (15 of 16), Syria (1 of 1), and Turkey  (18 of 23). Using the nomenclature of Achilli et al. [41] , all sequences belong exclusively to lineages from haplogroups that have previously been defined in present-day European domesticated cattle, namely T3 (n = 70), Q (n = 33), T2 (n = 6) and T, T5 or T1'2'3 (n = 4).
None of them belongs to a specific mtDNA motif referred to as haplogroup P that is dominating in the indigenous aurochs population of Europe  [26, 27, 42]. It is of note that the high frequency of haplogroup Q in ancient Southeastern Europe (between 50% and 29% in 5,500-5,000 BCE and 2,700-2,200 BCE, respectively) does not match present-day haplogroup distributions of taurine cattle from Europe, and particularly from the same area (combined frequency for T and Q in present-day Balkan and Greece: 1. 5 -2%  [43] ). It is also markedly higher than in all other ancient European groups (e. g. only 4% in Central/Western Europe (5,400-4,400 BCE)). See Additional file 4 for detail on haplogroup composition and frequency of haplogroup Q across the 13 spatiotemporal groups. There are 35 different mitochondrial lineages in the 193 prehistoric individuals, eight of which occur more than once in the dataset.
Non-unique haplotypes (H) were named according to their haplogroup and numbered consecutively (H1-H8). Additional files 4 and 7 provide a detailed overview on the distribution of haplogroups and shared and unique haplotypes across the 13 spatiotemporal groups. Only haplotypes called T3_H1, Q_H4, and T2_H7 occur more than twice in the dataset (114, 37, and 5 times, respectively), with T3_H1 also being predominant in present-day taurine cattle. Haplotype T3_H1 occurs in all of the ancient 13 spatiotemporal groups, Q_H4 in all except Spain 2,700-1,600 BCE and Southern France 5,500-4,500 BCE. It is of note that Q_H5, T_H6, and T2_H7 are restricted to the geographical groups of Iran/Syria and Southeastern Europe (Q_H5 in Iran/Syria 4,000-1,400 BCE and Southeastern Europe 6,200-5,500 BCE; T_H6 in Iran 7,000-5,000 BCE and Southeastern Europe 2,700-2,200 BCE; T2_H7 in Iran 7,000-5,000 BCE and Southeastern Europe 5,500-5,000 BCE).
Discussion The domestication process of taurine cattle Using an ancient (n = 193) and modern mtDNA dataset (n = 597) of domesticated cattle from the Near East, Anatolia and Europe for coalescent simulation and approximate Bayesian computation, we inferred a (joint) posterior mode of 81 female founder individuals at the beginning of the domestication process. This result is consistent with the previous estimate based on 15 ancient Iranian and 27 modern Near Eastern and Anatolian cattle  [23] , and demonstrates that this initial finding of a very strong Near Eastern bottleneck is robust even with a greatly expanded continent-wide data set, and is not biased by theoretically possible subsequent introgression from aurochs populations outside the Near East. It can therefore be concluded that domestic cattle indeed have a discrete and rather localised origin, very likely in Southeastern Anatolia and the Near East, a view that is consistent with a huge body of archaeozoological evidence from the 9 th millennium BCE  [44] [45] [46].
47 and 0. 4, respectively). Smaller genetic distances are observed between more adjacent areas, such as between Iran and Western Anatolia and between Iran and Southeastern Europe (0. 11 and 0. 17, respectively). Other statistics are equally consistent with the serial dilution model: Neolithic cattle from Iran yield the highest value for haplotype diversity in the whole dataset (0. 96). Haplotype diversity consistently decreases along the proposed two main Neolithisation routes, with the lowest values in remote areas, i. e. in Neolithic Central/ Western Europe and Southern France (0. 22 and 0. 00, respectively), while intermediate values are observed in between. Alternative scenarios of secondary domestications or traceable female gene-flow from wild aurochs in Europe have been discussed several times in the literature  [29, [47] [48] [49] [50] [51].
g. by expanding the existing dataset to encompass finds from diverse archaeological sites and later chronological phases. However, the existing dataset from the rest of Europe suggests that introgressions of local genes into the imported domestic cattle populations are rare and geographically restricted exceptions, or coming from male aurochs. Separate independent domestication(s) of European aurochs can almost with certainty be excluded. The strict separation of domestic cattle from their wild European relatives is very different to what can be observed in other animals. For example, pigs were imported to Europe in a similar way to cattle, but after a few centuries all their mitochondrial lineages were replaced through admixture with local wild boar  [52, 53].
Acknowledgements This work was funded by  German Archaeological Institute ,  Johannes Gutenberg-University Mainz , and  CNRS  ( Centre national de la recherche scientifique ). The authors would like to thank  Adrian Bӑlӑşescu ,  Cornelia Becker ,  Daniel Bradley ,  Altan Çilingiroğlu ,  Çiler Çilingiroğlu ,  Giuliano Cremonesi ,  Keith Dobney ,  Ceiridwen Edwards ,  Ralf Gleser ,  Angela Graefen ,  Jean Guilaine ,  Svend Hansen ,  Daniel Helmer ,  Robert Hofmann ,  Raiko Krauß ,  Marion Lichardus-Itten ,  Claire Manen ,  Ingo Motzenbäcker ,  Mehmet Özdoğan ,  Jean Roudil ,  Silviane Scharl ,  Nils Müller-Scheeßel ,  Wolfram Schier ,  Mona Schreiber ,  Elisabeth Stephan , and  Bernhard Weninger  for providing bone material and for helpful discussions.
2 ), and a rate of 0. 1 yr -1 sr -1 at z 5 (bottom panel of Fig. 2 ). These rates are consistent with the Fermi detection rate above this flux limit. Fig. 4  shows the fluence cumulative distribution of the simulated population. Color codes are as in Fig. 3. We note that the curves corresponding to different energy ranges are very similar at relatively low fluence values. This is because we show here the energy fluence, i. e. in units of erg cm -2 (as typically reported in GRB databases). In the appendix (Fig. A1 ) the same cumulative curves are shown with the photon fluence (i. e. in units of ph cm -2 ) which might also be useful in estimating the detection rate of GRBs for an instrument working with image triggers. As explained for the peak flux, each curve represented in Fig. 3  should be compared with the fluence limit in the corresponding energy range.
ACKNOWLEDGMENTS We acknowledge a  Prin-INAF 2011 grant  ( 1. 05. 01. 09. 15 ) and  ASI-INAF  contract  (I/004/11/1 ). We thank the anonymous referee for his/her valuable comments that improved the manuscript. Specific simulations for energy ranges not considered in this paper can be provided on request. JPO acknowledges partial support from the  UK Space Agency. DB is funded through  ARC  grant  DP110102034. DG acknowledges the financial support of the  Uni-vEarthS Labex program  at  Sorbonne Paris Cité  ( ANR-10-LABX-0023  and  ANR-11-IDEX-0005-02 ).
RNA-seq library preparation and analysis Library preparation and Illumina sequencing were performed at the École Normale Supérieure Genomic Platform (Paris, France). Messenger (polyA+) RNAs were purified from 800 ng of total RNA using oligo(dT). Libraries were prepared using the stranded RNA-Seq library preparation TruSeq RNA Sample Prep Kits (Illumina). Libraries were multiplexed by four on three single flow cell lanes and subjected to 50 bp paired-end read sequencing on a HiSeq 2000 device. A mean of 47 ± 6 million passing illumina quality filter reads was obtained for each of the 12 samples. Transcriptomic analysis was performed as three experimental replicates from which mean values were calculated and presented. RNA-Seq data analysis was done using the EOULSAN software version 1. 2. 2  (Jourdren et al.
, 2012). Before mapping, poly N read tails were trimmed, reads ≤ 40 bases were removed, and reads with quality mean ≤ 30 were discarded. Reads were then aligned against the T. reesei reference genome (http://genome. jgipsf. org/Trire2/Trire2. home. html) using Bowtie mapper (version 0. 12. 7) using the '-n 2 -l 34 -e 70 -k 2 --best' parameters. Only one alignment was kept in a given locus for each read, and read alignments matching on more than one locus were removed. To compute gene expression, the T. reesei genome annotation was used Gene expression was computed by counting all overlapping regions between alignments and referenced exons. To quantify the gene expression level, the relative transcript abundance was measured in reads per kilobase of exon per million mapped sequence reads (RPKM;  (Mortazavi et al.
, 2008) ). The log2 ratios of the RPKM values were used to identify differentially expressed genes. To keep only the most differentially expressed genes, thresholds of four (sophorose vs. glycerol in the retransformant) and five (retransformant vs. Δkap8 on sophorose) for log ratio were used. Read numbers < 100 were considered as (almost) absence of transcription and not chosen for the evaluation. Genes were identified by the aid of a completely manually annotated T. reesei genome database proprietary to C. P. K. The RNA-Seq gene expression data and raw fastq files are available at the GEO repository (http://www. ncbi. nlm. nih. gov/ geo/) under accession number GSE59600.