dataesr/scientific-paragraphs-categorization huggingface dataset:
Non-stationarity is common in experimental datasets. The good performance of some methods presented in the present study may stem from the stationary nature of the simulated data we have used. Some of these methods may perform poorly in nonstationary situations, which would require explicit modeling. In principle, the present framework allows one to test the robustness of methods to violations of stationarity assumptions-by comparing the estimates based on stationary and non-stationary simulations. We hope to address this particular issue in a future paper.
CONCLUSION The pragmatic contributions of this work are to illustrate how one can optimize analysis parameters and to explore the dependency of various functional connectivity measures on the underlying generative process and architecture. The particular analyses presented in this paper illustrate the optimization under some particular generative models. We hope that people will use the MATLAB code that we have made available to reproduce this sort of analysis with a focus on specific issues of interest; for example, the impact of variable haemodynamic delays over regions or nodes. This can be implemented simply by supplying node specific haemodynamic parameters to the fMRI simulations. Clearly, there are many other variations of the generative models and functional connectivity measures one might consider, using the procedures described above.
Our results demonstrate that no single method is optimal for all types of data. We propose that combining methods, knowing when they fail and when they succeed, is the obvious strategy. In our next communication using the framework established in this paper, we will present a MULtiple connectivity ANalysis (MULAN) algorithm. This scheme enables one to identify the most probable connectivity structures by integrating the results of the current methods. Subsequently, we will describe an extension that allows connectivity graphs to evolve dynamically. The schemes described in this paper are implemented in Matlab code and are available freely from in github: https:// github. com/HuifangWang/MULAN.
ACKNOWLEDGMENTS We are very grateful to  Pedro A. Valdés-Sosa ,  Fabrice Wendling ,  Marmaduke Woodman ,  Mathieu Golos  for helpful discussions, and  Andrea Brovelli  for useful discussions and technical help. This work was supported by grants from the  Fondation pour la Recherche Médicale en France FRM :  ING20121226265 , and the  French National Research Agency (ANR MUSIC, MINOS).
gambiae genome with exon lengths between 364 and 1400 bp. Selected exons were transferred to Geneious 5. 1. 5 software (www. geneious. com) and used for finding homologous sequences in the An. nili genome assembly (DDBJ/ EMBL/GenBank accession ATLZ00000000)  [25]  using Basic Local Alignment Search Tool (BLAST). The AnilD1 genome assembly of the An. nili Dinderesso strain is also available at VectorBase for BLAST and download (https://www. vectorbase. org/downloadinfo/anopheles-nili-dinderessocontigsanild1fagz). If no BLAST hits were present, another exon or gene was then selected. Appropriate exons from the An. gambiae PEST genome that had significant e-values (,1e-10) in BLAST searches against the An. nili genome were the candidate genes for further BLAST analysis. These exons were used to BLAST against the An.
gambiae M5 (M form, Mali strain) (https://www. vectorbase. org/organisms/ anopheles-gambiae/mali-nih-m-form/m5) and G4 (S form, Pimperena strain) (https://www. vectorbase. org/organisms/anophelesgambiae/pimperena-s-form/g4) genome assemblies  [34] , the An. stephensi AsteI1 (DDBJ/EMBL/GenBank accession ALPR00000000, VectorBase: https://www. vectorbase. org/downloadinfo/anophelesstephensi-indianscaffoldsastei1fagz) [12, 26] , the C. quinquefasciatus (https://www. vectorbase. org/organisms/culex-quinquefasciatus/ johannesburg/cpipj1)  [27] , and A. aegypti (https://www. vectorbase. org/organisms/aedes-aegypti/liverpool-lvp/aaegl1)  [28]  genome assemblies, as well as An. funestus transcriptome sequences (http:// funcgen. vectorbase. org/annotated-transcriptome/Crawford_et_al_ Anopheles_funestus)  [29].
Gene alignment and phylogenetic analyses Orthologous sequences with significant e-values in the BLAST search were then transferred to Molecular Evolutionary Genetics Analysis (MEGA 5. 05) program  [37]. The sequences were aligned using ClustalW alignment option in MEGA. Alignments were performed by adding the presumed most closely related species followed by the outgroup species. The sequence alignments are available upon request. Phylogenetic trees for each gene were constructed using a NJ method  [38]  in MEGA and a ML based method in RAxML version 7. 5. 3 [39]. A general time reversible (GTR) model with gamma distributed rate heterogeneity of nucleotide substitution (GTR + C) was used in the ML analysis. Support values for each clade were generated in RAxML by 1000 rapid bootstrap replicates.
The 49 genes in 5 chromosomal arms were concatenated into a dataset of 42,300 bp and similarly analyzed using a RAxML tree search, while treating each gene as a separate unlinked partition. PhyloBayes version 3. 3 was then used to estimate node divergence times under a relaxed clock log normal model  [40, 41]. We used the best RAxML tree as a starting topology for PhyloBayes tree searches. Three fossils were used to calibrate the phylogeny: Anopheles dominicanus, 34 million years ago (mya)  [42] , Culex winchesteri, 34 mya  [43] , and Paleoculicis minutus, 70 mya,  [44]. We assigned calibration fossils to the following nodes in the analysis: (1) An. dominicanus to the most recent node shared by An. gambiae and An. nili, (2) C. winchesteri to the most recent node shared by C. quinquefasciatus, and Ae.
aegypti, and P. minutus to the most recent node shared by An. nili and Ae. aegypti. Placement of fossil calibrations were based on the global similarity or intuitive method that searches for the extant taxon that best corresponds to the fossil based on morphological similarity  [45]. The best ML tree from the concatenated dataset was used as a rooted starting topology in the PhyloBayes analysis. Upon completion of the analysis, one fifth of the total chain length was discarded as burnin, and the posterior distribution of likelihoods, branch lengths, and rates were averaged and divergence dates summarized by the command: ''readdiv''. Individual trees were visualized in FigTree version 1. 4. 0  [46]  and consensus trees (those with a full complement of the 8 genomes) in DensiTree 2. 0  [47].
DNA fragment length, alignment and matrix assembly Based on the data obtained from VectorBase  [48] , the length of each chromosomal arm (in base pairs) is the following: X, 24393108; 2R, 61545105; 2L, 49364325; 3R, 53200684 and 3L, 41963435 (Table  1 ). In proportion to the length of each arm: 3, 19, 13, 8 and 6 genes were selected from X (Table  S1 ), 2R (Table  S2 ), 2L (Table  S3 ), 3R (Table  S4 ) and 3L (Table  S5 ), respectively. Our sequencing data consisted of $364 bp-long gene fragments resulting in a total alignment of 41,124 bp based on the An. gambiae AgamP3 genome assembly. We obtained orthologous sequences of the selected genes from 8 genome assemblies representing 6 species. Support values for each clade were generated by 1000 bootstrap replicates and the reliability of nodes in the phylogenetic trees were assessed based on 70% as the cut-off value  [49].
funestus + An. stephensi clade. We attempted to compare our phylogenetic approach to other previously used methods. The only other phylogenetic study that included An. gambiae, An. funestus, An. nili, and An. stephensi used mtDNA and nuclear rDNA  [9]. A parsimony bootstrap consensus tree based on the mtDNA sequence had a very low phylogenetic signal (resulting from highly variable COI and COII sequences) and, therefore, could not resolve the phylogeny with confidence. A Bayesian phylogenetic analysis using rDNA sequences placed the An. gambiae complex sister to the remaining species of Cellia, including An. stephensi, An. funestus and An. nili  [9]. However,  Marshall et al. 2005  included seven Cellia species not represented in our study. Although potentially related to taxon sampling and/ or a smaller dataset, the trees-even with the seven additional taxa pruned-are in contradiction with our concatenated trees, which consistently show the branching pattern: (An.
Small RNA library preparation and Sequencing protocol Small RNA libraries were constructed starting from 25-50 ng of total plasma RNA using the TruSeq SBS Kit v5-GA, while two additional libraries were constructed using the TruSeq SBS v3-HS kit, both from Illumina, according to the manufacturer's instructions. The quantities of RNA used for small RNA libraries preparation were further checked with a Qubit Fluorometer (Life Technologies). Libraries were sequenced (single read) on a GA-IIx or on a HiSeq1000 Illumina sequencer. The raw reads have been deposited at the European Nucleotide Archive (ENA) with accession number PRJEB6619.
Post-sequencing analysis First, a set of unique chicken miRNA precursors was built from the miRBase (version 19)  [14]  and Ensembl (version 72) databases. While miRBase is composed of experimentally identified miRNAs, Ensembl also includes precursor predictions based on stem-loop structure and sequence homology. The secondary structures of these precursors were then computed using the RNAfold tool from the Vienna RNA package  [15]. To assign read count to mature miRNAs, all miRBase mature miRNAs were mapped on the set of precursors without allowing any mismatches. When miRNAs from multiple species, including chicken, mapped at the same position on a precursor, the chicken miRNA annotation was retained. Using this approach we annotated 985 mature miRNAs (including 791 known chicken miRNAs from miRBase and 194 putative orthologs) based upon 1080 non-redundant precursors.
For the final set of 41 differentially abundant miRNAs (Table  1 ), the mature miRNA names were updated using the current miRBase release (version 20). Next, cutadapt v1. 2. 1  [16]  was used to perform 39 adaptor sequence removal and trim low-quality ends of reads. Reads between 19 nt and 24 nt in length were then collapsed to remove redundancy using an in-house python script. Bowtie v0. 12. 8  [17]  was used to map collapsed reads to the set of chicken precursors, allowing at maximum one mismatch. To discard hairpins with a read distribution inconsistent with Drosha and Dicer processing sites (i. e. , reads tilled across the precursor), we expected at least a 3:1 ratio between reads that matched on any of the stem-loop arms and reads located in the loop. For the remaining hairpins, reads that mapped inside the loop were not considered for quantification.
Putative new mature miRNAs were named based upon the name of the hairpin on which they were located when no known mature miRNA matched the same precursor, or from the name of the miRNA mapping on the opposite strand of the precursor. The suffixes ''-5p'' and ''-3p'' were added if the new miRNA mapped on the 59 arm or the 39 arm, respectively. Sequences that did not map to any precursor were then successively re-aligned with the same tool and criteria against the Gallus gallus genome release 4. 0, the RFAM database depleted from the miRNAs, and chicken cDNAs retrieved from the Ensembl database.
Differential expression (abundance) analyses The term ''differential abundance'' was used when referring to miRNAs circulating in plasma. Differential analysis of the small RNA-seq expression data was done using the R/Bioconductor packages edgeR (version 3. 2. 3) [18]  and HTSFilter (version 1. 0. 0) [19]. Briefly, a negative binomial generalized linear model (GLM) was fit for each miRNA, including a sample-specific normalization factor to adjust for systematic differences among samples due to variable sequencing depth and RNA composition, a fixed line effect (R2 and R+), and a fixed feeding effect (FD and RF). As recommended in  [19] , after estimating pergene parameters for the full dataset, miRNAs with weak abundance levels across all samples were filtered using HTSFilter, leaving 148 for further analysis.
16. 5) [21]. For plasma samples, the qPCR abundance data were obtained for each animal from the two feeding conditions (FD, RF), while qPCR data for the liver samples were obtained from different animals in each feeding condition because of the necessity to euthanize the animals for tissue sampling. Using the log 2 -transformed abundance relative to the threshold cycle (CT) value (2 2DC T ), a linear model was fit for each miRNA. For the plasma data the linear model included fixed effects for line (R2 and R+), feeding group (FD and RF), and batch (as plasma samples were collected in two batches), as well as a random effect for each animal to account for the correlation between measurements made on each individual before and after re-feeding. For the liver data the animal random effect and sampling batch effect were omitted because not necessary.
Co-abundance analysis of miRNAs As for the differential analysis (see above), we adopted the term of ''coabundance'' when referring to plasma miRNAs. Groups of co-abundant miRNAs were identified from the miRNA-seq data using the clustering approach implemented in the R package HTSCluster (version 1. 2)  [24]. Briefly, we assumed that the population of miRNAs arises from several distinct subpopulations or clusters, each of which can be modeled separately. The filtered population of a Condition: RF vs. FD; Line: R+ vs. R2; Condition within each chicken line: RF vs. FD (R+) and RF vs. FD (R2); Line within each feeding condition: R+ vs. R2 (RF) and R+ vs. R2 (FD). b Cluster number (see Figure  3 ). doi:10. 1371/journal. pone. 0114598. t001 miRNAs used for the differential analysis were thus modeled using a Poisson mixture model, where parameter estimation was performed using an Expectation-Maximization algorithm and the appropriate number of clusters present in the data (K55) was identified using the Integrated Completed Likelihood (ICL) model selection criterion.
Comparison to miRNAs identified in red jungle fowl tissues We made use of the miRNAs identified by the Chickspress database (available at http://geneatlas. arl. arizona. edu; SRA Bioproject Accession PRJNA204941) to evaluate the relative tissue abundance of differentially expressed miRNAs across a variety of red jungle fowl tissues. The Chickspress miRNA database comprises 659 million single-end, 50 bp reads mapped to the galGal4 version of the chicken genome. miRNA data were collected from two, 20-month-old red jungle fowl (one male, one female) across a variety of tissues: male adrenal gland, male and female adipose tissue, cerebellum, cerebrum, gonad, hypothalamus, heart, kidney, liver, lung, breast-muscle, sciatic nerve, proventriculus and spleen. The database contains mappings to known miRNAs (miRBase v20;  [14] ) as well as novel miRNAs identified using the miRTrap software  [25]  for each tissue.
Target gene predictions and functional analysis An in silico search for putative binding sites of differentially abundant miRNAs was performed using TargetScan 6. 0  [26] ; note that non-conserved miRNAs (ENSGALT00000042483-3p, ENSGALT00000043002-3p, ENSGALT00000043002-5p, gga-miR-1736-3p) and those with borderline differential abundance (adjusted P-values of 0. 0498; ccr-miR-133a-5p, mmu-miR-144-5p, gga-miR-20a, aca-miR-499-3p) were not included in the functional analysis. Perl scripts and UTR sequences from 23-species alignments were downloaded from the TargetScan website (http://www. targetscan. org). Predictions were ranked according to the context+ score, which models the contribution of different context parameters on site efficiency including site-type, 39-pairing, local AU, position, target site abundance, and seed-pairing stability  [27].
sourceforge. net/index. shtml). The Cufflinks v2. 1. 1 package  (Roberts et al. , 2011) , which includes Cuffcompare, Cuffmerge and Cuffdiff; was used to quantify transcript abundance in terms of Fragment Per Kilobase Million (FPKM), allowing the discovery of new transcripts. Parameters were set as default using the GTF option, bias correction, and applying the total hits norm. We then compared the new transcriptomes to existing annotated transcripts using Cuffcompare. We ran Cuffdiff first on the conserved, known transcripts, and then on the combined transcriptome, which includes both previously annotated and new transcripts, to examine tissue specific differences in gene expression between midguts and salivary glands. The clusters of differently expressed genes were visualized using the R/Bioconductor cummeRbund package (http://compbio.
, in press ). The resulting enrichment files were binned into 10 bp windows covering the entire A. gambiae genome reference sequence. We then referenced the 10 bin normalized and subtracted ChIP enrichments to gene coordinates of the AgamP4 gene set and calculated average enrichments per gene across a region that includes the gene body scaled to 500 bp, flanked by 200 bp before the TSS and after the Transcription Termination Site (TTS), respectively. The same type of analysis was performed by referencing ChIP data to gene coordinates from the "custom" gene set generated by CuffCompare, which merges novel and conserved transcripts (see above). We then applied K-means clustering to the matrix of enrichment values for H3K27me3 to organize genes based on their similarities into 3 clusters using Cluster v3.
Functional analysis of genes Functional analyses were performed on genes obtained from the clustering approach corresponding to the various enrichment profiles (see results), and that intersect with H3K27ac and H3K27me3 peaks identified by MACS or SICER. Sequences for the various gene sets were retrieved by mapping VectorBase gene IDs against the UniProtKB database (http://beta. uniprot. org/). We employed Blast2Go (http://www. blast2go. de/) to conduct Gene Ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways annotations. Fisher's exact test was used to retrieve significantly enriched GO terms for genes marked with H3K27ac and H3K27me3 using Blast2Go. GO functional categories are defined as those containing at least five genes and a minimum enrichment score of 1. 3 (p-value < 0.
GENE EXPRESSION AND TRANSCRIPT DISCOVERY RNA library processing and quality metrics are shown in Table  1. The expression analysis on the assembly of reads obtained from A. gambiae female mosquito tissues, midguts and salivary glands, resulted in a total of 13,969 novel transcripts that correspond to newly discovered non-annotated loci (class code "u": Unknown, intergenic transcript); combined GTF and BED files available at GEO GSE59773) as well as transcripts categorized as class code "="correspond in most cases to extensions of previously annotated genes based on intron matches. Most novel transcripts (6907 in midguts and 6943 in salivary glands) show relative abundances, expressed as Fragments Per Kilobase of exon model per Million mapped fragments (FPKM), above the 95 percentile of expression for the distribution of transcript abundances, which we estimated correspond to a value of 0.
26 for midguts and 0. 56 for salivary glands (lower bound of the 95% confidence interval (CI) for the distribution of transcript abundance). Thus transcripts that show FPKM values above this threshold can be considered as reliable in terms of novel transcript discovery and gene expression differences. Blasting a subset of these transcripts against non-redundant protein databases confirms that they are novel (data not shown). This supports the notion that there is a large number of genes in the reference genome of A. gambiae that remain to be discovered and/or properly annotated. The comparison between transcripts obtained from midguts and salivary glands identified a total of 23 differentially expressed genes, 23 TSS and 21 isoforms switching events (p-value < 0. 01, q-value < 0. 05) (Figure  1 ).
FUNCTIONAL ANNOTATION OF HISTONE MODIFICATION PROFILES Genes clustered based on their histone modification profile, either marked with H3K27ac or H3K27me3, were assigned to GO accession numbers and classified into functional categories under three major classes (biological process, cellular component, and molecular function). We found multiple GO categories associated with each cluster (Table  S2 ). Genes enriched in either H3K27ac or H3K27me3, were assigned to multiple different KEGG pathways. Of these, pathways involved in metabolism of amino acids and signaling pathways were top ranked hits for genes marked with H3K27ac or H3K27me3 (Figure  S2 ). The genes associated to each histone PTM (see Figure  2  for cluster names) also differ in the relative enrichment of GO terms (Figure  3 , p < 0.
ACKNOWLEDGMENTS We thank The  Genomic Services Lab  at the  HudsonAlpha Institute for Biotechnology  for their help in preparing RNA-seq libraries and performing Illumina sequencing of RNA-seq and ChIP-Seq samples. We thank  A. Nicot ,  M. N. Lacroix , for assistance with mosquito breeding,  A. Kotlar  and  K. Van Bortle  for advice in the data analysis. This work was supported by  U. S. Public Health Service  Award  R02GM035463  from the  National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
ACKNOWLEDGMENT This work was supported by the European Commission in the framework of the FP7 Network of Excellence in Wireless COMmunications NEWCOM# (Grant agreement no. 318306) and by the Catalan and Spanish Governments under SGR (2009SGR1046) and CICYT (TEC2011-29006-C03-01), respectively. The authors would like to thank the anonymous reviewers, in particular reviewer 1, for their helpful suggestions which helpled to substantially improve the paper. APPENDIX: PARTIAL CSIT While above we have shown that adaptive HARQ can be successfully appied to the block fading channel, in which the transmitter only knows the average SNR γ, in many situations more knowledge on the channel state might be available. A widely accepted model assumes that the receiver performs channel estimation and prediction and sends the predicted channel coefficients via en error-free feedback link to the transmitter  [48] -  [51].
Our aim is to expand the toolkit available to biologists rather than proposing a method globally superior to another, since the particular biological problem should play an important role in selecting the tool. The paper is organized as follows. First, we introduce the two populations of marble trout that we used as a motivating example and case study. We then present the Empirical Bayes approach to parameter estimation as implemented in the module ADMB-RE (Automatic Differentiation Model Builder -Random-Effects) of the software ADMB  [37]  and apply the EB approach to the joint estimation of shared and individual variation in growth from longitudinal data using a parameter-rich von Bertalanffy growth function. For the case study of marble trout, we introduce environmental predictors of the von Bertalanffy growth function's parameters k (rate of growth) and L ? (asymptotic size) such as population density in the first year of fish life and year-of-birth cohort, and test whether their inclusion, in addition to individual random effects, improves model performance.
The last sampling occasion included in the dataset was June 2012. In Gacnik the last cohort included was the one born in 2010. Due to a flood that almost completely wiped out the population in 2007  [38] , the last cohort included for Zakojska was the one born in 2008. Density of fish of age 1 and older (number m 22 ) was (mean6sd) 0. 0560. 04 in Zakojska from 1998 to 2012 and 0. 1660. 07 in Gacnik from 2000 to 2012. In total, 1 067 unique fish were included in the Zakojska dataset and 4 764 in the Gacnik dataset.