dataesr/scientific-paragraphs-categorization huggingface dataset:
This is why DAPAR is accompanied by ProStaR, a package that relies on Shiny technology (http://shiny. rstudio. com/) to dynamically build web-based GUI to DAPAR functionalities. All the user has to do is to copy-paste the following command lines source ('http://www. bioconductor. org/biocLite. R') biocLite ('DAPAR'); biocLite ('Prostar'); library (Prostar); Prostar () in the R console to open the GUI and to start the quantitative analysis by a series of clicks. Moreover, ProStaR is also available in server mode: a single (server) machine is installed and maintained with R, DAPAR and ProStaR, on which each practitioner connects through a given URL. This makes ProStaR particularly suited for proteomics labs where a single bioinformatician deploys and maintains the tools that are used by the proteomicians for their data analyses.
In addition, to providing menus devoted to each of the five processing steps (filtering, normalization, imputation, aggregation and differential analysis), ProStaR provides import/export functionalities, as well as a 'descriptive statistics' menu where it is possible to visualize the dataset in hands, so as to best understand it or to produce display elements for publications. The packages DAPAR and ProStaR are separated for two reasons: first, ProStaR may be bypassed by any R coder that may want to directly access the low level functions of DAPAR, script their own pipelines and reproduce them in a better and simpler way. Second, the DAPAR functions can be directly mapped to other GUI (such as for instance ProLine software-http://proline. profiproteomics. fr/), so as to provide the same statistical pipeline in a different computational environment.
Funding This work was supported by  ANR-2010-GENOM-BTV-002-01  ( Chloro-Types ),  ANR-10-INBS-08  ( ProFI  project, ' Infrastructures Nationales en Biologie et Sante ', 'Investissements d'Avenir') ,  EU   FP7 program  ( Prime-XS  Project, Contract no. 262067 ), the  Prospectom  project (Mastodons 2012  CNRS Challenge ) and the  BBSRC Strategic Longer  and  Larger grant  ( Award BB/L002817/1 ). The authors thank the  PRABI (Pole Rhone-Alpes BioInformatique)  for providing its computing and hosting facilities.
This paper presents a hardware module design for the forward Binary Discrete Cosine Transform (BinDCT) and its implementation on a field programmable gate array device. Different architectures of the BinDCT module were explored to ensure the maximum efficiency. The elaboration of these architectures included architectural design, timing and pipeline analysis, hardware description language modeling, design synthesis, and implementation. The developed BinDCT hardware module presents a high efficiency in terms of operating frequency and.
Introduction The Discrete Cosine Transform (DCT) is widely used in video and image processing. However, its direct implementation is not very efficient because it is a floating-point transform, and it is characterized by complex loops. In fact, floating-point algorithms are slow in software and require more silicon in hardware implementation  [1] [2] [3]. Nevertheless, most video and image processing applications are subject to hard real time constraints, and the DCT must be calculated in a very short time. To decrease the complexity of this transform, several integer-friendly approximations of the DCT have been proposed  [4, 5]. The Binary Discrete Cosine Transform (BinDCT)  [6]  represents one of the most known DCT approximations. It works only with integer numbers, and it uses just sums and shifts.
Also, a comparison with the hardware implementation of several existing DCT approximations is performed to demonstrate the efficiency of the suggested BinDCT implementation. The purpose here is to put forward an efficient hardware module of the BinDCT that can be employed in highly constrained systems. The rest of the paper is organized as follows. The methodology section provides a review of the BinDCT algorithm and its structure, the architecture of the different BinDCT stages, and the global hardware architecture of the 2D transform. In the results and disscussion section, the implementation details and operations of the proposed 2D-BinDCT architectures are discussed. A comparison with the hardware implementations state of the art of the DCT approximations is also made in the latter section.
In addition, a comparison of the Peak Signal-to-Nose Ratio (PSNR) results of the reconstructed Lena image with the C7 configuration of the BinDCT, the floating version and the fast integer version of the source code from the Independent JPEG Group (IJG) was performed. It was demonstrated that the performances of the employed BinDCT were close to the floating DCT implementation. In particular, when the quality factor is below 90, the difference between the C7 configuration of the BinDCT and the floating DCT will be below 0. 5 dB. Besides, when the quality factor is above 90, the performance of the BinDCT will be much better than the fast integer version. The C7 BinDCT configuration coefficients are the following: P 1 ¼ 1=2; U 1 ¼ 1=2; P2 ¼ 1; U 2 ¼ 1=2; P 3 ¼ 1=4; U3 ¼ 1=4; P 4 ¼ 1=2; U 4 ¼ 1=4 ¼ U 0 4 þ U 00 4 ¼ 1=2 þ 1=4; P5 ¼ 1=2 In Fig.
1 , the U 4 parameter, which is used to calculate the Z1 output of stage 2 (Z 1 = U 4 Â Z 0 + a 6 ), is transformed to an addition of U 0 4 and U 00 4 coefficients, and an intermediate sum is introduced (H = U 0 4 Â Z 0 + a 6 ). The addition and shift operations corresponding to the Pi and Ui coefficients are defined as follows: The 2D-BinDCT module is composed of two 1D-BinDCT modules and a transpose memory block. The first 1D-BinDCT block is applied to 8 Â 8 matrix input data, and the second one is applied to the transpose matrix of the obtained 1D-BinDCT (8 Â 8 matrix). The transposition is ensured by the transpose memory block. The architecture of a 1D-BinDCT hardware module is composed of an input block with 8 registers and 4 hardware blocks corresponding to the four 1D-BinDCT stages.
BinDCT hardware implementation Hardware architecture of the different BinDCT stages Each 1D-BinDCT block takes as an input eight line values, which have to be introduced simultaneously. In each of eight clock cycles, these 8 values are recovered from the serial input (Xin) and transmitted in parallel to the BinDCT stage 1 through the ''input block". Fig. 2  gives the hardware structure of the input block. It consists of 8 registers used to store the 8 line values (X0-X7) to be introduced to the 1D-BinDCT block and 8 shift registers for serial to parallel transformation of the input values. As shown in Fig. 2 , this module is controlled by a counter (cntr8). Every time the counter output reaches a value of 8 (eight cycles), the load control input of the memory registers (X0-reg,. , X7-reg) is activated and the 8 recovered values are loaded into these registers.
As it can be seen in the timing diagrams, the different intermediate coefficients (ai, zi, bi, di and yi) are generated in a periodic way relative to the cntr-8-1 values. This latter is utilized to control the different multiplexers and registers of the first 1D_BinDCT architecture for data selection and register load activation. Cntr8-2 is used for the second 1D_BinDCT control, in the same way as cntr8-1. Nevertheless, this counter does not begin working with the system start. It is enabled when all the 1D-BinDCT coefficients are calculated and transposed. In this time, the 2nd 1D-BinDCT input block must begin getting the input values. The control signals (Alu, Sel, En, Alu1, etc. ) of the different BinDCT stage modules are directly commanded by the ''cntr-8" counter. In each counting cycle, some operations are to be performed to calculate particular coefficients, as defined in the timing diagram related to each implementation solution.
A second transpose memory block with a demultiplexer after the ''Before-SRAM" block and a multiplexer just after the two ''transpose memory" blocks is to be added. These two transpose memories are going to be used to alternately store the intermediate results generated by the first 1D-BinDCT module. When the second BinDCT module is reads data from one memory block, the first 1D-BinDCT module will write into the other one. After the required number of cycles to make the first BinDCT coefficient available on the out port, the outputs will be generated in every clock cycle. For a continuous execution mode, our architectures can attain a throughput of 378 Mega pixels per second (Mpps). This throughput can increase if a more recent FPGA family has been used (for example, it becomes 390 Mpps for a Virtex 7 FPGA).
Background: Seabirds are important components of marine ecosystems, both as predators and as indicators of ecological change, being conspicuous and sensitive to changes in prey abundance. To determine whether fluctuations in population sizes are localised or indicative of large-scale ecosystem change, we must first understand population structure and dispersal. King penguins are long-lived seabirds that occupy a niche across the sub-Antarctic zone close to the Polar Front. Colonies have very different histories of exploitation, population recovery, and expansion. Results: We investigated the genetic population structure and patterns of colonisation of king penguins across their current range using a dataset of 5154 unlinked, high-coverage single nucleotide polymorphisms generated via restriction site associated DNA sequencing (RADSeq).
1 ). Overall, king penguins are a highly mobile marine species with huge potential for dispersal; however, genetic divergence among colonies may exist as a result of nonphysical barriers, such as philopatry, local adaptation or isolation by colonisation  [35]. We therefore hypothesised that breeding colonies on different archipelagos would constitute genetically distinct populations. To test this hypothesis we generated a dataset of more than 5000 unlinked single nucleotide polymorphisms (SNPs) using restriction site associated DNA sequencing (RADSeq)  [36]  for king penguins from four colonies spread across their range (Fig. 1 ). We aimed to identify population structure, as well as distinct phylogenetic lineages that may have been associated with past glacial refugia. Previous studies have shown that king penguin numbers were much reduced during the last ice age  [37] , and the species' range may have been contracted into refugia at unknown locations  [38].
Sequencing DNA was extracted from the 64 blood samples using a QIAGEN DNeasy Blood and Tissue Kit following the manufacturer's protocol, but modified to include 40 μL proteinase K at the digestion step and with the incubation time extended to 3 h. The samples were treated with 1 μL Riboshredder (Epicentre) to reduce RNA contamination. DNA concentration was measured with a Qubit (ThermoFisher Scientific) and high molecular weight was confirmed on a 1 % gel. We sequenced the mitochondrial hypervariable region (HVR; 620 base pairs; GenBank accessions: KX857217-KX857259) because this marker has revealed phylogeographic patterns within other penguin species  [28, 29, 41]. The HVR was amplified in all samples using primers F-0225 (5'-GGAACCTCCC AAAGAGTACCA) and R-INR (5'-CCAACCAGATGT ATCGGTGA)  [28].
PCR products were sequenced using the Sanger method by Macrogen Europe. Geneious v5. 5. 9 was used for alignment. We employed RADSeq to generate a dataset of genome-wide SNPs to assess population structure among the king penguin colonies. RAD libraries were prepared using the SbfI restriction enzyme, which was chosen because it produces a large number of RAD loci in king penguins  [37]. RADSeq for all individuals was performed at the Edinburgh Genomics Facility, University of Edinburgh (https://genomics. ed. ac. uk/) as described in Gonen et al. [42]  after Etter et al. [43]. Briefly, 250 ng of DNA per individual was digested with SbfI-HF (NEB), followed by ligation to barcoded P1 adapters. The uniquely barcoded individuals were pooled into multiplexed libraries, and each library sheared into fragments of ∼ 300-400 bp.
Bioinformatics FastQC was used to assess read quality and check for adapter contamination. We used process_radtags within the Stacks pipeline v1. 35  [44, 45]  to de-multiplex, trim and clean reads. We then truncated reads to 113 bp to exclude the four terminal bases in order to avoid poor sequence quality. We excluded read pairs in which either read had uncalled bases, a low quality score and/or a barcode or cut-site with more than one mismatch. The remaining paired reads were aligned to the emperor penguin reference genome (http://gigadb. org/dataset/100005) using bwa-mem  [46]. We prevented terminal alignments by enforcing a clipping penalty of 100. Reads with more than five mismatches, multiple alignments and/or more than two indels were removed using a custom python script (filter. py, available online  [47] ).
We removed PCR duplicates with Picardtools (http://broadinstitute. github. io/picard). We used the Stacks pipeline (pstackscstackssstacksrxstackscstackssstacks -populations) to prepare a dataset of unlinked, filtered SNPs from the RAD reads, following many of the suggestions outlined in the framework of Benestan et al. [48]. In pstacks we selected a minimum stack depth of six reads mapping to the same location and used the bounded SNP model with a significance level of α = 0. 05, an upper bound of 0. 1 and a lower bound of 0. 0041 (corresponding to the highest sequencing error rate recorded by phiX spikes in the sequencing lanes). All 64 individuals were used to build the catalog in cstacks. In rxstacks we removed confounded loci (those with a biologically implausible number of haplotypes, such as from repetitive sequences or paralogous loci) with a conservative confidence limit of 0.
25. Also in rxstacks, we removed excess haplotypes from individuals as well as any loci with a mean log likelihood < -10. Further filtering was conducted in the populations module. We removed SNPs with a minor allele frequency (MAF) < 0. 01 because these are likely to be the result of sequencing errors. We also removed loci with a heterozygosity > 0. 5, as these could be paralogs  [48]. A single SNP per RAD-tag was chosen at random in order to remove tightly linked SNPs from the dataset. We also specified that a locus must be present in all colonies to be included in the final dataset, as well as genotyped in at least 80 % of individuals from each colony. We then removed any SNPs with a mean coverage exceeding 100X using vcftools v0. 1. 13 [49]  to avoid SNPs from repetitive regions of the genome.
Outlier loci detection We investigated whether SNPs were potentially under selection before proceeding with population genetic analyses, because loci under either directional or balancing selection violate the assumption of neutrality that is a caveat of most population genetic methods. We used a Bayesian F ST outlier test as implemented in BayeScan 2. 1  [53]  to identify loci to be discarded from the neutral dataset. BayeScan has been shown to have good power for detecting loci genuinely under selection under a range of demographic scenarios, but with an accompanying high false-positive rate  [54]. Given that our reason for testing for outlier loci was to obtain a truly neutral dataset, we are not concerned by the high false-positive rate in this case. We set the prior odds of neutrality parameter at five, which refers to the probability that a given locus in the dataset is under selection (i.
Contemporary population structure The genetic structuring among king penguin colonies was assessed using several different methods. Firstly, the Weir and Cockerham  [56]  unbiased estimator of F ST was calculated between all pairs of colonies using Genodive v2. 0b27  [57]. The hypothesis of departure from panmixia was tested with 5000 random permutations of the data to determine the statistical significance of each pairwise F ST value between colonies, with the significance level adjusted for multiple testing using Sequential Goodness of Fit (SGoF+)  [58]. To identify the number of genetic populations ("clusters") among the 64 individuals, we used the find. clusters K-means clustering algorithm within the adegenet package  [50, 51] , retaining all principal components. We also used a Bayesian clustering approach with a Markov chain Monte Carlo (MCMC) sampling procedure within structure v2.
Each group was divided into a training set and a hold-out set and we identified the most informative SNPs for colony assignment using the training set in TRES v1. 0  [66]. We used the Informative for Assignment test (I n ) to identify ancestry informative markers (AIMs), as I n has been shown to be the most powerful method for estimating ancestry proportions  [67]. For population assignment tests it is recommended to trial different numbers of SNPs, therefore, we exported the top 100, 200, 500, 1000 and 2000 most informative SNPs. These SNP datasets were used to assign the hold-out set of individuals to their populations of origin within Genodive. If the minor allele was not sampled in either population (i. e. its frequency was zero) the frequency was replaced with 0. 005 as recommended by Paetkau et al.
Past population patterns We used a species tree approach, as implemented in SNAPP  [69]  within BEAST v2. 4. 0  [70] , to estimate the evolutionary relationships and order of splitting among the geographically isolated colonies to determine whether any of the colonies may have been glacial refugia in the past, as well as the source of the new Falkland Islands colony. SNAPP uses a coalescent method to infer species trees from unlinked biallelic markers, such as SNPs. SNAPP is highly computationally demanding and analysis of the full dataset of individuals was implausible. We therefore selected two representative individuals from each colony (i. e. four haplotypes) for analysis, and to ensure consistency of the posterior we ran the analysis twice with different randomly-selected colony representatives.
Any SNPs that were no longer polymorphic within the reduced datasets were removed from analysis, leaving datasets of 2668 and 2626 SNPs. The mutation rates (u and v) were calculated from the data, rather than estimated as part of the MCMC. We ran the MCMC for 5 million generations with a burn-in of 10 %. This was more than sufficient for convergence, with Tracer v1. 6  [71]  indicating ESSs > 4000. The likelihood plots were also visually inspected for convergence. The Bayesian method results in not a single topology, but a posterior distribution of the possible topologies; we used DensiTree v2. 0. 1  [72]  to visualise the entire posterior distributions of trees as a cloudogram, excluding a 10 % burn-in. We used RAxML v8. 2. 7  [73]  to infer maximum likelihood (ML) phylogenies among the full dataset of king penguin individuals.
We applied an ascertainment bias correction to the likelihood calculations, as recommended when using SNPs to account for the lack of invariant sites  [74]. For the ascertainment correction to function, all invariant sites must be removed. In practice, this means that an alignment site consisting of only heterozygotes and homozygotes for a single allele (e. g. an alignment site that is only Rs and As with no Gs) is considered potentially invariant by RAxML and must be removed. We filtered out such sites using the Phrynomics R script (https://rstudio. stat. washington. edu/ shiny/phrynomics/). After this filtering step 1727 SNPs remained in the dataset. We conducted a rapid bootstrap analysis and search for the best-scoring maximum likelihood tree in a single program run using the MREbased bootstopping criterion  [75]  to ascertain when sufficient bootstrap replicates had been generated.
All searches were conducted under the GTRGAMMA nucleotide substitution model. We also conducted a ML search on the HVR sequences, because HVR has been shown to resolve distinct phylogenetic lineages within Adélie penguins  [29, 41, 76] , emperor penguins  [28]  and gentoo penguins  [31, 41]. We used the same search protocol as for the SNP dataset, but without an ascertainment bias correction. Finally, we constructed a median-joining haplotype network for the HVR sequences using PopArt (http://popart. otago. ac. nz).
Genotyping The 64 king penguin samples yielded 6. 27-55. 9 million unpaired reads per individual, with an average of 15. 7 million reads per individual. On average, 97. 3 % of reads per individual passed the quality filters in process_radtags and, of these, an average of 97. 7 % successfully aligned to the emperor penguin reference genome. After specifying a minimum stack depth of six, a total of 34,171 RAD-tags remained, containing 35,766 SNPs. Our SNP filtering protocols resulted in a final dataset of 5154 SNPs  [47]  for use in subsequent analyses. Of these we detected no loci that were putatively under selection (BayeScan output available online  [47] ) and none that were out of HWE in > 50 % of colonies. There were no notable differences in genetic diversity measures (number of private alleles, expected heterozygosity, observed heterozygosity or nucleotide diversity) among colonies (Table  1 ).
003-0. 005), indicating only subtle genetic differences between these pairs of colonies. Therefore there are at least two slightly differentiated genetic populations among the sampled colonies: (1) the South Georgia population and (2) a population including the Falkland Islands, Crozet and Macquarie. DAPC was unable to distinguish among the four sampled colonies or between the two slightly differentiated populations, with the distribution of individuals overlapping in both scenarios (Fig. 3 ). For the individual-based population assignment tests, the 100 SNP dataset was found to be best at assigning the test set of individuals back to their population of origin. However, the test performed poorly, with only seven individuals assigned correctly out of the 16 individuals in the test dataset.
Phylogeography We attempted to ascertain the branching structure among colonies using the species tree approach implemented in SNAPP. We have presented the full posterior distribution of trees in order to highlight the uncertainty in the topology (Fig. 4 ). The majority of the topologies support the grouping of the Falkland and Crozet Islands colonies (Fig. 4 ), congruent with our structure and pairwise F ST results. However, aside from this one clade, the rest of the branching structure among the colonies is unresolved. We constructed maximum likelihood phylogenies for the full set of individuals using both HVR and the dataset of SNPs in order to determine if there are any strongly supported phylogenetic lineages that are not necessarily affiliated with the contemporary colony sites. The MRE bootstopping-criterion was satisfied by 550 and 800 bootstraps for the SNP and HVR searches, respectively.
The best-scoring likelihood and majority rule extended consensus trees for the SNP dataset had very low support across the entire topology, with only a single node having a branch support value > 50 (topology not shown). The HVR topology did not show any more resolution, with 75 % of nodes in the tree having branch support values < 50 and no evidence of any wellsupported phylogenetic lineages (topology not shown). A median joining network of the haplotypes of the mitochondrial HVR also showed no clear phylogeographic pattern and no evidence of ancestral haplotypes (Additional file 1: Figure  S1 ). Overall, there are no distinct lineages among king penguins, no remnant signatures of refugia and no evidence for the order of colonisation of the islands.
Acknowledgements We are grateful to  Jan Cheek  and the  warden of Johnson's Harbour ,  Derek Petterson , for access to the Volunteer Point colony, and  Andy Black ,  Sarah Crofts ,  Judy Horsburgh  and  Marion Nicolaus  for their help in sample collection. We would like to thank  Edinburgh Genomics  staff for RAD library preparation and sequencing and gratefully acknowledge the use of the  University of Oxford Advanced Research Computing (ARC)  facility in carrying out this work (http://dx. doi. org/10. 5281/zenodo. 22558).