dataesr/scientific-paragraphs-categorization huggingface dataset:
Users can provide a list of values to be tested for M, the maximum number of nucleotides that may be different between stacks (assembly of exactly matching reads) to be merged into a single locus (http://creskolab. uoregon. edu/stacks/param_tut. php). Individual loci are then merged into a catalog of loci with cstacks (Stacks). Users can provide a list of values for the parameter n, the number of mismatches allowed between individual loci when generating the catalog. Exploring alternate parameterization allows the user to find a good compromise for merging orthologous loci from distant species, whilst ensuring that paralogs and nonhomologous loci are not merged. Users can then perform loci and sample selection to build datasets that fit with their prior knowledge of the studied species. They can fix a minimum number of loci required for a sample to be kept in the analysis, or retain only loci for which at least a given number of samples have sequences (a list of values can be provided).
Users can also choose to remove loci in which paralogs and non-homologous sequences are probably merged together. Phylip-formatted files that meet the selection criteria are produced by RADIS. Finally, combined datasets (concatenation of the full sequence of each locus) are analyzed using RAxML  (Stamatakis, 2006a,b)  to produce phylogenetic trees. Users can delay implementation of RAxML analyses in order to increase the number of cpus to be used. Explicit names are used for output directories and files making the results obtained with different sets of parameters easily distinguished and compared (e. g. stacks_ M2n4S12L10000. sel. phy is the phylip-formatted combined dataset obtained when individual loci are built using M ¼ 2 (M2, ustacks), the catalog of loci is built using n ¼ 4 (n4, cstacks), only sample with at least 10 000 loci (L10000) and loci for which at least 12 samples have sequences (S12) are selected (.
Evaluation using empirical data To test the program, we reanalyzed the raw data from  Cruaud et al. (2014). Experimental design was as follows: DNA from 31 samples was first digested with PstI and P1 adaptors containing 5 or 6 bp barcodes were then ligated. Paired-end sequencing of the library (2 * 100 nt) was performed on a single lane of a HiSeq 2000. Raw Illumina data were processed with RADIS. pl using Stacks-1. 32 and RAxML 8. 2. 0-ALPHA on 8-cores of a 16-core Linux, 2. 9 GHz, 64 GB RAM computer. In the R ADIS. cfg file, radis_nttrim_read1_5p was set to 5 (to remove the overhang of the restriction site), radi-s_nttrim_read2_3p was set to 5 (to remove low quality bases) while radis_nttrim_read1_3p and radis_nttrim_read2_5p were set to 0. M was set to 2 and 4 values of n were tested  (4, 6, 8, 10).
Conclusion By facilitating testing the impact of different parameter combinations, the pipeline RADIS automates and standardizes the analyses of RADseq data for phylogenetic inference. The program may prove useful to evaluate the robustness of the results to the options chosen to process real RAD-seq data, or to carry out simulation studies. Most importantly, RADIS could also help to assess how different clustering methods may impact tree topology (e. g. Stacks versus PyRAD).
Cancer Cancer, and in particular glioma, was also considered. Marc Mesnil (Université de Poitiers, France) noted that the most common brain tumor in adults, glioblastoma (grade IV glioma) is not curable because of recurrence due to high invasion capacity. In a study covering gliomas from grades II to IV (85 tumors), he observed that even if Cx43 expression decreases in higher grade tumors some glioblastoma cells still express it. He mentioned experimental data suggesting that lack of Cx43 expression favors cell proliferation while its presence increases invasion capacity. This was confirmed using human glioblastoma cells in which Cx43 was detected in invadopodia able to digest gelatin. Decreasing its expression by shRNAs demonstrated that Cx43 favors invadopodia growth while its hemichannel function inhibits this process.
Acknowledgements The authors wish to acknowledge the co-sponsorship of the Workshop by the  Foundation Hugot of the Collège de France ,  Consulat Général de France in Vancouver ,  Zealand Pharma ,  FirstString Research , and Theranexus. Funding for publication costs was provided by a grant to CCN and CG from  Brain Canada ,  Michael Smith Foundation for Health Research, Genome BC  and the  Pacific Alzheimer's Research Foundation. The authors thank the participants of the Workshop for their helpful suggestions and apologize to researchers whose work was not included as part of this Workshop due to organizational constraints.
Participants We recruited 14 adult participants (Mean age: 41. 5 years; SD: 14. 9; range: 25-69) with an equal number of males and females. All were residents of Newcastle upon Tyne, UK, or the surrounding area. In determining numbers of participants, we were guided by the work of  Guest et al. (2006)  who asked 'how many interviews are enough' for data saturation to occur. Using data from 60 in-depth interviews, they found that saturation occurred within the first 12 interviews. Note that this study was carried out in accordance with the recommendations of Northumbria University Ethics Committee with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki.
Analysis Interview sessions were audio recorded, transcribed, and sentence-by-sentence analysis was employed using NVivo qualitative software. Initial data review was conducted by reading and rereading the transcripts for instances of actual or anticipated reminiscence. The process followed principles of thematic analysis  (Braun and Clarke, 2006)  as well as template analysis (see  King, 2004 ) using an existing reminiscence framework  (Westerhof and Bohlmeijer, 2014)  where we examined evidence of social, instrumental, and integrative reminiscence. Note, however, that we were open to these 'codes' being modified in light of actual participant data. A credibility check on coding was conducted by the second author, following guidance from  Elliott et al. (1999).
DISCUSSION This study has investigated the role of transformed or 'slow' social media as a reminiscence tool for both young and old. Responding to van den Hoven's (2014) observation that "Digital media, consisting of bits and bytes, are just not as visible and present as physical or tangible media are" (p. 370), we have engaged with participants to create printed books from their social media profiles and assessed the reminiscence value of such books. Participant responses were measured against existing theoretical frameworks regarding the role and function of reminiscence. Our data generally supported the three overarching reminiscence categories described by  Westerhof and Bohlmeijer (2014)  which we have described in terms of the three acts of connecting with others, learning from the past and building self-knowledge, although we should note that our examples of learning from the past (instrumental strategies) were rather limited.
These sentiments were often accompanied with the realization that without printing their Facebook content, they would not have recalled the activities they posted about online at any other time. The books encouraged participants to think about sharing their experiences with loved ones, compared to the online review of Facebook which was something people said they were most likely to do alone, if at all. Using social media as a reminiscence tool was not unproblematic. Participants struggled to manage vast quantities of data and felt that the website offered them a fairly restricted set of tools with which to create their book. Many participants said they would prefer better editorial control and that they would gladly spend the time to make the book a truly tailored artifact. Our participants recognized that the quality of the finished books was dependent upon the quality of their social media profilei.
e. , coverage, and is hence of particular interest when considering sequencing technology's current trends that are producing longer fragments. Results: Given the potential relevance of efficient haplotyping in several analysis pipelines, we have designed and engineered PWHATSHAP, a parallel, high-performance version of WHATSHAP. PWHATSHAP is embedded in a toolkit developed in Python and supports genomics datasets in standard file formats. Building on WHATSHAP, PWHATSHAP exhibits the same complexity exploring a number of possible solutions which is exponential in the coverage of the dataset. The parallel implementation on multi-core architectures allows for a relevant reduction of the execution time for haplotyping, while the provided results enjoy the same high accuracy as that provided by WHATSHAP, which increases with coverage.
It is for these reasons that the authors of  [19]  introduced WHATSHAP, the first fixed-parameter tractable (FPT)  [20]  approach for solving the weighed minimum error correction (wMEC)  [21]  (and hence, the MEC problem) where coverage is the only parameter. The runtime of this approach is linear in the number of SNPs per read, which is the term that will increase by orders of magnitude as longer and longer reads become available. A distinguishing feature of WHATSHAP with regards to the other currently available proposals is that it is exponential in the sequencing coverage and not in in read length. This appears to be very relevant when considering current trends in future generation sequencing technologies: technical improvements will clearly yield longer reads. The WHATSHAP algorithm has been implemented in a freely available toolkit (https:// bitbucket.
org/whatshap/whatshap). Because WHATSHAP is the first approach in this promising direction, it appeared worthwhile to speed up its implementation by parallelising it. This paper presents PWHATSHAP, an optimised parallelisation of WHAT-SHAP, and its implementation in a toolkit which is also freely distributed (also available at https://bitbucket. org/ whatshap/whatshap). PWHATSHAP has been a developing project, evolving together with the very active development of WHATSHAP. Preliminary results on the parallelisation experiment of the core structure of the algorithm were reported in  [22]. In this paper we report on the parallelisation of the latest version of WHATSHAP, which has matured into an integrated framework engineered according to the current trends in genetic applications, and capable of analysing data in standard file formats (such as BAM and VCF) used in genomic analysis.
The merits of this work are: i) The PWHATSHAP project provides the research community with a freely available application, which can easily be embedded in analysis pipelines requiring the solution of haplotyping problems. The core of the parallel haplotyping algorithm consists of an advanced and optimised implementation tailored to multi-core architectures. Such an enhanced core has now been engineered in the integrated framework described above, supporting standard data formats. This is a major engineering step, requiring the embedding of several C++ core functions, coherently running as a parallel application, into a framework developed in Python. This allowed the PWHATSHAP project to move from a prototype development phase to a mature, open-source product. Haplotyping can be typically employed in larger pipelines, for instance including other typically expensive steps, such as data acquisition and result analysis.
The provision of efficient solutions to haplotyping, such as PWHATSHAP, empowers more accurate analysis in all those contexts. ii) The incremental construction of haplotypes in WHATSHAP is the type of algorithm whose parallelisation is very difficult. These algorithms process a large amount of data and are therefore sensitive to the availability of sufficiently large amounts of memory (RAM). Their exponential complexity (in time, but with direct implications on space complexity), and the huge datasets currently available, easily make memory availability a critical parameter. Parallelising one of the problems of this type represents an engineering challenge. The solution adopted is supported by the FastFlow framework  [23] , which provides high-level parallel programming constructs, such as skeletons and parallel design patterns.
Thanks to the high-level programming paradigm adopted, it has been possible to build PWHATSHAP retaining most of the overall structure and code of WHATSHAP. The chosen paradigm has also the advantage to limit the need for mutual exclusion mechanisms, known to be typically slow. The clear performance improvement obtained supports the efficient treatment of large datasets and high coverage. It is important to note that the presented results can be obtained by computers that may easily equip current state-of-the-art genomic laboratories. Such improvement in the computational efficiency of haplotyping, made available at affordable costs, may be key in several analysis pipelines. iii) A comprehensive evaluation of the obtained results has been carried out, both theoretically and experimentally.
First, the correctness of PWHATSHAP has been validated against WHATSHAP: both applications return identical results in terms of the wMEC score of the computed optimal solutions. Following correctness, the accuracy of PWHATSHAP has been discussed in terms of the accuracy of WHATSHAP, which is known to be strong. We discuss various aspects of the accuracy of WHATSHAP and review the several constraints under which the competing approaches to haplotyping work. PWHATSHAP emerges as an accurate and largely applicable approach. The efficiency of PWHATSHAP is discussed against theoretical complexity results, and validated by means of experimental results over benchmark datasets. Overall, the large applicability and accuracy of PWHATSHAP, together with its increased efficiency, make it a reference player in the quick developing quest for solutions to the haplotyping problem.
In the next section, Methods, the problem of haplotyping will be defined and the WHATSHAP approach described. Then, the details of the construction of PWHATSHAP are illustrated and the choices made in the engineering of the parallel solution discussed. An account of FastFlow, the supporting high-level parallel programming framework, concludes the section. The Results section evaluates the performances of PWHAT- SHAP. Two main parameters are considered to illustrate the validity of the developed application: accuracy of the returned results, and efficiency of the computation. The accuracy of PWHATSHAP builds on top of the accuracy of WHATSHAP, as discussed. Efficiency, instead, is demonstrated by means of suitable experimental results on benchmark datasets. Concluding remarks follow.
Both R and S contain each the whole set of reads that have been identified as belonging to the same haplotype. However, conflict-free bipartitioningss rarely can be found in existing datasets because of sequencing and mapping errors. Therefore, it is important to be able to determine a minimum-weight set of corrections to such errors capable of making the bipartitioning conflict-free. As an example, the following fragment matrix F has not a conflict-free bipartitioning of its fragments (f 1 , f 2 and f 3 , one each row): F = ⎛ ⎝ 1 9 1 9 0 3 1 8 -0 8 ⎞ ⎠ Subscripts are a measure of confidence of each datum, i. e. , v i,j , the cost to be paid to "correct" it. The minimum cost, conflict-free bipartitioning R = {f 1 , f 2 }, S = {f 3 } can be obtained by correcting the element f 2,1 , i. e. , flipping it to a 1 at a cost of 3.
Several FPT algorithms for the MEC have been developed in  [19, 24, 32, 33]. Nonetheless, the complexity of  [32]  is exponential in the read length, or the number of SNPs per read, which will soon become larger quite quickly with the developments of sequencing techniques. In turn, HapCol (  [33] ) requires the fragment matrix to be gapless (that is, in a row of F, no '-' can occur with 0's and 1's both on the left and on the right), which exclude the applicability to datasets with paired end reads. Also, given that the HapCol is exponential in the number of corrections (and in the coverage too, but less strongly that WHATSHAP), then it actually solves a constrained version of the MEC problem where the number of correction is bounded a priori. WHATSHAP  [19, 24]  is an algorithm that is fixed parameter tractable in the coverage, rather than in read length It is hence much more suitable to the trends in development of current sequencing techniques.
PWHATSHAP: high-performance WHATSHAP on multi-core architectures The focus of this work is on parallelising the core haplotyping algorithm embedded in the WHATSHAP integrated toolkit described above. The main rationale behind such a choice are the desirable properties of WHATSHAP: solving wMEC with a complexity that does not depend on read length, but is exponential only in the sequencing coverage. This appeared to be particularly relevant when considering the future trend of sequencing technology, which are inching towards longer reads. Furthermore, solving the weighted version of the problem caters to its accuracy. Two main approaches to parallelisation can be followed, respectively focusing on the haplotyping of a single chromosome or many of them. Actually, single chromosome datasets that can be decomposed in "independent" sets of SNPs, i.
Each parallel executor processes a number of the elements (rows) of the column of the cost matrix under consideration, that is, each executor evaluates some of the bipartitionings (R, S) of F j , which are the active reads on column j. A columnbased decomposition, as well as hybrid solutions possibly mixing the two approaches, are the scope of future work. The first step when moving from the sequential design of WHATSHAP to a row-based parallel implementation was profiling the efficiency of WHATSHAP in terms of the time needed to generate the j-th column of C, the minimum cost matrix C (see p. 5). This is useful to determine whether a column of a given coverage requires enough work to be worth parallelising it. Table  1  shows data from a profiling test on a given dataset. The time required by the sequential algorithm for processing a column is reported in the second row, according to the column dimension.
This is a function of the number of possible bipartitionings of the active reads on the column, i. e. , it depends on the coverage (there are ∼ 2 c possible bipartitionings for coverage c). It is easy to appreciate its exponential growth. From the results summarised in the table, the cost for the smaller columns (coverage < 15) is negligible, less than one ms, therefore not justifying the parallel overhead. Differently, when c > 15, the cost varies from a few milliseconds to a few seconds for each column (for c > 25). Columns with coverages bigger than 16/18 are worth being parallelised. What is also interesting is to gauge how many columns worth being parallelised are present in a given dataset. This of course is highly dependent on the specific dataset, but carried out experiments show that a sufficiently large number of high-coverage columns justify the parallelisation, as shown in the section Results.
PWHATSHAP: the parallel implementation The focus of the present work is the parallelisation of the core WHATSHAP haplotyping algorithm, which is a component of a larger application whose main module is written in Python, with Cython used for interfacing Python and C++. Our starting point is the WHATSHAP core algorithm written in C++, which is actually embedded into a larger, multi-language application, making the development of the parallel version very elaborated, for instance requiring us to work on the edge of different programming paradigms during both debugging and tuning. The parallel construction of the minimum cost matrix C proceeds independently over the possible bipartitionings (R, S) of the current column F j. We aimed to exploit the maximum possible parallelism in this construction by exploiting both task and data parallelism.
For this, is necessary to cut the application of parallel computing to kernels exploiting a minimum level of granularity. As we shall discuss in the Results section, for PWHAT- SHAP the threshold value is set around coverage 20, this value being almost independent of the input dataset considered. The proposed parallelisation is quite direct and, importantly, requires minimal changes to the original sequential WHATSHAP code. Furthermore, a high degree of parallelisation is involved due to the many entries of the large fragment table F corresponding to many (small) tasks that can be executed in parallel on the available cores.
Results and discussion The PWHATSHAP project focused on the design and development of a high-performance, parallel application for the solution of the haplotype problem. This has been done building upon the WHATSHAP framework, an evolving tool-kit which currently supports several stages in the haplotyping pipeline and supports data analysis in standard formats. As illustrated, the choice of WHAT-SHAP is justified by its performance in terms of accuracy, i. e. , being able to provide solutions with a low percentage of errors, and its interesting computational complexity, which depends on the coverage of data sets rather than on the length of reads. This appeared as a desirable property in the light of the future trends in sequencing technologies that will yield longer and longer reads. Indeed, other proposals based on similar approaches to computational complexity are being developed.
Accuracy In this section we compare the accuracy of PWHAT-SHAP against the accuracy of state of the art approaches to haplotyping. As explained, this is done by exploiting existing data about the accuracy of WHATSHAP, given that PWHATSHAP exhibits the same behaviour as WHATSHAP. In order to make this explicit, we will use (P)WHATSHAP where appropriate in the rest of this section. The accuracy of reconstructed haplotypes can be validated by considering both error rate  [39] , that is the count of phased variants presenting some discrepancies, and phased positions, that is the count of genomic positions for which a phased prediction can be identified out of all the phasable positions in the whole dataset. (P)WHATSHAP is compared to three tools which have been specifically designed for the long reads coming from third generation sequencing technologies: ProbHap  [40] , a recent approach that uses a probabilistic graphical model to exactly optimise a specific likelihood function; RefHap  [41] , a heuristic method presenting very high accuracy; and HapCol  [33]  Both a real and a synthetic data set have been considered for the comparison.
The real dataset (the sample NA12878) was analysed in the HapMap project  [41]  and it is a standard benchmark for haplotyping algorithms designed to work with long reads, since the haplotype of this patient, and also those of her parents, was independently reconstructed using genome sequencing techniques. The dataset consists of 271,184 reads with average length of ∼40 kb and with average coverage of ∼3x. Variant calls have been achieved using the GATK  [42]  considering the 1,252,769 positions covered by the NA12878 dataset and are trio-phased. (P)WHATSHAP, RefHap, HapCol, and ProbHap have been tested on each chromosome independently. The dataset used does not include paired end reads because HapCol cannot handle them. Moreover, despite the fact that (P)WHATSHAP and Hap-Col can compute haplotypes outside the all-heterozygous hypothesis (which allows for a better handling of sequencing errors, since it permits to consider a SNP site homozygous also if its column is non-monotone), in this test case, the all-heterozygous assumption was enforced for all the tools.