dataesr/scientific-paragraphs-categorization huggingface dataset:
k-bounded-All-MPR problem The main concern of this section is to discuss the effect of bounding the distance of the host switch events. The variables that will be defined here must be considered relative to a fixed dataset and a fixed cost vector. We denote by S(k) the set of optimal solutions obtained when the maximum distance allowed for a host switch is k, and denote by opt k their cost. We also denote by opt * the optimum cost of an acyclic reconciliation (without any bound on the host switch distance): observe that this value is in general For some datasets, the number of optimal time-feasible solutions may be huge when k is unbounded. In some cases, however, by introducing a bound on k we can greatly reduce the number of time-feasible solutions while keeping their optimality. For all datasets whose number of acyclic solutions is positive for unbounded k, we identified k start (minimum k whose optimal cost is equal to the optimal cost obtained for unbounded k) and we searched for the minimum k ≥ k start whose number of acyclic solutions is non zero.
We executed this procedure for every pair (dataset, cost vector) for which the number of optimal acyclic solutions is positive. In the first column, the values for k = k start and k are given. #AC/#AC denotes the number of optimal acyclic solutions for the case when the switches are unbounded and the case when they are bounded by k , respectively. The same relation is shown for the total number of optimal solutions in the column #T/#T. NP-hard to determine. Clearly, if S(∞) contains at least one time-feasible solution then opt ∞ = opt *. However, this is not always the case (see Table  1  for some examples): let us then consider the case where opt ∞ < opt *. We are now interested in finding an upper bound on the value of opt * by making use of the possibility given by EUCALYPT to limit the distance of switches.
Conclusions We presented in this paper a software, EUCALYPT, that can find one optimal reconciliation of a pair of host and parasite trees, can compute the number of all optimal solutions, and can enumerate them all. The first two problems are handled in polynomial time, while the enumeration has a polynomial delay complexity. EUCALYPT also displays the classes of solutions observed, where two solutions are in a same class if the number of each event in the two is the same. We show that although the number of classes of solutions may be considerably smaller than the total number of optimal reconciliations, it nevertheless may remain very high even for relatively small trees. Finally, we introduced the k-bounded-All-MPR problem and showed how it could be applied either to find optimal time-feasible solutions when the parsimonious method found none, or to reduce their number if this is too large to be handled in practice for further analysis.
EUCALYPT takes a nexus file as input and generates all the information related to the reconciliations described in the paper. The datasets used in this paper are also available on the website of the software. All the results of this study point to the necessity of introducing new criteria besides parsimony in the model for an optimal reconciliation. The idea of imposing an evolutionary distance to the host switches is one possible criterion when it can be justified from a biological point of view. Other types of information, such as for instance geographic, might enable also to indicate that certain mappings of internal vertices are impossible. One next improvement of EUCALYPT will therefore be to allow the user to indicate that certain associations of internal vertices should be forbidden.
Acknowledgements The authors would like to thank  Valentino Bruni  who was the first to notice that CORE-PA may miss some optimal solutions when enumerating all. This result was presented in his bachelor thesis [34]. The research leading to the results presented in this paper was funded by the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement no. [247073]10, and by the  French  project  ANR MIRI BLAN08-1335497. It was supported by the  ANR funded LabEx ECOFECT. The fourth author was partially supported by the  Italian Ministry of Education, University, and Research (MIUR).
Genomic translocation events frequently underlie cancer development through generation of gene fusions with oncogenic properties. Identification of such fusion transcripts by transcriptome sequencing might help to discover new potential therapeutic targets. We developed TRUP (Tumor-specimen suited RNA-seq Unified Pipeline) (https://github. com/ruping/TRUP), a computational approach that combines split-read and read-pair analysis with de novo assembly for the identification of chimeric transcripts in cancer specimens. We apply TRUP to RNA-seq data of different tumor types, and find it to be more sensitive than alternative tools in detecting chimeric transcripts, such as secondary rearrangements in EML4-ALK-positive lung tumors, or recurrent inactivating rearrangements affecting RASSF8.
TRUP: A pipeline for detecting fusion genes in cancer In order to detect fusion transcripts from PE RNA-seq data, we need to identify the fusion point from the sequencing read alignments. Discordant mapping of mate pairs, which include chimeric as well as partial alignments of an individual read, are reported by GSNAP  [11]  or STAR  [12]. To guarantee high sensitivity, TRUP collects all candidate regions containing potential breakpoints suggested by those abnormal alignments. Additionally, for each candidate region, de novo assembly is performed using de Bruijn graphs ('Velvet')  [13]  and a modified version of Velvet (Oases) that employs additional filters to afford optimized merging of multiple assemblies, specifically of transcriptome sequencing data  [14] , with the aim to construct possible contigs from each region by leveraging dependency among reads.
After sensitive split-read mapping and specific de novo assembly, fusion candidates are filtered and ranked based on repeat content and number of reads supporting the fusion points (Figure  1 ; Materials and Methods). In order to evaluate the performance TRUP, we initially applied a preliminary version of TRUP (v1. 0) to the well-characterized lung cancer cell-lines H3122 and H2228, which are known to harbor different variants of the EML4-ALK fusion gene  [15] , as well as to five lung adenocarcinoma tumor specimens that had been found positive for ALK rearrangements by FISH. On average, 50 million PE reads were uniquely mapped to the human genome (Additional file 1). We considered as high confidence candidates those chimeric transcripts that matched the following requirements: inter-or intra-chromosomal rearrangements; at least five independent reads supporting the breakpoint (either reads that span or read-pairs that encompass the fusion-point, referred as spanning reads and encompassing reads, respectively); and a nonrepetitive sequence across the fusion-point (unless the chimeric transcript was also covered by encompassing reads).
We found that below 5x most of the candidates called were artifacts of the pipeline or barely expressed chimeric transcripts difficult to validate by RT-PCR. In the seven samples analyzed, 20 chimeric transcripts matched the above-mentioned requirements. Out of these 20, 17 In a first step, TRUP aligns the read pairs onto the genome allowing discovery of chimeric alignments (read pair id p2 and p7 in the cartoon) and partial alignments (p1, p3, p6, and p8). To guarantee a sensitive detection of candidate regions containing potential breakpoint, relaxed criteria are adopted to call breakpoints from chimeric/partial alignments, as well as from entirely aligned discordant pairs (p4 and p5). Subsequently, to reach high accuracy, de novo assembly is performed on a candidate region by using the read pairs anchored in this region.
Lastly, bona fide breakpoints relative to the genome are identified from the assembled sequences. A fusion candidate is called if it attracts a sufficient number of supporting reads. While the mapping and assembly steps adopt the state-of-the-art algorithms, the breakpoint searching and fusion calling steps are novel (Materials and methods). (85%) were validated by RT-PCR and Sanger sequencing across the fusion-point, or by FISH in the case of EML4-ALK (Table  1 ). These results were used to build an improved version of TRUP (v2. 0), which not only recovered all the above-mentioned validated candidates but also identified 28 additional high-confident ones (Additional file 2). For all subsequent analyses version 2. 0 was used. In order to determine the robustness and accuracy of TRUP we applied our pipeline to a published PE RNAseq dataset of small cell lung cancer  [16].
We applied TRUP to the cell-line data, where experimental validation of candidates was possible. We were able to identify two novel fusion transcripts affecting histone modifiers: one predicted to inactivate the histone acetyltransferase CREBBP in the cell line, N417, and the other one leading to the inactivation of the TAF6like RNA polymerase II p300/CBP-associated factor (PCAF)-associated factor (TAF6L) in the cell line, H187 (Additional file 3). These results are in agreement with previous studies in which alterations of histone modifiers by rearrangements were reported  [17, 18] , and support the important role that these genes might have in the development and maintenance of small cell lung cancer.
By using relaxed criteria requiring at least two supporting reads with a minimum of one read spanning the fusion-point, the seven tools reported from four to 542 fusion transcripts in sample S00054 (Figure  3c ). Since the full set of true-positive predictions was not available we took the set of predictions shared across tools as gold set. There were in total 21 fusion transcripts detected by at least two independent tools, including three experimentally validated ones. Despite the low stringency settings, most alternative tools were only able to detect the EML4-ALK fusion but not the additional two experimentally validated chimeric transcripts. Three tools showed a high number of unique predictions suggesting a high number of false-positives. In case of FusionMap this might be due to the fact that this tool primarily relies on a single method (split-read mapping) not offering the use of a complementary approach for eliminating false-positives.
Nevertheless, such a recall is still higher than that of other fusion prediction tools. We compared TopHat-Fusion and TRUP in more detail since they showed the best performance (the harmonic mean of recall and precision for TRUP is 0. 62 and for TopHat-Fusion is 0. 49, highest among all the tools) with a total of 20 and 40 predicted fusion events, respectively (Additional files 8 and 9). For candidates with very low coverage, disagreements between the two tools were observed, indicating higher uncertainty for calling fusion transcripts with low expression. After manually checking the calls unique to TRUP, we only found a single candidate that might be considered as a false positive. This candidate exhibited a breakpoint located in a repetitive region and also showed a low spanning score, which summarizes the confidence of supporting evidence of the spanning reads (Materials and methods).
Conclusions Taken together, TRUP is a new tool for the identification of chimeric transcripts using PE RNA-seq data, which shows a balance between sensitivity, specificity, and computational efficiency for prediction of fusion events. TRUP afforded the identification of new fusion events in the context of EML4-ALK, which were genomically linked in one case, suggesting that in some cases EML4-ALK occurs as a balanced translocation. We furthermore detected inactivating rearrangements affecting RASSF8, supporting its role as a tumor suppressor gene in cancer.
kmer is set in the genome database for GSNAP (16 bp or less). Within the nearby ±200 bp region of X, TRUP requires the existence of at least t 1 (default: 2) other supporting read alignments with discordant mates consistent with X. (iii) For partially aligned X with a shorter unmapped segment having a discordant mate, TRUP requires at least t 2 (default: 3) other discordant read alignments consistent with X. (iv) For a group of t 3 (default: 4) entirely aligned discordant read pairs that are all consistent with a fusion point, the potential breakpoint will be set to the locations where the reads' 3′ ends extend farthest. If STAR is used as mapper, the chimeric junctions for each read produced by STAR are incorporated into the breakpoint calls, in addition to the ones identified by scanning the mapping results.
An additional filtration step was used to filter out those predictions with low (less than 5) total supporting reads (non-redundant), with low spanning score (less than 1) and/or those with both breakpoints residing in self-chain region or repetitive region. Isoform junctions for the same gene fusion were merged. TRUP also has several other modules for RNA-seq analysis in general, such as quality assessment, gene/ transcript expression quantification, and differential expression analysis. TRUP is available at  [28].
Acknowledgements The study as well as written informed consent documents had been approved by the  Institutional Review Board of the University of Cologne. Additional biospecimens for this study were obtained from the  Victorian Cancer Biobank, Melbourne, Australia. The  Institutional Review Board  (IRB) of each participating institution approved collection and use of all patient specimens in this study. We are indebted to the patients donating their tumor specimens as part of the  Clinical Lung Cancer Genome Project initiative. We thank  Marek Franitza ,  Graziella Bosco , and  Belarmina Valdes Naves  for their technical assistance. We furthermore thank the regional computing center at the  University of Cologne (RRZK)  for providing the CPU time on the DFG-funded supercomputer 'CHEOPS' as well as the support.
This work was supported by the  Deutsche Krebshilfe  as part of the small-cell lung cancer genome-sequencing consortium (grant ID:  109679  to RKT, MP, RB, PN, MV, and SAH). Additional funds were provided by the  EU-Framework program  CURELUNG ( HEALTH-F2-2010-258677  to RKT, JW, and EB); by the  German federal state North Rhine Westphalia (NRW)  and the  European Union  ( European Regional Development Fund: Investing In Your Future) within PerMed NRW  (grant  005-1111-0025  to RKT, JW, and RB); by the  Deutsche Forschungsgemeinschaft  through  TH1386/3-1  (to RKT) and through  SFB832  ( TP6  to RKT,  RTU , and JW;  TP5  to LCH); by the  German Ministry of Science and Education (BMBF) as part of the NGFNplus program  (grant  01GS08101  to RKT, JW, and PN) and as part of the  eMed program  (grant  01ZX1303A  to RKT, RB, and JW); by the  Deutsche Krebshilfe as part of the Oncology Centers of Excellence funding program  (RKT, RB, and JW); and by  Stand Up To Cancer -American Association for Cancer Research   Innovative Research Grant  ( SU2C-AACR-IR60109  to RKT).
While discussion forums and live meetups can provide opportunities to share experience, they are de-contexualised spaces, divorced from the immediate content of the course and not exploiting the situational context of the learners. Studies have shown that collecting data in context can enable more authentic and personally relevant learning  (Kravcik, et. al, 2004)  as well as offering possibilities of live connections between remote learning contexts and classrooms  (Bergin et al. , 2007). The iSpot platform developed at The Open University is an example of a learning technology that exploits situational context (http://www. ispotnature. org). This site enables people to make observations of nature (such as birds, animals, plants and fungi) by taking a photograph of an interesting natural item using the iSpot mobile application (app) and adding a proposed identification.
Other people in the iSpot community, among them wildlife experts, offer additional information and a more accurate identification. The system adds further information and links, e. g. to Wikipedia pages, based on the species identification and other meta-data such as the location. A sophisticated reputation management system  (Clow & Makriyannis, 2011)  awards credit and online badges to people who make many observations, offer identifications that are confirmed by others, and gain confirmed identifications from experts. This type of learner-contributed contextual content could be included in a MOOC to bring benefits from the engagement of thousands of learners. A MOOC on meteorology, for example, could benefit from the activity of thousands of learners contributing weather data matched to specific times and locations.
The Sense-it mobile app, also from The Open University, gives access to all the sensors on an Android phone or tablet 1. A user can choose one or more sensor, select the sampling rate, record data samples over a period of time and upload this to a shared website to be combined with data from other users. By automatically logging time and location (from GPS), the data can be visualised on a map or timeline. Modern mobile devices have over 20 sensors including atmospheric pressure, light, sound, ambient temperature, humidity, magnetic field, and others such as wind speed can be approximated by software (from wind noise over the sound sensor). Similarly, for MOOCs related to health, mobile phones can measure heart-rate, and software can log step counts and sleep movement as well as provide nutrition information on meals.
The technology must also scale appropriately. Greater access through mobile technology demands support for a wide range of devices and more concurrent connections. There are learning benefits to be derived from rich and varied resources created by learners, but these materials need to be stored, validated, managed, shared and archived. A fundamental social issue is the need for localisation, to accommodate different languages and different perspectives. For example, the iSpot platform described earlier enables people to identify and share observations of wildlife. The developers of iSpot recruited acknowledged wildlife experts to 'kick start' the service by providing expert validation of the initial contributions. But each country has different flora and fauna, so extending this to other countries requires finding experts with local knowledge.
Spaced repetition for vocabulary is one example of timesensitive learning. The companion problem is that universal high speed access has still not been achieved in most countries. Sensor pack for situated real world data: mobile devices have many sensors that extend the human senses with 'superpowers' to detect magnetic field or infrared radiation. Conventional multimedia capture, such as video or audio, or data logging, such as accelerometer or positioning information, can enrich the experience of inquiry science and enable the capture and sharing of experience in context. Connectivity for sharing between locations and cultures: the connectivity of mobile allows learners to share experiences and benefit from crowd learning by working with others worldwide towards a common goal. While some sites such as iSpot and StackExchange have shown the value of crowd learning, issues of moderation, authority and scholarship need to be explored further for MOOC courses.
During the first LHC run, the CMS experiment collected tens of Petabytes of collision and simulated data, which need to be distributed among dozens of computing centres with low latency in order to make efficient use of the resources. While the desired level of throughput has been successfully achieved, it is still common to observe transfer workflows that cannot reach full completion in a timely manner due to a small fraction of stuck files which require operator intervention. For this reason, in 2012 the CMS transfer management system, PhEDEx, was instrumented with a monitoring system to measure file transfer latencies, and to predict the completion time for the transfer of a data set. The operators can detect abnormal patterns in transfer latencies while the transfer is still in progress, and monitor the long-term performance of the transfer infrastructure to plan the data placement strategy.
Based on the data collected for one year with the latency monitoring system, we present a study on the different factors that contribute to transfer completion time. As case studies, we analyze several typical CMS transfer workflows, such as distribution of collision event data from CERN or upload of simulated event data from the Tier-2 centres to the archival Tier-1 centres. For each workflow, we present the typical patterns of transfer latencies that have been identified with the latency monitor. We identify the areas in PhEDEx where a development effort can reduce the latency, and we show how we are able to detect stuck transfers which need operator intervention. We propose a set of metrics to alert about stuck subscriptions and prompt for manual intervention, with the aim of improving transfer completion times.
Introduction The CMS experiment  [1]  at the LHC accelerator is concluding the first Long Shutdown (LS1) after a successful first run of data taking (Run-1), with Run-2 starting in Summer 2015. In the original CMS Computing model  [2] , one of the main concepts of the data management  [3]  was that jobs go where the data is, and no data moves in response to job submissions. In such model, the importance of adequate policies and tools for data placement is vital. Over the years, this motivated the CMS Computing project to design, build and operate a robust and reliable solution to perform transfers of massive volumes of data among computing centres of the Worldwide LHC Computing Grid (WLCG)  [4, 5] , called PhEDEx  [6, 7, 8, 9]. PhEDEx is a reliable and scalable dataset replication system based on a central database on an Oracle instance running at CERN and a set of highly specialised, loosely-coupled, stateless software agents distributed at sites.
In production for CMS since more than 10 years, PhEDEx moved 150 PB during Run-1, and it is currently moving about 2. 5 PB per week among about 60 sites. The PhEDEx design aims at providing the highest possible transfer completion rate, despite possible infrastructural unreliabilities, achieved via intelligent fail-over tactics and automatic retrials. During the several years of its operations, including the first LHC data taking period (Run-1) and the first LHC Long Shutdown (LS1), a large set of data concerning the latencies observed in all transfers between all Tiers has been collected. The study of this data set is allowing a categorisation of the different root sources of such latencies and shaping the strategy to attach this problem and increase the overall performance of the PhEDEx system.
Instrumenting PhEDEx to collect latency data The atomic unit for transfer operations in PhEDEx is the file block: an arbitrary group of O(100-1000) files in the same dataset. To achieve scalability, PhEDEx doesn't keep a permanent record of the states of individual files: all information is aggregated at the level of block after transfers are completed. This level of detail is sufficient for replica location, but it is not enough to identify problems that increase latency in block transfers: for example, there is no way to distinguish between the case of a high-latency transfer proceeding at a low regular rate, and the case of a transfer for which the latency is dominated by a few stuck files in the "transfer tail". For this reason, in 2012 we instrumented the central agents of PhEDEx with a detailed latency monitoring system  [10] , collecting file-level information on transfers in historical monitoring tables to complement block-level information, and providing corresponding Data Service APIs to retrieve the monitoring data for further analysis.