bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Rolling Stones promoting Monty Python
Authors: Sorlin Sandrine 
Published: April 2018

Abstract
Based on Text World Theory, this chapter offers a fine-grained analysis of how irony and banter are likely to be processed by viewers watching a promotional video but also highlights the pragmatic functions of such an indirect strategy. It dissects a 1:40 tongue-in-cheek video which served as an introduction to the 2014
Monty Python Live (Mostly) press conference. It features Mick Jagger accusing the coming troupe of being “wrinkly old men trying to relive their youth”, using irony and banter as powerful means of promotion. Bringing together different theoretical frameworks of irony that are usually described as competing, this chapter highlights how expectations are twisted in the “text-world” of the video through the use of (mock) dramatic irony, generating pragmatic paradoxes.
Machinability Study on Milling Kenaf Fiber Reinforced Plastic Composite Materials using Design of Experiments
Authors: Azmi H. , Haron C. H. C. , Ghani J. A. , Suhaily M. , Yuzairi A. R. 
Published: April 2018

Abstract
The surface roughness (Ra) and delamination factor (Fd) of a milled kenaf reinforced plastic composite materials are depending on the milling parameters (spindle speed, feed rate and depth of cut). Therefore, a study was carried out to investigate the relationship between the milling parameters and their effects on a kenaf reinforced plastic composite materials. The composite panels were fabricated using vacuum assisted resin transfer moulding (VARTM) method. A full factorial design of experiments was use as an initial step to screen the significance of the parameters on the defects using Analysis of Variance (ANOVA). If the curvature of the collected data shows significant, Response Surface Methodology (RSM) is then applied for obtaining a quadratic modelling equation that has more reliable in expressing the optimization. Thus, the objective of this research is obtaining an optimum setting of milling parameters and modelling equations to minimize the surface roughness (Ra) and delamination factor (Fd) of milled kenaf reinforced plastic composite materials. The spindle speed and feed rate contributed the most in affecting the surface roughness and the delamination factor of the kenaf composite materials.
COSSMO: predicting competitive alternative splice site selection using deep learning
Authors: Bretschneider Hannes , Gandhi Shreshth , Deshwar Amit G , Zuberi Khalid , Frey Brendan J 
Published: April 2018

Abstract

Motivation
Alternative splice site selection is inherently competitive and the probability of a given splice site to be used also depends on the strength of neighboring sites. Here, we present a new model named the competitive splice site model (COSSMO), which explicitly accounts for these competitive effects and predicts the percent selected index (PSI) distribution over any number of putative splice sites. We model an alternative splicing event as the choice of a 3′ acceptor site conditional on a fixed upstream 5′ donor site or the choice of a 5′ donor site conditional on a fixed 3′ acceptor site. We build four different architectures that use convolutional layers, communication layers, long short-term memory and residual networks, respectively, to learn relevant motifs from sequence alone. We also construct a new dataset from genome annotations and RNA-Seq read data that we use to train our model.

Results
COSSMO is able to predict the most frequently used splice site with an accuracy of 70% on unseen test data, and achieve an R2 of 0.6 in modeling the PSI distribution. We visualize the motifs that COSSMO learns from sequence and show that COSSMO recognizes the consensus splice site sequences and many known splicing factors with high specificity.

Availability and implementation
Model predictions, our training dataset, and code are available from http://cossmo.genes.toronto.edu.

Supplementary information
Supplementary data are available at Bioinformatics online.
A scalable estimator of SNP heritability for biobank-scale data
Authors: Wu Yue , Sankararaman Sriram 
Published: April 2018

Abstract

Motivation
Heritability, the proportion of variation in a trait that can be explained by genetic variation, is an important parameter in efforts to understand the genetic architecture of complex phenotypes as well as in the design and interpretation of genome-wide association studies. Attempts to understand the heritability of complex phenotypes attributable to genome-wide single nucleotide polymorphism (SNP) variation data has motivated the analysis of large datasets as well as the development of sophisticated tools to estimate heritability in these datasets. Linear mixed models (LMMs) have emerged as a key tool for heritability estimation where the parameters of the LMMs, i.e. the variance components, are related to the heritability attributable to the SNPs analyzed. Likelihood-based inference in LMMs, however, poses serious computational burdens.

Results
We propose a scalable randomized algorithm for estimating variance components in LMMs. Our method is based on a method-of-moment estimator that has a runtime complexity O(NMB) for N individuals and M SNPs (where B is a parameter that controls the number of random matrix-vector multiplications). Further, by leveraging the structure of the genotype matrix, we can reduce the time complexity to O(NMBmax( log⁡3N, log⁡3M)).
We demonstrate the scalability and accuracy of our method on simulated as well as on empirical data. On standard hardware, our method computes heritability on a dataset of 500 000 individuals and 100 000 SNPs in 38 min.

Availability and implementation
The RHE-reg software is made freely available to the research community at: https://github.com/sriramlab/RHE-reg.
AmpUMI: design and analysis of unique molecular identifiers for deep amplicon sequencing
Authors: Clement Kendell , Farouni Rick , Bauer Daniel E , Pinello Luca 
Published: April 2018

Abstract

Motivation
Unique molecular identifiers (UMIs) are added to DNA fragments before PCR amplification to discriminate between alleles arising from the same genomic locus and sequencing reads produced by PCR amplification. While computational methods have been developed to take into account UMI information in genome-wide and single-cell sequencing studies, they are not designed for modern amplicon-based sequencing experiments, especially in cases of high allelic diversity. Importantly, no guidelines are provided for the design of optimal UMI length for amplicon-based sequencing experiments.

Results
Based on the total number of DNA fragments and the distribution of allele frequencies, we present a model for the determination of the minimum UMI length required to prevent UMI collisions and reduce allelic distortion. We also introduce a user-friendly software tool called AmpUMI to assist in the design and the analysis of UMI-based amplicon sequencing studies. AmpUMI provides quality control metrics on frequency and quality of UMIs, and trims and deduplicates amplicon sequences with user specified parameters for use in downstream analysis.

Availability and implementation
AmpUMI is open-source and freely available at http://github.com/pinellolab/AmpUMI.
The Kappa platform for rule-based modeling
Authors: Boutillier Pierre , Maasha Mutaamba , Li Xing , Medina-Abarca Héctor F , Krivine Jean , Feret Jérôme , Cristescu Ioana , Forbes Angus G , Fontana Walter 
Published: April 2018

Abstract

Motivation
We present an overview of the Kappa platform, an integrated suite of analysis and visualization techniques for building and interactively exploring rule-based models. The main components of the platform are the Kappa Simulator, the Kappa Static Analyzer and the Kappa Story Extractor. In addition to these components, we describe the Kappa User Interface, which includes a range of interactive visualization tools for rule-based models needed to make sense of the complexity of biological systems. We argue that, in this approach, modeling is akin to programming and can likewise benefit from an integrated development environment. Our platform is a step in this direction.

Results
We discuss details about the computation and rendering of static, dynamic, and causal views of a model, which include the contact map (CM), snaphots at different resolutions, the dynamic influence network (DIN) and causal compression. We provide use cases illustrating how these concepts generate insight. Specifically, we show how the CM and snapshots provide information about systems capable of polymerization, such as Wnt signaling. A well-understood model of the KaiABC oscillator, translated into Kappa from the literature, is deployed to demonstrate the DIN and its use in understanding systems dynamics. Finally, we discuss how pathways might be discovered or recovered from a rule-based model by means of causal compression, as exemplified for early events in EGF signaling.

Availability and implementation
The Kappa platform is available via the project website at kappalanguage.org. All components of the platform are open source and freely available through the authors’ code repositories.
Deep neural networks and distant supervision for geographic location mention extraction
Authors: Magge Arjun , Weissenbacher Davy , Sarker Abeed , Scotch Matthew , Gonzalez-Hernandez Graciela 
Published: April 2018

Abstract

Motivation
Virus phylogeographers rely on DNA sequences of viruses and the locations of the infected hosts found in public sequence databases like GenBank for modeling virus spread. However, the locations in GenBank records are often only at the country or state level, and may require phylogeographers to scan the journal articles associated with the records to identify more localized geographic areas. To automate this process, we present a named entity recognizer (NER) for detecting locations in biomedical literature. We built the NER using a deep feedforward neural network to determine whether a given token is a toponym or not. To overcome the limited human annotated data available for training, we use distant supervision techniques to generate additional samples to train our NER.

Results
Our NER achieves an F1-score of 0.910 and significantly outperforms the previous state-of-the-art system. Using the additional data generated through distant supervision further boosts the performance of the NER achieving an F1-score of 0.927. The NER presented in this research improves over previous systems significantly. Our experiments also demonstrate the NER’s capability to embed external features to further boost the system’s performance. We believe that the same methodology can be applied for recognizing similar biomedical entities in scientific literature.
DeepFam: deep learning based alignment-free method for protein family modeling and prediction
Authors: Seo Seokjun , Oh Minsik , Park Youngjune , Kim Sun 
Published: April 2018

Abstract

Motivation
A large number of newly sequenced proteins are generated by the next-generation sequencing technologies and the biochemical function assignment of the proteins is an important task. However, biological experiments are too expensive to characterize such a large number of protein sequences, thus protein function prediction is primarily done by computational modeling methods, such as profile Hidden Markov Model (pHMM) and k-mer based methods. Nevertheless, existing methods have some limitations; k-mer based methods are not accurate enough to assign protein functions and pHMM is not fast enough to handle large number of protein sequences from numerous genome projects. Therefore, a more accurate and faster protein function prediction method is needed.

Results
In this paper, we introduce DeepFam, an alignment-free method that can extract functional information directly from sequences without the need of multiple sequence alignments. In extensive experiments using the Clusters of Orthologous Groups (COGs) and G protein-coupled receptor (GPCR) dataset, DeepFam achieved better performance in terms of accuracy and runtime for predicting functions of proteins compared to the state-of-the-art methods, both alignment-free and alignment-based methods. Additionally, we showed that DeepFam has a power of capturing conserved regions to model protein families. In fact, DeepFam was able to detect conserved regions documented in the Prosite database while predicting functions of proteins. Our deep learning method will be useful in characterizing functions of the ever increasing protein sequences.

Availability and implementation
Codes are available at https://bhi-kimlab.github.io/DeepFam.
Unsupervised embedding of single-cell Hi-C data
Authors: Liu Jie , Lin Dejun , Yardımcı Galip Gürkan , Noble William Stafford 
Published: April 2018

Abstract

Motivation
Single-cell Hi-C (scHi-C) data promises to enable scientists to interrogate the 3D architecture of DNA in the nucleus of the cell, studying how this structure varies stochastically or along developmental or cell-cycle axes. However, Hi-C data analysis requires methods that take into account the unique characteristics of this type of data. In this work, we explore whether methods that have been developed previously for the analysis of bulk Hi-C data can be applied to scHi-C data. We apply methods designed for analysis of bulk Hi-C data to scHi-C data in conjunction with unsupervised embedding.

Results
We find that one of these methods, HiCRep, when used in conjunction with multidimensional scaling (MDS), strongly outperforms three other methods, including a technique that has been used previously for scHi-C analysis. We also provide evidence that the HiCRep/MDS method is robust to extremely low per-cell sequencing depth, that this robustness is improved even further when high-coverage and low-coverage cells are projected together, and that the method can be used to jointly embed cells from multiple published datasets.
Gene prioritization using Bayesian matrix factorization with genomic and phenotypic side information
Authors: Zakeri Pooya , Simm Jaak , Arany Adam , ElShal Sarah , Moreau Yves 
Published: April 2018

Abstract

Motivation
Most gene prioritization methods model each disease or phenotype individually, but this fails to capture patterns common to several diseases or phenotypes. To overcome this limitation, we formulate the gene prioritization task as the factorization of a sparsely filled gene-phenotype matrix, where the objective is to predict the unknown matrix entries. To deliver more accurate gene-phenotype matrix completion, we extend classical Bayesian matrix factorization to work with multiple side information sources. The availability of side information allows us to make non-trivial predictions for genes for which no previous disease association is known.

Results
Our gene prioritization method can innovatively not only integrate data sources describing genes, but also data sources describing Human Phenotype Ontology terms. Experimental results on our benchmarks show that our proposed model can effectively improve accuracy over the well-established gene prioritization method, Endeavour. In particular, our proposed method offers promising results on diseases of the nervous system; diseases of the eye and adnexa; endocrine, nutritional and metabolic diseases; and congenital malformations, deformations and chromosomal abnormalities, when compared to Endeavour.

Availability and implementation
The Bayesian data fusion method is implemented as a Python/C++ package: https://github.com/jaak-s/macau. It is also available as a Julia package: https://github.com/jaak-s/BayesianDataFusion.jl. All data and benchmarks generated or analyzed during this study can be downloaded at https://owncloud.esat.kuleuven.be/index.php/s/UGb89WfkZwMYoTn.

Supplementary information
Supplementary data are available at Bioinformatics online.
A space and time-efficient index for the compacted colored de Bruijn graph
Authors: Almodaresi Fatemeh , Sarkar Hirak , Srivastava Avi , Patro Rob 
Published: April 2018

Abstract

Motivation
Indexing reference sequences for search—both individual genomes and collections of genomes—is an important building block for many sequence analysis tasks. Much work has been dedicated to developing full-text indices for genomic sequences, based on data structures such as the suffix array, the BWT and the FM-index. However, the de Bruijn graph, commonly used for sequence assembly, has recently been gaining attention as an indexing data structure, due to its natural ability to represent multiple references using a graphical structure, and to collapse highly-repetitive sequence regions. Yet, much less attention has been given as to how to best index such a structure, such that queries can be performed efficiently and memory usage remains practical as the size and number of reference sequences being indexed grows large.

Results
We present a novel data structure for representing and indexing the compacted colored de Bruijn graph, which allows for efficient pattern matching and retrieval of the reference information associated with each k-mer. As the popularity of the de Bruijn graph as an index has increased over the past few years, so have the number of proposed representations of this structure. Existing structures typically fall into two categories; those that are hashing-based and provide very fast access to the underlying k-mer information, and those that are space-frugal and provide asymptotically efficient but practically slower pattern search. Our representation achieves a compromise between these two extremes. By building upon minimum perfect hashing and making use of succinct representations where applicable, our data structure provides practically fast lookup while greatly reducing the space compared to traditional hashing-based implementations. Further, we describe a sampling scheme for this index, which provides the ability to trade off query speed for a reduction in the index size. We believe this representation strikes a desirable balance between speed and space usage, and allows for fast search on large reference sequences.
Finally, we describe an application of this index to the taxonomic read assignment problem. We show that by adopting, essentially, the approach of Kraken, but replacing k-mer presence with coverage by chains of consistent unique maximal matches, we can improve the space, speed and accuracy of taxonomic read assignment.

Availability and implementation
pufferfish is written in C++11, is open source, and is available at https://github.com/COMBINE-lab/pufferfish.

Supplementary information
Supplementary data are available at Bioinformatics online.
On the Role of Different Age Groups and Pertussis Vaccines During the 2012 Outbreak in Wisconsin
Authors: Goldstein Edward , Worby Colin J , Lipsitch Marc 
Published: April 2018

Abstract

Background
There is limited information on the roles of different age groups in propagating pertussis outbreaks, and on the impact of vaccination on pertussis transmission in the community.

Methods
The relative roles of different age groups in propagating the 2012 pertussis outbreak in Wisconsin were evaluated using the relative risk (RR) statistic that measures the change in the group’s proportion among all detected cases before vs after the epidemic peak. The impact of vaccination in different age groups against infection (that is potentially different from the protective effect against detectable disease) was evaluated using the odds ratios (ORs), within each age group, for being vaccinated vs undervaccinated before vs after the outbreak’s peak.

Results
The RR statistic suggests that children aged 13–14 years played the largest relative role during the outbreak’s ascent (with estimates consistent across the 3 regions in Wisconsin that were studied), followed by children aged 7–8, 9–10, and 11–12 years. Young children and older teenagers and adults played more limited relative roles during the outbreak. Results of the vaccination status analysis for the fifth dose of DTaP (for children aged 7–8 years: OR, 0.44; 95% confidence interval [CI], 0.23–0.86; for children aged 9–10 years: OR, 0.51; 95% CI, 0.27–0.95); and for Tdap for children aged 13–14 years (OR, 0.38, 95% CI, 0.16–0.89) are consistent with protective effect against infection.

Conclusions
While our epidemiological findings for the fifth dose of DTaP and for Tdap are consistent with protective effect against infection, further studies, including those estimating vaccine effectiveness against infection/transmission to others particularly for pertussis vaccines for adolescents, are needed to evaluate the impact of vaccination on the spread of pertussis in the community.
Orbital solution leading to an acceptable interpretation for the enigmatic gamma-ray binary HESS J0632+057
Authors: Yuki Moritani , Kawano Takafumi , Chimasu Sho , Kawachi Akiko , Takahashi Hiromitsu , Takata Jumpei , Carciofi Alex C 
Published: April 2018

Abstract
High-dispersion spectroscopic monitoring of HESS J0632+057 has been carried out over four orbital cycles in order to search for orbital modulation, covering the entire orbital phase. We have measured the radial velocity of the Hα emission line with the method introduced by Shafter, Szkody, and Thorstensen (1986, ApJ, 308, 765), which has been successfully applied to some Be stars. The velocity is seen to increase much earlier than expected for the orbital period of 315 d, and much more steeply than expected at around “apastron.” The period of the Hα modulation is found to be $308^{+26}_{-23}$ d. We have also analyzed Swift/XRT data from 2009 to 2015 to study the orbital modulation, selecting the data with good statistics (≥30 counts). With additional two-year data to the previous works, the orbital period has been updated to $313^{+11}_{-8}$ d, which is consistent with the previous X-ray periods and the spectroscopic one. Previous XMM-Newton and Chandra observations prefer a period of 313 d. With the new period, assuming that Hα velocities accurately trace the motion of the Be star, we have derived a new set of orbital parameters. In the new orbit, which is less eccentric (e ≃ 0.6), two outbursts occur: after apastron and just after periastron. Also, the column density in bright phase ($4.7^{+0.9}_{-08}\times 10^{21} \, \mathrm{cm}^{-2}$) is higher than in faint phase (2.2 ± 0.5 × 1021 cm−2). These facts suggest that outbursts occur when the compact object passes nearby/through the Be disk. The mass function implies that the mass of the compact object is less than 2.5 M⊙, assuming that the mass of the Be star is 13.2–18.2 M⊙ (Aragona et al. 2010, ApJ, 724, 306), unless the inclination is extremely small. The photon index indicates that the spectra become softer when the system is bright. These suggest that the compact object is a pulsar.
High-energy astrophysics and the search for sources of gravitational waves
Authors: O'Brien P. T. , Evans P. 
Published: April 2018

The dawn of the gravitational-wave (GW) era has sparked a greatly renewed interest into possible links between sources of high-energy radiation and GWs. The most luminous high-energy sources—gamma-ray bursts (GRBs)—have long been considered as very likely sources of GWs, particularly from short-duration GRBs, which are thought to originate from the merger of two compact objects such as binary neutron stars and a neutron star–black hole binary. In this paper, we discuss: (i) the high-energy emission from short-duration GRBs; (ii) what other sources of high-energy radiation may be observed from binary mergers; and (iii) how searches for high-energy electromagnetic counterparts to GW events are performed with current space facilities. While current high-energy facilities, such as Swift and Fermi, play a crucial role in the search for electromagnetic counterparts, new space missions will greatly enhance our capabilities for joint observations. We discuss why such facilities, which incorporate new technology that enables very wide-field X-ray imaging, are required if we are to truly exploit the multi-messenger era.This article is part of a discussion meeting issue ‘The promises of gravitational-wave astronomy’.