bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bayesian coalescent inference of in-host evolution using Next Generation Sequencing
Authors: Leen Gayle , Baguelin Marc 
Published: September 2018

AbstractWithin an infected individual, influenza virus exists as a heterogeneous population of variants. When representing the viral population as a consensus sequence, information about minority variants is lost. However, using next generation sequencing (NGS), it is possible to identify nucleotide substitutions which segregate at low frequencies in the viral population, and can give insight into the within-host processes that drive the virus’s evolution, and is a step towards understanding the dynamics of the disease. During the course of an infection, mutations may occur, and at each segregating site, the frequency of the derived allele in the population will fluctuate. We develop a method which can use information about the relative frequencies of mutations in NGS data from a viral population sampled at multiple time points, to infer past population dynamics with a Bayesian skyline model. By using coalescent theory, we analytically derive the joint allele frequency spectrum for a population across multiple time points, and relate this to the coalescent intervals generated from the skyline model. We demonstrate the model on data taken from populations of equine influenza virus sampled during an infection, and show that it is possible to infer a posterior distribution of effective viral population size through time. We also show how the model can be used to infer the probability that a mutation occurred within-host, as opposed to being an ancestral mutation which occurred prior to infection.Author SummaryWhen a host is infected by a virus, many particles of the infecting agent enter the body of the host. This viral population is composed of many closely related viruses that continue diversifying by mutating while reproducing in the host. New sequencing technologies allow the quantifying of the proportion of the different variants present in the host at a particular time. Unfortunately, the data resulting from such sequencing techniques are difficult to interpret as they consist of many unlinked copies of relatively small fragments of genetic code distributed along the genome of the virus.We designed a method combining models of virus genealogies and frequency of mutations appearing in the data to reconstruct the variation of the viral population inside the host. It also allows us to time the apparition of particular variants. This could be useful to detect if a particular mutation (e.g. providing drug resistance) has appeared in host or was circulating before. We applied our method to data of within-host evolution of equine influenza.
Iron and Heme Coordinate Erythropoiesis through HRI-Mediated Regulation of Protein Translation and Gene Expression
Authors: Zhang Shuping , Macias-Garcia Alejandra , Ulirsch Jacob C. , Velazquez Jason , Butty Vincent L. , Levine Stuart S. , Sankaran Vijay G. , Chen Jane-Jane 
Published: September 2018

AbstractIron and heme play central roles in red blood cell production. However, the mechanisms by which iron and heme levels coordinate erythropoiesis remain incompletely understood. HRI is a heme-regulated kinase that controls translation by phosphorylating eIF2α. Here, we investigate the global impact of iron, heme and HRI on protein translation in vivo in murine primary erythroblasts using ribosome profiling. By defining the underlying changes in translation during iron and HRI deficiencies, we validate known regulators of this process, including Atf4, and identify novel pathways such as co-regulation of ribosomal protein mRNA translation. Surprisingly, we found that heme and HRI pathways, but not iron-regulated pathways, mediate the major protein translational and transcriptional responses to iron deficiency in erythroblasts in vivo and thereby identify previously unappreciated regulators of erythropoiesis. Our genome-wide study uncovers the major impact of the HRI-mediated integrated stress response for the adaptation to iron deficiency anemia.
Streaming histogram sketching for rapid microbiome analytics
Authors: Rowe Will P. M. , Carrieri Anna Paola , Alcon-Giner Cristina , Caim Shabhonam , Shaw Alex , Sim Kathleen , Kroll J Simon , Hall Lindsay J. , Pyzer-Knapp Edward O. , Winn Martyn D. 
Published: September 2018

AbstractMotivationThe growth in publically available microbiome data in recent years has yielded an invaluable resource for genomic research; allowing for the design of new studies, augmentation of novel datasets and reanalysis of published works. This vast amount of microbiome data, as well as the widespread proliferation of microbiome research and the looming era of clinical metagenomics, means there is an urgent need to develop analytics that can process huge amounts of data in a short amount of time.To address this need, we propose a new method for the compact representation of microbiome sequencing data using similarity-preserving sketches of streaming k-mer spectra. These sketches allow for dissimilarity estimation, rapid microbiome catalogue searching, and classification of microbiome samples in near real-time.ResultsWe apply streaming histogram sketching to microbiome samples as a form of dimensionality reduction, creating a compressed ‘histosketch’ that can be used to efficiently represent microbiome k-mer spectra. Using public microbiome datasets, we show that histosketches can be clustered by sample type using pairwise Jaccard similarity estimation, consequently allowing for rapid microbiome similarity searches via a locality sensitive hashing indexing scheme. Furthermore, we show that histosketches can be used to train machine learning classifiers to accurately label microbiome samples. Specifically, using a collection of 108 novel microbiome samples from a cohort of premature neonates, we trained and tested a Random Forest Classifier that could accurately predict whether the neonate had received antibiotic treatment (95% accuracy, precision 97%) and could subsequently be used to classify microbiome data streams in less than 12 seconds.We provide our implementation, Histosketching Using Little K-mers (HULK), which can histosketch a typical 2GB microbiome in 50 seconds on a standard laptop using 4 cores, with the sketch occupying 3000 bytes of disk space.AvailabilityOur implementation (HULK) is written in Go and is available at: https://github.com/will-rowe/hulk (MIT License)
Spider mites escape bacterial infection by avoiding contaminated food
Authors: Zélé Flore , Santos-Matos Gonçalo , Figueiredo Alexandre , Eira Cátia , Pinto Catarina , Laurentino Telma , Sucena Élio , Magalhães Sara 
Published: September 2018

AbstractTo fight infection, arthropods rely on the deployment of an innate immune response but also upon physical/chemical barriers and avoidance behaviours. However, most studies focus on immunity, with other defensive mechanisms being relatively overlooked.We have previously shown that the spider mite Tetranychus urticae does not mount an induced immune response towards systemic bacterial infections, entailing very high mortality rates. Therefore, we hypothesized that other defence mechanisms may be operating to minimize infection risk. Here, we test (a) if spider mites are also highly susceptible to other infection routes - spraying and feeding - and (b) if they display avoidance behaviours towards infected food. Individuals sprayed with or fed on Escherichia coli or Pseudomonas putida survived less than the control, pointing to a deficient capacity of the gut epithelium, and possibly of the cuticle, to contain bacteria. Additionally, we found that spider mites prefer uninfected food to food contaminated with bacteria, a choice that probably does not rely on olfactory cues.Our results suggest that spider mites may rely mostly on avoidance behaviours to minimize bacterial infection and highlight the multi-layered nature of immune strategies present in arthropods.
Topological Reinforcement as a Principle of Modularity Emergence in Brain Networks
Authors: Damicelli Fabrizio , Hilgetag Claus C. , Hütt Marc-Thorsten , Messé Arnaud 
Published: September 2018

AbstractModularity is a ubiquitous topological feature of structural brain networks at various scales. While a variety of potential mechanisms have been proposed, the fundamental principles by which modularity emerges in neural networks remain elusive. We tackle this question with a plasticity model of neural networks derived from a purely topological perspective. Our topological reinforcement model acts enhancing the topological overlap between nodes, iteratively connecting a randomly selected node to a non-neighbor with the highest topological overlap, while pruning another network link at random. This rule reliably evolves synthetic random networks toward a modular architecture. Such final modular structure reflects initial ‘proto-modules’, thus allowing to predict the modules of the evolved graph. Subsequently, we show that this topological selection principle might be biologically implemented as a Hebbian rule. Concretely, we explore a simple model of excitable dynamics, where the plasticity rule acts based on the functional connectivity between nodes represented by co-activations. Results produced by the activity-based model are consistent with the ones from the purely topological rule, showing a consistent final network configuration. Our findings suggest that the selective reinforcement of topological overlap may be a fundamental mechanism by which brain networks evolve toward modular structure.
The essential genome of the crenarchaeal model Sulfolobus islandicus
Authors: Zhang Changyi , Phillips Alex P. R. , Wipfler Rebecca L. , Olsen Gary J. , Whitaker Rachel J. 
Published: September 2018

AbstractSulfolobus islandicus is a model experimental system in the TACK superphylum of the Archaea, a key lineage in the evolutionary history of cell biology. Here we report a genome-wide identification of the repertoire of genes essential to S. islandicus growth in culture. We confirm previous targeted gene knockouts, uncover the non-essentiality of functions assumed to be essential to the Sulfolobus cell, including the proteinaceous S-layer, and highlight key essential genes whose functions are yet to be determined. Phyletic distributions illustrate the potential transitions that have occurred during the evolution of this contemporary archaeal cell and highlight the sets of genes that may have been associated with each transition. We use this comparative context as a lens to focus future research on archaea-specific uncharacterized essential genes for which future functional data would provide valuable insights into the evolutionary history of the contemporary cell.
Modulating CRISPR gene drive activity through nucleocytoplasmic localization of Cas9 in S. cerevisiae
Authors: Goeckel Megan E. , Basgall Erianna M. , Lewis Isabel C. , Goetting Samantha C. , Yan Yao , Halloran Megan , Finnigan Gregory C. 
Published: September 2018

ABSTRACTThe bacterial CRISPR/Cas genome editing system has provided a major breakthrough in molecular biology. One use of this technology is within a nuclease-based gene drive. This type of system can install a genetic element within a population at unnatural rates. Combatting of vector-borne diseases carried by metazoans could benefit from a delivery system that bypasses traditional Mendelian laws of segregation. Recently, laboratory studies in fungi, insects, and even mice, have demonstrated successful propagation of CRISPR gene drives and the potential utility of this type of mechanism. However, current gene drives still face challenges including evolved resistance, containment, and the consequences of application in wild populations. In this study, we use an artificial gene drive system in budding yeast to explore mechanisms to modulate nuclease activity of Cas9 through its nucleocytoplasmic localization. We examine non-native nuclear localization sequences on Cas9 fusion proteins in vivo and demonstrate that appended signals can titrate gene drive activity and serve as a potential molecular safeguard.
Simultaneous SNP selection and adjustment for population structure in high dimensional prediction models
Authors: Bhatnagar Sahir R , Yang Yi , Lu Tianyuan , Schurr Erwin , Loredo-Osti JC , Forest Marie , Oualkacha Karim , Greenwood Celia MT 
Published: September 2018

AbstractComplex traits are known to be influenced by a combination of environmental factors and rare and common genetic variants. However, detection of such multivariate associations can be compromised by low statistical power and confounding by population structure. Linear mixed effects models (LMM) can account for correlations due to relatedness but have not been applicable in high-dimensional (HD) settings where the number of fixed effect predictors greatly exceeds the number of samples. False positives or false negatives can result from two-stage approaches, where the residuals estimated from a null model adjusted for the subjects’ relationship structure are sub-sequently used as the response in a standard penalized regression model. To overcome these challenges, we develop a general penalized LMM with a single random effect calledggmixfor simultaneous SNP selection and adjustment for population structure in high dimensional prediction models.We develop a blockwise coordinate descent algorithm with automatic tuning parameter selection which is highly scalable, computationally efficient and has theoretical guarantees of convergence. Through simulations and three real data examples, we show thatggmixleads to more parsimonious models compared to the two-stage approach or principal component adjustment with better prediction accuracy. Our method performs well even in the presence of highly correlated markers, and when the causal SNPs are included in the kinship matrix.ggmixcan be used to construct polygenic risk scores and select instrumental variables in Mendelian randomization studies.Our algorithms are available in an R package (https://github.com/greenwoodlab/ggmix).1Author SummaryThis work addresses a recurring challenge in the analysis and interpretation of genetic association studies: which genetic variants can best predict and are independently associated with a given phenotype in the presence of population structure ? Not controlling confounding due to geographic population structure, family and/or cryptic relatedness can lead to spurious associations. Much of the existing research has therefore focused on modeling the association between a phenotype and a single genetic variant in a linear mixed model with a random effect. However, this univariate approach may miss true associations due to the stringent significance thresholds required to reduce the number of false positives and also ignores the correlations between markers. We propose an alternative method for fitting high-dimensional multivariable models, which selects SNPs that are independently associated with the phenotype while also accounting for population structure. We provide an efficient implementation of our algorithm and show through simulation studies and real data examples that our method outperforms existing methods in terms of prediction accuracy and controlling the false discovery rate.
Assessing the digital library research output: bibliometric analysis from 2002 to 2016
Authors: Ahmad Khurshid , Jian Ming Zheng , Rafi Muhammad 
Published: September 2018

Purpose
The purpose of this bibliometric analysis is to quantify the prominence and impact of published literature within the field. The research has been conducted with the intent of obtaining the expansion and characteristics of the literature on digital library.

Design/methodology/approach
More than 4,206 documents found in the period of 2002-2016 were collected from the ISI Web of Science and were analyzed to explore the annual productivity, yearly citation, most cited articles, prolific authors, eminent journals of the subject, productivity of institutes and contribution of countries. These results are based on the types of documents (articles, reviews, proceedings papers, book reviews, editorial materials and book chapters).

Findings
The core findings are that the most productive year of publication was 2016, and the growth of citation increased rapidly; the top source of title is electronic library. The result shows that the USA dominates in the research output, with Illinois University securing the first position; the most prolific author is Fourie I from South Africa. In the document types category, the most cited sources are research articles.

Originality/value
This research is useful for the researchers interested in the field of bibliometrics as it postulates an inclusive indication of provenance in the field of library and information science.
A cylindrical insert for parabolic trough solar collector
Authors: Bellos Evangelos , Daniil Ilias , Tzivanidis Christos 
Published: September 2018

Purpose
The purpose of this paper is to investigate a cylindrical flow insert for a parabolic trough solar collector. Centrally placed and eccentric placed inserts are investigated in a systematic way to determine which configuration leads to the maximum thermal enhancement.

Design/methodology/approach
The analysis is performed in SolidWorks Flow Simulation with a validated computational fluid dynamics model. Moreover, the useful heat production and the pumping work demand increase are evaluated using the exergy and the overall efficiency criteria. The different scenarios are compared for inlet temperature of 600 K, flow rate of 100 L/min and Syltherm 800 as the working fluid. Moreover, the inlet temperature is examined from 450 to 650 K, and the diameter of the insert is investigated up to 50 mm.

Findings
According to the final results, the use of a cylindrical insert of 30 mm diameter is the most sustainable choice which leads to 0.56 per cent thermal efficiency enhancement. This insert was examined in various eccentric positions, and it is found that the optimum location is 10 mm over the initial position in the vertical direction. The thermal enhancement, in this case, is about 0.69 per cent. The pumping work demand was increased about three times with the insert of 30 mm, but the absolute values of this parameter are too low compared to the useful heat production. So, it is proved that the increase in the pumping work is not able to eliminate the useful heat production increase. Moreover, the thermal enhancement is found to be greater at higher temperature levels and can reach up to 1 per cent for an inlet temperature of r650 K.

Originality/value
The present work is a systematic investigation of the cylindrical flow insert in a parabolic trough collector. Different diameters of this insert, as well as different positions in two dimensions, are examined using a parametrization of angle-radius. To the authors’ knowledge, there is no other study in the literature that investigates the presented many cases systematically with the followed methodology on parabolic trough collectors. Moreover, the results of this work are evaluated with various criteria (thermal, exergy and overall efficiency), something which is not found in the literature.
Approach to activity engagement and differences in activity participation in chronic pain: A five‐day observational study
Authors: Andrews Nicole E. , Strong Jenny , Meredith Pamela J. , Branjerdporn Grace S. 
Published: September 2018

Background/aimActivity pacing is one of the most widely endorsed interventions used by occupational therapists to assist clients to manage chronic pain conditions. It targets two behaviours that are thought to be maladaptive: activity avoidance and overactivity (activity engagement that severely aggravates pain). However, in more recent years, the potential for activity pacing to negatively impact activity participation has been recognised which deters habitually overactive individuals from adopting the self‐management strategy. The main aim of this study was to evaluate if variances in activity participation can be explained by how individuals approach activity engagement when in pain.MethodsSixty‐eight adults with chronic pain completed a demographic questionnaire, the Pain and Activity Relations Questionnaire (PARQ), and recorded their participation in activities for five days using a paper diary. Two of the authors independently coded the recorded activities into one of three time‐use categories: rest, productivity or leisure/social. A MANCOVA model was produced to examine differences in time use across four ‘approach to activity engagement’ categories which were determined by scores on the PARQ.ResultsA significant multivariate effect was found. Univariate comparisons revealed that ‘overactives’ (high overactivity, low avoidance) and ‘pacers’ (low overactivity, low avoidance) spent a similar amount of time resting over the five‐day period. ‘Overactives’ spent the most amount of time on productive tasks and the least amount of time on social/leisure activities out of the four subgroups.ConclusionResults suggest that activity pacing does not negatively impact on activity participation in chronic pain populations. ‘Pacers’ spent a similar amount of time resting, and had a slightly better balance between productive tasks and leisure/social activities, when compared to ‘overactives’. The results of this study can be incorporated into patient education and highlight potential treatment avenues for individuals with chronic pain who are habitually overactive.
Arthropod biodiversity in olive groves under two soil management systems: presence versus absence of herbaceous cover crop
Authors: Carpio Antonio J. , Castro Jesús , Tortosa Francisco S. 
Published: September 2018

AbstractAgricultural intensification has greatly affected biodiversity in recent decades. One common and recent transformation that has taken place in Mediterranean countries is olive grove intensification, although its effects on biodiversity have rarely been studied.In the present study, we investigated the effect of soil management practices, with versus without soil cover crops, on the arthropod abundance, richness, diversity and species composition in Andalusia olive groves, South Spain.The results obtained show that these soil cover crops positively affect the diversity of arthropods, along with other variables such as the diversity of the plant community and Shannon index of the landscape. By contrast, the number of arthropods was higher in olive groves with bare ground as a result of the abundance of two families (Entomobryidae and Aeolothripidae), which comprised more than 50% of all the individuals collected.Rank abundance diagrams and multivariate analyses showed differences between the two types of soil management systems with respect to the composition of families.In conclusion, soil cover crops strongly improve the diversity of arthropods in olive orchards, which helps to maintain biodiversity in these intensive agroecosystems at the same time as improving other ecosystems services, such as soil fertility and the prevention of soil erosion.