bluuebunny/crossref_metadata_2025_split huggingface dataset:
Elimination of reference mapping bias reveals robust immune related allele-specific expression in crossbred sheep
Authors: Salavati Mazdak , Bush Stephen J. , Palma-Vera Sergio , McCulloch Mary E. B. , Hume David A. , Clark Emily L. 
Published: April 2019

AbstractPervasive allelic variation at both gene and single nucleotide level (SNV) between individuals is commonly associated with complex traits in humans and animals. Allele-specific expression (ASE) analysis, using RNA-Seq, can provide a detailed annotation of allelic imbalance and infer the existence of cis-acting transcriptional regulation. However, variant detection in RNA-Seq data is compromised by biased mapping of reads to the reference DNA sequence. In this manuscript we describe an unbiased standardised computational pipeline for allele-specific expression analysis using RNA-Seq data, which we have adapted and developed using tools available under open licence. The analysis pipeline we present is designed to minimise reference bias while providing accurate profiling of allele-specific expression across tissues and cell types. Using this methodology, we were able to profile pervasive allelic imbalance across tissues and cell types, at both the gene and SNV level, in Texel x Scottish Blackface sheep, using the sheep gene expression atlas dataset. ASE profiles were pervasive in each sheep and across all tissue types investigated. However, ASE profiles shared across tissues were limited and instead they tended to be highly tissue-specific. These tissue-specific ASE profiles may underlie the expression of economically important traits and could be utilized as weighted SNVs, for example, to improve the accuracy of genomic selection in breeding programmes for sheep. An additional benefit of the pipeline is that it does not require parental genotypes and can therefore be applied to other RNA-Seq datasets for livestock, including those available on the Functional Annotation of Animal Genomes (FAANG) data portal. This study is the first global characterisation of moderate to extreme ASE in tissues and cell types from sheep. We have applied a robust methodology for ASE profiling, to provide both a novel analysis of the multi-dimensional sheep gene expression atlas dataset, and a foundation for identifying the regulatory and expressed elements of the genome that are driving complex traits in livestock.
Calibrating seed-based alignment heuristics with Sesame
Authors: Filion Guillaume J. , Cortini Ruggero , Zorita Eduard 
Published: April 2019

AbstractThe increasing throughput of DNA sequencing technologies creates a need for faster algorithms. The fate of most reads is to be mapped to a reference sequence, typically a genome. Modern mappers rely on heuristics to gain speed at a reasonable cost for accuracy. In the seeding heuristic, short matches between the reads and the genome are used to narrow the search to a set of candidate locations. Several seeding variants used in modern mappers show good empirical performance but they are difficult to calibrate or to optimize for lack of theoretical results. Here we develop a theory to estimate the probability that the correct location of a read is filtered out during seeding, resulting in mapping errors. We describe the properties of simple exact seeds, skip-seeds and MEM seeds (Maximal Exact Match seeds). The main innovation of this work is to use concepts from analytic combinatorics to represent reads as abstract sequences, and to specify their generative function to estimate the probabilities of interest. We provide several algorithms, which combined together give a workable solution for the problem of calibrating seeding heuristics for short reads. We also provide a C implementation of these algorithms in a library called Sesame. These results can improve current mapping algorithms and lay the foundation of a general strategy to tackle sequence alignment problems. The Sesame library is open source and available for download at https://github.com/gui11aume/sesame.
Gene Therapy for Follistatin Mitigates Systemic Metabolic Inflammation and Post-Traumatic Osteoarthritis in High-Fat Diet-Induced Obese Mice
Authors: Tang Ruhang , Harasymowicz Natalia S. , Wu Chia-Lung , Collins Kelsey H. , Choi Yun-Rak , Oswald Sara J. , Guilak Farshid 
Published: April 2019

ABSTRACTObesity-associated inflammation and loss of muscle function play critical roles in the development of osteoarthritis (OA); thus, therapies that target muscle tissue may provide novel approaches to restoring metabolic and biomechanical dysfunction associated with obesity. Recent studies indicate that follistatin (FST), a protein which binds myostatin and activin, may have the potential to enhance muscle formation while neutralizing inflammation induced by these proteins. Here, we hypothesized that adeno-associated virus (AAV9) delivery of FST will enhance muscle formation and mitigate metabolic inflammation and knee OA caused by a high fat diet in mice. Obese mice receiving AAV-mediated FST delivery exhibited decreased inflammatory adipokines and cytokines systemically in the serum as well as locally in the joint synovial fluid. Regardless of diet, mice receiving FST gene therapy were protected from post-traumatic OA and bone remodeling induced by joint injury. While obesity disrupted the mitochondrial oxidative phosphorylation (OXPHOS) system in adipocytes, gene therapy for FST restored the key proteins involved in mitochondrial biogenesis, such as PPARγ coactivator 1α and AKT protein kinase 1, leading to the browning of white adipose tissue. Taken together, these findings suggest that FST gene therapy may provide a multifactorial therapeutic approach for injury-induced OA and metabolic inflammation in obesity.
Recessive genetic effects on type 2 diabetes-related metabolites in a consanguineous population
Authors: Demirkan Ayşe , Liu Jun , Amin Najaf , van Klinken Jan B , van Dijk Ko Willems , van Duijn Cornelia M. 
Published: April 2019

AbstractAutozygosity, meaning inheritance of an ancestral allele in the homozygous state is known to lead bi-allelic mutations that manifest their effects through the autosomal recessive inheritance pattern. Autosomal recessive mutations are known to be the underlying cause of several Mendelian metabolic diseases, especially among the offspring of related individuals. In line with this, inbreeding coefficient of an individual as a measure of cryptic autozygosity among the general population is known to lead adverse metabolic outcomes including Type 2 diabetes (T2DM); a multifactorial metabolic disease for which the recessive genetic causes remain unknown. In order to unravel such effects for multiple metabolic facades of the disease, we investigated the relationship between the excess of homozygosity and the metabolic signature of T2DM. We included a set of 53 metabolic phenotypes, including 47 metabolites, T2DM and five T2DM risk factors, measured in a Dutch genetic isolate of 2,580 people. For 20 of these markers, we identified 29 regions of homozygous (ROHs) associated with the nominal significance of P-value < 1.0 × 10−3. By performing association according to the recessive genetic model within these selected regions, we identified and replicated two intronic variants: rs6759814 located inKCNH7associated with valine and rs1573707 located inPTPRTassociated with IDL-free cholesterol and IDL-phospholipids. Additionally, we identified a rare intronic SNV inTBR1for which the homozygous individuals were enriched for obesity. Interestingly, all three genes are mainly neuronally expressed and pointed out the involvement of glutamergic synaptic transmission pathways in the regulation of metabolic pathways. Taken together our study underline the additional benefits of model supervised analysis, but also seconds the involvement of the central nervous system in T2DM and obesity pathogenesis.
Rapid, Low-Cost Detection of Water Contaminants Using RegulatedIn VitroTranscription
Authors: Alam Khalid K. , Jung Jaeyoung K. , Verosloff Matthew S. , Clauer Phillip R. , Lee Jeong Wook , Capdevila Daiana A. , Pastén Pablo A. , Giedroc David P. , Collins James J. , Lucks Julius B. 
Published: April 2019

ABSTRACTSynthetic biology has enabled the development of powerful nucleic acid diagnostic technologies for detecting pathogens and human health biomarkers. Here we expand the reach of synthetic biology-enabled diagnostics by developing a cell-free biosensing platform that usesRNAoutputsensorsactivated by ligandinduction (ROSALIND) to detect harmful contaminants in aqueous samples. ROSALIND consists of three programmable components: highly-processive RNA polymerases, allosteric transcription factors, and synthetic DNA transcription templates. Together, these components allosterically regulate thein vitrotranscription of a fluorescence-activating RNA aptamer: in the absence of a target compound, transcription is blocked, while in its presence a fluorescent signal is produced. We demonstrate that ROSALIND can be configured to detect a range of water contaminants, including antibiotics, toxic small molecules, and metals. Our cell-free biosensing platform, which can be freeze-dried for field deployment, creates a new capability for point-of-use monitoring of molecular species to address growing global crises in water quality and human health.
Macromolecular crowding links ribosomal protein gene dosage to growth rate inVibrio cholerae
Authors: Soler-Bistué Alfonso , Aguilar-Pierlé Sebastián , Garcia-Garcerá Marc , Val Marie-Eve , Sismeiro Odile , Varet Hugo , Sieira Rodrigo , Krin Evelyne , Skovgaard Ole , Comerci Diego J. , Rocha Eduardo P. C. , Mazel Didier 
Published: April 2019

AbstractRibosomal protein (RP) genes locate near the replication origin (oriC) in fast-growing bacteria, which is thought to have been selected as a translation optimization strategy. Relocation ofS10-spc-αlocus (S10), which codes for most of the RP, to ectopic genomic positions shows that its relative distance to theoriCcorrelates to a reduction on its dosage, its expression, and bacterial growth rate. Deep-sequencing revealed that S10 relocation altered chromosomal replication dynamics and genome-wide transcription. Such changes increased as a function oforiC-S10 distance. Strikingly, in this work we observed that protein production capacity was independent of S10 position. Since RP constitute a large proportion of cell mass, lower S10 dosage could lead to changes in macromolecular crowding, impacting cell physiology. Accordingly, cytoplasm fluidity was higher in mutants where S10 is most distant fromoriC. In hyperosmotic conditions, when crowding differences are minimized, the growth rate and replication dynamics were highly alleviated in these strains. Therefore, on top of its essential function in translation, RP genomic location contributes to sustain optimal macromolecular crowding. This is a novel mechanism coordinating DNA replication with bacterial growth.
Dopamine modulates learning-related changes in dynamic striatal-cortical connectivity in Parkinson’s disease
Authors: Gerraty Raphael T. , Sharp Madeleine E. , Buch Amanda , Bassett Danielle S. , Shohamy Daphna 
Published: April 2019

AbstractLearning from reinforcement is thought to depend on striatal dopamine inputs, which serve to update the value of actions by modifying connections in widespread cortico-striatal circuits. While considerable research has described the activity of individual striatal and midbrain regions in reinforcement learning, the broader role for dopamine in modulating network-level processes has been difficult to decipher. To examine whether dopamine modulates circuit-level dynamic connectivity during learning, we characterized the effects of dopamine on learning-related dynamic functional connectivity estimated from fMRI data acquired in patients with Parkinson’s disease. Patients with Parkinson’s disease have severe dopamine depletion in the striatum and are treated with dopamine replacement drugs, providing an opportunity to compare learning and network dynamics when patients are in a low dopamine state (off drugs) versus a high dopamine state (on drugs). We assessed the relationship between dopamine and dynamic connectivity while patients performed a probabilistic reversal learning task. We found that reversal learning altered dynamic network flexibility in the striatum and that this effect was dependent on dopaminergic state. We also found that dopamine modulated changes in connectivity between the striatum and specific task-relevant visual areas of inferior temporal cortex, providing empirical support for theories stipulating that value is updated through changes in cortico-striatal circuits. These results suggest that dopamine exerts a widespread effect on neural circuitry and network dynamics during reinforcement learning.
Assembly-free single-molecule nanopore sequencing recovers complete virus genomes from natural microbial communities
Authors: Beaulaurier John , Luo Elaine , Eppley John , Uyl Paul Den , Dai Xiaoguang , Turner Daniel J , Pendelton Matthew , Juul Sissel , Harrington Eoghan , DeLong Edward F. 
Published: April 2019

AbstractViruses are the most abundant biological entities on Earth, and play key roles in host ecology, evolution, and horizontal gene transfer. Despite recent progress in viral metagenomics, the inherent genetic complexity of virus populations still poses technical difficulties for recovering complete virus genomes from natural assemblages. To address these challenges, we developed an assembly-free, single-molecule nanopore sequencing approach enabling direct recovery of high-quality viral genome sequences from environmental samples. Our method yielded over a thousand high quality, full-length draft virus genome sequences that could not be fully recovered using short read assembly approaches applied to the same samples. Additionally, novel DNA sequences were discovered whose repeat structures, gene contents and concatemer lengths suggested that they represent phage-inducible chromosomal islands that were packaged as concatemers within phage particles. Our new approach provided novel insight into genome structures, population biology, and ecology of naturally occurring viruses and viral parasites.
The ‘breakpoint’ in transmission for soil-transmitted helminths: dynamical behaviour near the equilibria and the impact of infected human migration
Authors: Hardwick Robert J. , Vegvari Carolin , Truscott James E. , Anderson Roy M. 
Published: April 2019

AbstractBuilding on past research, we here develop an analytic framework for describing the dynamics of the transmission of soil-transmitted helminth (STH) parasitic infections near the transmission breakpoint and equilibria of endemic infection and disease extinction, while allowing for perturbations in the infectious reservoir of the parasite within a defined location. This perturbation provides a model for the effect of infected human movement between villages with differing degrees of parasite control induced by mass drug administration (MDA). Analysing the dynamical behaviour around the unstable equilibrium, known as the transmission ‘breakpoint’, we illustrate how slowly-varying the dynamics are and develop an understanding of how discrete ‘pulses’ in the release of transmission stages (eggs or larvae, depending on the species of STH), due to infected human migration between villages, can lead to perturbations in the deterministic transmission dynamics. Such perturbations are found to have the potential to undermine targets for parasite elimination as a result of MDA and/or improvements in water and sanitation provision. We extend our analysis by developing a simple stochastic model and analytically investigate the uncertainty this induces in the dynamics. Where appropriate, all analytical results are supported by numerical analyses.
Assessment of Tuberculosis incidence and treatment success rates of the indigenous Maká community in Paraguay
Authors: Fröberg J. , Sequera V.G. , Tostmann A. , Aguirre S. , Magis-Escurra C. 
Published: April 2019

SUMMARYSettingIn Paraguay, 1.8% of the population are indigenous people. The Maká community mainly live in urbanized areas in the Central Region. This study focuses on the epidemiology of tuberculosis (TB) among indigenous Maká and the non-indigenous people living in the Central Region, the biggest metropolitan area of the Paraguay.ObjectivesThis study aims to analyze the TB incidence and treatment success rate of the urbanized Maká indigenous populationDesignRetrospective cohort study of 6,147 registered TB patients with 387 Maká indigenous people, from 2005-2017.ResultsCompared to the non-indigenous population in the Central Region, the Maká had a 66 times higher TB incidence, a lower median age at diagnosis (3 vs. 33 years; P<0.001), less bacteriological diagnosis (55.0% vs. 77.8%; P<0.001), and a higher treatment success rate of 75.2% vs. 67.8%. Directly observed therapy coverage was higher among the Maká (89.4% vs. 47.1%; P<0.001).ConclusionsThe Maká showed a disproportionately high TB incidence in children. Treatment success rates did not reach the WHO standards of 85%. If the diagnosis in children from this period can be confirmed, the public health system should intensify their focus on the Maká, increasing case finding and contact tracing activities in the whole population.
A high-precision hybrid algorithm for predicting eukaryotic protein subcellular localization
Authors: Zhang Dahan , Huang Haiyun , Bai Xiaogang , Fang Xiaodong , Zhang Yi 
Published: April 2019

ABSTRACTMotivationSubcellular location plays an essential role in protein synthesis, transport, and secretion, thus it is an important step in understanding the mechanisms of trait-related proteins. Generally, homology methods provide reliable homology-based results with small E-values. We must resort to pattern recognition algorithms (SVM, Fisher discriminant, KNN, random forest, etc.) for proteins that do not share significant homologous domains with known proteins. However, satisfying results are seldom obtained.ResultsHere, a novel hybrid method “Basic Local Alignment Search Tool+Smith-Waterman+Needleman-Wunsch” or BLAST+SWNW, has been obtained by integrating a loosened E-value Basic Local Alignment Search Tool (BLAST) with the Smith-Waterman (SW) and Needleman-Wunsch (NW) algorithms, and this method has been introduced to predict protein subcellular localization in eukaryotes. When tested on Dataset I and Dataset II, BLAST+SWNW showed an average accuracy of 97.18% and 99.60%, respectively, surpassing the performance of other algorithms in predicting eukaryotic protein subcellular localization.Availability and ImplementationBLAST+SWNW is an open source collaborative initiative available in the GitHub repository (https://github.com/ZHANGDAHAN/BLAST-SWNW-for-SLP or http://202.206.64.158:80/link/72016CAC26E4298B3B7E0EAF42288935)Contactzhaqi1972@163.com; zhangdahan@genetics.ac.cnSupplementary InformationSupplementary data are available at PLOS Computational Biology online.
A new method for inferring timetrees from temporally sampled molecular sequences
Authors: Miura Sayaka , Tamura Koichiro , Kosakovsky Pond Sergei L. , Huuki Louise A. , Priest Jessica , Deng Jiamin , Kumar Sudhir 
Published: April 2019

ABSTRACTPathogen timetrees are phylogenies scaled to time. They reveal the temporal history of a pathogen spread through the populations as captured in the evolutionary history of strains. These timetrees are inferred by using molecular sequences of pathogenic strains sampled at different times. That is, temporally sampled sequences enable the inference of sequence divergence times. Here, we present a new approach (RelTime with Dated Tips [RTDT]) to estimating pathogen timetrees based on the relative rate framework underlying the RelTime approach. RTDT does not require many of the priors demanded by Bayesian approaches, and it has light computing requirements. We found RTDT to be accurate on simulated datasets evolved under a variety of branch rates models. Interestingly, we found two non-Bayesian methods (RTDT and Least Squares Dating [LSD]) to perform similar to or better than the Bayesian approaches available in BEAST and MCMCTree programs. RTDT method was found to generally outperform all other methods for phylogenies in with autocorrelated evolutionary rates. In analyses of empirical datasets, RTDT produced dates that were similar to those from Bayesian analyses. Speed and accuracy of the new method, as compared to the alternatives, makes it appealing for analyzing growing datasets of pathogenic strains. Cross-platform MEGA X software, freely available from http://www.megasoftware.net, now contains the new method for use through a friendly graphical user interface and in high-throughput settings.AUTHOR SUMMARYPathogen timetrees trace the origins and evolutionary histories of strains in populations, hosts, and outbreaks. The tips of these molecular phylogenies often contain sampling time information because the sequences were generally obtained at different times during the disease outbreaks and propagation. We have developed a new method for inferring timetrees for phylogenies with tip dates, which improves on widely-used Bayesian methods (e.g., BEAST) in computational efficiency and does not require prior specification of population parameters, branch rate model, or clock model. We performed extensive computer simulation and found that RTDT performed better than the other methods for the estimation of divergence times at deep node in phylogenies where evolutionary rates were autocorrelated. The new method is available in the cross-platform MEGA software package that provides a graphical user interface, and allows use via a command line in scripting and high throughput analysis (www.megasoftware.net).
Process excellence the key for digitalisation
Authors: Martinez Felipe 
Published: April 2019

Purpose
The new digital era brings several opportunities and challenges that accelerate the need to implement digital solutions in organisations. This speed of change creates many questions, but unfortunately, references cases to answer them or general guidelines to succeed are still rare. Therefore, the purpose of this paper is to present four cases that illustrate the procedure followed by companies to introduce digital elements in their operations (automotive industry supplier, toy industry, catering company and circuit trainings company). Each case offers inspiration and knowledge about the procedure to introduce digital elements and facilitates the proposal of a general procedure that aims to become guidance for practitioners.

Design/methodology/approach
The research implements comparative analysis in a multi-case design. It sensitises the concept of “digitalisation path” as a research instrument. Projects documentation and informal conversational interviews allow the researchers to describe each organisation and their digitalisation path. The interviewees review, enhance and approve the cases. Furthermore, the research compares the paths and summarises the findings into a digitalisation path model that explains the digitalisation of these organisations and it serves as a guide for other organisations.

Findings
The digitalisation paths evidence the necessity of business process management to develop digitalisation. Technology is not the goal, but the instrument and process excellence is the driver to introduce new technologies in the operations. Furthermore, the four organisations share a similar pattern and activities within their digitalisation paths.

Research limitations/implications
The multi-case methodology lacks generalisation, but it provides detail over the digitalisation paths.

Practical implications
The paper presents a guideline for practitioners to introduce digital elements in the organisation.

Originality/value
The paper presents a practical perspective to introduce digitalisation in organisations, which is currently rare in literature.