bluuebunny/crossref_metadata_2025_split huggingface dataset:
Living Donor Liver Transplant for Alcoholic Liver Disease: Data from the Adult-to-adult Living Donor Liver Transplantation Study
Authors: Braun Hillary J. , Dodge Jennifer L. , Grab Joshua D. , Syed Shareef M. , Roll Garrett R. , Freise Chris E. , Roberts John P. , Ascher Nancy L. 
Published: May 2019

Background.
Alcoholic liver disease (ALD) accounts for 15%–30% of transplants performed in the United States and Europe; however, the data on living donor liver transplantation (LDLT) for ALD remain sparse. The purpose of this study was to examine the outcomes following LDLT for ALD using data from the adult-to-adult living donor liver transplantation (A2ALL) study, which represents the largest Western experience with adult-to-adult LDLT.

Methods.
A retrospective review of A2ALL data collected between 1998 and 2014 was performed. Patients were excluded if they received a deceased donor liver transplant. Demographic data, postoperative outcomes and complications, graft and patient survival, and predictors of graft and patient survival were assessed.

Results.
Of the 1065 patients who underwent LDLT during the study time period, 168 (15.8%) were transplanted for a diagnosis of ALD. Comparing patients who underwent transplant for ALD with those who were transplanted for other etiologies of liver disease, there was no significant difference in graft survival at 1 (88% versus 84%), 5 (76% versus 74%), or 10 years following transplant (55% versus 61%, P = 0.29). Similarly, there was no difference in patient survival at 1 (94% versus 91%), 5 (83% versus 79%), or 10 years following transplant (61% versus 66%, P = 0.32).

Conclusions.
LDLT for ALD results in excellent 1-, 5-, and 10-year graft and patient survival. Patients with ALD and impaired renal function have a higher risk of graft loss and death. These findings support the notion that early LDLT for patients with ALD may help optimize outcomes.
Translating surveillance data into incidence estimates
Authors: Bourhis Y. , Gottwald T. , van den Bosch F. 
Published: May 2019

Monitoring a population for a disease requires the hosts to be sampled and tested for the pathogen. This results in sampling series from which we may estimate the disease incidence, i.e. the proportion of hosts infected. Existing estimation methods assume that disease incidence does not change between monitoring rounds, resulting in an underestimation of the disease incidence. In this paper, we develop an incidence estimation model accounting for epidemic growth with monitoring rounds that sample varying incidence. We also show how to accommodate the asymptomatic period that is the characteristic of most diseases. For practical use, we produce an approximation of the model, which is subsequently shown to be accurate for relevant epidemic and sampling parameters. Both the approximation and the full model are applied to stochastic spatial simulations of epidemics. The results prove their consistency for a very wide range of situations. The estimation model is made available as an online application.This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.
New methodologies for the estimation of population vulnerability to diseases: a case study of Lassa fever and Ebola in Nigeria and Sierra Leone
Authors: Kajero Olumayowa , Del Rio Vilas Victor , Wood James L. N. , Lo Iacono Giovanni 
Published: May 2019

Public health practitioners require measures to evaluate how vulnerable populations are to diseases, especially for zoonoses (i.e. diseases transmitted from animals to humans) given their pandemic potential. These measures would be valuable to support strategic and operational decision making and allocation of resources. Although vulnerability is well defined for natural hazards, for public health threats the concept remains undetermined. Here, we develop new methodologies to: (i) quantify the impact of zoonotic diseases and the capacity of countries to cope with these diseases, and (ii) combine these two measures (impact and capacity) into one overall vulnerability indicator. The adaptive capacity is calculated from estimations of disease mortality, although the method can be adapted for diseases with no or low mortality but high morbidity. As an example, we focused on the vulnerability of Nigeria and Sierra Leone to Lassa Fever and Ebola. We develop a simple analytical form that can be used to estimate vulnerability scores for different spatial units of interest, e.g. countries or regions. We show how some populations can be highly vulnerable despite low impact threats. We finally outline future research to more comprehensively inform vulnerability with the incorporation of relevant factors depicting local heterogeneities (e.g. bio-physical and socio-economic factors).This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.
Transmission routes of rare seasonal diseases: the case of norovirus infections
Authors: Rushton Stephen P. , Sanderson Roy A. , Reid William D. K. , Shirley Mark D. F. , Harris John P. , Hunter Paul R. , O'Brien Sarah J. 
Published: May 2019

Norovirus (NoV) is the most commonly recognized cause of acute gastroenteritis, with over a million cases globally per year. While usually self-limiting, NoV poses a substantial economic burden because it is highly contagious and there are multiple transmission routes. Infection occurs through inhalation of vomitus; faecal-oral spread; and food, water and environmental contamination. While the incidence of the disease is predictably seasonal, much less is known about the relative contribution of the various exposure pathways in causing disease. Additionally, asymptomatic excretion and viral shedding make forecasting disease burden difficult. We develop a novel stochastic dynamic network model to investigate the contributions of different transmission pathways in multiple coupled social networks representing schools, hospitals, care-homes and family households in a community setting. We analyse how the networks impact on transmission. We used ward-level demographic data from Northumberland, UK to create a simulation cohort. We compared the results with extant data on NoV cases from the IID2 study. Connectivity across the simulated cohort was high. Cases of NoV showed marked seasonality, peaking in early winter and declining through the summer. For the first time, we show that fomites and food appear to be the most important exposure routes in determining the population burden of disease.This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.
Applying optimal control theory to complex epidemiological models to inform real-world disease management
Authors: Bussell E. H. , Dangerfield C. E. , Gilligan C. A. , Cunniffe N. J. 
Published: May 2019

Mathematical models provide a rational basis to inform how, where and when to control disease. Assuming an accurate spatially explicit simulation model can be fitted to spread data, it is straightforward to use it to test the performance of a range of management strategies. However, the typical complexity of simulation models and the vast set of possible controls mean that only a small subset of all possible strategies can ever be tested. An alternative approach—optimal control theory—allows the best control to be identified unambiguously. However, the complexity of the underpinning mathematics means that disease models used to identify this optimum must be very simple. We highlight two frameworks for bridging the gap between detailed epidemic simulations and optimal control theory: open-loop and model predictive control. Both these frameworks approximate a simulation model with a simpler model more amenable to mathematical analysis. Using an illustrative example model, we show the benefits of using feedback control, in which the approximation and control are updated as the epidemic progresses. Our work illustrates a new methodology to allow the insights of optimal control theory to inform practical disease management strategies, with the potential for application to diseases of humans, animals and plants.
This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.
Rigorous surveillance is necessary for high confidence in end-of-outbreak declarations for Ebola and other infectious diseases
Authors: Thompson Robin N. , Morgan Oliver W. , Jalava Katri 
Published: May 2019

The World Health Organization considers an Ebola outbreak to have ended once 42 days have passed since the last possible exposure to a confirmed case. Benefits of a quick end-of-outbreak declaration, such as reductions in trade/travel restrictions, must be balanced against the chance of flare-ups from undetected residual cases. We show how epidemiological modelling can be used to estimate the surveillance level required for decision-makers to be confident that an outbreak is over. Results from a simple model characterizing an Ebola outbreak suggest that a surveillance sensitivity (i.e. case reporting percentage) of 79% is necessary for 95% confidence that an outbreak is over after 42 days without symptomatic cases. With weaker surveillance, unrecognized transmission may still occur: if the surveillance sensitivity is only 40%, then 62 days must be waited for 95% certainty. By quantifying the certainty in end-of-outbreak declarations, public health decision-makers can plan and communicate more effectively.
This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This issue is linked with the earlier theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.
dearseq: a variance component score test for RNA-Seq differential analysis that effectively controls the false discovery rate
Authors: Gauthier Marine , Agniel Denis , Thiébaut Rodolphe , Hejblum Boris P. 
Published: May 2019

AbstractRNA-seq studies are growing in size and popularity. We provide evidence that the most commonly used methods for differential expression analysis (DEA) may yield too many false positive results in some situations. We presentdearseq, a new method for DEA which controls the FDR without making any assumption about the true distribution of RNA-seq data. We show thatdearseqcontrols the FDR while maintaining strong statistical power compared to the most popular methods. We demonstrate this behavior with mathematical proofs, simulations, and a real data set from a study of Tuberculosis, where our method produces fewer apparent false positives.
High-throughput microfluidic micropipette aspiration device to probe time-scale dependent nuclear mechanics in intact cells
Authors: Davidson Patricia M. , Fedorchak Gregory R. , Mondésert-Deveraux Solenne , Bell Emily S. , Isermann Philipp , Aubry Denis , Allena Rachele , Lammerding Jan 
Published: May 2019

AbstractThe mechanical properties of the cell nucleus are increasingly recognized as critical in many biological processes. The deformability of the nucleus determines the ability of immune and cancer cells to migrate through tissues and across endothelial cell layers, and changes to the mechanical properties of the nucleus can serve as novel biomarkers in processes such as cancer progression and stem cell differentiation. However, current techniques to measure the viscoelastic nuclear mechanical properties are often time consuming, limited to probing one cell at a time, or require expensive, highly specialized equipment. Furthermore, many current assays do not measure time-dependent properties, which are characteristic of viscoelastic materials. Here, we present an easy-to-use microfluidic device that applies the well-established approach of micropipette aspiration, adapted to measure many cells in parallel. The device design allows rapid loading and purging of cells for measurements, and minimizes clogging by large particles or clusters of cells. Combined with a semi-automated image analysis pipeline, the microfluidic device approach enables significantly increased experimental throughput. We validated the experimental platform by comparing computational models of the fluid mechanics in the device with experimental measurements of fluid flow. In addition, we conducted experiments on cells lacking the nuclear envelope protein lamin A/C and wild-type controls, which have well-characterized nuclear mechanical properties. Fitting time-dependent nuclear deformation data to power law and different viscoelastic models revealed that loss of lamin A/C significantly altered the elastic and viscous properties of the nucleus, resulting in substantially increased nuclear deformability. Lastly, to demonstrate the versatility of the devices, we characterized the viscoelastic nuclear mechanical properties in a variety of cell lines and experimental model systems, including human skin fibroblasts from an individual with a mutation in the lamin gene associated with dilated cardiomyopathy, healthy control fibroblasts, induced pluripotent stem cells (iPSCs), and human tumor cells. Taken together, these experiments demonstrate the ability of the microfluidic device and automated image analysis platform to provide robust, high throughput measurements of nuclear mechanical properties, including time-dependent elastic and viscous behavior, in a broad range of applications.
Physiological determinants of the increase in oxygen consumption during exercise in individuals with stroke
Authors: Oyake Kazuaki , Baba Yasuto , Ito Nao , Suda Yuki , Murayama Jun , Mochida Ayumi , Kondo Kunitsugu , Otaka Yohei , Momose Kimito 
Published: May 2019

AbstractBackgroundUnderstanding the physiological limitations of the increase in oxygen consumption (V̇O2) during exercise is essential to improve cardiorespiratory fitness in individuals with stroke. However, the physiological determinants of the increase in V̇O2 during exercise have not been examined using multivariate analysis in individuals with stroke. This study aimed to identify the physiological determinants of the increase in V̇O2 during a graded exercise in terms of the respiratory function, cardiac function, and ability of skeletal muscles to extract oxygen.MethodsEighteen individuals with stroke (60.1 ± 9.4 years of age, 67.1 ± 30.8 days poststroke) underwent a graded exercise test for the assessment of cardiorespiratory response to exercise. The increase in V̇O2 from rest to ventilatory threshold and that from rest to peak exercise were measured as a dependent variable. The increases in respiratory rate, tidal volume, heart rate, stroke volume, and arterial-venous oxygen difference from rest to ventilatory threshold and those from rest to peak exercise were measured as independent variables.ResultsFrom rest to ventilatory threshold, the increases in heart rate (β = 0.546) and arterial-venous oxygen difference (β = 0.398) were significant determinants of the increase in V̇O2 (adjusted R2 = 0.703, p < 0.001). From rest to peak exercise, the increases in tidal volume (β = 0.611) and heart rate (β = 0.353) were significant determinants of the increase in V̇O2 (adjusted R2 = 0.702, p < 0.001).ConclusionV̇O2 is well-known to increase nearly linearly with increasing heart rate; however, our results suggest that arterial-venous oxygen difference and tidal volume are also significant physiological determinants of the increase in V̇O2 from rest to ventilatory threshold and that from rest to peak exercise, respectively. Our findings could potentially contribute to the development of appropriate therapies to improve cardiorespiratory fitness in individuals with stroke.
Casilio-ME: Enhanced CRISPR-based DNA demethylation by RNA-guided coupling methylcytosine oxidation and DNA repair pathways
Authors: Taghbalout Aziz , Du Menghan , Jillette Nathaniel , Rosikiewicz Wojciech , Rath Abhijit , Heinen Christopher D. , Li Sheng , Cheng Albert W. 
Published: May 2019

ABSTRACTWe have developed a methylation editing toolbox, Casilio-ME, that enables not only RNA-guided methylcytosine editing by targeting TET1 to genomic sites, but also by co-delivering TET1 and protein factors that couple methylcytosine oxidation to DNA repair activities, and/or promote TET1 to achieve enhanced activation of methylation-silenced genes. Delivery of TET1 activity by Casilio-ME1 robustly altered the CpG methylation landscape of promoter regions and activated methylation-silenced genes. We augmented Casilio-ME1 to simultaneously deliver the TET1-catalytic domain and GADD45A (Casilio-ME2) or NEIL2 (Casilio-ME3) to streamline removal of oxidized cytosine intermediates to enhance activation of targeted genes. Using two-in-one effectors or modular effectors, Casilio-ME2 and Casilio-ME3 remarkably boosted gene activation and methylcytosine demethylation of targeted loci. We expanded the toolbox to enable a stable and expression-inducible system for broader application of the Casilio-ME platforms. This work establishes an advanced platform for editing DNA methylation to enable transformative research investigations interrogating DNA methylomes.
Global distribution and evolution ofMycobacterium bovislineages
Authors: Zimpel Cristina Kraemer , Patané José Salvatore L. , Proença Guedes Aureliano Coelho , de Souza Robson F. , Silva-Pereira Taiana T. , Soler Camargo Naila C. , de Souza Filho Antônio F. , Ikuta Cássia Y. , Ferreira Neto José Soares , Setubal João Carlos , Heinemann Marcos Bryan , Sa Guimaraes Ana Marcia 
Published: May 2019

AbstractMycobacterium bovisis the main causative agent of zoonotic tuberculosis in humans and frequently devastates livestock and wildlife worldwide. Previous studies suggested the existence of genetic groups ofM. bovisstrains based on limited DNA markers (a.k.a. clonal complexes), and the evolution and ecology of this pathogen has been only marginally explored at the global level. We have screened over 2,600 publicly availableM. bovisgenomes and newly sequenced two wildlifeM. bovisstrains, gathering 823 genomes from 21 countries and 21 host-species, including humans, to complete a phylogenomic analyses. We propose the existence of four distinct global lineages ofM. bovis(Lb1, Lb2, Lb3 and Lb4) underlying the current disease distribution. These lineages are not fully represented by clonal complexes and are dispersed based on geographic location rather than host species. Our data divergence analysis agreed with previous studies reporting independent archeological data of ancientM. bovis[(South Siberian infected skeletons at ∼2,000 years BP (before present)] and indicates that extantM. bovisoriginated during the Roman period, subsequently dispersing across the world with the discovery and settlement of the New World and Oceania, directly influenced by trades among countries.
Analysis of Heterogeneous Genomic Samples Using Image Normalization and Machine Learning
Authors: Basodi Sunitha , Baykal Pelin Icer , Zelikovsky Alex , Skums Pavel , Pan Yi 
Published: May 2019

AbstractBackgroundAnalysis of heterogeneous populations such as viral quasispecies is one of the most challenging bioinformatics problems. Although machine learning models are becoming to be widely employed for the analysis of sequencing data associated with such populations, their straightforward application is impeded by multiple challenges associated with technological limitations and biases, difficulty of selection of relevant features and need to compare genomic datastes of different sizes and structures.MethodsWe propose a novel preprocessing approach to transform irregular genomic data into normalized image data. Such representation allows to restate the problems of classification and comparison of heterogeneous populations as image classification problems which can be solved using variety of available machine learning tools. We then apply the proposed approach to two important molecular epidemiology problems: inference of viral infection stage and detection of viral transmission clusters and outbreaks using next-generation sequencing data.ResultsThe infection staging method has been applied to HCV HVR1 samples collected from 108 recently and 257 chronically infected individuals. The SVM-based image classification approach achieved more than 95% accuracy for both recently and chronically HCV-infected individuals. Clustering has been performed on the data collected from 33 epidemiologically curated outbreaks, yielding more than 97% accuracy.AvailabilityThe developed software is freely available at https://bitbucket.org/adv_bio_coll/chronic_vs_clinic
White matter pathways supporting individual differences in epistemic and perceptual curiosity
Authors: Valji Ashvanti , Priemysheva Alisa , Hodgetts Carl J. , Costigan Alison G. , Parker Greg D. , Graham Kim S. , Lawrence Andrew D. , Gruber Matthias J. 
Published: May 2019

AbstractAcross the lifespan, curiosity motivates us to learn, yet curiosity varies strikingly between individuals. Such individual differences have been shown for two distinct dimensions of curiosity:epistemic curiosity(EC), the desire to acquire conceptual knowledge, andperceptual curiosity(PC), the desire for sensory information. It is not known, however, whether both dimensions of curiosity depend on different brain networks and whether inter-individual differences in curiosity depend on variation in anatomical connectivity within these networks. Here, we investigated the neuroanatomical connections underpinning individual variation in trait curiosity. Fifty-one female participants underwent a two-shell diffusion MRI sequence and completed questionnaires measuring EC and PC. Using deterministic spherical deconvolution tractography we extracted microstructural metrics (fractional anisotropy (FA) and mean diffusivity (MD)) from two key white matter tracts: the fornix (implicated in novelty processing, exploration, information seeking and episodic memory) and the inferior longitudinal fasciculus (ILF) (implicated in semantic learning and memory). In line with our predictions, we found that EC – but not PC – correlated with ILF microstructure. Fornix microstructure, in contrast, correlated with both EC and PC, with posterior hippocampal fornix fibres - associated with posterior hippocampal network connectivity - linked to PC specifically. These findings suggest that differences in distinct dimensions of curiosity map systematically onto specific white matter tracts underlying well characterized brain networks. Furthermore, the results pave the way to study the anatomical substrates of inter-individual differences in dimensions of trait curiosity that motivate the learning of distinct forms of knowledge and skills.