bluuebunny/crossref_metadata_2025_split huggingface dataset:
Observed Changes in Natural Killer and T cell Phenotypes with Evaluation of Immune Outcome in a Longitudinal Cohort Following Sofosbuvir-Based Therapy for Chronic Hepatitis C Infection
Authors: Stevenson Timothy J , Barbour Youssef , McMahon Brian J , Townshend-Bulson Lisa , Hewitt Annette M , Espera Hannah G F , Homan Chriss , Holck Peter , Luna Sarah V , Knall Cindy , Simons Brenna C 
Published: May 2019

Abstract

Background
Chronic hepatitis C virus (HCV) infection diminishes immune function through cell exhaustion and repertoire alteration. Direct acting antiviral (DAA)-based therapy can restore immune cell subset function and reduce exhaustion states. However, the extent of immune modulation following DAA-based therapy and the role that clinical and demographic factors play remain unknown.

Methods
We examined natural killer (NK) cell, CD4+, and CD8+ T cell subsets along with activation and exhaustion phenotypes across an observational study of sofosbuvir-based treatment for chronic HCV infection. Additionally, we examined the ability of clinical variables and duration of infection to predict 12 weeks of sustained virologic response (SVR12) immune marker outcomes.

Results
We show that sofosbuvir-based therapy restores NK cell subset distributions and reduces chronic activation by SVR12. Likewise, T cell subsets, including HCV-specific CD8+ T cells, show reductions in chronic exhaustion markers by SVR12. Immunosuppressive CD4+ regulatory T cells decrease at 4-weeks treatment and SVR12. We observe the magnitude and direction of change in immune marker values from pretreatment to SVR12 varies greatly among participants. Although we observed associations between the estimated date of infection, HCV diagnosis date, and extent of immune marker outcome at SVR12, our regression analyses did not indicate any factors as strong SVR12 outcome predictors.

Conclusion
Our study lends further evidence of immune changes following sofosbuvir-based therapy. Further investigation beyond SVR12 and into factors that may predict posttreatment outcome is warranted.
COMPARISON OF BNCT DOSIMETRY CALCULATIONS USING DIFFERENT GEANT4 PHYSICS LISTS
Authors: Chen Zhao , Yang Peng , Lei Qin , Wen Yumei , He Donglin , Wu Zhangwen , Gou Chengjun 
Published: May 2019

Abstract
A comparison of Geant4 physics lists is conducted in the calculation of the total absorbed dose, boron dose, and non-boron dose in phantom, and the total depth-dose, boron depth-dose, and non-boron depth-dose along the beam axis for neutrons in a range of 0.0253 eV to 10 MeV. Physics processes are included for neutrons, photons, and charged particles, and calculations are conducted for neutrons and secondary particles. The results obtained from QBBC, QGSP_BERT, and neutron high precision physics lists with and without S(α, β) data are compared with the FLUKA values. Neutron high precision physics lists with S(α, β) data showed the best agreement with FLUKA in the studied energy range.
A dual role for peripheral GDNF signaling in nociception and cardiovascular reflexes
Authors: Queme Luis F. , Weyler Alex A. , Cohen Elysia R. , Hudgins Renita C , Jankowski Michael P. 
Published: May 2019

AbstractGroup III/IV muscle afferents transduce nociceptive signals and modulate exercise pressor reflexes (EPR). However, the mechanisms governing afferent responsiveness to dually modulate these processes are not well characterized. We and others have shown that ischemic injury can induce both nociception-related behaviors and exacerbated EPRs in the same mice. This correlated with primary muscle afferent sensitization and increased expression of glial cell line-derived neurotrophic factor (GDNF) in injured muscle and increased expression of GDNF family receptor α1 (GFRα1) in DRGs. Here we report that increased GDNF/GFRα1 signaling to sensory neurons from ischemia/reperfusion affected muscle modulated nociceptive-like behaviors, increased EPRs, and group III/IV muscle afferent sensitization. This appeared to have taken effect through increased CREB/CREB-binding protein mediated expression of the purinergic receptor P2X5 in the DRGs. Muscle GDNF signaling to neurons may play an important dual role in nociception and sympathetic reflexes and could provide a novel therapeutic target for treating complications from ischemic injuries.
Association of Obesity with Health Care Costs: A Mendelian Randomization study
Authors: Kurz Christoph , Laxy Michael 
Published: May 2019

AbstractCausal effect estimates for the association of obesity with health care costs can be biased by reversed causation and omitted variables. In this study, we use genetic variants as instrumental variables to overcome these limitations. We estimate the effect of body mass index (BMI) and waist-to-hip ratio (WHR) on total health care costs using data from a German observational study. We find that the model using genetic instruments identifies additional annual costs of 189€ for a one unit increase in BMI, and additional 1165€ for a 0.1 unit increase in WHR. This is more than two times higher than estimates from linear regression without instrumental variables. We found little evidence of a non-linear relationship between BMI or WHR and health care costs. Our results imply that the use of genetic instruments can be a powerful tool for estimating causal effects in health economic evaluation that might be superior to other types of instruments where there is a strong association with a modifiable risk factor.
ADHD genetic liability and physical health outcomes - A two-sample Mendelian randomization study
Authors: Leppert Beate , Riglin Lucy , Dardani Christina , Thapar Ajay , Staley James R , Tilling Kate , Smith George Davey , Thapar Anita , Stergiakouli Evie 
Published: May 2019

AbstractObjectiveAttention-deficit/hyperactivity disorder (ADHD) has been associated with a broad range of physical health problems, including cardiometabolic, neurological and immunological conditions. Determining whether ADHD plays a causal role in these associations is of great importance for treatment and prevention but also because comorbid health problems further increase the serious social and economic impacts of ADHD on individuals and their families.MethodsWe used a two-sample Mendelian randomization (MR) approach to examine the causal relationships between genetic liability for ADHD and previously implicated physical health conditions. 11 genetic variants associated with ADHD were obtained from the latest summary statistics. Consistent effects obtained from IVW, weighted median and MR Egger methods were taken forward for sensitivity analysis, including bidirectional MR and multivariable MR (MVMR).ResultsWe found evidence of a causal effect of genetic liability for ADHD on childhood obesity (OR:1.29 (95% CI:1.02,1.63)) and coronary artery disease (CAD) (OR:1.11 (95% CI:1.03,1.19)) with consistent results across different MR approaches. There was further evidence for a bidirectional relationship between genetic liability for ADHD and childhood obesity. The effect of genetic liability for ADHD on CAD was independent of smoking heaviness but was attenuated when simultaneously controlling for childhood obesity. There was little evidence for a causal effect on other cardiometabolic, immunological, neurological disorders and lung cancer.ConclusionOur findings strengthen the argument for early treatment and support for children with ADHD and their families and especially promoting physical activity and providing them with dietary advice to reduce future risk for developing CAD.Key MessageEpidemiological studies have reported observational associations between ADHD and adult onset physical health outcomes.Mendelian Randomization can be used to assess causal associations for ADHD on health outcomes that would traditionally require long term follow-up and may suffer confoundingWe found that genetic liability for ADHD was associated with coronary artery disease and there was evidence for a bidirectional association between genetic liability for ADHD and childhood obesityMultivariable mendelian randomization suggests that the link between genetic liability and coronary artery disease might partially act through childhood obesity but was independent of smoking heavinessThere was little evidence of a causal of ADHD on other cardiometabolic and immunological diseases.
Distributed dynamic coding for spatial working memory in hippocampal-prefrontal networks
Authors: Hernan AE , Mahoney JM , Curry W , Mawe S , Scott RC 
Published: May 2019

AbstractSpatial working memory (SWM) is a central cognitive process during which the hippocampus and prefrontal cortex (PFC) encode and maintain spatial information for subsequent decision-making. This occurs in the context of ongoing computations relating to spatial position, recall of long-term memory, attention, amongst many others. To establish how intermittently presented information is integrated with ongoing computations we recorded single units, in both hippocampus and PFC, in control rats and those with a brain malformation during performance of a SWM task. Neurons that encode intermittent task parameters are also well-modulated in time and incorporated into a functional network across regions. Our results implicate a model in which ongoing oscillatory coordination among neurons in the hippocampal-PFC network defines a functional network that is poised to receive sensory inputs that are then integrated and multiplexed as working memory. These dynamics are systematically altered in disease and may provide potential targets for stimulation-based therapies.
Integrating exposure knowledge and serum suspect screening as a new approach to biomonitoring: An application in firefighters and office workers
Authors: Grashow Rachel , Bessonneau Vincent , Gerona Roy R. , Wang Aolin , Trowbridge Jessica , Lin Thomas , Buren Heather , Rudel Ruthann A. , Morello-Frosch Rachel 
Published: May 2019

ABSTRACTBackgroundWomen firefighters are exposed to recognized and probable carcinogens, yet there are few studies of chemical exposures and associated health concerns, such as breast cancer. Biomonitoring often requires a priori selection of compounds to be measured, and so may not detect important, lesser known, exposures.ObjectivesThe Women Firefighters Biomonitoring Collaborative (WFBC) created a biological sample archive and conducted a general suspect screen (GSS) to address this data gap.MethodsUsing liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF/MS) we sought to identify candidate chemicals of interest in serum samples from 83 women firefighters (FF) and 79 office workers (OW) in San Francisco. Through the GSS approach we identified chemical peaks by matching accurate mass from serum samples against a custom chemical database of 740 slightly polar phenolic and acidic compounds, including many of relevance to firefighting or breast cancer etiology. We then selected chemicals for confirmation based on a priori criteria: 1) detection frequency or peak area differences between OW and FF; 2) evidence of mammary carcinogenicity, estrogenicity, or genotoxicity; and 3) not currently measured in large biomonitoring studies.ResultsWe detected 620 chemicals that matched 300 molecular formulas in the WFBC database, including phthalate metabolites, phosphate flame retardant metabolites, phenols, pesticides, nitro-and nitroso-compounds, and per- and polyfluoroalkyl substances. The average number of chemicals from the database that were detected in participants was 72 and 70 in FF and OW, respectively. We confirmed 8 of the 20 prioritized suspect chemicals –including two alkylphenols, ethyl paraben, BPF, PFOSAA, benzophenone-3, benzyl p-hydroxybenzoate, and triphenyl phosphate--by running a matrix spike of the reference standards and using m/z, retention time and the confirmation of at least two fragment ions as criteria for matching.ConclusionGSS provides a powerful high-throughput approach to identify and prioritize novel chemicals for biomonitoring and health studies.
Consensus Machine Learning for Gene Target Selection in Pediatric AML Risk
Authors: Smith Jenny , Maden Sean K. , Lee David , Buie Ronald , Peddu Vikas , Shean Ryan , Busby Ben 
Published: May 2019

AbstractAcute myeloid leukemia (AML) is a cancer of hematopoietic systems that poses high population burden, especially among pediatric populations. AML presents with high molecular heterogeneity, complicating patient risk stratification and treatment planning. While molecular and cytogenetic subtypes of AML are well described, significance of subtype-specific gene expression patterns is poorly understood and effective modeling of these patterns with individual algorithms is challenging. Using a novel consensus machine learning approach, we analyzed public RNA-seq and clinical data from pediatric AML patients (N = 137 patients) enrolled in the TARGET project.We used a binary risk classifier (Low vs. Not-Low Risk) to study risk-specific expression patterns in pediatric AML. We applied the following workflow to identify important gene targets from RNA-seq data: (1) Reduce data dimensionality by identification of differentially expressed genes for AML risk (N = 1984 loci); (2) Optimize algorithm hyperparameters for each of 4 algorithm types (lasso, XGBoost, random forest, and SVM); (3) Study ablation test results for penalized methods (lasso and XGBoost); (4) Bootstrap Boruta permutations with a novel consensus importance metric.We observed recurrently selected features across hyperparameter optimizations, ablation tests, and Boruta permutation bootstrap iterations, including HOXA9 and putative cofactors including MEIS1. Consensus feature selection from Boruta bootstraps identified a larger gene set than single penalized algorithm runs (lasso or XGBoost), while also including correlated and predictive genes from ablation tests.We present a consensus machine learning approach to identify gene targets of likely importance for pediatric AML risk. The approach identified a moderately sized set of recurrent important genes from across 4 algorithm types, including genes identified across ablation tests with penalized algorithms (HOXA9 and MEIS1). Our approach mitigates exclusion biases of penalized algorithms (lasso and XGBoost) while obviating arbitrary importance cutoffs for other types (SVM and random forest). This approach is readily generalizable for research of other heterogeneous diseases, single-assay experiments, and high-dimensional data. Resources and code to recreate our findings are available online.
Mice tune out not in: Violation of prediction drives auditory saliency
Authors: Rogalla Meike M. , Rauser Inga , Schulze Karsten , Osterhagen Lasse , Hildebrandt K Jannis 
Published: May 2019

SummarySuccessful navigation in complex acoustic scenes requires focusing on relevant sounds while ignoring irrelevant distractors. It has been argued that the ability to track stimulus statistics and generate predictions supports the choice what to attend and what to ignore. However, the role of these predictions about future auditory events in drafting decisions remains elusive. While most psychophysical studies in humans indicate that expected stimuli serve as implicit cues attracting attention, most work studying physiological auditory processing in animals highlights the detection of unexpected, surprising stimuli. Here, we tested whether in the mouse, target probability is used as an implicit cue attracting attention or whether detection is biased towards low-probability deviants using an auditory detection task. We implemented a probabilistic choice model to investigate whether a possible dependence on stimulus statistics arises from short term serial correlations or from integration over longer periods. Our results demonstrate that target detectability in mice decreases with increasing probability, contrary to humans. We suggest that mice indeed track probability over a time scale of at least several minutes but do not use this information in the same way as humans do: instead of maximizing reward by focusing on high-probability targets, the saliency of a target is determined by surprise.
An active inference approach to modeling structure learning: concept learning as an example case
Authors: Smith Ryan , Schwartenbeck Philipp , Parr Thomas , Friston Karl J. 
Published: May 2019

AbstractWithin computational neuroscience, the algorithmic and neural basis of structure learning remains poorly understood. Concept learning is one primary example, which requires both a type of internal model expansion process (adding novel hidden states that explain new observations), and a model reduction process (merging different states into one underlying cause and thus reducing model complexity via meta-learning). Although various algorithmic models of concept learning have been proposed within machine learning and cognitive science, many are limited to various degrees by an inability to generalize, the need for very large amounts of training data, and/or insufficiently established biological plausibility. Using concept learning as an example case, we introduce a novel approach for modeling structure learning – and specifically state-space expansion and reduction – within the active inference framework and its accompanying neural process theory. Our aim is to demonstrate its potential to facilitate a novel line of active inference research in this area. The approach we lay out is based on the idea that a generative model can be equipped with extra (hidden state or cause) ‘slots’ that can be engaged when an agent learns about novel concepts. This can be combined with a Bayesian model reduction process, in which any concept learning – associated with these slots – can be reset in favor of a simpler model with higher model evidence. We use simulations to illustrate this model’s ability to add new concepts to its state space (with relatively few observations) and increase the granularity of the concepts it currently possesses. We also simulate the predicted neural basis of these processes. We further show that it can accomplish a simple form of ‘one-shot’ generalization to new stimuli. Although deliberately simple, these simulation results highlight ways in which active inference could offer useful resources in developing neurocomputational models of structure learning. They provide a template for how future active inference research could apply this approach to real-world structure learning problems and assess the added utility it may offer.
High contribution ofPelagibacteralesto bacterial community composition and activity in spring blooms off Kerguelen Island (Southern Ocean)
Authors: Dinasquet J. , Landa M. , Obernosterer I. 
Published: May 2019

AbstractThe ecology of Pelagibacterales (SAR11 clade), the most abundant bacterial group in the ocean, has been intensively studied in temperate and tropical ocean regions, but the distribution patterns of this clade remains largely unexplored in the Southern Ocean. Through amplicon sequencing of the 16S rRNA genes, we assessed the contribution of Pelagibacterales to bacterial community composition in the naturally iron fertilized region off Kerguelen Island (Southern Ocean). We investigated the upper 300 m water column at seven sites located in early spring phytoplankton blooms and at one site in HNLC waters. Despite pronounced vertical patterns of the bacterioplankton assemblages, the SAR11 clade had high relative abundances at all depths and sites, averaging 40% (±15%) of the total community relative abundance. Micro-autoradiography combined with CARD-FISH further revealed that the SAR11 clade contributed substantially (45-60% in surface waters) to bacterial biomass production (as determined by3H leucine incorporation). A clear niche partitioning of the further resolved SAR11 subclades was observed with depth layers, but differences among sites were detectable for only a few subclades. Our study provides novel observations of the distribution and contribution to the marine carbon cycle of the SAR11 clade in the cold waters of the Southern Ocean.
ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific cells
Authors: Danziger Samuel A , Gibbs David L , Shmulevich Ilya , McConnell Mark , Trotter Matthew WB , Schmitz Frank , Reiss David J , Ratushny Alexander V 
Published: May 2019

AbstractImmune cell infiltration of tumors can be an important component for determining patient outcomes, e.g. by inferring immune cell presence by deconvolving gene expression data drawn from a heterogenous mix of cell types. One particularly powerful family of deconvolution techniques uses signature matrices of genes that uniquely identify each cell type as determined from cell type purified gene expression data. Many methods of this type have been recently published, often including new signature matrices appropriate for a single purpose, such as investigating a specific type of tumor. The package ADAPTS helps users make the most of this expanding knowledge base by introducing a framework for cell type deconvolution. ADAPTS implements modular tools for customizing signature matrices for new tissue types by adding custom cell types or building new matrices de novo, including from single cell RNAseq data. It includes a common interface to several popular deconvolution algorithms that use a signature matrix to estimate the proportion of cell types present in heterogenous samples. ADAPTS also implements a novel method for clustering cell types into groups that are hard to distinguish by deconvolution and then re-splitting those clusters using hierarchical deconvolution. We demonstrate that the techniques implemented in ADAPTS improve the ability to reconstruct the cell types present in a single cell RNAseq data set in a blind predictive analysis. ADAPTS is currently available for use in R on CRAN and GitHub.
Genetic homogeneity in face of morphological heterogeneity in the harbor porpoises from the Black Sea and adjacent waters
Authors: Chehida Yacine Ben , Thumloup Julie , Vishnyakova Karina , Gol’din Pavel , Fontaine Michael C. 
Published: May 2019

AbstractIsolated from North Atlantic populations, the Black Sea harbor porpoise (Phocoena phocoena relicta) is listed as Endangered due to the massive population decline triggered by historical hunting, and subsequently through fisheries bycatch, and other human activities. Of paramount importance for its conservation, is the characterization of the population structure. While morphological heterogeneity suggested population subdivision, previous genetic studies have failed to find any differences. Here, we investigated the population genetic structure of 144 harbor porpoises sampled opportunistically from across the entire subspecies range including the Aegean, Marmara, Black, and Azov Seas. Genetic variation of across one-fourth of the mitochondrial genome, in combination with the analysis of ten microsatellite loci revealed a nearly complete genetic homogeneity. While simulations show that this inability to reject panmixia does not stem from a lack of power (power to detectFSTof 0.008). A genetic time-lag effect limiting our ability to detect population subdivision is also unlikely when effective population size is low, as is the case here. For now, genetic panmixia among porpoises of the Black Sea and adjacent waters cannot be rejected. Population subdivision may well exist, but conclusive evidence would require an improved sampling providing suitable contrasts (e.g., age, sex, season). Also, a genome scale study providing access to neutral and selected genetic variation may reveal cryptic differentiation indicative of ecologically subdivisions. As a precautionary approach, definition of management units should be based on evidence of population heterogeneity obtained from multidisciplinary approaches rather than just genetics.