bluuebunny/crossref_metadata_2025_split huggingface dataset:
Decomposition disentangled: a test of the multiple mechanisms by which nitrogen enrichment alters litter decomposition
Authors: Pichon Noémie A. , Cappelli Seraina , Soliveres Santiago , Hölzel Norbert , Klaus Valentin H. , Kleinebecker Till , Allan Eric 
Published: June 2019

SummaryNitrogen (N) enrichment has direct effects on ecosystem functioning by altering soil abiotic conditions and indirect effects by reducing plant diversity and shifting plant functional composition from dominance by slow to fast growing species. Litter decomposition is a key ecosystem function and is affected by N enrichment either by a change in litter quality (the recalcitrance of the plant material) or through a change in soil quality (the abiotic and biotic components of the soil that affect decomposition). The relative importance of soil and litter quality and how the direct and effects of N alter them remains poorly known.We designed a large grassland field experiment manipulating N enrichment, plant species richness and functional composition in a full factorial design. We used three complementary litter bag experiments and a novel structural equation modelling approach to quantify the relative effects of the treatments on litter and soil quality and their importance for total decomposition.Our results indicate that total decomposition was mostly driven by changes in litter quality rather than soil quality. Litter quality was affected by the nutrient contents (N and calcium) and structural components of the litter (leaf dry matter content, fibres). N enrichment increased litter decomposition mostly indirectly through a shift in functional composition toward faster growing plant species producing higher quality litter. N enrichment also had effects on soil, by directly and indirectly affected vegetation cover, but this had relatively few consequences for the total decomposition rate.Synthesis. Our approach provides a mechanistic tool to test the drivers of litter decomposition across different ecosystems. Our results show that functional composition is more important than richness or soil quality in determining litter decomposition and that N enrichment effects mainly occur via above- rather than belowground processes. This highlights the importance of considering shifts in plant species composition when assessing the effects of N enrichment on decomposition.
Herbal tablet of Pueraria tuberosa water extract suppresses the alloxan induced liver damage and hyperglycemia in rats
Authors: Pandey Harsh , Srivastava Shivani , Tripathi Yamini Bhusan 
Published: June 2019

AbstractAimTo study the protective response of herbal formulation (tablets) of Pueraria tuberosa water extract (PTAB) on alloxan induced rat diabetic model.MethodologyAlloxan (120 mg/kg bw) was injected intraperitonially. Rats were divided into three groups: group 1 as normal, group 2 as diabetic control and group 3 were given PTAB upto 14 days. Blood glucose and liver function tests were done using their respective kits. Hematoxylene and eosin staining was done to evaluate the morphological changes in liver tissues. Through immunohistochemistry, we have checked the protein expression of VEGF, MMP9 and ki67.ResultPTAB significantly decreases blood glucose level in a time dependent manner up to 14 days. As compared to diabetic control, PTAB decreases SGOT, SGPT and alkaline phosphates after 14 days of treatment. In diabetic control, the morphology of liver tissues were found damaged due to deformed hepatocytes and dilated lobules. Most of the hepatocytes after PTAB treatment were comparatively found similar to normal rats tissues, along with dilated blood vessels and normalized liver lobules. In addition to these results, PTAB suppresses the expressions of VEGF and MMP 9.ConclusionPTAB was found to act as an effective hypoglycemic agent. In addition to this, PTAB also found to inhibit inflammation and apoptosis of liver. Thus, can be taken as a potential drug in liver diseases.
Distinct roles of parvalbumin and somatostatin interneurons in the synchronization of spike-times in the neocortex
Authors: Jang Hyun Jae , Chung Hyowon , Rowland James M. , Richards Blake A. , Kohl Michael M. , Kwag Jeehyun 
Published: June 2019

AbstractSynchronization of precise spike-times across multiple neurons carries information about sensory stimuli. Inhibitory interneurons are suggested to promote this synchronization, but it is unclear whether distinct interneuron subtypes provide different contributions. To test this, we examined single-unit recordings from barrel cortex in vivo and used optogenetics to determine the contribution of two classes of inhibitory interneurons: parvalbumin (PV)- and somatostatin (SST)-positive interneurons to spike-timing synchronization across cortical layers. We found that PV interneurons preferentially promote the synchronization of spike-times when instantaneous firing-rates are low (<12 Hz), whereas SST interneurons preferentially promote the synchronization of spike-times when instantaneous firing-rates are high (>12 Hz). Furthermore, using a computational model, we demonstrate that these effects can be explained by PV and SST interneurons having preferential contribution to feedforward and feedback inhibition, respectively. Our findings demonstrate that distinct subtypes of inhibitory interneurons have frequency-selective roles in spatio-temporal synchronization of precise spike-times.
Insights into Membrane Protein-Lipid Interactions from Free Energy Calculations
Authors: Corey Robin A. , Vickery Owen N. , Sansom Mark S. P. , Stansfeld Phillip J. 
Published: June 2019

AbstractIntegral membrane proteins are regulated by specific interactions with lipids from the surrounding bilayer. The structures of protein-lipid complexes can be determined through a combination of experimental and computational approaches, but the energetic basis of these interactions is difficult to resolve. Molecular dynamic simulations provide the primary computational technique to estimate the free energies of these interactions. We demonstrate that the energetics of protein-lipid interactions may be reliably and reproducibly calculated using three simulation-based approaches: potential of mean force calculations. alchemical free energy perturbation, and well-tempered metadynamics. We employ these techniques within the framework of a coarse-grained force field, and apply them to both bacterial and mammalian membrane protein-lipid systems. We demonstrate good agreement between the different techniques, providing a robust framework for their automated implementation within a pipeline for annotation of newly determined membrane protein structures.
Robust estimation of heritability and predictive accuracy in plant breeding: evaluation using simulation and empirical data
Authors: Lourenço Vanda M , Ogutu Joseph O , Piepho Hans-Peter 
Published: June 2019

AbstractBackgroundGenomic prediction (GP) is used in animal and plant breeding to help identify the best genotypes for selection. One of the most important measures of the effectiveness and reliability of GP in plant breeding is predictive accuracy. An accurate estimate of this measure is thus central to GP. Moreover, regression models are the models of choice for analyzing field trial data in plant breeding. However, models that use the classical likelihood typically perform poorly, often resulting in biased parameter estimates, when their underlying assumptions are violated. This typically happens when data are contaminated with outliers. These biases often translate into inaccurate estimates of heritability and predictive accuracy, compromising the performance of GP. Since phenotypic data are susceptible to contamination, improving the methods for estimating heritability and predictive accuracy can enhance the performance of GP. Robust statistical methods provide an intuitively appealing and a theoretically well justified framework for overcoming some of the drawbacks of classical regression, most notably the departure from the normality assumption. We compare the performance of robust and classical approaches to two recently published methods for estimating heritability and predictive accuracy of GP using simulation of several plausible scenarios of random and block data contamination with outliers and commercial maize and rye breeding datasets.ResultsThe robust approach generally performed as good as or better than the classical approach in phenotypic data analysis and in estimating the predictive accuracy of heritability and genomic prediction under both the random and block contamination scenarios. Notably, it consistently outperformed the classical approach under the random contamination scenario. Analyses of the empirical maize and rye datasets further reinforce the stability and reliability of the robust approach in the presence of outliers or missing data.ConclusionsThe proposed robust approach enhances the predictive accuracy of heritability and genomic prediction while alleviating the need for performing outlier detection for a broad range of simulation scenarios and empirical breeding datasets. Accordingly, plant breeders should seriously consider regularly using the robust alongside the classical approach and increasing the number of replicates to three or more, to further enhance the accuracy of the robust approach.
Cas9HF1 enhanced specificity inUstilago maydis
Authors: Zuo Weiliang , Depotter Jasper RL , Doehlemann Gunther 
Published: June 2019

AbstractThe clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 system is widely used as a tool to precisely manipulate genomic sequence targeted by sgRNA (single guide RNA) and is adapted in different species for genome editing. One of the major concerns of CRISPR-Cas9 is the possibility of off-target effects, which can be remedied by the deployment of high fidelity Cas9 variants.Ustilago maydisis a maize fungal pathogen, which has served as a model organism for biotrophic pathogens for decades. The successful adaption of CRISPR-Cas9 inU. maydisgreatly facilitated effector biology studies. Here, we constructed anU. maydisreporter strain that allowsin vivoquantification of efficiency and target specificity of three high fidelity Cas9 variants, Cas9HF1, Cas9esp1.1 and Cas9hypa. This approach identified Cas9HF1 as most specific Cas9 variant inU. maydis. Furthermore, whole genome sequencing showed absence of off-target effects inU. maydisby CRISPR-Cas9 editing.
Modeling and Characterization of Inter-Individual Variability in CD8 T Cell Responses in Mice
Authors: Audebert Chloe , Laubreton Daphné , Arpin Christophe , Gandrillon Olivier , Marvel Jacqueline , Crauste Fabien 
Published: June 2019

AbstractTo develop vaccines it is mandatory yet challenging to account for inter-individual variability during immune responses. Even in laboratory mice, T cell responses of single individuals exhibit a high heterogeneity that may come from genetic backgrounds, intra-specific processes (e.g. antigen-processing and presentation) and immunization protocols.To account for inter-individual variability in CD8 T cell responses in mice, we propose a dynamical model coupled to a statistical, nonlinear mixed effects model. Average and individual dynamics during a CD8 T cell response are characterized in different immunization contexts (vaccinia virus and tumor). On one hand, we identify biological processes that generate inter-individual variability (activation rate of naive cells, the mortality rate of effector cells, and dynamics of the immunogen). On the other hand, introducing categorical covariates to analyze two different immunization regimens, we highlight the steps of the response impacted by immunogens (priming, differentiation of naive cells, expansion of effector cells and generation of memory cells). The robustness of the model is assessed by confrontation to new experimental data.Our approach allows to investigate immune responses in various immunization contexts, when measurements are scarce or missing, and contributes to a better understanding of inter-individual variability in CD8 T cell immune responses.
Auditory predictions and prediction errors in response to self-initiated vowels
Authors: Knolle Franziska , Schwartze Michael , Schröger Erich , Kotz Sonja A. 
Published: June 2019

AbstractIt has been suggested that speech production is accomplished by an internal forward model, reducing processing activity directed to self-produced speech in the auditory cortex. The current study uses an established N1-suppression paradigm comparing self- and externally-initiated natural speech sounds to answer two questions:Are forward predictions generated to process complex speech sounds, such as vowels, initiated via a button press?Are prediction errors regarding self-initiated deviant vowels reflected in the corresponding ERP components?Results confirm an N1-suppression in response to self-initiated speech sounds. Furthermore, our results suggest that predictions leading to the N1-suppression effect are specific, as self-initiated deviant vowels do not elicit an N1-suppression effect. Rather, self-initiated deviant vowels elicit an enhanced N2b and P3a compared to externally-generated deviants, externally-generated standard, or self-initiated standards, again confirming prediction specificity.Results show that prediction errors are salient in self-initiated auditory speech sounds, which may lead to more efficient error correction in speech production.
The ATPase cycle of Human Muscle Myosin II Isoforms: Adaptation of a single mechanochemical cycle for different physiological roles
Authors: Johnson Chloe A. , Walklate Jonathan , Svicevic Marina , Mijailovich Srboljub M. , Vera Carlos , Karabina Anastasia , Leinwand Leslie A. , Geeves Michael A. 
Published: June 2019

AbstractStriated muscle myosins are encoded by a large gene family in all mammals, including human. These isoforms define several of the key characteristics of the different striated muscle fiber types including maximum shortening velocity. We have previously used recombinant isoforms of the motor domains of eight different human myosin isoforms to define the actin.myosin cross-bridge cycle in solution. Here, we use a recently developed modeling approach MUSICO to explore how well the experimentally defined cross-bridge cycles for each isoform in solution can predict the characteristics of muscle fiber contraction including duty ratio, shortening velocity, ATP economy and the load dependence of these parameters. The work shows that the parameters of the cross-bridge cycle predict many of the major characteristics of each muscle fiber type and raises the question of what sequence changes are responsible for these characteristics.
Increased O-GlcNAcylation rapidly decreases GABAAR currents in hippocampus yet depresses neuronal output
Authors: Stewart Luke T. , Abiraman Kavitha , Chatham John C. , McMahon Lori L. 
Published: June 2019

AbstractO-GlcNAcylation, a post-translational modification involving O-linkage of β-N-acetylglucosamine to Ser/Thr residues on target proteins, is increasingly recognized as a critical regulator of brain function in health and disease. Enzymes that catalyze O-GlcNAcylation are found at both presynaptic and postsynaptic sites, and O-GlcNAcylated proteins localize to synaptosomes. An acute increase in O-GlcNAcylation induces long-term depression (LTD) of excitatory transmission at hippocampal CA3-CA1 synapses, and depresses hyperexcitable circuits in vitro and in vivo. Yet, no study has investigated how O-GlcNAcylation modulates the efficacy of inhibitory neurotransmission. Here we show an acute increase in O-GlcNAc dampens GABAergic currents onto principal cells in rodent hippocampus likely through a postsynaptic mechanism, and has a variable effect on the excitation/inhibition balance. The overall effect of increased O-GlcNAc is reduced synaptically-driven spike probability via synaptic depression and decreased intrinsic excitability. Our results position O-GlcNAcylation as a novel regulator of the overall excitation/inhibition balance and neuronal output.
Multi-Subject Stochastic Blockmodels for Adaptive Analysis of Individual Differences in Human Brain Network Cluster Structure
Authors: Pavlović Dragana M. , Guillaume Bryan R. L. , Towlson Emma K. , Kuek Nicole M. Y. , Afyouni Soroosh , Vértes Petra E. , Yeo Thomas B. T. , Bullmore Edward T. , Nichols Thomas E. 
Published: June 2019

AbstractThere is great interest in elucidating the cluster structure of brain networks in terms of modules, blocks or clusters of similar nodes. However, it is currently challenging to handle data on multiple subjects since most of the existing methods are applicable only on a subject-by-subject basis or for analysis of a group average network. The main limitation of per-subject models is that there is no obvious way to combine the results for group comparisons, and of group-averaged models that they do not reflect the variability between subjects. Here, we propose two novel extensions of the classical Stochastic Blockmodel (SBM) that use a mixture model to estimate blocks or clusters of connected nodes, combined with a regression model to capture the effects on cluster structure of individual differences on subject-level covariates. Multi-subject Stochastic Blockmodels (MS-SBM) can flexibly account for between-subject variability in terms of a homogenous or heterogeneous effect on connectivity of covariates such as age or diagnostic status. Using synthetic data, representing a range of block sizes and cluster structures, we investigate the accuracy of the estimated MS-SBM parameters as well as the validity of inference procedures based on Wald, likelihood ratio and Monte Carlo permutation tests. We show that multi-subject SBMs recover the true cluster structure of synthetic networks more accurately and adaptively than standard methods for modular decomposition. Permutation tests of MS-SBM parameters were more robustly valid for statistical inference and Type I error control than tests based on standard asymptotic assumptions. Applied to analysis of multi-subject resting state fMRI networks (13 healthy volunteers; 12 people with schizophrenia; N = 268 brain regions), we show that the Heterogeneous Stochastic Blockmodel estimates ‘core-on-modules’ architecture. The intra-block and inter-block connection weights vary between individual participants and can be modelled as a logistic function of subject-level covariates like age or diagnostic status. Multi-subject Stochastic Blockmodels are likely to be useful tools for statistical analysis of individual differences in human brain graphs and other networks whose prior cluster structure needs to be estimated from the data.
Scale-dependent effects of geography, host ecology, and host genetics, on species composition and co-occurrence in a stickleback parasite metacommunity
Authors: Bolnick Daniel I. , Resetarits Emlyn J. , Ballare Kimberly , Stuart Yoel E. , Stutz William E. 
Published: June 2019

ABSTRACTA core goal of ecology is to understand the abiotic and biotic variables that regulate species distributions and community composition. A major obstacle is that the rules governing species distribution can change with spatial scale. Here, we illustrate this point using data from a spatially nested metacommunity of parasites infecting a metapopulation of threespine stickleback fish from 34 lakes on Vancouver Island, British Columbia. Parasite communities differ among host individuals within each host population and between host populations. The distribution of each parasite taxon depends, to varying degrees, on individual host traits (e.g., mass, diet) and on host population characteristics (e.g., lake size, mean diet). However, in most cases, a given parasite was regulated by different factors at the host-individual and host-population scales, contributing to scale-dependent patterns of parasite-species co-occurrence.
Neuropathology of RAN translation proteins in Fragile X-associated Tremor/Ataxia Syndrome
Authors: Krans Amy , Skariah Geena , Zhang Yuan , Bayly Bryana , Todd Peter K. 
Published: June 2019

AbstractCGG repeat expansions in FMR1 cause the neurodegenerative disorder Fragile X-associated Tremor/Ataxia Syndrome (FXTAS). Ubiquitinated neuronal intranuclear inclusions (NIIs) are the neuropathological hallmark of FXTAS. Both sense strand derived CGG repeats and antisense strand derived CCG repeats support non-AUG initiated (RAN) translation of homopolymeric proteins in potentially 6 different reading frames. However, the relative abundance of these proteins in FXTAS brains and their co-localization with each other and NIIs is lacking. Here we describe rater-blinded assessment of immunohistochemical and immunofluorescence staining with newly generated antibodies to different CGG RAN translation products in FXTAS and control brains as well as co-staining with ubiquitin, p62/SQSTM1, and ubiquilin 2. We find that both FMRpolyG and a second CGG repeat derived RAN translation product, FMRpolyA, accumulate in aggregates in FXTAS brains. FMRpolyG is a near-obligate component of both ubiquitin-positive and p62-positive NIIs in FXTAS, with occurrence of aggregates in 20% of all hippocampal neurons and >90% of all inclusions. A subset of these inclusions also stain positive for the ALS/FTD associated protein ubiquilin 2. Ubiquitinated inclusions and FMRpolyG+ aggregates are rarer in cortex and cerebellum. Intriguingly, FMRpolyG staining is also visible in control neuronal nuclei. In contrast to FMRpolyG, staining for FMRpolyA and CCG antisense derived RAN translation products were less abundant and were infrequent components of FMRpolyG+ inclusions. In conclusion, RAN translated FMRpolyG is a common component of ubiquitin and p62 positive inclusions in FXTAS patient brains.
Oct4-mediated inhibition of Lsd1 activity promotes the active and primed state of pluripotency enhancers
Authors: AlAbdi Lama , Saha Debapriya , He Ming , Dar Mohd Saleem , Utturkar Sagar M. , Sudyanti Putu Ayu , McCune Stephen , Spears Brice H. , Breedlove James A. , Lanman Nadia A. , Gowher Humaira 
Published: June 2019

SummaryAn aberrant increase in pluripotency gene (PpG) expression due to enhancer reactivation could induce stemness and enhance tumorigenicity of cancer stem cells. Silencing of PpG enhancers (PpGe) during embryonic stem cell differentiation involves Lsd1–mediated H3K4me1 demethylation and DNA methylation. Here, we observed retention of H3K4me1 and DNA hypomethylation at PpGe associated with a partial repression of PpGs in F9 embryonal carcinoma cells (ECCs) post-differentiation. H3K4me1 demethylation in F9 ECCs could not be rescued by Lsd1 overexpression. Given our observation that H3K4me1 demethylation is accompanied by strong Oct4 repression in P19 ECCs, we tested if Oct4 interaction with Lsd1 affects its catalytic activity. Our data show a dose-dependent inhibition of Lsd1 activity by Oct4 and retention of H3K4me1 at PpGe in Oct4 overexpressing P19 ECCs. These data suggest that Lsd1-Oct4 interaction in cancer stem cells could establish a primed enhancer state that is susceptible to reactivation leading to aberrant PpG expression.