bluuebunny/crossref_metadata_2025_split huggingface dataset:
Balanced nutrient requirements for maize in the Northern Nigerian Savanna: Parameterization and validation of QUEFTS model
Authors: Shehu Bello M. , Lawan Bassam A. , Jibrin Jibrin M. , Kamara Alpha Y. , Mohammed Ibrahim B. , Rurinda Jairos , Zingore Shamie , Craufurd Peter , Vanlauwe Bernard , Merckx Roel 
Published: April 2019

AbstractEstablishing balanced nutrient requirements for maize (Zea mays L.) in the Northern Nigerian Savanna is paramount to develop site-specific fertilizer recommendations to increase maize yield, profits of farmers and avoid negative environmental impacts of fertilizer use. The model QUEFTS (QUantitative Evaluation of Fertility of Tropical Soils) was used to estimate balanced nitrogen (N), phosphorus (P) and potassium (K) requirements for maize production in the Northern Nigerian Savanna. Data from on-farm nutrient omission trials conducted in 2015 and 2016 rainy seasons in two agro-ecological zones in the Northern Nigerian Savanna (i.e. Northern Guinea Savanna “NGS” and Sudan Savanna “SS”) were used to parameterize and validate the QUEFTS model. The relations between indigenous soil N, P, and K supply and soil properties were not well described with the QUEFTS default equations and consequently new and better fitting equations were derived. The average fertilizer recovery fractions of N, P and K in the NGS were generally comparable with the QUEFTS default values, but lower recovery fractions of these nutrients were observed in the SS. The parameters of maximum accumulation (a) and dilution (d) in kg grain per kg nutrient for the QUEFTS model obtained were respectively 35 and 79 for N, 200 and 527 for P and 25 and 117 for K in the NGS zone and 32 and 79 for N, 164 and 528 for P and 24 and 136 for K in the SS zone. The model predicted a linear relationship between grain yield and above-ground nutrient uptake until yield reached about 50 to 60% of the yield potential. When the yield target reached 60% of the potential yield (i.e. 6.0 tonnes per hectare), the model showed above-ground nutrient uptake of 19.4, 3.3 and 23.0 kg N, P, and K, respectively, per one tonne of maize grain in the NGS, and 17.3, 5.3 and 26.2 kg N, P and K, respectively, per one tonne of maize grain in the SS. These results suggest an average NPK ratio in the plant dry matter of about 5.9:1:7.0 for maize in the NGS and 3.3:1:4.9 for maize in the SS. There was a close agreement between observed and parameterized QUEFTS predicted yields across the two agro-ecological zones (R2 = 0.70 for the NGS and 0.86 for the SS). We concluded that the QUEFTS model can be used for balanced nutrient requirement estimations and development of site-specific fertilizer recommendations for maize intensification in the Northern Nigerian Savanna.
Amelioration of muscular dystrophy phenotype in mdx mice by inhibition of Flt1
Authors: Verma Mayank , Shimizu-Motohashi Yuko , Asakura Yoko , Ennen James , Bosco Jennifer , Zou Zhiwei , Fong Guo-Hua , Josiah Serene , Keefe Dennis , Asakura Atsushi 
Published: April 2019

AbstractDuchenne muscular dystrophy (DMD) is an X-linked recessive genetic disease in which the dystrophin coding for a membrane stabilizing protein is mutated. Recently, the vasculature has also shown to be perturbed in DMD and DMD modelmdxmice. Data-mining DMD transcriptomics revealed the defects were correlated to a vascular endothelial growth factor (VEGF) signaling pathway. To reveal the relationship between DMD and VEGF signaling,mdxmice were crossed with constitutive (CAG/CreERTM:Flt1LoxP/LoxP) and endothelial cell-specific conditional gene knockout mice (Cdh5CreERT2:Flt1LoxP/LoxP) forFlt1which is a decoy receptor for VEGF. Previous work demonstrated that heterozygous globalFlt1knockout mice increased vascular density and improved DMD phenotypes when crossed with DMD modelmdxandmdx:utrn-/-mice. Here, we showed that while constitutive deletion ofFlt1is detrimental to the skeletal muscle function, endothelial cell-specificFlt1deletion resulted in increased vascular density and improvement in the DMD-associated phenotype in themdxmice. These decreases in pathology, including improved muscle histology and function, were recapitulated inmdxmice given anti-FLT1 peptides or monoclonal antibodies, which blocked VEGF-FLT1 binding. The histological and functional improvement of dystrophic muscle by FLT1 blockade provides a novel pharmacological strategy for the potential treatment of DMD.
Effects of MCHM on yeast metabolism
Authors: Pupo Amaury , Ku Kang Mo , Gallagher Jennifer E. G. 
Published: April 2019

AbstractOn January 2014 approximately 10,000 gallons of crude 4-Methylcyclohexanemethanol (MCHM) and propylene glycol phenol ether (PPH) were accidentally released into the Elk River, West Virginia, contaminating the tap water of around 300,000 residents. Crude MCHM is an industrial chemical used as flotation reagent to clean coal. At the time of the spill, MCHM’s toxicological data were limited, an issue that have been addressed by different studies focused on understanding the immediate and long-term effects of MCHM on human health and the environment. UsingS. cerevisiaeas a model organism we study the effect of acute exposition to crude MCHM on metabolism. Yeasts were treated with MCHM 3.9 mM in YPD for 30 minutes. Polar and lipid metabolites were extracted from cells by a chloroform-methanol-water mixture. The extracts were then analyzed by direct injection ESI-MS and by GC-MS. The metabolomics analysis was complemented with flux balance analysis simulations done with genome-scale metabolic network models (GSMNM) of MCHM treated vs non-treated control. We integrated the effect of MCHM on yeast gene expression from RNA-Seq data within these GSMNM. 181 and 66 metabolites were identified by the ESI-MS and GC-MS procedures, respectively. From these 38 and 34 relevant metabolites were selected from ESI-MS and GC-MS respectively, for 72 unique compounds. MCHM induced amino acid accumulation, via its effects on amino acid metabolism, as well as a potential impairment of ribosome biogenesis. MCHM affects phospholipid biosynthesis and decrease the levels of ergosterol, with a potential impact in the biophysical properties of yeast cellular membranes. The FBA simulations were able to reproduce the deleterious effect of MCHM on cell’s growth and suggest that the effect of MCHM on ubiquinol:ferricytochrome c reductase reaction, caused by the under-expression ofCYT1gene, could be the driven force behind the observed effect on yeast metabolism and growth.
Probing and predicting ganglion cell responses to smooth electrical stimulation in healthy and blind mouse retina
Authors: Höfling Larissa , Berens Philipp , Zeck Günther 
Published: April 2019

ABSTRACTRetinal implants are used to replace lost photoreceptors in blind patients suffering from retinopathies such as retinitis pigmentosa. Patients wearing implants regain some rudimentary visual function. However, it is severely limited compared to normal vision because non-physiological stimulation strategies fail to selectively activate different retinal pathways at sufficient spatial and temporal resolution. The development of improved stimulation strategies is rendered difficult by the large space of potential stimuli. Here we systematically explore a subspace of potential stimuli by electrically stimulating healthy and blind mouse retina in epiretinal configuration using smooth Gaussian white noise delivered by a high-density CMOS-based microelectrode array. We identify linear filters of retinal ganglion cells (RGCs) by fitting a linear-nonlinear-Poisson (LNP) model. Our stimulus evokes fast, reliable, and spatially confined spiking responses in RGC which are accurately predicted by the LNP model. Furthermore, we find diverse shapes of linear filters in the linear stage of the model, suggesting diverse preferred electrical stimuli of RGCs. Our smooth electrical stimulus could provide a starting point of a model-guided search for improved stimuli for retinal prosthetics.
Mechanical constraints influence microtubule orientation in plant cells
Authors: Bachmann S , Froese R , Cytrynbaum EN 
Published: April 2019

ABSTRACTIn growing plant cells, parallel ordering of microtubules (MTs) along the inner surface of the cell membrane influences the direction of cell expansion and thereby plant morphology. For correct expansion of organs that primarily grow by elongating, such as roots and stems, MTs must bend in the high-curvature direction along the cylindrically shaped cell membrane in order to form the required circumferential arrays. Previous studies, which have recapitulated the self-organization of these arrays, ignored MT mechanics and assumed MTs follow geodesics of the cell surface. Here, we show, through analysis of a derived Euler-Lagrange equation, that an elastic MT constrained to a cylindrical surface will deflect away from geodesics and toward low curvature directions to minimize bending energy. This occurs when the curvature of the cell surface is relatively high for a given anchor density. In the limit of infinite anchor density, MTs always follow geodesics. We compare our analytical predictions to measured curvatures and anchor densities and find that the regime in which cells are forming these cortical arrays straddles the region of parameter space in which arrays must form under the antagonistic influence of this mechanically induced deflection. Although this introduces a potential obstacle to forming circumferentially orientated arrays that needs to be accounted for in the models, it also raises the question of whether plants use this mechanical phenomenon to regulate array orientation. The model also constitutes an elegant generalization of the classical Euler-bucking instability along with an intrinsic unfolding of the associated pitchfork bifurcation.SIGNIFICANCE STATEMENTThe cortical microtubule array in plant cells is a dynamic structure whose organization influences cell function, growth, and plant morphology. During cell growth, the array must be highly ordered and perpendicular to the axis of expansion. In other instances, it can be disordered or axially oriented. Experimental and modelling studies have outlined the importance of various microtubule-microtubule interactions in the ordering and orienting process. We explore the influence of cell shape on the bending of microtubules that has been largely ignored to date. We find that cell shape can induce microtubule bending that steers them away from high-curvature directions. This bending force can act as an obstacle to proposed mechanisms for array organization and also as a mechanism for regulating reorientation of the array.
Four ways to fit an ion channel model
Authors: Clerx M. , Beattie K.A. , Gavaghan D.J. , Mirams G.R. 
Published: April 2019

ABSTRACTComputational models of the cardiac action potential are increasingly being used to investigate the effects of genetic mutations, predict pro-arrhythmic risk in drug development, and to guide clinical interventions. These safety-critical applications, and indeed our understanding of the cardiac action potential, depend on accurate characterisation of the underlying ionic currents. Four different methods can be found in the literature to fit ionic current models to single-cell measurements: (Method 1) fitting model equations directly to time constant, steady-state, and I-V summary curves; (Method 2) fitting by comparing simulated versions of these summary curves to their experimental counterparts; (Method 3) fitting to the current traces themselves from a range of protocols; and (Method 4) fitting to a single current trace from an information-rich voltage clamp protocol. We compare these methods using a set of experiments in which hERG1a current from single Chinese Hamster Ovary (CHO) cells was characterised using multiple fitting protocols and an independent validation protocol. We show that Methods 3 and 4 provide the best predictions on the independent validation set, and that the short information-rich protocols of Method 4 can replace much longer conventional protocols without loss of predictive ability. While data for Method 2 is most readily available from the literature, we find it performs poorly compared to Methods 3 and 4 both in accuracy of predictions and computational efficiency. Our results demonstrate how novel experimental and computational approaches can improve the quality of model predictions in safety-critical applications.Statement of SignificanceMathematical models have been constructed to capture and share our understanding of the kinetics of ion channel currents for almost 70 years, and hundreds of models have been developed, using a variety of techniques. We compare how well four of the main methods fit data, how reliable and efficient the process of fitting is, and how predictive the resulting models are for physiological situations. The most widely-used traditional approaches based on current-voltage and time constant-voltage curves do not produce the most predictive models. Short, optimised experimental voltage clamp protocols can be used to create models that are as predictive as ones derived from traditional protocols, opening up possibilities for measuring ion channel kinetics faster, more accurately and in single cells. As these models often form part of larger multi-scale action potential and tissue electrophysiology models, improved ion channel kinetics models could influence the findings of thousands of simulation studies.
Sleep is bi-directionally modified by amyloid beta oligomers
Authors: Özcan Güliz Gürel , Lim Sumi , Leighton Patricia L.A. , Allison W. Ted , Rihel Jason 
Published: April 2019

SUMMARYDisrupted sleep is a major feature of Alzheimer’s Disease (AD), often arising years before symptoms of cognitive decline. Prolonged wakefulness exacerbates the production of amyloid-beta (Aβ) species, a major driver of AD progression, suggesting that sleep loss further accelerates AD through a vicious cycle. However, the mechanisms by which Aβ affects sleep are unknown. We demonstrate in zebrafish that Aβ acutely and reversibly enhances or suppresses sleep as a function of oligomer length. Genetic disruptions revealed that short Aβ oligomers induce acute wakefulness through Adrenergic receptor b2 (Adrb2) and Progesterone membrane receptor component 1 (Pgrmc1), while longer Aβ forms induce sleep through a pharmacologically tractable Prion Protein (PrP) signalling cascade. Our data indicate that Aβ can trigger a bi-directional sleep/wake switch. Alterations to the brain’s Aβ oligomeric milieu, such as during the progression of AD, may therefore disrupt sleep via changes in acute signalling events.HIGHLIGHTSAmyloid beta oligomers can drive either sleep or wakefulness, depending on their sizeWakefulness driven by short amyloid beta oligomers requires binding partners Adrenergic Beta Receptor 2 and Pgrmc1Long amyloid beta oligomers drive sleep through interaction with Prion ProteinThe in vivo sleep effects of amyloid beta can be pharmacologically blocked by targeting several steps of the Amyloid beta-Prion Protein signalling cascade.
A Deep Learning Approach for Rapid Mutational Screening in Melanoma
Authors: Kim Randie H. , Nomikou Sofia , Coudray Nicolas , Jour George , Dawood Zarmeena , Hong Runyu , Esteva Eduardo , Sakellaropoulos Theodore , Donnelly Douglas , Moran Una , Hatzimemos Aristides , Weber Jeffrey S. , Razavian Narges , Aifantis Ioannis , Fenyo David , Snuderl Matija , Shapiro Richard , Berman Russell S. , Osman Iman , Tsirigos Aristotelis 
Published: April 2019

AbstractImage-based analysis as a rapid method for mutation detection can be advantageous in research or clinical settings when tumor tissue is limited or unavailable for direct testing. Here, we applied a deep convolutional neural network (CNN) to whole slide images of melanomas from 256 patients and developed a fully automated model that first selects for tumor-rich areas (Area Under the Curve AUC=0.96) then predicts for the presence of mutated BRAF in our test set (AUC=0.72) Model performance was cross-validated on melanoma images from The Cancer Genome Atlas (AUC=0.75). We confirm that the mutated BRAF genotype is linked to phenotypic alterations at the level of the nucleus through saliency mapping and pathomics analysis, which reveal that cells with mutated BRAF exhibit larger and rounder nuclei. Not only do these findings provide additional insights on how BRAF mutations affects tumor structural characteristics, deep learning-based analysis of histopathology images have the potential to be integrated into higher order models for understanding tumor biology, developing biomarkers, and predicting clinical outcomes.
Structural Learning of Proteins Using Graph Convolutional Neural Networks
Authors: Zamora-Resendiz Rafael , Crivelli Silvia 
Published: April 2019

AbstractThe exponential growth of protein structure databases has motivated the development of efficient deep learning methods that perform structural analysis tasks at large scale, ranging from the classification of experimentally determined proteins to the quality assessment and ranking of computationally generated protein models in the context of protein structure prediction. Yet, the literature discussing these methods does not usually interpret what the models learned from the training or identify specific data attributes that contribute to the classification or regression task. While 3D and 2D CNNs have been widely used to deal with structural data, they have several limitations when applied to structural proteomics data. We pose that graph-based convolutional neural networks (GCNNs) are an efficient alternative while producing results that are interpretable. In this work, we demonstrate the applicability of GCNNs to protein structure classification problems. We define a novel spatial graph convolution network architecture which employs graph reduction methods to reduce the total number of trainable parameters and promote abstraction in interme-diate representations. We show that GCNNs are able to learn effectively from simplistic graph representations of protein structures while providing the ability to interpret what the network learns during the training and how it applies it to perform its task. GCNNs perform comparably to their 2D CNN counterparts in predictive performance and they are outperformed by them in training speeds. The graph-based data representation allows GCNNs to be a more efficient option over 3D CNNs when working with large-scale datasets as preprocessing costs and data storage requirements are negligible in comparison.
Growth-dependent activation of protein kinases suggests a mechanism for measuring cell growth
Authors: Jasani Akshi , Huynh Tiffany , Kellogg Douglas R. 
Published: April 2019

AbstractIn all cells, progression through the cell cycle occurs only when sufficient growth has occurred. Thus, cells must translate growth into a proportional signal that can be used to measure and transmit information about growth. Previous genetic studies in budding yeast suggested that related kinases called Gin4 and Hsl1 could play roles in mechanisms that measure bud growth; however, interpretation of the data was complicated by the use of gene deletions that cause complex terminal phenotypes. Here, we used the first conditional alleles of Gin4 and Hsl1 to more precisely define their functions. We show that excessive bud growth during a prolonged mitotic delay is an immediate consequence of inactivating Gin4 and Hsl1. Thus, acute loss of Gin4 and Hsl1 causes cells to behave as though they cannot detect that bud growth has occurred. We further show that Gin4 and Hsl1 undergo gradual hyperphosphorylation during bud growth that is dependent upon growth and correlated with the extent of growth. Moreover, gradual hyperphosphorylation of Gin4 during bud growth requires binding to anionic phospholipids that are delivered to the growing bud. While alternative models are possible, the data suggest that signaling lipids delivered to the growing bud generate a growth-dependent signal that could be used to measure bud growth.
FermentativeEscherichia colimakes a substantial contribution to H2production in coculture with phototrophicRhodopseudomonas palustris
Authors: Sangani Amee A. , McCully Alexandra L. , LaSarre Breah , McKinlay James B. 
Published: April 2019

AbstractIndividual species within microbial communities can combine their attributes to produce services that benefit society, such as the transformation of renewable resources into valuable chemicals. Under defined genetic and environmental conditions, fermentativeEscherichia coliand phototrophicRhodopseudomonas palustrisexchange essential carbon and nitrogen, respectively, to establish a mutualistic relationship. In this relationship, each species produces H2biofuel as a byproduct of their metabolism. However, the extent to which each species contributes to H2production and the factors that influence their relative contributions were previously unknown. By comparing H2yields in cocultures pairingR. palustriswith either wild-typeE. colior a formate hydrogenlyase mutant that is incapable of H2production, we determined the relative contribution of each species to total H2production. Our results indicate thatE. colicontributes between 32% and 86% of the H2produced in coculture depending on the level of ammonium excreted by theR. palustrispartner. AnR. palustrisstrain that stimulated rapidE. coligrowth through a high level of ammonium excretion resulted in earlier accumulation of formate and acidic conditions that allowedE. colito be the major contributor to H2production.
Inverse Transformed Encoding Models – a solution to the problem of correlated trial-by-trial parameter estimates in fMRI decoding
Authors: Soch Joram , Allefeld Carsten , Haynes John-Dylan 
Published: April 2019

AbstractTechniques of multivariate pattern analysis (MVPA) can be used to decode the discrete experimental condition or a continuous modulator variable from measured brain activity during a particular trial. In functional magnetic resonance imaging (fMRI), trial-wise response amplitudes are sometimes estimated from the measured signal using a general linear model (GLM) with one onset regressor for each trial. When using rapid event-related designs with trials closely spaced in time, those estimates are highly variable and serially correlated due to the temporally extended shape of the hemodynamic response function (HRF). Here, we describe inverse transformed encoding modelling (ITEM), a principled approach of accounting for those serial correlations and decoding from the resulting estimates, at low computational cost and with no loss in statistical power. We use simulated data to show that ITEM outperforms the current standard approach in terms of decoding accuracy and analyze empirical data to demonstrate that ITEM is capable of visual reconstruction from fMRI signals.