bluuebunny/crossref_metadata_2025_split huggingface dataset:
Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases
Authors: Alamil M. , Hughes J. , Berthier K. , Desbiez C. , Thébaud G. , Soubeyrand S. 
Published: May 2019

Pathogen sequence data have been exploited to infer who infected whom, by using empirical and model-based approaches. Most of these approaches exploit one pathogen sequence per infected host (e.g. individual, household, field). However, modern sequencing techniques can reveal the polymorphic nature of within-host populations of pathogens. Thus, these techniques provide a subsample of the pathogen variants that were present in the host at the sampling time. Such data are expected to give more insight on epidemiological links than a single sequence per host. In general, a mechanistic viewpoint to transmission and micro-evolution has been followed to infer epidemiological links from these data. Here, we investigate an alternative approach grounded on statistical learning. The idea consists of learning the structure of epidemiological links with a pseudo-evolutionary model applied to training data obtained from contact tracing, for example, and using this initial stage to infer links for the whole dataset. Such an approach has the potential to be particularly valuable in the case of a risk of erroneous mechanistic assumptions, it is sufficiently parsimonious to allow the handling of big datasets in the future, and it is versatile enough to be applied to very different contexts from animal, human and plant epidemiology.
This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’.
A new mechanistic model of weather-dependent Septoria tritici blotch disease risk
Authors: Chaloner Thomas M. , Fones Helen N. , Varma Varun , Bebber Daniel P. , Gurr Sarah J. 
Published: May 2019

We present a new mechanistic model for predicting Septoria tritici blotch (STB) disease, parameterized with experimentally derived data for temperature- and wetness-dependent germination, growth and death of the causal agent,Zymoseptoria tritici. The output of this model (A) was compared with observed disease data for UK wheat over the period 2002–2016. In addition, we compared the output of a second model (B), in which experimentally derived parameters were replaced by a modified version of a publishedZ. triticithermal performance equation, with the same observed disease data. Neither model predicted observed annual disease, but model A was able to differentiate UK regions with differing average disease risks over the entire period. The greatest limitations of both models are: broad spatial resolution of the climate data, and lack of host parameters. Model B is further limited by its lack of explicitly defined pathogen death, leading to a cumulative overestimation of disease over the course of the growing season. Comparison of models A and B demonstrates the importance of accounting for the temperature-dependency of pathogen processes important in the initiation and progression of disease. However, effective modelling of STB will probably require similar experimentally derived parameters for host and environmental factors, completing the disease triangle.This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’.
Assessing the potential impact of vector-borne disease transmission following heavy rainfall events: a mathematical framework
Authors: Chowell G. , Mizumoto K. , Banda J. M. , Poccia S. , Perrings C. 
Published: May 2019

Predicting the impact of natural disasters such as hurricanes on the transmission dynamics of infectious diseases poses significant challenges. In this paper, we put forward a simple modelling framework to investigate the impact of heavy rainfall events (HREs) on mosquito-borne disease transmission in temperate areas of the world such as the southern coastal areas of the USA. In particular, we explore the impact of the timing of HREs relative to the transmission season via analyses that test the sensitivity of HRE-induced epidemics to variation in the effects of rainfall on the dynamics of mosquito breeding capacity, and the intensity and temporal profile of human population displacement patterns. The recent Hurricane Harvey in Texas motivates the simulations reported. Overall, we find that the impact of vector-borne disease transmission is likely to be greater the earlier the HREs occur in the transmission season. Simulations based on data for Hurricane Harvey suggest that the limited impact it had on vector-borne disease transmission was in part because of when it occurred (late August) relative to the local transmission season, and in part because of the mitigating effect of the displacement of people. We also highlight key data gaps related to models of vector-borne disease transmission in the context of natural disasters.
This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’.
Predicting plant disease epidemics from functionally represented weather series
Authors: Shah D. A. , Paul P. A. , De Wolf E. D. , Madden L. V. 
Published: May 2019

Epidemics are often triggered by specific weather patterns favouring the pathogen on susceptible hosts. For plant diseases, models predicting epidemics have therefore often emphasized the identification of early season weather patterns that are correlated with a disease outcome at some later point. Toward that end, window-pane analysis is an exhaustive search algorithm traditionally used in plant pathology for mining correlations in a weather series with respect to a disease endpoint. Here we show, with reference to Fusarium head blight (FHB) of wheat, that a functional approach is a more principled analytical method for understanding the relationship between disease epidemics and environmental conditions over an extended time series. We used scalar-on-function regression to model a binary outcome (FHB epidemic or non-epidemic) relative to weather time series spanning 140 days relative to flowering (when FHB infection primarily occurs). The functional models overall fit the data better than previously described standard logistic regression (lr) models. Periods much earlier than heretofore realized were associated with FHB epidemics. The findings were used to create novel weather summary variables which, when incorporated into lr models, yielded a new set of models that performed as well as existing lr models for real-time predictions of disease risk.
This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’.
Tmod2 is a regulator of cocaine responses through control of striatal and cortical excitability, and drug-induced plasticity
Authors: Mitra Arojit , Deats Sean P. , Dickson Price E. , Zhu Jiuhe , Gardin Justin , Nieman Brian J. , Henkelman R. Mark , Tsai Nien-Pei , Chesler Elissa J. , Zhang Zhong-Wei , Kumar Vivek 
Published: May 2019

AbstractDrugs of abuse induce neuroadaptations, including synaptic plasticity, that are critical for transition to addiction, and genes and pathways that regulate these neuroadaptations are potential therapeutic targets. Tropomodulin 2 (Tmod2) is an actin-regulating gene that plays an important role in synapse maturation and dendritic arborization and has been implicated in substance-abuse and intellectual disability in humans. Here we mine the KOMP2 data and find that Tmod2 knockout mice show emotionality phenotypes that are predictive of addiction vulnerability. Detailed addiction phenotyping showed that Tmod2 deletion does not affect the acute locomotor response to cocaine administration. However, sensitized locomotor responses are highly attenuated in these knockouts, indicating perturbed drug-induced plasticity. In addition, Tmod2 mutant animals do not self-administer cocaine indicating lack of hedonic responses to cocaine. Whole brain MR imaging shows differences in brain volume across multiple regions although transcriptomic experiments did not reveal perturbations in gene co-expression networks. Detailed electrophysiological characterization of Tmod2 KO neurons, showed increased spontaneous firing rate of early postnatal and adult cortical and striatal neurons. Cocaine-induced synaptic plasticity that is critical for sensitization is either missing or reciprocal in Tmod2 KO nucleus accumbens shell medium spiny neurons, providing a mechanistic explanation of the cocaine response phenotypes. Combined, these data provide compelling evidence that Tmod2 is a major regulator of plasticity in the mesolimbic system and regulates the reinforcing and addictive properties of cocaine.Significance statementWe identify, characterize, and establish tropomodulin 2 (Tmod2), an actin-regulating gene exclusively expressed in neurons, as an important regulator of addiction-related phenotypes. We show that Tmod2, knockout mice (Tmod2 KO) exhibit phenotypes that are predictive of addiction. In detailed addiction phenotyping, we find the Tmod2 regulates cocaine sensitization and self-administration. We explore anatomical, transcriptional, electrophysiological mechanisms of this regulation. Combined these studies provide compelling evidence that Tmod2 is critical for synaptic plasticity necessary for transition to addiction.
Reply to “A discriminative learning approach to differential expression analysis for single-cell RNA-seq”
Authors: Becht Etienne , Zhao Edward , Amezquita Robert , Gottardo Raphael 
Published: May 2019

AbstractMultivariate logistic regression (mLR) has been recently proposed by Ntranos et al. to perform gene differential expression analyses of single-cell RNA-sequencing (scRNAseq) data. Herein we reproduce and extend some of their findings. We notably show that while mLR performs better in simulated datasets, these simulations do not recapitulate important features of experimental datasets. Indeed, our results suggest that MAST followed by Sidak aggregation of the p-values perform better than mLR on experimental datasets. Overall, we highlight that most of the new results obtained by Ntranos et al is likely due to the quantification of scRNAseq data at the transcript or transcript compatibility classes level, rather than the use of mLR.
Lights, Camera, Path Splitter: A New Approach for Truly Simultaneous Dual Optical Mapping of the Heart with a Single Camera
Authors: Jaimes Rafael , McCullough Damon , Siegel Bryan , Swift Luther , Hiebert James , McInerney Daniel , Posnack Nikki Gillum 
Published: May 2019

ABSTRACTBackgroundOptical mapping of transmembrane voltage and intracellular calcium is a powerful tool for investigating cardiac physiology and pathophysiology. However, simultaneous dual mapping of two fluorescent probes remains technically challenging. We introduce a novel, easy-to-use approach that requires a path splitter, single camera and excitation light to simultaneously acquire voltage and calcium signals from whole heart preparations, which can be applied to other physiological models – including neurons and isolated cardiomyocytes.ResultsComplementary probes were selected that could be excited with a single wavelength light source. Langendorff-perfused hearts (rat, swine) were stained and imaged using a sCMOS camera outfitted with an optical path splitter to simultaneously acquire two emission fields at high spatial and temporal resolution. Voltage (RH237) and calcium (Rhod2) signals were acquired concurrently on a single sensor, resulting in two 384×256 images at 814 frames per second. At this frame rate, the signal-to-noise ratio was 47 (RH237) and 85 (Rhod2). Imaging experiments were performed on small rodent hearts, as well as larger pig hearts with sufficient optical signals. In separate experiments, each dye was used independently to assess crosstalk and demonstrate signal specificity. Additionally, the effect of ryanodine on myocardial calcium transients was validated – with no measurable effect on the amplitude of optical action potentials. To demonstrate spatial resolution, ventricular tachycardia was induced –resulting in the novel finding that spatially discordant calcium alternans can be present in different regions of the heart, even when electrical alternans remain concordant. The described system excels in providing a wide field of view and high spatiotemporal resolution for a variety of cardiac preparations.ConclusionsWe report the first multiparametric mapping system that simultaneously acquires calcium and voltage signals from cardiac preparations, using a path splitter, single camera and excitation light. This approach eliminates the need for multiple cameras, excitation light patterning or frame interleaving. These features can aid in the adoption of dual mapping technology by the broader cardiovascular research community, and decrease the barrier of entry into panoramic heart imaging, as it reduces the number of required cameras.
Ribosomal DNA methylation as stable biomarkers for detection of cancer in plasma
Authors: Zhang Xianglin , Fang Huan , Zhang Wei , Zhong Bixi , Li Yanda , Wang Xiaowo 
Published: May 2019

AbstractBackgroundRecently, liquid biopsy for cancer detection has pursued great progress. However, there are still a lack of high quality markers. It is a challenge to detect cancer stably and accurately in plasma cell free DNA (cfDNA), when the ratio of cancer signal is low. Repetitive genes or elements may improve the robustness of signals. In this study, we focused on ribosomal DNA which repeats hundreds of times in human diploid genome and investigated performances for cancer detection in plasma.ResultsWe collected bisulfite sequencing samples including normal tissues and 4 cancer types and found that intergenic spacer (IGS) of rDNA has high methylation levels and low variation in normal tissues and plasma. Strikingly, IGS of rDNA shows significant hypo-methylation in tumors compared with normal tissues. Further, we collected plasma bisulfite sequencing data from 224 healthy subjects and cancer patients. Means of AUC in testing set were 0.96 (liver cancer), 0.94 (lung cancer and), 0.92 (colon cancer) with classifiers using only 10 CpG sites. Due to the feature of high copy number, when liver cancer plasma WGBS was down-sampled to 10 million raw reads (0.25× whole genome coverage), the prediction performance decreased only a bit (mean AUC=0.93). Finally, methylation of rDNA could also be used for monitor cancer progression and treatment.ConclusionTaken together, we provided the high-resolution map of rDNA methylation in tumors and supported that methylation of rDNA was a competitive and robust marker for detecting cancer and monitoring cancer progression in plasma.
Controlling and co-ordinating chitinase secretion in aSerratia marcescenspopulation
Authors: de Assis Alcoforado Costa Marília , Owen Richard A. , Tammsalu Triin , Buchanan Grant , Palmer Tracy , Sargent Frank 
Published: May 2019

ABSTRACTSerratia marcescensis a γ-Proteobacterium and an opportunistic animal and insect pathogen. The bacterium exhibits a complex extracellular protein ‘secretome’ comprising numerous enzymes, toxins and effector molecules. One component of the secretome is the ‘chitinolytic machinery’, which is a set of at least four chitinases that allow the use of insoluble extracellular chitin as sole carbon and nitrogen source. Secretion of the chitinases across the outer membrane is governed by thechiWXYZoperon encoding a holin/endopeptidase pair. Expression of thechiWXYZoperon is co-ordinated with the chitinase genes and is also bimodal, since normally only 1% of the population expresses the chitinolytic machinery. In this work, the role of the ChiR protein in chitinase production has been explored. Using live cell imaging and flow cytometry, ChiR was shown to govern the co-ordinated regulation ofchiWXYZwith bothchiAandchiC. Moreover, overexpression ofchiRalone was able to increase the proportion of the cell population expressing chitinase genes to >60%. In addition, quantitative label-free proteomic analysis of cells overexpressingchiRestablished that ChiR regulates the entire chitinolytic machinery. The proteomic experiments also revealed a surprising link between the regulation of the chitinolytic machinery and the production of proteins involved in the metabolism of nitrogen compounds such as nitrate and nitrite. The research demonstrates for the first time that ChiR plays a critical role in controlling bimodal gene expression inS. marcescens, and provides new evidence of a clear link between chitin breakdown and nitrogen metabolism.IMPORTANCEThe opportunistic pathogenSerratia marcescenssecretes chitinases through the action of thechiWXYZoperon, which encodes a holin/endopeptidase pair. Expression ofchiWXYZis normally bimodal, with only 1% of the population transcribing these genes. In this work, it is demonstrated that overexpression ofchiRinduces exquisitely co-ordinated holin/endopeptidase and chitinase gene expression in the majority of the population of cells, establishing that ChiR is a key player in biomodal gene expression. This work also reveals for the first time that co-operating pathways are induced by ChiR, including enzymes involved in ammonia, nitrite and nitrate metabolism. This work expands knowledge of basic bacterial physiology and could have applications in the biomedical and biotechnological research fields.
CRISPR-Cas9 mutated pregnane x receptor (pxr) retains pregnenolone-induced expression of cytochrome p450 family 3, subfamily A, polypeptide 65 (cyp3a65) in zebrafish (Danio rerio) larvae
Authors: Salanga Matthew C. , Brun Nadja R. , Francolini Rene , Stegeman John J. , Goldstone Jared V. 
Published: May 2019

AbstractPregnane x receptor (PXR) is a nuclear receptor that regulates transcriptional responses to drug or xenobiotic exposure in many vertebrate species. One key response is the induction of cytochrome P450 3A (CYP3A) transcription. PXR is a promiscuous receptor activated by a wide range of ligands that can differ across species, making functional studies on its role in the chemical defensome, most relevant when approached in a species-specific manner. Genetic knockout studies in mammals have shown a requirement for PXR in ligand-dependent activation of CYP3A expression or reporter gene activity. Transient knockdown in zebrafish revealed a similar requirement, however it is not known what the effect of a genetic knockout would be in the zebrafish model. Here, we report on generation of two zebrafish lines each carrying a genetic deletion in the pxr coding region, predicted to result in loss of a functional gene product. To our surprise zebrafish larvae, homozygous for either of the pxr mutant alleles, retain their ability to induce cyp3a65 mRNA expression following exposure to the established zebrafish Pxr ligand, pregnenolone (PN). Thus, zebrafish carrying pxr alleles with sizable deletions in either the DNA binding or the ligand binding domains do not yield a loss-of-function phenotype, suggesting that a compensatory mechanism is responsible for cyp3a65 induction. Two alternative possibilities are that Pxr is not required for the effect or that truncated yet functional mutant Pxr is sufficient for the effect.
Community driven dynamics of oscillatory network responses to threat
Authors: Muthuraman M , Chirumamilla VC , Koirala N , Anwar AR , Tüscher O , Vogt J , Horstmann P , Meyer B , Bonano GA , Gonzalez-Escamilla G , Groppa S 
Published: May 2019

AbstractPhysiological responses to threat stimuli involve neural synchronized oscillations in cerebral networks with distinct organization properties. Community architecture within these networks and its dynamic adaptation could play a critical role in achieving optimal physiological responses.Here we applied dynamic network analyses to address the early phases of threat processing at the millisecond level, describing multi-frequency (theta and alpha) integration and basic reorganization properties (flexibility and clustering) that drive physiological responses. We quantified cortical and subcortical network interactions and captured illustrative reconfigurations using community allegiance as essential fingerprints of large-scale adaptation.A theta band driven community reorganization of key anatomical regions forming the threat network (TN) along with transitions of nodes from the dorsal attention (DAN) and salience (SN) circuits predict the optimal physiological response to threat. We show that increase flexibility of the community network architecture drives the physiological responses during instructed threat processing. Nodal switches modulate the directionality of information flows in the involved circuits.These results provide a captivating perspective of flexible network responses to threat and shed new light on basic physiological principles relevant for the development of stress- and threat-related mental disorders.
Brain-predicted age difference score is related to specific cognitive functions: A multi-site replication analysis
Authors: Boyle R. , Jollans L. , Rueda-Delgado L.M. , Rizzo R. , Yener G.G. , McMorrow J.P. , Knight S.P. , Carey D. , Robertson I.H. , Emek-Savaş D.D. , Stern Y. , Kenny R.A. , Whelan R. 
Published: May 2019

AbstractBrain-predicted age difference scores are calculated by subtracting chronological age from ‘brain’ age, which is estimated using neuroimaging data. Positive scores reflect accelerated ageing and are associated with increased mortality risk and poorer physical function. To date, however, the relationship between brain-predicted age difference scores and specific cognitive functions has not been systematically examined using appropriate statistical methods. First, applying machine learning to 1,359 T1-weighted MRI scans, we predicted the relationship between chronological age and voxel-wise grey matter data. This model was then applied to MRI data from three independent datasets, significantly predicting chronological age in each dataset: Dokuz Eylül University (n=175), the Cognitive Reserve/Reference Ability Neural Network study (n=380), and The Irish Longitudinal Study on Ageing (n=487). Each independent dataset had rich neuropsychological data. Brain-predicted age difference scores were significantly negatively correlated with performance on measures of general cognitive status (two datasets); processing speed, visual attention, and cognitive flexibility (three datasets); visual attention and cognitive flexibility (two datasets); and semantic verbal fluency (two datasets). As such, there is firm evidence of correlations between increased brain-predicted age differences and reduced cognitive function in some domains that are implicated in cognitive ageing.