Impact of cerebellar-specific genetic and circuit manipulations on the behavioral phenotype and cerebellar physiology in murine autism models

Clinical evidence suggests that developmental cerebellar injury and cerebello-cortical connectivity abnormalities are often present in autism. In mouse models, cerebellar-specific deletions of autism risk genes, or temporally constrained, developmental manipulations of cerebellar circuits, elicit autistic-like behaviors. Nonetheless, behavioral and electrophysiological findings are inconsistent within and across models. Additionally, while cerebellar manipulations during development can induce autistic phenotypes, studies of early cerebellar function and connectivity are scarce. In this review, we discuss the impact of cerebellar-specific genetic mutations and circuit manipulations on adult behavior and cerebellar neuronal activity in murine autism models. We also explore how cerebellar development can impact the establishment of mature circuits, and we consider the existing gaps regarding the use of murine models to elucidate the cerebellar role in autism.</p

Simulation of a Clustering Scheme for Vehicular Ad Hoc Networks Using a DEVS-based Virtual Laboratory Environment

ANT 2018, The 9th International Conference on Ambient Systems, Networks and Technologies, Porto, PORTUGAL, 08-/05/2018 - 11/05/2018Protocol design is usually based on the functional models developed according to the needs of the system. In Intelligent Transport Systems (ITS), the features studied regarding Vehicular Ad hoc Networks (VANET) include self-organizing, routing, reliability, quality of service, and security. Simulation studies on ITS-dedicated routing protocols usually focus on their performance in specific scenarios. However, the evolution of transportation systems towards autonomous vehicles requires robust protocols with proven or at least guaranteed properties. Though formal approaches provide powerful tools for system design, they cannot be used for every types of ITS components. Our goal is to develop new tools combining formal tools such as Event-B with DEVS-based (Discrete Event System Specification) virtual laboratories in order to design the models of ITS components which simulation would allow proving and verifying their properties in large-scale scenarios. This paper presents the models of the different components of a VANET realized with the Virtual Laboratory Environment (VLE). We point out the component models fitting to formal modeling, and proceed to the validation of all designed models through a simulation scenario based on real-world road traffic data

A new clustering structure for VANET

The Vehicular Ad-Hoc Network (VANET) paradigm offers the opportunity of extending Intelligent Transport System (ITS) by supporting its applications through vehicle-to-vehicle communications, notably in the areas where the infrastructure is inexistent, in failure, or overloaded. However, the complexity induced by ad hoc network management raises many challenges that have to be solved such as the sharing of bandwidth resources, the limitations on the duration of the connections between the vehicles, and the application-specific quality of service (QoS) requirements. Recently, the Chain-Branch-Leaf clustering scheme (CBL) has been proposed for vehicle-to-vehicle ad hoc routing that combines the information of road configuration, vehicle mobility, and link quality in order to build an efficient clustering connecting the entire VANET through a flexible backbone. This work presents a comparative study between the native Multipoint relaying clustering used in the Optimized Link State Routing (OLSR) and CBL scheme. The results show that CBL reduces significantly the routing traffic overhead compared to native OLSR, thus freeing up more bandwidth for ITS applications and reducing the IP delays for peer-to-peer applications

Multivariate error modeling and uncertainty quantification using importance (re-)weighting for Monte Carlo simulations in particle transport

Fast and accurate predictions of uncertainties in the computed dose are crucial for the determination of robust treatment plans in radiation therapy. This requires the solution of particle transport problems with uncertain parameters or initial conditions. Monte Carlo methods are often used to solve transport problems especially for applications which require high accuracy. In these cases, common non-intrusive solution strategies that involve repeated simulations of the problem at different points in the parameter space quickly become infeasible due to their long run-times. Intrusive methods however limit the usability in combination with proprietary simulation engines. In our previous paper [51], we demonstrated the application of a new non-intrusive uncertainty quantification approach for Monte Carlo simulations in proton dose calculations with normally distributed errors on realistic patient data. In this paper, we introduce a generalized formulation and focus on a more in-depth theoretical analysis of this method concerning bias, error and convergence of the estimates. The multivariate input model of the proposed approach further supports almost arbitrary error correlation models. We demonstrate how this framework can be used to model and efficiently quantify complex auto-correlated and time-dependent errors.Comment: 26 pages, 10 figures, [v2]: corrected title of figure

Activated PI3Kδ syndrome, an immunodeficiency disorder, leads to sensorimotor deficits recapitulated in a murine model.

The phosphoinositide-3-kinase (PI3K) family plays a major role in cell signaling and is predominant in leukocytes. Gain-of-function (GOF) mutations in the PIK3CD gene lead to the development of activated PI3Kδ syndrome (APDS), a rare primary immunodeficiency disorder. A subset of APDS patients also displays neurodevelopmental delay symptoms, suggesting a potential role of PIK3CD in cognitive and behavioural function. However, the extent and nature of the neurodevelopmental deficits has not been previously quantified. Here, we assessed the cognitive functions of two APDS patients, and investigated the causal role of the PIK3CD GOF mutation in neurological deficits using a murine model of this disease. We used p110δE1020K knock-in mice, harbouring the most common APDS mutation in patients. We found that APDS patients present with visuomotor deficits, exacerbated by autism spectrum disorder comorbidity, whereas p110δE1020K mice exhibited impairments in motor behaviour, learning and repetitive behaviour patterning. Our data indicate that PIK3CD GOF mutations increase the risk for neurodevelopmental deficits, supporting previous findings on the interplay between the nervous and the immune system. Further, our results validate the knock-in mouse model, and offer an objective assessment tool for patients that could be incorporated in diagnosis and in the evaluation of treatments

Nanoquartz in Late Permian C1 coal and the high incidence of female lung cancer in the Pearl River Origin area: a retrospective cohort study

<p>Abstract</p> <p>Background</p> <p>The Pearl River Origin area, Qujing District of Yunnan Province, has one of the highest female lung cancer mortality rates in China. Smoking was excluded as a cause of the lung cancer excess because almost all women were non-smokers. Crystalline silica embedded in the soot emissions from coal combustion was found to be associated with the lung cancer risk in a geographical correlation study. Lung cancer rates tend to be higher in places where the Late Permian C1 coal is produced. Therefore, we have hypothesized the two processes: C1 coal combustion --> nanoquartz in ambient air --> lung cancer excess in non-smoking women.</p> <p>Methods/Design</p> <p>We propose to conduct a retrospective cohort study to test the hypothesis above. We will search historical records and compile an inventory of the coal mines in operation during 1930–2009. To estimate the study subjects' retrospective exposure, we will reconstruct the historical exposure scenario by burning the coal samples, collected from operating or deserted coal mines by coal geologists, in a traditional firepit of an old house. Indoor air particulate samples will be collected for nanoquartz and polycyclic aromatic hydrocarbons (PAHs) analyses. Bulk quartz content will be quantified by X-ray diffraction analysis. Size distribution of quartz will be examined by electron microscopes and by centrifugation techniques. Lifetime cumulative exposure to nanoquartz will be estimated for each subject. Using the epidemiology data, we will examine whether the use of C1 coal and the cumulative exposure to nanoquartz are associated with an elevated risk of lung cancer.</p> <p>Discussion</p> <p>The high incidence rate of lung cancer in Xuan Wei, one of the counties in the current study area, was once attributed to high indoor air concentrations of PAHs. The research results have been cited for qualitative and quantitative cancer risk assessment of PAHs by the World Health Organization and other agencies. If nanoquartz is found to be the main underlying cause of the lung cancer epidemic in the study area, cancer potency estimates for PAHs by the international agencies based on the lung cancer data in this study setting should then be updated.</p

The Genomic Analysis of Lactic Acidosis and Acidosis Response in Human Cancers

The tumor microenvironment has a significant impact on tumor development. Two important determinants in this environment are hypoxia and lactic acidosis. Although lactic acidosis has long been recognized as an important factor in cancer, relatively little is known about how cells respond to lactic acidosis and how that response relates to cancer phenotypes. We develop genome-scale gene expression studies to dissect transcriptional responses of primary human mammary epithelial cells to lactic acidosis and hypoxia in vitro and to explore how they are linked to clinical tumor phenotypes in vivo. The resulting experimental signatures of responses to lactic acidosis and hypoxia are evaluated in a heterogeneous set of breast cancer datasets. A strong lactic acidosis response signature identifies a subgroup of low-risk breast cancer patients having distinct metabolic profiles suggestive of a preference for aerobic respiration. The association of lactic acidosis response with good survival outcomes may relate to the role of lactic acidosis in directing energy generation toward aerobic respiration and utilization of other energy sources via inhibition of glycolysis. This “inhibition of glycolysis” phenotype in tumors is likely caused by the repression of glycolysis gene expression and Akt inhibition. Our study presents a genomic evaluation of the prognostic information of a lactic acidosis response independent of the hypoxic response. Our results identify causal roles of lactic acidosis in metabolic reprogramming, and the direct functional consequence of lactic acidosis pathway activity on cellular responses and tumor development. The study also demonstrates the utility of genomic analysis that maps expression-based findings from in vitro experiments to human samples to assess links to in vivo clinical phenotypes

Lactic Acidosis Triggers Starvation Response with Paradoxical Induction of TXNIP through MondoA

Although lactic acidosis is a prominent feature of solid tumors, we still have limited understanding of the mechanisms by which lactic acidosis influences metabolic phenotypes of cancer cells. We compared global transcriptional responses of breast cancer cells in response to three distinct tumor microenvironmental stresses: lactic acidosis, glucose deprivation, and hypoxia. We found that lactic acidosis and glucose deprivation trigger highly similar transcriptional responses, each inducing features of starvation response. In contrast to their comparable effects on gene expression, lactic acidosis and glucose deprivation have opposing effects on glucose uptake. This divergence of metabolic responses in the context of highly similar transcriptional responses allows the identification of a small subset of genes that are regulated in opposite directions by these two conditions. Among these selected genes, TXNIP and its paralogue ARRDC4 are both induced under lactic acidosis and repressed with glucose deprivation. This induction of TXNIP under lactic acidosis is caused by the activation of the glucose-sensing helix-loop-helix transcriptional complex MondoA:Mlx, which is usually triggered upon glucose exposure. Therefore, the upregulation of TXNIP significantly contributes to inhibition of tumor glycolytic phenotypes under lactic acidosis. Expression levels of TXNIP and ARRDC4 in human cancers are also highly correlated with predicted lactic acidosis pathway activities and associated with favorable clinical outcomes. Lactic acidosis triggers features of starvation response while activating the glucose-sensing MondoA-TXNIP pathways and contributing to the “anti-Warburg” metabolic effects and anti-tumor properties of cancer cells. These results stem from integrative analysis of transcriptome and metabolic response data under various tumor microenvironmental stresses and open new paths to explore how these stresses influence phenotypic and metabolic adaptations in human cancers