Disease Progression Modeling and Prediction through Random Effect Gaussian Processes and Time Transformation

The development of statistical approaches for the joint modelling of the temporal changes of imaging, biochemical, and clinical biomarkers is of paramount importance for improving the understanding of neurodegenerative disorders, and for providing a reference for the prediction and quantification of the pathology in unseen individuals. Nonetheless, the use of disease progression models for probabilistic predictions still requires investigation, for example for accounting for missing observations in clinical data, and for accurate uncertainty quantification. We tackle this problem by proposing a novel Gaussian process-based method for the joint modeling of imaging and clinical biomarker progressions from time series of individual observations. The model is formulated to account for individual random effects and time reparameterization, allowing non-parametric estimates of the biomarker evolution, as well as high flexibility in specifying correlation structure, and time transformation models. Thanks to the Bayesian formulation, the model naturally accounts for missing data, and allows for uncertainty quantification in the estimate of evolutions, as well as for probabilistic prediction of disease staging in unseen patients. The experimental results show that the proposed model provides a biologically plausible description of the evolution of Alzheimer's pathology across the whole disease time-span as well as remarkable predictive performance when tested on a large clinical cohort with missing observations.Comment: 13 pages, 2 figure

The neurodevelopmental continuum towards a neurodevelopmental gradient hypothesis

In contrast to the categorical approach of the current nosographic system, in the last decades increasing literature is suggesting that psychiatric disorders may be better conceptualized as a continuum, which would feature as a common basis a neurodevelopmental alteration. The “neurodevelopmental continuum” (NC) is a theoretical framework supported by several empirical evidences in multiple fields of research. The conceptual core of this model is that an alteration in brain development, the expression of which would be determined by the intertwined relationships between genetic and environmental factors, may constitute the common underpinning of different kinds of mental disorders. Moreover, the NC theory also implies that psychiatric conditions could be placed along a gradient, where autism spectrum disorder (ASD) with intellectual disabilities would be the most severe expression of an alteration of the “social brain development”, followed by other DSM-5 neurodevelopmental phenotypes characterized by a milder impairment. This model would subsequently include, along a decreasing neurodevelopmental gradient, other psychiatric conditions such as schizophrenia and mood disorders as well as eating and anxiety disorders, encompassing also non-psychopathological personality traits. From a cognitive point of view, the link between neurodevelopmental alterations and vulnerability towards psychopathology could be identified in an impairment of the proprioceptive experience and of the interoceptive inference, which would prevent the patient to properly define his own subjectivity and to adequately place him-self in the relational space. The conceptual framework proposed here may allow significant changes in both research and clinical settings, eventually leading to improve therapeutic and prevention strategies

On interfaces between cell populations with different mobilities

Partial differential equations describing the dynamics of cell population densities from a fluid mechanical perspective can model the growth of avascular tumours. In this framework, we consider a system of equations that describes the interaction between a population of dividing cells and a population of non-dividing cells. The two cell populations are characterised by different mobilities. We present the results of numerical simulations displaying two-dimensional spherical waves with sharp interfaces between dividing and non-dividing cells. Furthermore, we numerically observe how different ratios between the mobilities change the morphology of the interfaces, and lead to the emergence of finger-like patterns of invasion above a threshold. Motivated by these simulations, we study the existence of one-dimensional travelling wave solutions

A COMPOSITE MODEL FOR THE SIMULATION OF SKIING TECHNIQUES

INTRODUCTION In this work we present a model for skiing technique analysis and simulation: it consists of a man model, an equipment model and a contact (ski-snow) model. Such a model is the basis for a deeper understanding of the interaction between skier and equipment and its use will be profitable in various applications such as: equipment optimisation and technique improvement. Moreover this simulation technique can be profitably used for teaching the basic principles of skiing. MATERIAL AND METHODS To build our model we combined the methods used for multibody systems dynamic analysis (man model with finite element techniques (ski model). The human body model consists of 3D chains of rigid bodies: according to the "sophistication" of the simulation we use 16 segments, with 39'internal d.0.f (full man model), or 7 segments, with 6 internal d.0.f . To describe rigid body dynamics and kinematics (man model) we adopt a method based on homogeneous matrices (Casolo 1995): both the absolute and the relative position, velocity and acceleration are described by 4x4 matrices, as well as the inertial properties and the external loads. This approach allows to embed both the linear and angular terms in the same formalism. To derive the equation of motion a Lagrangian approach was adopted, leading to this expression: Mq+C(cf.q.t) = Fl(q.q,t) +Ft(q,q) where M is the mass matrix, C contain the weight, the centrifugal and Coriolis effect, Ft contains joint torques, F2 represent the action exchanged with ski through the bindings and the vector q contains joints laws of motion. The model can be used to perform direct and inverse dynamics analysis of skiing, since it allows the input of joint torques and/or joint relative movements, that can be experimental data or can be generated by scratch, by a law of motion preprocessor. Skis are modelled with Finite Element techniques. The internal structure of a ski is quite complex: different material, with complex arrangement, are employed giving rise to properties (stiffness, damping and mass) which can be determined by experimental measures or by complex FE analysis. These properties can be quite well reproduced by means of a simplified model consisting of 3D beam elements . Some geometrical features, such as camber and sidecut, can be easily reproduced. Ski equations of motion, in matrix form, are: M9+ q v r e l + Kq&f = F,,I +Fnlon-ski f F.+.ki - cn,,wn where M, C, K are, respectively, the ski mass, damping and stiffness matrices. The ski load consists of three terms: weight, action exerted by the skier through the bindings and the contact action exerted by the snow. A simple contact model has been also developed, based on the assumption that the snow reacts both to ski deepening, sliding and skidding. This simple model can take into account, for example, the effect of ski vibration on the ski-snow interaction. RESULTS Some simulations have been performed to test model capabilities: we analysed the effect of ski torsional stiffness, as well as the amount of sidecut, on skier trajectory during traverse and turns. The model is also used to simulate the aerial phase of a free-style jump and the following landing phase. In all of these cases simulation can be an useful tool for predicting the effect of changing joint movements (i.e varying skiing technique) and equipment characteristics. A sensitivity analysis can be a first step toward a technique and equipment optimisation. References Casolo F., Legnani G., Righettini P., Zappa B. "A homogeneous matrix approach to 3D kinematics and dynamics", TMM (in press)

Mesangial cell abnormalities in spontaneously hypertensive rats before the onset of hypertension

Mesangial cell abnormalities in spontaneously hypertensive rats before the onset of hypertension. To identify kidney biosynthetic abnormalities that may precede the onset of hypertension, we studied the expression of flbronectin (FN) and collagen IV (Coll IV) in young SHR (4 weeks of age) whose systolic blood pressure was normal and similar to that of age-matched control WKY rats. In isolated glomeruli the level of FN protein assessed by immunoblotting tended to be lower in the SHR than in the WKY rats. By Northern analysis the FN/actin mRNA ratio was significantly lower in glomeruli from SHR (0.56 ± 0.47) than in glomeruli from WKY rats (2.0 ± 0.8). These abnormalities were maintained in vitro since the expression of FN was significantly lower in SHR than in WKY cultured mesangial cells (FN/actin mRNA ratio = 0.84 ± 0.46 vs. 1.9 ± 0.7, P = 0.029). No differences in Coll IV mRNA or protein levels were observed in SHR glomeruli and mesangial cells when compared with WKY rats. The levels of aortic FN and Coll IV mRNAs were not different in SHR and WKY rats. In addition, mesangial cells from SHR showed a significantly higher growth rate than those from WKY. The biosynthetic and proliferative abnormalities observed in the SHR mesangial cells appear to reflect genetic characteristics, and could provide novel insights into cellular mechanisms linking the genetics of hypertension with predisposition to glomerular pathology

An \emph{ab initio} method for locating characteristic potential energy minima of liquids

It is possible in principle to probe the many--atom potential surface using density functional theory (DFT). This will allow us to apply DFT to the Hamiltonian formulation of atomic motion in monatomic liquids [\textit{Phys. Rev. E} {\bf 56}, 4179 (1997)]. For a monatomic system, analysis of the potential surface is facilitated by the random and symmetric classification of potential energy valleys. Since the random valleys are numerically dominant and uniform in their macroscopic potential properties, only a few quenches are necessary to establish these properties. Here we describe an efficient technique for doing this. Quenches are done from easily generated "stochastic" configurations, in which the nuclei are distributed uniformly within a constraint limiting the closeness of approach. For metallic Na with atomic pair potential interactions, it is shown that quenches from stochastic configurations and quenches from equilibrium liquid Molecular Dynamics (MD) configurations produce statistically identical distributions of the structural potential energy. Again for metallic Na, it is shown that DFT quenches from stochastic configurations provide the parameters which calibrate the Hamiltonian. A statistical mechanical analysis shows how the underlying potential properties can be extracted from the distributions found in quenches from stochastic configurations

Evolutionary dynamics of phenotype-structured populations: from individual-level mechanisms to population-level consequences

Epigenetic mechanisms are increasingly recognised as integral to the adaptation of species that face environmental changes. In particular, empirical work has provided important insights into the contribution of epigenetic mechanisms to the persistence of clonal species, from which a number of verbal explanations have emerged that are suited to logical testing by proof-of-concept mathematical models. Here, we present a stochastic agent-based model and a related deterministic integrodifferential equation model for the evolution of a phenotype-structured population composed of asexually-reproducing and competing organisms which are exposed to novel environmental conditions. This setting has relevance to the study of biological systems where colonising asexual populations must survive and rapidly adapt to hostile environments, like pathogenesis, invasion and tumour metastasis. We explore how evolution might proceed when epigenetic variation in gene expression can change the reproductive capacity of individuals within the population in the new environment. Simulations and analyses of our models clarify the conditions under which certain evolutionary paths are possible and illustrate that while epigenetic mechanisms may facilitate adaptation in asexual species faced with environmental change, they can also lead to a type of “epigenetic load” and contribute to extinction. Moreover, our results offer a formal basis for the claim that constant environments favour individuals with low rates of stochastic phenotypic variation. Finally, our model provides a “proof of concept” of the verbal hypothesis that phenotypic stability is a key driver in rescuing the adaptive potential of an asexual lineage and supports the notion that intense selection pressure can, to an extent, offset the deleterious effects of high phenotypic instability and biased epimutations, and steer an asexual population back from the brink of an evolutionary dead end

Dissecting the dynamics of epigenetic changes in phenotype-structured populations exposed to fluctuating environments

An enduring puzzle in evolutionary biology is to understand how individuals and populations adapt to fluctuating environments. Here we present an integro-differential model of adaptive dynamics in a phenotype-structured population whose fitness landscape evolves in time due to periodic environmental oscillations. The analytical tractability of our model allows for a systematic investigation of the relative contributions of heritable variations in gene expression, environmental changes and natural selection as drivers of phenotypic adaptation. We show that environmental fluctuations can induce the population to enter an unstable and fluctuation-driven epigenetic state. We demonstrate that this can trigger the emergence of oscillations in the size of the population, and we establish a full characterisation of such oscillations. Moreover, the results of our analyses provide a formal basis for the claim that higher rates of epimutations can bring about higher levels of intrapopulation heterogeneity, whilst intense selection pressures can deplete variation in the phenotypic pool of asexual populations. Finally, our work illustrates how the dynamics of the population size is led by a strong synergism between the rate of phenotypic variation and the frequency of environmental oscillations, and identifies possible ecological conditions that promote the maximisation of the population size in fluctuating environments

Modeling the Effects of Space Structure and Combination Therapies on Phenotypic Heterogeneity and Drug Resistance in Solid Tumors

Histopathological evidence supports the idea that the emergence of phenotypic heterogeneity and resistance to cytotoxic drugs can be considered as a process of selection in tumor cell populations. In this framework, can we explain intra-tumor heterogeneity in terms of selection driven by the local cell environment? Can we overcome the emergence of resistance and favor the eradication of cancer cells by using combination therapies? Bearing these questions in mind, we develop a model describing cell dynamics inside a tumor spheroid under the effects of cytotoxic and cytostatic drugs. Cancer cells are assumed to be structured as a population by two real variables standing for space position and the expression level of a phenotype of resistance to cytotoxic drugs. The model takes explicitly into account the dynamics of resources and anticancer drugs as well as their interactions with the cell population under treatment. We analyze the effects of space structure and combination therapies on phenotypic heterogeneity and chemotherapeutic resistance. Furthermore, we study the efficacy of combined therapy protocols based on constant infusion and bang–bang delivery of cytotoxic and cytostatic drugs