Generation of human computational models with machine learning

Services in smart environments pursue to increase the quality of people?s lives. The most important issues when developing this kind of environments is testing and validating such services. These tasks usually imply high costs and annoying or unfeasible real-world testing. In such cases, artificial societies may be used to simulate the smart environment (i.e. physical environment, equipment and humans). With this aim, the CHROMUBE methodology guides test engineers when modeling human beings. Such models reproduce behaviors which are highly similar to the real ones. Originally, these models are based on automata whose transitions are governed by random variables. Automaton?s structure and the probability distribution functions of each random variable are determined by a manual test and error process. In this paper, it is presented an alternative extension of this methodology which avoids the said manual process. It is based on learning human behavior patterns automatically from sensor data by using machine learning techniques. The presented approach has been tested on a real scenario, where this extension has given highly accurate human behavior models,

Decreased ability of HIV-1 Tat protein-treated accessory cells to organize cellular clusters is associated with partial activation of T cells

It has been shown in several animal models that HIV infection of accessory cells (ACs) plays an important role in development of AIDS. Here, we report that ACs treated with HIV-1 Tat protein (Tat-ACs) have a decreased ability to organize cellular aggregates as compared with untreated ACs, resulting in incomplete activation of T cells in responses to anti-CD3 mAb or staphylococcal enterotoxin B stimulation. The T cells failed to up-regulate adhesion molecules CD11a and CD2 on the cell surface and had reduced proliferative responses, as determined by [3H]thymidine incorporation, but they obtained lymphoblast-like morphology and expressed early activation antigens on the cell surface such as Fas and CD69 and interleukin 2 receptor, at comparable levels as those T cells undergoing a maximal proliferation. These results suggest that the Tat-AC-induced defect occurs in the late, but not in the early, phases of T cell activation. Normal expression of cell surface Fas antigen accompanied by defects in late activation thus may result in the susceptibility of these T cells to apoptosis. Our studies suggest that dysfunction, hyperactivation, and susceptibility to apoptosis, as observed with T cells isolated from HIV-infected individuals, may be, at least in part, a consequence of abnormal functions of ACs.

The role of the D2 dopamine receptor (D2R) in A2A adenosine receptor (A2AR)-mediated behavioral and cellular responses as revealed by A2A and D2 receptor knockout mice

The A2AR is largely coexpressed with D2Rs and enkephalin mRNA in the striatum where it modulates dopaminergic activity. Activation of the A2AR antagonizes D2R-mediated behavioral and neurochemical effects in the basal ganglia through a mechanism that may involve direct A2AR–D2R interaction. However, whether the D2R is required for the A2AR to exert its neural function is an open question. In this study, we examined the role of D2Rs in A2AR-induced behavioral and cellular responses, by using genetic knockout (KO) models (mice deficient in A2ARs or D2Rs or both). Behavioral analysis shows that the A2AR agonist 2–4-(2-carboxyethyl)phenethylamino-5′-N-ethylcarboxamidoadenosine reduced spontaneous as well as amphetamine-induced locomotion in both D2 KO and wild-type mice. Conversely, the nonselective adenosine antagonist caffeine and the A2AR antagonist 8-(3-chlorostyryl)caffeine produced motor stimulation in mice lacking the D2R, although the stimulation was significantly attentuated. At the cellular level, A2AR inactivation counteracted the increase in enkephalin expression in striatopallidal neurons caused by D2R deficiency. Consistent with the D2 KO phenotype, A2AR inactivation partially reversed both acute D2R antagonist (haloperidol)-induced catalepsy and chronic haloperidol-induced enkephalin mRNA expression. Together, these results demonstrate that A2ARs elicit behavioral and cellular responses despite either the genetic deficiency or pharmacological blockade of D2Rs. Thus, A2AR-mediated neural functions are partially independent of D2Rs. Moreover, endogenous adenosine acting at striatal A2ARs may be most accurately viewed as a facilitative modulator of striatal neuronal activity rather than simply as an inhibitory modulator of D2R neurotransmission.

Regional and cellular fractionation of working memory

This chapter recounts efforts to dissect the cellular and circuit basis of a memory system in the primate cortex with the goal of extending the insights gained from the study of normal brain organization in animal models to an understanding of human cognition and related memory disorders. Primates and humans have developed an extraordinary capacity to process information “on line,” a capacity that is widely considered to underlay comprehension, thinking, and so-called executive functions. Understanding the interactions between the major cellular constituents of cortical circuits—pyramidal and nonpyramidal cells—is considered a necessary step in unraveling the cellular mechanisms subserving working memory mechanisms and, ultimately, cognitive processes. Evidence from a variety of sources is accumulating to indicate that dopamine has a major role in regulating the excitability of the cortical circuitry upon which the working memory function of prefrontal cortex depends. Here, I describe several direct and indirect intercellular mechanisms for modulating working memory function in prefrontal cortex based on the localization of dopamine receptors on the distal dendrites and spines of pyramidal cells and on interneurons in the prefrontal cortex. Interactions between monoamines and a compromised cortical circuitry may hold the key to understanding the variety of memory disorders associated with aging and disease.

Checkpoints in the progression of autoimmune disease: lessons from diabetes models.

In the last few years, data from experiments employing transgenic models of autoimmune disease have strengthened a particular concept of autoimmunity: disease results not so much from cracks in tolerance induction systems, leading to the generation of anti-self repertoire, as from the breakdown of secondary systems that keep these cells in check. T cells with anti-self specificities are readily found in disease-free individuals but ignore target tissues. This is also the case in some transgenic models, in spite of overwhelming numbers of autoreactive cells. In other instances, local infiltration and inflammation result, but they are well tolerated for long periods of time and do not terminally destroy target tissue. We review the possible molecular and cellular mechanisms that underlie these situations, with a particular emphasis on the destruction of pancreatic beta cells in transgenic models of insulin-dependent disease.

Thermal model for charring rate calculation in wooden cellular slabs under fire

Wood is a natural and traditional building material, as popular today as ever, and presents advantages. Physically, wood is strong and stiff, but compared with other materials like steel is light and flexible. Wood material can absorb sound very effectively and it is a relatively good heat insulator. But dry wood burns quite easily and produces a great deal of heat energy. The main disadvantage is the high level of combustion when exposed to fire, where the point at which it catches fire is from 200–400°C. After fire exposure, is need to determine if the charred wooden structures are safe for future use. Design methods require the use of computer modelling to predict the fire exposure and the capacity of structures to resist those action. Also, large or small scale experimental tests are necessary to calibrate and verify the numerical models. The thermal model is essential for wood structures exposed to fire, because predicts the charring rate as a function of fire exposure. The charring rate calculation of most structural wood elements allows simple calculations, but is more complicated for situations where the fire exposure is non-standard and in wood elements protected with other materials. In this work, the authors present different case studies using numerical models, that will help professionals analysing woods elements and the type of information needed to decide whether the charred structures are adequate or not to use. Different thermal models representing wooden cellular slabs, used in building construction for ceiling or flooring compartments, will be analysed and submitted to different fire scenarios (with the standard fire curve exposure). The same numerical models, considering insulation material inside the wooden cellular slabs, will be tested to compare and determine the fire time resistance and the charring rate calculation.

Thermal model for charring rate calculation in wooden cellular slabs under fire

Wood is a natural and traditional building material, as popular today as ever, and presents advantages. Physically, wood is strong and stiff, but compared with other materiais like steel is light and flexible. Wood material can absorb sound very effectively and it is a relatively good heat insulator. But dry wood does bum quite easily md produces a great deal ofheat energy. The main disadvantage is the high levei ofcombustion when exposed to fíre, where the point at which it catches fire is fi-om 200-400°C. After fu-e exposure, is need to determine if the charred wooden stmctures are safe for future use. Design methods require the use ofcomputer modelling to predict the fíre exposure and the capacity ofstructures to resist fhose action. Also, large or small scale experimental tests are necessary to calibrate and verify the numerical models. The thermal model is essential for wood stmctures exposed to fire, because predicts the charring rate as a fünction offire exposure. The charring rate calculation ofmost stmctural wood elements allows simple calculations, but is more complicated for situations where the fire exposure is non-standard and in wood elements protected with other materiais.

Logical Modeling and Dynamical Analysis of Cellular Networks

The logical (or logic) formalism is increasingly used to model regulatory and signaling networks. Complementing these applications, several groups contributed various methods and tools to support the definition and analysis of logical models. After an introduction to the logical modeling framework and to several of its variants, we review here a number of recent methodological advances to ease the analysis of large and intricate networks. In particular, we survey approaches to determine model attractors and their reachability properties, to assess the dynamical impact of variations of external signals, and to consistently reduce large models. To illustrate these developments, we further consider several published logical models for two important biological processes, namely the differentiation of T helper cells and the control of mammalian cell cycle.

Cellular-block-lined grade control structure : hydraulic model investigation /

"August 1973."

Statistical Hurdle Models for Single Cell Gene Expression: Differential Expression and Graphical Modeling

Thesis (Ph.D.)--University of Washington, 2016-06

Semliki Forest virus and Kunjin virus RNA replicons elicit comparable cellular immunity but distinct humoral immunity

RNA replicons offer a number of qualities which make them attractive as vaccination vectors. Both alphavirus and flavivirus replicon vaccines have been investigated in preclinical models yet there has been little direct comparison of the two vector systems. To determine whether differences in the biology of the two vectors influence immunogenicity, we compared two prototypic replicon vectors based on Semliki Forest virus (SFV) (alphavirus) and Kunjin virus (KUN) (flavivirus). Both vectors when delivered as naked RNAs elicited comparable CD8+ T cell responses but the SFV vectors elicited greater humoral responses to an encoded cytoplasmic antigen beta-galactosidase. Studies in MHC class II-deficient mice revealed that neither vector could overcome the dependence of CD4+ T cell help in the development of humoral and cellular responses following immunization. These studies indicate that the distinct biology of the two replicon systems may differentially impact the adaptive immune response and this may need to be considered when designing vaccination strategies. (c) 2005 Elsevier Ltd. All rights reserved.

Simulation of Brugada syndrome using cellular and three-dimensional whole-heart modeling approaches

Brugada syndrome (BS) is a genetic disease identified by an abnormal electrocardiogram ( ECG) ( mainly abnormal ECGs associated with right bundle branch block and ST-elevation in right precordial leads). BS can lead to increased risk of sudden cardiac death. Experimental studies on human ventricular myocardium with BS have been limited due to difficulties in obtaining data. Thus, the use of computer simulation is an important alternative. Most previous BS simulations were based on animal heart cell models. However, due to species differences, the use of human heart cell models, especially a model with three-dimensional whole-heart anatomical structure, is needed. In this study, we developed a model of the human ventricular action potential (AP) based on refining the ten Tusscher et al (2004 Am. J. Physiol. Heart Circ. Physiol. 286 H1573 - 89) model to incorporate newly available experimental data of some major ionic currents of human ventricular myocytes. These modified channels include the L-type calcium current (ICaL), fast sodium current (I-Na), transient outward potassium current (I-to), rapidly and slowly delayed rectifier potassium currents (I-Kr and I-Ks) and inward rectifier potassium current (I-Ki). Transmural heterogeneity of APs for epicardial, endocardial and mid-myocardial (M) cells was simulated by varying the maximum conductance of IKs and Ito. The modified AP models were then used to simulate the effects of BS on cellular AP and body surface potentials using a three-dimensional dynamic heart - torso model. Our main findings are as follows. (1) BS has little effect on the AP of endocardial or mid-myocardial cells, but has a large impact on the AP of epicardial cells. (2) A likely region of BS with abnormal cell AP is near the right ventricular outflow track, and the resulting ST-segment elevation is located in the median precordium area. These simulation results are consistent with experimental findings reported in the literature. The model can reproduce a variety of electrophysiological behaviors and provides a good basis for understanding the genesis of abnormal ECG under the condition of BS disease.

Stochastic models for regulatory networks of the genetic toggle switch

Bistability arises within a wide range of biological systems from the A phage switch in bacteria to cellular signal transduction pathways in mammalian cells. Changes in regulatory mechanisms may result in genetic switching in a bistable system. Recently, more and more experimental evidence in the form of bimodal population distributions indicates that noise plays a very important role in the switching of bistable systems. Although deterministic models have been used for studying the existence of bistability properties under various system conditions, these models cannot realize cell-to-cell fluctuations in genetic switching. However, there is a lag in the development of stochastic models for studying the impact of noise in bistable systems because of the lack of detailed knowledge of biochemical reactions, kinetic rates, and molecular numbers. in this work, we develop a previously undescribed general technique for developing quantitative stochastic models for large-scale genetic regulatory networks by introducing Poisson random variables into deterministic models described by ordinary differential equations. Two stochastic models have been proposed for the genetic toggle switch interfaced with either the SOS signaling pathway or a quorum-sensing signaling pathway, and we have successfully realized experimental results showing bimodal population distributions. Because the introduced stochastic models are based on widely used ordinary differential equation models, the success of this work suggests that this approach is a very promising one for studying noise in large-scale genetic regulatory networks.

Parallel implementation of stochastic simulation for large scale cellular processes

Experimental and theoretical studies have shown the importance of stochastic processes in genetic regulatory networks and cellular processes. Cellular networks and genetic circuits often involve small numbers of key proteins such as transcriptional factors and signaling proteins. In recent years stochastic models have been used successfully for studying noise in biological pathways, and stochastic modelling of biological systems has become a very important research field in computational biology. One of the challenge problems in this field is the reduction of the huge computing time in stochastic simulations. Based on the system of the mitogen-activated protein kinase cascade that is activated by epidermal growth factor, this work give a parallel implementation by using OpenMP and parallelism across the simulation. Special attention is paid to the independence of the generated random numbers in parallel computing, that is a key criterion for the success of stochastic simulations. Numerical results indicate that parallel computers can be used as an efficient tool for simulating the dynamics of large-scale genetic regulatory networks and cellular processes