Metabolomics-on-a-chip and predictive systems toxicology in microfluidic bioartificial organs

The world faces complex challenges for chemical hazard assessment. Microfluidic bioartificial organs enable the spatial and temporal control of cell growth and biochemistry, critical for organ-specific metabolic functions and particularly relevant to testing the metabolic dose-response signatures associated with both pharmaceutical and environmental toxicity. Here we present an approach combining a microfluidic system with (1)H NMR-based metabolomic footprinting, as a high-throughput small-molecule screening approach. We characterized the toxicity of several molecules: ammonia (NH(3)), an environmental pollutant leading to metabolic acidosis and liver and kidney toxicity; dimethylsulfoxide (DMSO), a free radical-scavenging solvent; and N-acetyl-para-aminophenol (APAP, or paracetamol), a hepatotoxic analgesic drug. We report organ-specific NH(3) dose-dependent metabolic responses in several microfluidic bioartificial organs (liver, kidney, and cocultures), as well as predictive (99% accuracy for NH(3) and 94% for APAP) compound-specific signatures. Our integration of microtechnology, cell culture in microfluidic biochips, and metabolic profiling opens the development of so-called "metabolomics-on-a-chip" assays in pharmaceutical and environmental toxicology.     

Analytical modeling of H‐shape distributed topology with bandpass negative group delay behavior

This article describes an analytical modeling of a distributed negative group delay (NGD) circuit presenting a H‐shape topology. It acts as an innovative typical transmission line (TL) based topology with a feedback connected with an open‐stub termination. The equivalent circuit diagram is established based on TL representation of H‐topology constituting elements. The S‐matrix analytical model is developed. To validate the analytical model, a proof‐of‐concept prototype implemented in microstrip topology is designed, simulated, fabricated, and measured. The compared calculated, simulated, and measured S‐parameters are in very good agreement. In addition, the microstrip prototype confirm the bandpass NGD behavior of the H‐topology circuit with NGD value of approximately ?2 ns around the center frequency 1.67 GHz.     

What causes a neuron to spike?

The computation performed by a neuron can be formulated as a combination of dimensional reduction in stimulus space and the nonlinearity inherent in a spiking output. White noise stimulus and reverse correlation (the spike-triggered average and spike-triggered covariance) are often used in experimental neuroscience to "ask" neurons which dimensions in stimulus space they are sensitive to and to characterize the nonlinearity of the response. In this article, we apply reverse correlation to the simplest model neuron with temporal dynamics-the leaky integrate-and-fire model-and find that for even this simple case, standard techniques do not recover the known neural computation. To overcome this, we develop novel reverse-correlation techniques by selectively analyzing only "isolated" spikes and taking explicit account of the extended silences that precede these isolated spikes. We discuss the implications of our methods to the characterization of neural adaptation. Although these methods are developed in the context of the leaky integrate-and-fire model, our findings are relevant for the analysis of spike trains from real neurons.     

Regular set of representatives for time-constrained MSC graphs

Systems involving both time and concurrency are notoriously difficult to analyze. Existing decidability results apply when clocks on different processes cannot be compared or when the set of timed executions is regular. We prove new decidability results for timed concurrent systems, requiring neither restriction. We consider the formalism of time-constrained MSC graphs (TC-MSC graphs for short) introduced in Akshay et al. (2007) [1], and study if the set of timed executions generated by a TC-MSC graph is empty. This emptiness problem is known to be undecidable in general (Gastin et al., 2009) [11]. Our approach consists of finding a regular set R of representative timed executions, i.e., such that every timed execution of the system has an equivalent, up to commutation, timed execution in R. This allows us to solve the emptiness problem under the assumption that the TC-MSC graph is prohibited from (1) forcing any basic scenario to take an arbitrarily long time to complete and (2) enforcing unboundedly many events to occur within one unit of time.     

Diagnosis from scenarios

Diagnosis of a system consists in providing explanations to a supervisor from a partial observation of the system and a model of possible executions. This paper proposes a partial order diagnosis algorithm that recovers sets of scenarios which correspond to a given observation. Systems are modeled using High-level Message Sequence Charts (HMSCs), and the diagnosis is given as a new HMSC, which behaviors are all explanations of the partial observation. The main difficulty is that some actions of the monitored system are unobservable but may still induce some causal ordering among observed events. We first give an offline centralized diagnosis algorithm, then we discuss a decentralized version of this algorithm. We then give an online diagnosis algorithm, and define syntactic criteria under which the memory used can be bounded. This allows us to give a complete diagnosis framework for infinite state systems, with a strong emphasis on concurrency and causal ordering in behaviors. The last contribution of the paper is an application of diagnosis techniques to a security problem called anomaly detection. Anomaly detection consists in comparing what occurs in the system with usual/expected behaviors, and raising an alarm when some unusual behavior (meaning a potential attack) occurs.     

Bayesian update of dialogue state: A POMDP framework for spoken dialogue systems

This paper describes a statistically motivated framework for performing real-time dialogue state updates and policy learning in a spoken dialogue system. The framework is based on the partially observable Markov decision process (POMDP), which provides a well-founded, statistical model of spoken dialogue management. However, exact belief state updates in a POMDP model are computationally intractable so approximate methods must be used. This paper presents a tractable method based on the loopy belief propagation algorithm. Various simplifications are made, which improve the efficiency significantly compared to the original algorithm as well as compared to other POMDP-based dialogue state updating approaches. A second contribution of this paper is a method for learning in spoken dialogue systems which uses a component-based policy with the episodic Natural Actor Critic algorithm.The framework proposed in this paper was tested on both simulations and in a user trial. Both indicated that using Bayesian updates of the dialogue state significantly outperforms traditional definitions of the dialogue state. Policy learning worked effectively and the learned policy outperformed all others on simulations. In user trials the learned policy was also competitive, although its optimality was less conclusive. Overall, the Bayesian update of dialogue state framework was shown to be a feasible and effective approach to building real-world POMDP-based dialogue systems.     

The geographic distribution of Mexican health sciences research

The distribution of Mexican health science publications according to the states of origin, institutions, main cities has been measured in four main health science bibliographic databases. The results showed that Mexican health sciences research activities are highly skewed.     

Mathematical modelling of the thin layer solar drying of banana, mango and cassava

The main objectives of this paper are firstly to investigate the behaviour of the thin layer drying of plantain banana, mango and cassava experimentally in a direct solar dryer and secondly to perform mathematical modelling by using thin layer drying models encountered in literature. The variation of the moisture content of the products studied and principal drying parameters are analysed. Seven statistical models, which are empirical or semi-empirical, are tested to validate the experimental data. A non-linear regression analysis using a statistical computer program is used to evaluate the constants of the models. The Henderson and Pabis drying model is found to be the most suitable for describing the solar drying curves of plantain banana, mango and cassava. The drying data of these products have been analysed to obtain the values of the effective diffusivity during the falling drying rate phase.     

Dispersion parameters of cadmium chloride doped PVA-PVP blend films

Solution cast films of poly vinylalcohol (PVA) – poly vinylpyrrolidone (PVP) polymer blend, doped with different concentrations (from 0.5 wt% up to 40 wt%) of cadmium chloride (CdCl₂) have been studied by analysing the data recorded using optical (UV-Vis) spectrometry. The modified band structure of PVA -PVP blends doped with CdCl₂ has been studied using Moss model and Wemple - DiDomenco model. Optical parameters such as transmittance (T), reflectance (R), refractive index (n), average oscillator energy (E₀), oscillator dispersion energy (E_d), real and imaginary part of dielectric constant (ε_r and ε_i) have been determined. The ratio of charge carrier concentration to effective mass (N/m^*), plasma frequency (ω_p), average oscillator wavelength (λ_o), oscillator strength (S_o), optical conductivity (σ) and optical momenta of spectra (M_?1 and M_?3) have been estimated for the PVA-PVP blend films doped with CdCl₂.     

Numerical Study of the Dynamics and Structure of a Partial Dam-Break Flow Using the VOF Method

Water Resources Management - This paper aims to evaluate the effects of the opening width of a dam site on the evolution of partial dam-break waves over a fixed dry bed. The volume of fluid (VOF)...