Diffusion Kurtosis Imaging (DKI) is a diffusion-weighted technique which overcomes limitations of the commonly used diffusion tensor imaging approach. This technique models non-Gaussian behaviour of water diffusion by the diffusion kurtosis tensor (KT), which can be used to provide indices of tissue heterogeneity and a better characterisation of the spatial architecture of tissue microstructure. In this study, the geometry of the KT is elucidated using synthetic data generated from multi-compartmental models, where diffusion heterogeneity between intra and extra-cellular media are taken into account, as well as the sensitivity of the results to each model parameter and to synthetic noise. Furthermore, based on the assumption that maxima of the KT are distributed perpendicularly to the direction of well aligned fibres, a novel algorithm for estimating fibre direction directly from the KT is proposed and compared to the fibre directions extracted from DKI based orientation distribution function (ODF) estimates previously proposed in the literature. Synthetic data results showed that, for fibres crossing at high intersection angles, direction estimates extracted directly from the KT have smaller errors than the DKI based ODF estimation approaches (DKI-ODF). Nevertheless, the proposed method showed smaller angular resolution and lower stability to changes of the simulation parameters. On real data, tractography performed on these KT fibre estimates suggests a higher sensitivity than the DKI based ODF in resolving lateral corpus callosum fibres reaching the pre-central cortex when diffusion acquisition is performed with five b-values. Using faster acquisition schemes, KT based tractography did not show improved performance over the DKI-ODF procedures. Nevertheless, it is shown that direct KT fibres estimates are more adequate for computing a generalized version of radial kurtosis maps.
Thermodynamically Consistent Force Fields for the Assembly of Inorganic, Organic, and Biological Nanostructures: The INTERFACE Force Field
- Langmuir : the ACS journal of surfaces and colloids
- Published about 5 years ago
The complexity of molecular recognition and assembly of biotic-abiotic interfaces at a scale of 1 to 1000 nm can be understood more effectively using simulation tools along with laboratory instrumentation. We discuss current capabilities and limitations of atomistic force fields and explain a strategy to obtain dependable parameters for inorganic compounds that has been developed and tested over the last decade. Parameter developments include several silicates, aluminates, metals, oxides, sulfates, and apatites that are summarized in what we call the INTERFACE force field. The INTERFACE force field operates as an extension of common harmonic force fields (PCFF, COMPASS, CHARMM, AMBER, GROMACS, and OPLS-AA) by employing the same functional form and combination rules to enable simulations of inorganic-organic and inorganic-biomolecular interfaces. The parameterization builds on in-depth understanding of physical-chemical properties at the atomic scale to assign each parameter, especially atomic charges and van-der-Waals constants, as well as on the validation of macroscale physical-chemical properties for each compound in comparison to measurements. The approach eliminates large discrepancies between computed and measured bulk and surface properties up to two orders of magnitude using other parameterization protocols and increases the transferability of the parameters by introducing thermodynamic consistency. As a result, a wide range of properties can be computed in quantitative agreement with experiment, including densities, surface energies, solid-water interface tensions, anisotropies of interfacial energies of different crystal facets, adsorption energies of biomolecules, thermal, and mechanical properties. Applications include insight into the assembly of inorganic-organic multiphase materials, recognition of inorganic facets by biomolecules, growth and shape preferences of nanocrystals and nanoparticles, as well as thermal transitions and nanomechanics. Limitations and opportunities for further development are described.
New signal processing techniques have enabled the use of the vectorcardiogram (VCG) for the detection of cardiac ischemia. Thus, we studied this signal during ventricular depolarization in 80 ischemic patients, before undergoing angioplasty, and 52 healthy subjects with the objective of evaluating the vectorcardiographic difference between both groups so leading to their subsequent classification. For that matter, seven QRS-loop parameters were analyzed, i.e.: (a) Maximum Vector Magnitude; (b) Volume; © Planar Area; (d) Maximum Distance between Centroid and Loop; (e) Angle between XY and Optimum Plane; (f) Perimeter and, (g) Area-Perimeter Ratio. For comparison, the conventional ST-Vector Magnitude (ST(VM)) was also calculated. Results indicate that several vectorcardiographic parameters show significant differences between healthy and ischemic subjects. The identification of ischemic patients via discriminant analysis using ST(VM) produced 73.2% Sensitivity (Sens) and 73.9% Specificity (Spec). In our study, the QRS-loop parameter with the best global performance was Volume, which achieved Sens=64.5% and Spec=74.6%. However, when all QRS-loop parameters and ST(VM) were combined, we obtained Sens=88.5% and Spec=92.1%. In conclusion, QRS loop parameters can be accepted as a complement to conventional ST(VM) analysis in the identification of ischemic patients.
Reward enhancement by nicotine has been suggested as an important phenomenon contributing toward tobacco abuse and dependence. Reinforcement value is a multifaceted construct not fully represented by any single measure of response strength. The present study evaluated the changes in the reinforcement value of a visual stimulus in 16 male Sprague-Dawley rats using the reinforcer demand technique proposed by Hursh and Silberberg. The different parameters of the model have been shown to represent differing facets of reinforcement value, including intensity, perseverance, and sensitivity to changes in response cost. Rats lever-pressed for 1-min presentations of a compound visual stimulus over blocks of 10 sessions across a range of response requirements (fixed ratio 1, 2, 4, 8, 14, 22, 32). Nicotine (0.4 mg/kg, base) or saline was administered 5 min before each session. Estimates from the demand model were calculated between nicotine and saline administration conditions within subjects and changes in reinforcement value were assessed as differences in Q0, Pmax, Omax, and essential value. Nicotine administration increased operant responding across the entire range of reinforcement schedules tested, and uniformly affected model parameter estimates in a manner suggesting increased reinforcement value of the visual stimulus.
We offer and test a simple operationalization of hedonic and eudaimonic well-being (“happiness”) as mediating variables that link outcomes to motivation. In six evolutionary agent-based simulation experiments, we compared the relative performance of agents endowed with different combinations of happiness-related traits (parameter values), under four types of environmental conditions. We found (i) that the effects of attaching more weight to longer-term than to momentary happiness and of extending the memory for past happiness are both stronger in an environment where food is scarce; (ii) that in such an environment “relative consumption,” in which the agent’s well-being is negatively affected by that of its neighbors, is more detrimental to survival when food is scarce; and (iii) that having a positive outlook, under which agents' longer-term happiness is increased by positive events more than it is decreased by negative ones, is generally advantageous.
Controlled attenuation parameter (CAP) evaluated with transient elastography (FibroScan) is a recent method for non invasive assessment of steatosis. Its usefulness in clinical practice is unknown. We prospectively investigated the determinants of CAP failure and the relationships between CAP and clinical or biological parameters in a large cohort of consecutive patients.
This study develops the basic idea of Pütter and Bertalanffy addressing the allometric scaling of anabolism and catabolism on somatic growth dynamics. We proposed a standardized form of the Pütter-Bertalanffy equation (PBE), which is given as the extended model of Richards function, and subsequently solved it. The analytical solution of the PBE was defined by an incomplete beta function and can take a wide range of shapes in its growth curve. The mathematical behavior of PBE due to the change in parameter values was briefly discussed. Most forms of solution consistently hold the implicit functional type with respect to the variable of body size.
Spike-timing-dependent plasticity (STDP) is an important synaptic dynamics that is capable of shaping the complex spatiotemporal activity of neural circuits. In this study, we examine the effects of STDP on the spatiotemporal patterns of a spatially extended, two-dimensional spiking neural circuit. We show that STDP can promote the formation of multiple, localized spiking wave patterns or multiple spike timing sequences in a broad parameter space of the neural circuit. Furthermore, we illustrate that the formation of these dynamic patterns is due to the interaction between the dynamics of ongoing patterns in the neural circuit and STDP. This interaction is analyzed by developing a simple model able to capture its essential dynamics, which give rise to symmetry breaking. This occurs in a fundamentally self-organizing manner, without fine-tuning of the system parameters. Moreover, we find that STDP provides a synaptic mechanism to learn the paths taken by spiking waves and modulate the dynamics of their interactions, enabling them to be regulated. This regulation mechanism has error-correcting properties. Our results therefore highlight the important roles played by STDP in facilitating the formation and regulation of spiking wave patterns that may have crucial functional roles in brain information processing.
Contaminated site remediation is generally difficult, time consuming, and expensive. As a result ranking may aid in efficient allocation of resources. In order to rank the priorities of contaminated sites, input parameters relevant to contaminant fate and transport, and exposure assessment should be as accurate as possible. Yet, in most cases these parameters are vague or not precise. Most of the current remediation priority ranking methodologies overlook the vagueness in parameter values or do not go beyond assigning a contaminated site to a risk class. The main objective of this study is to develop an alternative remedial priority ranking system (RPRS) for contaminated sites in which vagueness in parameter values is considered. RPRS aims to evaluate potential human health risks due to contamination using sufficiently comprehensive and readily available parameters in describing the fate and transport of contaminants in air, soil, and groundwater. Vagueness in parameter values is considered by means of fuzzy set theory. A fuzzy expert system is proposed for the evaluation of contaminated sites and a software (ConSiteRPRS) is developed in Microsoft Office Excel 2007 platform. Rankings are employed for hypothetical and real sites. Results show that RPRS is successful in distinguishing between the higher and lower risk cases.
A Bayesian approach for characterization of soft tissue viscoelasticity in acoustic radiation force imaging
- International journal for numerical methods in biomedical engineering
- Published over 2 years ago
Biomechanical imaging techniques based on acoustic radiation force (ARF) have been developed to characterize the viscoelasticity of soft tissue by measuring the motion excited by ARF noninvasively. The unknown stress distribution in the region of excitation (ROE) limits an accurate inverse characterization of soft tissue viscoelasticity, and single degree-of-freedom (SDF) simplified models have been applied to solve the inverse problem approximately. In this study, the ARF induced creep imaging is employed to estimate the time constant of a Voigt viscoelastic tissue model, and an inverse finite element (FE) characterization procedure based on a Bayesian formulation is presented. The Bayesian approach aims to estimate a reasonable quantification of the probability distributions of soft tissue mechanical properties in the presence of measurement noise and model parameter uncertainty. Gaussian process (GP) metamodeling is applied to provide a fast statistical approximation based on a small number of computationally expensive FE-model runs. Numerical simulation results demonstrate that the Bayesian approach provides an efficient and practical estimation of the probability distributions of time constant in the ARF induced creep imaging. In a comparison study with the SDF models, the Bayesian approach with FE models improves the estimation results even in the presence of large uncertainty levels of the model parameters. This article is protected by copyright. All rights reserved.