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The established Hoeffding-Blum-Kiefer-Rosenblatt independence test statistic is investigated for partly not identically distributed data. Surprisingly, it turns out that the statistic has the well-known distribution-free limiting null distribution of the classical criterion under standard regularity conditions. An application is testing goodness-of-fit for the regression function in a non parametric random effects meta-regression model, where the consistency is obtained as well. Simulations investigate size and power of the approach for small and moderate sample sizes. A real data example based on clinical trials illustrates how the test can be used in applications.
Inference on the basis of high-dimensional and functional data are two topics which are discussed frequently in the current statistical literature. A possibility to include both topics in a single approach is working on a very general space for the underlying observations, such as a separable Hilbert space. We propose a general method for consistently hypothesis testing on the basis of random variables with values in separable Hilbert spaces. We avoid concerns with the curse of dimensionality due to a projection idea. We apply well-known test statistics from nonparametric inference to the projected data and integrate over all projections from a specific set and with respect to suitable probability measures. In contrast to classical methods, which are applicable for real-valued random variables or random vectors of dimensions lower than the sample size, the tests can be applied to random vectors of dimensions larger than the sample size or even to functional and high-dimensional data. In general, resampling procedures such as bootstrap or permutation are suitable to determine critical values. The idea can be extended to the case of incomplete observations. Moreover, we develop an efficient algorithm for implementing the method. Examples are given for testing goodness-of-fit in a one-sample situation in [1] or for testing marginal homogeneity on the basis of a paired sample in [2]. Here, the test statistics in use can be seen as generalizations of the well-known Cramérvon-Mises test statistics in the one-sample and two-samples case. The treatment of other testing problems is possible as well. By using the theory of U-statistics, for instance, asymptotic null distributions of the test statistics are obtained as the sample size tends to infinity. Standard continuity assumptions ensure the asymptotic exactness of the tests under the null hypothesis and that the tests detect any alternative in the limit. Simulation studies demonstrate size and power of the tests in the finite sample case, confirm the theoretical findings, and are used for the comparison with concurring procedures. A possible application of the general approach is inference for stock market returns, also in high data frequencies. In the field of empirical finance, statistical inference of stock market prices usually takes place on the basis of related log-returns as data. In the classical models for stock prices, i.e., the exponential Lévy model, Black-Scholes model, and Merton model, properties such as independence and stationarity of the increments ensure an independent and identically structure of the data. Specific trends during certain periods of the stock price processes can cause complications in this regard. In fact, our approach can compensate those effects by the treatment of the log-returns as random vectors or even as functional data.
GaAs-based Gunn diodes with graded AlGaAs hot electron injector heterostructures have been developed under the special needs in automotive applications. The fabrication of the Gunn diode chips was based on total substrate removal and processing of integrated Au heat sinks. Especially, the thermal and RF behavior of the diodes have been analyzed by DC, impedance and S-parameter measurements. The electrical investigations have revealed the functionality of the hot electron injector. An optimized layer structure could fulfill the requirements in adaptive cruise control (ACC) systems at 77 GHz with typical output power between 50 and 90 mW.
Two single-incision mini-slings used for treating urinary incontinence in women are compared with respect to the stresses they produce in their surrounding tissue. In an earlier paper we experimentally observed that these implants produce considerably different stress distributions in a muscle tissue equivalent. Here we perform 2D finite element analyses to compare the shear stresses and normal stresses in the tissue equivalent for the two meshes and to investigate their failure behavior. The results clearly show that the Gynecare TVT fails for increasing loads in a zipper-like manner because it gradually debonds from the surrounding tissue. Contrary to that, the tissue at the ends of the DynaMesh-SIS direct may rupture but only at higher loads. The simulation results are in good agreement with the experimental observations thus the computational model helps to interpret the experimental results and provides a tool for qualitative evaluation of mesh implants.
Human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CM) today are widely used for the investigation of normal electromechanical cardiac function, of cardiac medication and of mutations. Computational models are thus established that simulate the behavior of this kind of cells. This section first motivates the modeling of hiPS-CM and then presents and discusses several modeling approaches of microscopic and macroscopic constituents of human-induced pluripotent stem cell-derived and mature human cardiac tissue. The focus is led on the mapping of the computational results one can achieve with these models onto mature human cardiomyocyte models, the latter being the real matter of interest. Model adaptivity is the key feature that is discussed because it opens the way for modeling various biological effects like biological variability, medication, mutation and phenotypical expression. We compare the computational with experimental results with respect to normal cardiac function and with respect to inotropic and chronotropic drug effects. The section closes with a discussion on the status quo of the specificity of computational models and on what challenges have to be solved to reach patient-specificity.
Effectiveness of the edge-based smoothed finite element method applied to soft biological tissues
(2012)
We present an electromechanically coupled computational model for the investigation of a thin cardiac tissue construct consisting of human-induced pluripotent stem cell-derived atrial, ventricular and sinoatrial cardiomyocytes. The mechanical and electrophysiological parts of the finite element model, as well as their coupling are explained in detail. The model is implemented in the open source finite element code Code_Aster and is employed for the simulation of a thin circular membrane deflected by a monolayer of autonomously beating, circular, thin cardiac tissue. Two cardio-active drugs, S-Bay K8644 and veratridine, are applied in experiments and simulations and are investigated with respect to their chronotropic effects on the tissue. These results demonstrate the potential of coupled micro- and macroscopic electromechanical models of cardiac tissue to be adapted to experimental results at the cellular level. Further model improvements are discussed taking into account experimentally measurable quantities that can easily be extracted from the obtained experimental results. The goal is to estimate the potential to adapt the presented model to sample specific cell cultures.
We present an electromechanically coupled Finite Element model for cardiac tissue. It bases on the mechanical model for cardiac tissue of Hunter et al. that we couple to the McAllister-Noble-Tsien electrophysiological model of purkinje fibre cells. The corresponding system of ordinary differential equations is implemented on the level of the constitutive equations in a geometrically and physically nonlinear version of the so-called edge-based smoothed FEM for plates. Mechanical material parameters are determined from our own pressure-deflection experimental setup. The main purpose of the model is to further examine the experimental results not only on mechanical but also on electrophysiological level down to ion channel gates. Moreover, we present first drug treatment simulations and validate the model with respect to the experiments.
Malaria infection remains a significant risk for much of the population of tropical and subtropical areas, particularly in developing countries. Therefore, it is of high importance to develop sensitive, accurate and inexpensive malaria diagnosis tests. Here, we present a novel aptamer-based electrochemical biosensor (aptasensor) for malaria detection by impedance spectroscopy, through the specific recognition between a highly discriminatory DNA aptamer and its target Plasmodium falciparum lactate dehydrogenase (PfLDH). Interestingly, due to the isoelectric point (pI) of PfLDH, the aptasensor response showed an adjustable detection range based on the different protein net-charge at variable pH environments. The specific aptamer recognition allows sensitive protein detection with an expanded detection range and a low detection limit, as well as a high specificity for PfLDH compared to analogous proteins. The specific feasibility of the aptasensor is further demonstrated by detection of the target PfLDH in human serum. Furthermore, the aptasensor can be easily regenerated and thus applied for multiple usages. The robustness, sensitivity, and reusability of the presented aptasensor make it a promising candidate for point-of-care diagnostic systems.
Optimization of passivation layers for corrosion protection of silicon-based microelectrode arrays
(2000)
The hybrid K+/Ca2+ sensor based on laser scanned silicon transducer for multi-component analysis
(2002)
Frequency mixing magnetic detection (FMMD) is a sensitive and selective technique to detect magnetic nanoparticles (MNPs) serving as probes for binding biological targets. Its principle relies on the nonlinear magnetic relaxation dynamics of a particle ensemble interacting with a dual frequency external magnetic field. In order to increase its sensitivity, lower its limit of detection and overall improve its applicability in biosensing, matching combinations of external field parameters and internal particle properties are being sought to advance FMMD. In this study, we systematically probe the aforementioned interaction with coupled Néel–Brownian dynamic relaxation simulations to examine how key MNP properties as well as applied field parameters affect the frequency mixing signal generation. It is found that the core size of MNPs dominates their nonlinear magnetic response, with the strongest contributions from the largest particles. The drive field amplitude dominates the shape of the field-dependent response, whereas effective anisotropy and hydrodynamic size of the particles only weakly influence the signal generation in FMMD. For tailoring the MNP properties and parameters of the setup towards optimal FMMD signal generation, our findings suggest choosing large particles of core sizes dc > 25 nm nm with narrow size distributions (σ < 0.1) to minimize the required drive field amplitude. This allows potential improvements of FMMD as a stand-alone application, as well as advances in magnetic particle imaging, hyperthermia and magnetic immunoassays.
Magnetic nanoparticle relaxation in biomedical application: focus on simulating nanoparticle heating
(2021)
Dual frequency magnetic excitation of magnetic nanoparticles (MNP) enables enhanced biosensing applications. This was studied from an experimental and theoretical perspective: nonlinear sum-frequency components of MNP exposed to dual-frequency magnetic excitation were measured as a function of static magnetic offset field. The Langevin model in thermodynamic equilibrium was fitted to the experimental data to derive parameters of the lognormal core size distribution. These parameters were subsequently used as inputs for micromagnetic Monte-Carlo (MC)-simulations. From the hysteresis loops obtained from MC-simulations, sum-frequency components were numerically demodulated and compared with both experiment and Langevin model predictions. From the latter, we derived that approximately 90% of the frequency mixing magnetic response signal is generated by the largest 10% of MNP. We therefore suggest that small particles do not contribute to the frequency mixing signal, which is supported by MC-simulation results. Both theoretical approaches describe the experimental signal shapes well, but with notable differences between experiment and micromagnetic simulations. These deviations could result from Brownian relaxations which are, albeit experimentally inhibited, included in MC-simulation, or (yet unconsidered) cluster-effects of MNP, or inaccurately derived input for MC-simulations, because the largest particles dominate the experimental signal but concurrently do not fulfill the precondition of thermodynamic equilibrium required by Langevin theory.
Heating efficiency of magnetic nanoparticles decreases with gradual immobilization in hydrogels
(2019)
Many efforts are made worldwide to establish magnetic fluid hyperthermia (MFH) as a treatment for organ-confined tumors. However, translation to clinical application hardly succeeds as it still lacks of understanding the mechanisms determining MFH cytotoxic effects. Here, we investigate the intracellular MFH efficacy with respect to different parameters and assess the intracellular cytotoxic effects in detail. For this, MiaPaCa-2 human pancreatic tumor cells and L929 murine fibroblasts were loaded with iron-oxide magnetic nanoparticles (MNP) and exposed to MFH for either 30 min or 90 min. The resulting cytotoxic effects were assessed via clonogenic assay. Our results demonstrate that cell damage depends not only on the obvious parameters bulk temperature and duration of treatment, but most importantly on cell type and thermal energy deposited per cell during MFH treatment. Tumor cell death of 95% was achieved by depositing an intracellular total thermal energy with about 50% margin to damage of healthy cells. This is attributed to combined intracellular nanoheating and extracellular bulk heating. Tumor cell damage of up to 86% was observed for MFH treatment without perceptible bulk temperature rise. Effective heating decreased by up to 65% after MNP were internalized inside cells.
Biomedical applications of magnetic nanoparticles (MNP) fundamentally rely on the particles’ magnetic relaxation as a response to an alternating magnetic field. The magnetic relaxation complexly depends on the interplay of MNP magnetic and physical properties with the applied field parameters. It is commonly accepted that particle core size is a major contributor to signal generation in all the above applications, however, most MNP samples comprise broad distribution spanning nm and more. Therefore, precise knowledge of the exact contribution of individual core sizes to signal generation is desired for optimal MNP design generally for each application. Specifically, we present a magnetic relaxation simulation-driven analysis of experimental frequency mixing magnetic detection (FMMD) for biosensing to quantify the contributions of individual core size fractions towards signal generation. Applying our method to two different experimental MNP systems, we found the most dominant contributions from approx. 20 nm sized particles in the two independent MNP systems. Additional comparison between freely suspended and immobilized MNP also reveals insight in the MNP microstructure, allowing to use FMMD for MNP characterization, as well as to further fine-tune its applicability in biosensing.
Magnetic nanoparticles (MNPs) are used as therapeutic and diagnostic agents for local delivery of heat and image contrast enhancement in diseased tissue. Besides magnetization, the most important parameter that determines their performance for these applications is their magnetic relaxation, which can be affected when MNPs immobilize and agglomerate inside tissues. In this letter, we investigate different MNP agglomeration states for their magnetic relaxation properties under excitation in alternating fields and relate this to their heating efficiency and imaging properties. With focus on magnetic fluid hyperthermia, two different trends in MNP heating efficiency are measured: an increase by up to 23% for agglomerated MNP in suspension and a decrease by up to 28% for mixed states of agglomerated and immobilized MNP, which indicates that immobilization is the dominant effect. The same comparatively moderate effects are obtained for the signal amplitude in magnetic particle spectroscopy.