Search

This paper reports the levitation of micro-organisms in the sheath of radio-frequency (RF) plasma. Based on simple force-balance calculations, the required electric field for the levitation of micro-organisms has been estimated. It is predicted that micro-organisms could levitate at the periphery of the sheath. Experimental results demonstrate the levitation of two species of bacillus micro-organisms Escherichia coli (E. coli) and Klebsiella pneumoniae in an RF plasma. The almost monodiverse Klebsiella pneumoniae levitating in the RF plasma formed an ordered state similar to a 2-D Coulomb crystal. Diverse E. coli particles demonstrate the tendency that the levitating height of micro-organisms decreases as the radius of particles increases. Levitating micro-organisms in plasmas can be referred as dusty plasmas. [ABSTRACT FROM AUTHOR]

The estimation of soil moisture and crop biomass based on differential interferometry is questioned by the influence of intercepted rain (i.e., plant surface moisture) on repeat-pass observables. The magnitude, the origin of this effect, as well as its dependence on system and crop biophysical parameters have been only marginally addressed so far. This paper intends to investigate these aspects within the frame of a laboratory experiment carried out in a highly controlled electromagnetic environment. The collection of multifrequency and fully polarimetric scatterometer profiles offers a distinctive data set, which helps to understand the variations of the interferometric observables in response to a varying plant surface moisture. These changes are assessed by comparing the predictions of a first-order scattering solution with the impact found in the experimental data. Furthermore, the connection between plant surface moisture and differential interferometric observables (i.e., the magnitude and phase of the interferometric coherence) is empirically tested with the aid of regression techniques. Irrespective of frequency and polarization, intercepted water is found to impact the interferometric coherence in a similar way as changes in soil and/or plant water status, i.e., the increase of the sensor to target optical path. Changes of plant surface moisture might be erroneously mistaken either for soil water content or fresh biomass variations. Therefore, this paper raises the possibility that, in certain circumstances, intercepted water might represent a potential source of bias for the estimation of these two land surface parameters. [ABSTRACT FROM PUBLISHER]

Human body communication (HBC) has emerged as an alternative to radio wave communication for connecting low power, miniaturized wearable, and implantable devices in, on, and around the human body. HBC uses the human body as the communication channel between on-body devices. Previous studies characterizing the human body channel has reported widely varying channel response much of which has been attributed to the variation in measurement setup. This calls for the development of a unifying bio-physical model of HBC, supported by in-depth analysis and an understanding of the effect of excitation, termination modality on HBC measurements. This paper characterizes the human body channel up to 1 MHz frequency to evaluate it as a medium for the broadband communication. The communication occurs primarily in the electro-quasistatic (EQS) regime at these frequencies through the subcutaneous tissues. A lumped bio-physical model of HBC is developed, supported by experimental validations that provide insight into some of the key discrepancies found in previous studies. Voltage loss measurements are carried out both with an oscilloscope and a miniaturized wearable prototype to capture the effects of non-common ground. Results show that the channel loss is strongly dependent on the termination impedance at the receiver end, with up to 4 dB variation in average loss for different termination in an oscilloscope and an additional 9 dB channel loss with wearable prototype compared to an oscilloscope measurement. The measured channel response with capacitive termination reduces low-frequency loss and allows flat-band transfer function down to 13 KHz, establishing the human body as a broadband communication channel. Analysis of the measured results and the simulation model shows that instruments with 50 Ω input impedance (Vector Network Analyzer, Spectrum Analyzer) provides pessimistic estimation of channel loss at low frequencies. Instead, high impedance and capacitive termination should be used at the receiver end for accurate voltage mode loss measurements of the HBC channel at low frequencies. The experimentally validated bio-physical model shows that capacitive voltage mode termination can improve the low frequency loss by up to 50 dB, which helps broadband communication significantly. [ABSTRACT FROM AUTHOR]

Solving inverse problems is central in geosciences and remote sensing. The radiative transfer models (RTMs) represent mathematically the physical laws that rule the phenomena in remote sensing applications (forward models). The numerical inversion of the RTM equations is a challenging and computationally demanding problem. For this reason, often the application of a simpler statistical regression is preferred. In general, the regression models predict the biophysical parameter of interest from the corresponding received radiance, learning a mapping from in situ data. However, this approach does not employ the physical information encoded in the RTMs. An alternative strategy, which attempts to include the physical knowledge, consists in learning a regression model trained using simulated data by an RTM code. In this paper, we introduce a nonlinear nonparametric regression model that combines the benefits of the two aforementioned approaches. The inversion is performed considering jointly both real observations and RTMsimulated data. The proposed joint Gaussian process (JGP) provides a solid framework for exploiting the regularities between the two types of data, in order to perform inverse modeling. The JGP automatically detects the relative quality of the simulated and real data, and combines them properly. This occurs by learning an additional hyperparameter with respect to a standard Gaussian process model, so that the novel scheme is at the same time simple and robust, i.e., capable of adapting to different scenarios. The advantages of the JGP method compared with benchmark strategies are shown considering synthetic and real data in different experiments. Specifically, we consider leaf area index retrieval from Landsat data combined with simulated data generated by the PROSAIL model. [ABSTRACT FROM AUTHOR]

Goal: In this paper, we used in silico patient-specific models constructed from three-dimensional delayed-enhanced magnetic resonance imaging (DE-MRI) to simulate intracardiac electrograms (EGM). These included electrically abnormal EGM as these are potential radiofrequency ablation (RFA) targets. Methods: We generated signals with distinguishable macroscopic normal and abnormal characteristics by constructing MRI-based patient-specific structural heart models and by solving the simplified biophysical Mitchell–Schaeffer model of cardiac electrophysiology (EP). Then, we simulated intracardiac EGM by modeling a recording catheter using a dipole approach. Results: Qualitative results show that simulated EGM resemble clinical signals. Additionally, the quantitative assessment of signal features extracted from the simulated EGM showed statistically significant differences (p $<$ 0.0001) between the distributions of normal and abnormal EGM, similarly to what is observed on clinical data. Conclusion: We demonstrate the feasibility of coupling simplified cardiac EP models with imaging data to generate intracardiac EMG. Significance: These results are a step forward in the direction of the preoperative and noninvasive identification of ablation targets to guide RFA therapy. [ABSTRACT FROM AUTHOR]

Frequency-invariant beam patterns (FIBPs) are often required by systems using an array of sensors to process broadband signals. Although several methods have been proposed to design a broadband beamformer (typically characterized by a finite impulse response (FIR) filter for each sensor) with an FIBP, until now, the case in which the spatial aperture is shorter than the involved wavelengths has very rarely been considered. In such a case, the use of a superdirective beam pattern is essential to attaining an efficient system. In this context, robustness to array imperfections and random errors is a very crucial feature. In this paper, a method to design a robust broadband beamformer that produces an FIBP for a data-independent superdirective array is proposed and compared with other potential approaches. The method generates a far-field beam pattern that reproduces the desired profile over a very wide frequency band (also if the array is shorter than the wavelength;) and is based on a stochastic approach to the direct synthesis of the FIR filters. The very simple implementation and the resulting robustness of the attained filter-and-sum beamformer to array imperfections increase the applicability of the system. This fact is particularly important in the context of a audio signal processing carried out by microphone arrays, which is the main application considered in this paper. [ABSTRACT FROM AUTHOR]

Objective: In this paper, we explore the dependence of sliding window correlation (SWC) results on different parameters of correlating signals. The SWC is extensively used to explore the dynamics of functional connectivity (FC) networks using resting-state functional MRI (rsfMRI) scans. These scanned signals often contain multiple amplitudes, frequencies, and phases. However, the exact values of these parameters are unknown. Two recent studies explored the relationship of window length and frequencies (minimum/maximum) in the correlating signals. Methods: We extend the findings of these studies by using two deterministic signals with multiple amplitudes, frequencies, and phases. Afterward, we modulate one of the signals to introduce dynamics (nonstationarity) in their relationship. We also explore the relationship of window length and frequency band for real rsfMRI data. Results: For deterministic signals, the spurious fluctuations due to the method itself minimize, and the SWC estimates the stationary correlation when frequencies in the signals have specific relationship. For dynamic relationship also, the undesirable frequencies were removed under specific conditions for the frequencies. For real rsfMRI data, the SWC results varied with frequencies and window length. Conclusion: In the absence of any “ground truth” for different parameters in real rsfMRI signals, the SWC with a constant window size may not be a reliable method to study the dynamics of the FC. Significance: This study reveals the parametric dependencies of the SWC and its limitation as a method to analyze dynamics of FC networks in the absence of any ground truth. [ABSTRACT FROM AUTHOR]

Pseudarthrosis is an exceedingly common, costly, and morbid complication in the treatment of long bone fractures and after spinal fusion surgery. Electrical bone growth stimulation (EBGS) presents a unique approach to accelerate healing and promote fusion success rates. Over the past three decades, increased experience and widespread use of EBGS devices has led to significant improvements in stimulation paradigms and clinical outcomes. In this paper, we comprehensively review the literature and examine the history, scientific evidence, available technology, and clinical applications for EBGS. We summarize indications, limitations, and provide an overview of cost-effectiveness and future directions of EBGS technology. Various models of electrical stimulation have been proposed and marketed as adjuncts for spinal fusions and long bone fractures. Clinical studies show variable safety and efficacy of EBGS under different conditions and clinical scenarios. While the results of clinical trials do not support indiscriminate EBGS utilization for any bone injury, the evidence does suggest that EBGS is desirable and cost efficient for certain orthopedic indications, especially when used in combination with standard, first-line treatments. This review should serve as a reference to inform practicing clinicians of available treatment options, facilitate evidence-based decision making, and provide a platform for further research. [ABSTRACT FROM AUTHOR]

In this paper, we face the problem of collecting training samples for regression problems under an active learning perspective. In particular, we propose various active learning strategies specifically developed for regression approaches based on Gaussian processes (GPs) and support vector machines (SVMs). For GP regression, the first two strategies are based on the idea of adding samples that are dissimilar from the current training samples in terms of covariance measure, while the third one uses a pool of regressors in order to select the samples with the greater disagreements between the different regressors. Finally, the last strategy exploits an intrinsic GP regression outcome to pick up the most difficult and hence interesting samples to label. For SVM regression, the method based on the pool of regressors and two additional strategies based on the selection of the samples distant from the current support vectors in the kernel-induced feature space are proposed. The experimental results obtained on simulated and real data sets show that the proposed strategies exhibit a good capability to select samples that are significant for the regression process, thus opening the way to the active learning approach for remote-sensing regression problems. [ABSTRACT FROM PUBLISHER]

In this paper, we propose an embedding technique for univariate single-channel biomedical signals to apply projective subspace techniques. Biomedical signals are often recorded as 1-D time series; hence, they need to be transformed to multidimensional signal vectors for subspace techniques to be applicable. The transformation can be achieved by embedding an observed signal in its delayed coordinates. We propose the application of two nonlinear subspace techniques to embedded multidimensional signals and discuss their relation. The techniques consist of modified versions of singular-spectrum analysis (SSA) and kernel principal component analysis (KPCA). For illustrative purposes, both nonlinear subspace projection techniques are applied to an electroencephalogram (EEG) signal recorded in the frontal channel to extract its dominant electrooculogram (EOG) interference. Furthermore, to evaluate the performance of the algorithms, an experimental study with artificially mixed signals is presented and discussed. [ABSTRACT FROM AUTHOR]

This study explores some problems to analyze time-course gene expression data by state-space models (SSMs). One problem is regarding the methods of parameter estimation and determination of the dimension of the internal state variable. Although several methods have been applied, there are few literature studies which with to compare them. Thus, this paper gives a brief review of the existing literature that use the SSM to analyze the gene expression time-course data. Another problem is the identifiability of the model. If the parameters of SSMs are simply estimated without any constraints for parameter space, they lack identifiability. To identify a system uniquely, it requires a specific algorithm to estimate the parameters with some constraints. For that purpose, an identifiable form of SSMs and an algorithm for estimating parameters are derived. The last problem is the extraction of biological information by interpreting the estimated parameters, such as mechanism of gene regulations at the module level. For that one, this paper explores methods to extract further information using the estimated parameters, that is, reconstruction of a module network from time-course gene expression data [ABSTRACT FROM PUBLISHER]

Remotely sensed, angle-based vegetation indices that measure vegetation amounts by the angle between an approximated soil line and a simulated vegetation isoline in the red-near-infrared reflectance space were developed and evaluated in this paper. Ünsalan and Boyer previously proposed an angle-based vegetation index, θ (denoted as θNDVI in this paper), based on the normalized difference vegetation index (NDVI) with the objective of overcoming the saturation problem in the NDVI. However, θNDVI did not consider strong soil background influences present in the NDVI. To reduce soil background noise, an angle-based vegetation index, θSAVI, based on the soil-adjusted vegetation index (SAVI), was derived using trigonometric analysis. The performance of θNDVI and θSAVI was evaluated and compared with their corresponding vegetation indices, NDVI and SAVI. The soil background influence on θNDVI was found to be as significant as that on the NDVI. θNDVI was found to be more sensitive to vegetation amount than the NDVI at low vegetation density levels, but less sensitive to vegetation fraction at high vegetation density levels. Thus, the saturation effect at high vegetation density levels encountered in the NDVI was not mitigated by θNDVI. By contrast, θSAVI exhibited insignificant soil background effects and weaker saturation, as in SAVI, but also improved upon the dynamic range of SAVI. Analyses and evaluation suggest that θSAVI is an optimal vegetation index to assess and monitor vegetation cover across the entire range of vegetation fraction density levels and over a wide variety of soil backgrounds. [ABSTRACT FROM AUTHOR]

A convenient way to analyze blood-oxygen-level-dependent functional magnetic resonance imaging data consists of modeling the whole brain as a stationary, linear system characterized by its transfer function: the hemodynamic response function (HRF). HRF estimation, though of the greatest interest, is still under investigation, for the problem is ill-conditioned. In this paper, we recall the most general Bayesian model for HRF estimation and show how It can beneficially be translated in terms of Bayesian graphical models, leading to 1) a clear and efficient representation of all structural and functional relationships entailed by the model, and 2) a straightforward numerical scheme to approximate the Joint posterior distribution, allowing for estimation of the HRF, as well as all other model parameters. We finally apply this novel technique on both simulations and real data. [ABSTRACT FROM AUTHOR]

Hyperspectral image analysis has benefited from an array of methods that take advantage of the increased spectral depth compared to multispectral sensors; however, the focus of these developments has been on supervised classification methods. Lack of a priori knowledge regarding land cover characteristics can make unsupervised classification methods preferable under certain circumstances. An unsupervised classification technique is presented that utilizes physically relevant basis functions to model the reflectance spectra. These fit parameters used to generate the basis functions allow clustering based on spectral characteristics rather than spectral channels and provide both noise and data reduction. Histogram splitting of the fit parameters is then used as a means of producing an unsupervised classification. Unlike current unsupervised classification techniques that rely primarily on Euclidian distance measures to determine similarity, the unsupervised classification technique uses the natural splitting of the fit parameters associated with the basis functions creating clusters that are similar in terms of physical parameters. The data set used in this work utilizes the publicly available data collected at Indian Pines, Indiana. This data set provides reference data allowing for comparisons of the efficacy of different unsupervised data analysis. The unsupervised histogram splitting technique presented in this paper is shown to be better than the standard unsupervised ISODATA clustering technique with an overall accuracy of 34.3/19.0% before merging and 40.9/39.2% after merging. This improvement is also seen as an improvement of kappa before/after merging of 24.8/30.5 for the histogram splitting technique compared to 15.8/28.5 for ISODATA. [ABSTRACT FROM PUBLISHER]

The red blood cells (RBC) aggregation is of current basic science and clinical interest, as a determinant of blood microcirculation. Thus, the measurement and assessment of the RBC aggregation property (aggregability) and aggregation state at different physiologic conditions of a human individual or laboratory animal are an important issue. In this paper, in order to assess the dynamics of RBC interaction, optical tweezers were used to probe the forces during the RBC doublet formation or disruption. We show that in autologous plasma, RBC aggregating and disaggregating forces have different absolute values, ca 2–4 pN and dozens of piconewton, correspondingly. We speculate that in plasma, RBC aggregation and disaggregation processes have different driving forces. [ABSTRACT FROM PUBLISHER]

This study considers the computationally determined thermal profile of a fully clothed, finely discretized, heterogeneous human body model, subject to the maximum allowable reference level for a 1-GHz radio frequency electromagnetic field for a worker, and also subject to adverse environmental conditions, including high humidity and high ambient temperature. An initial observation is that while electromagnetic fields at the occupational safety limit will contribute an additional thermal load to the tissues, and subsequently, cause an elevated temperature, the magnitude of this effect is far outweighed by that due to the conditions including the ambient temperature, relative humidity, and the type of clothing worn. It is envisaged that the computational modeling approach outlined in this paper will be suitably modified in future studies to evaluate the thermal response of a body at elevated metabolic rates, and for different body shapes and sizes including children and pregnant women. [ABSTRACT FROM PUBLISHER]

In this paper, we provide a broad overview of models and technologies pertaining to transcranial current brain stimulation (tCS), a family of related noninvasive techniques including direct current (tDCS), alternating current (tACS), and random noise current stimulation (tRNS). These techniques are based on the delivery of weak currents through the scalp (with electrode current intensity to area ratios of about 0.3-5~\A/m^2) at low frequencies (\typically< \1 \kHz) resulting in weak electric fields in the brain (with amplitudes of about 0.2-2 V/m). Here we review the biophysics and simulation of noninvasive, current-controlled generation of electric fields in the human brain and the models for the interaction of these electric fields with neurons, including a survey of in vitro and in vivo related studies. Finally, we outline directions for future fundamental and technological research. [ABSTRACT FROM AUTHOR]

This paper proposes a novel antenna for dental implants. The proposed antenna can be attached to minimally invasive biomedical devices to monitor health conditions. Based on a combination of Archimedean spirals and a Hilbert-based curve, this 3D folded antenna was embedded on a ceramic denture (ZrO2), and operates within the medical radio (MedRadio) band. An omnidirectional radiation pattern was obtained from simulations of human models to eliminate specific orientation dependence. A realistic measurement of an oral cavity was also performed under the Institutional Review Board (IRB) protocol to evaluate its practical biomedical effects. A miniature antenna with a total area of less than 245 mm^2 was designed and implemented. The measured performance achieved an antenna gain of -6.78 dBi, and had an impedance bandwidth of approximately 60 MHz. Therefore, a compact high-gain antenna with a large bandwidth was achieved. [ABSTRACT FROM PUBLISHER]

A crystallike structure was successfully formed as a result of the levitation of a kind of bacillus microorganisms $Klebsiella ~pneumoniae$ in a radio frequency (RF) plasma. The crystallike structure was confirmed to be hexagonal from top-view photograph. Neutral gas friction cools the particles down to equilibrium positions. Field Emission Scanning Electron Microscope (FE-SEM) measurement suggests that RF plasma processes the surface of levitating microorganisms. [ABSTRACT FROM AUTHOR]

The delivery of high-frequency alternating currents has been shown to produce a focal and reversible conduction block in whole nerve and is a potential therapeutic option for various diseases and disorders involving pathological or undesired neurological activity. However, delivery of high-frequency alternating current to a nerve produces a finite burst of neuronal firing, called the onset response, before the nerve is blocked. Reduction or elimination of the onset response is very important to moving this type of nerve block into clinical applications since the onset response is likely to result in undesired muscle contraction and pain. This paper describes a study of the effect of nerve cuff electrode geometry (specifically, bipolar contact separation distance), and waveform amplitude on the magnitude and duration of the onset response. Electrode geometry and waveform amplitude were both found to affect these measures. The magnitude and duration of the onset response showed a monotonic relationship with bipolar separation distance and amplitude. The duration of the onset response varied by as much as 820% on average for combinations of different electrode geometries and waveform amplitudes. Bipolar electrodes with a contact separation distance of 0.5 mm resulted in the briefest onset response on average. Furthermore, the data presented in this study provide some insight into a biophysical explanation for the onset response. These data suggest that the onset response consists of two different phases: one phase which is responsive to experimental variables such as electrode geometry and waveform amplitude, and one which is not and appears to be inherent to the transition to the blocked state. This study has implications for nerve block electrode and stimulation parameter selection for clinical therapy systems and basic neurophysiology studies. [ABSTRACT FROM AUTHOR]

Birds, fish, sea turtles, and various other animals have been reported to sense the geomagnetic field and to use it for orientation, navigation, and homing. In recent years, exciting progress has been made towards elucidating the physical and structural basis of this remarkable phenomenon. This paper focuses on the two hypotheses that drive current research into magnetoreception. One proposal relies on the presence of molecules that undergo magnetically anisotropic chemical reactions due to transient formation of a radical pair. The proposed mechanism-essentially a chemical compass-is theoretically well-established and specifically designed behavioral experiments may indeed be interpreted that way, which has sparked a hunt for the molecules and structures in question. The ferrimagnetic transduction hypothesis, on the other hand, draws its plausibility from both theoretical considerations and the fact that magnetite has been detected in sensory neurons, with stable single-domain particles in fish and micrometer-scale clusters of superparamagnetic nanocrystals in birds. We discuss the limitations of our current knowledge and suggest future studies. [ABSTRACT FROM AUTHOR]

This paper presents a 2-D model to calculate the electric current densities and the biopotential differences generated due to a breast cancerous cell during the hyperpolarization of the G1/synthesis (G1/S) transition. The proposed model is based on semiconductor diffusion-drift analysis, and aims to understand the biophysics associated with growing breast cancerous cells. The effect of the duration of the G1/S transition, and the diffusivity and the mobility of the cancerous cell boundary is investigated. The results show that shorter G1/S transition durations, and higher diffusivity and mobility at the cell boundary provide higher magnitude of the electric signals. [ABSTRACT FROM AUTHOR]

This paper explores regularization options for the ill-posed spline coefficient equations in the realistic Laplacian computation. We investigate the use of the Tikhonov regularization, truncated singular value decomposition, and the so-called λ-correction with the regularization parameter chosen by the L-curve, generalized cross-validation, quasi-optimality, and the discrepancy principle criteria. The provided range of regularization techniques is much wider than in the previous works. The improvement of the realistic Laplacian is investigated by simulations on the three-shell spherical head model. The conclusion is that the best performance is provided by the combination of the Tikhonov regularization and the generalized cross-validation criterion—a combination that has never been suggested for this task before. [ABSTRACT FROM AUTHOR]

Selecting a model topology that realistically predicts biomechanical function remains an unsolved problem. Today's dominant modeling approach is to replicate experimental input/output data by performing parameter estimation on an assumed topology. In contrast, we propose that modeling some complex biomechanical systems requires the explicit and simultaneous exploration of model topology (i.e., the type, number, and organization of physics-based functional building blocks) and parameter values. In this paper, we use the example of modeling the notoriously complex tendon networks of the lingers to present three critical advances towards the goal of implementing this extended modeling paradigm. First,- we describe a novel computational environment to perform quasi-static simulations of arbitrary topologies of elastic structures undergoing large deformations. Second, we use this form of simulation to show that the assumed topology for the tendon network of a finger plays an important role in the propagation of tension to the finger joints. Third, we demonstrate the use of a novel inference algorithm that simultaneously explores the topology and parameter values for hidden synthetic tendon networks. We conclude by discussing critical issues of observability, separability, and uniqueness of topological features inferred from input/output data, and out- line the challenges that need to be overcome to apply this novel modeling paradigm to extract causal models in real anatomical systems. [ABSTRACT FROM AUTHOR]

This paper describes the design and micro-assembly process of a low-profile 3-D microelectrode array for mapping the functional organization of targeted areas of the central nervous system and for possible application in neural prostheses. The array consists of multiple planar complimentary metal-oxide-semiconductor stimulating probes and 3-D assembly components. Parylene-encapsulated gold beams supported by etch-stopped silicon braces allow the backends of the probes to be folded over to reduce the height of the array above the cortical surface. A process permitting parylene to be used at wafer level with bulk-silicon wet release has been reported. Spacers are used to fix the microassembled probes in position and are equipped with interlocking structures to facilitate the assembly process and increase yield. Four-probe 256-site 3-D arrays operate from ±5 V with an average per-channel power dissipation of 97 µW at full range stimulation with pulse widths of 100 µs at 500-Hz frequency. Thirty-two sites can be stimulated simultaneously with maximum currents of ±127 µA and a current resolution of ±1 µA. The microassembly techniques allow a variety of 3-D microstructures to be created from planar components. [ABSTRACT FROM AUTHOR]

Insects run with far greater speed and agility for their size than even the most advanced legged robots produced to date. The single-leg ground reaction forces of running insects such as the cockroach B. discoidalis provide valuable insight into the biomechanical basis for this rapid robust locomotion. To better study the running kinematics and biomechanics of these insects, a multiaxis silicon micromachined force sensor has been fabricated. The sensor consists of a 5.3-mm square plate that is supported at its corners by thin springlike beam elements. Each flexure beam is instrumented with two piezoresistive strain gauges, allowing the determination of both normal and inplane bending force components. Typical unamplified normal and in-plane flexure force sensitivities of 55 and 12 V/N, respectively, have been demonstrated for a sensor with 18-μm-thick flexures and a mechanical bandwidth of 1.3 kHz. Nominal normal force resolution is 2.2 nN/Hz1/2 at 1 kHz. This paper details the design, fabrication, calibration, performance, and analytical modeling of the first-generation micromachined ground reaction force sensor. Preliminary data obtained from running cockroaches show that this sensor represents a marked improvement in performance over the techniques previously available for studying small-animal biomechanics. [ABSTRACT FROM AUTHOR]

Over the last 20 years, there have been tremendous intellectual and technological advancements in the fields of muscle biophysics, biomechanics, and musculoskeletal modeling. These advances have fueled a revolution in integrative muscle physiology. Whereas 20 years ago the notion of understanding the function and design of a muscular system from the molecular to the whole animal level was a dream, it is now becoming a reality. Fish represent an exceptional model for understanding the function and design of the muscular system of vertebrates. There are two fundamental reasons for this preeminence. First, the unique anatomical separation of the different muscle fiber types has made fish the most tractable model (i.e., the use and properties of the different muscle fiber types can be most easily studied). Second, fish utilize the broadest range of movement than any vertebrate. This diversity of movement imposes a wide array of challenges on the muscular system of fish and at the same time enables physiologists/biomechanists to observe how these challenges have been met. These features have permitted us to extract a number of general principles of actuation and control that have evolved over millions of years. Rather than summarizing the considerable literature on fish muscle function and fish swimming, this paper will focus narrowly on a relatively few studies that permit us to extract principles of actuation. These principles may in turn provide some insights into the design and construction of autonomous underwater vehicles (AUVs). [ABSTRACT FROM AUTHOR]

The main goal of this paper is to study the feasibility of a novel implantable micro-system able to record information about tendon sliding movements by using contactiess measurement devices (magnetic sources and sensors). The system, named "Biomechatronic Position Transducer" (BPT), can be used for the implementation of advanced control strategies in neuro- prostheses. After a preliminary analysis based on finite element model simulations, an experimental setup was developed in order to simulate the recording conditions (the sensors fixed to the bones and the magnetic sources placed on the tendons). In order to limit the number of implanted components of the system, a fuzzy Mamdani-like architecture was developed to extract the information from the raw data. The results confirm the possibility of using the presented approach for developing an implantable micro-sensor able to extract kinematic information useful for the control of neuroprostheses. Future works will go in the direction of integrating and testing the sensors and the electronic circuitry (to provide power supply and to record the data) during in vitro and in situ experiments. [ABSTRACT FROM AUTHOR]

A large space of different neural models exists from simple mathematical abstrctions to detailed biophysical representations with strongly differing levels of complexity and biological relevance. Previous comparisons between models have looked at biological realism or mathematical tractability rather than expressive power. This paper, however, investigates whether more sophisticated models are better suited to a complex sensorimotor control task than simpler ones, or whether the more general nature of groups of the simpler neurons allows them to collectively solve complex tasks better despite their individual simplicity. Many models have been proposed or used for sensorimotor con- trol tasks such as the control of locomotion. Four such neural models with varying levels of complexity were chosen. Controllers made of networks of each neural type were evolved to generate locomotion in a simulated dynamically stable four-legged robot using a genetic algorithm. The problem domain was chosen as one for which no simple solution could be hand crafted and which, with its tight sensorimotor coupling, had strongly time-dependent properties as is common in many biological control tasks. Analysis of the results shows that the most complex and biologically based model is significantly better at walking control, even producing recognizable gaits. [ABSTRACT FROM AUTHOR]

This paper presents a finite element method used to evaluate the induced current density in a realistic model of the human head exposed to a time varying magnetic field. The tissue electric properties were varied to ascertain their influence on the induced currents. Current density magnitude and vector plots were generated throughout the tissue layers to determine the effects of tissue boundaries on the field. The current density magnitude correlated to the conductivity of the tissue in all the cases tested except where the tissue permittivity was raised to a level to allow for displacement currents. In this case, the permittivity of the tissue was the dominant factor. Current density components normal to the tissue interface were shown to exist in all solutions within the cortex contrary to the predictions of present models that rely on symmetrical geometries. Additionally, modifications in the cortical geometry were shown to perturb the field so that the site of activation could be altered in diseased patient populations. Finally, by varying the tissue permittivity values and the source frequency, we tested the effects of alpha dispersion theories on transcranial magnetic stimulation. [ABSTRACT FROM AUTHOR]

An improvement of the information transfer rate of brain-computer communication is necessary for the creation of more powerful and convenient applications. This paper presents and asynchronously controlled three-class brain-computer interface-based spelling device [virtual keyboard (VK)], operated by spontaneous electroencephalogram and modulated by motor imagery. Of the first results of three able-bodied subjects operating the VK, two were successful, showing an improvement of the spelling rate σ, the number of correctly spelled letters/min, up to σ = 3.38 (average σ = 1.99). [ABSTRACT FROM AUTHOR]

The aim of this paper is to quantify the coupling during phase-locking patterns by using the recurrence plot quantification approach. We found that the percent determinism of the recurrences and the entropy of recurrences--corrected for border effect induced by finite data lenght--succeeded in distinguishing three coupling conditions both in simulation signals and in real data from cardiorespiratory synchronization experiments. [ABSTRACT FROM AUTHOR]

In this paper, an anatomically accurate three-dimensional finite-element (FE) model of the human lumbar spine (L2-L3) was used to study the biomechanical effects of graded bilateral and unilateral facetectomies of L3 under anterior shear. The intact L2-L3 FE model was validated under compression, tension, and shear loading and the predicted responses matched well with experimental data. The gross external (translational and coupled) responses, flexibilities, and facet load were delineated for these iatrogenic changes. Results indicted that unilateral facetectomy of greater than 75% and bilateral facetectomy of 75% or more resection markedly alter the translational displacement and flexibilities of the motion segment. This study suggests that fixation or fusion to restore strength and stability of the lumbar spine may be required for surgical intervention of greater than 75% facetectomy. [ABSTRACT FROM AUTHOR]

In the biomagnetic inverse problem, it is important that the optimal method be selected, especially when estimating internal electrical sources for higher functions with poor information about their source profile. In this paper, internal sources of brain magnetic fields associated with short-term memory processes were estimated by: 1) a nonlinear parameter optimization method using a multidipole model and 2) a linear optimization method using an L1 minimum norm estimation (L1-MNE) technique. Both methods showed almost the same sources: in the inferior part of the occipital lobe, the supramarginal gyrus and the angular gyms, and the inferior frontal gyms. These results indicate the reliability and avail- ability of these methods for brain magnetic data associated with higher brain functions. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is a crucial biophysical parameter that is indispensable for many biophysical and climatic models. A neural network algorithm in conjunction with extensive canopy and atmospheric radiative transfer simulations is presented in this paper to estimate LAI from Landsat-7 Enhanced Thematic Mapper Plus data. Two schemes were explored; the first was based on surface reflectance, and the second on top-of-atmosphere (TOA) radiance. The implication of the second scheme is that atmospheric corrections are not needed for estimating the surface LAI. A soil reflectance index (SRI) was proposed to account for variable soil background reflectances. Ground-measured LAI data acquired at Beltsville, MD were used to validate both schemes. The results indicate that both methods can be used to estimate LAI accurately. The experiments also showed that the use of SRI is very critical. [ABSTRACT FROM AUTHOR]

Electroporation of Jurkat T-lymphoma human cells was investigated using 10-MHz continuous waves and benchmarked against that at 100 kHz. Both cell poration and cell death were simultaneously monitored by fluorescence microscopy, and found to occur under approximately four times higher voltages at 10 MHz than that at 100 kHz. This weaker-than-expected increase in poration threshold could be explained by detailed analysis of the distributed effect often ignored in electroporation studies. [ABSTRACT FROM AUTHOR]

Infrared-terahertz (IRT) resonant absorption complies with unique biomolecule fingerprints, including neurotransmitters, proteins, ribonucleic acid, and deoxyribonucleic acid, rendering it a suitable technology to achieve conformational variations and functional adjustments. Recent research has preliminarily confirmed that IRT waves can reversibly and nonthermally regulate gene expression, transmit biological information, transform protein function, and intervene in the interchange of material at the molecular level. Regarding macroscopic effects, IRT waves can enhance cell motility, improve brain ability, and affect mood. This review article attempts to provide comprehensive insight to illustrate the application of IRT waves in biophysics. Some typical experiments and equipment are summarized to demonstrate how to conduct IRT biophysics research. This cutting-edge field involves interesting research topics spanning physics and biology and opens an avenue to the performance of interdisciplinary research. [ABSTRACT FROM AUTHOR]

Transcranial magnetic resonance-guided focused ultrasound (tcMRgFUS) enables the noninvasive treatment of the deep brain. This capacity relies on the ability to focus acoustic energy through the in-tact skull, a feat that requires accurate estimates of the acoustic velocity in individual patient skulls. In current practice, these estimates are generated using a pretreatment computed tomography (CT) scan and then registered to a magnetic resonance (MR) dataset on the day of the treatment. Treatment safety and efficacy can be improved by eliminating the need to register the CT data to the MR images and by improving the accuracy of acoustic velocity measurements. In this study, we examine the capacity of MR to supplement or replace CT as a means of estimating velocity in the skull. We find that MR can predict velocity with less but comparable accuracy to CT. We then use micro-CT imaging to better understand the limitations of Hounsfield unit (HU)-based estimates of velocity, demonstrating that the macrostructure of pores in the skull contributes to the acoustic velocity of the bone. We find evidence that detailed T2 measurements provide information about pore macrostructure similar to the information obtained with micro-CT, offering a potential clinical mechanism for improving patient-specific estimates of acoustic velocity in the human skull. [ABSTRACT FROM AUTHOR]