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Cortical circuits in the brain have long been recognised for their information processing capabilities and have been studied both experimentally and theoretically via spiking neural networks. Neuromorphic engineers are primarily concerned with translating the computational capabilities of biological cortical circuits, using the Spiking Neural Network (SNN) paradigm, into in silico applications that can mimic the behaviour and capabilities of real biological circuits/systems. These capabilities include low power consumption, compactness, and relevant dynamics. In this paper, we propose a new accelerated-time circuit that has several advantages over its previous neuromorphic counterparts in terms of compactness, power consumption, and capability to mimic the outcomes of biological experiments. The presented circuit simulation results demonstrate that, in comparing the new circuit to previous published synaptic plasticity circuits, reduced silicon area and lower energy consumption for processing each spike is achieved. In addition, it can be tuned in order to closely mimic the outcomes of various spike timing- and rate-based synaptic plasticity experiments. The proposed circuit is also investigated and compared to other designs in terms of tolerance to mismatch and process variation. Monte Carlo simulation results show that the proposed design is much more stable than its previous counterparts in terms of vulnerability to transistor mismatch, which is a significant challenge in analog neuromorphic design. All these features make the proposed design an ideal circuit for use in large scale SNNs, which aim at implementing neuromorphic systems with an inherent capability that can adapt to a continuously changing environment, thus leading to systems with significant learning and computational abilities. [ABSTRACT FROM AUTHOR]

A recent paper by Daubechies et al. claims that two independent component analysis (ICA) algorithms, Infomax and FastICA, which are widely used for functional magnetic resonance imaging (fMRI) analysis, select for sparsity rather than independence. The argument was supported by a series of experiments on synthetic data. We show that these experiments fall short of proving this claim and that the ICA algorithms are indeed doing what they are designed to do: identify maximally independent sources. [ABSTRACT FROM AUTHOR]

Brain-computer interaction (BCI) and physiological computing are terms that refer to using processed neural or physiological signals to influence human interaction with computers, environment, and each other. A major challenge in developing these systems arises from the large individual differences typically seen in the neural/physiological responses. As a result, many researchers use individually-trained recognition algorithms to process this data. In order to minimize time, cost, and barriers to use, there is a need to minimize the amount of individual training data required, or equivalently, to increase the recognition accuracy without increasing the number of user-specific training samples. One promising method for achieving this is collaborative filtering, which combines training data from the individual subject with additional training data from other, similar subjects. This paper describes a successful application of a collaborative filtering approach intended for a BCI system. This approach is based on transfer learning (TL), active class selection (ACS), and a mean squared difference user-similarity heuristic. The resulting BCI system uses neural and physiological signals for automatic task difficulty recognition. TL improves the learning performance by combining a small number of user-specific training samples with a large number of auxiliary training samples from other similar subjects. ACS optimally selects the classes to generate user-specific training samples. Experimental results on 18 subjects, using both nearest neighbors and support vector machine classifiers, demonstrate that the proposed approach can significantly reduce the number of user-specific training data samples. This collaborative filtering approach will also be generalizable to handling individual differences in many other applications that involve human neural or physiological data, such as affective computing. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for the systematic implementation of low-power microelectronic circuits aimed at computing nonlinear cellular and molecular dynamics. The method proposed is based on the Nonlinear Bernoulli Cell Formalism (NBCF), an advanced mathematical framework stemming from the Bernoulli Cell Formalism (BCF) originally exploited for the modular synthesis and analysis of linear, time-invariant, high dynamic range, logarithmic filters. Our approach identifies and exploits the striking similarities existing between the NBCF and coupled nonlinear ordinary differential equations (ODEs) typically appearing in models of naturally encountered biochemical systems. The resulting continuous-time, continuous-value, low-power CytoMimetic electronic circuits succeed in simulating fast and with good accuracy cellular and molecular dynamics. The application of the method is illustrated by synthesising for the first time microelectronic CytoMimetic topologies which simulate successfully: 1) a nonlinear intracellular calcium oscillations model for several Hill coefficient values and 2) a gene-protein regulatory system model. The dynamic behaviours generated by the proposed CytoMimetic circuits are compared and found to be in very good agreement with their biological counterparts. The circuits exploit the exponential law codifying the low-power subthreshold operation regime and have been simulated with realistic parameters from a commercially available CMOS process. They occupy an area of a fraction of a square-millimetre, while consuming between 1 and 12 microwatts of power. Simulations of fabrication-related variability results are also presented. [ABSTRACT FROM AUTHOR]

Early stage detection of lymphoma cells is invaluable for providing reliable prognosis to patients. However, the purity of lymphoma cells in extracted samples from human patients' marrow is typically low. To address this issue, we report here our work on using optically-induced dielectrophoresis (ODEP) force to rapidly purify Raji cells' (a type of Burkitt's lymphoma cell) sample from red blood cells (RBCs) with a label-free process. This method utilizes dynamically moving virtual electrodes to induce negative ODEP force of varying magnitudes on the Raji cells and RBCs in an optically-induced electrokinetics (OEK) chip. Polarization models for the two types of cells that reflect their discriminate electrical properties were established. Then, the cells' differential velocities caused by a specific ODEP force field were obtained by a finite element simulation model, thereby established the theoretical basis that the two types of cells could be separated using an ODEP force field. To ensure that the ODEP force dominated the separation process, a comparison of the ODEP force with other significant electrokinetics forces was conducted using numerical results. Furthermore, the performance of the ODEP-based approach for separating Raji cells from RBCs was experimentally investigated. The results showed that these two types of cells, with different concentration ratios, could be separated rapidly using externally-applied electrical field at a driven frequency of 50 kHz at 20 Vpp. In addition, we have found that in order to facilitate ODEP-based cell separation, Raji cells' adhesion to the OEK chip's substrate should be minimized. This paper also presents our experimental results of finding the appropriate bovine serum albumin concentration in an isotonic solution to reduce cell adhesion, while maintaining suitable medium conductivity for electrokinetics-based cell separation. In short, we have demonstrated that OEK technology could be a promising tool for efficient and effective purification of Raji cells from RBCs. [ABSTRACT FROM AUTHOR]

During the last decade markerless motion capture techniques have gained an increasing interest in the biomechanics community. In the clinical field, however, the application of markerless techniques is still debated. This is mainly due to a limited number of papers dedicated to the comparison with the state of the art of marker based motion capture, in term of repeatability of the three dimensional joints' kinematics. In the present work the application of markerless technique to data acquired with a marker-based system was investigated. All videos and external data were recorded with the same motion capture system and included the possibility to use markerless and marker-based methods simultaneously. Three dimensional markerless joint kinematics was estimated and compared with the one determined with traditional marker based systems, through the evaluation of root mean square distance between joint rotations. In order to compare the performance of markerless and marker-based systems in terms of clinically relevant joint angles estimation, the same anatomical frames of reference were defined for both systems. Differences in calibration and synchronization of the cameras were excluded by applying the same wand calibration and lens distortion correction to both techniques. Best results were achieved for knee flexion-extension angle, with an average root mean square distance of 11.75 deg, corresponding to 18.35% of the range of motion. Sagittal plane kinematics was estimated better than on the other planes also for hip and ankle (root mean square distance of 17.62 deg e.g. 44.66%, and 7.17 deg e.g. 33.12%), meanwhile estimates for hip joint were the most incorrect. This technique enables users of markerless technology to compare differences with marker-based in order to define the degree of applicability of markerless technique. [ABSTRACT FROM AUTHOR]

This paper describes the design and implementation of a wireless neural telemetry system that enables new experimental paradigms, such as neural recordings during rodent navigation in large outdoor environments. RoSco, short for Rodent Scope, is a small lightweight user-configurable module suitable for digital wireless recording from freely behaving small animals. Due to the digital transmission technology, RoSco has advantages over most other wireless modules of noise immunity and online user-configurable settings. RoSco digitally transmits entire neural waveforms for 14 of 16 channels at 20 kHz with 8-bit encoding which are streamed to the PC as standard USB audio packets. Up to 31 RoSco wireless modules can coexist in the same environment on non-overlapping independent channels. The design has spatial diversity reception via two antennas, which makes wireless communication resilient to fading and obstacles. In comparison with most existing wireless systems, this system has online user-selectable independent gain control of each channel in 8 factors from 500 to 32,000 times, two selectable ground references from a subset of channels, selectable channel grounding to disable noisy electrodes, and selectable bandwidth suitable for action potentials (300 Hz–3 kHz) and low frequency field potentials (4 Hz–3 kHz). Indoor and outdoor recordings taken from freely behaving rodents are shown to be comparable to a commercial wired system in sorting for neural populations. The module has low input referred noise, battery life of 1.5 hours and transmission losses of 0.1% up to a range of 10 m. [ABSTRACT FROM AUTHOR]

The β1-adrenergic signaling system plays an important role in the functioning of cardiac cells. Experimental data shows that the activation of this system produces inotropy, lusitropy, and chronotropy in the heart, such as increased magnitude and relaxation rates of [Ca2+]i transients and contraction force, and increased heart rhythm. However, excessive stimulation of β1-adrenergic receptors leads to heart dysfunction and heart failure. In this paper, a comprehensive, experimentally based mathematical model of the β1-adrenergic signaling system for mouse ventricular myocytes is developed, which includes major subcellular functional compartments (caveolae, extracaveolae, and cytosol). The model describes biochemical reactions that occur during stimulation of β1-adrenoceptors, changes in ionic currents, and modifications of Ca2+ handling system. Simulations describe the dynamics of major signaling molecules, such as cyclic AMP and protein kinase A, in different subcellular compartments; the effects of inhibition of phosphodiesterases on cAMP production; kinetics and magnitudes of phosphorylation of ion channels, transporters, and Ca2+ handling proteins; modifications of action potential shape and duration; magnitudes and relaxation rates of [Ca2+]i transients; changes in intracellular and transmembrane Ca2+ fluxes; and [Na+]i fluxes and dynamics. The model elucidates complex interactions of ionic currents upon activation of β1-adrenoceptors at different stimulation frequencies, which ultimately lead to a relatively modest increase in action potential duration and significant increase in [Ca2+]i transients. In particular, the model includes two subpopulations of the L-type Ca2+ channels, in caveolae and extracaveolae compartments, and their effects on the action potential and [Ca2+]i transients are investigated. The presented model can be used by researchers for the interpretation of experimental data and for the developments of mathematical models for other species or for pathological conditions. [ABSTRACT FROM AUTHOR]

Brain computer interfaces (BCIs) offer a broad class of neurologically impaired individuals an alternative means to interact with the environment. Many BCIs are “synchronous” systems, in which the system sets the timing of the interaction and tries to infer what control command the subject is issuing at each prompting. In contrast, in “asynchronous” BCIs subjects pace the interaction and the system must determine when the subject’s control command occurs. In this paper we propose a new idea for BCI which draws upon the strengths of both approaches. The subjects are externally paced and the BCI is able to determine when control commands are issued by decoding the subject’s intention for initiating control in dedicated time slots. A single task with randomly interleaved trials was designed to test whether it can be used as stimulus for inducing initiation and non-initiation states when the sensory and motor requirements for the two types of trials are very nearly identical. Further, the essential problem on the discrimination between initiation state and non-initiation state was studied. We tested the ability of EEG spectral power to distinguish between these two states. Among the four standard EEG frequency bands, beta band power recorded over parietal-occipital cortices provided the best performance, achieving an average accuracy of 86% for the correct classification of initiation and non-initiation states. Moreover, delta band power recorded over parietal and motor areas yielded a good performance and thus could also be used as an alternative feature to discriminate these two mental states. The results demonstrate the viability of our proposed idea for a BCI design based on conventional EEG features. Our proposal offers the potential to mitigate the signal detection challenges of fully asynchronous BCIs, while providing greater flexibility to the subject than traditional synchronous BCIs. [ABSTRACT FROM AUTHOR]

Detection of dysphonia is useful for monitoring the progression of phonatory impairment for patients with Parkinson’s disease (PD), and also helps assess the disease severity. This paper describes the statistical pattern analysis methods to study different vocal measurements of sustained phonations. The feature dimension reduction procedure was implemented by using the sequential forward selection (SFS) and kernel principal component analysis (KPCA) methods. Four selected vocal measures were projected by the KPCA onto the bivariate feature space, in which the class-conditional feature densities can be approximated with the nonparametric kernel density estimation technique. In the vocal pattern classification experiments, Fisher’s linear discriminant analysis (FLDA) was applied to perform the linear classification of voice records for healthy control subjects and PD patients, and the maximum a posteriori (MAP) decision rule and support vector machine (SVM) with radial basis function kernels were employed for the nonlinear classification tasks. Based on the KPCA-mapped feature densities, the MAP classifier successfully distinguished 91.8% voice records, with a sensitivity rate of 0.986, a specificity rate of 0.708, and an area value of 0.94 under the receiver operating characteristic (ROC) curve. The diagnostic performance provided by the MAP classifier was superior to those of the FLDA and SVM classifiers. In addition, the classification results indicated that gender is insensitive to dysphonia detection, and the sustained phonations of PD patients with minimal functional disability are more difficult to be correctly identified. [ABSTRACT FROM AUTHOR]

The vasculature of body tissues is continuously subject to remodeling processes originating at the micro-vascular level. The formation of new blood vessels (angiogenesis) is essential for a number of physiological and pathophysiological processes such as tissue regeneration, tumor development and the integration of artificial tissues. There are currently no time-lapsed in vivo imaging techniques providing information on the vascular network at the capillary level in a non-destructive, three-dimensional and high-resolution fashion. This paper presents a novel imaging framework based on contrast enhanced micro-computed tomography (micro-CT) for hierarchical in vivo quantification of blood vessels in mice, ranging from largest to smallest structures. The framework combines for the first time a standard morphometric approach with densitometric analysis. Validation tests showed that the method is precise and robust. Furthermore, the framework is sensitive in detecting different perfusion levels after the implementation of a murine ischemia-reperfusion model. Correlation with both histological data and micro-CT analysis of vascular corrosion casts confirmed accuracy of the method. The newly developed time-lapsed imaging approach shows high potential for in vivo monitoring of a number of different physiological and pathological conditions in angiogenesis and vascular development. [ABSTRACT FROM AUTHOR]

Nowadays, brain signals are employed in various scientific and practical fields such as Medical Science, Cognitive Science, Neuroscience, and Brain Computer Interfaces. Hence, the need for robust signal analysis methods with adequate accuracy and generalizability is inevitable. The brain signal analysis is faced with complex challenges including small sample size, high dimensionality and noisy signals. Moreover, because of the non-stationarity of brain signals and the impacts of mental states on brain function, the brain signals are associated with an inherent uncertainty. In this paper, an evidence-based combining classifiers method is proposed for brain signal analysis. This method exploits the power of combining classifiers for solving complex problems and the ability of evidence theory to model as well as to reduce the existing uncertainty. The proposed method models the uncertainty in the labels of training samples in each feature space by assigning soft and crisp labels to them. Then, some classifiers are employed to approximate the belief function corresponding to each feature space. By combining the evidence raised from each classifier through the evidence theory, more confident decisions about testing samples can be made. The obtained results by the proposed method compared to some other evidence-based and fixed rule combining methods on artificial and real datasets exhibit the ability of the proposed method in dealing with complex and uncertain classification problems. [ABSTRACT FROM AUTHOR]

The limited ability to vascularize and perfuse thick, cell-laden tissue constructs has hindered efforts to engineer complex tissues and organs, including liver, heart and kidney. The emerging field of modular tissue engineering aims to address this limitation by fabricating constructs from the bottom up, with the objective of recreating native tissue architecture and promoting extensive vascularization. In this paper, we report the elements of a simple yet efficient method for fabricating vascularized tissue constructs by fusing biodegradable microcapsules with tunable interior environments. Parenchymal cells of various types, (i.e. trophoblasts, vascular smooth muscle cells, hepatocytes) were suspended in glycosaminoglycan (GAG) solutions (4%/1.5% chondroitin sulfate/carboxymethyl cellulose, or 1.5 wt% hyaluronan) and encapsulated by forming chitosan-GAG polyelectrolyte complex membranes around droplets of the cell suspension. The interior capsule environment could be further tuned by blending collagen with or suspending microcarriers in the GAG solution These capsule modules were seeded externally with vascular endothelial cells (VEC), and subsequently fused into tissue constructs possessing VEC-lined, inter-capsule channels. The microcapsules supported high density growth achieving clinically significant cell densities. Fusion of the endothelialized, capsules generated three dimensional constructs with an embedded network of interconnected channels that enabled long-term perfusion culture of the construct. A prototype, engineered liver tissue, formed by fusion of hepatocyte-containing capsules exhibited urea synthesis rates and albumin synthesis rates comparable to standard collagen sandwich hepatocyte cultures. The capsule based, modular approach described here has the potential to allow rapid assembly of tissue constructs with clinically significant cell densities, uniform cell distribution, and endothelialized, perfusable channels. [ABSTRACT FROM AUTHOR]

Accurate and robust brain extraction is a critical step in most neuroimaging analysis pipelines. In particular, for the large-scale multi-site neuroimaging studies involving a significant number of subjects with diverse age and diagnostic groups, accurate and robust extraction of the brain automatically and consistently is highly desirable. In this paper, we introduce population-specific probability maps to guide the brain extraction of diverse subject groups, including both healthy and diseased adult human populations, both developing and aging human populations, as well as non-human primates. Specifically, the proposed method combines an atlas-based approach, for coarse skull-stripping, with a deformable-surface-based approach that is guided by local intensity information and population-specific prior information learned from a set of real brain images for more localized refinement. Comprehensive quantitative evaluations were performed on the diverse large-scale populations of ADNI dataset with over 800 subjects (55∼90 years of age, multi-site, various diagnosis groups), OASIS dataset with over 400 subjects (18∼96 years of age, wide age range, various diagnosis groups), and NIH pediatrics dataset with 150 subjects (5∼18 years of age, multi-site, wide age range as a complementary age group to the adult dataset). The results demonstrate that our method consistently yields the best overall results across almost the entire human life span, with only a single set of parameters. To demonstrate its capability to work on non-human primates, the proposed method is further evaluated using a rhesus macaque dataset with 20 subjects. Quantitative comparisons with popularly used state-of-the-art methods, including BET, Two-pass BET, BET-B, BSE, HWA, ROBEX and AFNI, demonstrate that the proposed method performs favorably with superior performance on all testing datasets, indicating its robustness and effectiveness. [ABSTRACT FROM AUTHOR]

With great potential for assisting radiological image interpretation and decision making, content-based image retrieval in the medical domain has become a hot topic in recent years. Many methods to enhance the performance of content-based medical image retrieval have been proposed, among which the relevance feedback (RF) scheme is one of the most promising. Given user feedback information, RF algorithms interactively learn a user’s preferences to bridge the “semantic gap” between low-level computerized visual features and high-level human semantic perception and thus improve retrieval performance. However, most existing RF algorithms perform in the original high-dimensional feature space and ignore the manifold structure of the low-level visual features of images. In this paper, we propose a new method, termed dual-force ISOMAP (DFISOMAP), for content-based medical image retrieval. Under the assumption that medical images lie on a low-dimensional manifold embedded in a high-dimensional ambient space, DFISOMAP operates in the following three stages. First, the geometric structure of positive examples in the learned low-dimensional embedding is preserved according to the isometric feature mapping (ISOMAP) criterion. To precisely model the geometric structure, a reconstruction error constraint is also added. Second, the average distance between positive and negative examples is maximized to separate them; this margin maximization acts as a force that pushes negative examples far away from positive examples. Finally, the similarity propagation technique is utilized to provide negative examples with another force that will pull them back into the negative sample set. We evaluate the proposed method on a subset of the IRMA medical image dataset with a RF-based medical image retrieval framework. Experimental results show that DFISOMAP outperforms popular approaches for content-based medical image retrieval in terms of accuracy and stability. [ABSTRACT FROM AUTHOR]

Cell survival in complex, vascularized tissues, has been implicated as a major bottleneck in advancement of therapies based on cardiac tissue engineering. This limitation motivates the search for small, inexpensive molecules that would simultaneously be cardio-protective and vasculogenic. Here, we present peptide sequence QHREDGS, based upon the fibrinogen-like domain of angiopoietin-1, as a prime candidate molecule. We demonstrated previously that QHREDGS improved cardiomyocyte metabolism and mitigated serum starved apoptosis. In this paper we further demonstrate the potency of QHREDGS in its ability to enhance endothelial cell survival, metabolism and tube formation. When endothelial cells were exposed to the soluble form of QHREDGS, improvements in endothelial cell barrier functionality, nitric oxide production and cell metabolism (ATP levels) in serum starved conditions were found. The functionality of the peptide was then examined when conjugated to collagen-chitosan hydrogel, a potential carrier for in vivo application. The presence of the peptide in the hydrogel mitigated paclitaxel induced apoptosis of endothelial cells in a dose dependent manner. Furthermore, the peptide modified hydrogels stimulated tube-like structure formation of encapsulated endothelial cells. When integrin αvβ3 or α5β1were antibody blocked during cell encapsulation in peptide modified hydrogels, tube formation was abolished. Therefore, the dual protective nature of the novel peptide QHREDGS may position this peptide as an appealing augmentation for collagen-chitosan hydrogels that could be used for biomaterial delivered cell therapies in the settings of myocardial infarction. [ABSTRACT FROM AUTHOR]

Changes in electroencephalography (EEG) amplitude modulations have recently been linked with early-stage Alzheimer’s disease (AD). Existing tools available to perform such analysis (e.g., detrended fluctuation analysis), however, provide limited gains in discriminability power over traditional spectral based EEG analysis. In this paper, we explore the use of an innovative EEG amplitude modulation analysis technique based on spectro-temporal signal processing. More specifically, full-band EEG signals are first decomposed into the five well-known frequency bands and the envelopes are then extracted via a Hilbert transform. Each of the five envelopes are further decomposed into four so-called modulation bands, which were chosen to coincide with the delta, theta, alpha and beta frequency bands. Experiments on a resting-awake EEG dataset collected from 76 participants (27 healthy controls, 27 diagnosed with mild-AD, and 22 with moderate-AD) showed significant differences in amplitude modulations between the three groups. Most notably, i) delta modulation of the beta frequency band disappeared with an increase in disease severity (from mild to moderate AD), ii) delta modulation of the theta band appeared with an increase in severity, and iii) delta modulation of the beta frequency band showed to be a reliable discriminant feature between healthy controls and mild-AD patients. Taken together, it is hoped that the developed tool can be used to assist clinicians not only with early detection of Alzheimer’s disease, but also to monitor its progression. [ABSTRACT FROM AUTHOR]

Background: Swallowing dysfunction (also known as dysphagia), which results in a deterioration of nutritional intake, slows rehabilitation and causes aspiration pneumonia, is very common following neurological impairments. Although videofluorographic (VF) examination is widely used for detecting aspiration, an objective and non-invasive method for assessing swallowing function has yet to be established because of a lack of adequate devices and protocols. In this paper, a bend sensor whose resistance is altered by bending was introduced to monitor swallowing-related laryngeal movement. Methods: Six healthy male volunteers were recruited in the present study. Specific time points on the signal waveform produced by the bend sensor were defined to describe laryngeal movement by differential analysis. Additionally, the physiological significance of the obtained waveform was confirmed by analyzing the sequential correlations between the signal waveform from the bend sensor and hyoid bone kinetics simultaneously recorded by VF. Results: Seven time points were successfully defined on the signal waveform to reference laryngeal movement. Each time point was well correlated with certain VF events, with evidence of no significant time lags, and there were positive correlations between waveform time points and matched VF events. Furthermore, obvious similarities were noticed between the duration of each phase on the signal waveform and the duration of the matched hyoid bone activity. Conclusions: The present monitoring system using a bend sensor might be useful for observing the temporal aspects of laryngeal movement during swallowing, and it was well coordinated with hyoid bone movement. [ABSTRACT FROM AUTHOR]

The rapid detection and identification of infectious disease pathogens is a critical need for healthcare in both developed and developing countries. As we gain more insight into the genomic basis of pathogen infectivity and drug resistance, point-of-care nucleic acid testing will likely become an important tool for global health. In this paper, we present an inexpensive, handheld, battery-powered instrument designed to enable pathogen genotyping in the developing world. Our Microfluidic Biomolecular Amplification Reader (µBAR) represents the convergence of molecular biology, microfluidics, optics, and electronics technology. The µBAR is capable of carrying out isothermal nucleic acid amplification assays with real-time fluorescence readout at a fraction of the cost of conventional benchtop thermocyclers. Additionally, the µBAR features cell phone data connectivity and GPS sample geotagging which can enable epidemiological surveying and remote healthcare delivery. The µBAR controls assay temperature through an integrated resistive heater and monitors real-time fluorescence signals from 60 individual reaction chambers using LEDs and phototransistors. Assays are carried out on PDMS disposable microfluidic cartridges which require no external power for sample loading. We characterize the fluorescence detection limits, heater uniformity, and battery life of the instrument. As a proof-of-principle, we demonstrate the detection of the HIV-1 integrase gene with the µBAR using the Loop-Mediated Isothermal Amplification (LAMP) assay. Although we focus on the detection of purified DNA here, LAMP has previously been demonstrated with a range of clinical samples, and our eventual goal is to develop a microfluidic device which includes on-chip sample preparation from raw samples. The µBAR is based entirely around open source hardware and software, and in the accompanying online supplement we present a full set of schematics, bill of materials, PCB layouts, CAD drawings, and source code for the µBAR instrument with the goal of spurring further innovation toward low-cost genetic diagnostics. [ABSTRACT FROM AUTHOR]

Traction force microscopy (TFM) is a quantitative technique for measuring cellular traction force, which is important in understanding cellular mechanotransduction processes. Traditional TFM has a significant limitation in that it has a low measurement throughput, commonly one per TFM dish, due to a lack of cell position information. To obtain enough cellular traction force data, an onerous workload is required including numerous TFM dish preparations and heavy cell-seeding activities, creating further difficulty in achieving identical experimental conditions among batches. In this paper, we present an improved-throughput TFM method using the well-developed microcontact printing technique and chemical modifications of linking microbeads to the gel surface to address these limitations. Chemically linking the microbeads to the gel surface has no significant influence on cell proliferation, morphology, cytoskeleton, and adhesion. Multiple pairs of force loaded and null force fluorescence images can be easily acquired by means of manual microscope with the aid of a fluorescence micropattern made by microcontact printing. Furthermore, keeping the micropattern separate from cells by using gels effectively eliminates the potential negative effect of the micropattern on the cells. This novel design greatly improves the analysis throughput of traditional TFM from one to at least twenty cells per petri dish without losing unique advantages, including a high spatial resolution of traction measurements. This newly developed method will boost the investigation of cell-matrix mechanical interactions. [ABSTRACT FROM AUTHOR]

Biological fluids fulfill key functionalities such as hydrating, protecting, and nourishing cells and tissues in various organ systems. They are capable of these versatile tasks owing to their distinct structural and viscoelastic properties. Characterizing the viscoelastic properties of bio-fluids is of pivotal importance for monitoring the development of certain pathologies as well as engineering synthetic replacements. Laser Speckle Rheology (LSR) is a novel optical technology that enables mechanical evaluation of tissue. In LSR, a coherent laser beam illuminates the tissue and temporal speckle intensity fluctuations are analyzed to evaluate mechanical properties. The rate of temporal speckle fluctuations is, however, influenced by both optical and mechanical properties of tissue. Therefore, in this paper, we develop and validate an approach to estimate and compensate for the contributions of light scattering to speckle dynamics and demonstrate the capability of LSR for the accurate extraction of viscoelastic moduli in phantom samples and biological fluids of varying optical and mechanical properties. [ABSTRACT FROM AUTHOR]

Background: Physical activity is inversely correlated to morbidity and mortality risk. Large cohort studies use wearable accelerometer devices to measure physical activity objectively, providing data potentially relevant to identify different activity patterns and to correlate these to health-related outcome measures. A method to compute relevant characteristics of such data not only with regard to duration and intensity, but also to regularity of activity events, is necessary. The aims of this paper are to propose a new method – the ATLAS index (Activity Types from Long-term Accelerometric Sensor data) – to derive generic measures for distinguishing different characteristic activity phenotypes from accelerometer data, to propose a comprehensive graphical representation, and to conduct a proof-of-concept with long-term measurements from different devices and cohorts. Methods: The ATLAS index consists of the three dimensions regularity (reg), duration (dur) and intensity (int) of relevant activity events identified in long-term accelerometer data. It can be regarded as a 3D vector and represented in a 3D cube graph. 12 exemplary data sets of three different cohort studies with 99,467 minutes of data were chosen for concept validation. Results: Five archetypical activity types are proposed along with their dimensional characteristics (insufficiently active: low reg, int and dur; busy bee: low dur and int, high reg; cardio-active: medium reg, int and dur, endurance athlete: high reg, int and dur; and weekend warrior: high int and dur, low reg). The data sets are displayed in one common graph, indicating characteristic differences in activity patterns. Conclusion: The ATLAS index incorporates the relevant regularity dimension apart from the widely-used measures of duration and intensity. Along with the 3D representation, it allows to compare different activity types in cohort study populations, both visually and computationally using vector distance measures. Further research is necessary to validate the ATLAS index in order to find normative values and group centroids. [ABSTRACT FROM AUTHOR]

Remote dynamic three-dimensional (3D) scene reconstruction renders the motion structure of a 3D scene remotely by means of both the color video and the corresponding depth maps. It has shown a great potential for telepresence applications like remote monitoring and remote medical imaging. Under this circumstance, video-rate and high resolution are two crucial characteristics for building a good depth map, which however mutually contradict during the depth sensor capturing. Therefore, recent works prefer to only transmit the high-resolution color video to the terminal side, and subsequently the scene depth is reconstructed by estimating the motion vectors from the video, typically using the propagation based methods towards a video-rate depth reconstruction. However, in most of the remote transmission systems, only the compressed color video stream is available. As a result, color video restored from the streams has quality losses, and thus the extracted motion vectors are inaccurate for depth reconstruction. In this paper, we propose a precise and robust scheme for dynamic 3D scene reconstruction by using the compressed color video stream and their inaccurate motion vectors. Our method rectifies the inaccurate motion vectors by analyzing and compensating their quality losses, motion vector absence in spatial prediction, and dislocation in near-boundary region. This rectification ensures the depth maps can be compensated in both video-rate and high resolution at the terminal side towards reducing the system consumption on both the compression and transmission. Our experiments validate that the proposed scheme is robust for depth map and dynamic scene reconstruction on long propagation distance, even with high compression ratio, outperforming the benchmark approaches with at least 3.3950 dB quality gains for remote applications. [ABSTRACT FROM AUTHOR]

This paper discusses modeling and automatic feedback control of (postural and rest) tremor for adaptive-control-methodology-based estimation of deep brain stimulation (DBS) parameters. The simplest linear oscillator-based tremor model, between stimulation amplitude and tremor, is investigated by utilizing input-output knowledge. Further, a nonlinear generalization of the oscillator-based tremor model, useful for derivation of a control strategy involving incorporation of parametric-bound knowledge, is provided. Using the Lyapunov method, a robust adaptive output feedback control law, based on measurement of the tremor signal from the fingers of a patient, is formulated to estimate the stimulation amplitude required to control the tremor. By means of the proposed control strategy, an algorithm is developed for estimation of DBS parameters such as amplitude, frequency and pulse width, which provides a framework for development of an automatic clinical device for control of motor symptoms. The DBS parameter estimation results for the proposed control scheme are verified through numerical simulations. [ABSTRACT FROM AUTHOR]

Background: Calcium phosphate cement (CPC) can be molded or injected to form a scaffold in situ, which intimately conforms to complex bone defects. Bioactive glass (BG) is known for its unique ability to bond to living bone and promote bone growth. However, it was not until recently that literature was available regarding CPC-BG applied as an injectable graft. In this paper, we reported a novel injectable CPC-BG composite with improved properties caused by the incorporation of BG into CPC. Materials and Methods: The novel injectable bioactive cement was evaluated to determine its composition, microstructure, setting time, injectability, compressive strength and behavior in a simulated body fluid (SBF). The in vitro cellular responses of osteoblasts and in vivo tissue responses after the implantation of CPC-BG in femoral condyle defects of rabbits were also investigated. Results: CPC-BG possessed a retarded setting time and markedly better injectability and mechanical properties than CPC. Moreover, a new Ca-deficient apatite layer was deposited on the composite surface after immersing immersion in SBF for 7 days. CPC-BG samples showed significantly improved degradability and bioactivity compared to CPC in simulated body fluid (SBF). In addition, the degrees of cell attachment, proliferation and differentiation on CPC-BG were higher than those on CPC. Macroscopic evaluation, histological evaluation, and micro-computed tomography (micro-CT) analysis showed that CPC-BG enhanced the efficiency of new bone formation in comparison with CPC. Conclusions: A novel CPC-BG composite has been synthesized with improved properties exhibiting promising prospects for bone regeneration. [ABSTRACT FROM AUTHOR]

Several works have reported on the reconstruction of 2D/3D limb kinematics from low-frequency EEG signals using linear regression models based on positive correlation values between the recorded and the reconstructed trajectories. This paper describes the mathematical properties of the linear model and the correlation evaluation metric that may lead to a misinterpretation of the results of this type of decoders. Firstly, the use of a linear regression model to adjust the two temporal signals (EEG and velocity profiles) implies that the relevant component of the signal used for decoding (EEG) has to be in the same frequency range as the signal to be decoded (velocity profiles). Secondly, the use of a correlation to evaluate the fitting of two trajectories could lead to overly-optimistic results as this metric is invariant to scale. Also, the correlation has a non-linear nature that leads to higher values for sinus/cosinus-like signals at low frequencies. Analysis of these properties on the reconstruction results was carried out through an experiment performed in line with previous studies, where healthy participants executed predefined reaching movements of the hand in 3D space. While the correlations of limb velocity profiles reconstructed from low-frequency EEG were comparable to studies in this domain, a systematic statistical analysis revealed that these results were not above the chance level. The empirical chance level was estimated using random assignments of recorded velocity profiles and EEG signals, as well as combinations of randomly generated synthetic EEG with recorded velocity profiles and recorded EEG with randomly generated synthetic velocity profiles. The analysis shows that the positive correlation results in this experiment cannot be used as an indicator of successful trajectory reconstruction based on a neural correlate. Several directions are herein discussed to address the misinterpretation of results as well as the implications on previous invasive and non-invasive works. [ABSTRACT FROM AUTHOR]

The study of light propagation in turbid media has attracted extensive attention in the field of biomedical optical molecular imaging. In this paper, we present a software platform for the simulation of light propagation in turbid media named the “Molecular Optical Simulation Environment (MOSE)”. Based on the gold standard of the Monte Carlo method, MOSE simulates light propagation both in tissues with complicated structures and through free-space. In particular, MOSE synthesizes realistic data for bioluminescence tomography (BLT), fluorescence molecular tomography (FMT), and diffuse optical tomography (DOT). The user-friendly interface and powerful visualization tools facilitate data analysis and system evaluation. As a major measure for resource sharing and reproducible research, MOSE aims to provide freeware for research and educational institutions, which can be downloaded at http://www.mosetm.net. [ABSTRACT FROM AUTHOR]

Most EEG-based brain-computer interface (BCI) paradigms include specific electrode positions. As the structures and activities of the brain vary with each individual, contributing channels should be chosen based on original records of BCIs. Phase measurement is an important approach in EEG analyses, but seldom used for channel selections. In this paper, the phase locking and concentrating value-based recursive feature elimination approach (PLCV-RFE) is proposed to produce robust-EEG channel selections in a P300 speller. The PLCV-RFE, deriving from the phase resetting mechanism, measures the phase relation between EEGs and ranks channels by the recursive strategy. Data recorded from 32 electrodes on 9 subjects are used to evaluate the proposed method. The results show that the PLCV-RFE substantially reduces channel sets and improves recognition accuracies significantly. Moreover, compared with other state-of-the-art feature selection methods (SSNRSF and SVM-RFE), the PLCV-RFE achieves better performance. Thus the phase measurement is available in the channel selection of BCI and it may be an evidence to indirectly support that phase resetting is at least one reason for ERP generations. [ABSTRACT FROM AUTHOR]

Despite numerous technology advances, bioreactors are still mostly utilized as functional black-boxes where trial and error eventually leads to the desirable cellular outcome. Investigators have applied various computational approaches to understand the impact the internal dynamics of such devices has on overall cell growth, but such models cannot provide a comprehensive perspective regarding the system dynamics, due to limitations inherent to the underlying approaches. In this study, a novel multi-paradigm modeling platform capable of simulating the dynamic bidirectional relationship between cells and their microenvironment is presented. Designing the modeling platform entailed combining and coupling fully an agent-based modeling platform with a transport phenomena computational modeling framework. To demonstrate capability, the platform was used to study the impact of bioreactor parameters on the overall cell population behavior and vice versa. In order to achieve this, virtual bioreactors were constructed and seeded. The virtual cells, guided by a set of rules involving the simulated mass transport inside the bioreactor, as well as cell-related probabilistic parameters, were capable of displaying an array of behaviors such as proliferation, migration, chemotaxis and apoptosis. In this way the platform was shown to capture not only the impact of bioreactor transport processes on cellular behavior but also the influence that cellular activity wields on that very same local mass transport, thereby influencing overall cell growth. The platform was validated by simulating cellular chemotaxis in a virtual direct visualization chamber and comparing the simulation with its experimental analogue. The results presented in this paper are in agreement with published models of similar flavor. The modeling platform can be used as a concept selection tool to optimize bioreactor design specifications. [ABSTRACT FROM AUTHOR]

This paper introduces novel vacuum/compression valves (VCVs) utilizing paraffin wax. A VCV is implemented by sealing the venting channel/hole with wax plugs (for normally-closed valve), or to be sealed by wax (for normally-open valve), and is activated by localized heating on the CD surface. We demonstrate that the VCV provides the advantages of avoiding unnecessary heating of the sample/reagents in the diagnostic process, allowing for vacuum sealing of the CD, and clear separation of the paraffin wax from the sample/reagents in the microfluidic process. As a proof of concept, the microfluidic processes of liquid flow switching and liquid metering is demonstrated with the VCV. Results show that the VCV lowers the required spinning frequency to perform the microfluidic processes with high accuracy and ease of control. [ABSTRACT FROM AUTHOR]

Functional transcrannial Doppler (fTCD) is used for monitoring the hemodynamics characteristics of major cerebral arteries. Its resting-state characteristics are known only when considering the maximal velocity corresponding to the highest Doppler shift (so called the envelope signals). Significantly more information about the resting-state fTCD can be gained when considering the raw cerebral blood flow velocity (CBFV) recordings. In this paper, we considered simultaneously acquired envelope and raw CBFV signals. Specifically, we collected bilateral CBFV recordings from left and right middle cerebral arteries using 20 healthy subjects (10 females). The data collection lasted for 15 minutes. The subjects were asked to remain awake, stay silent, and try to remain thought-free during the data collection. Time, frequency and time-frequency features were extracted from both the raw and the envelope CBFV signals. The effects of age, sex and body-mass index were examined on the extracted features. The results showed that the raw CBFV signals had a higher frequency content, and its temporal structures were almost uncorrelated. The information-theoretic features showed that the raw recordings from left and right middle cerebral arteries had higher content of mutual information than the envelope signals. Age and body-mass index did not have statistically significant effects on the extracted features. Sex-based differences were observed in all three domains and for both, the envelope signals and the raw CBFV signals. These findings indicate that the raw CBFV signals provide valuable information about the cerebral blood flow which can be utilized in further validation of fTCD as a clinical tool. [ABSTRACT FROM AUTHOR]

This paper describes a human-computer interface based on electro-oculography (EOG) that allows interaction with a computer using eye movement. The EOG registers the movement of the eye by measuring, through electrodes, the difference of potential between the cornea and the retina. A new pair of EOG glasses have been designed to improve the user's comfort and to remove the manual procedure of placing the EOG electrodes around the user's eye. The interface, which includes the EOG electrodes, uses a new processing algorithm that is able to detect the gaze direction and the blink of the eyes from the EOG signals. The system reliably enabled subjects to control the movement of a dot on a video screen. [ABSTRACT FROM AUTHOR]

This paper examines the multiple atlas random diffeomorphic orbit model in Computational Anatomy (CA) for parameter estimation and segmentation of subcortical and ventricular neuroanatomy in magnetic resonance imagery. We assume that there exist multiple magnetic resonance image (MRI) atlases, each atlas containing a collection of locally-defined charts in the brain generated via manual delineation of the structures of interest. We focus on maximum a posteriori estimation of high dimensional segmentations of MR within the class of generative models representing the observed MRI as a conditionally Gaussian random field, conditioned on the atlas charts and the diffeomorphic change of coordinates of each chart that generates it. The charts and their diffeomorphic correspondences are unknown and viewed as latent or hidden variables. We demonstrate that the expectation-maximization (EM) algorithm arises naturally, yielding the likelihood-fusion equation which the a posteriori estimator of the segmentation labels maximizes. The likelihoods being fused are modeled as conditionally Gaussian random fields with mean fields a function of each atlas chart under its diffeomorphic change of coordinates onto the target. The conditional-mean in the EM algorithm specifies the convex weights with which the chart-specific likelihoods are fused. The multiple atlases with the associated convex weights imply that the posterior distribution is a multi-modal representation of the measured MRI. Segmentation results for subcortical and ventricular structures of subjects, within populations of demented subjects, are demonstrated, including the use of multiple atlases across multiple diseased groups. [ABSTRACT FROM AUTHOR]

Electrolysis, electrochemotherapy with reversible electroporation, nanosecond pulsed electric fields and irreversible electroporation are valuable non-thermal electricity based tissue ablation technologies. This paper reports results from the first large animal study of a new non-thermal tissue ablation technology that employs "Synergistic electrolysis and electroporation" (SEE). The goal of this pre-clinical study is to expand on earlier studies with small animals and use the pig liver to establish SEE treatment parameters of clinical utility. We examined two SEE methods. One of the methods employs multiple electrochemotherapy-type reversible electroporation magnitude pulses, designed in such a way that the charge delivered during the electroporation pulses generates the electrolytic products. The second SEE method combines the delivery of a small number of electrochemotherapy magnitude electroporation pulses with a low voltage electrolysis generating DC current in three different ways. We show that both methods can produce lesion with dimensions of clinical utility, without the need to inject drugs as in electrochemotherapy, faster than with conventional electrolysis and with lower electric fields than irreversible electroporation and nanosecond pulsed ablation.

Recent research demonstrates that white matter of the brain contains not only long T2 components, but a minority of ultrashort T2* components. Adiabatic inversion recovery prepared dual echo ultrashort echo time (IR-dUTE) sequences can be used to selectively image the ultrashort T2* components in white matter of the brain using a clinical whole body scanner. The T2*s of the ultrashort T2* components can be quantified using mono-exponential decay fitting of the IR-dUTE signal at a series of different TEs. However, accurate T1 measurement of the ultrashort T2* components is technically challenging. Efficient suppression of the signal from the majority of long T2 components is essential for robust T1 measurement. In this paper we describe a novel approach to this problem based on the use of IR-dUTE data acquisitions with different TR and TI combinations to selectively detect the signal recovery of the ultrashort T2* components. Exponential recovery curve fitting provides efficient T1 estimation, with minimized contamination from the majority of long T2 components. A rubber phantom and a piece of bovine cortical bone were used for validation of this approach. Six healthy volunteers were studied. An averaged T2* of 0.32 ± 0.09 ms, and a short mean T1 of 226 ± 46 ms were demonstrated for the healthy volunteers at 3T.

Craniosynostosis (CS), the premature ossification of cranial sutures, is attributed to increased osteogenic potential of resident osteoblasts, yet the contribution of the surrounding extracellular matrix (ECM) on osteogenic differentiation is unclear. The osteoblast-secreted ECM provides binding sites for cellular adhesion and regulates the transport and signaling of osteoinductive factors secreted by the underlying dura mater. The binding affinity of each osteoinductive factor for the ECM may amplify or mute its relative effect, thus contributing to the rate of suture fusion. The purpose of this paper was to examine the role of ECM composition derived from calvarial osteoblasts on protein binding and its resultant effect on cell phenotype. We hypothesized that potent osteoinductive proteins present during sutural fusion (e.g., bone morphogenetic protein-2 (BMP-2) and transforming growth factor beta-1 (TGF-β1)) would exhibit distinct differences in binding when exposed to ECMs generated by human calvarial osteoblasts from unaffected control individuals (CI) or CS patients. Decellularized ECMs produced by osteoblasts from CI or CS patients were incubated in the presence of BMP-2 or TGF-β1, and the affinity of each protein was analyzed. The contribution of ECM composition to protein binding was interrogated by enzymatically modulating proteoglycan content within the ECM. BMP-2 had a similar binding affinity for each ECM, while TGF-β1 had a greater affinity for ECMs produced by osteoblasts from CI compared to CS patients. Enzymatic treatment of ECMs reduced protein binding. CS osteoblasts cultured on enzymatically-treated ECMs secreted by osteoblasts from CI patients in the presence of BMP-2 exhibited impaired osteogenic differentiation compared to cells on untreated ECMs. These data demonstrate the importance of protein binding to cell-secreted ECMs and confirm that protein-ECM interactions have an important role in directing osteoblastic differentiation of calvarial osteoblasts.

For many types of cells, behavior in two-dimensional (2D) culture differs from that in three-dimensional (3D) culture. Among biologists, 2D culture on treated plastic surfaces is currently the most popular method for cell culture. In 3D, no analogous standard method--one that is similarly convenient, flexible, and reproducible--exists. This paper describes a soft-lithographic method to encapsulate cells in 3D gel objects (modules) in a variety of simple shapes (cylinders, crosses, rectangular prisms) with lateral dimensions between 40 and 1000 microm, cell densities of 10(5)-10(8) cells/cm(3), and total volumes between 1x10(-7) and 8x10(-4) cm(3). By varying (i) the initial density of cells at seeding, and (ii) the dimensions of the modules, the number of cells per module ranged from 1 to 2500 cells. Modules were formed from a range of standard biopolymers, including collagen, Matrigel, and agarose, without the complex equipment often used in encapsulation. The small dimensions of the modules allowed rapid transport of nutrients by diffusion to cells at any location in the module, and therefore allowed generation of modules with cell densities near to those of dense tissues (10(8)-10(9) cells/cm(3)). This modular method is based on soft lithography and requires little special equipment; the method is therefore accessible, flexible, and well suited to (i) understanding the behavior of cells in 3D environments at high densities of cells, as in dense tissues, and (ii) developing applications in tissue engineering.

Given the critical risks to public health and safety that can involve lapses in attention (e.g., through implication in workplace accidents), researchers have sought to develop cognitive-state tracking technologies, capable of alerting individuals engaged in cognitively demanding tasks of potentially dangerous decrements in their levels of attention. The purpose of the present study was to address this issue through an investigation of the reliability of optical measures of cortical correlates of attention in conjunction with machine learning techniques to distinguish between states of full attention and states characterized by reduced attention capacity during a sustained attention task. Seven subjects engaged in a 30 minutes duration sustained attention reaction time task with near infrared spectroscopy (NIRS) monitoring over the prefrontal and the right parietal areas. NIRS signals from the first 10 minutes of the task were considered as characterizing the ‘full attention’ class, while the NIRS signals from the last 10 minutes of the task were considered as characterizing the ‘attention decrement’ class. A two-class support vector machine algorithm was exploited to distinguish between the two levels of attention using appropriate NIRS-derived signal features. Attention decrement occurred during the task as revealed by the significant increase in reaction time in the last 10 compared to the first 10 minutes of the task (p<.05). The results demonstrate relatively good classification accuracy, ranging from 65 to 90%. The highest classification accuracy results were obtained when exploiting the oxyhemoglobin signals (i.e., from 77 to 89%, depending on the cortical area considered) rather than the deoxyhemoglobin signals (i.e., from 65 to 66%). Moreover, the classification accuracy increased to 90% when using signals from the right parietal area rather than from the prefrontal cortex. The results support the feasibility of developing cognitive tracking technologies using NIRS and machine learning techniques. [ABSTRACT FROM AUTHOR]

Tissue engineering of annulus fibrosus (AF), the essential load-bearing disc component, remains challenging due to the intrinsic heterogeneity of AF tissue. In order to provide a set of characterization data of AF tissue, which serve as the benchmark for constructing tissue engineered AF, we analyzed tissues and cells from various radial zones of AF, i.e., inner AF (iAF), middle AF (mAF), and outer AF (oAF), using a rabbit model. We found that a radial gradient in the cellular, biochemical, and biomechanical characteristics of rabbit AF existed. Specifically, the iAF cells (iAFCs) had the highest expression of collagen-II and aggrecan genes, while oAF cells (oAFCs) had the highest collagen-I gene expression. The contents of DNA, total collagen and collagen-I sequentially increased from iAF, mAF to oAF, while glycosaminoglycan (GAG) and collagen-II levels decreased. The cell traction forces of primary AFCs gradually decreased from iAFCs, mAFCs to oAFCs, being 336.6±155.3, 199.0±158.8, and 123.8±76.1 Pa, respectively. The storage moduli of iAF, mAF, and oAF were 0.032±0.002, 2.121±0.656, and 4.130±0.159 MPa, respectively. These measurements have established a set of reference data for functional evaluation of the efficacy of AF tissue engineering strategies using a convenient and cost-effective rabbit model, the findings of which may be further translated to human research. [ABSTRACT FROM AUTHOR]

Electrochemical Impedance Spectroscopy (EIS) is a powerful technique that can be used to elicit information about an electrode interface. In this article, we highlight six principal processes by which the presence of microorganisms can affect impedance and show how one of these - the production of electroactive metabolites - changes the impedance signature of culture media containing Pseudomonas aeruginosa. EIS, was used in conjunction with a low cost screen printed carbon sensor to detect the presence of P. aeruginosa when grown in isolation or as part of a polymicrobial infection with Staphylococcus aureus. By comparing the electrode to a starting measurement, we were able to identify an impedance signature characteristic of P. aeruginosa. Furthermore, we are able to show that one of the changes in the impedance signature is due to pyocyanin and associated phenazine compounds. The findings of this study indicate that it might be possible to develop a low cost sensor for the detection of P. aeruginosa in important point of care diagnostic applications. In particular, we suggest that a development of the device described here could be used in a polymicrobial clinical sample such as sputum from a CF patient to detect P. aeruginosa. [ABSTRACT FROM AUTHOR]

We examine bidirectional brain-machine interfaces that control external devices in a closed loop by decoding motor cortical activity to command the device and by encoding the state of the device by delivering electrical stimuli to sensory areas. Although it is possible to design this artificial sensory-motor interaction while maintaining two independent channels of communication, here we propose a rule that closes the loop between flows of sensory and motor information in a way that approximates a desired dynamical policy expressed as a field of forces acting upon the controlled external device. We previously developed a first implementation of this approach based on linear decoding of neural activity recorded from the motor cortex into a set of forces (a force field) applied to a point mass, and on encoding of position of the point mass into patterns of electrical stimuli delivered to somatosensory areas. However, this previous algorithm had the limitation that it only worked in situations when the position-to-force map to be implemented is invertible. Here we overcome this limitation by developing a new non-linear form of the bidirectional interface that can approximate a virtually unlimited family of continuous fields. The new algorithm bases both the encoding of position information and the decoding of motor cortical activity on an explicit map between spike trains and the state space of the device computed with Multi-Dimensional-Scaling. We present a detailed computational analysis of the performance of the interface and a validation of its robustness by using synthetic neural responses in a simulated sensory-motor loop. [ABSTRACT FROM AUTHOR]

Gradient-echo echo-planar imaging (GE EPI) is the most commonly used approach to assess localized blood oxygen level dependent (BOLD) signal changes in real-time. Alternatively, real-time spin-echo single-voxel spectroscopy (SE SVS) has recently been introduced for spatially specific BOLD neurofeedback at 3 T and at 7 T. However, currently it is not known how neurofeedback based on real-time SE SVS compares to real-time GE EPI-based. We therefore compared both methods at high (3 T) and at ultra-high (7 T) magnetic field strengths. We evaluated standard quality measures of both methods for signals originating from the motor cortex, the visual cortex, and for a neurofeedback condition. At 3 T, the data quality of the real-time SE SVS and GE EPI R2* estimates were comparable. At 7 T, the data quality of the real-time GE EPI acquisitions was superior compared to those of the real-time SE SVS. Despite the somehow lower data quality of real-time SE SVS compared to GE EPI at 7 T, SE SVS acquisitions might still be an interesting alternative. Real-time SE SVS allows for a direct and subject-specific T2* estimation and thus for a physiologically more plausible neurofeedback signal. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) and magnetoencephalography (MEG) are the two modalities for measuring neuronal dynamics at a millisecond temporal resolution. Different source analysis methods, to locate the dipoles in the brain from which these dynamics originate, have been readily applied to both modalities alone. However, direct comparisons and possible advantages of combining both modalities have rarely been assessed during voluntary movements using coherent source analysis. In the present study, the cortical and sub-cortical network of coherent sources at the finger tapping task frequency (2–4 Hz) and the modes of interaction within this network were analysed in 15 healthy subjects using a beamformer approach called the dynamic imaging of coherent sources (DICS) with subsequent source signal reconstruction and renormalized partial directed coherence analysis (RPDC). MEG and EEG data were recorded simultaneously allowing the comparison of each of the modalities separately to that of the combined approach. We found the identified network of coherent sources for the finger tapping task as described in earlier studies when using only the MEG or combined MEG+EEG whereas the EEG data alone failed to detect single sub-cortical sources. The signal-to-noise ratio (SNR) level of the coherent rhythmic activity at the tapping frequency in MEG and combined MEG+EEG data was significantly higher than EEG alone. The functional connectivity analysis revealed that the combined approach had more active connections compared to either of the modalities during the finger tapping (FT) task. These results indicate that MEG is superior in the detection of deep coherent sources and that the SNR seems to be more vital than the sensitivity to theoretical dipole orientation and the volume conduction effect in the case of EEG. [ABSTRACT FROM AUTHOR]

G-protein-coupled receptors (GPCRs) transmit stimuli to intracellular signaling systems. Rhodopsin (Rh), which is a prototypical GPCR, possesses an 11-cis retinal. Photoisomerization of 11-cis to all-trans leads to structural changes in the protein of cytoplasmic loops, activating G-protein. Microbial rhodopsins are similar heptahelical membrane proteins that function as bacterial sensors, light-driven ion-pumps, or light-gated channels. They possess an all-trans retinal, and photoisomerization to 13-cis triggers structural changes in protein. Despite these similarities, there is no sequence homology between visual and microbial rhodopsins, and microbial rhodopsins do not activate G-proteins. In this study, new chimeric proton-pumping rhodopsins, proteorhodopsin (PR) and Gloeobacter rhodopsin (GR) were designed by replacing cytoplasmic loops with bovine Rh loops. Although G-protein was not activated by the PR chimeras, all 12 GR chimeras activated G-protein. The GR chimera containing the second cytoplasmic loop of bovine Rh did not activate G-protein. However, the chimera with a second and third double-loop further enhanced G-protein activation. Introduction of an E132Q mutation slowed the photocycle 30-fold and enhanced activation. The highest catalytic activity of the GR chimera was still 3,200 times lower than bovine Rh but only 64 times lower than amphioxus Go-rhodopsin. This GR chimera showed a strong absorption change of the amide-I band on a light-minus-dark difference FTIR spectrum which could represent a larger helical opening, important for G-protein activation. The light-dependent catalytic activity of this GR chimera makes it a potential optogenetic tool for enzymatic activation by light. [ABSTRACT FROM AUTHOR]

Integration of tissue-engineered bone grafts with the host bone is vital for the healing of critical-size bone defects. An important aspect of this process is bone resorption, which must be carried out by osteoclasts derived from the host. However, the mechanism underlying recruitment of host osteoclast precursors to graft sites remains unclear. Endothelial progenitor cells (EPCs) mobilize from the bone marrow into the circulation and home to sites of angiogenesis such as tissue remodeling. Since EPCs express SDF-1, and C/EBPβ is known to regulate SDF-1α expression, we hypothesized that EPCs may recruit CXCR4-expressing host osteoclast precursors to the repair area and that this recruitment may be mediated through C/EBPβ signaling. Using an inflammatory EPC model we showed that EPCs upregulate protein levels of both SDF-1α and C/EBPβ. A luciferase assay confirmed that C/EBPβ acts on the SDF-1α promoter in these cells, and that binding is increased under conditions of inflammation, while silencing of C/EBPβ reduces expression of SDF-1 α and C/EBPβ. Using RAW264.7 cells as a model of osteoclastic monocyte precursors, we investigated their responses to migratory factors in EPC conditioned medium. We showed that RAW264.7 cells migrate towards conditioned medium from EPCs treated with IL-1β, an effect which could be abolished by silencing C/EBPβ in EPCs, and was almost completely blocked by silencing CXCR4 in RAW264.7 cells. These findings show that EPCs respond to inflammatory stimuli by signaling to osteoclast precursors via SDF-1, and that C/EBPβ mediates this response. [ABSTRACT FROM AUTHOR]

Recent progress in information and communication technologies has made it possible to measure heart rate (HR) and normalized pulse volume (NPV), which are important physiological indices, using only a smartphone. This has been achieved with reflection mode photoplethysmography (PPG), by using a smartphone’s embedded flash as a light source and the camera as a light sensor. Despite its widespread use, the method of PPG is susceptible to motion artifacts as physical displacements influence photon propagation phenomena and, thereby, the effective optical path length. Further, it is known that the wavelength of light used for PPG influences the photon penetration depth and we therefore hypothesized that influences of motion artifact could be wavelength-dependant. To test this hypothesis, we made measurements in 12 healthy volunteers of HR and NPV derived from reflection mode plethysmograms recorded simultaneously at three different spectral regions (red, green and blue) at the same physical location with a smartphone. We then assessed the accuracy of the HR and NPV measurements under the influence of motion artifacts. The analyses revealed that the accuracy of HR was acceptably high with all three wavelengths (all rs > 0.996, fixed biases: −0.12 to 0.10 beats per minute, proportional biases: r = −0.29 to 0.03), but that of NPV was the best with green light (r = 0.791, fixed biases: −0.01 arbitrary units, proportional bias: r = 0.11). Moreover, the signal-to-noise ratio obtained with green and blue light PPG was higher than that of red light PPG. These findings suggest that green is the most suitable color for measuring HR and NPV from the reflection mode photoplethysmogram under motion artifact conditions. We conclude that the use of green light PPG could be of particular benefit in ambulatory monitoring where motion artifacts are a significant issue. [ABSTRACT FROM AUTHOR]

Epicardial high-density electrical mapping is a well-established experimental instrument to monitor in vivo the activity of the atria in response to modulations of the autonomic nervous system in sinus rhythm. In regions that are not accessible by epicardial mapping, noncontact endocardial mapping performed through a balloon catheter may provide a more comprehensive description of atrial activity. We developed a computer model of the canine right atrium to compare epicardial and noncontact endocardial mapping. The model was derived from an experiment in which electroanatomical reconstruction, epicardial mapping (103 electrodes), noncontact endocardial mapping (2048 virtual electrodes computed from a 64-channel balloon catheter), and direct-contact endocardial catheter recordings were simultaneously performed in a dog. The recording system was simulated in the computer model. For simulations and experiments (after atrio-ventricular node suppression), activation maps were computed during sinus rhythm. Repolarization was assessed by measuring the area under the atrial T wave (ATa), a marker of repolarization gradients. Results showed an epicardial-endocardial correlation coefficients of 0.80 and 0.63 (two dog experiments) and 0.96 (simulation) between activation times, and a correlation coefficients of 0.57 and 0.46 (two dog experiments) and 0.92 (simulation) between ATa values. Despite distance (balloon-atrial wall) and dimension reduction (64 electrodes), some information about atrial repolarization remained present in noncontact signals. [ABSTRACT FROM AUTHOR]