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Objective To evaluate examiner variability in a superficial skin marker model of canine stifle kinematics. Study Design Experimental. Animals Six clinically normal dogs. Methods Dogs had 11 retroreflective markers fixed to the skin on the right hindlimb. Dogs were trotted 5 times through the calibrated testing space and this was repeated on 4 different testing days. Examiner A applied all markers to a dog and collected 6 good trials for analysis. The markers were then removed and Examiner B immediately repeated the process on the same dog. This was repeated for each dog on the 4 testing days. The dogs were trotted at a velocity of 1.70–2.10 m/s through the testing space to obtain the dynamic data sets. Comparisons were performed with Fourier analysis and Generalized Indicator Function Analysis (GIFA). Significance was set at P

Neural data analysis has increasingly incorporated causal information to study circuit connectivity. Dimensional reduction forms the basis of most analyses of large multivariate time series. Here, we present a new, multitaper-based decomposition for stochastic, multivariate time series that acts on the covariance of the time series at all lags, $C(\tau)$, as opposed to standard methods that decompose the time series, $\mathbf{X}(t)$, using only information at zero-lag. In both simulated and neural imaging examples, we demonstrate that methods that neglect the full causal structure may be discarding important dynamical information in a time series., Comment: This invited paper was presented at the Asilomar 50th Conference on Signals, Systems, and Computers

Recent evidence suggests that neural information is encoded in packets and may be flexibly routed from region to region. We have hypothesized that neural circuits are split into sub-circuits where one sub-circuit controls information propagation via pulse gating and a second sub-circuit processes graded information under the control of the first sub-circuit. Using an explicit pulse-gating mechanism, we have been able to show how information may be processed by such pulse-controlled circuits and also how, by allowing the information processing circuit to interact with the gating circuit, decisions can be made. Here, we demonstrate how Hebbian plasticity may be used to supplement our pulse-gated information processing framework by implementing a machine learning algorithm. The resulting neural circuit has a number of structures that are similar to biological neural systems, including a layered structure and information propagation driven by oscillatory gating with a complex frequency spectrum., This invited paper was presented at the 50th Asilomar Conference on Signals, Systems and Computers

Line attractors in neuronal networks have been suggested to be the basis of many brain functions, such as working memory, oculomotor control, head movement, locomotion, and sensory processing. In this paper, we make the connection between line attractors and pulse-gating in feedforward neuronal networks. In this context, because of their neutral stability along a one-dimensional manifold, line attractors are associated with a time-translational invariance that allows graded information to be propagated from one neuronal population to the next. To understand how pulse-gating manifests itself in a high-dimensional, non-linear, feedforward integrate-and-fire network, we use a Fokker-Planck approach to analyze system dynamics. We make a connection between pulse-gated propagation in the Fokker-Planck and population-averaged mean-field (firing rate) models, then identify an approximate line attractor in state space as the essential structure underlying graded information propagation. An analysis of the line attractor shows that it consists of three fixed points: a central saddle with an unstable manifold along the line and stable manifolds orthogonal to the line, which is surrounded on either side by stable fixed points. Along the manifold defined by the fixed points, slow dynamics give rise to a ghost. We show that this line attractor arises at a cusp catastrophe, where a fold bifurcation develops as a function of synaptic noise; and that the ghost dynamics near the fold of the cusp underly the robustness of the line attractor. Understanding the dynamical aspects of this cusp catastrophe allows us to show how line attractors can persist in biologically realistic neuronal networks and how the interplay of pulse gating, synaptic coupling and neuronal stochasticity can be used to enable attracting one-dimensional manifolds and thus, dynamically control the processing of graded information., Comment: 7 pages, 5 figures

Compiling quantum algorithms for near-term quantum computers (accounting for connectivity and native gate alphabets) is a major challenge that has received significant attention both by industry and academia. Avoiding the exponential overhead of classical simulation of quantum dynamics will allow compilation of larger algorithms, and a strategy for this is to evaluate an algorithm's cost on a quantum computer. To this end, we propose a variational hybrid quantum-classical algorithm called quantum-assisted quantum compiling (QAQC). In QAQC, we use the overlap between a target unitary $U$ and a trainable unitary $V$ as the cost function to be evaluated on the quantum computer. More precisely, to ensure that QAQC scales well with problem size, our cost involves not only the global overlap ${\rm Tr} (V^\dagger U)$ but also the local overlaps with respect to individual qubits. We introduce novel short-depth quantum circuits to quantify the terms in our cost function, and we prove that our cost cannot be efficiently approximated with a classical algorithm under reasonable complexity assumptions. We present both gradient-free and gradient-based approaches to minimizing this cost. As a demonstration of QAQC, we compile various one-qubit gates on IBM's and Rigetti's quantum computers into their respective native gate alphabets. Furthermore, we successfully simulate QAQC up to a problem size of 9 qubits, and these simulations highlight both the scalability of our cost function as well as the noise resilience of QAQC. Future applications of QAQC include algorithm depth compression, black-box compiling, noise mitigation, and benchmarking., Comment: 19 + 10 pages, 14 figures. Added larger scale implementations and proof that cost function is DQC1-hard

Light sheet fluorescence microscopy (LSFM) is a powerful tool for investigating model organisms including zebrafish. However, due to scattering and refractive index variations within the sample, the resulting image often suffers from low contrast. Structured illumination (SI) has been combined with scanned LSFM to remove out-of-focus and scattered light using square-law detection. Here, we demonstrate that the combination of LSFM with linear reconstruction SI can further increase resolution and contrast in the vertical and axial directions compared to the widely adopted root-mean square reconstruction method while using the same input images. We apply this approach to imaging neural activity in 7-day postfertilization zebrafish larvae. We imaged two-dimensional sections of the zebrafish central nervous system in two colors at an effective frame rate of 7 frames per second.

Although current research recognizes robust interdependence among family members, it is not yet evident that such principles have fully integrated into existing systems of care for military and veteran families. Such gaps can create disadvantages in delivering effective support, prevention, and treatment, while including families may yield significant advantages. This article highlights theoretical frameworks and empirical evidence that illustrate the relevance of systemic approaches to supporting service members, veterans, and their families. We discuss examples of family-centered approaches already in place and identify gaps in existing systems of care.

The efficiency and accuracy of cortical maps from optical imaging experiments have been improved using periodic stimulation protocols. The resulting data analysis requires the detection and estimation of periodic information in a multivariate dataset. To date, these analyses have relied on discrete Fourier transform (DFT) sinusoid estimates. Multitaper methods have become common statistical tools in the analysis of univariate time series that can give improved estimates. Here, we extend univariate multitaper harmonic analysis methods to the multivariate, imaging context. Given the hypothesis that a coherent oscillation across many pixels exists within a specified bandwidth, we investigate the problem of its detection and estimation in noisy data by constructing Hotelling's generalized T2-test. We then extend the investigation of this problem in two contexts, that of standard canonical variate analysis (CVA) and that of generalized indicator function analysis (GIFA) which is often more robust in extracting a signal in spatially correlated noise. We provide detailed information on the fidelities of the mean estimates found with our methods and comparison with DFT estimates. Our results indicate that GIFA provides particularly good estimates of harmonic signals in spatially correlated noise and is useful for detecting small amplitude harmonic signals in applications such as biological imaging measurements where spatially correlated noise is common. We demonstrate the power of our methods with an optical imaging dataset of the periodic response to a periodically rotating oriented drifting grating stimulus experiment in cat visual cortex.

In this paper, we extend our framework for constructing low-dimensional dynamical system models of large-scale neuronal networks of mammalian primary visual cortex. Our dimensional reduction procedure consists of performing a suitable linear change of variables and then systematically truncating the new set of equations. The extended framework includes modeling the effect of neglected modes as a stochastic process. By parametrizing and including stochasticity in one of two ways we show that we can improve the systems-level characterization of our dimensionally reduced neuronal network model. We examined orientation selectivity maps calculated from the firing rate distribution of large-scale simulations and stochastic dimensionally reduced models and found that by using stochastic processes to model the neglected modes, we were able to better reproduce the mean and variance of firing rates in the original large-scale simulations while still accurately predicting the orientation preference distribution.

The use of nondestructive NMR spectroscopy for enzymatic studies offers unique opportunities to identify nearly all enzymatic byproducts and detect unstable short-lived products or intermediates at the molecular level; however, numerous challenges must be overcome before it can become a widely used tool. The biosynthesis of acetyl-coenzyme A (acetyl-CoA) by acetyl-CoA synthetase is used here as a case study for the development of an analytical NMR-based time-course assay platform. We describe an algorithm to deconvolve superimposed spectra into spectra for individual molecules, and further develop a model to simulate the acetyl-CoA synthetase enzyme reaction network using the data derived from time-course NMR. Simulation shows indirectly that synthesis of acetyl-CoA is mediated via an enzyme-bound intermediate (possibly acetyl-AMP) and is accompanied by a nonproductive loss from an intermediate. The ability to predict enzyme function based on partial knowledge of the enzymatic pathway topology is also discussed.

Objective: To model the kinematics of the canine stifle in 3 dimensions using the Joint Coordinate System (JCS) and compare the JCS method with linear and segmental models. Study Design: In vivo biomechanical study. Animals: Normal adult mixed breed dogs (n=6). Methods: Dogs had 10 retroreflective markers affixed to the skin on the right pelvic limb. Dogs were walked and trotted 5 times through the calibrated space and the procedure was repeated 5 days later. Sagittal flexion and extension angle waveforms acquired during each trial with all 3 models (JCS, Linear, and Segmental) were produced simultaneously during each gait. The JCS method provided additional internal/external and abduction/adduction angles. Comparison of sagittal flexion and extension angle waveforms was performed with generalized indicator function analysis (GIFA) and Fourier analysis. A normalization procedure was performed. Results: Each model provided consistent equivalent sagittal flexion–extension data. The JCS provided consistent additional internal/external and abduction/adduction. Sagittal waveform differences were found between methods and testing days for each dog at a walk and a trot with both GIFA and Fourier analysis. After normalization, differences were less with Fourier analysis and were unaltered with GIFA. Conclusions: Whereas all methods produced similar flexion–extension waveforms, JCS provided additional valuable data. Clinical Relevance: The JCS model provided sagittal plane flexion/extension data as well as internal/external rotation and abduction/adduction data.

Systems-level neurophysiological data reveal coherent activity that is distributed across large regions of cortex. This activity is often thought of as an emergent property of recurrently connected networks. The fact that this activity is coherent means that populations of neurons may be thought of as the carriers of information, not individual neurons. Therefore, systems-level descriptions of functional activity in the network often find their simplest form as combinations of the underlying neuronal variables. In this paper, we provide a general framework for constructing low-dimensional dynamical systems that capture the essential systems-level information contained in large-scale networks of neurons. We demonstrate that these dimensionally-reduced models are capable of predicting the response to previously un-encountered input and that the coupling between systems-level variables can be used to reconstruct cellular-level functional connectivities. Furthermore, we show that these models may be constructed even in the absence of complete information about the underlying network.

We investigate the effect that noise has on the evolution of measurement strategies and competition in populations of organisms with sensory systems of differing fidelities. We address two questions motivated by experimental and theoretical work on sensory systems in noisy environments: (1) How complex must a sensory system be in order to face the need to develop adaptive measurement strategies that change depending on the noise level? (2) Does the principle of competitive exclusion for sensory systems force one population to win out over all others? We find that the answer to the first question is that even very simple sensory systems will need to change measurement strategies depending on the amount of noise in the environment. Interestingly, the answer to the second question is that, in general, at most two populations with different fidelity sensory systems may co-exist within a single environment.

This article describes the exposure of refugees, and particularly refugee children, to trauma, loss, and severe hardship in their countries of origin, while fleeing to their host countries, and after arrival in the host country. It then discusses acute psychosocial interventions for traumatized children and families, in particular the "Psychological First Aid" and "Skills for Psychological Recovery" guidelines developed by the National Child Traumatic Stress Network and the National Center for Posttraumatic Stress Disorder. It concludes by discussing the need to establish an evidence base for the effectiveness of such interventions.

The Sheng-Suzuki theorem states that all exponential operator splitting methods of order greater than 2 must contain negative time integration. There have been claims in the literature that higher-order splitting methods for deterministic parabolic equations are unstable due to this fact. We show stability for a class of higher-order splitting methods for integrating deterministic parabolic equations. We note that problems with backwards time integration will still exist for stochastic integration methods for which information is lost and backward timesteps become ill-defined. Therefore, completely positive splitting methods, such as those developed by Chin, still have an important place. We present numerical results from first-, second-, third-and fourth-order methods showing that the error becomes increasingly small as the order increases.

Neural oscillations can enhance feature recognition (Azouz and Gray Proceedings of the National Academy of Sciences of the United States of America, 97, 8110---8115 2000), modulate interactions between neurons (Womelsdorf et al. Science, 316, 1609---01612 2007), and improve learning and memory (Markowska et al. The Journal of Neuroscience, 15, 2063---2073 1995). Numerical studies have shown that coherent spiking can give rise to windows in time during which information transfer can be enhanced in neuronal networks (Abeles Israel Journal of Medical Sciences, 18, 83---92 1982; Lisman and Idiart Science, 267, 1512---1515 1995, Salinas and Sejnowski Nature Reviews. Neuroscience, 2, 539---550 2001). Unanswered questions are: 1) What is the transfer mechanism? And 2) how well can a transfer be executed? Here, we present a pulse-based mechanism by which a graded current amplitude may be exactly propagated from one neuronal population to another. The mechanism relies on the downstream gating of mean synaptic current amplitude from one population of neurons to another via a pulse. Because transfer is pulse-based, information may be dynamically routed through a neural circuit with fixed connectivity. We demonstrate the transfer mechanism in a realistic network of spiking neurons and show that it is robust to noise in the form of pulse timing inaccuracies, random synaptic strengths and finite size effects. We also show that the mechanism is structurally robust in that it may be implemented using biologically realistic pulses. The transfer mechanism may be used as a building block for fast, complex information processing in neural circuits. We show that the mechanism naturally leads to a framework wherein neural information coding and processing can be considered as a product of linear maps under the active control of a pulse generator. Distinct control and processing components combine to form the basis for the binding, propagation, and processing of dynamically routed information within neural pathways. Using our framework, we construct example neural circuits to 1) maintain a short-term memory, 2) compute time-windowed Fourier transforms, and 3) perform spatial rotations. We postulate that such circuits, with automatic and stereotyped control and processing of information, are the neural correlates of Crick and Koch's zombie modes.

Current quantum computing architectures lack the size and fidelity required for universal fault-tolerant operation, limiting the practical implementation of key quantum algorithms to all but the smallest problem sizes. In this work we propose an alternative method for general-purpose quantum computation that is ideally suited for such “prethreshold” superconducting hardware. Computations are performed in the n-dimensional single-excitation subspace (SES) of a system of n tunably coupled superconducting qubits. The approach is not scalable, but allows many operations in the unitary group SU(n) to be implemented by a single application of the Hamiltonian, bypassing the need to decompose a desired unitary into elementary gates. This feature makes large, nontrivial quantum computations possible within the available coherence time. We show how to use a programmable SES chip to perform fast amplitude amplification and phase estimation, two versatile quantum subalgorithms. We also show that an SES processor is well suited for Hamiltonian simulation, specifically simulation of the Schr¨ odinger equation with a real but otherwise arbitrary n × n Hamiltonian matrix. We discuss the utility and practicality of such a universal quantum simulator, and propose its application to the study of realistic atomic and molecular collisions.

Motivated by a problem in the evolution of sensory systems where gains obtained by improvements in detection are offset by increased costs, we prove several results about the dynamics of replicator equations with an n x n game matrix of the form: A( ij ) = a( i )b( j ) - c( i ). First, we show that, generically, for this class of game matrix, all equilibria must be on the 1-skeleton of the simplex, and that all interior solutions must limit to the boundary. Second, for the particular ordering, a1a2...an and b1b2...bn, which is most natural in the study of the evolution of sensory systems, we show that topological restrictions require a unique local attractor in every face of the simplex. We conjecture that the unique local attractor for the full simplex is, in fact, a global attractor, and prove this for nor = 5. In a separate argument supporting the conjecture, we show that there can be no chain recurrent invariant set entirely contained in the 1-skeleton of the simplex. Finally, we discuss the special, non-generic case and give a local description of the dynamics when there is an interior equilibrium.

To evaluate examiner variability in a superficial skin marker model of canine stifle kinematics.Experimental.Six clinically normal dogs.Dogs had 11 retroreflective markers fixed to the skin on the right hindlimb. Dogs were trotted 5 times through the calibrated testing space and this was repeated on 4 different testing days. Examiner A applied all markers to a dog and collected 6 good trials for analysis. The markers were then removed and Examiner B immediately repeated the process on the same dog. This was repeated for each dog on the 4 testing days. The dogs were trotted at a velocity of 1.70-2.10 m/s through the testing space to obtain the dynamic data sets. Comparisons were performed with Fourier analysis and Generalized Indicator Function Analysis (GIFA). Significance was set at P .05 for all comparisons.Fourier analysis and GIFA found differences within and between examiners. Fourier analysis found no differences in sagittal and transverse planes for the experienced (A) and novice examiner (B), respectively. Fourier analysis detected fewer differences for the experienced examiner (A).Variability occurs within and between examiners using the same kinematic model. Transverse and frontal plane kinematics produce variable results between examiners. Prior experience with the model reduces the amount of variability and results in consistent and repeatable sagittal plane kinematic data collection.

In optical imaging experiments of primary visual cortex, visual stimuli evoke a complicated dynamics. Typically, any stimulus with sufficient contrast evokes a response. Much of the response is the same regardless of which stimulus is presented. For instance, when oriented drifting gratings are presented to the visual system, over 90% of the response is the same from orientation to orientation. Small differences may be seen, however, between the responses to different orientations. A problem in the analysis of optical measurements of the response to stimulus in cortical tissue is the distinction of the 'global' or 'non-specific' response from the 'differential' or 'stimulus-specific' response. This problem arises whenever the signal of interest is the difference in response to various stimuli and is evident in many kinds of uni- and multivariate data. To this end, we present enhancements to a frequency-based method that we previously introduced called the periodic stacking method. These enhancements allow us to separately estimate the dynamics of both the average signal across all stimuli (the 'global' response) and deviations from the average amongst the various stimuli (the 'stimulus-specific' response) evoked in response to a set of stimuli. We also discuss improvements in the signal-to-noise ratio, relative to standard trial averaging methods, that result from the data-adaptive smoothing in our method.

In many experimental circumstances, heart dynamics are, to a good approximation, periodic. For this reason, it makes sense to use high-resolution methods in the frequency domain to visualize the spectrum of imaging data of the heart and to estimate the deterministic signal content and extract the periodic signal from background noise in experimental data. In this paper, we describe the first application of a new method that we call cardiac rhythm analysis which uses a combination of principal component analysis and multitaper harmonic analysis to extract periodic, deterministic signals from high-resolution imaging data of cardiac electrical activity, We show that this method significantly increases the signal-to-noise ratio of our recordings, allowing for better visualization of signal dynamics and more accurate quantification of the properties of electrical conduction. We visualize the spectra of three cardiac data sets of mouse hearts exhibiting sinus rhythm, paced rhythm and monomorphic tachycardia. Then, for pedagogical purposes, we investigate the tachycardia more closely, demonstrating the presence of two distinct periodicities in the re-entrant tachycardia. Analysis of the tachycardia shows that cardiac rhythm analysis not only allows for better visualization of electrical activity, but also provides new opportunities to study multiple periodicities in signal dynamics.

Social support is an important influence on success during lifestyle modification attempts. According to the theory of expressed emotion, support that is perceived by the recipient as critical or overprotective is unhelpful and perhaps harmful. This article describes an intervention for couples and preliminary assessment of its feasibility and acceptability by the nurses who trialed it. Four staff nurses and 2 advanced practice nurses administered the intervention to 15 couples and reported on the benefits and limitations of the intervention. One individual in each couple had a chronic or an acute cardiac illness. The nurses perceived the intervention as valuable; even a single session stimulated discussion between the couple about important issues. Complexity, logistic barriers, and time constraints limited the technique. The authors conclude that this intervention may be most useful to clinical nurse specialists who have additional education, training in communication, and teaching and consulting skills.

While many tools exist for identifying and quantifying individual cell types, few methods are available to assess the relationships between cell types in organs and tissues and how these relationships change during aging or disease states. We present a quantitative method for evaluating cellular organization, using the mouse thymus as a test organ. The thymus is the primary lymphoid organ responsible for generating T cells in vertebrates, and its proper structure and organization is essential for optimal function. Our method, Multitaper Circularly Averaged Spectral Analysis (MiCASA), identifies differences in the tissue-level organization with high sensitivity, including defining a novel type of phenotype by measuring variability as a specific parameter. MiCASA provides a novel and easily implemented quantitative tool for assessing cellular organization., There are few methods available that can quantify relationships between cell types in tissue images. Here the authors present a quantitative method to evaluate cellular organization, validated in the mouse thymus and spinal cord, called Multitaper Circularly Averaged Spectral Analysis (MiCASA).

The thymus is essential for proper development and maintenance of a T-cell repertoire that can respond to newly encountered antigens, but its function can be adversely affected by internal factors such as pregnancy and normal aging or by external stimuli such as stress, infection, chemotherapy and ionizing radiation. We have utilized a unique archive of thymus tissues, obtained from 165 individuals, exposed to the 1945 atomic bomb blast in Hiroshima, to study the long-term effects of receiving up to ~3 Gy dose of ionizing radiation on human thymus function. A detailed morphometric analysis of thymus activity and architecture in these subjects at the time of their natural deaths was performed using bright-field immunohistochemistry and dual-color immunofluorescence and compared to a separate cohort of nonexposed control subjects. After adjusting for age-related effects, increased hallmarks of thymic involution were observed histologically in individuals exposed to either low (5–200 mGy) or moderate-to-high (>200 mGy) doses of ionizing radiation compared to unirradiated individuals (

Despite the controversy regarding clinical efficacy, dental hygienists use the diode laser as an adjunct to non-surgical periodontal therapy. The technique to maximize successful laser therapy outcome is controversial as well. The purpose of this review is to explore the scientific foundation of the controversy surrounding the use of the diode laser as an adjunct to non-surgical periodontal therapy. Further, this paper addresses the weaknesses in study design, the heterogeneity of methodology in the published clinical studies, especially the laser parameters, and how these issues impact the collective clinical and microbial data, and thus conclusions regarding clinical efficacy. Evaluation of the literature identifies possible mechanisms that could contribute to the varied, often conflicting results among laser studies that are the foundation of the controversy surrounding clinical efficacy. These mechanisms include current paradigms of periodontal biofilm behavior, tissue response to laser therapy being dependent on tissue type and health, and that the successful therapeutic treatment window is specific to the target tissue, biofilm composition, laser wavelength, and laser energy delivered. Lastly, this paper discusses laser parameters used in the various clinical studies, and how their diversity contributes to the controversy. Although this review does not establish clinical efficacy, it does reveal the scientific foundation of the controversy and the need for standardized, well designed randomized controlled clinical trials to develop specific guidelines for using the laser as an adjunct to non-surgical periodontal therapy. Using evidence-based laser guidelines would allow dental hygienists to provide more effective non-surgical periodontal care.

The clustering of baryons and cold dark matter induced by a single moving string is analyzed numerically making use of a new three-dimensional Eulerian cosmological hydro code$^{1)}$ which is based on the PPM method to track the baryons and the PIC method to evolve the dark matter particles. A long straight string moving with a speed comparable to $c$ induces a planar overdensity (a``wake"). Since the initial perturbation is a velocity kick towards the plane behind the string and there is no initial Newtonian gravitational line source, the baryons are trapped in the center of the wake, leading to an enhanced baryon to dark matter ratio. The cold coherent flow leads to very low post--shock temperatures of the baryonic fluid. In contrast, long strings with a lot of small-scale structure (which can be described by adding a Newtonian gravitational line source) move slowly and form filamentary objects. The large central pressure due to the gravitational potential causes the baryons to be expelled from the central regions and leads to a relative deficit in the baryon to dark matter ratio. In this case, the velocity of the baryons is larger, leading to high post-shock temperatures., Comment: 36 pages (including 19 figures), TeX (with phyzzx) type, mpeg simulations available at http://www.damtp.cam.ac.uk/user/ats25/

The ordering of scalar fields after a phase transition in which a group G of global symmetries is spontaneously broken to a subgroup H provides a possible explanation for the origin of structure in the Universe, as well as leading to observable effects in condensed-matter systems. The field dynamics can depend, in principle, on the geometry and topology of the vacuum manifold G/H; for example, texture configurations which collapse and unwind will exist if the third homotopy group {pi}{sub 3}(G/H) is nontrivial. We numerically simulate the evolution of texturelike configurations in a number of different models, in order to determine the extent to which the geometry and topology of the vacuum manifold influence the field evolution. We find that the dynamics is affected by whether or not the theory supports strings or monopoles [characterized by {pi}{sub 1}(G/H) and {pi}{sub 2}(G/H), respectively]. In some of the theories studied, configurations with initially spherically symmetric energy densities are unstable to nonspherical collapse; these theories are also found to nucleate defects during the collapse. Models that do not support monopoles or strings behave similarly to each other, regardless of the specific vacuum manifold. {copyright} {ital 1997} {ital The American Physical Society}

Previously, electrophysiological studies in adult zebrafish have been limited to slice preparations or to eye cup preparations and electrorentinogram recordings. This paper describes how an adult zebrafish can be immobilized, intubated, and used for in vivo electrophysiological experiments, allowing recording of neural activity. Immobilization of the adult requires a mechanism to deliver dissolved oxygen to the gills in lieu of buccal and opercular movement. With our technique, animals are immobilized and perfused with habitat water to fulfill this requirement. A craniotomy is performed under tricaine methanesulfonate (MS-222; tricaine) anesthesia to provide access to the brain. The primary electrode is then positioned within the craniotomy window to record extracellular brain activity. Through the use of a multitube perfusion system, a variety of pharmacological compounds can be administered to the adult fish and any alterations in the neural activity can be observed. The methodology not only allows for observations to be made regarding changes in neurological activity, but it also allows for comparisons to be made between larval and adult zebrafish. This gives researchers the ability to identify the alterations in neurological activity due to the introduction of various compounds at different life stages.

A number of quantum algorithms have been performed on small quantum computers; these include Shor's prime factorization algorithm, error correction, Grover's search algorithm and a number of analog and digital quantum simulations. Because of the number of gates and qubits necessary, however, digital quantum particle simulations remain untested. A contributing factor to the system size required is the number of ancillary qubits needed to implement matrix exponentials of the potential operator. Here, we show that a set of tunneling problems may be investigated with no ancillary qubits and a cost of one single-qubit operator per time step for the potential evolution. We show that physically interesting simulations of tunneling using 2 qubits (i.e. on 4 lattice point grids) may be performed with 40 single and two-qubit gates. Approximately 70 to 140 gates are needed to see interesting tunneling dynamics in three-qubit (8 lattice point) simulations., 4 pages, 2 figures

Evaluate the effect of marker placement on kinematics of the canine stifle in 3 distinct hindlimb models.In vivo biomechanical study.Normal adult mixed-breed dogs (n=5).Ten retroreflective markers were affixed to the skin on the right rear leg of each dog to establish normal stifle kinematics. Four additional markers were placed around the greater trochanter (GT), 2 m cranial, caudal, dorsal, and ventral to evaluate single marker placement variability on kinematic model data. Dogs were walked and trotted 5 times through the calibrated space. Sagittal flexion and extension angle waveforms were acquired during each trial with 3 models that were produced simultaneously during each gait. The GT marker was reassigned to 1 of the 4 additional locations (cranial, caudal, dorsal, and ventral) to alter the kinematic model. Comparison of sagittal flexion and extension angle waveforms was performed with Generalized Indicator Function Analysis.Each model provided consistent equivalent sagittal flexion-extension data. Analysis revealed statistically significant differences between all GT locations. The differences were greatest in the cranial and caudal locations for all models.Deviation of the GT marker in the cranial/caudal direction from an anatomically normal position produces a greater degree of difference than deviation in a dorsal/ventral direction.

The ability to map functional connectivity is necessary for the study of the flow of activity in neuronal circuits. Optical imaging of calcium indicators, including FRET-based genetically encoded indicators and extrinsic dyes, is an important adjunct to electrophysiology and is widely used to visualize neuronal activity. However, techniques for mapping functional connectivities with calcium imaging data have been lacking. We present a procedure to compute reduced functional couplings between neuronal ensembles undergoing seizure activity from ratiometric calcium imaging data in three steps: (1) calculation of calcium concentrations and neuronal firing rates from ratiometric data; (2) identification of putative neuronal populations from spatio-temporal time-series of neural bursting activity; and then, (3) derivation of reduced connectivity matrices that represent neuronal population interactions. We apply our method to the larval zebrafish central nervous system undergoing chemoconvulsant-induced seizures. These seizures generate propagating, central nervous system-wide neural activity from which population connectivities may be calculated. This automatic functional connectivity mapping procedure provides a practical and user-independent means for summarizing the flow of activity between neuronal ensembles.