Quantification of carboxyl-functionalized multiwall carbon nanotubes in plant tissues with programmed thermal analysis.

In this study, carboxyl functionalized multiwall carbon nanotubes (c-MWCNTs) in plant (lettuce [Lactuca sativa Bionda Ricciolina]) tissues were quantitatively analyzed with programmed thermal analysis coupled with a sequential digestion. Programmed thermal analysis evidenced a linear relationship between c-MWCNT-bound C and elemental C detected. A detection limit of 114-708 μg C g plant tissues (dry mass) was achieved for analysis of c-MWCNTs.

Individual differences in symptom severity and behavior predict neural activation during face processing in adolescents with autism.

Despite the impressive literature describing atypical neural activation in visuoperceptual face processing regions in autism, almost nothing is known about whether these perturbations extend to more affective regions in the circuitry and whether they bear any relationship to symptom severity or atypical behavior. Using fMRI, we compared face-, object-, and house-related activation in adolescent males with high-functioning autism (HFA) and typically developing (TD) matched controls. HFA adolescents exhibited hypo-activation throughout the core visuoperceptual regions, particularly in the right hemisphere, as well as in some of the affective/motivational face-processing regions, including the posterior cingulate cortex and right anterior temporal lobe.

Nanoparticle aggregation: challenges to understanding transport and reactivity in the environment.

Unique forms of manufactured nanomaterials, nanoparticles, and their suspensions are rapidly being created by manipulating properties such as shape, size, structure, and chemical composition and through incorporation of surface coatings. Although these properties make nanomaterial development interesting for new applications, they also challenge the ability of colloid science to understand nanoparticle aggregation in the environment and the subsequent effects on nanomaterial transport and reactivity. This review briefly covers aggregation theory focusing on Derjaguin-Landau-Verwey-Overbeak (DLVO)-based models most commonly used to describe the thermodynamic interactions between two particles in a suspension.

Environmental occurrences, behavior, fate, and ecological effects of nanomaterials: an introduction to the special series.

The release of engineered nanomaterials (ENMs) into the biosphere will increase as industries find new and useful ways to utilize these materials. Scientists and engineers are beginning to assess the material properties that determine the fate, transport, and effects of ENMs; however, the potential impacts of released ENMs on organisms, ecosystems, and human health remain largely unknown. This special collection of four review papers and four technical papers identifies many key and emerging knowledge gaps regarding the interactions between nanomaterials and ecosystems.

Design and implementation of spherical ultrasonic motor.

We present a mechanical design and implementation of spherical ultrasonic motor (SUSM) that is an actuator with multiple rotational degrees of freedom (multi-DOF). The motor is constructed of 3 annular stators and a spherical rotor and is much smaller and simpler than conventional multi-DOF mechanisms such as gimbals using servomotors. We designed a novel SUSM using experimental data from a single annular stator and a finite element method.

Data assimilation in large time-varying multidimensional fields.

In the physical sciences, e.g., meteorology and oceanography, combining measurements with the dynamics of the underlying models is usually referred to as data assimilation.

Three-dimensional modeling from two-dimensional video.

This paper presents the surface-based factorization method to recover three-dimensional (3-D) structure, i.e., the 3-D shape and 3-D motion, of a rigid object from a two-dimensional (2-D) video sequence.

Optimal tradeoff circular harmonic function correlation filter methods providing controlled in-plane rotation response.

Correlation methods are becoming increasingly attractive tools for image recognition and location. This renewed interest in correlation methods is spurred by the availability of high-speed image processors and the emergence of correlation filter designs that can optimize relevant figures of merit. In this paper, a new correlation filter design method is presented that allows one to optimally tradeoff among potentially conflicting correlation output performance criteria while achieving desired correlation peak value behavior in response to in-plane rotation of input images.

Reconstructing specimens using DIC microscope images.

Differential interference contrast (DIC) microscopy is a powerful visualization tool used to study live biological cells. Its use, however, has been limited to qualitative observations. The inherent nonlinear relationship between the object properties and the image intensity makes quantitative analysis difficult.

A musculotendon contribution for multijoint hand control.

For multijoint arm control, intersegmental dynamic properties have been observed to play a significant role. This paper focuses on the contribution of viscoelastic musculotendon properties during coordinated hand movements. Specifically, the musculotendon passive stiffness torque is shown to be more than 90% of the total passive torque, based on two-link planar model dynamics, during repetitive hand movements.

Towards hybrid swimming microrobots: bacteria assisted propulsion of polystyrene beads.

Compactness and efficiency of biomotors makes them superior to man-made actuators and a very attractive choice of actuation for micro/nanorobots. However, biomotors are difficult to work with due to complications associated with their isolation and reconstitution. To circumvent this problem, here we use flagellar motors inside the intact cell of S.

The effect of a distributed mass loading on the frequency response of a MEMS mesh resonator.

This paper reports on the development of an acoustic-wave biosensor based on integrated MEMS technology that promises high sensitivity and selectively without the need for molecular tagging or external optical equipment. The device works by detecting frequency shifts resulting from the selective binding of target molecules to the surface of a functionalized resonating polymer MEMS-composite membrane. Here, we characterize the frequency response of our metal-oxide MEMS resonators.

Applied force during vitreoretinal microsurgery with handheld instruments.

Applied force was measured in vivo during vitreoretinal surgery in rabbits, in three types of task: membrane peeling, vessel puncture/cannulation, and vessel dissection. Quantitative results are presented and compared with similar measurements taken in vitro in a porcine retina, in which no scleral interaction is present.

Active tremor compensation in microsurgery.

This work presents the development of an intelligent microsurgical instrument to perform real-time tremor compensation within a handheld tool. The intelligent instrument senses its own motion, distinguishes between voluntary and erroneous motion, and manipulates its tip to cancel the undesired component in real-time. The on-board sensing unit is made up of a magnetometer-aided all-accelerometer inertial measurement unit and sensor fusion is performed via a quaternion-based Kalman filtering.

Recovering genetic regulatory networks from micro-array data and location analysis data.

Learning large network (with hundreds of variables) is gaining interest of many researchers with the emergence of high-throughput biological data sources such as micro-array data. In this paper, we investigated the two popular large scale network structure learning algorithms, sparse candidate hill climbing (SCHC) and Grow-Shrinkage(GS) algorithm. The experiments show that in fact both of them have serious effectiveness problems when the number of variables(genes) is large compared to the number of instances(experimental conditions), which is a common case in micro-array data.

Learning overcomplete representations.

In an overcomplete basis, the number of basis vectors is greater than the dimensionality of the input, and the representation of an input is not a unique combination of basis vectors. Overcomplete representations have been advocated because they have greater robustness in the presence of noise, can be sparser, and can have greater flexibility in matching structure in the data. Overcomplete codes have also been proposed as a model of some of the response properties of neurons in primary visual cortex.

Detection filters and algorithm fusion for ATR.

Detection involves locating all candidate regions of interest (objects) in a scene independent of the object class with object distortions and contrast differences, etc., present. It is one of the most formidable problems in automatic target recognition, since it involves analysis of every local scene region.

Evolution of an artificial neural network based autonomous land vehicle controller.

This paper presents an evolutionary method for creating an artificial neural network based autonomous land vehicle controller. The evolved controllers perform better in unseen situations than those trained with an error backpropagation learning algorithm designed for this task. In this paper, an overview of the previous connectionist based approaches to this task is given, and the evolutionary algorithms used in this study are described in detail.

On the uniqueness of weights in single-layer perceptrons.

In this paper the geometric formulation of the single layer perceptron weight optimization problem previously described by Coetzee et al. (1993, 1996) is combined with results from other researchers on nonconvex set projections to describe sufficient conditions for uniqueness of weight solutions. It is shown that the perceptron data surface is pseudoconvex and has infinite folding, allowing for the specification of a region of desired vectors having unique projections purely in terms of the local curvature of the data surface.

On a natural homotopy between linear and nonlinear single-layer networks.

In this paper we formulate a homotopy approach for solving for the weights of a network by smoothly transforming a linear single layer network into a nonlinear perceptron network. While other researchers have reported potentially useful numerical results based on heuristics related to this approach, the work presented here provides the first rigorous exposition of the deformation process. Results include a complete description of how the weights relate to the data space, a proof of the global convergence and validity of the method, and a rigorous formulation of the generalized orthogonality theorem to provide a geometric perspective of the solution process.

Parallel data resampling and Fourier inversion by the scan-line method.

Fourier inversion is an efficient method for image reconstruction in a variety of applications, for example, in computed tomography and magnetic resonance imaging. Fourier inversion normally consists of two steps, interpolation of data onto a rectilinear grid, if necessary, and inverse Fourier transformation. Here, the authors present interpolation by the scan-line method, in which the interpolation algorithm is implemented in a form consisting only of row operations and data transposes.

Learning with limited numerical precision using the cascade-correlation algorithm.

A key question in the design of specialized hardware for simulation of neural networks is whether fixed-point arithmetic of limited numerical precision can be used with existing learning algorithms. An empirical study of the effects of limited precision in cascade-correlation networks on three different learning problems is presented. It is shown that learning can fail abruptly as the precision of network weights or weight-update calculations is reduced below a certain level, typically about 13 bits including the sign.

Competitive learning with generalized winner-take-all activation.

Competitive learning paradigms are usually defined with winner-take-all (WTA) activation rules. The paper develops a mathematical model for competitive learning paradigms using a generalization of the WTA activation rule (g-WTA). The model is a partial differential equation (PDE) relating the time rate of change in the ;density' of weight vectors to the divergence of a vector field called the neural flux.

Invariance and neural nets.

Application of neural nets to invariant pattern recognition is considered. The authors study various techniques for obtaining this invariance with neural net classifiers and identify the invariant-feature technique as the most suitable for current neural classifiers. A novel formulation of invariance in terms of constraints on the feature values leads to a general method for transforming any given feature space so that it becomes invariant to specified transformations.