Gadolinium Doping Modulates the Enzyme-like Activity and Radical-Scavenging Properties of CeO Nanoparticles.

Their unique physicochemical properties and multi-enzymatic activity make CeO nanoparticles (CeO NPs) the most promising active component of the next generation of theranostic drugs. When doped with gadolinium ions, CeO NPs constitute a new type of contrast agent for magnetic resonance imaging, possessing improved biocatalytic properties and a high level of biocompatibility. The present study is focused on an in-depth analysis of the enzyme-like properties of gadolinium-doped CeO NPs (CeO:Gd NPs) and their antioxidant activity against superoxide anion radicals, hydrogen peroxide, and alkylperoxyl radicals.

Optogenetics Reveals Control of Cochlear Electromechanical Responses by Supporting Cells.

Cochlear sensitivity, essential for communication and exploiting the acoustic environment, results from sensory-motor outer hair cells (OHCs) operating in a structural scaffold of supporting cells and extracellular cortilymph within the organ of Corti (OoC). Cochlear sensitivity control is hypothesized to involve interaction between the OHCs and OoC supporting cells (e.g.

Liquid permeation and chemical stability of anodic alumina membranes.

A study on the chemical stability of anodic alumina membranes and their performance in long-term water and organic solvent permeation experiments is reported. Anodic alumina possesses high stability for both protonic and aprotonic organic solvents. However, serious degradation of the membrane occurs in pure water, leading to a drastic decrease of permeance (over 20% of the initial value after the passing of 0.

Size-Dependent Structure Relations between Nanotubes and Encapsulated Nanocrystals.

The structural organization of compounds in a confined space of nanometer-scale cavities is of fundamental importance for understanding the basic principles for atomic structure design at the nanolevel. Here, we explore size-dependent structure relations between one-dimensional PbTe nanocrystals and carbon nanotube containers in the diameter range of 2.0-1.

Nanomechanical humidity detection through porous alumina cantilevers.

We present here the behavior of the resonance frequency of porous anodic alumina cantilever arrays during water vapor adsorption and emphasize their possible use in the micromechanical sensing of humidity levels at least in the range of 10-22%. The sensitivity of porous anodic aluminium oxide cantilevers (Δf/Δm) and the humidity sensitivity equal about 56 Hz/pg and about 100 Hz/%, respectively. The approach presented here for the design of anodic alumina cantilever arrays by the combination of anodic oxidation and photolithography enables easy control over porosity, surface area, geometric and mechanical characteristics of the cantilever arrays for micromechanical sensing.

Controlled way to prepare quasi-1D nanostructures with complex chemical composition in porous anodic alumina.

Herein we propose a novel approach to the preparation of quasi-1D nanostructures with various chemical compositions based on infiltration of colloidal solution into the asymmetric anodic alumina membrane. The proposed technique was successfully applied for the preparation of ordered arrays of the magnetically hard anisotropic hexaferrite nanostructures.

Fabrication of artificial opals by electric-field-assisted vertical deposition.

We present a new technique for large-scale fabrication of colloidal crystals with controllable quality and thickness. The method is based on vertical deposition in the presence of a DC electric field normal to the conducting substrate. The crystal structure and quality are quantitatively characterized by microradian X-ray diffraction, scanning electron microscopy, and optical reflectometry.

Targeting the lymphotoxin-beta receptor with agonist antibodies as a potential cancer therapy.

The lymphotoxin-beta receptor (LT beta R) is a tumor necrosis factor receptor family member critical for the development and maintenance of various lymphoid microenvironments. Herein, we show that agonistic anti-LT beta R monoclonal antibody (mAb) CBE11 inhibited tumor growth in xenograft models and potentiated tumor responses to chemotherapeutic agents. In a syngeneic colon carcinoma tumor model, treatment of the tumor-bearing mice with an agonistic antibody against murine LT beta R caused increased lymphocyte infiltration and necrosis of the tumor.

Topology of gene expression networks as revealed by data mining and modeling.

Motivation: Interpretation of high-throughput gene expression profiling requires a knowledge of the design principles underlying the networks that sustain cellular machinery. Recently a novel approach based on the study of network topologies has been proposed. This methodology has proven to be useful for the analysis of a variety of biological systems, including metabolic networks, networks of protein-protein interactions, and gene networks that can be derived from gene expression data.

Kirrel2, a novel immunoglobulin superfamily gene expressed primarily in beta cells of the pancreatic islets.

A novel immunoglobulin superfamily (Igsf) protein gene was discovered by computational analysis of human draft genomic DNA, and multiple cDNA clones were obtained. The protein encoded by this gene contains five Ig domains, one transmembrane domain, and an intracellular domain. It has significant similarity with several known Igsf proteins, including Drosophila RST (irregular chiasm C-roughest) protein and mammalian KIRREL (kin of irregular chiasm C-roughest), NEPH1, and NPHS1 (nephrin) proteins.

Comparison of gene expression patterns induced by treatment of human umbilical vein endothelial cells with IFN-alpha 2b vs. IFN-beta 1a: understanding the functional relationship between distinct type I interferons that act through a common receptor.

We analyzed whether interferon-alpha 2b (IFN-alpha 2b) and IFN-beta 1a engage their common receptor to generate activated receptor complexes possessing distinct signaling properties. Human vascular endothelial cells (HUVEC) are 100-1000-fold more sensitive to IFN-beta 1a than to IFN-alpha 2b in in vitro assays. An nonarray-based expression profiling (GeneCalling) technology was employed to compare the patterns and levels of gene expression induced by these IFN as the broadest means by which signaling events could be measured.

Analysis of temporal gene expression profiles: clustering by simulated annealing and determining the optimal number of clusters.

Motivation: Cluster analysis of genome-wide expression data from DNA microarray hybridization studies has proved to be a useful tool for identifying biologically relevant groupings of genes and samples. In the present paper, we focus on several important issues related to clustering algorithms that have not yet been fully studied.

Results: We describe a simple and robust algorithm for the clustering of temporal gene expression profiles that is based on the simulated annealing procedure.

Local multiple sequence alignment using dead-end elimination.

Motivation: Local multiple sequence alignment is a basic tool for extracting functionally important regions shared by a family of protein sequences. We present an effectively polynomial-time algorithm for rigorously solving the local multiple alignment problem.

Results: The algorithm is based on the dead-end elimination procedure that makes it possible to avoid an exhaustive search.

GeneMark.hmm: new solutions for gene finding.

The number of completely sequenced bacterial genomes has been growing fast. There are computer methods available for finding genes but yet there is a need for more accurate algorithms. The GeneMark.

Cortical dynamic tuning: the role of intrinsic connections, as revealed by modeling.

A current challenge in computational neuroscience is to elucidate the role of cortical circuitry in information processing and in generating motor output. Our understanding of the functional significance of specifically organized feedback connections is progressing rapidly as researchers establish the equivalence of theoretical models to biological neural circuits. Modeling studies of different neural structures, along with quantitative comparisons of model performance to biological data, have recently helped to identify the basic features of synaptic connectivity that may play important roles in cortical operations.

A simulated actuator driven by motor cortical signals.

One problem in motor control concerns the mechanism whereby the central nervous system translates the motor cortical command encoded in cell activity into a coordinated contraction of limb muscles to generate a desired motor output. This problem is closely related to the design of adaptive systems that transform neuronal signals chronically recorded from the motor cortex into the physiologically appropriate motor output of multijoint prosthetic limbs. In this study we demonstrated how this transformation can be carried out by an artificial neural network using as command signals the actual impulse activity obtained from recordings in the motor cortex of monkeys during the performance of a task that required the exertion of force in different directions.

Neural computations underlying the exertion of force: a model.

We have developed a model that simulates possible mechanisms by which supraspinal neuronal signals coding forces could converge in the spinal cord and provide an ongoing integrated signal to the motoneuronal pools whose activation results in the exertion of force. The model consists of a three-layered neural network connected to a two-joint-six-muscle model of the arm. The network layers represent supraspinal populations, spinal cord interneurons, and motoneuronal pools.

Modeling motor cortical operations by an attractor network of stochastic neurons.

Understanding the neural computations performed by the motor cortex requires biologically plausible models that account for cell discharge patterns revealed by neurophysiological recordings. In the present study the motor cortical activity underlying movement generation is modeled as the dynamic evolution of a large fully recurrent network of stochastic spiking neurons with noise superimposed on the synaptic transmission. We show that neural representations of the learned movement trajectories can be stored in the connectivity matrix in such a way that, when activated, a particular trajectory evolves in time as a dynamic attractor of the system while individual neurons fire irregularly with large variability in their interspike intervals.

Overlapping neural networks for multiple motor engrams.

The hypothesis was tested that learned movement trajectories of different shapes can be stored in, and generated by, largely overlapping neural networks. Indeed, it was possible to train a massively interconnected neural network to generate different shapes of internally stored, dynamically evolving movement trajectories using a general-purpose core part, common to all networks, and a special-purpose part, specific for a particular trajectory. The weights of connections between the core units do not carry any information about trajectories.

Directional operations in the motor cortex modeled by a neural network of spiking neurons.

A neural network with realistically modeled, spiking neurons is proposed to model ensemble operations of directionally tuned neurons in the motor cortex. The model reproduces well directional operations previously identified experimentally, including the prediction of the direction of an upcoming movement in reaching tasks and the rotation of the neuronal population vector in a directional transformation task.

Cognitive neurophysiology of the motor cortex.

A major challenge of current neuroscience is to elucidate the brain mechanisms that underlie cognitive function. There is no doubt that cognitive processing in the brain engages large populations of cells. This article explores the logic of investigating these problems by combining psychological studies in human subjects and neurophysiological studies of neuronal populations in the motor cortex of behaving monkeys.

A dynamical neural network model for motor cortical activity during movement: population coding of movement trajectories.

As a dynamical model for motor cortical activity during hand movement we consider an artificial neural network that consists of extensively interconnected neuron-like units and performs the neuronal population vector operations. Local geometrical parameters of a desired curve are introduced into the network as an external input. The output of the model is a time-dependent direction and length of the neuronal population vector which is calculated as a sum of the activity of directionally tuned neurons in the ensemble.