Ligand density-dependent influence of arginine–glycine–aspartate functionalized gold nanoparticles on osteogenic and adipogenic differentiation of mesenchymal stem cells

Extracellular matrix (ECM) plays a very important role in regulating cell function and fate. It is highly desirable to fabricate biomimetic models to investigate the role of ECM in stem cell differentiation. In this study, arginine–glycine–aspartate (RGD)-modified gold nanoparticles (Au NPs) with tunable surface ligand density were prepared to mimic the ECM microenvironment. Their effect on osteogenic and adipogenic differentiation of human mesenchymal stem cells (MSCs) was investigated. The biomimetic Au NPs were taken up by MSCs in a ligand density-dependent manner. The biomimetic NPs with a high RGD density had an inhibitive effect on the alkaline phosphatase (ALP) activity, calcium deposition, and osteogenic marker gene expression of MSCs. Their effect on oil droplet formation and adipogenic marker gene expression was negative when RGD density was low, while their effect was promotive when RGD density was high. The biomimetic Au NPs regulated the osteogenic and adipogenic differentiation of MSCs mainly through affecting the focal adhesion and cytoskeleton. This study highlights the roles of biomimetic NPs on stem cell differentiation that could provide a meaningful strategy in fabricating functional biomaterials for tissue engineering and biomedical applications.

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Translational lemmas for DLOGTIME-uniform circuits,alternating TMs,and PRAMs

We present translational lemmas for the three standard models of parallel computation, and apply them to obtain tight hierarchy results. It is shown that, for arbitrarily small rational constant , (i) there is a language which can be accepted by a -uniform circuit family of depth and size but not by any -uniform circuit family of depth and size , (ii) there is a language which can be accepted by a -time -space ATM with l worktapes but not by any -time -space ATM with the same l worktapes if the number of tape symbols is fixed, and (iii) there is a language which can be accepted by a -time PRAM with processors but not by any -time PRAM with processors. Here, c > 0, d ≥ 1, r ₁ > 1, and r ₂ ≥ 1 are arbitrary rational constants, and l ≥ 2 is an arbitrary integer. Preliminary versions of different parts of this paper appeared in Proc. MCU 2004 (LNCS 3354) and Proc. FCT 2005 (LNCS 3623).     

Simultaneous rate-synchrony codes in populations of spiking neurons

Firing rates and synchronous firing are often simultaneously relevant signals, and they independently or cooperatively represent external sensory inputs, cognitive events, and environmental situations such as body position. However, how rates and synchrony comodulate and which aspects of inputs are effectively encoded, particularly in the presence of dynamical inputs, are unanswered questions. We examine theoretically how mixed information in dynamic mean input and noise input is represented by dynamic population firing rates and synchrony. In a subthreshold regime, amplitudes of spatially uncorrelated noise are encoded up to a fairly high input frequency, but this requires both rate and synchrony output channels. In a suprathreshold regime, means and common noise amplitudes can be simultaneously and separately encoded by rates and synchrony, respectively, but the input frequency for which this is possible has a lower limit.     

Learning commutative deterministic finite state automata in polynomial time

We consider the problem of learning the commutative subclass of regular languages in the on-line model of predicting {0,1∼-valued functions from examples and reinforcements due to Littlestone [7,4]. We show that the entire class of commutative deterministic finite state automata (CDFAs) of an arbitrary alphabet sizek is predictable inO(s ^k) time with the worst case number of mistakes bounded above byO(s ^kk logs), wheres is the number of states in the target DFA. As a corollary, this result implies that the class of CDFAs is also PAC-learnable from random labeled examples in timeO(s ^k) with sample complexity, using a different class of representations. The mistake bound of our algorithm is within a polynomial, for a fixed alphabet size, of the lower boundO(s+k) we obtain by calculating the VC-dimension of the class. Our result also implies the predictability of the class of finite sets of commutative DFAs representing the finite unions of the languages accepted by the respective DFAs. Part of this work was supported by the Office of Naval Research under contract number N00014-87-K-0401 while the author was at the Department of Computer and Information Science, University of Pennsylvania, and N0014-86-K-0454 while at the Department of Computer and Information Sciences, U.C. Santa Cruz. The author’s email address is abe@IBL.CL.nec.co.jp     

Role of disorder-order transformation in consolidation of ceramics

The ordering of stacking-disordered silicon carbide prepared from the elements by high energy ball milling was investigated during sintering. A sharp increase in density in the temperature region 1700–1800°C was associated with a decrease in the disorder. Samples which had low disorder density showed a more continuous sintering behavior with temperature. Highly dense (up to 99% relative density) SiC can be obtained at 1900°C under a pressure of 70 MPa with no hold time. Similar results were observed for structurally disordered carbon with 10 at% of boron. The sintering behavior exhibited an abrupt density increase in the narrow temperature region of 1450–1600°C and was associated with disorder-order transformation.     

Efficient distribution-free population learning of simple concepts

We consider a variant of the ‘population learning model’ proposed by Kearns and Seung [8], in which the learner is required to be ‘distribution-free’ as well as computationally efficient. A population learner receives as input hypotheses from a large population of agents and produces as output its final hypothesis. Each agent is assumed to independently obtain labeled sample for the target concept and output a hypothesis. A polynomial time population learner is said to PAC-learn a concept class, if its hypothesis is probably approximately correct whenever the population size exceeds a certain bound which is polynomial, even if the sample size for each agent is fixed at some constant. We exhibit some general population learning strategies, and some simple concept classes that can be learned by them. These strategies include the ‘supremum hypothesis finder’, the ‘minimum superset finder’ (a special case of the ‘supremum hypothesis finder’), and various voting schemes. When coupled with appropriate agent algorithms, these strategies can learn a variety of simple concept classes, such as the ‘high–low game’, conjunctions, axis-parallel rectangles and others. We give upper bounds on the required population size for each of these cases, and show that these systems can be used to obtain a speed up from the ordinary PAC-learning model [11], with appropriate choices of sample and population sizes. With the population learner restricted to be a voting scheme, what we have is effectively a model of ‘population prediction’, in which the learner is to predict the value of the target concept at an arbitrarily drawn point, as a threshold function of the predictions made by its agents on the same point. We show that the population learning model is strictly more powerful than the population prediction model. Finally, we consider a variant of this model with classification noise, and exhibit a population learner for the class of conjunctions in this model. This revised version was published online in June 2006 with corrections to the Cover Date.     

Theranostic Agent Combining Fullerene Nanocrystals and Gold Nanoparticles for Photoacoustic Imaging and Photothermal Therapy

Developing photoactivatable theranostic platforms with integrated functionalities of biocompatibility, targeting, imaging contrast, and therapy is a promising approach for cancer diagnosis and therapy. Here, we report a theranostic agent based on a hybrid nanoparticle comprising fullerene nanocrystals and gold nanoparticles (FGNPs) for photoacoustic imaging and photothermal therapy. Compared to gold nanoparticles and fullerene crystals, FGNPs exhibited stronger photoacoustic signals and photothermal heating characteristics by irradiating light with an optimal wavelength. Our studies demonstrated that FGNPs could kill cancer cells due to their photothermal heating characteristics in vitro. Moreover, FGNPs that are accumulated in tumor tissue via the enhanced permeation and retention effect can visualize tumor tissue due to their photoacoustic signal in tumor xenograft model mice. The theranostic agent with FGNPs shows promise for cancer therapy.     

Return Link Systems for Wireless Mobile Camera Using 42 GHz-Band with Frequency and Space Diversity Techniques

In this paper, return link systems for wireless mobile camera using 42 GHz-band in multipath fading environments are proposed. The return link systems for wireless mobile camera require wireless transmission with high reliability as same as the conventional wired camera systems. The proposed return link systems achieve transmission with high reliability by taking into consideration frequency and space diversity techniques. The proposed systems can be classified as 3 three types of return link systems according to diversity techniques： FD （frequency diversity） systems, SD （space diversity） systems and FD＋SD system （frequency and space diversity）. Computer simulations are carried out in order to confirm the effectiveness of the proposed return link systems. It is confirmed that the proposed three types of return link systems have an advantage about BER performances and effectively exploit frequency and space resources.     

Organic–Inorganic Nanohybrids as Novel Thermoelectric Materials: Hybrids of Polyaniline and Bismuth(III) Telluride Nanoparticles

Thermoelectric materials have received much attention recently from the viewpoint of global environmental issues and effective utilization of energy resources. Especially those effective at relatively low temperature, such as below 100°C, which are usually abandoned without use, have become noteworthy recently. From this point of view, organic thermoelectric materials are most attractive, because they could be prepared at low cost and applied in various locations due to their flexibility. We have investigated the thermoelectric properties of organic conducting polymers such as polyaniline, polypyrrole, and polyphenylenevinylene, and succeeded in increasing the thermoelectric performance by selecting dopants, stretching conducting films, etc. Recently we have focused on new systems of organic–inorganic hybrid thermoelectric materials. Herein we present the preparation of a novel system of hybrids of polyaniline and bismuth(III) telluride nanoparticles, starting from bismuth(III) chloride and tetrachlorotellurium by using polyvinylpyrrolidone as a protecting reagent, as well as their thermoelectric properties. The hybrids prepared by this particular method showed much higher thermoelectric performance than the starting organic conducting polymer.     

Spatial mapping of electronic states in κ-(BEDT-TTF)2X using infrared reflectivity

We review our recent work on spatial inhomogeneity of the electronic states in the strongly correlated molecular conductors κ-(BEDT-TTF)₂X. Spatial mapping of infrared spectra (SMIS) is used for imaging the distribution of the local electronic states. In molecular materials, the infrared response of the specific molecular vibration mode with a strong electron–molecular vibration coupling can reflect the electronic states via the change in the vibration frequency. By spatially mapping the frequency shift of the molecular vibration mode, an electronic phase separation has been visualized near the first-order Mott transition in the bandwidth-controlled organic conductor κ-(BEDT-TTF)₂Cu[N(CN)₂]Br. In addition to reviewing SMIS of the phase separation, we briefly mention the electronic and optical properties of κ-(BEDT-TTF)₂X.