A Trabecular Bone Explant Model of Osteocyte–Osteoblast Co-Culture for Bone Mechanobiology

The osteocyte network is recognized as the major mechanical sensor in the bone remodeling process, and osteocyte-osteoblast communication acts as an important mediator in the coordination of bone formation and turnover. In this study, we developed a novel 3D trabecular bone explant co-culture model that allows live osteocytes situated in their native extracellular matrix environment to be interconnected with seeded osteoblasts on the bone surface. Using a low-level medium perfusion system, the viability of in situ osteocytes in bone explants was maintained for up to 4 weeks, and functional gap junction intercellular communication (GJIC) was successfully established between osteocytes and seeded primary osteoblasts. Using this novel co-culture model, the effects of dynamic deformational loading, GJIC, and prostaglandin E-2 (PGE(2)) release on functional bone adaptation were further investigated. The results showed that dynamical deformational loading can significantly increase the PGE(2) release by bone cells, bone formation, and the apparent elastic modulus of bone explants. However, the inhibition of gap junctions or the PGE(2) pathway dramatically attenuated the effects of mechanical loading. This 3D trabecular bone explant co-culture model has great potential to fill in the critical gap in knowledge regarding the role of osteocytes as a mechano-sensor and how osteocytes transmit signals to regulate osteoblasts function and skeletal integrity as reflected in its mechanical properties.

Forecasting Daily Chlorophyll a Concentration during the Spring Phytoplankton Bloom Period in Xiangxi Bay of the Three-Gorges Reservoir by Means of a Recurrent Artificial Neural Network

A recurrent artificial neural network was used for 0-and 7-days-ahead forecasting of daily spring phytoplankton bloom dynamics in Xiangxi Bay of Three-Gorges Reservoir with meteorological, hydrological, and limnological parameters as input variables. Daily data from the depth of 0.5 m was used to train the model, and data from the depth of 2.0 m was used to validate the calibrated model. The trained model achieved reasonable accuracy in predicting the daily dynamics of chlorophyll a both in 0-and 7-days-ahead forecasting. In 0-day-ahead forecasting, the R-2 values of observed and predicted data were 0.85 for training and 0.89 for validating. In 7-days-ahead forecasting, the R-2 values of training and validating were 0.68 and 0.66, respectively. Sensitivity analysis indicated that most ecological relationships between chlorophyll a and input environmental variables in 0-and 7-days-ahead models were reasonable. In the 0-day model, Secchi depth, water temperature, and dissolved silicate were the most important factors influencing the daily dynamics of chlorophyll a. And in 7-days-ahead predicting model, chlorophyll a was sensitive to most environmental variables except water level, DO, and NH3N.

Using the moving window incorporated neural network to forecast the population behavior of Nostocales spp. in the River Darling, Australia

The paper demonstrates the nonstationarity of algal population behaviors by analyzing the historical populations of Nostocales spp. in the River Darling, Australia. Freshwater ecosystems are more likely to be nonstationary, instead of stationary. Nonstationarity implies that only the near past behaviors could forecast the near future for the system. However, nonstionarity was not considered seriously in previous research efforts for modeling and predicting algal population behaviors. Therefore the moving window technique was incorporated with radial basis function neural network (RBFNN) approach to deal with nonstationarity when modeling and forecasting the population behaviors of Nostocales spp. in the River Darling. The results showed that the RBFNN model could predict the timing and magnitude of algal blooms of Nostocales spp. with high accuracy. Moreover, a combined model based on individual RBFNN models was implemented, which showed superiority over the individual RBFNN models. Hence, the combined model was recommended for the modeling and forecasting of the phytoplankton populations, especially for the forecasting.

Modeling phytoplankton dynamics in the River Darling (Australia) using the radial basis function neural network

A radial basis function neural network was employed to model the abundance of cyanobacteria. The trained network could predict the populations of two bloom forming algal taxa with high accuracy, Nostocales spp. and Anabaena spp., in the River Darling, Australia. To elucidate the population dynamics for both Nostocales spp. and Anabaena spp., sensitivity analysis was performed with the following results. Total Kjeldahl nitrogen had a very strong influence on the abundance of the two algal taxa, electrical conductivity had a very strong negative relationship with the population of the two algal species, and flow was identified as one dominant factor influencing algal blooms after a scatter plot revealed that high flow could significantly reduce the algal biomass for both Nostocales spp. and Anabaena spp. Other variables such as turbidity, color, and pH were less important in determining the abundance and succession of the algal blooms.

Measurement of a reciprocal four-port transmission line structure using the 16-term error model

A new method to measure reciprocal four-port structures, using a 16-term error model, is presented. The measurement is based on 5 two-port calibration standards connected to two of the ports, while the network analyzer is connected to the two remaining ports. Least-squares-fit data reduction techniques are used to lower error sensitivity. The effect of connectors is deembedded using closed-form equations. (C) 2007 Wiley Periodicals, Inc.

Rate-equation-based circuit model of high-speed semiconductor lasers

Based oil rare equations of semiconductor laser, a symbolically-defined model for optical transmission system performance evaluation and network characterization in both time- and frequency domains is presented. The steady-state and small-signal characteristics, such as current-photon density curve, current-voltage curve, and input impedance, call be predicted from this model. Two important dynamic characteristics, second-order harmonic distortion and two-tone third-order intermodulation products, are evaluated under different driving conditions. Experiments show that the simulated results agree well with the published data. (c) 2007 Wiley Periodicals, Inc.

The design algorithm of an associative memory neural network

A design algorithm of an associative memory neural network is proposed. The benefit of this design algorithm is to make the designed associative memory model can implement the hoped situation. On the one hand, the designed model has realized the nonlinear association of infinite value pattern from n dimension space to m dimension space. The result has improved the ones of some old associative memory neural network. On the other hand, the memory samples are in the centers of the fault-tolerant. In average significance the radius of the memory sample fault-tolerant field is maximum.

Lossless data compression scheme based on neural network and SVM

First, the compression-awaited data are regarded Lis character strings which are produced by virtual information source mapping M. then the model of the virtual information source M is established by neural network and SVM. Last we construct a lossless data compression (coding) scheme based oil neural network and SVM with the model, an integer function and a SVM discriminant. The scheme differs from the old entropy coding (compressions) inwardly, and it can compress some data compressed by the old entropy coding.

A 16-term error model based on linear equations of voltage and current variables

Formulation of a 16-term error model, based on the four-port ABCD-matrix and voltage and current variables, is outlined. Matrices A, B, C, and D are each 2 x 2 submatrices of the complete 4 x 4 error matrix. The corresponding equations are linear in terms of the error parameters, which simplifies the calibration process. The parallelism with the network analyzer calibration procedures and the requirement of five two-port calibration measurements are stressed. Principles for robust choice of equations are presented. While the formulation is suitable for any network analyzer measurement, it is expected to be a useful alternative for the nonlinear y-parameter approach used in intrinsic semiconductor electrical and noise parameter measurements and parasitics' deembedding.

Kinetic Model Study on Enzymatic Hydrolysis of Cellulose Using Artificial Neural Networks

Enzymatic hydrolysis of cellulose was highly complex because of the unclear enzymatic mechanism and many factors that affect the heterogeneous system. Therefore, it is difficult to build a theoretical model to study cellulose hydrolysis by cellulase. Artificial neural network (ANN) was used to simulate and predict this enzymatic reaction and compared with the response surface model (RSM). The independent variables were cellulase amount X-1, substrate concentration X-2, and reaction time X-3, and the response variables were reducing sugar concentration Y-1 and transformation rate of the raw material Y-2. The experimental results showed that ANN was much more suitable for studying the kinetics of the enzymatic hydrolysis than RSM. During the simulation process, relative errors produced by the ANN model were apparently smaller than that by RSM except one and the central experimental points. During the prediction process, values produced by the ANN model were much closer to the experimental values than that produced by RSM. These showed that ANN is a persuasive tool that can be used for studying the kinetics of cellulose hydrolysis catalyzed by cellulase.

Biomimetic (topological) pattern recognition - A new model of pattern recognition theory and its application

A new theoretical model of Pattern Recognition principles was proposed, which is based on "matter cognition" instead of "matter classification" in traditional statistical Pattern Recognition. This new model is closer to the function of human being, rather than traditional statistical Pattern Recognition using "optimal separating" as its main principle. So the new model of Pattern Recognition is called the Biomimetic Pattern Recognition (BPR)(1). Its mathematical basis is placed on topological analysis of the sample set in the high dimensional feature space. Therefore, it is also called the Topological Pattern Recognition (TPR). The fundamental idea of this model is based on the fact of the continuity in the feature space of any one of the certain kinds of samples. We experimented with the Biomimetic Pattern Recognition (BPR) by using artificial neural networks, which act through covering the high dimensional geometrical distribution of the sample set in the feature space. Onmidirectionally cognitive tests were done on various kinds of animal and vehicle models of rather similar shapes. For the total 8800 tests, the correct recognition rate is 99.87%. The rejection rate is 0.13% and on the condition of zero error rates, the correct rate of BPR was much better than that of RBF-SVM.

Electrochemiluminescence detection of NADH and ethanol based on partial sulfonation of sol-gel network with gold nanoparticles

We developed a stable, sensitive electrochemiluminescence (ECL) biosensor based on the synthesis of a new sol-gel material with the ion-exchange capacity sol-gel to coimmobilize the Ru(bpy)(3)(2+) and enzyme. The partial sulfonated (3-mercaptopropyl)-trimethoxysilane sol-gel (PSSG) film acted as both an ion exchanger for the immobilization of Ru(bpy)(3)(2+) and a matrix to immobilize gold nanoparticles (AuNPs). The AuNPs/PSSG/Ru(bpy)(3)(2+) film modified electrode allowed sensitive the ECL detection of NADH as low as 1 nM. Such an ability of AuNPs/PSSG/Ru(bpy)(3)(2+) film to promote the electron transfer between Ru(bpy)(3)(2+) and the electrode suggested a new, promising biocompatible platform for the development of dehydrogenase-based ECL biosensors. With alcohol dehydrogenase (ADH) as a model, we then constructed an ethanol biosensor, which had a linear range of 5 mu M to 5.2 mM with a detection limit of 12 nM.

Quantifying robustness and dissipation cost of yeast cell cycle network: The funneled energy landscape perspectives

We study the origin of robustness of yeast cell cycle cellular network through uncovering its underlying energy landscape. This is realized from the information of the steady-state probabilities by solving a discrete set of kinetic master equations for the network. We discovered that the potential landscape of yeast cell cycle network is funneled toward the global minimum, G1 state. The ratio of the energy gap between G1 and average versus roughness of the landscape termed as robustness ratio ( RR) becomes a quantitative measure of the robustness and stability for the network. The funneled landscape is quite robust against random perturbations from the inherent wiring or connections of the network. There exists a global phase transition between the more sensitive response or less self-degradation phase leading to underlying funneled global landscape with large RR, and insensitive response or more self-degradation phase leading to shallower underlying landscape of the network with small RR. Furthermore, we show that the more robust landscape also leads to less dissipation cost of the network. Least dissipation and robust landscape might be a realization of Darwinian principle of natural selection at cellular network level. It may provide an optimal criterion for network wiring connections and design.

IONIC-CONDUCTIVITY AND DYNAMIC MECHANICAL RELAXATION OF A 2-COMPONENT EPOXY NETWORK-LICLO4 ELECTROLYTE SYSTEM

The correlation between mechanical relaxation and ionic conductivity was investigated in a two-component epoxy network-LiClO4 electrolyte system. The network was composed of diglycidyl ether of polyethylene glycol (DGEPEG) and triglycidyl ether of glycerol (TGEG). The effects of salt concentration, molecular weight of PEG in DGEPEG and the proportion of DGEPEG (1000) in DGEPEG/TGEG ratio on the ionic conductivity and the mechanical relaxation of the system were studied. It was found that, among the three influential factors, the former reinforces the network chains, reduces the free volume fraction and thus increases the relaxation time of the segmental motion, which in turn lowers the ionic conductivity of the specimen. Conversely, the latter two increase the free volume and thus the chain flexibility, showing an opposite effect. From the iso-free-volume plot of the shift factor log at and reduced ionic conductivity, it is noted that the plot can be used to examine the temperature dependence of segmental mobility and seems to be useful to judge whether the incorporated salt has been dissociated completely. Besides, the ionic conductivity and relaxation time at constant reference temperature are linearly correlated with each other in all the three cases. This result gives an additional experimental confirmation of the coordinated motion model of the ionic hopping with the moving polymer chain segment, which is generally used to explain the ionic conduction in non-glassy amorphous polymer electrolytes.

A new model to estimate significant wave heights with ERS-1/2 scatterometer data

A new model is proposed to estimate the significant wave heights with ERS-1/2 scatterometer data. The results show that the relationship between wave parameters and radar backscattering cross section is similar to that between wind and the radar backscattering cross section. Therefore, the relationship between significant wave height and the radar backscattering cross section is established with a neural network algorithm, which is, if the average wave period is <= 7s, the root mean square of significant wave height retrieved from ERS-1/2 data is 0.51 m, or 0.72 m if it is >7s otherwise.