An ontology-based approach for data integration in regionally interoperable healthcare systems

In order to best utilize the limited resource of medical resources, and to reduce the cost and improve the quality of medical treatment, we propose to build an interoperable regional healthcare systems among several levels of medical treatment organizations. In this paper, our approaches are as follows:(1) the ontology based approach is introduced as the methodology and technological solution for information integration; (2) the integration framework of data sharing among different organizations are proposed(3)the virtual database to realize data integration of hospital information system is established. Our methods realize the effective management and integration of the medical workflow and the mass information in the interoperable regional healthcare system. Furthermore, this research provides the interoperable regional healthcare system with characteristic of modularization, expansibility and the stability of the system is enhanced by hierarchy structure.

Multi-agent based modeling and simulation of complex system in hospital

This study puts forward a method to model and simulate the complex system of hospital on the basis of multi-agent technology. The formation of the agents of hospitals with intelligent and coordinative characteristics was designed, the message object was defined, and the model operating mechanism of autonomous activities and coordination mechanism was also designed. In addition, the Ontology library and Norm library etc. were introduced using semiotic method and theory, to enlarge the method of system modelling. Swarm was used to develop the multi-agent based simulation system, which is favorable for making guidelines for hospital's improving it's organization and management, optimizing the working procedure, improving the quality of medical care as well as reducing medical charge costs.

A semiotic approach of multi-agent simulation for policy evaluation

Nowadays the changing environment becomes the main challenge for most of organizations, since they have to evaluate proper policies to adapt to the environment. In this paper, we propose a multi-agent simulation method to evaluate policies based on complex adaptive system theory. Furthermore, we propose a semiotic EDA (Epistemic, Deontic, Axiological) agent model to simulate agent's behavior in the system by incorporating the social norms reflecting the policy. A case study is also provided to validate our approach. Our research present better adaptability and validity than the qualitative analysis and experiment approach and the semiotic agent model provides high creditability to simulate agents' behavior.

An ontological approach to personalized medical knowledge recommendation

Knowledge recommendation has become a promising method in supporting the clinicians decisions and improving the quality of medical services in the constantly changing clinical environment. However, current medical knowledge management systems cannot understand users requirements accurately and realize personalized recommendation. Therefore this paper proposes an ontological approach based on semiotic principles to personalized medical knowledge recommendations. In particular, healthcare domain knowledge is conceptualized and an ontology-based user profile is built. Furthermore, the personalized recommendation mechanism is illustrated.

An ontological approach to personalized medical knowledge matching

Knowledge management has become a promising method in supporting the clinicians′ decisions and improving the quality of medical services in the constantly changing clinical environment. However, current medical knowledge management systems cannot understand users′ requirements accurately and realize personalized matching. Therefore this paper proposes an ontological approach based on semiotic principles to personalized medical knowledge matching. In particular, healthcare domain knowledge is conceptualized and an ontology-based user profile is built. Furthmore, the personalized matching mechanism and algorithm are illustrated.

Dimensionality reduction assisted tensor clustering

This paper is concerned with tensor clustering with the assistance of dimensionality reduction approaches. A class of formulation for tensor clustering is introduced based on tensor Tucker decomposition models. In this formulation, an extra tensor mode is formed by a collection of tensors of the same dimensions and then used to assist a Tucker decomposition in order to achieve data dimensionality reduction. We design two types of clustering models for the tensors: PCA Tensor Clustering model and Non-negative Tensor Clustering model, by utilizing different regularizations. The tensor clustering can thus be solved by the optimization method based on the alternative coordinate scheme. Interestingly, our experiments show that the proposed models yield comparable or even better performance compared to most recent clustering algorithms based on matrix factorization.

B-spline neural network based single-carrier frequency domain equalisation for Hammerstein channels

A practical single-carrier (SC) block transmission with frequency domain equalisation (FDE) system can generally be modelled by the Hammerstein system that includes the nonlinear distortion effects of the high power amplifier (HPA) at transmitter. For such Hammerstein channels, the standard SC-FDE scheme no longer works. We propose a novel Bspline neural network based nonlinear SC-FDE scheme for Hammerstein channels. In particular, we model the nonlinear HPA, which represents the complex-valued static nonlinearity of the Hammerstein channel, by two real-valued B-spline neural networks, one for modelling the nonlinear amplitude response of the HPA and the other for the nonlinear phase response of the HPA. We then develop an efficient alternating least squares algorithm for estimating the parameters of the Hammerstein channel, including the channel impulse response coefficients and the parameters of the two B-spline models. Moreover, we also use another real-valued B-spline neural network to model the inversion of the HPA’s nonlinear amplitude response, and the parameters of this inverting B-spline model can be estimated using the standard least squares algorithm based on the pseudo training data obtained as a byproduct of the Hammerstein channel identification. Equalisation of the SC Hammerstein channel can then be accomplished by the usual one-tap linear equalisation in frequency domain as well as the inverse Bspline neural network model obtained in time domain. The effectiveness of our nonlinear SC-FDE scheme for Hammerstein channels is demonstrated in a simulation study.

On-line Gaussian mixture density estimator for adaptive minimum bit-error-rate beamforming receivers

We develop an on-line Gaussian mixture density estimator (OGMDE) in the complex-valued domain to facilitate adaptive minimum bit-error-rate (MBER) beamforming receiver for multiple antenna based space-division multiple access systems. Specifically, the novel OGMDE is proposed to adaptively model the probability density function of the beamformer’s output by tracking the incoming data sample by sample. With the aid of the proposed OGMDE, our adaptive beamformer is capable of updating the beamformer’s weights sample by sample to directly minimize the achievable bit error rate (BER). We show that this OGMDE based MBER beamformer outperforms the existing on-line MBER beamformer, known as the least BER beamformer, in terms of both the convergence speed and the achievable BER.

Joint multiple dictionary learning for tensor sparse coding

Traditional dictionary learning algorithms are used for finding a sparse representation on high dimensional data by transforming samples into a one-dimensional (1D) vector. This 1D model loses the inherent spatial structure property of data. An alternative solution is to employ Tensor Decomposition for dictionary learning on their original structural form —a tensor— by learning multiple dictionaries along each mode and the corresponding sparse representation in respect to the Kronecker product of these dictionaries. To learn tensor dictionaries along each mode, all the existing methods update each dictionary iteratively in an alternating manner. Because atoms from each mode dictionary jointly make contributions to the sparsity of tensor, existing works ignore atoms correlations between different mode dictionaries by treating each mode dictionary independently. In this paper, we propose a joint multiple dictionary learning method for tensor sparse coding, which explores atom correlations for sparse representation and updates multiple atoms from each mode dictionary simultaneously. In this algorithm, the Frequent-Pattern Tree (FP-tree) mining algorithm is employed to exploit frequent atom patterns in the sparse representation. Inspired by the idea of K-SVD, we develop a new dictionary update method that jointly updates elements in each pattern. Experimental results demonstrate our method outperforms other tensor based dictionary learning algorithms.

Yield and nitrogen accumulation and partitioning in winter wheat under elevated CO2: A 3-year free-air CO2 enrichment experiment

Fossil fuel combustion and deforestation have resulted in a rapid increase in atmospheric [CO2] since the 1950’s, and it will reach about 550 μmol mol−1 in 2050. Field experiments were conducted at the Free-air CO2 Enrichment facility in Beijing, China. Winter wheat was grown to maturity under elevated [CO2] (550 ± 17 μmol mol−1) and ambient [CO2] (415 ± 16 μmol mol−1), with high nitrogen (N) supply (HN, 170 kg N ha−1) and low nitrogen supply (LN, 100 kg N ha−1) for three growing seasons from 2007 to 2010. Elevated [CO2] increased wheat grain yield by 11.4% across the three years. [CO2]-induced yield enhancements were 10.8% and 11.9% under low N and high N supply, respectively. Nitrogen accumulation under elevated [CO2] was increased by 12.9% and 9.2% at the half-way anthesis and ripening stage across three years, respectively. Winter wheat had higher nitrogen demand under elevated [CO2] than ambient [CO2], and grain yield had a stronger correlation with plant N uptake after anthesis than before anthesis at high [CO2]. Our results suggest that regulating on the N application rate and time, is likely important for sustainable grain production under future CO2 climate.

Increased liver stiffness in alcoholic liver disease: differentiating fibrosis from steatohepatitis

To test if inflammation also interferes with liver stiffness (LS) assessment in alcoholic liver disease (ALD) and to provide a clinical algorithm for reliable fibrosis assessment in ALD by FibroScan (FS).

Mechanisms of species divergence through visual adaptation and sexual selection: Perspectives from a cichlid model system

The theory of ecological speciation suggests that assortative mating evolves most easily when mating preferences are;directly linked to ecological traits that are subject to divergent selection. Sensory adaptation can play a major role in this process,;because selective mating is often mediated by sexual signals: bright colours, complex song, pheromone blends and so on. When;divergent sensory adaptation affects the perception of such signals, mating patterns may change as an immediate consequence.;Alternatively, mating preferences can diverge as a result of indirect effects: assortative mating may be promoted by selection;against intermediate phenotypes that are maladapted to their (sensory) environment. For Lake Victoria cichlids, the visual environment;constitutes an important selective force that is heterogeneous across geographical and water depth gradients. We investigate;the direct and indirect effects of this heterogeneity on the evolution of female preferences for alternative male nuptial colours;(red and blue) in the genus Pundamilia. Here, we review the current evidence for divergent sensory drive in this system, extract;general principles, and discuss future perspectives

No evidence for a genetic association between female mating preference and male secondary sexual trait in a Lake Victoria cichlid fish

Sexual selection by female mating preference for male nuptial coloration has been suggested as a driving force in the rapid speciation of Lake Victoria cichlid fish. This process could have been facilitated or accelerated by genetic associations between female preference loci and male coloration loci. Preferences, as well as coloration, are heritable traits and are probably determined by more than one gene. However, little is known about potential genetic associations between these traits. In turbid water, we found a population that is variable in male nuptial coloration from blue to yellow to red. Males at the extreme ends of the phenotype distribution resemble a reproductively isolated species pair in clear water that has diverged into one species with blue-grey mates and one species with bright red males. Females of the turbid water population vary in mating preference coinciding with the male phenotype distribution. For the current study, these females were mated to blue males. We measured the coloration of the sires and male offspring. Parents-offspring regression showed that the sires did not affect male offspring coloration, which confirms earlier findings that the blue species breeds true. In contrast, male offspring coloration was determined by the identity of the dams, which suggests that there is heritable variation in male color genes between females. However, we found that mating preferences of the dams were not correlated with male offspring coloration. Thus, there is no evidence for strong genetic linkage between mating preference and the preferred trait in this population [Current Zoology 56 (1): 57-64 2010].

Bifidobacterium lactis attenuates onset of inflammation in a murine model of colitis

To assess the anti-inflammatory effect of the probiotic Bifidobacterium lactis (B. lactis) in an adoptive transfer model of colitis.

Effect of preoperative FOLFOX chemotherapy on CCL20/CCR6 expression in colorectal liver metastases

To evaluate the influence of preoperative FOLFOX chemotherapy on CCL20/CCR6 expression in liver metastases of stage IV colorectal cancer (CRC) patients.