WSN based intelligent cold chain management

This paper presents a cold chain monitoring system which is implemented by using ubiquitous computing technologies, Radio Frequency Identification (RFID) & Wireless Sensor Network (WSN). In this paper, we discuss how cold supply chain works and how we can monitor and control cold supply chain by using wireless tracking and sensing technologies. We propose a prototype design which will provide a well controlled and transparent cold chain system, which could help the users to manage their products’ environmental data in real time during the life cycle. Moreover, we highlight how the availability of product trace data in combination with historical condition-monitoring data can facilitate decision-making processes enhancing supply chain’s performance. Finally we discuss the integration works of these two technologies together in the cold supply chain management system. Hardware and software platform of WSN used in this system are also described in this paper

Transcription factor activity estimation based on particle swarm optimization and fast network component analysis

Proceedings of the IEEE Engineering in Medicine and Biology Society Conference, 2010, p. 1061-1064Transcription factors (TFs) play an important role in regulating the expression of genes. The accurate measurement of transcription factor activities (TFAs) depends on a series of experimental technologies of molecular biology and is intractable in most practical situations. Some signal processing methods for blind source separation have been applied in the prediction of TFAs from gene expression data. Most of such methods make use of statistical properties of the gene expression data only, leading to the inaccurate detection of TFAs. In contrast, network component analysis (NCA) can provide much improved result through utilizing the structural information of the gene regulatory network. However, the structure of the gene regulatory network, required by NCA, is not available in most practical cases so that NCA is not directly applicable. In this paper, we propose to use particle swarm optimization (PSO) to find the most plausible network structure iteratively from the gene expression data, with the assistance of recently developed fast algorithm for network component analysis (FastNCA). This novel approach to TFA inference can thus take advantage of NCA, even when the required network structure is unknown. The effectiveness of our novel approach has been demonstrated by applications to both simulated data and real gene expression microarray data, in the sense that TFAs can be inferred with high accuracy. © 2010 IEEE.published_or_final_versio

Meta-analysis on gene regulatory networks discovered by pairwise Granger causality

Identifying regulatory genes partaking in disease development is important to medical advances. Since gene expression data of multiple experiments exist, combining results from multiple gene regulatory network discoveries offers higher sensitivity and specificity. However, data for multiple experiments on the same problem may not possess the same set of genes, and hence many existing combining methods are not applicable. In this paper, we approach this problem using a number of meta-analysis methods and compare their performances. Simulation results show that vote counting is outperformed by methods belonging to the Fisher's chi-square (FCS) family, of which FCS test is the best. Applying FCS test to the real human HeLa cell-cycle dataset, degree distributions of the combined network is obtained and compared with previous works. Consulting the BioGRID database reveals the biological relevance of gene regulatory networks discovered using the proposed method.published_or_final_versio

Modelling uncertainty in transcriptome measurements enhances network component analysis of yeast metabolic cycle

Using high throughput DNA binding data for transcription factors and DNA microarray time course data, we constructed four transcription regulatory networks and analysed them using a novel extension to the network component analysis (NCA) approach. We incorporated probe level uncertainties in gene expression measurements into the NCA analysis by the application of probabilistic principal component analysis (PPCA), and applied the method to data from yeast metabolic cycle. Analysis shows statistically significant enhancement to periodicity in a large fraction of the transcription factor activities inferred from the model. For several of these we found literature evidence of post-transcriptional regulation. Accounting for probe level uncertainty of microarray measurements leads to improved network component analysis. Transcription factor profiles showing greater periodicity at their activity levels, rather than at the corresponding mRNA levels, for over half the regulators in the networks points to extensive post-transcriptional regulations. ©2009 IEEE.published_or_final_versio

Modulation of mitochondrial calcium as a pharmacological target for Alzheimer's disease

Perturbed neuronal calcium homeostasis is a prominent feature in Alzheimer's disease (AD). Mitochondria accumulate calcium ions (Ca2+) for cellular bioenergetic metabolism and suppression of mitochondrial motility within the cell. Excessive Ca2+ uptake into mitochondria often leads to mitochondrial membrane permeabilization and induction of apoptosis. Ca2+ is an interesting second messenger which can initiate both cellular life and death pathways in mitochondria. This review critically discusses the potential of manipulating mitochondrial Ca2+ concentrations as a novel therapeutic opportunity for treating AD. This review also highlights the neuroprotective role of a number of currently available agents that modulate different mitochondrial Ca2+ transport pathways. It is reasoned that these mitochondrial Ca2+ modulators are most effective in combination with agents that increase the Ca2+ buffering capacity of mitochondria. Modulation of mitochondrial Ca2+ handling is a potential pharmacological target for future development of AD treatments. © 2010 Elsevier B.V.postprin

An integrated approach for identifying wrongly labeled samples when performing classification in microarray data

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Large-scale customized indemnificatory housing area system design: Shenzhen 2011 "1unit - 100families - 10000residents" comprehensive gold award program in indemnificatory housing design contest: E-dot

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Application of Granger causality to gene regulatory network discovery

Article no. 6314142Granger causality (GC) has been applied to gene regulatory network discovery using DNA microarray time-series data. Since the number of genes is much larger than the data length, a full model cannot be applied in a straightforward manner, hence GC is often applied to genes pairwisely. In this paper, firstly we investigate with synthetic data and point out how spurious causalities (false discoveries) may emerge in pairwise GC detection. In addition, spurious causalities may also arise if the order of the vector autoregressive model is not high enough. Therefore, besides using a suitable model order, we recommend a full model over pairwise GC. This is possible if pairwise GC is first used to identify a network of interactions among only a few genes, and then all these interactions are validated with a full model again. If a full model is not possible, we recommend using model validation techniques to remove spurious discoveries. Secondly, we apply pairwise GC with model validation to a real dataset (HeLa). To estimate the model order, the Akaike information criterion is found to be more suitable than the Bayesian information criterion. Degree distribution and network hubs are obtained and compared with previous publications. The hubs tend to act as sources of interactions rather than receivers of interactions. © 2012 IEEE.published_or_final_versio

Dihydroxynaphthalene-based mimicry of fungal melanogenesis for multifunctional coatings

Material-independent adhesive action derived from polycatechol structures has been intensively studied due to its high applicability in surface engineering. Here, we for the first time demonstrate that a dihydroxynaphthalene-based fungal melanin mimetic, which exhibit a catechol-free structure, can act as a coating agent for material-independent surface modifications on the nanoscale. This mimetic was made by using laccase to catalyse the oxidative polymerization of specifically 2,7-dihydroxynaphthalene. Analyses of the product of this reaction, using Fourier transform infrared-attenuated total reflectance and X-ray photoelectron spectroscopy, bactericidal action, charge-dependent sorption behaviour, phenol content, Zeta potential measurements and free radical scavenging activity, yielded results consistent with it containing hydroxyphenyl groups. Moreover, nuclear magnetic resonance analyses of the product revealed that C-O coupling and C-C coupling were the main mechanisms for its synthesis, thus clearly excluding a catechol structure in the polymerization. This product, termed poly(2,7-DHN), was successfully deposited onto a wide variety of solid surfaces, including metals, polymeric materials, ceramics, biosurfaces and mineral complexes. The melanin-like polymerization could be used to co-immobilize other organic molecules, forming functional surfaces. In addition, the hydroxyphenyl group contained in the coated poly(2,7-DHN) induced secondary metal chelation/reduction and adhesion with proteins, suggesting the potential of this poly(2,7-DHN) layer to serve as a platform material for a variety of surface engineering applications. Moreover, the novel physicochemical properties of the poly(2,7-DHN) illuminate its potential applications as bactericidal, radical-scavenging and pollutant-sorbing agents.1143Ysciescopu