Energy Efficient Ant Colony Algorithms for Data Aggregation in Wireless Sensor Networks

In this paper, a family of ant colony algorithms called DAACA for data aggregation has been presented which contains three phases: the initialization, packet transmission and operations on pheromones. After initialization, each node estimates the remaining energy and the amount of pheromones to compute the probabilities used for dynamically selecting the next hop. After certain rounds of transmissions, the pheromones adjustment is performed periodically, which combines the advantages of both global and local pheromones adjustment for evaporating or depositing pheromones. Four different pheromones adjustment strategies are designed to achieve the global optimal network lifetime, namely Basic-DAACA, ES-DAACA, MM-DAACA and ACS-DAACA. Compared with some other data aggregation algorithms, DAACA shows higher superiority on average degree of nodes, energy efficiency, prolonging the network lifetime, computation complexity and success ratio of one hop transmission. At last we analyze the characteristic of DAACA in the aspects of robustness, fault tolerance and scalability.Comment: To appear in Journal of Computer and System Science

Protein kinase CK2α is overexpressed in colorectal cancer and modulates cell proliferation and invasion via regulating EMT-related genes

<p>Abstract</p> <p>Background</p> <p>Protein kinase CK2 is a highly conserved, ubiquitous protein serine/threonine kinase that phosphorylates many substrates and has a global role in numerous biological and pathological processes. Overexpression of the protein kinase CK2α subunit (CK2α) has been associated with the malignant transformation of several tissues, with not nearly as much focus on the role of CK2α in colorectal cancer (CRC). The aims of this study are to investigate the function and regulatory mechanism of CK2α in CRC development.</p> <p>Methods</p> <p>Expression levels of CK2α were analyzed in 144 patients (104 with CRC and 40 with colorectal adenoma) by immunohistochemistry. Proliferation, senescence, motility and invasion assays as well as immunofluorescence staining and western blots were performed to assess the effect of CK2α in CRC.</p> <p>Results</p> <p>The immunohistochemical expression of nuclear CK2α was stronger in tumor tissues than in adenomas and normal colorectal tissues. Suppression of CK2α by small-interfering RNA or the CK2α activity inhibitor emodin inhibited proliferation of CRC cells, caused G0/G1 phase arrest, induced cell senescence, elevated the expression of p53/p21 and decreased the expression of C-myc. We also found that knockdown of CK2α suppressed cell motility and invasion. Significantly, CK2α inhibition resulted in β-catenin transactivation, decreased the expression levels of vimentin and the transcription factors snail1 and smad2/3, and increased the expression of E-cadherin, suggesting that CK2α regulates the epithelial-mesenchymal transition (EMT) process in cancer cells.</p> <p>Conclusions</p> <p>Our results indicate that CK2α plays an essential role in the development of CRC, and inhibition of CK2α may serve as a promising therapeutic strategy for human CRC.</p

Cloning and characterization of maize ZmSPK1, a homologue to nonfermenting1-related protein kinase2

SnRK2s play important roles in plant stresses responses. One full-length cDNA encoding a SnRK2b homologue was isolated from maize by RT-PCR and named as ZmSPK1 (for stress-induced protein kinase). The ZmSPK1 protein has 364 amino acids with an estimated molecular mass of 41.8 KD and an isoelectric point of 5.8. The deduced protein sequence has the closest identities to the members of SnRK2b group. RT-PCR analysis showed that the ZmSPK1 expression was induced by mannitol, salt and abscisic acid (ABA). Furthermore, in different tissues the ZmSPK1 showed different expression patterns and was most abundant in reproductive organs. These results suggested that ZmSPK1 might play multiple roles in abiotic stress resistance pathways, as well as in plant reproductive development.Key words: Zea mays L., SnRK2b, expression pattern, abiotic stres

Learning biological neuronal networks with artificial neural networks: neural oscillations

First-principles-based modelings have been extremely successful in providing crucial insights and predictions for complex biological functions and phenomena. However, they can be hard to build and expensive to simulate for complex living systems. On the other hand, modern data-driven methods thrive at modeling many types of high-dimensional and noisy data. Still, the training and interpretation of these data-driven models remain challenging. Here, we combine the two types of methods to model stochastic neuronal network oscillations. Specifically, we develop a class of first-principles-based artificial neural networks to provide faithful surrogates to the high-dimensional, nonlinear oscillatory dynamics produced by neural circuits in the brain. Furthermore, when the training data set is enlarged within a range of parameter choices, the artificial neural networks become generalizable to these parameters, covering cases in distinctly different dynamical regimes. In all, our work opens a new avenue for modeling complex neuronal network dynamics with artificial neural networks.Comment: 18 pages, 8 figure