Testing Microfluidic Fully Programmable Valve Arrays (FPVAs)

Fully Programmable Valve Array (FPVA) has emerged as a new architecture for the next-generation flow-based microfluidic biochips. This 2D-array consists of regularly-arranged valves, which can be dynamically configured by users to realize microfluidic devices of different shapes and sizes as well as interconnections. Additionally, the regularity of the underlying structure renders FPVAs easier to integrate on a tiny chip. However, these arrays may suffer from various manufacturing defects such as blockage and leakage in control and flow channels. Unfortunately, no efficient method is yet known for testing such a general-purpose architecture. In this paper, we present a novel formulation using the concept of flow paths and cut-sets, and describe an ILP-based hierarchical strategy for generating compact test sets that can detect multiple faults in FPVAs. Simulation results demonstrate the efficacy of the proposed method in detecting manufacturing faults with only a small number of test vectors.Comment: Design, Automation and Test in Europe (DATE), March 201

Exploring complex miRNA-mRNA interactions with Bayesian networks by splitting-averaging strategy

© 2009 Liu et al; licensee BioMed Central Ltd.Background: microRNAs (miRNAs) regulate target gene expression by controlling their mRNAs post-transcriptionally. Increasing evidence demonstrates that miRNAs play important roles in various biological processes. However, the functions and precise regulatory mechanisms of most miRNAs remain elusive. Current research suggests that miRNA regulatory modules are complicated, including up-, down-, and mix-regulation for different physiological conditions. Previous computational approaches for discovering miRNA-mRNA interactions focus only on down-regulatory modules. In this work, we present a method to capture complex miRNA-mRNA interactions including all regulatory types between miRNAs and mRNAs. Results: We present a method to capture complex miRNA-mRNA interactions using Bayesian network structure learning with splitting-averaging strategy. It is designed to explore all possible miRNA-mRNA interactions by integrating miRNA-targeting information, expression profiles of miRNAs and mRNAs, and sample categories. We also present an analysis of data sets for epithelial and mesenchymal transition (EMT). Our results show that the proposed method identified all possible types of miRNA-mRNA interactions from the data. Many interactions are of tremendous biological significance. Some discoveries have been validated by previous research, for example, the miR-200 family negatively regulates ZEB1 and ZEB2 for EMT. Some are consistent with the literature, such as LOX has wide interactions with the miR-200 family members for EMT. Furthermore, many novel interactions are statistically significant and worthy of validation in the near future. Conclusions: This paper presents a new method to explore the complex miRNA-mRNA interactions for different physiological conditions using Bayesian network structure learning with splitting-averaging strategy. The method makes use of heterogeneous data including miRNA-targeting information, expression profiles of miRNAs and mRNAs, and sample categories. Results on EMT data sets show that the proposed method uncovers many known miRNA targets as well as new potentially promising miRNA-mRNA interactions. These interactions could not be achieved by the normal Bayesian network structure learning.Bing Liu, Jiuyong Li, Anna Tsykin, Lin Liu, Arti B. Gaur and Gregory J. Goodal

Improving SeNA-CNN by Automating Task Recognition

Catastrophic forgetting arises when a neural network is not capable of preserving the past learned task when learning a new task. There are already some methods proposed to mitigate this problem in arti cial neural networks. In this paper we propose to improve upon our previous state-of-the-art method, SeNA-CNN, such as to enable the automatic recognition in test time of the task to be solved and we experimentally show that it has excellent results. The experiments show the learning of up to 4 di erent tasks with a single network, without forgetting how to solve previous learned tasks.info:eu-repo/semantics/publishedVersio

Self-organized Boolean game on networks

A model of Boolean game with only one free parameter $p$ that denotes the strength of herd behavior is proposed where each agent acts according to the information obtained from his neighbors in network and those in the minority are rewarded. The simulation results indicate that the dynamic of system is sensitive to network topology, where the network of larger degree variance, i.e. the system of greater information heterogeneity, leads to less system profit. The system can self-organize to a stable state and perform better than random choice game, although only the local information is available to the agents. In addition, in heterogeneity networks, the agents with more information gain more than those with less information for a wide extent of herd strength $p$.Comment: 5 pages, 5 eps figure

Observation and Modeling of Gravity Wave Propagation through Reflection and Critical Layers above Andes Lidar Observatory at Cerro Pachón, Chile

A complex gravity wave event was observed from 04:30 to 08:10 UTC on 16 January 2015 by a narrow-band sodium lidar and an all-sky airglow imager located at Andes Lidar Observatory (ALO) in Cerro Pachón (30.25∘S, 70.73∘W), Chile. The gravity wave packet had a period of 18–35 min and a horizontal wavelength of about 40–50 km. Strong enhancements of the vertical wind perturbation, exceeding10 m s−1, were found at ∼90 km and ∼103 km, consistent with nearly evanescent wave behavior near a reflection layer. A reduction in vertical wavelength was found as the phase speed approached the background wind speed near ∼93 km. A distinct three-layered structure was observed in the lidar data due to refraction of the wave packet. A fully nonlinear model was used to simulate this event, which successfully reproduced the amplitudes and layered structure seen in observations. The model results provide dynamical insight, suggesting that a double reflection occurring at two separate heights caused the large vertical wind amplitudes, while the three-layered structure in the temperature perturbation was a result of relatively stable regions at those altitudes. The event provides a clear perspective on the filtering processes to which short-period, small-scale gravity waves are subject in mesosphere and lower thermosphere

A novel role for 3, 4-dichloropropionanilide (DCPA) in the inhibition of prostate cancer cell migration, proliferation, and hypoxia-inducible factor 1alpha expression

Background The amide class compound, 3, 4-dichloropropionanilide (DCPA) is known to affect multiple signaling pathways in lymphocyte and macrophage including the inhibition of NF-κB ability. However, little is known about the effect of DCPA in cancer cells. Hypoxia-inducible factor 1 (HIF-1) regulates the expression of many genes including vascular endothelial growth factor (VEGF), heme oxygenase 1, inducible nitric oxide synthase, aldolase, enolase, and lactate dehydrogenase A. HIF-1 expression is associated with tumorigenesis and angiogenesis. Methods We used Transwell assay to study cell migration, and used immunoblotting to study specific protein expression in the cells. Results In this report, we demonstrate that DCPA inhibited the migration and proliferation of DU145 and PC-3 prostate cancer cells induced by serum, insulin, and insulin-like growth factor I (IGF-I). We found that DCPA inhibited HIF-1 expression in a subunit-specific manner in these cancer cell lines induced by serum and growth factors, and decreased HIF-1α expression by affecting its protein stability. Conclusion DCPA can inhibit prostate cancer cell migration, proliferation, and HIF-1α expression, suggesting that DCPA could be potentially used for therapeutic purpose for prostate cancer in the future

Theoretical study on pp --> p n pi+ reaction at medium energies

The $pp\to p n \pi^+$ reaction is a channel with the largest total cross section for pp collision in COSY/CSR energy region. In this work, we investigate individual contributions from various $N^*$ and $\Delta^{*}$ resonances with mass up to about 2 GeV for the $pp\to p n \pi^+$ reaction. We extend a resonance model, which can reproduce the observed total cross section quite well, to give theoretical predictions of various differential cross sections for the present reaction at $T_p=2.88$ GeV. It could serve as a reference for identifying new physics in the future experiments at HIRFL-CSR.Comment: talk at STORI08, Sept. 2008, Lanzhou, Chin