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Although activating mutations in RAS oncogenes are known to result in aberrant signaling through multiple pathways, the role of microRNAs (miRNAs) in the Ras oncogenic program remains poorly characterized. Here we demonstrate that Ras activation leads to repression of the miR-143/145 cluster in cells of human, murine, and zebrafish origin. Loss of miR-143/145 expression is observed frequently in KRAS mutant pancreatic cancers, and restoration of these miRNAs abrogates tumorigenesis. miR-143/145 down-regulation requires the Ras-responsive element-binding protein (RREB1), which represses the miR-143/145 promoter. Additionally, KRAS and RREB1 are targets of miR-143/miR-145, revealing a feed-forward mechanism that potentiates Ras signaling.

We report on the fabrication of ZnO-channel charge injection memory thin-film transistors (TFTs). Our non-volatile memory TFT has a thin pentacene on top of a dielectric sandwich which has 3 nm-thin inserted potential well as a hole trap layer. The thin pentacene in contact with Au top electrode supports effective hole injection from pentacene into the inserted well under +8 V programming gate pulse while those injected holes are effectively ejected out under −8 V pulse, so that ZnO channel below the dielectric may have two different current states: write and erase. Our device operates at less than 2 V and shows a retention time of about 1000 s after programmed at +8 V, along with an effective program/erase ratio of 5–20.

Molecular layer deposition (MLD) technique can be used for preparation of various organic-inorganic nanohybrid superlattices at a gas-phase. The MLD method is a self-controlled layer-by-layer growth process under vacuum conditions, and is perfectly compatible with the atomic layer deposition (ALD) method. In this paper, we fabricated a new type organic-inorganic nanohybrid thin film using MLD method combined with ALD. A self-assembled organic layer (SAOL) was formed at 170 °C using MLD with repeated sequential adsorptions of C=C terminated alkylsilane and zirconium hydroxyl with ozone activation. A ZrO 2 inorganic nanolayer was deposited at the same temperature using ALD with alternating surface-saturating reactions of Zr(OC(CH 3 ) 3 ) 4 and H 2 O. The prepared SAOL-ZrO 2 organic-inorganic nanohybrid films exhibited good mechanical stability, excellent insulating properties, and relatively high dielectric constant k (∼16). They were then used as a 23 nm-thick dielectric for low voltage pentacene-based thin film transistors, which showed a maximum field effect mobility of 0.63 cm 2 /V s, operating at ― 1 V with an on/off current ratio of ∼10 3 .

Transparent electronics has been one of the key terminologies forecasting the ubiquitous technology era. Several researchers have thus extensively developed transparent oxide-based thin-film transistors (TFTs) on glass and plastic substrates. However, work in transparent electronics has been limited mostly to high-voltage devices operating at more than a few tens of volts, and has mainly focused on transparent display drivers. Low-voltage logic devices, such as transparent complementary inverters, operating in an electrically stable and photo-stable manner, are now very necessary to practically realize transparent electronics. Electrically stable dielectrics with high strength and high capacitance must also be proposed to support this mission, and simultaneously these dielectrics must be compatible with both n- and p-channel TFTs in device fabrication. Here, a nanohybrid dielectric layer that is composed of multiple units of inorganic oxide and organic self-assembled monolayer is proposel to support a transparent complementary TFT inverter operating at 3 V.

We report on the fabrication of pentacene-based thin-film transistors (TFTs) with a 230 nm-thick double polymer dielectric composed of 30 nm-thin low- k poly-4-vinyphenol (PVP) and 200 nm-thick high- k poly(vinylidene fluoride/trifluoroethylene) [P(VDF–TrFE)] dielectric on polyethersulfone (PES) films. Our 230 nm-thick double (high- k /low- k ) polymer showed a good dielectric strength of ∼2 MV/cm, a high capacitance of 26 nF/cm 2 with k = ∼7. Based on this double polymer dielectric, our flexible pentacene TFT displayed a high saturation mobility of 1.22 cm 2 /V s, a threshold voltage of −2.5 V, and on/off ratio of 10 3 , stably operating under −5 V.

We report a low-temperature fabrication of mixed-organic–inorganic nanohybrid superlattices for high-k thin stable gate dielectrics on flexible substrates. The self-assembled organic layers (SAOLs) were grown by repeated sequential adsorptions of C C-terminated alkylsilane and metal (Al or Ti) hydroxyl with ozone activation, which was called “molecular layer deposition (MLD)”. The MLD method is a self-controlled layer-by-layer growth process under vacuum conditions, and is perfectly compatible with the atomic layer deposition (ALD) method. The TiO2 and Al2O3 inorganic layers were grown by ALD, which relies on sequential saturated surface reactions resulting in the formation of a monolayer in each sequence and is a potentially powerful method for preparing high quality multicomponent superlattices. The MLD method combined with ALD (MLD–ALD) was applied to fabricate SAOLs-Al2O3–SAOLs-TiO2 nanohybrid superlattices on polycarbonate substrates with accurate control of film thickness, large-scale uniformity, excellent conformality, good reproducibility, multilayer processing capability, sharp interfaces, and excellent film qualities at relatively low temperature. The prepared ultrathin nanohybrid films exhibited good thermal and mechanical stability, good flexibility, excellent insulating properties, and relatively high dielectric constant k (6–11). The MLD–ALD method is an ideal fabrication technique for various flexible electronic devices.

We describe our method to predict the direction of conditional probabilistic dependencies between clusters of random variables. Selected variables called `gateway variables` are used to predict the conditional probabilistic dependency relations between clusters. The direction of conditional probabilistic dependencies between clusters are predicted by finding directed acyclic graph (DAG)-shaped dependency structure between the gateway variables. We show that our method shows meaningful prediction results in determining directions of conditional probabilistic dependencies between clusters.

We propose a quatitative annotation method for edges in Bayesian networks using given sets of condition-specific data. Bayesian network model has been used widely in various fields to infer probabilistic dependency relationships between entities in target systems. Besides the need for identifying dependency relationships, the annotation of edges in Bayesian networks is required to analyze the meaning of learned Bayesian networks. We assume the training data is composed of several condition-specific data sets. The contribution of each condition-specific data set to each edge in the learned Bayesian network is measured using the ratio of likelihoods between network structures of including and missing the specific edge. The proposed method can be a good approach to make quantitative annotation for learned Bayesian network structures while previous annotation approaches only give qualitative one.

Scientific literature is the most reliable and comprehensive source of knowledge for scientific and biomedical information. Citation information in the literature is also reliable source for linking between literatures. We proposed CiNet, a graphic user interface based tool that extracts the trend of the research using citation information. We can navigate related literatures and extract keywords from the linked literature using this tool. These extracted keywords will be helpful to researchers who want to survey the information.

In this article a new approach for solving the Reactive Hoist Scheduling Problem is proposed. The investigation focuses on the case where multiple hoists, sharing the same rail, are used to convey a single type of jobs along the line according to a given production sequence. It is based on two distinct, though coupled, multi-agent systems. The first system is responsible for making a decision about the input date for the next job, while the second system is concerned with the assignment of transfer operations of jobs between tanks to the hoists. These multi-agent systems use two different types of auction mechanisms to attain cooperation.

In this paper, the conventional k-modes-type algorithms for clustering categorical data are extended by representing the clusters of categorical data with k-populations instead of the hard-type centroids used in the conventional algorithms. Use of a population-based centroid representation makes it possible to preserve the uncertainty inherent in data sets as long as possible before actual decisions are made. The k-populations algorithm was found to give markedly better clustering results through various experiments.

In this paper the conventional subtractive clustering method is extended by calculating the mountain value of each data point based on a kernel-induced distance instead of the conventional sum-of-squares distance. The kernel function is a generalization of the distance metric that measures the distance between two data points as the data points are mapped into a high dimensional space. Use of the kernel function makes it possible to cluster data that is linearly non-separable in the original space into homogeneous groups in the transformed high dimensional space. Application of the conventional subtractive method and the kernel-based subtractive method to well-known data sets showed the superiority of the proposed approach.

By using a kernel function, data that are not easily separable in the original space can be clustered into homogeneous groups in the implicitly transformed high-dimensional feature space. Kernel k-means algorithms have recently been shown to perform better than conventional k-means algorithms in unsupervised classification. However, few reports have examined the benefits of using a kernel function and the relative merits of the various kernel clustering algorithms with regard to the data distribution. In this study, we reformulated four representative clustering algorithms based on a kernel function and evaluated their performances for various data sets. The results indicate that each kernel clustering algorithm gives markedly better performance than its conventional counterpart for almost all data sets. Of the kernel clustering algorithms studied in the present work, the kernel average linkage algorithm gives the most accurate clustering results.

Motivation: Clustering has been used as a popular technique for finding groups of genes that show similar expression patterns under multiple experimental conditions. Many clustering methods have been proposed for clustering gene-expression data, including the hierarchical clustering, k-means clustering and self-organizing map (SOM). However, the conventional methods are limited to identify different shapes of clusters because they use a fixed distance norm when calculating the distance between genes. The fixed distance norm imposes a fixed geometrical shape on the clusters regardless of the actual data distribution. Thus, different distance norms are required for handling the different shapes of clusters. Results: We present the Gustafson--Kessel (GK) clustering method for microarray gene-expression data. To detect clusters of different shapes in a dataset, we use an adaptive distance norm that is calculated by a fuzzy covariance matrix (F) of each cluster in which the eigenstructure of F is used as an indicator of the shape of the cluster. Moreover, the GK method is less prone to falling into local minima than the k-means and SOM because it makes decisions through the use of membership degrees of a gene to clusters. The algorithmic procedure is accomplished by the alternating optimization technique, which iteratively improves a sequence of sets of clusters until no further improvement is possible. To test the performance of the GK method, we applied the GK method and well-known conventional methods to three recently published yeast datasets, and compared the performance of each method using the Saccharomyces Genome Database annotations. The clustering results of the GK method are more significantly relevant to the biological annotations than those of the other methods, demonstrating its effectiveness and potential for clustering gene-expression data. Availability: The software was developed using Java language, and can be executed on the platforms that JVM (Java Virtual Machine) is running. It is available from the authors upon request. Contact: dhlee@bisl.kaist.ac.kr Supplementary information: Supplementary data are available at http://dragon.kaist.ac.kr/gk

Motivation: The structural interaction of proteins and their domains in networks is one of the most basic molecular mechanisms for biological cells. Topological analysis of such networks can provide an understanding of and solutions for predicting properties of proteins and their evolution in terms of domains. A single paradigm for the analysis of interactions at different layers, such as domain and protein layers, is needed. Results: Applying a colored vertex graph model, we integrated two basic interaction layers under a unified model: (1) structural domains and (2) their protein/complex networks. We identified four basic and distinct elements in the model that explains protein interactions at the domain level. We searched for motifs in the networks to detect their topological characteristics using a pruning strategy and a hash table for rapid detection. We obtained the following results: first, compared with a random distribution, a substantial part of the protein interactions could be explained by domain-level structural interaction information. Second, there were distinct kinds of protein interaction patterns classified by specific and distinguishable numbers of domains. The intermolecular domain interaction was the most dominant protein interaction pattern. Third, despite the coverage of the protein interaction information differing among species, the similarity of their networks indicated shared architectures of protein interaction network in living organisms. Remarkably, there were only a few basic architectures in the model (>10 for a 4-node network topology), and we propose that most biological combinations of domains into proteins and complexes can be explained by a small number of key topological motifs. Contact: [email protected]

In this paper, the problem of traditional validity indices when applied to the Gustafson-Kessel (GK) clustering are reviewed. A new cluster validity index for the GK algorithm is proposed. This validity index is defined as the average value of the relative degrees of sharing of all possible pairs of fuzzy clusters in the system. It computes the overlap of each pair of fuzzy clusters by considering the degree of sharing of each data point in the overlap. The optimal number of clusters is obtained by minimizing the validity index. Experiments in which the proposed validity index and several traditional validity indices were applied to 6 data sets highlight the superior qualities of the proposed index. The results indicate that the proposed validity index is very reliable.

A new cluster validity index is proposed that determines the optimal partition and optimal number of clusters for fuzzy partitions obtained from the fuzzy c-means algorithm. The proposed validity index exploits an overlap measure and a separation measure between clusters. The overlap measure, which indicates the degree of overlap between fuzzy clusters, is obtained by computing an inter-cluster overlap. The separation measure, which indicates the isolation distance between fuzzy clusters, is obtained by computing a distance between fuzzy clusters. A good fuzzy partition is expected to have a low degree of overlap and a larger separation distance. Testing of the proposed index and nine previously formulated indexes on well-known data sets showed the superior effectiveness and reliability of the proposed index in comparison to other indexes.

In this paper the conventional fuzzy k-modes algorithm for clustering categorical data is extended by representing the clusters of categorical data with fuzzy centroids instead of the hard-type centroids used in the original algorithm. Use of fuzzy centroids makes it possible to fully exploit the power of fuzzy sets in representing the uncertainly in the classification of categorical data. To test the proposed approach, the proposed algorithm and two conventional algorithms (the k-modes and fuzzy k-modes algorithms) were used to cluster three categorical data sets. The proposed method was found to give markedly better clustering results.

When ranking fuzzy values, there is an ambiguity in selecting the most-preferred ranked sequence from a collection of candidate sequences because every sequence has a possibility to which the preference relations in the sequence are correct. In this paper, we propose a method for ranking the sequences of fuzzy values that assigns a preference degree to each ranked sequence. Each preference degree indicates the relative preference of the given sequence over the other sequences. The proposed method gives information regarding which sequence could be classified as the most-preferred sequence and which sequences as alternatives.

A novel initialization scheme for the fuzzy c-means (FCM) algorithm is proposed for the color clustering problem. Given a set of color points, the proposed initialization scheme extracts the most vivid and distinguishable colors, referred to here as the dominant colors. The color points closest to these dominant colors are selected as the initial centroids in the FCM calculations. To obtain the dominant colors and their closest color points, we introduce reference colors and define a fuzzy membership model between a color point and a reference color. The effectiveness and reliability of the proposed method is demonstrated through various color clustering examples.

In this paper, the conventional k-modes and fuzzy k-modes algorithms for clustering categorical data is extended by representing the clusters of categorical data with fuzzy centroids instead of the hard-type centroids used in the original algorithm. The hard-type centroids of the traditional algorithms had difficulties in dealing with ambiguous boundary data, which might be misclassified and lead to thelocal optima. Use of fuzzy centroids makes it possible to fully exploit the power of fuzzy sets in representing the uncertainty in the classification of categorical data. The distance measure between data and fuzzy centroids is more precise and effective than those of the k-modes and fuzzy k-modes. To test the proposed approach, the proposed algorithm and two conventional algorithms were used to cluster three categorical data sets. The proposed method was found to give markedly better clustering results.

A new cluster validity index is proposed for fuzzy partitions obtained from Fuzzy C-Means algorithm. The proposed validity index exploits an inter-cluster proximity between fuzzy clusters. The inter-cluster proximity is used to measure the degree of overlap between clusters. A low proximity value indicates well-partitioned clusters. The best fuzzy c-partition is obtained by minimizing the inter-cluster proximity with respect to c. Well-known data sets are tested to show the effectiveness and reliability of the proposed index.

Static and dynamic fracture of interfaces between orthotropic and isotropic materials were studied using photoelasticity. In this study, a bi-material specimen made of PSM-1 ® and Scotchply ® 1002, a unidirectional glass fiber reinforced epoxy composite, was used. Two fiber orientations, fibers parallel to the interface ( α =0°) and fibers perpendicular to the interface ( α =90°) were considered. Center crack bi-material specimens having different crack lengths were loaded quasi-statically and the full-field isochromatics were recorded using a digital camera. The complex stress intensity factor corresponding to each crack length was calculated from the isochromatics and the values were compared to that obtained from boundary collocation method. Dynamic interfacial fracture was studied with an edge crack bi-material geometry for the two different fiber orientations. The isochromatics around the propagating crack were recorded using a digital high-speed camera. The fracture parameters such as crack speed, complex stress intensity factor and energy release rate were extracted from the isochromatics using the asymptotic stress field equations. The complex stress intensity factor obtained from the static experiments was in close agreement with that calculated using the boundary collocation method. The results also indicated that the fiber orientation with respect to the interface influences the fracture parameters for stationary and propagating cracks.

A new cluster validity index is proposed that determines the optimal partition and optimal number of clusters for fuzzy partitions obtained from the fuzzy c-means algorithm. The proposed validity index exploits an overlap measure and a separation measure between clusters. The overlap measure is obtained by computing an inter-cluster overlap. The separation measure is obtained by computing a distance between fuzzy clusters. A good fuzzy partition is expected to have a low degree of overlap and a larger separation distance. Testing of the proposed index and nine previously formulated indexes on well-known data sets showed the superior effectiveness and reliability of the proposed index in comparison to other indexes.

We report on the fabrication of a transparent photostable cell circuit composed of drive and resistor diodes which are face-to-face connected to each other with different device area. The diodes consisted of e-beam evaporated p-NiO on sputter-deposited n-ZnO for p/n diode formation on indium-tin-oxide glass. Our transparent diodes show photostable rectifying behavior, about 103 on/off current ratio and even dynamic rectification at a maximum frequency of 100 Hz AC input signal in ambient light. The noticeable photo-responsivity of the circuit was obtained only under ultraviolet (UV) light. We conclude that our transparent diode circuit is promising in enriching the field of transparent device electronics. (© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

Type-1 fuzzy set is used to show the uncertainty in a given value. But there are many situations where it needs to be extended to type-2 fuzzy set because it can be also difficult to determine the crisp membership function itself. Type-2 fuzzy systems have the advantage that they are more expressive and powerful than type-1 fuzzy systems, but they require many operations defined for type-1 fuzzy sets need to be extended in the domain of type-2 fuzzy sets. In this paper, comparison and ranking methods for type-2 fuzzy sets are proposed. It is based on the satisfaction function that produces the comparison results considering the actual values of the given type-2 fuzzy sets with their possibilities. Some properties of the proposed method are also analyzed.

Biological networks show various types of biological relationships which respond to various biomedical contexts, such as diseases and drugs. Therefore, the construction of a context-specific biological network is very important to those seeking to discover the underlying biological principles with respect to biomedical contexts. In recent studies, attempts to construct a context-specific network by determining the biological relationships from very large public biological databases and from the literature have been emerged. Therefore, finding biological relationships of which the contexts are related to specific contexts of interest is an important research issue. However, there are limitations in previous studies in that they only found a limited range of contexts and only considered pathological features from ontology. In this research, two strategies are employed to overcome these limitations. First, continuous similarity for all context term pairs is measured and biological relationships which have contexts with higher levels of similarity than an established similarity threshold are included in a context-specific network. Secondly, molecular biological features stemming from the similarity between context-associated protein-protein interaction networks is employed and integrated with the pathological similarity from ontology. The performance of the proposed method was evaluated by F-score comparisons and by the ROC curves, with the result showing higher overall performances than those of previous methods. The context-specific biological network in this research will promote additional research on the underlying biological principles of biomedical contexts.

The calcium palmitate monolayer formed from palmitic acid on calcium-containing substrate can be examined regarding the study of hydration by applying the quartz crystal microbalance (QCM) technique. Monolayers of calcium palmitate were deposited on piezoelectric crystal plate by the Langmuir-Blodgett (LB) technique. Analysis of complete conversion to calcium palmitate was performed by identification of its infrared spectra. The estimation of the hydration for the multilayered calcium palmitate is compared with the data of the multilayered calcium stearate in the literature. The QCM measurement for determining hydration number is based on the results of McCaffrey, particularly employed in the treatment of drying in vacuum (R.R. McCaffrey, S. Bruckenstein, P.N. Prasad, Langmuir 2 (1986) 228). In our experiments, the frequency change of the oscillating quartz transducer for the skimmed calcium palmitate also increased after drying in vacuum. Hydration behavior of the multilayered calcium palmitate LB film is discussed, and it was shown that the hydration of calcium palmitate exists as monohydrate, though its hydrophobicity is relatively a little low compared with that of calcium stearate, which has a longer hydrocarbon chain length.

Toy web is defined as web-based interactive contents for children and teen agers to improve their cognitive ability. Its market is just emerging now and we would like foresight how its user interface in 2030 will look like. In order to foresight we have to identify key drivers and related variables. We used STEPPER for this, and 3 Layer timed-SWOT(three present and future SWOT(strength, weakness, opportunity, threat) analyses based on three key drivers; technology, education, policies). The final step for the future foresight is to drive the future strategies and foresight integration, thereby creating toy web foresight scenarios. Our overall foresight process is summarized as follows

We propose new support vector machines (SVMs) that incorporate the geometric distribution of an input data set by associating each data point with a possibilistic membership, which measures the relative strength of the self class membership. By using a possibilistic distance measure based on the possibilistic membership, we reformulate conventional SVMs in three ways. The proposed methods are shown to have better classification performance than conventional SVMs in various tests.

Electronic Medical Records (EMRs) enable the sharing of patient medical data whenever it is needed and also are used as a tool for building new medical technology and patient recommendation systems. Since EMRs include patients' private data, access is restricted to researchers. Thus, an anonymizing technique is necessary that keeps patients' private data safe while not damaging useful medical information. k-member clustering anonymization approaches k-anonymization as a clustering issue. The objective of the k-member clustering problem is to gather records that will minimize the data distortion during data generalization. Most of the previous clustering techniques include random seed selection. However, randomly selecting a cluster seed will provide inconsistent performance. The authors propose a k-member cluster seed selection algorithm (KMCSSA) that is distinct from the previous clustering methods. Instead of randomly selecting a cluster seed, the proposed method selects the seed based on the closeness centrality to provide consistent information loss (IL) and to reduce the information distortion. An adult database from University of California Irvine Machine Learning Repository was used for the experiment. By comparing the proposed method with two previous methods, the experimental results shows that KMCSSA provides superior performance with respect to information loss. The authors provide a privacy protection algorithm that derives consistent information loss and reduces the overall information distortion.

Sonar has been the Navy's workhorse for detection and discrimination of underwater objects from clutter through analysis of image phenomena. However, new phenomena need to be identified to accommodate a growing class of unexploded ordnance targets with more varied shapes and sizes, and difficulties increase for buried ordnance because the wave attenuation and inhomogeneity in ocean sediments make detection less predictable and high-resolution imaging more difficult. Even when imaging can be done, important image phenomena (e.g., highlight/shadow details) are lost. Therefore, modeling, data collection, and data processing/analysis has been performed at NSWC PCD to build a target response database that could be exploited both for statistical automated target recognition studies based on existing feature types and for isolation of promising physical phenomena that classifiable features could be extracted from without requiring high image resolution. Several methods to build this database have been pursued, including data collection in full-scale field experiments in the Gulf of Mexico, data collection in NSWC PCD's freshwater pond facility, data collection within a small freshwater tank using scaled targets on scaled glass bead sediments, and data generation through finite element simulations. In this paper, results from each of these methods are presented and compared to assess the tradeoffs inherent in them.