ALBERT

Description: Replication in herpesvirus genomes is a major concern of public health as they multiply rapidly during the lytic phase of infection that cause maximum damage to the host cells. Earlier research has established that sites of replication origin are dominated by high concentration of rare palindrome sequences of DNA. Computational methods are devised based on scoring to determine the concentration of palindromes. In this paper, we propose both extraction and localization of rare palindromes in an automated manner. Discrete Cosine Transform (DCT-II), a widely recognized image compression algorithm is utilized here to extract palindromic sequences based on their reverse complimentary symmetry property of existence. We formulate a novel approach to localize the rare palindrome clusters by devising a Minimum Quadratic Entropy (MQE) measure based on the Renyi’s Quadratic Entropy (RQE) function. Experimental results over a large number of herpesvirus genomes show that the RQE based scoring of rare palindromes have higher order of sensitivity, and lesser false alarm in detecting concentration of rare palindromes and thereby sites of replication origin.

Description: GO relation embodies some aspects of existence dependency. If GO term x is existence-dependent on GO term y , the presence of y implies the presence of x . Therefore, the genes annotated with the function of the GO term y are usually functionally and semantically related to the genes annotated with the function of the GO term x . A large number of gene set enrichment analysis methods have been developed in recent years for analyzing gene sets enrichment. However, most of these methods overlook the structural dependencies between GO terms in GO graph by not considering the concept of existence dependency. We propose in this paper a biological search engine called RSGSearch that identifies enriched sets of genes annotated with different functions using the concept of existence dependency. We observe that GO term x cannot be existence-dependent on GO term y , if x and y have the same specificity (biological characteristics). After encoding into a numeric format the contributions of GO terms annotating target genes to the semantics of their lowest common ancestors (LCAs), RSGSearch uses microarray experiment to identify the most significant LCA that annotates the result genes. We evaluated RSGSearch experimentally and compared it with five gene set enrichment systems. Results showed marked improvement.

Description: The Tikhonov regularized nonnegative matrix factorization (TNMF) is an NMF objective function that enforces smoothness on the computed solutions, and has been successfully applied to many problem domains including text mining, spectral data analysis, and cancer clustering. There is, however, an issue that is still insufficiently addressed in the development of TNMF algorithms, i.e., how to develop mechanisms that can learn the regularization parameters directly from the data sets. The common approach is to use fixed values based on a priori knowledge about the problem domains. However, from the linear inverse problems study it is known that the quality of the solutions of the Tikhonov regularized least square problems depends heavily on the choosing of appropriate regularization parameters. Since least squares are the building blocks of the NMF, it can be expected that similar situation also applies to the NMF. In this paper, we propose two formulas to automatically learn the regularization parameters from the data set based on the L-curve approach. We also develop a convergent algorithm for the TNMF based on the additive update rules. Finally, we demonstrate the use of the proposed algorithm in cancer clustering tasks.

Description: Analysis of probability distributions conditional on species trees has demonstrated the existence of anomalous ranked gene trees (ARGTs), ranked gene trees that are more probable than the ranked gene tree that accords with the ranked species tree. Here, to improve the characterization of ARGTs, we study enumerative and probabilistic properties of two classes of ranked labeled species trees, focusing on the presence or avoidance of certain subtree patterns associated with the production of ARGTs. We provide exact enumerations and asymptotic estimates for cardinalities of these sets of trees, showing that as the number of species increases without bound, the fraction of all ranked labeled species trees that are ARGT-producing approaches $1$ . This result extends beyond earlier existence results to provide a probabilistic claim about the frequency of ARGTs.

Description: The existence of various types of correlations among the expressions of a group of biologically significant genes poses challenges in developing effective methods of gene expression data analysis. The initial focus of computational biologists was to work with only absolute and shifting correlations. However, researchers have found that the ability to handle shifting-and-scaling correlation enables them to extract more biologically relevant and interesting patterns from gene microarray data. In this paper, we introduce an effective shifting-and-scaling correlation measure named Shifting and Scaling Similarity (SSSim), which can detect highly correlated gene pairs in any gene expression data. We also introduce a technique named Intensive Correlation Search (ICS) biclustering algorithm, which uses SSSim to extract biologically significant biclusters from a gene expression data set. The technique performs satisfactorily with a number of benchmarked gene expression data sets when evaluated in terms of functional categories in Gene Ontology database.

Description: Attractors in gene regulatory networks represent cell types or states of cells. In system biology and synthetic biology, it is important to generate gene regulatory networks with desired attractors. In this paper, we focus on a singleton attractor, which is also called a fixed point. Using a Boolean network (BN) model, we consider the problem of finding Boolean functions such that the system has desired singleton attractors and has no undesired singleton attractors. To solve this problem, we propose a matrix-based representation of BNs. Using this representation, the problem of finding Boolean functions can be rewritten as an Integer Linear Programming (ILP) problem and a Satisfiability Modulo Theories (SMT) problem. Furthermore, the effectiveness of the proposed method is shown by a numerical example on a WNT5A network, which is related to melanoma. The proposed method provides us a basic method for design of gene regulatory networks.

Description: In this paper, we study Copy Number Variation (CNV) data.The underlying process generating CNV segments is generally assumed to be memory-less, giving rise to an exponential distribution of segment lengths. In this paper, we provide evidence from cancer patient data, which suggests that this generative model is too simplistic , and that segment lengths follow a power-law distribution instead . We conjecture a simple preferential attachment generative model that provides the basis for the observed power-law distribution. We then show how an existing statistical method for detecting cancer driver genes can be improved by incorporating the power-law distribution in the null model.

Description: Proteins fold into complex three-dimensional shapes. Simplified representations of their shapes are central to rationalise, compare, classify, and interpret protein structures. Traditional methods to abstract protein folding patterns rely on representing their standard secondary structural elements (helices and strands of sheet) using line segments. This results in ignoring a significant proportion of structural information. The motivation of this research is to derive mathematically rigorous and biologically meaningful abstractions of protein folding patterns that maximize the economy of structural description and minimize the loss of structural information. We report on a novel method to describe a protein as a non-overlapping set of parametric three dimensional curves of varying length and complexity. Our approach to this problem is supported by information theory and uses the statistical framework of minimum message length (MML) inference. We demonstrate the effectiveness of our non-linear abstraction to support efficient and effective comparison of protein folding patterns on a large scale.

Description: The organization of global protein interaction networks (PINs) has been extensively studied and heatedly debated. We revisited this issue in the context of the analysis of dynamic organization of a PIN in the yeast cell cycle. Statistically significant bimodality was observed when analyzing the distribution of the differences in expression peak between periodically expressed partners. A close look at their behavior revealed that date and party hubs derived from this analysis have some distinct features. There are no significant differences between them in terms of protein essentiality, expression correlation and semantic similarity derived from gene ontology (GO) biological process hierarchy. However, date hubs exhibit significantly greater values than party hubs in terms of semantic similarity derived from both GO molecular function and cellular component hierarchies. Relating to three-dimensional structures, we found that both single- and multi-interface proteins could become date hubs coordinating multiple functions performed at different times while party hubs are mainly multi-interface proteins. Furthermore, we constructed and analyzed a PPI network specific to the human cell cycle and highlighted that the dynamic organization in human interactome is far more complex than the dichotomy of hubs observed in the yeast cell cycle.

Description: The articles in this special section were presented at the 2012 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS 2012) that was held in Washington DC from December 2nd to 4th.

Description: Disk additions to an RAID-6 storage system can increase the I/O parallelism and expand the storage capacity simultaneously. To regain load balance among all disks including old and new, RAID-6 scaling requires moving certain data blocks onto newly added disks. Existing approaches to RAID-6 scaling, restricted by preserving a round-robin data distribution, require migrating all the data, which results in an expensive cost for RAID-6 scaling. In this paper, we propose RS6—a new approach to accelerating RDP RAID-6 scaling by reducing disk I/Os and XOR operations. First, RS6 minimizes the number of data blocks to be moved while maintaining a uniform data distribution across all data disks. Second, RS6 piggybacks parity updates during data migration to reduce the cost of maintaining consistent parities. Third, RS6 selects parameters of data migration so as to reduce disk I/Os for parity updates. Our mathematical analysis indicates that RS6 provides uniform data distribution, minimal data migration, and fast data addressing. We also conducted extensive simulation experiments to quantitatively characterize the properties of RS6. The results show that, compared with existing “moving-everything” Round-Robin approaches, RS6 reduces the number of blocks to be moved by 60.0%–88.9%, and saves the migration time by 40.27%–69.88%.

Description: This paper focuses on designing a distributed medium access control algorithm for fairly sharing network resources among contending stations in an 802.11 wireless network. Because the notion of fairness is not universal and there lacks a rigorous analysis on the relationships among the four types of most popular fairness criteria, we first mathematically prove that there exist certain connections between these types of fairness criteria. We then propose an efficient medium access algorithm that aims at achieving time fairness and throughput enhancement in a fully distributed manner. The core idea of our proposed algorithm lies in that each station needs to select an appropriate contention window size so as to fairly share the channel occupancy time and maximize the throughput under the time fairness constraint. The derivation of the proper contention window size is addressed rigorously. We evaluate the performance of our proposed algorithm through an extensive simulation study, and the evaluation results demonstrate that our proposed algorithm leads to nearly perfect time fairness, high throughput, and low collision overhead.

Description: This paper investigates the limits of adaptive voltage scaling (AVS) applied to commercial FPGAs which do not specifically support voltage adaptation. An adaptive power architecture based on a modified design flow is created with in-situ detectors and dynamic reconfiguration of clock management resources. AVS is a power-saving technique that enables a device to regulate its own voltage and frequency based on workload, process and operating conditions in a closed-loop configuration. It results in significant improved energy profiles compared with dynamic voltage frequency scaling (DVFS) in which the device uses a number of pre-calculated valid working points. The results of deploying AVS in FPGAs with in-situ detectors shows power and energy savings exceeding 85 percent compared with nominal voltage operation at the same frequency. The in-situ detector approach compares favorably with critical path replication based on delay lines since it avoids the need of cumbersome and error-prone delay line calibration.

Description: by Ryan Tasseff, Anjali Bheda-Malge, Teresa DiColandrea, Charles C. Bascom, Robert J. Isfort, Richard Gelinas The hair cycle is a dynamic process where follicles repeatedly move through phases of growth, retraction, and relative quiescence. This process is an example of temporal and spatial biological complexity. Understanding of the hair cycle and its regulation would shed light on many other complex systems relevant to biological and medical research. Currently, a systematic characterization of gene expression and summarization within the context of a mathematical model is not yet available. Given the cyclic nature of the hair cycle, we felt it was important to consider a subset of genes with periodic expression. To this end, we combined several mathematical approaches with high-throughput, whole mouse skin, mRNA expression data to characterize aspects of the dynamics and the possible cell populations corresponding to potentially periodic patterns. In particular two gene clusters, demonstrating properties of out-of-phase synchronized expression, were identified. A mean field, phase coupled oscillator model was shown to quantitatively recapitulate the synchronization observed in the data. Furthermore, we found only one configuration of positive-negative coupling to be dynamically stable, which provided insight on general features of the regulation. Subsequent bifurcation analysis was able to identify and describe alternate states based on perturbation of system parameters. A 2-population mixture model and cell type enrichment was used to associate the two gene clusters to features of background mesenchymal populations and rapidly expanding follicular epithelial cells. Distinct timing and localization of expression was also shown by RNA and protein imaging for representative genes. Taken together, the evidence suggests that synchronization between expanding epithelial and background mesenchymal cells may be maintained, in part, by inhibitory regulation, and potential mediators of this regulation were identified. Furthermore, the model suggests that impairing this negative regulation will drive a bifurcation which may represent transition into a pathological state such as hair miniaturization.

Description: by Varsha Dhankani, J. Nathan Kutz, Joshua T. Schiffer Herpes simplex virus-2 (HSV-2) is a chronic reactivating infection that leads to recurrent shedding episodes in the genital tract. A minority of episodes are prolonged, and associated with development of painful ulcers. However, currently, available tools poorly predict viral trajectories and timing of reactivations in infected individuals. We employed principal components analysis (PCA) and singular value decomposition (SVD) to interpret HSV-2 genital tract shedding time series data, as well as simulation output from a stochastic spatial mathematical model. Empirical and model-derived, time-series data gathered over 〉30 days consists of multiple complex episodes that could not be reduced to a manageable number of descriptive features with PCA and SVD. However, single HSV-2 shedding episodes, even those with prolonged duration and complex morphologies consisting of multiple erratic peaks, were consistently described using a maximum of four dominant features. Modeled and clinical episodes had equivalent distributions of dominant features, implying similar dynamics in real and simulated episodes. We applied linear discriminant analysis (LDA) to simulation output and identified that local immune cell density at the viral reactivation site had a predictive effect on episode duration, though longer term shedding suggested chaotic dynamics and could not be predicted based on spatial patterns of immune cell density. These findings suggest that HSV-2 shedding patterns within an individual are impossible to predict over weeks or months, and that even highly complex single HSV-2 episodes can only be partially predicted based on spatial distribution of immune cell density.

Description: by Marcin J. Skwark, Daniele Raimondi, Mirco Michel, Arne Elofsson Given sufficient large protein families, and using a global statistical inference approach, it is possible to obtain sufficient accuracy in protein residue contact predictions to predict the structure of many proteins. However, these approaches do not consider the fact that the contacts in a protein are neither randomly, nor independently distributed, but actually follow precise rules governed by the structure of the protein and thus are interdependent. Here, we present PconsC2, a novel method that uses a deep learning approach to identify protein-like contact patterns to improve contact predictions. A substantial enhancement can be seen for all contacts independently on the number of aligned sequences, residue separation or secondary structure type, but is largest for β -sheet containing proteins. In addition to being superior to earlier methods based on statistical inferences, in comparison to state of the art methods using machine learning, PconsC2 is superior for families with more than 100 effective sequence homologs. The improved contact prediction enables improved structure prediction.

Description: by Veronika Boskova, Sebastian Bonhoeffer, Tanja Stadler Quantifying epidemiological dynamics is crucial for understanding and forecasting the spread of an epidemic. The coalescent and the birth-death model are used interchangeably to infer epidemiological parameters from the genealogical relationships of the pathogen population under study, which in turn are inferred from the pathogen genetic sequencing data. To compare the performance of these widely applied models, we performed a simulation study. We simulated phylogenetic trees under the constant rate birth-death model and the coalescent model with a deterministic exponentially growing infected population. For each tree, we re-estimated the epidemiological parameters using both a birth-death and a coalescent based method, implemented as an MCMC procedure in BEAST v2.0. In our analyses that estimate the growth rate of an epidemic based on simulated birth-death trees, the point estimates such as the maximum a posteriori/maximum likelihood estimates are not very different. However, the estimates of uncertainty are very different. The birth-death model had a higher coverage than the coalescent model, i.e. contained the true value in the highest posterior density (HPD) interval more often (2–13% vs. 31–75% error). The coverage of the coalescent decreases with decreasing basic reproductive ratio and increasing sampling probability of infecteds. We hypothesize that the biases in the coalescent are due to the assumption of deterministic rather than stochastic population size changes. Both methods performed reasonably well when analyzing trees simulated under the coalescent. The methods can also identify other key epidemiological parameters as long as one of the parameters is fixed to its true value. In summary, when using genetic data to estimate epidemic dynamics, our results suggest that the birth-death method will be less sensitive to population fluctuations of early outbreaks than the coalescent method that assumes a deterministic exponentially growing infected population.

Description: by Junaid Hassan, Linda L. Bergaust, I. David Wheat, Lars R. Bakken In response to impending anoxic conditions, denitrifying bacteria sustain respiratory metabolism by producing enzymes for reducing nitrogen oxyanions/-oxides (NO x ) to N 2 (denitrification). Since denitrifying bacteria are non-fermentative, the initial production of denitrification proteome depends on energy from aerobic respiration. Thus, if a cell fails to synthesise a minimum of denitrification proteome before O 2 is completely exhausted, it will be unable to produce it later due to energy-limitation. Such entrapment in anoxia is recently claimed to be a major phenomenon in batch cultures of the model organism Paracoccus denitrificans on the basis of measured e − -flow rates to O 2 and NO x . Here we constructed a dynamic model and explicitly simulated actual kinetics of recruitment of the cells to denitrification to directly and more accurately estimate the recruited fraction (). Transcription of nirS is pivotal for denitrification, for it triggers a cascade of events leading to the synthesis of a full-fledged denitrification proteome. The model is based on the hypothesis that nirS has a low probability (, h −1 ) of initial transcription, but once initiated, the transcription is greatly enhanced through positive feedback by NO, resulting in the recruitment of the transcribing cell to denitrification. We assume that the recruitment is initiated as [O 2 ] falls below a critical threshold and terminates (assuming energy-limitation) as [O 2 ] exhausts. With  = 0.005 h −1 , the model robustly simulates observed denitrification kinetics for a range of culture conditions. The resulting (fraction of the cells recruited to denitrification) falls within 0.038–0.161. In contrast, if the recruitment of the entire population is assumed, the simulated denitrification kinetics deviate grossly from those observed. The phenomenon can be understood as a ‘bet-hedging strategy’: switching to denitrification is a gain if anoxic spell lasts long but is a waste of energy if anoxia turns out to be a ‘false alarm’.

Description: by Thomas R. Caulfield, Fabienne C. Fiesel, Elisabeth L. Moussaud-Lamodière, Daniel F. A. R. Dourado, Samuel C. Flores, Wolfdieter Springer Loss-of-function mutations in PINK1 or PARKIN are the most common causes of autosomal recessive Parkinson's disease. Both gene products, the Ser/Thr kinase PINK1 and the E3 Ubiquitin ligase Parkin, functionally cooperate in a mitochondrial quality control pathway. Upon stress, PINK1 activates Parkin and enables its translocation to and ubiquitination of damaged mitochondria to facilitate their clearance from the cell. Though PINK1-dependent phosphorylation of Ser65 is an important initial step, the molecular mechanisms underlying the activation of Parkin's enzymatic functions remain unclear. Using molecular modeling, we generated a complete structural model of human Parkin at all atom resolution. At steady state, the Ub ligase is maintained inactive in a closed, auto-inhibited conformation that results from intra-molecular interactions. Evidently, Parkin has to undergo major structural rearrangements in order to unleash its catalytic activity. As a spark, we have modeled PINK1-dependent Ser65 phosphorylation in silico and provide the first molecular dynamics simulation of Parkin conformations along a sequential unfolding pathway that could release its intertwined domains and enable its catalytic activity. We combined free (unbiased) molecular dynamics simulation, Monte Carlo algorithms, and minimal-biasing methods with cell-based high content imaging and biochemical assays. Phosphorylation of Ser65 results in widening of a newly defined cleft and dissociation of the regulatory N-terminal UBL domain. This motion propagates through further opening conformations that allow binding of an Ub-loaded E2 co-enzyme. Subsequent spatial reorientation of the catalytic centers of both enzymes might facilitate the transfer of the Ub moiety to charge Parkin. Our structure-function study provides the basis to elucidate regulatory mechanisms and activity of the neuroprotective Parkin. This may open up new avenues for the development of small molecule Parkin activators through targeted drug design.

Description: In microprocessor-based systems, such as the cloud computing infrastructure, high reliability is essential. As multiprocessor systems become more widespread and increasingly complex, system-level diagnosis will increasingly be adopted to determine their robustness. In this paper, we consider a pessimistic diagnostic strategy for hypermesh multiprocessor systems under the PMC model. The pessimistic strategy is a diagnostic process whereby all faulty processors are correctly identified and at most one fault-free processor may be misjudged to be a faulty processor. We first determine the pessimistic diagnosability of a hypermesh to be ${2}{{n}}({{k}} - {1}) - {{k}}$ . We then propose an efficient pessimistic diagnostic algorithm to identify at most ${ 2}{{n}}({{k}} - { 1}) - {{k}}$ faults in ${{O}}({{N}})$ time, where ${mbi{k}}$ is the radix, ${mbi{n}}$ is the number of dimensions, and ${{N}} = {{k^n}}$ is the total number of processors. This result is superior to the best precise diagnostic algorithm, which runs in ${{O}}({{N}}{log _{{k}}}{{N}})$ time. Furthermore, the Cartesian product network, a subgraph of the hypermesh and the proposed algorithm can be employed to determine faults in the product network.

Description: In a top- $k$ Geometric Intersection Query (top- $k$ GIQ) problem, a set of $n$ weighted, geometric objects in ${bb R}^d$ is to be pre-processed into a compact data structure so that for any query geometric object, $q$ , and integer $k>0$ , the $k$ largest-weight objects intersected by $q$ can be reported efficiently. While the top- $k$ problem has been studied extensively for non-geometric problems (e.g., recommender systems), the geometric version has received little attention. This paper gives a general technique to solve any top-

Description: Rule induction method based on rough set theory (RST) has received much attention recently since it may generate a minimal set of rules from the decision system for real-life applications by using of attribute reduction and approximations. The decision system may vary with time, e.g., the variation of objects, attributes and attribute values. The reduction and approximations of the decision system may alter on Attribute Values’ Coarsening and Refining (AVCR), a kind of variation of attribute values, which results in the alteration of decision rules simultaneously. This paper aims for dynamic maintenance of decision rules $w.r.t.$ AVCR. The definition of minimal discernibility attribute set is proposed firstly, which aims to improve the efficiency of attribute reduction in RST. Then, principles of updating decision rules in case of AVCR are discussed. Furthermore, the rough set-based methods for updating decision rules in the inconsistent decision system are proposed. The complexity analysis and extensive experiments on UCI data sets have verified the effectiveness and efficiency of the proposed methods.

Description: A major mining task for binary matrixes is the extraction of approximate top- (k) patterns that are able to concisely describe the input data. The top- (k) pattern discovery problem is commonly stated as an optimization one, where the goal is to minimize a given cost function, see the accuracy of the data description. In this work, we review several greedy algorithms, and discuss PaNDa + , an algorithmic framework able to optimize different cost functions generalized into a unifying formulation. We evaluated the goodness of the algorithm by measuring the quality of the extracted patterns. We adapted standard quality measures to assess the capability of the algorithm to discover both the items and transactions of the patterns embedded in the data. The evaluation was conducted on synthetic data, where patterns were artificially embedded, and on real-world text collection, where each document is labeled with a topic. Finally, in order to qualitatively evaluate the usefulness of the discovered patterns, we exploited PaNDa + to detect overlapping communities in a bipartite network. The results show that PaNDa + is able to discover high-quality patterns in both synthetic and real-world datasets.

Description: Mining network evolution has emerged as an intriguing research topic in many domains such as data mining, social networks, and machine learning. While a bulk of research has focused on mining evolutionary patterns of homogeneous networks (e.g., networks of friends), however, most real-world networks are heterogeneous, containing objects of different types, such as authors, papers, venues, and terms in a bibliographic network. Modeling co-evolution of multityped objects can capture richer information than that on single-typed objects alone. For example, studying co-evolution of authors, venues, and terms in a bibliographic network can tell better the evolution of research areas than just examining co-author network or term network alone. In this paper, we study mining co-evolution of multityped objects in a special type of heterogeneous networks, called star networks, and examine how the multityped objects influence each other in the network evolution. A hierarchical Dirichlet process mixture model-based evolution model is proposed, which detects the co-evolution of multityped objects in the form of multityped cluster evolution in dynamic star networks. An efficient inference algorithm is provided to learn the proposed model. Experiments on several real networks (DBLP, Twitter, and Delicious) validate the effectiveness of the model and the scalability of the algorithm.

Description: In this work, we define cost-free learning (CFL) formally in comparison with cost-sensitive learning (CSL). The main difference between them is that a CFL approach seeks optimal classification results without requiring any cost information, even in the class imbalance problem. In fact, several CFL approaches exist in the related studies, such as sampling and some criteria-based approaches. However, to our best knowledge, none of the existing CFL and CSL approaches are able to process the abstaining classifications properly when no information is given about errors and rejects. Based on information theory, we propose a novel CFL which seeks to maximize normalized mutual information of the targets and the decision outputs of classifiers. Using the strategy, we can handle binary/multi-class classifications with/without abstaining. Significant features are observed from the new strategy. While the degree of class imbalance is changing, the proposed strategy is able to balance the errors and rejects accordingly and automatically. Another advantage of the strategy is its ability of deriving optimal rejection thresholds for abstaining classifications and the “equivalent” costs in binary classifications. The connection between rejection thresholds and ROC curve is explored. Empirical investigation is made on several benchmark data sets in comparison with other existing approaches. The classification results demonstrate a promising perspective of the strategy in machine learning.

Description: Multivariate time series are common in many application domains, particularly in industrial processes with a large number of sensors installed for process monitoring and control. Often, such data encapsulate complex relations among individual series. This paper presents a new type of patterns in multivariate time series, referred to as temporal associations, to capture a wide range of local relations along and across individual series. A scalable algorithm is developed to discover frequent associations by incorporating (1) redundancy pruning of patterns in single time series and (2) two conditions to avoid over-counting the occurrences of associations, thus greatly reducing the space and runtime complexity of the discovery process. A statistical significance measure is also introduced for ranking and post-pruning discovered associations. To evaluate the proposed method, synthetic data sets and a real world data set taken from the time series mining repository as well as a large data set obtained from a delayed coking plant are used. The experiments demonstrated that the discovered associations capture the local relations in multiple time series and that the proposed method is scalable to large data sets.

Description: Short texts are popular on today’s web, especially with the emergence of social media. Inferring topics from large scale short texts becomes a critical but challenging task for many content analysis tasks. Conventional topic models such as latent Dirichlet allocation (LDA) and probabilistic latent semantic analysis (PLSA) learn topics from document-level word co-occurrences by modeling each document as a mixture of topics, whose inference suffers from the sparsity of word co-occurrence patterns in short texts. In this paper, we propose a novel way for short text topic modeling, referred as biterm topic model (BTM) . BTM learns topics by directly modeling the generation of word co-occurrence patterns (i.e., biterms) in the corpus, making the inference effective with the rich corpus-level information. To cope with large scale short text data, we further introduce two online algorithms for BTM for efficient topic learning. Experiments on real-word short text collections show that BTM can discover more prominent and coherent topics, and significantly outperform the state-of-the-art baselines. We also demonstrate the appealing performance of the two online BTM algorithms on both time efficiency and topic learning.

Description: In the literature about association analysis, many interestingness measures have been proposed to assess the quality of obtained association rules in order to select a small set of the most interesting among them. In the particular case of hierarchically organized items and generalized association rules connecting them, a measure that dealt appropriately with the hierarchy would be advantageous. Here we present the further developments of a new class of such hierarchical interestingness measures and compare them with a large set of conventional measures and with three hierarchical pruning methods from the literature. The aim is to find interesting pairwise generalized association rules connecting the concepts of multiple ontologies. Interested in the broad empirical evaluation of interestingness measures, we compared the rules obtained by 37 methods on four real world data sets against predefined ground truth sets of associations. To this end, we adopted a framework of instance-based ontology matching and extended the set of performance measures by two novel measures: relation learning recall and precision which take into account hierarchical relationships.

Description: A highly comparative, feature-based approach to time series classification is introduced that uses an extensive database of algorithms to extract thousands of interpretable features from time series. These features are derived from across the scientific time-series analysis literature, and include summaries of time series in terms of their correlation structure, distribution, entropy, stationarity, scaling properties, and fits to a range of time-series models. After computing thousands of features for each time series in a training set, those that are most informative of the class structure are selected using greedy forward feature selection with a linear classifier. The resulting feature-based classifiers automatically learn the differences between classes using a reduced number of time-series properties, and circumvent the need to calculate distances between time series. Representing time series in this way results in orders of magnitude of dimensionality reduction, allowing the method to perform well on very large data sets containing long time series or time series of different lengths. For many of the data sets studied, classification performance exceeded that of conventional instance-based classifiers, including one nearest neighbor classifiers using euclidean distances and dynamic time warping and, most importantly, the features selected provide an understanding of the properties of the data set, insight that can guide further scientific investigation.

Description: The explosive usage of social media produces massive amount of unlabeled and high-dimensional data. Feature selection has been proven to be effective in dealing with high-dimensional data for efficient learning and data mining. Unsupervised feature selection remains a challenging task due to the absence of label information based on which feature relevance is often assessed. The unique characteristics of social media data further complicate the already challenging problem of unsupervised feature selection, e.g., social media data is inherently linked, which makes invalid the independent and identically distributed assumption, bringing about new challenges to unsupervised feature selection algorithms. In this paper, we investigate a novel problem of feature selection for social media data in an unsupervised scenario. In particular, we analyze the differences between social media data and traditional attribute-value data, investigate how the relations extracted from linked data can be exploited to help select relevant features, and propose a novel unsupervised feature selection framework, LUFS, for linked social media data. We systematically design and conduct systemic experiments to evaluate the proposed framework on data sets from real-world social media websites. The empirical study demonstrates the effectiveness and potential of our proposed framework.

Description: The discovery of process models from event logs has emerged as one of the crucial problems for enabling the continuous support in the life-cycle of an information system. However, in a decade of process discovery research, the algorithms and tools that have appeared are known to have strong limitations in several dimensions. The size of the logs and the formal properties of the model discovered are the two main challenges nowadays. In this paper we propose the use of numerical abstract domains for tackling these two problems, for the particular case of the discovery of Petri nets. First, numerical abstract domains enable the discovery of general process models, requiring no knowledge (e.g., the bound of the Petri net to derive) for the discovery algorithm. Second, by using divide and conquer techniques we are able to control the size of the process discovery problems. The methods proposed in this paper have been implemented in a prototype tool and experiments are reported illustrating the significance of this fresh view of the process discovery problem.

Description: Given a real world graph, how should we lay-out its edges? How can we compress it? These questions are closely related, and the typical approach so far is to find clique-like communities, like the ‘cavemen graph’, and compress them. We show that the block-diagonal mental image of the ‘cavemen graph’ is the wrong paradigm, in full agreement with earlier results that real world graphs have no good cuts. Instead, we propose to envision graphs as a collection of hubs connecting spokes, with super-hubs connecting the hubs, and so on, recursively. Based on the idea, we propose the SlashBurn method to recursively split a graph into hubs and spokes connected only by the hubs. We also propose techniques to select the hubs and give an ordering to the spokes, in addition to the basic SlashBurn. We give theoretical analysis of the proposed hub selection methods. Our view point has several advantages: (a) it avoids the ‘no good cuts’ problem, (b) it gives better compression, and (c) it leads to faster execution times for matrix-vector operations, which are the back-bone of most graph processing tools. Through experiments, we show that SlashBurn consistently outperforms other methods for all data sets, resulting in better compression and faster running time. Moreover, we show that SlashBurn with the appropriate spokes ordering can further improve compression while hardly sacrificing the running time.

Description: In this paper, we introduce “task trail” to understand user search behaviors. We define a task to be an atomic user information need, whereas a task trail represents all user activities within that particular task, such as query reformulations, URL clicks. Previously, web search logs have been studied mainly at session or query level where users may submit several queries within one task and handle several tasks within one session. Although previous studies have addressed the problem of task identification, little is known about the advantage of using task over session or query for search applications. In this paper, we conduct extensive analyses and comparisons to evaluate the effectiveness of task trails in several search applications: determining user satisfaction, predicting user search interests, and suggesting related queries. Experiments on large scale data sets of a commercial search engine show that: (1) Task trail performs better than session and query trails in determining user satisfaction; (2) Task trail increases webpage utilities of end users comparing to session and query trails; (3) Task trails are comparable to query trails but more sensitive than session trails in measuring different ranking functions; (4) Query terms from the same task are more topically consistent to each other than query terms from different tasks; (5) Query suggestion based on task trail is a good complement of query suggestions based on session trail and click-through bipartite. The findings in this paper verify the need of extracting task trails from web search logs and enhance applications in search and recommendation systems.

Description: This paper studies the problem of mining named entity translations by aligning comparable corpora. Current state-of-the-art approaches mine a translation pair by aligning an entity graph in one language to another based on node similarity or propagated similarity of related entities. However, they, building on the assumption of “symmetry”, quickly deteriorate on “weakly” comparable corpora with some asymmetry. In this paper, we pursue two directions for overcoming relation and entity asymmetry respectively. The first approach starts from weakly comparable corpora (for high recall) then ensures precision by selective propagation only to entities of symmetric relations. The second approach starts from parallel corpora (for high precision) then enhances recall by extending the translation matrix based on node similarity and contextual similarity. Our experimental results on English-Chinese corpora show that both approaches are effective and complementary. Our combined approach outperforms the best-performing baseline in terms of F1-score by up to 0.28.

Description: The knowledge remembered by the human body and reflected by the dexterity of body motion is called embodied knowledge. In this paper, we propose a new method using singular value decomposition for extracting embodied knowledge from the time-series data of the motion. We compose a matrix from the time-series data and use the left singular vectors of the matrix as the patterns of the motion and the singular values as a scalar, by which each corresponding left singular vector affects the matrix. Two experiments were conducted to validate the method. One is a gesture recognition experiment in which we categorize gesture motions by two kinds of models with indexes of similarity and estimation that use left singular vectors. The proposed method obtained a higher correct categorization ratio than principal component analysis (PCA) and correlation efficiency (CE). The other is an ambulation evaluation experiment in which we distinguished the levels of walking disability. The first singular values derived from the walking acceleration were suggested to be a reliable criterion to evaluate walking disability. Finally we discuss the characteristic and significance of the embodied knowledge extraction using the singular value decomposition proposed in this paper.

Description: Edit distance is widely used for measuring the similarity between two strings. As a primitive operation, edit distance based string similarity search is to find strings in a collection that are similar to a given query string using edit distance. Existing approaches for answering such string similarity queries follow the filter-and-verify framework by using various indexes. Typically, most approaches assume that indexes and data sets are maintained in main memory. To overcome this limitation, in this paper, we propose B $^+$ -tree based approaches to answer edit distance based string similarity queries, and hence, our approaches can be easily integrated into existing RDBMSs. In general, we answer string similarity search using pruning techniques employed in the metric space in that edit distance is a metric. First, we split the string collection into partitions according to a set of reference strings. Then, we index strings in all partitions using a single B $^+$ -tree based on the distances of these strings to their corresponding reference strings. Finally, we propose two approaches to efficiently answer range and KNN queries, respectively, based on the B $^+$ -tree. We prove that the optimal partitioning of the data set is an NP-hard problem, and therefore propose a heuristic approach for selecting the reference strings greedily and present an optimal partition assignment strategy to minimize the expected number of strings that need to be verified during the query evaluation. Through extensive experiments over a variety of real data sets, we demonstrate that our B $^+$ -tree based approaches provide superior performance over state-of-the-art techniques on both range and KNN queries in most cases.

Description: A top- k query retrieves the best (k) tuples by assigning scores for each tuple in a target relation with respect to a user-specific scoring function. This paper studies the problem of constructing an indexing structure for supporting top- k queries over varying scoring functions and retrieval sizes. The existing research efforts can be categorized into three approaches: list- , layer- , and view-based approaches. In this paper, we mainly focus on the layer-based approach that pre-materializes tuples into consecutive multiple layers. We first propose a dual-resolution layer that consists of coarse-level and fine-level layers. Specifically, we build coarse-level layers using skylines , and divide each coarse-level layer into fine-level sublayers using convex skylines . To make our proposed dual-resolution layer scalable , we then address the following optimization directions: 1) index construction; 2) disk-based storage scheme; 3) the design of the virtual layer; and 4) index maintenance for tuple updates. Our evaluation results show that our proposed method is more scalable than the state-of-the-art methods.

Description: by The PLOS Computational Biology Staff

Description: by The PLOS Computational Biology Staff

Description: The constantly growing amount ofWeb content and the success of the SocialWeb lead to increasing needs for Web archiving. These needs go beyond the pure preservationo of Web pages. Web archives are turning into “community memories” that aim at building a better understanding of the public view on, e.g., celebrities, court decisions and other events. Due to the size of the Web, the traditional “collect-all” strategy is in many cases not the best method to build Web archives. In this paper, we present the ARCOMEM (From Future Internet 2014, 6 689 Collect-All Archives to Community Memories) architecture and implementation that uses semantic information, such as entities, topics and events, complemented with information from the Social Web to guide a novel Web crawler. The resulting archives are automatically enriched with semantic meta-information to ease the access and allow retrieval based on conditions that involve high-level concepts..

Description: Increased availability of multi-platform genomics data on matched samples has sparked research efforts to discover how diverse molecular features interact both within and between platforms. In addition, simultaneous measurements of genetic and epigenetic characteristics illuminate the roles their complex relationships play in disease progression and outcomes. However, integrative methods for diverse genomics data are faced with the challenges of ultra-high dimensionality and the existence of complex interactions both within and between platforms. We propose a novel modeling framework for integrative analysis based on decompositions of the large number of platform-specific features into a smaller number of latent features. Subsequently we build a predictive model for clinical outcomes accounting for both within- and between-platform interactions based on Bayesian model averaging procedures. Principal components, partial least squares and non-negative matrix factorization as well as sparse counterparts of each are used to define the latent features, and the performance of these decompositions is compared both on real and simulated data. The latent feature interactions are shown to preserve interactions between the original features and not only aid prediction but also allow explicit selection of outcome-related features. The methods are motivated by and applied to a glioblastoma multiforme data set from The Cancer Genome Atlas to predict patient survival times integrating gene expression, microRNA, copy number and methylation data. For the glioblastoma data, we find a high concordance between our selected prognostic genes and genes with known associations with glioblastoma. In addition, our model discovers several relevant cross-platform interactions such as copy number variation associated gene dosing and epigenetic regulation through promoter methylation. On simulated data, we show that our proposed method successfully incorporates interactions within and between g- nomic platforms to aid accurate prediction and variable selection. Our methods perform best when principal components are used to define the latent features.

Description: In Cyber-Physical Networked Systems (CPNS), the adversary can inject false measurements into the controller through compromised sensor nodes, which not only threaten the security of the system, but also consume network resources. To deal with this issue, a number of en-route filtering schemes have been designed for wireless sensor networks. However, these schemes either lack resilience to the number of compromised nodes or depend on the statically configured routes and node localization, which are not suitable for CPNS. In this paper, we propose a Polynomial-based Compromise-Resilient En-route Filtering scheme (PCREF), which can filter false injected data effectively and achieve a high resilience to the number of compromised nodes without relying on static routes and node localization. PCREF adopts polynomials instead of Message Authentication Codes (MACs) for endorsing measurement reports to achieve resilience to attacks. Each node stores two types of polynomials: authentication polynomial and check polynomial, derived from the primitive polynomial, and used for endorsing and verifying the measurement reports. Through extensive theoretical analysis and experiments, our data shows that PCREF achieves better filtering capacity and resilience to the large number of compromised nodes in comparison to the existing schemes.

Description: The Resistive Random Access Memory (RRAM) is a new type of non-volatile memory based on the resistive memory device. Researchers are currently moving from resistive device development to memory circuit design and implementation, hoping to fabricate memory chips that can be deployed in the market in the near future. However, so far the low manufacturing yield is still a major issue. In this paper, we propose defect and fault models specific to RRAM, i.e., the Over-Forming (OF) defect and the Read-One-Disturb (R1D) fault. We then propose a March algorithm to cover these defects and faults in addition to the conventional RAM faults, which is called March C*. We also develop a novel squeeze-search scheme to identify the OF defect, which leads to the Stuck-At Fault (SAF). The proposed test algorithm is applied to a first-cut 4-Mb HfO 2 -based RRAM test chip. Results show that OF defects and R1D faults do exist in the RRAM chip. We also identify specific failure patterns from the test results, which are shown to be induced by multiple short defects between bit-lines. By identifying the defects and faults, designers and process engineers can improve the RRAM yield in a more cost-effective way.

Description: In response to the increasing ubiquity of multicore processors, applications are usually designed or deployed to make each core busy. Unfortunately, lock contention within operating systems can limit the scalability of multicore systems so severely that an increase in the number of cores can actually lead to reduced performance (i.e., scalability collapse). Existing lock implementations have disadvantages in scalability, power consumption, and energy efficiency. In this paper, we observe that the number of tasks requesting a lock has a significant correlation with the occurrence of scalability collapse. Based on this observation, a lock implementation that allows tasks waiting for a lock to either spin or enter a power-saving state based on the number of requesters is proposed. Our lock protocol is called requester-based lock and is implemented in the Linux kernel to replace its default spin lock. Based on the results of a sensitivity analysis, we find that the best policy, in practice, for a task waiting for a lock to be granted is to enter the power-saving state immediately after noticing the lock cannot be acquired. Our requester-based lock scheme is evaluated using intensive benchmarking on AMD 32-core and Intel 40-core systems. Experimental results suggest that our lock avoids scalability collapse completely for most applications and shows better scalability, power consumption, and energy efficiency than previous work. Besides, the requester-based lock is extensible, which means using together with other kinds of spin locks can provide better scalability and energy efficiency.

Description: Functionally equivalent web services can be composed to form more reliable service-oriented systems. However, the choice of fault tolerance strategy can have a significant effect on the quality-of-service (QoS) of the resulting service-oriented systems. In this paper, we investigate the problem of selecting an optimal fault tolerance strategy for building reliable service-oriented systems. We formulate the user requirements as local and global constraints and model the selection of fault tolerance strategy as an optimization problem. A heuristic algorithm is proposed to efficiently solve the optimization problem. Fault tolerance strategy selection for semantically related tasks is also investigated in this paper. Large-scale real-world experiments are conducted to illustrate the benefits of the proposed approach. The experimental results show that our problem modeling approach and the proposed selection algorithm make it feasible to manage the fault tolerance of complex service-oriented systems both efficiently and effectively.

Description: This paper describes an end-to-end system implementation of a transactional memory (TM) programming model on top of the hardware transactional memory (HTM) of the Blue Gene/Q machine. The TM programming model supports most C/C++ programming constructs using a best-effort HTM and the help of a complete software stack including the compiler, the kernel, and the TM runtime. An extensive evaluation of the STAMP and the RMS-TM benchmark suites on BG/Q is the first of its kind in understanding characteristics of running TM workloads on real hardware TM. The study reveals several interesting insights on the overhead and the scalability of BG/Q HTM with respect to sequential execution, coarse-grain locking, and software TM.

Description: Radio Frequency Identification (RFID) technology has been widely used in inventory management in many scenarios, e.g., warehouses, retail stores, hospitals, etc. This paper investigates a challenging problem of complete identification of missing tags in large-scale RFID systems. Although this problem has attracted extensive attention from academy and industry, the existing work can hardly satisfy the stringent real-time requirements. In this paper, a Slot Filter-based Missing Tag Identification (SFMTI) protocol is proposed to reconcile some expected collision slots into singleton slots and filter out the expected empty slots as well as the unreconcilable collision slots, thereby achieving the improved time-efficiency. The theoretical analysis is conducted to minimize the execution time of the proposed SFMTI. We then propose a cost-effective method to extend SFMTI to the multi-reader scenarios. The extensive simulation experiments and performance results demonstrate that the proposed SFMTI protocol outperforms the most promising Iterative ID-free Protocol (IIP) by reducing nearly 45% of the required execution time, and is just within a factor of 1.18 from the lower bound of the minimum execution time.

Description: Memristor-based memory technology, also referred to as resistive RAM (RRAM), is one of the emerging memory technologies potentially to replace conventional semiconductor memories such as SRAM, DRAM, and flash. Existing research on such novel circuits focuses mainly on the integration between CMOS and non-CMOS, fabrication techniques, and reliability improvement. However, research on (manufacturing) test for yield and quality improvement is still in its infancy stage. This paper presents fault analysis and modeling for open defects based on electrical simulation, introduces fault models, and proposes test approaches for RRAMs. The fault analysis reveals that unique faults occur in addition to some conventional memory faults, and the detection of such unique faults cannot be guaranteed with just the application of traditional march tests. The paper also presents a new Design-for-Testability (DfT) concept to facilitate the detection of the unique faults. Two DfT schemes are developed by exploiting the access time duration and supply voltage level of the RRAM cells, and their simulation results show that the fault coverage can be increased with minor circuit modification. As the fault behavior may vary due to process variations, the DfT schemes are extended to be programmable to track the changes and further improve the fault/defect coverage.

Description: Runtime power management using dynamic voltage and frequency scaling (DVFS) has been extensively studied for video processing applications. But there is only a little work on game power management although gaming applications are now widely run on battery-operated portable devices like mobile phones. Taking a cue from video power management, where PID controllers have been successfully used, they were recently applied to game workload prediction and DVFS. However, the use of hand-tuned PID controller gains on relatively short game plays left open questions on the robustness of the controller and the sensitivity of prediction quality on the choice of the gain values. In this paper, we try to systematically answer these questions. We first show that from the space of PID controller gain values, only a small subset leads to good game quality and power savings. Further, the choice of this set highly depends on the scene and the game application. For most gain values the controller becomes unstable, which can lead to large oscillations in the processor’s frequency setting and thereby poor results. We then study a number of time series models, such as a Least Mean Squares (LMS) Linear Predictor and its generalizations in the form of Autoregressive Moving Average (ARMA) models. These models learn most of the relevant model parameters iteratively as the game progresses, thereby dramatically reducing the complexity of manual parameter estimation. This makes them deployable in real setups, where all game plays and even game applications are not a priori known. We have evaluated each of these models (PID, LMS, and ARMA) for a variety of games—ranging from Quake II to more recent closed-source games such as Crysis, Need for Speed—Shift and World in Conflict—with very encouraging results. To the best of our knowledge, this is the first work that systematically explores (a) the feasibility of manually tuning PID controller parameters for p- wer management, (b) time series models for workload prediction for gaming applications, and (c) power management for closed-source games.

Description: Ciphertext Policy Attribute-Based Encryption (CP-ABE) enforces expressive data access policies and each policy consists of a number of attributes. Most existing CP-ABE schemes incur a very large ciphertext size, which increases linearly with respect to the number of attributes in the access policy. Recently, Herranz proposed a construction of CP-ABE with constant ciphertext. However, Herranz do not consider the recipients’ anonymity and the access policies are exposed to potential malicious attackers. On the other hand, existing privacy preserving schemes protect the anonymity but require bulky, linearly increasing ciphertext size. In this paper, we proposed a new construction of CP-ABE, named Privacy Preserving Constant CP-ABE (denoted as PP-CP-ABE) that significantly reduces the ciphertext to a constant size with any given number of attributes. Furthermore, PP-CP-ABE leverages a hidden policy construction such that the recipients’ privacy is preserved efficiently. As far as we know, PP-CP-ABE is the first construction with such properties. Furthermore, we developed a Privacy Preserving Attribute-Based Broadcast Encryption (PP-AB-BE) scheme. Compared to existing Broadcast Encryption (BE) schemes, PP-AB-BE is more flexible because a broadcasted message can be encrypted by an expressive hidden access policy, either with or without explicit specifying the receivers. Moreover, PP-AB-BE significantly reduces the storage and communication overhead to the order of ${mbi {O}}(log {mbi {N}})$ , where ${mbi {N}}$ is the system size. Also, we proved, using information theoretical approaches, PP-AB-BE attains minimal bound on storage overhead for each user to cover all possible subgroups in the communication system.

Description: Recent mobile devices adopt high-performance processors to support various functions. As a side effect, higher performance inevitably leads to power density increase, eventually resulting in thermal problems. In order to alleviate the thermal problems, off-the-shelf mobile devices rely on dynamic voltage-frequency scaling (DVFS)-based dynamic thermal management (DTM) schemes. Unfortunately, in the DVFS-based DTM schemes, an excessive number of DTM operations worsen not only performance but also power efficiency. In this paper, we propose a temperature-aware DVFS scheme for Android-based mobile devices to optimize power or performance depending on the option. We evaluate our scheme in the off-the-shelf mobile device. Our evaluation results show that our scheme saves energy consumption by 12.7%, on average, when we use the power optimizing option. Our scheme also enhances the performance by 6.3%, on average, by using the performance optimizing scheme, still reducing the energy consumption by 6.7%.

Description: by Adviti Naik, Damjana Rozman, Aleš Belič Current state-of-the-art mathematical models to investigate complex biological processes, in particular liver-associated pathologies, have limited expansiveness, flexibility, representation of integrated regulation and rely on the availability of detailed kinetic data. We generated the SteatoNet, a multi-pathway, multi-tissue model and in silico platform to investigate hepatic metabolism and its associated deregulations. SteatoNet is based on object-oriented modelling, an approach most commonly applied in automotive and process industries, whereby individual objects correspond to functional entities. Objects were compiled to feature two novel hepatic modelling aspects: the interaction of hepatic metabolic pathways with extra-hepatic tissues and the inclusion of transcriptional and post-transcriptional regulation. SteatoNet identification at normalised steady state circumvents the need for constraining kinetic parameters. Validation and identification of flux disturbances that have been proven experimentally in liver patients and animal models highlights the ability of SteatoNet to effectively describe biological behaviour. SteatoNet identifies crucial pathway branches (transport of glucose, lipids and ketone bodies) where changes in flux distribution drive the healthy liver towards hepatic steatosis, the primary stage of non-alcoholic fatty liver disease. Cholesterol metabolism and its transcription regulators are highlighted as novel steatosis factors. SteatoNet thus serves as an intuitive in silico platform to identify systemic changes associated with complex hepatic metabolic disorders.

Description: by Xiaolin Tang, Mourad Bendjennat, Saveez Saffarian Vesicular stomatitis virus (VSV) is the prototype for negative sense non segmented (NNS) RNA viruses which include potent human and animal pathogens such as Rabies, Ebola and measles. The polymerases of NNS RNA viruses only initiate transcription at or near the 3′ end of their genome template. We measured the dissociation constant of VSV polymerases from their whole genome template to be 20 pM. Given this low dissociation constant, initiation and sustainability of transcription becomes nontrivial. To explore possible mechanisms, we simulated the first hour of transcription using Monte Carlo methods and show that a one-time initial dissociation of all polymerases during entry is not sufficient to sustain transcription. We further show that efficient transcription requires a sliding mechanism for non-transcribing polymerases and can be realized with different polymerase-polymerase interactions and distinct template topologies. In conclusion, we highlight a model in which collisions between transcribing and sliding non-transcribing polymerases result in release of the non-transcribing polymerases allowing for redistribution of polymerases between separate templates during transcription and suggest specific experiments to further test these mechanisms.

Description: by The PLOS Computational Biology Staff

Description: by Ganesh Shahane, Chirag Parsania, Durba Sengupta, Manali Joshi The human β 2 -adrenergic receptor (β 2 AR), a member of the G-protein coupled receptor (GPCR) family, is expressed in bronchial smooth muscle cells. Upon activation by agonists, β 2 AR causes bronchodilation and relief in asthma patients. The N-terminal polymorphism of β 2 AR at the 16 th position, Arg16Gly, has warranted a lot of attention since it is linked to variations in response to albuterol (agonist) treatment. Although the β 2 AR is one of the well-studied GPCRs, the N-terminus which harbors this mutation, is absent in all available experimental structures. The goal of this work was to study the molecular level differences between the N-terminal variants using structural modeling and atomistic molecular dynamics simulations. Our simulations reveal that the N-terminal region of the Arg variant shows greater dynamics than the Gly variant, leading to differential placement. Further, the position and dynamics of the N-terminal region, further, affects the ligand binding-site accessibility. Interestingly, long-range effects are also seen at the ligand binding site, which is marginally larger in the Gly as compared to the Arg variant resulting in the preferential docking of albuterol to the Gly variant. This study thus reveals key differences between the variants providing a molecular framework towards understanding the variable drug response in asthma patients.

Description: by Krishna Praneeth Kilambi, Kavan Reddy, Jeffrey J. Gray Protein-protein interactions depend on a host of environmental factors. Local pH conditions influence the interactions through the protonation states of the ionizable residues that can change upon binding. In this work, we present a pH-sensitive docking approach, pHDock, that can sample side-chain protonation states of five ionizable residues (Asp, Glu, His, Tyr, Lys) on-the-fly during the docking simulation. pHDock produces successful local docking funnels in approximately half (79/161) the protein complexes, including 19 cases where standard RosettaDock fails. pHDock also performs better than the two control cases comprising docking at pH 7.0 or using fixed, predetermined protonation states. On average, the top-ranked pHDock structures have lower interface RMSDs and recover more native interface residue-residue contacts and hydrogen bonds compared to RosettaDock. Addition of backbone flexibility using a computationally-generated conformational ensemble further improves native contact and hydrogen bond recovery in the top-ranked structures. Although pHDock is designed to improve docking, it also successfully predicts a large pH-dependent binding affinity change in the Fc–FcRn complex, suggesting that it can be exploited to improve affinity predictions. The approaches in the study contribute to the goal of structural simulations of whole-cell protein-protein interactions including all the environmental factors, and they can be further expanded for pH-sensitive protein design.

Description: Han propose a new method for parallel decimal multiplication with redundant partial products. They compare the performance of their multiplier with some previous relevant works, based on analytical and synthesis results. We have noted that the claimed critical delay path in (IEEE Trans. Computers, vol. 62, no. 5, pp. 956–968, May 2013) is faster than the actual critical delay path. Therefore, comparison results seem to be deceptive. For example, our accurate analytical evaluation devaluated the claimed speed advantage over the multiplier of (Microelectronics J., vol. 40, no. 10, pp. 1471–1481, Oct. 2009). Furthermore, we synthesized both multipliers, to show synthesis results confirm those of analytical evaluation.

Description: by Tadashi Ando, Jeffrey Skolnick DNA binding proteins efficiently search for their cognitive sites on long genomic DNA by combining 3D diffusion and 1D diffusion (sliding) along the DNA. Recent experimental results and theoretical analyses revealed that the proteins show a rotation-coupled sliding along DNA helical pitch. Here, we performed Brownian dynamics simulations using newly developed coarse-grained protein and DNA models for evaluating how hydrodynamic interactions between the protein and DNA molecules, binding affinity of the protein to DNA, and DNA fluctuations affect the one dimensional diffusion of the protein on the DNA. Our results indicate that intermolecular hydrodynamic interactions reduce 1D diffusivity by 30%. On the other hand, structural fluctuations of DNA give rise to steric collisions between the CG-proteins and DNA, resulting in faster 1D sliding of the protein. Proteins with low binding affinities consistent with experimental estimates of non-specific DNA binding show hopping along the CG-DNA. This hopping significantly increases sliding speed. These simulation studies provide additional insights into the mechanism of how DNA binding proteins find their target sites on the genome.

Description: by Krzysztof Puszynski, Alberto Gandolfi, Alberto d'Onofrio In this work we investigate, by means of a computational stochastic model, how tumor cells with wild-type p53 gene respond to the drug Nutlin, an agent that interferes with the Mdm2-mediated p53 regulation. In particular, we show how the stochastic gene-switching controlled by p53 can explain experimental dose-response curves, i.e., the observed inter-cell variability of the cell viability under Nutlin action. The proposed model describes in some detail the regulation network of p53, including the negative feedback loop mediated by Mdm2 and the positive loop mediated by PTEN, as well as the reversible inhibition of Mdm2 caused by Nutlin binding. The fate of the individual cell is assumed to be decided by the rising of nuclear-phosphorylated p53 over a certain threshold. We also performed in silico experiments to evaluate the dose-response curve after a single drug dose delivered in mice, or after its fractionated administration. Our results suggest that dose-splitting may be ineffective at low doses and effective at high doses. This complex behavior can be due to the interplay among the existence of a threshold on the p53 level for its cell activity, the nonlinearity of the relationship between the bolus dose and the peak of active p53, and the relatively fast elimination of the drug.

Description: by Sven Jahnke, Raoul-Martin Memmesheimer, Marc Timme Reliable signal transmission constitutes a key requirement for neural circuit function. The propagation of synchronous pulse packets through recurrent circuits is hypothesized to be one robust form of signal transmission and has been extensively studied in computational and theoretical works. Yet, although external or internally generated oscillations are ubiquitous across neural systems, their influence on such signal propagation is unclear. Here we systematically investigate the impact of oscillations on propagating synchrony. We find that for standard, additive couplings and a net excitatory effect of oscillations, robust propagation of synchrony is enabled in less prominent feed-forward structures than in systems without oscillations. In the presence of non-additive coupling (as mediated by fast dendritic spikes), even balanced oscillatory inputs may enable robust propagation. Here, emerging resonances create complex locking patterns between oscillations and spike synchrony. Interestingly, these resonances make the circuits capable of selecting specific pathways for signal transmission. Oscillations may thus promote reliable transmission and, in co-action with dendritic nonlinearities, provide a mechanism for information processing by selectively gating and routing of signals. Our results are of particular interest for the interpretation of sharp wave/ripple complexes in the hippocampus, where previously learned spike patterns are replayed in conjunction with global high-frequency oscillations. We suggest that the oscillations may serve to stabilize the replay.

Description: by Michael M. H. Graf, Lin Zhixiong, Urban Bren, Dietmar Haltrich, Wilfred F. van Gunsteren, Chris Oostenbrink The flavoenzyme pyranose dehydrogenase (PDH) from the litter decomposing fungus Agaricus meleagris oxidizes many different carbohydrates occurring during lignin degradation. This promiscuous substrate specificity makes PDH a promising catalyst for bioelectrochemical applications. A generalized approach to simulate all 32 possible aldohexopyranoses in the course of one or a few molecular dynamics (MD) simulations is reported. Free energy calculations according to the one-step perturbation (OSP) method revealed the solvation free energies (ΔG solv ) of all 32 aldohexopyranoses in water, which have not yet been reported in the literature. The free energy difference between β- and α-anomers (ΔG β-α ) of all d-stereoisomers in water were compared to experimental values with a good agreement. Moreover, the free-energy differences (ΔG) of the 32 stereoisomers bound to PDH in two different poses were calculated from MD simulations. The relative binding free energies (ΔΔG bind ) were calculated and, where available, compared to experimental values, approximated from K m values. The agreement was very good for one of the poses, in which the sugars are positioned in the active site for oxidation at C1 or C2. Distance analysis between hydrogens of the monosaccharide and the reactive N5-atom of the flavin adenine dinucleotide (FAD) revealed that oxidation is possible at HC1 or HC2 for pose A, and at HC3 or HC4 for pose B. Experimentally detected oxidation products could be rationalized for the majority of monosaccharides by combining ΔΔG bind and a reweighted distance analysis. Furthermore, several oxidation products were predicted for sugars that have not yet been tested experimentally, directing further analyses. This study rationalizes the relationship between binding free energies and substrate promiscuity in PDH, providing novel insights for its applicability in bioelectrochemistry. The results suggest that a similar approach could be applied to study promiscuity of other enzymes.

Description: Learn about metasystems, their characteristics, and the challenges IT professionals and systems engineers face in designing and managing such systems.

Description: Before writing a single line of code, software engineers can increase application assurance by instituting the practice recommendations articulated in their enterprise architecture. Many Common Weakness Enumerations (CWEs) can be addressed in the architecture and design phases of the development life cycle. Architectural and design flaws found late in the SDLC can be costly to repair; often, these flaws are so baked into the application that they're resistant to code patches. The only viable response might be to catalogue their existence for a later redesign of the application. Moreover, patches to flaws can inject additional defects as well as alert adversaries to the existence of these flaws.

Description: Due to instability and poor identification ability of single pyroelectric infrared (PIR) detector for human target identification, this paper proposes a new approach to fuse the information collected from multiple PIR sensors for human identification. Firstly, Fast Fourier Transform (FFT), Short Time Fourier Transform (STFT), Wavelet Transform (WT) and Wavelet Packet Transform (WPT) are adopted to extract features of the human body, which can be achieved by single PIR sensor. Then, we apply Principal Component Analysis (PCA) and Support Vector Machine (SVM) to reduce the characteristic dimensions and to classify the human targets, respectively. Finally, Fuzzy Comprehensive Evaluation (FCE) is utilized to fuse recognition results from multiple PIR sensors to finalize human identification. The pyroelectric characteristics under scenarios with different people and/or different paths are analyzed by various experiments, and the recognition results with/without fusion procedure are also shown and compared. The experimental results demonstrate our scheme has improved efficiency for human identification.

Description: Cloud computing provides flexibility and agility to meet growing business needs in a dynamic and competitive landscape. Banking, financial services, and insurance sector organizations are interested in exploring cloud services as a technology, provided that security and privacy are ensured. One solution is a community cloud, in which cloud services are targeted for organizations with common objectives and security controls. The Indian Banking Community Cloud (IBCC) initiative of the Institute for Development and Research in Banking Technology in Hyderabad, India, provides cloud-based services exclusively for Indian banks. In this article, the authors describe the IBCC architecture, along with its implementation details, cloud services offered, security and disaster-recovery aspects, deployment challenges, and future work. This article is part of a special issue on advancing cloud computing.

Description: Motivated by a continually increasing demand for applications that depend on machine comprehension of text-based content, researchers in both academia and industry have developed innovative solutions for automated information extraction from text. In this article, the authors focus on a subset of such tools--semantic taggers--that not only extract and disambiguate entities mentioned in the text but also identify topics that unambiguously describe the text's main themes. The authors offer insight into the process of semantic tagging, the capabilities and specificities of today's semantic taggers, and also indicate some of the criteria to be considered when choosing a tagger.

Description: The objectives of datacenter consolidation are cost reduction and superior services. A datacenter consolidation plan includes minimizing investments in IT infrastructure and buildings and reducing power consumption related to cooling. Such a process requires scalable planning and implementation. Virtualization is the most popular and cost-effective technology for datacenter consolidation. In this article, the author runs a cost-benefit analysis of virtualization and datacenter consolidation using the Global Virtual datacenter online calculator and VMware's ROI CO ((for) return on investment/total cost of ownership) calculator version 3.0.

Description: The adoption of various converging trends in IT, such as cloud computing, the Internet of Things (IoT), crypto-currency, autonomous systems, and big data, challenge traditional notions of program management and highlight the importance of computational networks.

Description: Provides a listing of current staff, committee members and society officers.

Description: by Alex N. Nguyen Ba, Bob Strome, Jun Jie Hua, Jonathan Desmond, Isabelle Gagnon-Arsenault, Eric L. Weiss, Christian R. Landry, Alan M. Moses Gene duplication is an important evolutionary mechanism that can result in functional divergence in paralogs due to neo-functionalization or sub-functionalization. Consistent with functional divergence after gene duplication, recent studies have shown accelerated evolution in retained paralogs. However, little is known in general about the impact of this accelerated evolution on the molecular functions of retained paralogs. For example, do new functions typically involve changes in enzymatic activities, or changes in protein regulation? Here we study the evolution of posttranslational regulation by examining the evolution of important regulatory sequences (short linear motifs) in retained duplicates created by the whole-genome duplication in budding yeast. To do so, we identified short linear motifs whose evolutionary constraint has relaxed after gene duplication with a likelihood-ratio test that can account for heterogeneity in the evolutionary process by using a non-central chi-squared null distribution. We find that short linear motifs are more likely to show changes in evolutionary constraints in retained duplicates compared to single-copy genes. We examine changes in constraints on known regulatory sequences and show that for the Rck1/Rck2, Fkh1/Fkh2, Ace2/Swi5 paralogs, they are associated with previously characterized differences in posttranslational regulation. Finally, we experimentally confirm our prediction that for the Ace2/Swi5 paralogs, Cbk1 regulated localization was lost along the lineage leading to SWI5 after gene duplication. Our analysis suggests that changes in posttranslational regulation mediated by short regulatory motifs systematically contribute to functional divergence after gene duplication.

Description: by Ritwik K. Niyogi, Peter Shizgal, Peter Dayan Given the option, humans and other animals elect to distribute their time between work and leisure, rather than choosing all of one and none of the other. Traditional accounts of partial allocation have characterised behavior on a macroscopic timescale, reporting and studying the mean times spent in work or leisure. However, averaging over the more microscopic processes that govern choices is known to pose tricky theoretical problems, and also eschews any possibility of direct contact with the neural computations involved. We develop a microscopic framework, formalized as a semi-Markov decision process with possibly stochastic choices, in which subjects approximately maximise their expected returns by making momentary commitments to one or other activity. We show macroscopic utilities that arise from microscopic ones, and demonstrate how facets such as imperfect substitutability can arise in a more straightforward microscopic manner.

Description: by Hua Cheng, R. Dustin Schaeffer, Yuxing Liao, Lisa N. Kinch, Jimin Pei, Shuoyong Shi, Bong-Hyun Kim, Nick V. Grishin Understanding the evolution of a protein, including both close and distant relationships, often reveals insight into its structure and function. Fast and easy access to such up-to-date information facilitates research. We have developed a hierarchical evolutionary classification of all proteins with experimentally determined spatial structures, and presented it as an interactive and updatable online database. ECOD (Evolutionary Classification of protein Domains) is distinct from other structural classifications in that it groups domains primarily by evolutionary relationships (homology), rather than topology (or “fold”). This distinction highlights cases of homology between domains of differing topology to aid in understanding of protein structure evolution. ECOD uniquely emphasizes distantly related homologs that are difficult to detect, and thus catalogs the largest number of evolutionary links among structural domain classifications. Placing distant homologs together underscores the ancestral similarities of these proteins and draws attention to the most important regions of sequence and structure, as well as conserved functional sites. ECOD also recognizes closer sequence-based relationships between protein domains. Currently, approximately 100,000 protein structures are classified in ECOD into 9,000 sequence families clustered into close to 2,000 evolutionary groups. The classification is assisted by an automated pipeline that quickly and consistently classifies weekly releases of PDB structures and allows for continual updates. This synchronization with PDB uniquely distinguishes ECOD among all protein classifications. Finally, we present several case studies of homologous proteins not recorded in other classifications, illustrating the potential of how ECOD can be used to further biological and evolutionary studies.

Description: by Norman E. Davey, Venkata P. Satagopam, Salvador Santiago-Mozos, Carlos Villacorta-Martin, Tanmay A. M. Bharat, Reinhard Schneider, John A. G. Briggs Huge research effort has been invested over many years to determine the phenotypes of natural or artificial mutations in HIV proteins—interpretation of mutation phenotypes is an invaluable source of new knowledge. The results of this research effort are recorded in the scientific literature, but it is difficult for virologists to rapidly find it. Manually locating data on phenotypic variation within the approximately 270,000 available HIV-related research articles, or the further 1,500 articles that are published each month is a daunting task. Accordingly, the HIV research community would benefit from a resource cataloguing the available HIV mutation literature. We have applied computational text-mining techniques to parse and map mutagenesis and polymorphism information from the HIV literature, have enriched the data with ancillary information and have developed a public, web-based interface through which it can be intuitively explored: the HIV mutation browser. The current release of the HIV mutation browser describes the phenotypes of 7,608 unique mutations at 2,520 sites in the HIV proteome, resulting from the analysis of 120,899 papers. The mutation information for each protein is organised in a residue-centric manner and each residue is linked to the relevant experimental literature. The importance of HIV as a global health burden advocates extensive effort to maximise the efficiency of HIV research. The HIV mutation browser provides a valuable new resource for the research community. The HIV mutation browser is available at: http://hivmut.org.

Description: by Julijana Gjorgjieva, Rebecca A. Mease, William J. Moody, Adrienne L. Fairhall Diverse ion channels and their dynamics endow single neurons with complex biophysical properties. These properties determine the heterogeneity of cell types that make up the brain, as constituents of neural circuits tuned to perform highly specific computations. How do biophysical properties of single neurons impact network function? We study a set of biophysical properties that emerge in cortical neurons during the first week of development, eventually allowing these neurons to adaptively scale the gain of their response to the amplitude of the fluctuations they encounter. During the same time period, these same neurons participate in large-scale waves of spontaneously generated electrical activity. We investigate the potential role of experimentally observed changes in intrinsic neuronal properties in determining the ability of cortical networks to propagate waves of activity. We show that such changes can strongly affect the ability of multi-layered feedforward networks to represent and transmit information on multiple timescales. With properties modeled on those observed at early stages of development, neurons are relatively insensitive to rapid fluctuations and tend to fire synchronously in response to wave-like events of large amplitude. Following developmental changes in voltage-dependent conductances, these same neurons become efficient encoders of fast input fluctuations over few layers, but lose the ability to transmit slower, population-wide input variations across many layers. Depending on the neurons' intrinsic properties, noise plays different roles in modulating neuronal input-output curves, which can dramatically impact network transmission. The developmental change in intrinsic properties supports a transformation of a networks function from the propagation of network-wide information to one in which computations are scaled to local activity. This work underscores the significance of simple changes in conductance parameters in governing how neurons represent and propagate information, and suggests a role for background synaptic noise in switching the mode of information transmission.

Description: In creating the Open Networking Foundation's conformance testing program for the OpenFlow networking specification, economic, technological, and market drivers must be harmonized, allowing for the simultaneous development of consumer confidence, industry competition, and trustworthy product validation.

Description: Recently, Apple admitted that revealing photos of celebrities had been released on the Internet due to security breaches associated with its iCloud and Find My iPhone systems.

Description: An integrity level defines a required level of confidence that a system satisfies critical properties related to relevant risk criteria. However, integrity level terms and definitions differ across industry sectors, and this hampers a common understanding and application of integrity levels.

Description: Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.

Description: Software-defined networking opens up new possibilities for architectures based on open source components, promising improved orchestration and agility, lower operational costs, and--most important--a wave of innovation. The Web extra at http://youtu.be/pdG2btcyyK8 is a video in which authors Christian Esteve Rothenberg, Roy Chua, and Thomas Nadeau present a slideshow and discuss how software-defined networking opens up new possibilities for architectures based on open-source components, promising improved orchestration and agility, lower operational costs, and a new wave of innovation.

Description: Why do dynamic power-management technologies that dramatically improve datacenter server energy efficiency continue to go unleveraged?

Description: To adequately address climate change, we need novel data-science methods that account for the spatiotemporal and physical nature of climate phenomena. Only then will we be able to move from statistical analysis to scientific insights.

Description: Amateur software developers might lack precise technical skills, but they bring detailed knowledge of their environments to the table. The first Web extra at http://youtu.be/r-kIJQu4iDQ is an audio recording of author David Alan Grier reading his Errant Hashtag column in which he discusses how amateur software developers might lack precise technical skills but bring detailed knowledge of their environments to the table. The second Web extra at http://youtu.be/EDKeN9mVfwk is an audio recording of author David Alan Grier discussing a recent report on electronic voting by the Atlantic Council, a Washington DC think tank, that shows that e-voting is still a risk that citizens of democracies and engineers should take into account.

Description: A summary of articles recently published in IEEE Computer Society magazines.

Description: The past decade has seen a dramatic increase in the amount of data captured and made available to scientists for research. This increase amplifies the difficulty scientists face in finding the data most relevant to their information needs. In prior work, we hypothesized that Information Retrieval-style ranked search can be applied to data sets to help a scientist discover the most relevant data amongst the thousands of data sets in many formats, much like text-based ranked search helps users make sense of the vast number of Internet documents. To test this hypothesis, we explored the use of ranked search for scientific data using an existing multi-terabyte observational archive as our test-bed. In this paper, we investigate whether the concept of varying relevance, and therefore ranked search, applies to numeric data—that is, are data sets are enough like documents for Information Retrieval techniques and evaluation measures to apply? We present a user study that demonstrates that data set similarity resonates with users as a basis for relevance and, therefore, for ranked search. We evaluate a prototype implementation of ranked search over data sets with a second user study and demonstrate that ranked search improves a scientist's ability to find needed data.

Description: In recent years, probabilistic data management has received a lot of attention due to several applications that deal with uncertain data: RFID systems, sensor networks, data cleaning, scientific and biomedical data management, and approximate schema mappings. Query evaluation is a challenging problem in probabilistic databases, proved to be #P-hard. A general method for query evaluation is based on the lineage of the query and reduces the query evaluation problem to computing the probability of a propositional formula. The main approaches proposed in the literature to approximate probabilistic queries confidence computation are based on Monte Carlo simulation, or formula compilation into decision diagrams (e.g., d-trees). The former executes a polynomial, but with too many, iterations, while the latter is polynomial for easy queries, but may be exponential in the worst case. We designed a new optimized Monte Carlo algorithm that drastically reduces the number of iterations and proposed an efficient parallel version that we implemented on GPU. Thanks to the elevated degree of parallelism provided by the GPU, combined with the linear speedup of our algorithm, we managed to reduce significantly the long running time required by a sequential Monte Carlo algorithm. Experimental results show that our algorithm is so efficient as to be comparable with the formula compilation approach, but with the significant advantage of avoiding exponential behavior.

Description: Graph-based ranking models have been widely applied in information retrieval area. In this paper, we focus on a well known graph-based model - the Ranking on Data Manifold model, or Manifold Ranking (MR). Particularly, it has been successfully applied to content-based image retrieval, because of its outstanding ability to discover underlying geometrical structure of the given image database. However, manifold ranking is computationally very expensive, which significantly limits its applicability to large databases especially for the cases that the queries are out of the database (new samples). We propose a novel scalable graph-based ranking model called Efficient Manifold Ranking (EMR), trying to address the shortcomings of MR from two main perspectives: scalable graph construction and efficient ranking computation. Specifically, we build an anchor graph on the database instead of a traditional $k$ -nearest neighbor graph, and design a new form of adjacency matrix utilized to speed up the ranking. An approximate method is adopted for efficient out-of-sample retrieval. Experimental results on some large scale image databases demonstrate that EMR is a promising method for real world retrieval applications.

Description: Product quantization-based approaches are effective to encode high-dimensional data points for approximate nearest neighbor search. The space is decomposed into a Cartesian product of low-dimensional subspaces, each of which generates a sub codebook. Data points are encoded as compact binary codes using these sub codebooks, and the distance between two data points can be approximated efficiently from their codes by the precomputed lookup tables. Traditionally, to encode a subvector of a data point in a subspace, only one sub codeword in the corresponding sub codebook is selected, which may impose strict restrictions on the search accuracy. In this paper, we propose a novel approach, named optimized cartesian K-means (ock-means), to better encode the data points for more accurate approximate nearest neighbor search. In ock-means, multiple sub codewords are used to encode the subvector of a data point in a subspace. Each sub codeword stems from different sub codebooks in each subspace, which are optimally generated with regards to the minimization of the distortion errors. The high-dimensional data point is then encoded as the concatenation of the indices of multiple sub codewords from all the subspaces. This can provide more flexibility and lower distortion errors than traditional methods. Experimental results on the standard real-life data sets demonstrate the superiority over state-of-the-art approaches for approximate nearest neighbor search.

Description: This paper considers the problem of determinizing probabilistic data to enable such data to be stored in legacy systems that accept only deterministic input. Probabilistic data may be generated by automated data analysis/enrichment techniques such as entity resolution, information extraction, and speech processing. The legacy system may correspond to pre-existing web applications such as Flickr, Picasa, etc. The goal is to generate a deterministic representation of probabilistic data that optimizes the quality of the end-application built on deterministic data. We explore such a determinization problem in the context of two different data processing tasks—triggers and selection queries. We show that approaches such as thresholding or top-1 selection traditionally used for determinization lead to suboptimal performance for such applications. Instead, we develop a query-aware strategy and show its advantages over existing solutions through a comprehensive empirical evaluation over real and synthetic datasets.

Description: Identifying which text corpus leads in the context of a topic presents a great challenge of considerable interest to researchers. Recent research into lead-lag analysis has mainly focused on estimating the overall leads and lags between two corpora. However, real-world applications have a dire need to understand lead-lag patterns both globally and locally. In this paper, we introduce TextPioneer , an interactive visual analytics tool for investigating lead-lag across corpora from the global level to the local level. In particular, we extend an existing lead-lag analysis approach to derive two-level results. To convey multiple perspectives of the results, we have designed two visualizations, a novel hybrid tree visualization that couples a radial space-filling tree with a node-link diagram and a twisted-ladder-like visualization. We have applied our method to several corpora and the evaluation shows promise, especially in support of text comparison at different levels of detail.

Description: by Moritz Helias, Tom Tetzlaff, Markus Diesmann Correlated neuronal activity is a natural consequence of network connectivity and shared inputs to pairs of neurons, but the task-dependent modulation of correlations in relation to behavior also hints at a functional role. Correlations influence the gain of postsynaptic neurons, the amount of information encoded in the population activity and decoded by readout neurons, and synaptic plasticity. Further, it affects the power and spatial reach of extracellular signals like the local-field potential. A theory of correlated neuronal activity accounting for recurrent connectivity as well as fluctuating external sources is currently lacking. In particular, it is unclear how the recently found mechanism of active decorrelation by negative feedback on the population level affects the network response to externally applied correlated stimuli. Here, we present such an extension of the theory of correlations in stochastic binary networks. We show that (1) for homogeneous external input, the structure of correlations is mainly determined by the local recurrent connectivity, (2) homogeneous external inputs provide an additive, unspecific contribution to the correlations, (3) inhibitory feedback effectively decorrelates neuronal activity, even if neurons receive identical external inputs, and (4) identical synaptic input statistics to excitatory and to inhibitory cells increases intrinsically generated fluctuations and pairwise correlations. We further demonstrate how the accuracy of mean-field predictions can be improved by self-consistently including correlations. As a byproduct, we show that the cancellation of correlations between the summed inputs to pairs of neurons does not originate from the fast tracking of external input, but from the suppression of fluctuations on the population level by the local network. This suppression is a necessary constraint, but not sufficient to determine the structure of correlations; specifically, the structure observed at finite network size differs from the prediction based on perfect tracking, even though perfect tracking implies suppression of population fluctuations.

Description: by John G. Koland Upon the ligand-dependent dimerization of the epidermal growth factor receptor (EGFR), the intrinsic protein tyrosine kinase (PTK) activity of one receptor monomer is activated, and the dimeric receptor undergoes self-phosphorylation at any of eight candidate phosphorylation sites (P-sites) in either of the two C-terminal (CT) domains. While the structures of the extracellular ligand binding and intracellular PTK domains are known, that of the ∼225-amino acid CT domain is not, presumably because it is disordered. Receptor phosphorylation on CT domain P-sites is critical in signaling because of the binding of specific signaling effector molecules to individual phosphorylated P-sites. To investigate how the combination of conventional substrate recognition and the unique topological factors involved in the CT domain self-phosphorylation reaction lead to selectivity in P-site phosphorylation, we performed coarse-grained molecular simulations of the P-site/catalytic site binding reactions that precede EGFR self-phosphorylation events. Our results indicate that self-phosphorylation of the dimeric EGFR, although generally believed to occur in trans , may well occur with a similar efficiency in cis , with the P-sites of both receptor monomers being phosphorylated to a similar extent. An exception was the case of the most kinase-proximal P-site-992, the catalytic site binding of which occurred exclusively in cis via an intramolecular reaction. We discovered that the in cis interaction of P-site-992 with the catalytic site was facilitated by a cleft between the N-terminal and C-terminal lobes of the PTK domain that allows the short CT domain sequence tethering P-site-992 to the PTK core to reach the catalytic site. Our work provides several new mechanistic insights into the EGFR self-phosphorylation reaction, and demonstrates the potential of coarse-grained molecular simulation approaches for investigating the complexities of self-phosphorylation in molecules such as EGFR (HER/ErbB) family receptors and growth factor receptor PTKs in general.

Description: by Yi-Fei Huang, G. Brian Golding A critical question in biology is the identification of functionally important amino acid sites in proteins. Because functionally important sites are under stronger purifying selection, site-specific substitution rates tend to be lower than usual at these sites. A large number of phylogenetic models have been developed to estimate site-specific substitution rates in proteins and the extraordinarily low substitution rates have been used as evidence of function. Most of the existing tools, e.g. Rate4Site, assume that site-specific substitution rates are independent across sites. However, site-specific substitution rates may be strongly correlated in the protein tertiary structure, since functionally important sites tend to be clustered together to form functional patches. We have developed a new model, GP4Rate, which incorporates the Gaussian process model with the standard phylogenetic model to identify slowly evolved regions in protein tertiary structures. GP4Rate uses the Gaussian process to define a nonparametric prior distribution of site-specific substitution rates, which naturally captures the spatial correlation of substitution rates. Simulations suggest that GP4Rate can potentially estimate site-specific substitution rates with a much higher accuracy than Rate4Site and tends to report slowly evolved regions rather than individual sites. In addition, GP4Rate can estimate the strength of the spatial correlation of substitution rates from the data. By applying GP4Rate to a set of mammalian B7-1 genes, we found a highly conserved region which coincides with experimental evidence. GP4Rate may be a useful tool for the in silico prediction of functionally important regions in the proteins with known structures.

Description: by Kasper Jensen, Gianni Panagiotou, Irene Kouskoumvekaki Awareness that disease susceptibility is not only dependent on genetic make up, but can be affected by lifestyle decisions, has brought more attention to the role of diet. However, food is often treated as a black box, or the focus is limited to few, well-studied compounds, such as polyphenols, lipids and nutrients. In this work, we applied text mining and Naïve Bayes classification to assemble the knowledge space of food-phytochemical and food-disease associations, where we distinguish between disease prevention/amelioration and disease progression. We subsequently searched for frequently occurring phytochemical-disease pairs and we identified 20,654 phytochemicals from 16,102 plants associated to 1,592 human disease phenotypes. We selected colon cancer as a case study and analyzed our results in three directions; i) one stop legacy knowledge-shop for the effect of food on disease, ii) discovery of novel bioactive compounds with drug-like properties, and iii) discovery of novel health benefits from foods. This works represents a systematized approach to the association of food with health effect, and provides the phytochemical layer of information for nutritional systems biology research.

Description: by Aidan I. Brown, Peter K. Kim, Andrew D. Rutenberg Peroxisomes are membrane-bound organelles within eukaryotic cells that post-translationally import folded proteins into their matrix. Matrix protein import requires a shuttle receptor protein, usually PEX5, that cycles through docking with the peroxisomal membrane, ubiquitination, and export back into the cytosol followed by deubiquitination. Matrix proteins associate with PEX5 in the cytosol and are translocated into the peroxisome lumen during the PEX5 cycle. This cargo translocation step is not well understood, and its energetics remain controversial. We use stochastic computational models to explore different ways the AAA ATPase driven removal of PEX5 may couple with cargo translocation in peroxisomal importers of mammalian cells. The first model considered is uncoupled, in which translocation is spontaneous, and does not immediately depend on PEX5 removal. The second is directly coupled, in which cargo translocation only occurs when its PEX5 is removed from the peroxisomal membrane. The third, novel, model is cooperatively coupled and requires two PEX5 on a given importomer for cargo translocation — one PEX5 with associated cargo and one with ubiquitin. We measure both the PEX5 and the ubiquitin levels on the peroxisomes as we vary the matrix protein cargo addition rate into the cytosol. We find that both uncoupled and directly coupled translocation behave identically with respect to PEX5 and ubiquitin, and the peroxisomal ubiquitin signal increases as the matrix protein traffic increases. In contrast, cooperatively coupled translocation behaves dramatically differently, with a ubiquitin signal that decreases with increasing matrix protein traffic. Recent work has shown that ubiquitin on mammalian peroxisome membranes can lead to selective degradation by autophagy, or ‘pexophagy.’ Therefore, the high ubiquitin level for low matrix cargo traffic with cooperatively coupled protein translocation could be used as a disuse signal to mediate pexophagy. This mechanism may be one way that cells could regulate peroxisome numbers.