Digital Commons Network^™

Unttangling Irregular Actin Cytoskeleton Architectures In Tomograms Of The Cell With Struwwel Tracer, Salim Sazzed, Peter Scheible, Jing He, Willy Wriggers

Computer Science Faculty Publications

In this work, we established, validated, and optimized a novel computational framework for tracing arbitrarily oriented actin filaments in cryo-electron tomography maps. Our approach was designed for highly complex intracellular architectures in which a long-range cytoskeleton network extends throughout the cell bodies and protrusions. The irregular organization of the actin network, as well as cryo-electron-tomography-specific noise, missing wedge artifacts, and map dimensions call for a specialized implementation that is both robust and efficient. Our proposed solution, Struwwel Tracer, accumulates densities along paths of a specific length in various directions, starting from locally determined seed points. The highest-density paths originating …

Detecting Deceptive Dark-Pattern Web Advertisements For Blind Screen-Reader Users, Satwick Ram Kodandaram, Mohan Sunkara, Sampath Jayarathna, Vikas Ashok

Computer Science Faculty Publications

Advertisements have become commonplace on modern websites. While ads are typically designed for visual consumption, it is unclear how they affect blind users who interact with the ads using a screen reader. Existing research studies on non-visual web interaction predominantly focus on general web browsing; the specific impact of extraneous ad content on blind users' experience remains largely unexplored. To fill this gap, we conducted an interview study with 18 blind participants; we found that blind users are often deceived by ads that contextually blend in with the surrounding web page content. While ad blockers can address this problem via …

A Hybrid Deep Learning Approach For Crude Oil Price Prediction, Hind Aldabagh, Xianrong Zheng, Ravi Mukkamala

Computer Science Faculty Publications

Crude oil is one of the world’s most important commodities. Its price can affect the global economy, as well as the economies of importing and exporting countries. As a result, forecasting the price of crude oil is essential for investors. However, crude oil price tends to fluctuate considerably during significant world events, such as the COVID-19 pandemic and geopolitical conflicts. In this paper, we propose a deep learning model for forecasting the crude oil price of one-step and multi-step ahead. The model extracts important features that impact crude oil prices and uses them to predict future prices. The prediction model …

Systematizing Confidence In Open Research And Evidence (Score), Nazanin Alipourfard, Beatrix Arendt, Daniel M. Benjamin, Noam Benkler, Michael Bishop, Mark Burstein, Martin Bush, James Caverlee, Yiling Chen, Chae Clark, Anna Dreber Almenberg, Timothy M. Errington, Fiona Fidler, Nicholas Fox, Aaron Frank, Hannah Fraser, Scott Friedman, Ben Gelman, James Gentile, Jian Wu, Et Al., Score Collaboration

Computer Science Faculty Publications

Assessing the credibility of research claims is a central, continuous, and laborious part of the scientific process. Credibility assessment strategies range from expert judgment to aggregating existing evidence to systematic replication efforts. Such assessments can require substantial time and effort. Research progress could be accelerated if there were rapid, scalable, accurate credibility indicators to guide attention and resource allocation for further assessment. The SCORE program is creating and validating algorithms to provide confidence scores for research claims at scale. To investigate the viability of scalable tools, teams are creating: a database of claims from papers in the social and behavioral …

Electroencephalogram (Eeg) For Delineating Objective Measure Of Autism Spectrum Disorder, Sampath Jayarathna, Yasith Jayawardana, Mark Jaime, Sashi Thapaliya

Computer Science Faculty Publications

Autism spectrum disorder (ASD) is a developmental disorder that often impairs a child's normal development of the brain. According to CDC, it is estimated that 1 in 6 children in the US suffer from development disorders, and 1 in 68 children in the US suffer from ASD. This condition has a negative impact on a person's ability to hear, socialize, and communicate. Subjective measures often take more time, resources, and have false positives or false negatives. There is a need for efficient objective measures that can help in diagnosing this disease early as possible with less effort. EEG measures the …

Auditing Snomed Ct Hierarchical Relations Based On Lexical Features Of Concepts In Non-Lattice Subgraphs, Licong Cui, Olivier Bodenreider, Jay Shi, Guo-Qiang Zhang

Computer Science Faculty Publications

Objective—We introduce a structural-lexical approach for auditing SNOMED CT using a combination of non-lattice subgraphs of the underlying hierarchical relations and enriched lexical attributes of fully specified concept names. Our goal is to develop a scalable and effective approach that automatically identifies missing hierarchical IS-A relations.

Methods—Our approach involves 3 stages. In stage 1, all non-lattice subgraphs of SNOMED CT’s IS-A hierarchical relations are extracted. In stage 2, lexical attributes of fully-specified concept names in such non-lattice subgraphs are extracted. For each concept in a non-lattice subgraph, we enrich its set of attributes with attributes from its ancestor …

A Dynamic Programming Algorithm For Finding The Optimal Placement Of A Secondary Structure Topology In Cryo-Em Data, Abhishek Biswas, Desh Ranjan, Mohammad Zubair, Jing He

Computer Science Faculty Publications

The determination of secondary structure topology is a critical step in deriving the atomic structures from the protein density maps obtained from electron cryomicroscopy technique. This step often relies on matching the secondary structure traces detected from the protein density map to the secondary structure sequence segments predicted from the amino acid sequence. Due to inaccuracies in both sources of information, a pool of possible secondary structure positions needs to be sampled. One way to approach the problem is to first derive a small number of possible topologies using existing matching algorithms, and then find the optimal placement for each …

Biological Network Motif Detection And Evaluation, Wooyoung Kim, Min Li, Jianxin Wang, Yi Pan

Computer Science Faculty Publications

Background: Molecular level of biological data can be constructed into system level of data as biological networks. Network motifs are defined as over- represented small connected subgraphs in networks and they have been used for many biological applications. Since network motif discovery involves computationally challenging processes, previous algorithms have focused on computational efficiency. However, we believe that the biological quality of network motifs is also very important.

Results: We define biological network motifs as biologically significant subgraphs and traditional network motifs are differentiated as structural network motifs in this paper. We develop five algorithms, namely, EDGEGO-BNM, EDGEBETWEENNESS-BNM, NMF-BNM, NMFGO-BNM and …

Parallel Progressive Multiple Sequence Alignment On Reconfigurable Meshes, Ken Nguyen, Yi Pan, Ge Nong

Computer Science Faculty Publications

Background: One of the most fundamental and challenging tasks in bio-informatics is to identify related sequences and their hidden biological significance. The most popular and proven best practice method to accomplish this task is aligning multiple sequences together. However, multiple sequence alignment is a computing extensive task. In addition, the advancement in DNA/RNA and Protein sequencing techniques has created a vast amount of sequences to be analyzed that exceeding the capability of traditional computing models. Therefore, an effective parallel multiple sequence alignment model capable of resolving these issues is in a great demand.

Results: We design O(1) run-time solutions …

A Comparison Of The Functional Modules Identified From Time Course And Static Ppi Network Data, Xiwei Tang, Jianxin Wang, Binbin Liu, Min Li, Gang Chen, Yi Pan

Computer Science Faculty Publications

Background: Cellular systems are highly dynamic and responsive to cues from the environment. Cellular function and response patterns to external stimuli are regulated by biological networks. A protein-protein interaction (PPI) network with static connectivity is dynamic in the sense that the nodes implement so-called functional activities that evolve in time. The shift from static to dynamic network analysis is essential for further understanding of molecular systems.

Results: In this paper, Time Course Protein Interaction Networks (TC- PINs) are reconstructed by incorporating time series gene expression into PPI networks. Then, a clustering algorithm is used to create functional modules from three …

In-Degree Dynamics Of Large-Scale P2p Systems, Zhongmei Yao, Daren B. H. Cline, Dmitri Loguinov

Computer Science Faculty Publications

This paper builds a complete modeling framework for understanding user churn and in-degree dynamics in unstructured P2P systems in which each user can be viewed as a stationary alternating renewal process. While the classical Poisson result on the superposition of n stationary renewal processes for n→∞ requires that each point process become sparser as n increases, it is often difficult to rigorously show this condition in practice. In this paper, we first prove that despite user heterogeneity and non-Poisson arrival dynamics, a superposition of edge-arrival processes to a live user under uniform selection converges to a Poisson process when …

Identifying Protein Complexes From Interaction Networks Based On Clique Percolation And Distance Restriction, Jianxin Wang, Binbin Liu, Min Li, Yi Pan

Computer Science Faculty Publications

Background: Identification of protein complexes in large interaction networks is crucial to understand principles of cellular organization and predict protein functions, which is one of the most important issues in the post-genomic era. Each protein might be subordinate multiple protein complexes in the real protein-protein interaction networks.Identifying overlapping protein complexes from protein-protein interaction networks is a considerable research topic.

Result: As an effective algorithm in identifying overlapping module structures, clique percolation method (CPM) has a wide range of application in social networks and biological networks. However, the recognition accuracy of algorithm CPM is lowly. Furthermore, algorithm CPM is unfit to …

Evaluating Hydrology Preservation Of Simplified Terrain Representations, Christopher S. Stuetzle, W. Randolph Franklin, Barbara Cutler

Computer Science Faculty Publications

We present an error metric based on the potential energy of water flow to evaluate the quality of lossy terrain simplification algorithms. Typically, terrain compression algorithms seek to minimize RMS (root mean square) and maximum error. These metrics fail to capture whether a reconstructed terrain preserves the drainage network. A quantitative measurement of how accurately a drainage network captures the hydrology is important for determining the effectiveness of a terrain simplification technique. Having a measurement for testing and comparing different models has the potential to be widely used in numerous applications (flood prevention, erosion measurement, pollutant propagation, etc). In this …

Efficient Corona Training Protocols For Sensor Networks, Alan A. Bertossi, Stephan Olariu, Cristina M. Pinotti

Computer Science Faculty Publications

Phenomenal advances in nano-technology and packaging have made it possible to develop miniaturized low-power devices that integrate sensing, special-purpose computing, and wireless communications capabilities. It is expected that these small devices, referred to as sensors, will be mass-produced and deployed, making their production cost negligible. Due to their small form factor and modest non-renewable energy budget, individual sensors are not expected to be GPS-enabled. Moreover, in most applications, exact geographic location is not necessary, and all that the individual sensors need is a coarse-grain location awareness. The task of acquiring such a coarse-grain location awareness is referred to as training. …

An Improved Ant Colony Algorithm With Diversified Solutions Based On The Immune Strategy, Ling Qin, Yi Pan, Ling Chen, Yixin Chen

Computer Science Faculty Publications

Background: Ant colony algorithm has emerged recently as a new meta- heuristic method, which is inspired from the behaviours of real ants for solving NP-hard problems. However, the classical ant colony algorithm also has its defects of stagnation and premature. This paper aims at remedying these problems.

Results: In this paper, we propose an adaptive ant colony algorithm that simulates the behaviour of biological immune system. The solutions of the problem are much more diversified than traditional ant colony algorithms.

Conclusion: The proposed method for improving the performance of traditional ant colony algorithm takes into account the polarization of the …

A Novel Approach To Phylogenetic Tree Construction Using Stochastic Optimization And Clustering, Ling Qin, Yixin Chen, Yi Pan, Ling Chen

Computer Science Faculty Publications

Background: The problem of inferring the evolutionary history and constructing the phylogenetic tree with high performance has become one of the major problems in computational biology.

Results: A new phylogenetic tree construction method from a given set of objects (proteins, species, etc.) is presented. As an extension of ant colony optimization, this method proposes an adaptive phylogenetic clustering algorithm based on a digraph to find a tree structure that defines the ancestral relationships among the given objects.

Conclusion: Our phylogenetic tree construction method is tested to compare its results with that of the genetic algorithm (GA). Experimental results show that …

Upper Bounds To The Clique Width Of Graphs, Bruno Courcelle, Stephan Olariu

Computer Science Faculty Publications

Hierarchical decompositions of graphs are interesting for algorithmic purposes. Many NP complete problems have linear complexity on graphs with tree-decompositions of bounded width. We investigate alternate hierarchical decompositions that apply to wider classes of graphs and still enjoy good algorithmic properties. These decompositions are motivated and inspired by the study of vertex-replacement context-free graph grammars. The complexity measure of graphs associated with these decompositions is called clique width. In this paper we bound the clique width of a graph in terms of its tree width on the one hand, and of the clique width of its edge complement on …

Weak Bipolarizable Graphs, Stephan Olariu

Computer Science Faculty Publications

We characterize a new class of perfectly orderable graphs and give a polynomial-time recognition algorithm, together with linear-time optimization algorithms for this class of graphs.

An Algorithm For The Electromagnetic Scattering Due To An Axially Symmetric Body With An Impedance Boundary Condition, F. Stenger, M. Hagmann, J. Scheing

Computer Science Faculty Publications

Let B be a body in R³, and let S denote the boundary of B. The surface S is described by S = {(x, y, z): (x² + Y²)^½= ƒ(z), -1≤ z ≤ I}, where ƒ analytic function that is real and positive on (-1, 1) and ƒ(±1) = 0. An algorithm is described for computing the scattered field due to a plane wave incident field, under Leontovich boundary conditions. The Galerkin method of solution used here leads to a block diagonal matrix involving 2M …