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Browsing Journal Articles, BioMed Central and SpringerOpen by Department "Computer Science"
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- Acoustic differences between healthy and depressed people: a cross-situation studyWang, Jingying; Zhang, Lei; Liu, Tianli; Pan, Wei; Hu, Bin; Zhu, Tingshao (2019-10-15)Background Abnormalities in vocal expression during a depressed episode have frequently been reported in people with depression, but less is known about if these abnormalities only exist in special situations. In addition, the impacts of irrelevant demographic variables on voice were uncontrolled in previous studies. Therefore, this study compares the vocal differences between depressed and healthy people under various situations with irrelevant variables being regarded as covariates. Methods To examine whether the vocal abnormalities in people with depression only exist in special situations, this study compared the vocal differences between healthy people and patients with unipolar depression in 12 situations (speech scenarios). Positive, negative and neutral voice expressions between depressed and healthy people were compared in four tasks. Multiple analysis of covariance (MANCOVA) was used for evaluating the main effects of variable group (depressed vs. healthy) on acoustic features. The significances of acoustic features were evaluated by both statistical significance and magnitude of effect size. Results The results of multivariate analysis of covariance showed that significant differences between the two groups were observed in all 12 speech scenarios. Although significant acoustic features were not the same in different scenarios, we found that three acoustic features (loudness, MFCC5 and MFCC7) were consistently different between people with and without depression with large effect magnitude. Conclusions Vocal differences between depressed and healthy people exist in 12 scenarios. Acoustic features including loudness, MFCC5 and MFCC7 have potentials to be indicators for identifying depression via voice analysis. These findings support that depressed people’s voices include both situation-specific and cross-situational patterns of acoustic features.
- AgroSeek: a system for computational analysis of environmental metagenomic data and associated metadataLiang, Xiao; Akers, Kyle; Keenum, Ishi M.; Wind, Lauren L.; Gupta, Suraj; Chen, Chaoqi; Aldaihani, Reem; Pruden, Amy; Zhang, Liqing; Knowlton, Katharine F.; Xia, Kang; Heath, Lenwood S. (2021-03-10)Background Metagenomics is gaining attention as a powerful tool for identifying how agricultural management practices influence human and animal health, especially in terms of potential to contribute to the spread of antibiotic resistance. However, the ability to compare the distribution and prevalence of antibiotic resistance genes (ARGs) across multiple studies and environments is currently impossible without a complete re-analysis of published datasets. This challenge must be addressed for metagenomics to realize its potential for helping guide effective policy and practice measures relevant to agricultural ecosystems, for example, identifying critical control points for mitigating the spread of antibiotic resistance. Results Here we introduce AgroSeek, a centralized web-based system that provides computational tools for analysis and comparison of metagenomic data sets tailored specifically to researchers and other users in the agricultural sector interested in tracking and mitigating the spread of ARGs. AgroSeek draws from rich, user-provided metagenomic data and metadata to facilitate analysis, comparison, and prediction in a user-friendly fashion. Further, AgroSeek draws from publicly-contributed data sets to provide a point of comparison and context for data analysis. To incorporate metadata into our analysis and comparison procedures, we provide flexible metadata templates, including user-customized metadata attributes to facilitate data sharing, while maintaining the metadata in a comparable fashion for the broader user community and to support large-scale comparative and predictive analysis. Conclusion AgroSeek provides an easy-to-use tool for environmental metagenomic analysis and comparison, based on both gene annotations and associated metadata, with this initial demonstration focusing on control of antibiotic resistance in agricultural ecosystems. Agroseek creates a space for metagenomic data sharing and collaboration to assist policy makers, stakeholders, and the public in decision-making. AgroSeek is publicly-available at https://agroseek.cs.vt.edu/ .
- Analysis and remedy of negativity problem in hybrid stochastic simulation algorithm and its applicationChen, Minghan; Cao, Yang (2019-06-20)Background The hybrid stochastic simulation algorithm, proposed by Haseltine and Rawlings (HR), is a combination of differential equations for traditional deterministic models and Gillespie’s algorithm (SSA) for stochastic models. The HR hybrid method can significantly improve the efficiency of stochastic simulations for multiscale biochemical networks. Previous studies on the accuracy analysis for a linear chain reaction system showed that the HR hybrid method is accurate if the scale difference between fast and slow reactions is above a certain threshold, regardless of population scales. However, the population of some reactant species might be driven negative if they are involved in both deterministic and stochastic systems. Results This work investigates the negativity problem of the HR hybrid method, analyzes and tests it with several models including a linear chain system, a nonlinear reaction system, and a realistic biological cell cycle system. As a benchmark, the second slow reaction firing time is used to measure the effect of negative populations on the accuracy of the HR hybrid method. Our analysis demonstrates that usually the error caused by negative populations is negligible compared with approximation errors of the HR hybrid method itself, and sometimes negativity phenomena may even improve the accuracy. But for systems where negative species are involved in nonlinear reactions or some species are highly sensitive to negative species, the system stability will be influenced and may lead to system failure when using the HR hybrid method. In those circumstances, three remedies are studied for the negativity problem. Conclusions The results of different models and examples suggest that the Zero-Reaction rule is a good remedy for nonlinear and sensitive systems considering its efficiency and simplicity.
- Automatic layout and visualization of biclustersGrothaus, Gregory A.; Mufti, Adeel; Murali, T. M. (2006-09-04)Background Biclustering has emerged as a powerful algorithmic tool for analyzing measurements of gene expression. A number of different methods have emerged for computing biclusters in gene expression data. Many of these algorithms may output a very large number of biclusters with varying degrees of overlap. There are no systematic methods that create a two-dimensional layout of the computed biclusters and display overlaps between them. Results We develop a novel algorithm for laying out biclusters in a two-dimensional matrix whose rows (respectively, columns) are rows (respectively, columns) of the original dataset. We display each bicluster as a contiguous submatrix in the layout. We allow the layout to have repeated rows and/or columns from the original matrix as required, but we seek a layout of the smallest size. We also develop a web-based search interface for the user to query the genes and samples of interest and visualise the layout of biclusters matching the queries. Conclusion We demonstrate the usefulness of our approach on gene expression data for two types of leukaemia and on protein-DNA binding data for two growth conditions in Saccharomyces cerevisiae. The software implementing the layout algorithm is available at http://bioinformatics.cs.vt.edu/~murali/papers/bivoc.
- The Catchment Feature Model: A Device for Multimodal Fusion and a Bridge between Signal and SenseQuek, Francis (2004-09-18)The catchment feature model addresses two questions in the field of multimodal interaction: how we bridge video and audio processing with the realities of human multimodal communication, and how information from the different modes may be fused. We argue from a detailed literature review that gestural research has clustered around manipulative and semaphoric use of the hands, motivate the catchment feature model psycholinguistic research, and present the model. In contrast to “whole gesture” recognition, the catchment feature model applies a feature decomposition approach that facilitates cross-modal fusion at the level of discourse planning and conceptualization. We present our experimental framework for catchment feature-based research, cite three concrete examples of catchment features, and propose new directions of multimodal research based on the model.
- Cell cycle control and environmental response by second messengers in Caulobacter crescentusXu, Chunrui; Weston, Bronson R.; Tyson, John J.; Cao, Yang (2020-09-30)Background Second messengers, c-di-GMP and (p)ppGpp, are vital regulatory molecules in bacteria, influencing cellular processes such as biofilm formation, transcription, virulence, quorum sensing, and proliferation. While c-di-GMP and (p)ppGpp are both synthesized from GTP molecules, they play antagonistic roles in regulating the cell cycle. In C. crescentus, c-di-GMP works as a major regulator of pole morphogenesis and cell development. It inhibits cell motility and promotes S-phase entry by inhibiting the activity of the master regulator, CtrA. Intracellular (p)ppGpp accumulates under starvation, which helps bacteria to survive under stressful conditions through regulating nucleotide levels and halting proliferation. (p)ppGpp responds to nitrogen levels through RelA-SpoT homolog enzymes, detecting glutamine concentration using a nitrogen phosphotransferase system (PTS Ntr). This work relates the guanine nucleotide-based second messenger regulatory network with the bacterial PTS Ntr system and investigates how bacteria respond to nutrient availability. Results We propose a mathematical model for the dynamics of c-di-GMP and (p)ppGpp in C. crescentus and analyze how the guanine nucleotide-based second messenger system responds to certain environmental changes communicated through the PTS Ntr system. Our mathematical model consists of seven ODEs describing the dynamics of nucleotides and PTS Ntr enzymes. Our simulations are consistent with experimental observations and suggest, among other predictions, that SpoT can effectively decrease c-di-GMP levels in response to nitrogen starvation just as well as it increases (p)ppGpp levels. Thus, the activity of SpoT (or its homologues in other bacterial species) can likely influence the cell cycle by influencing both c-di-GMP and (p)ppGpp. Conclusions In this work, we integrate current knowledge and experimental observations from the literature to formulate a novel mathematical model. We analyze the model and demonstrate how the PTS Ntr system influences (p)ppGpp, c-di-GMP, GMP and GTP concentrations. While this model does not consider all aspects of PTS Ntr signaling, such as cross-talk with the carbon PTS system, here we present our first effort to develop a model of nutrient signaling in C. crescentus.
- Chromosome–nuclear envelope attachments affect interphase chromosome territories and entanglementKinney, Nicholas A.; Sharakhov, Igor V.; Onufriev, Alexey V. (2018-01-22)Background It is well recognized that the interphase chromatin of higher eukaryotes folds into non-random configurations forming territories within the nucleus. Chromosome territories have biologically significant properties, and understanding how these properties change with time during lifetime of the cell is important. Chromosome–nuclear envelope (Chr–NE) interactions play a role in epigenetic regulation of DNA replication, repair, and transcription. However, their role in maintaining chromosome territories remains unclear. Results We use coarse-grained molecular dynamics simulations to study the effects of Chr–NE interactions on the dynamics of chromosomes within a model of the Drosophila melanogaster regular (non-polytene) interphase nucleus, on timescales comparable to the duration of interphase. The model simulates the dynamics of chromosomes bounded by the NE. Initially, the chromosomes in the model are prearranged in fractal-like configurations with physical parameters such as nucleus size and chromosome persistence length taken directly from experiment. Time evolution of several key observables that characterize the chromosomes is quantified during each simulation: chromosome territories, chromosome entanglement, compactness, and presence of the Rabl (polarized) chromosome arrangement. We find that Chr–NE interactions help maintain chromosome territories by slowing down and limiting, but not eliminating, chromosome entanglement on biologically relevant timescales. At the same time, Chr–NE interactions have little effect on the Rabl chromosome arrangement as well as on how chromosome compactness changes with time. These results are rationalized by simple dimensionality arguments, robust to model details. All results are robust to the simulated activity of topoisomerase, which may be present in the interphase cell nucleus. Conclusions Our study demonstrates that Chr–NE attachments may help maintain chromosome territories, while slowing down and limiting chromosome entanglement on biologically relevant timescales. However, Chr–NE attachments have little effect on chromosome compactness or the Rabl chromosome arrangement.
- Collaborative behavior, performance and engagement with visual analytics tasks using mobile devicesChen, Lei; Liang, Hai-Ning; Lu, Feiyu; Papangelis, Konstantinos; Man, Ka L.; Yue, Yong (2020-11-22)Interactive visualizations are external tools that can support users’ exploratory activities. Collaboration can bring benefits to the exploration of visual representations or visualizations. This research investigates the use of co-located collaborative visualizations in mobile devices, how users working with two different modes of interaction and view (Shared or Non-Shared) and how being placed at various position arrangements (Corner-to-Corner, Face-to-Face, and Side-by-Side) affect their knowledge acquisition, engagement level, and learning efficiency. A user study is conducted with 60 participants divided into 6 groups (2 modes × 3 positions) using a tool that we developed to support the exploration of 3D visual structures in a collaborative manner. Our results show that the shared control and view version in the Side-by-Side position is the most favorable and can improve task efficiency. In this paper, we present the results and a set of recommendations that are derived from them.
- Comparing time series transcriptome data between plants using a network module finding algorithmLee, Jiyoung; Heath, Lenwood S.; Grene, Ruth; Li, Song (2019-06-01)Background Comparative transcriptome analysis is the comparison of expression patterns between homologous genes in different species. Since most molecular mechanistic studies in plants have been performed in model species, including Arabidopsis and rice, comparative transcriptome analysis is particularly important for functional annotation of genes in diverse plant species. Many biological processes, such as embryo development, are highly conserved between different plant species. The challenge is to establish one-to-one mapping of the developmental stages between two species. Results In this manuscript, we solve this problem by converting the gene expression patterns into co-expression networks and then apply network module finding algorithms to the cross-species co-expression network. We describe how such analyses are carried out using bash scripts for preliminary data processing followed by using the R programming language for module finding with a simulated annealing method. We also provide instructions on how to visualize the resulting co-expression networks across species. Conclusions We provide a comprehensive pipeline from installing software and downloading raw transcriptome data to predicting homologous genes and finding orthologous co-expression networks. From the example provided, we demonstrate the application of our method to reveal functional conservation and divergence of genes in two plant species.
- Comprehensive off-target analysis of dCas9-SAM-mediated HIV reactivation via long noncoding RNA and mRNA profilingZhang, Yonggang; Arango-Argoty, Gustavo; Li, Fang; Xiao, Xiao; Putatunda, Raj; Yu, Jun; Yang, Xiao-Feng; Wang, Hong; Watson, Layne T.; Zhang, Liqing; Hu, Wenhui (2018-09-10)Background CRISPR/CAS9 (epi)genome editing revolutionized the field of gene and cell therapy. Our previous study demonstrated that a rapid and robust reactivation of the HIV latent reservoir by a catalytically-deficient Cas9 (dCas9)-synergistic activation mediator (SAM) via HIV long terminal repeat (LTR)-specific MS2-mediated single guide RNAs (msgRNAs) directly induces cellular suicide without additional immunotherapy. However, potential off-target effect remains a concern for any clinical application of Cas9 genome editing and dCas9 epigenome editing. After dCas9 treatment, potential off-target responses have been analyzed through different strategies such as mRNA sequence analysis, and functional screening. In this study, a comprehensive analysis of the host transcriptome including mRNA, lncRNA, and alternative splicing was performed using human cell lines expressing dCas9-SAM and HIV-targeting msgRNAs. Results The control scrambled msgRNA (LTR_Zero), and two LTR-specific msgRNAs (LTR_L and LTR_O) groups show very similar expression profiles of the whole transcriptome. Among 839 identified lncRNAs, none exhibited significantly different expression in LTR_L vs. LTR_Zero group. In LTR_O group, only TERC and scaRNA2 lncRNAs were significantly decreased. Among 142,791 mRNAs, four genes were differentially expressed in LTR_L vs. LTR_Zero group. There were 21 genes significantly downregulated in LTR_O vs. either LTR_Zero or LTR_L group and one third of them are histone related. The distributions of different types of alternative splicing were very similar either within or between groups. There were no apparent changes in all the lncRNA and mRNA transcripts between the LTR_L and LTR_Zero groups. Conclusion This is an extremely comprehensive study demonstrating the rare off-target effects of the HIV-specific dCas9-SAM system in human cells. This finding is encouraging for the safe application of dCas9-SAM technology to induce target-specific reactivation of latent HIV for an effective “shock-and-kill” strategy.
- Computational approaches for discovery of common immunomodulators in fungal infections: towards broad-spectrum immunotherapeutic interventionsKidane, Yared H.; Lawrence, Christopher B.; Murali, T. M. (2013-10-07)Background Fungi are the second most abundant type of human pathogens. Invasive fungal pathogens are leading causes of life-threatening infections in clinical settings. Toxicity to the host and drug-resistance are two major deleterious issues associated with existing antifungal agents. Increasing a host’s tolerance and/or immunity to fungal pathogens has potential to alleviate these problems. A host’s tolerance may be improved by modulating the immune system such that it responds more rapidly and robustly in all facets, ranging from the recognition of pathogens to their clearance from the host. An understanding of biological processes and genes that are perturbed during attempted fungal exposure, colonization, and/or invasion will help guide the identification of endogenous immunomodulators and/or small molecules that activate host-immune responses such as specialized adjuvants. Results In this study, we present computational techniques and approaches using publicly available transcriptional data sets, to predict immunomodulators that may act against multiple fungal pathogens. Our study analyzed data sets derived from host cells exposed to five fungal pathogens, namely, Alternaria alternata, Aspergillus fumigatus, Candida albicans, Pneumocystis jirovecii, and Stachybotrys chartarum. We observed statistically significant associations between host responses to A. fumigatus and C. albicans. Our analysis identified biological processes that were consistently perturbed by these two pathogens. These processes contained both immune response-inducing genes such as MALT1, SERPINE1, ICAM1, and IL8, and immune response-repressing genes such as DUSP8, DUSP6, and SPRED2. We hypothesize that these genes belong to a pool of common immunomodulators that can potentially be activated or suppressed (agonized or antagonized) in order to render the host more tolerant to infections caused by A. fumigatus and C. albicans. Conclusions Our computational approaches and methodologies described here can now be applied to newly generated or expanded data sets for further elucidation of additional drug targets. Moreover, identified immunomodulators may be used to generate experimentally testable hypotheses that could help in the discovery of broad-spectrum immunotherapeutic interventions. All of our results are available at the following supplementary website: http://bioinformatics.cs.vt.edu/~murali/supplements/2013-kidane-bmc
- CoSREM: a graph mining algorithm for the discovery of combinatorial splicing regulatory elementsBadr, Eman; Heath, Lenwood S. (2015-09-04)Background Alternative splicing (AS) is a post-transcriptional regulatory mechanism for gene expression regulation. Splicing decisions are affected by the combinatorial behavior of different splicing factors that bind to multiple binding sites in exons and introns. These binding sites are called splicing regulatory elements (SREs). Here we develop CoSREM (Combinatorial SRE Miner), a graph mining algorithm to discover combinatorial SREs in human exons. Our model does not assume a fixed length of SREs and incorporates experimental evidence as well to increase accuracy. CoSREM is able to identify sets of SREs and is not limited to SRE pairs as are current approaches. Results We identified 37 SRE sets that include both enhancer and silencer elements. We show that our results intersect with previous results, including some that are experimental. We also show that the SRE set GGGAGG and GAGGAC identified by CoSREM may play a role in exon skipping events in several tumor samples. We applied CoSREM to RNA-Seq data for multiple tissues to identify combinatorial SREs which may be responsible for exon inclusion or exclusion across tissues. Conclusion The new algorithm can identify different combinations of splicing enhancers and silencers without assuming a predefined size or limiting the algorithm to find only pairs of SREs. Our approach opens new directions to study SREs and the roles that AS may play in diseases and tissue specificity.
- DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic dataArango-Argoty, Gustavo; Garner, Emily; Pruden, Amy; Heath, Lenwood S.; Vikesland, Peter J.; Zhang, Liqing (2018-02-01)Background Growing concerns about increasing rates of antibiotic resistance call for expanded and comprehensive global monitoring. Advancing methods for monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is especially needed for identifying potential resources of novel antibiotic resistance genes (ARGs), hot spots for gene exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequencing now enables direct access and profiling of the total metagenomic DNA pool, where ARGs are typically identified or predicted based on the “best hits” of sequence searches against existing databases. Unfortunately, this approach produces a high rate of false negatives. To address such limitations, we propose here a deep learning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two deep learning models, DeepARG-SS and DeepARG-LS, were constructed for short read sequences and full gene length sequences, respectively. Results Evaluation of the deep learning models over 30 antibiotic resistance categories demonstrates that the DeepARG models can predict ARGs with both high precision (> 0.97) and recall (> 0.90). The models displayed an advantage over the typical best hit approach, yielding consistently lower false negative rates and thus higher overall recall (> 0.9). As more data become available for under-represented ARG categories, the DeepARG models’ performance can be expected to be further enhanced due to the nature of the underlying neural networks. Our newly developed ARG database, DeepARG-DB, encompasses ARGs predicted with a high degree of confidence and extensive manual inspection, greatly expanding current ARG repositories. Conclusions The deep learning models developed here offer more accurate antimicrobial resistance annotation relative to current bioinformatics practice. DeepARG does not require strict cutoffs, which enables identification of a much broader diversity of ARGs. The DeepARG models and database are available as a command line version and as a Web service at http://bench.cs.vt.edu/deeparg.
- DIMPL: a distributed in-memory drone flight path builder systemShukla, Manu; Chen, Zhiqian; Lu, Chang-Tien (2018-07-12)Drones are increasingly being used to perform risky and labor intensive aerial tasks cheaply and safely. To ensure operating costs are low and flights autonomous, their flight plans must be pre-built. In existing techniques drone flight paths are not automatically pre-calculated based on drone capabilities and terrain information. Instead, they focus on adaptive shortest paths, manually determined paths, navigation through camera, images and/or GPS for guidance and genetic or geometric algorithms to guide the drone during flight, all of which makes flight navigation complex and risky. In this paper we present details of an automated flight plan builder DIMPL that pre-builds flight plans for drones tasked with surveying a large area to take photographs of electric poles to identify ones with hazardous vegetation overgrowth. The flight plans are built for subregions allowing the drones to navigate autonomously. DIMPL employs a distributed in-memory paradigm to process subregions in parallel and build flight paths in a highly efficient manner. Experiments performed with network and elevation datasets validated the efficiency of DIMPL in building optimal flight plans for a fleet of different types of drones and demonstrated the tremendous performance improvements possible using the distributed in-memory paradigm.
- Distinct patterns of SSR distribution in the Arabidopsis thaliana and rice genomesLawson, Mark J.; Zhang, Liqing (2006-02-21)Background Simple sequence repeats (SSRs) in DNA have been traditionally thought of as functionally unimportant and have been studied mainly as genetic markers. A recent handful of studies have shown, however, that SSRs in different positions of a gene can play important roles in determining protein function, genetic development, and regulation of gene expression. We have performed a detailed comparative study of the distribution of SSRs in the sequenced genomes of Arabidopsis thaliana and rice. Results SSRs in different genic regions - 5'untranslated region (UTR), 3'UTR, exon, and intron - show distinct patterns of distribution both within and between the two genomes. Especially notable is the much higher density of SSRs in 5'UTRs compared to the other regions and a strong affinity towards trinucleotide repeats in these regions for both rice and Arabidopsis. On a genomic level, mononucleotide repeats are the most prevalent type of SSRs in Arabidopsis and trinucleotide repeats are the most prevalent type in rice. Both plants have the same most common mononucleotide (A/T) and dinucleotide (AT and AG) repeats, but have little in common for the other types of repeats. Conclusion Our work provides insight into the evolution and distribution of SSRs in the two sequenced model plant genomes of monocots and dicots. Our analyses reveal that the distributions of SSRs appear highly non-random and vary a great deal in different regions of the genes in the genomes.
- Drug voyager: a computational platform for exploring unintended drug actionOh, Min; Ahn, Jaegyoon; Lee, Taekeon; Jang, Giup; Park, Chihyun; Yoon, Youngmi (2017-02-28)Background The dominant paradigm in understanding drug action focuses on the intended therapeutic effects and frequent adverse reactions. However, this approach may limit opportunities to grasp unintended drug actions, which can open up channels to repurpose existing drugs and identify rare adverse drug reactions. Advances in systems biology can be exploited to comprehensively understand pharmacodynamic actions, although proper frameworks to represent drug actions are still lacking. Results We suggest a novel platform to construct a drug-specific pathway in which a molecular-level mechanism of action is formulated based on pharmacologic, pharmacogenomic, transcriptomic, and phenotypic data related to drug response (http://databio.gachon.ac.kr/tools/). In this platform, an adoption of three conceptual levels imitating drug perturbation allows these pathways to be realistically rendered in comparison to those of other models. Furthermore, we propose a new method that exploits functional features of the drug-specific pathways to predict new indications as well as adverse reactions. For therapeutic uses, our predictions significantly overlapped with clinical trials and an up-to-date drug-disease association database. Also, our method outperforms existing methods with regard to classification of active compounds for cancers. For adverse reactions, our predictions were significantly enriched in an independent database derived from the Food and Drug Administration (FDA) Adverse Event Reporting System and meaningfully cover an Adverse Reaction Database provided by Health Canada. Lastly, we discuss several predictions for both therapeutic indications and side-effects through the published literature. Conclusions Our study addresses how we can computationally represent drug-signaling pathways to understand unintended drug actions and to facilitate drug discovery and screening.
- EpiViewer: an epidemiological application for exploring time series dataThorve, Swapna; Wilson, Mandy L.; Lewis, Bryan L.; Swarup, Samarth; Vullikanti, Anil Kumar S.; Marathe, Madhav V. (2018-11-22)Background Visualization plays an important role in epidemic time series analysis and forecasting. Viewing time series data plotted on a graph can help researchers identify anomalies and unexpected trends that could be overlooked if the data were reviewed in tabular form; these details can influence a researcher’s recommended course of action or choice of simulation models. However, there are challenges in reviewing data sets from multiple data sources – data can be aggregated in different ways (e.g., incidence vs. cumulative), measure different criteria (e.g., infection counts, hospitalizations, and deaths), or represent different geographical scales (e.g., nation, HHS Regions, or states), which can make a direct comparison between time series difficult. In the face of an emerging epidemic, the ability to visualize time series from various sources and organizations and to reconcile these datasets based on different criteria could be key in developing accurate forecasts and identifying effective interventions. Many tools have been developed for visualizing temporal data; however, none yet supports all the functionality needed for easy collaborative visualization and analysis of epidemic data. Results In this paper, we present EpiViewer, a time series exploration dashboard where users can upload epidemiological time series data from a variety of sources and compare, organize, and track how data evolves as an epidemic progresses. EpiViewer provides an easy-to-use web interface for visualizing temporal datasets either as line charts or bar charts. The application provides enhanced features for visual analysis, such as hierarchical categorization, zooming, and filtering, to enable detailed inspection and comparison of multiple time series on a single canvas. Finally, EpiViewer provides several built-in statistical Epi-features to help users interpret the epidemiological curves. Conclusions EpiViewer is a single page web application that provides a framework for exploring, comparing, and organizing temporal datasets. It offers a variety of features for convenient filtering and analysis of epicurves based on meta-attribute tagging. EpiViewer also provides a platform for sharing data between groups for better comparison and analysis. Our user study demonstrated that EpiViewer is easy to use and fills a particular niche in the toolspace for visualization and exploration of epidemiological data.
- Evidence for extensive heterotrophic metabolism, antioxidant action, and associated regulatory events during winter hardening in Sitka spruceCollakova, Eva; Klumas, Curtis; Suren, Haktan; Myers, Elijah; Heath, Lenwood S.; Holliday, Jason A.; Grene, Ruth (2013-04-30)Background Cold acclimation in woody perennials is a metabolically intensive process, but coincides with environmental conditions that are not conducive to the generation of energy through photosynthesis. While the negative effects of low temperatures on the photosynthetic apparatus during winter have been well studied, less is known about how this is reflected at the level of gene and metabolite expression, nor how the plant generates primary metabolites needed for adaptive processes during autumn. Results The MapMan tool revealed enrichment of the expression of genes related to mitochondrial function, antioxidant and associated regulatory activity, while changes in metabolite levels over the time course were consistent with the gene expression patterns observed. Genes related to thylakoid function were down-regulated as expected, with the exception of plastid targeted specific antioxidant gene products such as thylakoid-bound ascorbate peroxidase, components of the reactive oxygen species scavenging cycle, and the plastid terminal oxidase. In contrast, the conventional and alternative mitochondrial electron transport chains, the tricarboxylic acid cycle, and redox-associated proteins providing reactive oxygen species scavenging generated by electron transport chains functioning at low temperatures were all active. Conclusions A regulatory mechanism linking thylakoid-bound ascorbate peroxidase action with “chloroplast dormancy” is proposed. Most importantly, the energy and substrates required for the substantial metabolic remodeling that is a hallmark of freezing acclimation could be provided by heterotrophic metabolism.
- A framework for evaluating epidemic forecastsTabataba, Farzaneh Sadat; Chakraborty, Prithwish; Ramakrishnan, Naren; Venkatramanan, Srinivasan; Chen, Jiangzhuo; Lewis, Bryan L.; Marathe, Madhav V. (2017-05-15)Background Over the past few decades, numerous forecasting methods have been proposed in the field of epidemic forecasting. Such methods can be classified into different categories such as deterministic vs. probabilistic, comparative methods vs. generative methods, and so on. In some of the more popular comparative methods, researchers compare observed epidemiological data from the early stages of an outbreak with the output of proposed models to forecast the future trend and prevalence of the pandemic. A significant problem in this area is the lack of standard well-defined evaluation measures to select the best algorithm among different ones, as well as for selecting the best possible configuration for a particular algorithm. Results In this paper we present an evaluation framework which allows for combining different features, error measures, and ranking schema to evaluate forecasts. We describe the various epidemic features (Epi-features) included to characterize the output of forecasting methods and provide suitable error measures that could be used to evaluate the accuracy of the methods with respect to these Epi-features. We focus on long-term predictions rather than short-term forecasting and demonstrate the utility of the framework by evaluating six forecasting methods for predicting influenza in the United States. Our results demonstrate that different error measures lead to different rankings even for a single Epi-feature. Further, our experimental analyses show that no single method dominates the rest in predicting all Epi-features when evaluated across error measures. As an alternative, we provide various Consensus Ranking schema that summarize individual rankings, thus accounting for different error measures. Since each Epi-feature presents a different aspect of the epidemic, multiple methods need to be combined to provide a comprehensive forecast. Thus we call for a more nuanced approach while evaluating epidemic forecasts and we believe that a comprehensive evaluation framework, as presented in this paper, will add value to the computational epidemiology community.
- Functional bias in molecular evolution rate of Arabidopsis thalianaWarren, Andrew S.; Anandakrishnan, Ramu; Zhang, Liqing (2010-05-01)Background Characteristics derived from mutation and other mechanisms that are advantageous for survival are often preserved during evolution by natural selection. Some genes are conserved in many organisms because they are responsible for fundamental biological function, others are conserved for their unique functional characteristics. Therefore one would expect the rate of molecular evolution for individual genes to be dependent on their biological function. Whether this expectation holds for genes duplicated by whole genome duplication is not known. Results We empirically demonstrate here, using duplicated genes generated from the Arabidopsis thaliana α-duplication event, that the rate of molecular evolution of genes duplicated in this event depend on biological function. Using functional clustering based on gene ontology annotation of gene pairs, we show that some duplicated genes, such as defense response genes, are under weaker purifying selection or under stronger diversifying selection than other duplicated genes, such as protein translation genes, as measured by the ratio of nonsynonymous to synonymous divergence (dN/dS). Conclusions These results provide empirical evidence indicating that molecular evolution rate for genes duplicated in whole genome duplication, as measured by dN/dS, may depend on biological function, which we characterize using gene ontology annotation. Furthermore, the general approach used here provides a framework for comparative analysis of molecular evolution rate for genes based on their biological function.
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