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Zhidong Tu,
Mark P Keller,
Chunsheng Zhang,
Mary E Rabaglia,
Danielle M Greenawalt,
Xia Yang,
I-Ming Wang,
Hongyue Dai,
Matthew D Bruss,
Pek Y Lum,
Yun-Ping Zhou,
Daniel M Kemp, Christina Kendziorski,
Brian S Yandell,
Alan D Attie,
Eric E Schadt,
Jun Zhu
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ABSTRACT: Complex diseases result from molecular changes induced by multiple genetic factors and the environment. To derive a systems view of how genetic loci interact in the context of tissue-specific molecular networks, we constructed an F2 intercross comprised of >500 mice from diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains made genetically obese by the Leptin(ob/ob) mutation (Lep(ob)). High-density genotypes, diabetes-related clinical traits, and whole-transcriptome expression profiling in five tissues (white adipose, liver, pancreatic islets, hypothalamus, and gastrocnemius muscle) were determined for all mice. We performed an integrative analysis to investigate the inter-relationship among genetic factors, expression traits, and plasma insulin, a hallmark diabetes trait. Among five tissues under study, there are extensive protein-protein interactions between genes responding to different loci in adipose and pancreatic islets that potentially jointly participated in the regulation of plasma insulin. We developed a novel ranking scheme based on cross-loci protein-protein network topology and gene expression to assess each gene's potential to regulate plasma insulin. Unique candidate genes were identified in adipose tissue and islets. In islets, the Alzheimer's gene App was identified as a top candidate regulator. Islets from 17-week-old, but not 10-week-old, App knockout mice showed increased insulin secretion in response to glucose or a membrane-permeant cAMP analog, in agreement with the predictions of the network model. Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes.
PLoS Genetics 12/2012; 8(12):e1003107. · 8.69 Impact Factor
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ABSTRACT: R/EBcoexpress implements the approach of Dawson and Kendziorski using R, a freely available, open source statistical programming language. The approach identifies differential co-expression (DC) by examining the correlations among gene pairs using an empirical Bayesian approach, producing a false discovery rate controlled list of DC pairs. This interrogation of DC gene pairs complements but is distinct from differential expression analyses, under the general goal of understanding differential regulation across biological conditions. AVAILABILITY AND IMPLEMENTATION: R/EBcoexpress is freely available and hosted on Bioconductor; a source file and vignette may be found at http://www.bioconductor.org/packages/release/bioc/html/EBcoexpress.html
Bioinformatics 05/2012; 28(14):1939-40. · 5.47 Impact Factor
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ABSTRACT: Two challenging problems in the clinical study of cancer are the
characterization of cancer subtypes and the classification of individual
patients according to those subtypes. Statistical approaches addressing these
problems are hampered by population heterogeneity and challenges inherent in
data integration across high-dimensional, diverse covariates. We have developed
a survival-supervised latent Dirichlet allocation (survLDA) modeling framework
to address these concerns. LDA models have proven extremely effective at
identifying themes common across large collections of text, but applications to
genomics have been limited. Our framework extends LDA to the genome by
considering each patient as a `document' with `text' constructed from clinical
and high-dimensional genomic measurements. We then further extend the framework
to allow for supervision by a time-to-event response. The model enables the
efficient identification of collections of clinical and genomic features that
co-occur within patient subgroups, and then characterizes each patient by those
features. An application of survLDA to The Cancer Genome Atlas (TCGA) ovarian
project identifies informative patient subgroups that are characterized by
different propensities for exhibiting abnormal mRNA expression and
methylations, corresponding to differential rates of survival from primary
therapy.
02/2012;
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ABSTRACT: A common goal of microarray and related high-throughput genomic experiments is to identify genes that vary across biological condition. Most often this is accomplished by identifying genes with changes in mean expression level, so called differentially expressed (DE) genes, and a number of effective methods for identifying DE genes have been developed. Although useful, these approaches do not accommodate other types of differential regulation. An important example concerns differential coexpression (DC). Investigations of this class of genes are hampered by the large cardinality of the space to be interrogated as well as by influential outliers. As a result, existing DC approaches are often underpowered, exceedingly prone to false discoveries, and/or computationally intractable for even a moderately large number of pairs. To address this, an empirical Bayesian approach for identifying DC gene pairs is developed. The approach provides a false discovery rate controlled list of significant DC gene pairs without sacrificing power. It is applicable within a single study as well as across multiple studies. Computations are greatly facilitated by a modification to the expectation-maximization algorithm and a procedural heuristic. Simulations suggest that the proposed approach outperforms existing methods in far less computational time; and case study results suggest that the approach will likely prove to be a useful complement to current DE methods in high-throughput genomic studies.
Biometrics 10/2011; 68(2):455-65. · 1.83 Impact Factor
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ABSTRACT: We previously reported that mice with skin-specific deletion of stearoyl-CoA desaturase-1 (Scd1) recapitulated the skin phenotype and hypermetabolism observed in mice with a whole-body deletion of Scd1. In this study, we first performed a diet-induced obesity experiment at thermoneutral temperature (33°C) and found that skin-specific Scd1 knockout (SKO) mice still remain resistant to obesity. To elucidate the metabolic changes in the skin that contribute to the obesity resistance and skin phenotype, we performed microarray analysis of skin gene expression in male SKO and control mice fed a standard rodent diet. We identified an extraordinary number of differentially expressed genes that support the previously documented histological observations of sebaceous gland hypoplasia, inflammation and epidermal hyperplasia in SKO mice. Additionally, transcript levels were reduced in skin of SKO mice for genes involved in fatty acid synthesis, elongation and desaturation, which may be attributed to decreased abundance of key transcription factors including SREBP1c, ChREBP and LXRα. Conversely, genes involved in cholesterol synthesis were increased, suggesting an imbalance between skin fatty acid and cholesterol synthesis. Unexpectedly, we observed a robust elevation in skin retinol, retinoic acid and retinoic acid-induced genes in SKO mice. Furthermore, SEB-1 sebocytes treated with retinol and SCD inhibitor also display an elevation in retinoic acid-induced genes. These results highlight the importance of monounsaturated fatty acid synthesis for maintaining retinol homeostasis and point to disturbed retinol metabolism as a novel contributor to the Scd1 deficiency-induced skin phenotype.
PLoS ONE 01/2011; 6(5):e19734. · 4.09 Impact Factor
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Enpeng Zhao,
Mark P Keller,
Mary E Rabaglia,
Angie T Oler,
Donnie S Stapleton,
Kathryn L Schueler,
Elias Chaibub Neto,
Jee Young Moon,
Ping Wang,
I-Ming Wang, [......],
Robert Kleinhanz,
Jin Shang,
Yun-Ping Zhou,
Andrew D Howard,
Bei B Zhang, Christina Kendziorski,
Nancy A Thornberry,
Brian S Yandell,
Eric E Schadt,
Alan D Attie
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ABSTRACT: Type 2 diabetes results from severe insulin resistance coupled with a failure of b cells to compensate by secreting sufficient insulin. Multiple genetic loci are involved in the development of diabetes, although the effect of each gene on diabetes susceptibility is thought to be small. MicroRNAs (miRNAs) are noncoding 19-22-nucleotide RNA molecules that potentially regulate the expression of thousands of genes. To understand the relationship between miRNA regulation and obesity-induced diabetes, we quantitatively profiled approximately 220 miRNAs in pancreatic islets, adipose tissue, and liver from diabetes-resistant (B6) and diabetes-susceptible (BTBR) mice. More than half of the miRNAs profiled were expressed in all three tissues, with many miRNAs in each tissue showing significant changes in response to genetic obesity. Furthermore, several miRNAs in each tissue were differentially responsive to obesity in B6 versus BTBR mice, suggesting that they may be involved in the pathogenesis of diabetes. In liver there were approximately 40 miRNAs that were downregulated in response to obesity in B6 but not BTBR mice, indicating that genetic differences between the mouse strains play a critical role in miRNA regulation. In order to elucidate the genetic architecture of hepatic miRNA expression, we measured the expression of miRNAs in genetically obese F2 mice. Approximately 10% of the miRNAs measured showed significant linkage (miR-eQTLs), identifying loci that control miRNA abundance. Understanding the influence that obesity and genetics exert on the regulation of miRNA expression will reveal the role miRNAs play in the context of obesity-induced type 2 diabetes.
Mammalian Genome 10/2009; 20(8):476-85. · 2.89 Impact Factor
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ABSTRACT: The power of a microarray experiment derives from the identification of genes differentially regulated across biological conditions. To date, differential regulation is most often taken to mean differential expression, and a number of useful methods for identifying differentially expressed (DE) genes or gene sets are available. However, such methods are not able to identify many relevant classes of differentially regulated genes. One important example concerns differentially co-expressed (DC) genes.
We propose an approach, gene set co-expression analysis (GSCA), to identify DC gene sets. The GSCA approach provides a false discovery rate controlled list of interesting gene sets, does not require that genes be highly correlated in at least one biological condition and is readily applied to data from individual or multiple experiments, as we demonstrate using data from studies of lung cancer and diabetes.
The GSCA approach is implemented in R and available at www.biostat.wisc.edu/ approximately kendzior/GSCA/.
kendzior@biostat.wisc.edu
Supplementary data are available at Bioinformatics online.
Bioinformatics 09/2009; 25(21):2780-6. · 5.47 Impact Factor
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ABSTRACT: Network reconstruction is a main goal of many biological endeavors. Graphical Gaussian models (GGMs) are often used since the underlying assumptions are well understood, the graph is readily estimated by calculating the partial correlation (paCor) matrix, and its interpretation is straightforward. In spite of these advantages, GGMs are limited in that interactions are not accommodated as the underlying multivariate normality assumption allows for linear dependencies only. As we show, when applied in the presence of interactions, the GGM framework can lead to incorrect inference regarding dependence. Identifying the exact dependence structure in this context is a difficult problem, largely because an analogue of the paCor matrix is not available and dependencies can involve many nodes. We here present a computationally efficient approach to identify bivariate interactions in networks. A key element is recognizing that interactions have a marginal linear effect and as a result information about their presence can be obtained from the paCor matrix. Theoretical derivations for the exact effect are presented and used to motivate the approach; and simulations suggest that the method works well, even in fairly complicated scenarios. Practical advantages are demonstrated in analyses of data from a breast cancer study.
Biostatistics 08/2009; 10(4):706-18. · 2.14 Impact Factor
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Christine T Ferrara,
Ping Wang,
Elias Chaibub Neto,
Robert D Stevens,
James R Bain,
Brett R Wenner,
Olga R Ilkayeva,
Mark P Keller,
Daniel A Blasiole, Christina Kendziorski,
Brian S Yandell,
Christopher B Newgard,
Alan D Attie
PLoS Genetics 08/2008; 4(7). · 8.69 Impact Factor
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ABSTRACT: We previously reported that mice deficient in stearoyl-CoA desaturase-1 (Scd1) and maintained on a very low-fat (VLF) diet for 10 days developed severe loss of body weight, hypoglycemia, hypercholesterolemia, and many cholestasis-like phenotypes. To better understand the metabolic changes associated with these phenotypes, we performed microarray analysis of hepatic gene expression in chow- and VLF-fed female Scd1+/+ and Scd1-/- mice. We identified an extraordinary number of differentially expressed genes (>4,000 probe sets) in the VLF Scd1-/- relative to both VLF Scd1+/+ and chow Scd1-/- mice. Transcript levels were reduced for genes involved in detoxification and several facets of fatty acid metabolism including biosynthesis, elongation, desaturation, oxidation, transport, and ketogenesis. This pattern is attributable to the decreased mRNA abundance of several genes encoding key transcription factors, including LXRalpha, RXRalpha, FXR, PPARalpha, PGC-1beta, SREBP1c, ChREBP, CAR, DBP, TEF, and HLF. A robust induction of endoplasmic reticulum (ER) stress is indicated by enhanced splicing of XBP1, increased expression of the stress-induced transcription factors CHOP and ATF3, and elevated expression of several genes involved in the integrated stress and unfolded protein response pathways. The gene expression profile is also consistent with induction of an acute inflammatory response and macrophage recruitment. These results highlight the importance of monounsaturated fatty acid synthesis for maintaining metabolic homeostasis in the absence of sufficient dietary unsaturated fat and point to a novel cellular nutrient-sensing mechanism linking fatty acid availability and/or composition to the ER stress response.
Physiological Genomics 06/2008; 33(3):361-72. · 2.73 Impact Factor
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Mark P Keller,
YounJeong Choi,
Ping Wang,
Dawn Belt Davis,
Mary E Rabaglia,
Angie T Oler,
Donald S Stapleton,
Carmen Argmann,
Kathy L Schueler,
Steve Edwards,
H Adam Steinberg,
Elias Chaibub Neto,
Robert Kleinhanz,
Scott Turner,
Marc K Hellerstein,
Eric E Schadt,
Brian S Yandell, Christina Kendziorski,
Alan D Attie
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ABSTRACT: Insulin resistance is necessary but not sufficient for the development of type 2 diabetes. Diabetes results when pancreatic beta-cells fail to compensate for insulin resistance by increasing insulin production through an expansion of beta-cell mass or increased insulin secretion. Communication between insulin target tissues and beta-cells may initiate this compensatory response. Correlated changes in gene expression between tissues can provide evidence for such intercellular communication. We profiled gene expression in six tissues of mice from an obesity-induced diabetes-resistant and a diabetes-susceptible strain before and after the onset of diabetes. We studied the correlation structure of mRNA abundance and identified 105 co-expression gene modules. We provide an interactive gene network model showing the correlation structure between the expression modules within and among the six tissues. This resource also provides a searchable database of gene expression profiles for all genes in six tissues in lean and obese diabetes-resistant and diabetes-susceptible mice, at 4 and 10 wk of age. A cell cycle regulatory module in islets predicts diabetes susceptibility. The module predicts islet replication; we found a strong correlation between (2)H(2)O incorporation into islet DNA in vivo and the expression pattern of the cell cycle module. This pattern is highly correlated with that of several individual genes in insulin target tissues, including Igf2, which has been shown to promote beta-cell proliferation, suggesting that these genes may provide a link between insulin resistance and beta-cell proliferation.
Genome Research 06/2008; 18(5):706-16. · 13.61 Impact Factor
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Christine T Ferrara,
Ping Wang,
Elias Chaibub Neto,
Robert D Stevens,
James R Bain,
Brett R Wenner,
Olga R Ilkayeva,
Mark P Keller,
Daniel A Blasiole, Christina Kendziorski,
Brian S Yandell,
Christopher B Newgard,
Alan D Attie
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ABSTRACT: Although numerous quantitative trait loci (QTL) influencing disease-related phenotypes have been detected through gene mapping and positional cloning, identification of the individual gene(s) and molecular pathways leading to those phenotypes is often elusive. One way to improve understanding of genetic architecture is to classify phenotypes in greater depth by including transcriptional and metabolic profiling. In the current study, we have generated and analyzed mRNA expression and metabolic profiles in liver samples obtained in an F2 intercross between the diabetes-resistant C57BL/6 leptin(ob/ob) and the diabetes-susceptible BTBR leptin(ob/ob) mouse strains. This cross, which segregates for genotype and physiological traits, was previously used to identify several diabetes-related QTL. Our current investigation includes microarray analysis of over 40,000 probe sets, plus quantitative mass spectrometry-based measurements of sixty-seven intermediary metabolites in three different classes (amino acids, organic acids, and acyl-carnitines). We show that liver metabolites map to distinct genetic regions, thereby indicating that tissue metabolites are heritable. We also demonstrate that genomic analysis can be integrated with liver mRNA expression and metabolite profiling data to construct causal networks for control of specific metabolic processes in liver. As a proof of principle of the practical significance of this integrative approach, we illustrate the construction of a specific causal network that links gene expression and metabolic changes in the context of glutamate metabolism, and demonstrate its validity by showing that genes in the network respond to changes in glutamine and glutamate availability. Thus, the methods described here have the potential to reveal regulatory networks that contribute to chronic, complex, and highly prevalent diseases and conditions such as obesity and diabetes.
PLoS Genetics 04/2008; 4(3):e1000034. · 8.69 Impact Factor
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ABSTRACT: Phosphoinositides are a family of lipid signalling molecules that regulate many cellular functions in eukaryotes. Phosphatidylinositol-4,5-bisphosphate (PtdIns4,5P2), the central component in the phosphoinositide signalling circuitry, is generated primarily by type I phosphatidylinositol 4-phosphate 5-kinases (PIPKIalpha, PIPKIbeta and PIPKIgamma). In addition to functions in the cytosol, phosphoinositides are present in the nucleus, where they modulate several functions; however, the mechanism by which they directly regulate nuclear functions remains unknown. PIPKIs regulate cellular functions through interactions with protein partners, often PtdIns4,5P2 effectors, that target PIPKIs to discrete subcellular compartments, resulting in the spatial and temporal generation of PtdIns4,5P2 required for the regulation of specific signalling pathways. Therefore, to determine roles for nuclear PtdIns4,5P2 we set out to identify proteins that interacted with the nuclear PIPK, PIPKIalpha. Here we show that PIPKIalpha co-localizes at nuclear speckles and interacts with a newly identified non-canonical poly(A) polymerase, which we have termed Star-PAP (nuclear speckle targeted PIPKIalpha regulated-poly(A) polymerase) and that the activity of Star-PAP can be specifically regulated by PtdIns4,5P2. Star-PAP and PIPKIalpha function together in a complex to control the expression of select mRNAs, including the transcript encoding the key cytoprotective enzyme haem oxygenase-1 (refs 8, 9) and other oxidative stress response genes by regulating the 3'-end formation of their mRNAs. Taken together, the data demonstrate a model by which phosphoinositide signalling works in tandem with complement pathways to regulate the activity of Star-PAP and the subsequent biosynthesis of its target mRNA. The results reveal a mechanism for the integration of nuclear phosphoinositide signals and a method for regulating gene expression.
Nature 03/2008; 451(7181):1013-7. · 36.28 Impact Factor
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ABSTRACT: Genetic background affects the susceptibility to mammary tumor development in Apc(Min/+) mice. Here we report the identification of four novel modifier loci that influence different aspects of mammary tumor development in Apc(Min/+) mice. Analysis of tumor development in a backcross of (FVBB6 Apc(Min/+)) x B6 Apc(Min/+) mice has identified a modifier on chromosome 9 that significantly affects tumor multiplicity, and a modifier on chromosome 4 that significantly affects tumor latency and affects tumor number with suggestive significance. This modifier was also identified in a backcross involving 129X1/SvJ and B6 Apc(Min/+) mice. A modifier on chromosome 18 specifically affects tumor latency but not tumor number. Kaplan-Meier analysis suggests there is at least an additive interaction affecting tumor latency between the loci on chromosomes 4 and 18. We also identified a modifier locus on chromosome 6 that interacts with the loci on chromosome 4 and chromosome 9 to affect tumor number. These results suggest that multiple genetic loci control different aspects of mammary tumor development. None of these modifiers is associated with intestinal tumor susceptibility, which indicates that these modifiers act on tumor development in a tissue-specific manner.
Cancer Research 01/2008; 67(23):11226-33. · 7.86 Impact Factor
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ABSTRACT: In 2001, Sen and Churchill reported a general Bayesian framework for quantitative trait loci (QTL) mapping in inbred line crosses. The framework is a powerful one, as many QTL mapping methods can be represented as special cases and many important considerations are accommodated. These considerations include accounting for covariates, nonstandard crosses, missing genotypes, genotyping errors, multiple interacting QTL, and nonnormal as well as multivariate phenotypes. The dimension of a multivariate phenotype easily handled within the framework is bounded by the number of subjects, as a full-rank covariance matrix describing correlations across the phenotypes is required. We address this limitation and extend the Sen-Churchill framework to accommodate expression quantitative trait loci (eQTL) mapping studies, where high-dimensional gene-expression phenotypes are obtained via microarrays. Doing so allows for the precise comparison of existing eQTL mapping approaches and facilitates the development of an eQTL interval-mapping approach that shares information across transcripts and improves localization of eQTL. Evaluations are based on simulation studies and a study of diabetes in mice.
Genetics 11/2007; 177(2):761-71. · 4.01 Impact Factor
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ABSTRACT: Exenatide (Ex-4) is an antidiabetic drug that acts through the glucagon-like peptide 1 receptor and has recently been approved for the treatment of type 2 diabetes mellitus. Ex-4 also has been shown to affect beta cell gene expression and increase beta cell mass in rodent models of type 1 diabetes mellitus, but the mechanisms are not fully understood. We therefore analyzed the pathways affected by Ex-4 in human islets by using oligonucleotide microarrays and the PathwayStudio software (Ariadne Genomics, Rockville, MD). We identified the JAK1-STAT1 pathway as a novel target of Ex-4 and confirmed the Ex-4-mediated down-regulation of JAK1 and STAT1 by quantitative reverse transcription-polymerase chain reaction in human islets and INS-1 cells. JAK1-STAT1 is the major signaling pathway mediating the interferon gamma effects on beta cell apoptosis in type 1 diabetes mellitus. Thus, these findings suggest that Ex-4 treatment may also be beneficial in type 1 diabetes mellitus, where it may help protect beta cells from cytokine-induced cell death by inhibiting JAK1-STAT1.
Metabolism 08/2007; 56(7):915-8. · 2.66 Impact Factor
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ABSTRACT: Although both clustering and identification of differentially expressed genes are equally essential in most microarray studies, the two tasks are often conducted without regard to each other. This is clearly not the most efficient way of extracting information. The main aim of this article is to develop a coherent statistical method that can simultaneously cluster and detect differentially expressed genes. Through information sharing between the two tasks, the proposed approach gives more sensible clustering among genes and is more sensitive in identifying differentially expressed genes. The improvement over existing methods is illustrated in both our simulation results and a case study.
Biometrics 01/2007; 62(4):1089-98. · 1.83 Impact Factor
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ABSTRACT: Abstract
Background
Aging has been associated with widespread changes at the gene expression level in multiple mammalian tissues. We have used high density oligonucleotide arrays and novel statistical methods to identify specific transcriptional classes that may uncover biological processes that play a central role in mammalian aging.
Results
We identified 712 transcripts that are differentially expressed in young (5 month old) and old (25-month old) mouse skeletal muscle. Caloric restriction (CR) completely or partially reversed 87% of the changes in expression. Examination of individual genes revealed a transcriptional profile indicative of increased p53 activity in the older muscle. To determine whether the increase in p53 activity is associated with transcriptional activation of apoptotic targets, we performed RT-PCR on four well known mediators of p53-induced apoptosis: puma , noxa , tnfrsf10b and bok . Expression levels for these proapoptotic genes increased significantly with age (P < 0.05), while CR significantly lowered expression levels for these genes as compared to control fed old mice (P < 0.05). Age-related induction of p53-related genes was observed in multiple tissues, but was not observed in young SOD2<sup>+/- </sup>and GPX4<sup>+/- </sup>mice, suggesting that oxidative stress does not induce the expression of these genes. Western blot analysis confirmed that protein levels for both p21 and GADD45a, two established transcriptional targets of p53, were higher in the older muscle tissue.
Conclusion
These observations support a role for p53-mediated transcriptional program in mammalian aging and suggest that mechanisms other than reactive oxygen species are involved in the age-related transcriptional activation of p53 targets.
BMC Genomics. 01/2007;
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ABSTRACT: With high-throughput technologies now widely available, investigators can easily measure thousands of phenotypes for quantitative trait loci (QTL) mapping. Microarray measurements are particularly amenable to QTL mapping, as evidenced by a number of recent studies demonstrating utility across a broad range of biological endeavors. The early success stories have impelled a rapid increase in both the number and complexity of expression QTL (eQTL) experiments. Consequently, there is a need to consider the statistical principles involved in the design and analysis of these experiments and the methods currently being used. In this article we review these principles and methods and discuss the open questions most likely to yield significant progress toward increasing the amount of meaningful information obtained from eQTL mapping experiments.
Mammalian Genome 07/2006; 17(6):509-17. · 2.89 Impact Factor
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Journal of the American Statistical Association 02/2006; 101(December):1323-1332. · 1.99 Impact Factor
