University of Virginia

About: University of Virginia is a education organization based out in Charlottesville, Virginia, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 52543 authors who have published 113268 publications receiving 5220506 citations. The organization is also known as: U of V & UVa.

Improved Steiner tree approximation in graphs

Abstract: The Steiner tree problem in weighted graphs seeks a minimum weight connected subgraph containing a given subset of the vertices (terminals). We present a new polynomial-time heuristic with an approximation ratio approaching 1 + 1~___33 ~ 1.55, which improves upon the previously best-known approximation algorithm of [10] with performance ratio ~ 1.59. In quasi-bipartite graphs (i.e., in graphs where all non-terminals are pairwise disjoint), our algorithm achieves an approximation ratio of ~ 1.28, whereas the previously best method achieves an approximation ratio approachiug 1.5 [19]. For complete graphs with edge weights 1 and 2, we show that our heuristic has an approximation ratio approaching ~ 1.28, which 4 improves upon the previously best-known ratio of ~ [4]. Our method is considerably simpler and easier to implement than previous approaches. Our techniques can also be used to prove that the Iterated 1-Steiner heuristic [14] achieves an approximation ratio of 1.5 in quasi-blpartite graphs, thus providing the first known non-trivial performance ratio of this well-known method. 1 I n t r o d u c t i o n Given an arbi t rary weighted graph with a distinguished vertex subset, the Steiner Tree Problem asks for a minimum-cost subtree spanning the distinguished vertices. Steiner trees are important in various applications such as VLSI routing [14], wirelength es t imat ion [6], phylogenetic tree reconstruction in biology [11], and network routing [12]. The Steiner Tree Problem is N P h a r d even in the Euclidean or rectilinear metrics [8]. Arora established tha t Euclidean and rectilinear minimum-cost Steiner trees can be efficiently approximated arbitrari ly close to optimal [1]. On the other hand, unless P = N P , the Steiner Tree Problem in general graphs c~.nnot be approximated within a factor of 1 + e for sufficiently small e > 0 [4, 7]. For arbi t rary weighted graphs, the best Steiner approximat ion ratio achievable within polynomial t ime was gradually decreased from 2 to 1.59 in a series of works [20, 21, 2, 22, 18, 15, 10]. In this paper we present a polynomialt ime approximation scheme with a performance ratio approaching 1 + ~ ~ 1.55 which improves upon the previously best -known ratio of 1.59 due to "This work was supported by a Packard Foundation Fellowship, by a National Science Foundation Young Investigator Award (MIP-9457412), and by a GSU Research Initiation Grant.

Corrigendum: KLF4-dependent phenotypic modulation of smooth muscle cells has a key role in atherosclerotic plaque pathogenesis.

Abstract: Previous studies investigating the role of smooth muscle cells (SMCs) and macrophages in the pathogenesis of atherosclerosis have provided controversial results owing to the use of unreliable methods for clearly identifying each of these cell types. Here, using Myh11-CreER(T2) ROSA floxed STOP eYFP Apoe(-/-) mice to perform SMC lineage tracing, we find that traditional methods for detecting SMCs based on immunostaining for SMC markers fail to detect >80% of SMC-derived cells within advanced atherosclerotic lesions. These unidentified SMC-derived cells exhibit phenotypes of other cell lineages, including macrophages and mesenchymal stem cells (MSCs). SMC-specific conditional knockout of Kruppel-like factor 4 (Klf4) resulted in reduced numbers of SMC-derived MSC- and macrophage-like cells, a marked reduction in lesion size, and increases in multiple indices of plaque stability, including an increase in fibrous cap thickness as compared to wild-type controls. On the basis of in vivo KLF4 chromatin immunoprecipitation-sequencing (ChIP-seq) analyses and studies of cholesterol-treated cultured SMCs, we identified >800 KLF4 target genes, including many that regulate pro-inflammatory responses of SMCs. Our findings indicate that the contribution of SMCs to atherosclerotic plaques has been greatly underestimated, and that KLF4-dependent transitions in SMC phenotype are critical in lesion pathogenesis.

The Circle of Security project: attachment-based intervention with caregiver-pre-school child dyads.

Abstract: The Circle of Security intervention protocol is a 20-week, group-based, parent education and psychotherapy intervention designed to shift patterns of attachment-caregiving interactions in high-risk caregiver-child dyads to a more appropriate developmental pathway. All phases of the protocol, including the pre- and post-intervention assessments, and the intervention itself, are based on attachment theory and procedures, current research on early relationships, and object relations theory. Using edited videotapes of their interactions with their children, caregivers are encouraged: 1. to increase their sensitivity and appropriate responsiveness to the child's signals relevant to its moving away from to explore, and its moving back for comfort and soothing; 2. to increase their ability to reflect on their own and the child's behavior, thoughts and feelings regarding their attachment-caregiving interactions; and 3. to reflect on experiences in their own histories that affect their current caregiving patterns. In this paper we describe the conceptual background of the protocol, and the protocol itself. We then present a case study from our current data set of 75 dyads who have completed the protocol.

Text Classification Algorithms: A Survey

Abstract: In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper understanding of machine learning methods to be able to accurately classify texts in many applications. Many machine learning approaches have achieved surpassing results in natural language processing. The success of these learning algorithms relies on their capacity to understand complex models and non-linear relationships within data. However, finding suitable structures, architectures, and techniques for text classification is a challenge for researchers. In this paper, a brief overview of text classification algorithms is discussed. This overview covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods. Finally, the limitations of each technique and their application in the real-world problem are discussed.