Exact algorithms for the Steiner tree problem

In this thesis, the exact algorithms for the Steiner tree problem have been investigated. The Dreyfus-Wagner algorithm is a well-known dynamic programming method for computing minimum Steiner trees in general weighted graphs in time O(3k), where k is the number of the terminals. Firstly, two exact algorithms for the Steiner tree problem will be presented. The first one improves the running time of algorithm to O(2.684k) by showing that the optimum Steiner tree T can be partitioned into T = T1 [ T2 [ T3 in a certain way such that each Ti is a minimum Steiner tree in a suitable contracted graph Gi with less than k 2 terminals. The second algorithm is in time O((2 + )k) for any > 0. Every rectilinear Steiner tree problem admits an optimal tree T which is composed of tree stars. Moreover, the currently fastest algorithm for the rectilinear Steiner tree problem proceeds by composing an optimum tree T from tree star components in the cheapest way. F¨oßmeier and Kaufmann showed that any problem instance with k terminals has a number of tree stars in between 1.32k and 1.38k. We also present additional conditions on tree stars which allow us to further reduce the number of candidate components building the optimum Steiner tree to O(1.337k)

Social Media Attention Increases Article Visits: An Investigation on Article-Level Referral Data of PeerJ

In order to better understand the effect of social media in the dissemination of scholarly articles, employing the daily updated referral data of 110 PeerJ articles collected over a period of 345 days, we analyze the relationship between social media attention and article visitors directed by social media. Our results show that social media presence of PeerJ articles is high. About 68.18% of the papers receive at least one tweet from Twitter accounts other than @PeerJ, the official account of the journal. Social media attention increases the dissemination of scholarly articles. Altmetrics could not only act as the complement of traditional citation measures but also play an important role in increasing the article downloads and promoting the impacts of scholarly articles. There also exists a significant correlation among the online attention from different social media platforms. Articles with more Facebook shares tend to get more tweets. The temporal trends show that social attention comes immediately following publication but does not last long, so do the social media directed article views

The role of the age-dependent loss of [alpha](E)-catenin in increased acute kidney injury /

Includes vita.The aging kidney undergoes structural and functional alterations which make it more susceptible to acute kidney injury (AKI). Previous studies in our laboratory have shown that the aging kidney has a marked loss of alpha(E)-catenin in proximal tubular epithelium. alpha-Catenin, a key regulator of actin cytoskeleton, interacts with a variety of actin-binding proteins. Fascin 2 is an actin bundling protein that interacts with adhesion molecules and F-actin. In this work, we hypothesized that loss of alpha(E)-catenin leads to disruption of actin cytoskeleton which increases cisplatin-induced injury in aged kidney. A stable shRNA knock-down of alpha(E)-catenin was generated in NRK-52E cells (C2 cells); NT3 cells are the non-targeted control. We demonstrated that age-dependent loss of alpah(E)-catenin in renal tubule epithelial cells facilitates the Fas-mediated apoptotic signaling pathway in response to cisplatin-induced AKI injury. In addition, a cisplatin-induced loss of fascin 2 was observed in aged kidney. Overexpression of Fscn2 abolished increased cisplatin-induced apoptosis, mitochondrial dysfunction and oxidative stress in C2 cells compared with NT3 cells. In conclusion, this dissertation projects novel insight into understanding the increased incidence of AKI in aged kidney and identified a novel role of fascin 2 in renal epithelial cells, which depends on the functional interaction with alpha(E)-catenin and F-actin. These findings may lay the groundwork for new therapeutic approaches to AKI in aged patients in the future.Dr. Alan R. Parrish, Dissertation Supervisor.|Includes vita.Includes bibliographical references (pages 122-141)

Application Research of Combined Forecasting Based on Induced Ordered Weighted Averaging Operator

Aiming at the shortcomings of traditional weighted arithmetic combination forecasting model, using the induced ordered weighted averaging operator, according to the fitting accuracy of each single forecasting method at each time point of the sample interval to endue weighted, and with the error sum of squares as criterion, we establish a new combination forecasting model, which effectively improves the precision of combination forecasting. And the resident consumption levels were predicted using the model analysis

TNFR2 and Regulatory T Cells: Potential Immune Checkpoint Target in Cancer Immunotherapy

TNF has both proinflammatory and antiinflammatory effects. It binds to two structurally related but functionally distinct receptors TNFR1 and TNFR2. Unlike TNFR1 that is ubiquitously expressed, TNFR2 expression is more limited to myeloid and lymphoid cell lineages including a fraction of regulatory T cells (Treg). In general, TNFR1 is responsible for TNF-mediated cell apoptosis and death, and mostly induces proinflammatory reactions. However, TNFR2 mainly leads to functions related to cell survival and immune suppression. Treg play an indispensable role in maintaining immunological self-tolerance and restraining excessive immune reactions deleterious to the host. Impaired Treg-mediated immune regulation has been observed in various autoimmune diseases as well as in cancers. Therefore, Treg might provide an ideal therapeutic target for diseases where the immune balance is impaired and could benefit from the regulation of Treg properties. TNFR2 is highly expressed on Treg in mice and in humans, and TNFR2+ Treg reveal the most potent suppressive capacity. TNF-TNFR2 ligation benefits Treg proliferation, although the effect on Treg suppressive function remains controversial. Here, we will describe in detail the TNF-mediated regulation of Treg and the potential clinical applications in cancer immunotherapy as well as in autoimmune diseases, with the focus on human Treg subsets

Non-specific filtering of beta-distributed data.

BackgroundNon-specific feature selection is a dimension reduction procedure performed prior to cluster analysis of high dimensional molecular data. Not all measured features are expected to show biological variation, so only the most varying are selected for analysis. In DNA methylation studies, DNA methylation is measured as a proportion, bounded between 0 and 1, with variance a function of the mean. Filtering on standard deviation biases the selection of probes to those with mean values near 0.5. We explore the effect this has on clustering, and develop alternate filter methods that utilize a variance stabilizing transformation for Beta distributed data and do not share this bias.ResultsWe compared results for 11 different non-specific filters on eight Infinium HumanMethylation data sets, selected to span a variety of biological conditions. We found that for data sets having a small fraction of samples showing abnormal methylation of a subset of normally unmethylated CpGs, a characteristic of the CpG island methylator phenotype in cancer, a novel filter statistic that utilized a variance-stabilizing transformation for Beta distributed data outperformed the common filter of using standard deviation of the DNA methylation proportion, or its log-transformed M-value, in its ability to detect the cancer subtype in a cluster analysis. However, the standard deviation filter always performed among the best for distinguishing subgroups of normal tissue. The novel filter and standard deviation filter tended to favour features in different genome contexts; for the same data set, the novel filter always selected more features from CpG island promoters and the standard deviation filter always selected more features from non-CpG island intergenic regions. Interestingly, despite selecting largely non-overlapping sets of features, the two filters did find sample subsets that overlapped for some real data sets.ConclusionsWe found two different filter statistics that tended to prioritize features with different characteristics, each performed well for identifying clusters of cancer and non-cancer tissue, and identifying a cancer CpG island hypermethylation phenotype. Since cluster analysis is for discovery, we would suggest trying both filters on any new data sets, evaluating the overlap of features selected and clusters discovered

T-spline based unifying registration procedure for free-form surface workpieces in intelligent CMM

With the development of the modern manufacturing industry, the free-form surface is widely used in various fields, and the automatic detection of a free-form surface is an important function of future intelligent three-coordinate measuring machines (CMMs). To improve the intelligence of CMMs, a new visual system is designed based on the characteristics of CMMs. A unified model of the free-form surface is proposed based on T-splines. A discretization method of the T-spline surface formula model is proposed. Under this discretization, the position and orientation of the workpiece would be recognized by point cloud registration. A high accuracy evaluation method is proposed between the measured point cloud and the T-spline surface formula. The experimental results demonstrate that the proposed method has the potential to realize the automatic detection of different free-form surfaces and improve the intelligence of CMMs