Miracle-dz/UniFAR-FTUs huggingface dataset:
High-sensitivity C-reactive Protein Can Predict Major Adverse Cardiovascular Events in Korean Patients with Type 2 Diabetes

Inflammation is thought to play a role in the pathogenesis of major adverse cardiovascular events (MACE). It has been suggested that the measurement of markers of inflammation may aid in predicting the risk of such events. Here, the relationship between high-sensitivity C-reactive protein (hs-CRP) levels and MACE in Korean patients with type 2 diabetes is assessed. A retrospective cohort study was conducted as a follow-up among 1,558 patients with type 2 diabetes and without cardiovascular diseases over a mean period of 55.5 months. A Cox proportional-hazards model was used to determine whether increased hs-CRP levels are useful as a predictor for future MACE. The hazard ratio of MACE was 1.77 (95% CI; 1.16-2.71) in subjects who had the highest hs-CRP levels (> 0.21 mg/dL) compared to subjects who had the lowest hs-CRP levels (< 0.08 mg/dL), after adjusting for age, regular physical activity, current smoking, and duration of diabetes. The present results indicate that high hs-CRP levels can act as a predictor for the MACE occurrence in Korean patients with type 2 diabetes.
Adenocarcinoma Epigenetic Patterns in the Progression of Esophageal Updated Version

Esophageal adenocarcinoma (EAC) arises after normal squamous mucosa undergoes metaplasia to specialized columnar epithelium (intestinal metaplasia or Barrett’s esophagus), which can then ultimately progress to dysplasia and subsequent malignancy. Epigenetic studies of this model have thus far been limited to the DNA methylation analysis of a few genes. In this study, we analyzed a panel of 20 genes using a quantitative, high-throughput methylation assay, MethyLight. We used this broader approach to gain insight into concordant methylation behavior between genes and to generate epigenomic fingerprints for the different histological stages of EAC. Our study included a total of 104 tissue specimens from 51 patients with different stages of Barrett’s esophagus and/or associated adenocarcinoma. We screened 84 of these samples with the full panel of 20 genes and found distinct classes of methylation patterns in the different types of tissue. The most informative genes were those with an intermediate frequency of significant hypermethylation [ranging from 15% (CDKN2A) to 60% (MGMT) of the samples]. This group could be further subdivided into three classes, according to the absence ( CDKN2A, ESR1, and MYOD1) or presence (CALCA, MGMT, and TIMP3) of methylation in normal esophageal mucosa and stomach, or the infrequent methylation of normal esophageal mucosa accompanied by methylation in all normal stomach samples (APC). The other genes were less informative, because the frequency of hypermethylation was below 5% (ARF,CDH1, CDKN2B, GSTP1,MLH1, PTGS2, andTHBS1), completely absent (CTNNB1, RB1, TGFBR2, andTYMS1), or ubiquitous (HIC1 and MTHFR), regardless of tissue type. Each class undergoes unique epigenetic changes at different steps of disease progression of EAC, suggesting a step-wise loss of multiple protective barriers against CpG island hypermethylation. The aberrant hypermethylation occurs at many different loci in the same tissues, suggestive of an overall deregulation of methylation control in EAC tumorigenesis. However, we did not find evidence for a distinct group of tumors with a CpG island methylator phenotype. Finally, we found that normal and metaplastic tissues from patients with evidence of associated dysplasia or cancer had a significantly higher incidence of hypermethylation than similar tissues from patients with no further progression of their disease. The fact that the samples from these two groups of patients were histologically indistinguishable, yet molecularly distinct, suggests that the occurrence of such hypermethylation may provide a clinical tool to identify patients with premalignant Barrett’s who are at risk for further progression.
Classification with Extreme Learning Machine and ensemble algorithms over randomly partitioned data

In this age of Big Data, machine learning based data mining methods are extensively used to inspect large scale data sets. Deriving applicable predictive modeling from these type of data sets is a challenging obstacle because of their high complexity. Opportunity with high data availability levels, automated classification of data sets has become a critical and complicated function. In this paper, the power of applying MapReduce based Distributed AdaBoosting of Extreme Learning Machine (ELM) are explored to build reliable predictive bag of classification models. Thus, (i) dataset ensembles are build; (ii) ELM algorithm is used to build weak classification models; and (iii) build a strong classification model from a set of weak classification models. This training model is applied to the publicly available knowledge discovery and data mining datasets.
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We study a simple example of a sequential game illustrating problems connected with making rational decisions that are universal for social sciences. The set of chooser’s optimal decisions that manifest his preferences in case of a constant strategy of the adversary (the offering player), is investigated. It turns out that the order imposed by the player’s rational preferences can be intransitive. The presented quantitative results imply a revision of the ”common sense” opinions stating that preferences showing intransitivity are paradoxical and undesired.
Bradykinin Enhances Sympathetic Neurotransmission in Rat Blood Vessels

Bradykinin evokes endothelium-dependent relaxation in some vascular beds; on the other hand, the possibility has been demonstrated that in certain organs, such as the adrenal medulla or atria, bradykinin may enhance transmitter release from the sympathetic nerves. We hypothesized that bradykinin may also enhance postganglionic sympathetic neurotransmission in blood vessels. To test this hypothesis, we recorded excitatory junction potentials (EJPs), a measure of sympathetic purinergic neurotransmission, in rat mesenteric resistance arteries with a conventional microelectrode technique. EJPs were elicited by repetitive perivascular nerve stimulation (1 Hz, 20 to 50 V, 30 to 60 &mgr;s, 11 pulses). In this preparation, bradykinin (10−7 or 10−6 mol/L) significantly enhanced the amplitude of EJPs without altering the resting membrane potential. This effect of bradykinin was blocked by Hoe 140, a bradykinin B2 receptor antagonist, but not by des-Arg9,[Leu8]-bradykinin, a bradykinin B1 receptor antagonist. The cyclooxygenase inhibitor indomethacin or NO synthase inhibitor NG-nitro-l-arginine did not alter the effect of bradykinin. Captopril, an ACE inhibitor, but not candesartan, an angiotensin II type 1 receptor antagonist, enhanced the action of a low concentration (10−8 mol/L) of bradykinin on EJPs. These findings suggest that in rat mesenteric resistance arteries, bradykinin enhances sympathetic purinergic neurotransmission, presumably through presynaptic bradykinin B2 receptors. The clinical relevance of the present findings remains unclear; however, the fact that the ACE inhibitor, but not the angiotensin II type 1 receptor antagonist, enhanced the action of bradykinin on sympathetic neurotransmission may warrant further investigation.
Overexpression of Brain-Derived Neurotrophic Factor Enhances Sensory Innervation and Selectively Increases Neuron Number

Target-derived neurotrophin growth factors have significant effects on the development and maintenance of the mammalian somatosensory system. Studies of transgenic mice that overexpress neurotrophins NGF and neurotrophin 3 (NT-3) at high levels in skin have shown increased sensory neuron number and enhanced innervation of specific sensory ending types. The effects of two other members of this family, BDNF and NT-4, on sensory neuron development are less clear. This study examined the role of brain-derived neurotrophic factor (BDNF) using transgenic mice that overexpress BDNF in epithelial target tissues of sensory neurons. BDNF transgenic mice had an increase in peripheral innervation density and showed selective effects on neuron survival. Neuron number in trigeminal ganglia, DRG, and SCG were unchanged, although a 38% increase in neurons comprising the placode-derived nodose–petrosal complex occurred. BDNF transgenic skin showed notable enhancement of innervation to hair follicles as detected by PGP9.5 immunolabeling. In nonhairy plantar skin, Meissner corpuscle sensory endings were larger, and the number of Merkel cells with associated innervation was increased. In trigeminal ganglia, neurons expressing trkB receptor were increased threefold, whereas trkA-positive neurons doubled. Analysis of trkB by Northern, reverse transcription-PCR, and Western assays indicated a modest increase in the expression of the T1 truncated receptor and preferential distribution to the periphery. These data indicate that skin-derived BDNF does not enhance survival of cutaneous sensory neurons, although it does promote neurite innervation of specific sites and sensory end organs of the skin.
Title of Document: Graph Theoretic Connectivity Analysis of the Mid-atlantic Region of the United States Graph Theoretic Connectivity Analysis of the Mid-atlantic Region of the United States

Title of Document: GRAPH THEORETIC CONNECTIVITY ANALYSIS OF THE MID-ATLANTIC REGION OF THE UNITED STATES Joseph R. Ferrari, Master of Science, 2005 Directed By: Assistant Professor Maile C. Neel, Department of Natural Resource Science and Landscape Architecture Connectivity is critical for persistence of species in the face of anthropogenic habitat destruction and fragmentation. Graph theory is a relatively new method for quantifying connectivity that has tremendous potential, but landscape graph applications to date are limited to specific conservation situations with static proportions of habitat (P). This study provides a uniform evaluation of graph metrics across wide gradients in P in both random neutral landscapes and real, forested landscapes from the Mid-Atlantic region of the United States. Such an analysis provides a background that will be valuable for future interpretation of graph metrics. Results indicate that graph metrics have characteristic forms when plotted against P that can be exploited for conservation management. GRAPH THEORETIC CONNECTIVITY ANALYSIS OF THE MID-ATLANTIC REGION OF THE UNITED STATES
Origin of Activity and Stability Enhancement for Ag3PO4 Photocatalyst after Calcination

Pristine Ag3PO4 microspheres were synthesized by a co-precipitation method, followed by being calcined at different temperatures to obtain a series of calcined Ag3PO4 photocatalysts. This work aims to investigate the origin of activity and stability enhancement for Ag3PO4 photocatalyst after calcination based on the systematical analyses of the structures, morphologies, chemical states of elements, oxygen defects, optical absorption properties, separation and transfer of photogenerated electron-hole pairs, and active species. The results indicate that oxygen vacancies (VO˙˙) are created and metallic silver nanoparticles (Ag NPs) are formed by the reaction of partial Ag+ in Ag3PO4 semiconductor with the thermally excited electrons from Ag3PO4 and then deposited on the surface of Ag3PO4 microspheres during the calcination process. Among the calcined Ag3PO4 samples, the Ag3PO4-200 sample exhibits the best photocatalytic activity and greatly enhanced photocatalytic stability for photodegradation of methylene blue (MB) solution under visible light irradiation. Oxygen vacancies play a significantly positive role in the enhancement of photocatalytic activity, while metallic Ag has a very important effect on improving the photocatalytic stability. Overall, the present work provides some powerful evidences and a deep understanding on the origin of activity and stability enhancement for the Ag3PO4 photocatalyst after calcination.
miRDiabetes : A microRNA-Diabetes Association Database Constructed by Classification on Literature

MicroRNAs (miRNAs) are a growing class of non-coding RNAs that regulate gene expression by translational repression. miRNA plays a very important in many biological process. A role for miRNA in diabetes was first discovered in 2004 and miRNA-diabetes association has been an increasing interest since then. However, before this study, there is no computational tool available to be able to retrieve and gather literature on this topic. In this paper, we performed classification on the PubMed literature data to automatically retrieve associations between miRNAs and diabetes. After being verified, the retrieved associations are stored in a database called miRDiabetes, which is the first comprehensive database to collect articles that profile relations between miRNAs and diabetes. Performance study shows that our system can automatically determine relevancy of new entries with high accuracy. We also developed an application to facilitate regular updates on the database and built a website (http://mirdiabetes.ecu.edu) for researchers to search and download the miRDiabetes database.
Examining the (Lack of) Effects Associated with Leadership Training Participation in Higher Education

This study examined the differences between a multi-campus sample of university students who reported consistent participation in formal leadership development programs (n=414) and a comparison sample (n=153) with no prior experience across three diverse postsecondary institutions in the United States. Both samples were matched with regard to gender and racial identity, prior self-reported high school leadership training experience, and selfreported possession of formal leadership positions in co-curricular student organizations. Results suggest that students who report past consistent participation in postsecondary leadership training report levels of leadership capacity no different than those with no training. In addition, like their peers they also possess a cognitive model of leadership capacity that fails to differentiate leader self-efficacy, motivation to lead, and leadership skill.
Advanced algorithm partitioning of markov and color image segmentation

The color vision systems require a first step of classifying pixels in a given image into a discrete set of color classes. In this paper we introduce a new method of algorithm partitioning, and color image segmentation based on similarities or dissimilarities of the pixels. We consider fuzzy segmentation with markov, and normalized cut method. Experiments show these different processes used an effective solution on natural images, and computational efficiency. Finally, the algorithm has proven our process of experiments on gray scale, color, and texture images show promising segmentation results successful.
The Role of S7, A Subunit of the 19S Proteasome, in the Transcriptional Regulation of MHC II.

Induction of an adaptive, or antigen specific, immune response is critical for eliminating most infections. Pathogen clearance is accomplished primarily, by the actions of CD4+ T cells through their ability to recognize foreign antigens presented at the cell surface by major histocompatibility class II (MHC II) molecules. Consequently, the capacity to regulate expression of MHC molecules is essential to control the adaptive immune response. MHC molecules are regulated at the level of transcription by a master regulator, the class II transcriptional activator, CIITA. Thus, the expression of MHC II is directly related to proper CIITA activity. This thesis focuses on the novel role of S7, an ATPase subunit of the 19S proteasome, in the transcriptional regulation of CIITA and MHC II molecules. INDEX WORDS: MHC II, S7, proteasome, CIITA, adaptive immunity THE ROLE OF S7, A SUBUNIT OF THE 19S PROTEASOME, IN THE TRANSCRIPTIONAL REGULATION OF MHC II GENES by DAWSON GERHARDT A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in the College of Arts and Sciences Georgia State University
HLM1, an Essential Signaling Component in the Hypersensitive Response, Is a Member of the Cyclic Nucleotide–Gated Channel Ion Channel Family Online version contains Web-only data. Article, publication date, and citation information can be found at www.plantcell.org/cgi/doi/10.1105/tpc.006999.

The hypersensitive response (HR) in plants is a programmed cell death that is commonly associated with disease resistance. A novel mutation in Arabidopsis, hlm1, which causes aberrant regulation of cell death, manifested by a lesion-mimic phenotype and an altered HR, segregated as a single recessive allele. Broad-spectrum defense mechanisms remained functional or were constitutive in the mutant plants, which also exhibited increased resistance to a virulent strain of Pseudomonas syringae pv tomato. In response to avirulent strains of the same pathogen, the hlm1 mutant showed differential abilities to restrict bacterial growth, depending on the avirulence gene expressed by the pathogen. The HLM1 gene encodes a cyclic nucleotide–gated channel, CNGC4. Preliminary study of the HLM1/CNGC4 gene pro-duct in Xenopus oocytes (inside-out patch-clamp technique) showed that CNGC4 is permeable to both K+ and Na+ and is activated by both cGMP and cAMP. HLM1 gene expression is induced in response to pathogen infection and some pathogen-related signals. Thus, HLM1 might constitute a common downstream component of the signaling pathways leading to HR/resistance.
Generalized MMSE Beamforming for Downlink MIMO Systems

In this paper, a generalized MMSE beamforming is proposed for downlink multiple input multiple output (MIMO) systems. Unlike the conventional MMSE beamforming, cellular environments are considered where each user is randomly distributed in a cell and has the different temporal correlation of fading channel. We apply the proposed generalized MMSE beamforming to two important downlink MIMO scenarios, which are multiuser MIMO (MU-MIMO) and multicell MIMO (MC-MIMO). We derive the closed-form expressions of the generalized MMSE beamforming for both scenarios using convex optimization technique. The derived beamforming solution captures the impacts of the random geometry and different temporal correlations of users. Numerical results verify that the generalized MMSE beamforming achieves lower BER and higher average sum-rate than the previous beamforming schemes developed for MU-MIMO and MC-MIMO including the conventional MMSE beamforming.
A Self-Learning Structure to Improve the Efficiency of BlueChip Software

There have been serious concerns about hardware security from malicious modification of hardware design during design or manufacturing. Several hardware Trojan detection approaches have been proposed in past few years. In [1], Hicks et al. put forward a novel strategy named BlueChip to detect and nullify the hardware Trojans at design time. They also proposed a recovery mechanism in software layer to assist the modified hardware work. This detection and resistance method is useful while it has some flaws. Some legitimate circuits which were removed by BlueChip might invoke the recovery function continuously. In this paper, we propose a novel approach to improve the effectiveness of the BlueChip architecture by building a self-learning structure to let the circuit distinguish the removed legitimated circuits from the removed Trojan circuits.
Exploring the Molecular Epidemiology and Evolutionary Dynamics of Influenza A Virus in Taiwan

The evolution and population dynamics of human influenza in Taiwan is a microcosm of the viruses circulating worldwide, which has not yet been studied in detail. We collected 343 representative full genome sequences of human influenza A viruses isolated in Taiwan between 1979 and 2009. Phylogenetic and antigenic data analysis revealed that H1N1 and H3N2 viruses consistently co-circulated in Taiwan, although they were characterized by different temporal dynamics and degrees of genetic diversity. Moreover, influenza A viruses of both subtypes underwent internal gene reassortment involving all eight segments of the viral genome, some of which also occurred during non-epidemic periods. The patterns of gene reassortment were different in the two subtypes. The internal genes of H1N1 viruses moved as a unit, separately from the co-evolving HA and NA genes. On the other hand, the HA and NA genes of H3N2 viruses tended to segregate consistently with different sets of internal gene segments. In particular, as reassortment occurred, H3HA always segregated as a group with the PB1, PA and M genes, while N2NA consistently segregated with PB2 and NP. Finally, the analysis showed that new phylogenetic lineages and antigenic variants emerging in summer were likely to be the progenitors of the epidemic strains in the following season. The synchronized seasonal patterns and high genetic diversity of influenza A viruses observed in Taiwan make possible to capture the evolutionary dynamic and epidemiological rules governing antigenic drift and reassortment and may serve as a “warning” system that recapitulates the global epidemic.
Discovering Inter-Dimensional Rules in Dynamic Graphs

Data mining methods that exploit graph/network have become quite popular and a timely challenge is to consider the discovery of dynamic properties in evolving graphs or networks. In this paper, we consider the dynamic oriented graphs that can be encoded as n-ary relations with n ≤ 3 such that we have a least 3 dimensions: the dimensions of departure (tail) and arrival (head) vertices plus the time dimension. In other terms, it encodes the sequence of adjacency matrices of the graph. In such datasets, we propose a new semantics for inter-dimensional rules in dynamic graphs. We define rules that may involve subsets of any dimensions in their antecedents and their consequents and we propose the new objective interestingness measure called the exclusive confidence.We introduce a first algorithm for computing such inter-dimensional rules and we illustrate the added-value of exclusive confidence for supporting the discovery of relevant rules from a real-life dynamic graph.
Towards Integrated Services for Health Monitoring

The emergence of short-range wireless communications hold the promise of realizing the grand vision of the next generation communication networks in which devices follow a always best-connected pattern for anybody, to anything from anywhere at anytime. Short-range wireless communications offers easy access to the global information and communications infrastructure, facilitates seamless connectivity amongst a host of computing, communications and sensing devices that collaborate to form a supporting ambient and pervasive computing environment. In this paper, we describe the IS-Home project where we propose a novel autonomic communications middleware platform enabling a wide variety of integrated services to be installed, including health monitoring services. The motivation for the project is to take advantage of the opportunities that open and standardized protocols and technologies can represent in the future home environment.