Using Structure Indices for Efficient Approximation of Network Properties

Statistics on networks have become vital to the study of relational data drawn from areas including bibliometrics, fraud detection, bioinformatics, and the Internet. Calculating many of the most important measures—such as betweenness centrality, closeness centrality, and graph diameter—requires identifying short paths in these networks. However, finding these short paths can be intractable for even moderate-size networks. We introduce the concept of a network structure index (NSI), a composition of (1) a set of annotations on every node in the network and (2) a function that uses the annotations to estimate graph distance between pairs of nodes. We present several varieties of NSIs, examine their time and space complexity, and analyze their performance on synthetic and real data sets. We show that creating an NSI for a given network enables extremely efficient and accurate estimation of a wide variety of network statistics on that network

Temperature dependent absorption cross-sections of HNO3 and N2O5

Absorption cross-sections for HNO3 and N2O5 have been measured in the wavelength region 220-450 nm, using a dual beam diode array spectrometer with a spectral resolution of 0.3 nm. The results for both compounds are in good agreement with recommended values at room temperature. However, the cross-sections of both HNO3 and N2O5 show a marked reduction with decreasing temperature in the range 295-233 K. The calculated photolysis rate of HNO3 at the low temperatures and high solar zenith angles characteristic of the polar winter and spring is significantly lower than previously estimated

Morphological Changes and Immunohistochemical Expression of RAGE and its Ligands in the Sciatic Nerve of Hyperglycemic Pig (Sus Scrofa)

The aim of our project was to study the effect of streptozotocin (STZ)—induced hyperglycemia on sciatic nerve morphology, blood plasma markers and immunohistochemical expression of RAGE (the Receptor for Advanced Glycation End-products), and its ligands—S100B and Carboxymethyl Lysine (CML)-advanced glycation endproduct (AGE) in the laboratory pig. Six months after STZ—injections, blood plasma measurements, morphometric analysis of sciatic nerve fiber density, immunofluorescent distribution of potential molecular neuropathy contributors, ELISA measurement of plasma AGE level and HPLC analysis of sciatic nerve levels of one of the pre-AGE and the glycolysis intermediate products—methyl-glyoxal (MG) were performed. The results of our study revealed that STZ—injected animals displayed elevated levels of plasma glucose, gamma glutamyl transferase (GGT) and triglycerides. The sciatic nerve of STZ-injected pigs revealed significantly lower numbers of small-diameter myelinated fibers, higher immunoreactivity for RAGE and S100B and increased levels of MG as compared to control animals. Our results correspond to clinical findings in human patients with hyperglycemia/diabetes-evoked peripheral neuropathy and suggest that the domestic pig may be a suitable large animal model for the study of mechanisms underlying hyperglycemia-induced neurological complications in the peripheral nerve and may serve as a relevant model for the pre-clinical assessment of candidate drugs in neuropathy

Increased muscle blood supply and transendothelial nutrient and insulin transport induced by food intake and exercise: effect of obesity and ageing.

This review concludes that a sedentary lifestyle, obesity and ageing impair the vasodilator response of the muscle microvasculature to insulin, exercise and VEGF-A and reduce microvascular density. Both impairments contribute to the development of insulin resistance, obesity and chronic age-related diseases. A physically active lifestyle keeps both the vasodilator response and microvascular density high. Intravital microscopy has shown that microvascular units (MVUs) are the smallest functional elements to adjust blood flow in response to physiological signals and metabolic demands on muscle fibres. The luminal diameter of a common terminal arteriole (TA) controls blood flow through up to 20 capillaries belonging to a single MVU. Increases in plasma insulin and exercise/muscle contraction lead to recruitment of additional MVUs. Insulin also increases arteriolar vasomotion. Both mechanisms increase the endothelial surface area and therefore transendothelial transport of glucose, fatty acids (FAs) and insulin by specific transporters, present in high concentrations in the capillary endothelium. Future studies should quantify transporter concentration differences between healthy and at risk populations as they may limit nutrient supply and oxidation in muscle and impair glucose and lipid homeostasis. An important recent discovery is that VEGF-B produced by skeletal muscle controls the expression of FA transporter proteins in the capillary endothelium and thus links endothelial FA uptake to the oxidative capacity of skeletal muscle, potentially preventing lipotoxic FA accumulation, the dominant cause of insulin resistance in muscle fibres

The effect of different training modes on skeletal muscle microvascular density and endothelial enzymes controlling NO availability

It is becoming increasingly apparent that a high vasodilator response of the skeletal muscle microvasculature to insulin and exercise is of critical importance for adequate muscle perfusion and long-term microvascular and muscle metabolic health. Previous research has shown that a sedentary lifestyle, obesity, and ageing lead to impairments in the vasodilator response, while a physically active lifestyle keeps both microvascular density and vasodilator response high. To investigate the molecular mechanisms behind these impairments and the benefits of exercise training interventions, our laboratory has recently developed quantitative immunofluorescence microscopy methods to measure protein content of eNOS and NAD(P)Hoxidase specifically in the endothelial layer of capillaries and arterioles of human skeletal muscle. As eNOS produces NO and NAD(P)Hoxidase superoxide anions (quenching NO) we propose that the eNOS/NAD(P)Hoxidase protein ratio is a marker of vasodilator capacity. The novel methods show that endurance training (ET) and high intensity interval training (HIT) generally regarded as a time efficient alternative to ET, increase eNOS protein content and the eNOS/NADP(H) oxidase protein ratio in previously sedentary lean and obese young men. Resistance exercise training had smaller but qualitatively similar effects. Western blot data of other laboratories suggest that endurance exercise training leads to similar changes in sedentary elderly men. Future research will be required to investigate the relative importance of other sources and tissues in the balance between NO and O2- production seen by the vascular smooth muscle layer of terminal arterioles

Using structure indices for efficient approximation of network properties

Statistics on networks have become vital to the study of relational data drawn from areas including bibliometrics, fraud detection, bioinformatics, and the Internet. Calculating many of the most important measures—such as betweenness centrality, closeness centrality, and graph diameter—requires identifying short paths in these networks. However, finding these short paths can be intractable for even moderate-size networks. We introduce the concept of a network structure index (NSI), a composition of (1) a set of annotations on every node in the network and (2) a function that uses the annotations to estimate graph distance between pairs of nodes. We present several varieties of NSIs, examine their time and space complexity, and analyze their performance on synthetic and real data sets. We show that creating an NSI for a given network enables extremely efficient and accurate estimation of a wide variety of network statistics on that network

ABSTRACT The Case For Anomalous Link Discovery

In this paper, we describe the challenges inherent to the task of link prediction, and we analyze one reason why many link prediction models perform poorly. Specifically, we demonstrate the effects of the extremely large class skew associated with the link prediction task. We then present an alternate task — anomalous link discovery (ALD) — and qualitatively demonstrate the effectiveness of simple link prediction models for the ALD task. We show that even the simplistic structural models that perform poorly on link prediction can perform quite well at the ALD task

Leveraging Relational Representations for Causal Discovery

This thesis represents a synthesis of relational learning and causal discovery, two subjects at the frontier of machine learning research. Relational learning investigates algorithms for constructing statistical models of data drawn from of multiple types of interrelated entities, and causal discovery investigates algorithms for constructing causal models from observational data. My work demonstrates that there exists a natural, methodological synergy between these two areas of study, and that despite the sometimes onerous nature of each, their combination (perhaps counterintuitively) can provide advances in the state of the art for both. Traditionally, propositional (or flat ) data representations have dominated the statistical sciences. These representations assume that data consist of independent and identically distributed (iid) entities which can be represented by a single data table. More recently, data scientists have increasingly focused on relational data sets that consist of interrelated, heterogeneous entities. However, relational learning and causal discovery are rarely combined. Relational representations are wholly absent from the literature where causality is discussed explicitly. Instead, the literature on causality that uses the framework of graphical models assumes that data are independent and identically distributed. This unexplored topical intersection represents an opportunity for advancement --- by combining relational learning with causal reasoning, we can provide insight into the challenges found in each subject area. By adopting a causal viewpoint, we can clarify the mechanisms that produce previously identified pathologies in relational learning. Analogously, we can utilize relational data to establish and strengthen causal claims in ways that are impossible using only propositional representations

Relational Blocking for Causal Discovery

Blocking is a technique commonly used in manual statistical analysis to account for confounding variables. However, blocking is not currently used in automated learning algorithms. These algorithms rely solely on statistical conditioning as an operator to identify conditional independence. In this work, we present relational blocking as a new operator that can be used for learning the structure of causal models. We describe how blocking is enabled by relational data sets, where blocks are determined by the links in the network. By blocking on entities rather than conditioning on variables, relational blocking can account for both measured and unobserved variables. We explain the mechanism of these methods using graphical models and the semantics of d-separation. Finally, we demonstrate the effectiveness of relational blocking for use in causal discovery by showing how blocking can be used in the causal analysis of two real-world social media systems