Showing papers by "Columbia University published in 1998"

General methods for monitoring convergence of iterative simulations

Abstract: We generalize the method proposed by Gelman and Rubin (1992a) for monitoring the convergence of iterative simulations by comparing between and within variances of multiple chains, in order to obtain a family of tests for convergence. We review methods of inference from simulations in order to develop convergence-monitoring summaries that are relevant for the purposes for which the simulations are used. We recommend applying a battery of tests for mixing based on the comparison of inferences from individual sequences and from the mixture of sequences. Finally, we discuss multivariate analogues, for assessing convergence of several parameters simultaneously.

Epidemiology of Sarcopenia among the Elderly in New Mexico

Abstract: Muscle mass decreases with age, leading to "sarcopenia," or low relative muscle mass, in elderly people. Sarcopenia is believed to be associated with metabolic, physiologic, and functional impairments and disability. Methods of estimating the prevalence of sarcopenia and its associated risks in elderly populations are lacking. Data from a population-based survey of 883 elderly Hispanic and non-Hispanic white men and women living in New Mexico (the New Mexico Elder Health Survey, 1993-1995) were analyzed to develop a method for estimating the prevalence of sarcopenia. An anthropometric equation for predicting appendicular skeletal muscle mass was developed from a random subsample (n = 199) of participants and was extended to the total sample. Sarcopenia was defined as appendicular skeletal muscle mass (kg)/height2 (m2) being less than two standard deviations below the mean of a young reference group. Prevalences increased from 13-24% in persons under 70 years of age to >50% in persons over 80 years of age, and were slightly greater in Hispanics than in non-Hispanic whites. Sarcopenia was significantly associated with self-reported physical disability in both men and women, independent of ethnicity, age, morbidity, obesity, income, and health behaviors. This study provides some of the first estimates of the extent of the public health problem posed by sarcopenia.

Association of missense and 5′-splice-site mutations in tau with the inherited dementia FTDP-17

Abstract: Thirteen families have been described with an autosomal dominantly inherited dementia named frontotemporal dementia and parkinsonism linked to chromosome 17 (FTDP-17)(1-9), historically termed Pick's disease(10) Most FTDP-17 cases show neuronal and/or glial inclusions that stain positively with antibodies raised against the microtubule-associated protein Tau, although the Tau pathology varies considerably in both its quantity (or severity) and characteristics(1-8,12) Previous studies have mapped the FTDP-17 locus to a 2-centimorgan region on chromosome 17q2111; the tau gene also lies within this region We have now sequenced tau in FTDP-17 families and identified three missense mutations (G272V, P301L and R406W) and three mutations in the 5' splice site of exon in The splice-site mutations all destabilize a potential stem-loop structure which is probably involved in regulating the alternative splicing of exon10 (ref 13) This causes more frequent usage of the 5' splice site and an increased proportion of tan transcripts that include exon 10 The increase in exon 10(+) messenger RNA will increase the proportion of Tau containing four microtubule-binding repeats, which is consistent with the neuropathology described in several families with FTDP-17 (refs 12, 14)

Presentation Slides for 'Investor Psychology and Security Market Under and Overreactions'

Abstract: We propose a theory of securities market under- and overreactions based on two well-known psychological biases: investor overconfidence about the precision of private information; and biased self-attribution, which causes asymmetric shifts in investors' confidence as a function of their investment outcomes. We show that overconfidence implies negative long-lag autocorrelations, excess volatility, and, when managerial actions are correlated with stock mispricing, public-event-based return predictability. Biased self-attribution adds positive short-lag autocorrelations (momentum), short-run earnings drift, but negative correlation between future returns and long-term past stock market and accounting performance. The theory also offers several untested implications and implications for corporate financial policy. Prepublication version available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2017

Structure of an HIV gp120 envelope glycoprotein in complex with the CD4 receptor and a neutralizing human antibody

Abstract: The entry of human immunodeficiency virus (HIV) into cells requires the sequential interaction of the viral exterior envelope glycoprotein, gp120, with the CD4 glycoprotein and a chemokine receptor on the cell surface. These interactions initiate a fusion of the viral and cellular membranes. Although gp120 can elicit virus-neutralizing antibodies, HIV eludes the immune system. We have solved the X-ray crystal structure at 2.5 A resolution of an HIV-1 gp120 core complexed with a two-domain fragment of human CD4 and an antigen-binding fragment of a neutralizing antibody that blocks chemokine-receptor binding. The structure reveals a cavity-laden CD4-gp120 interface, a conserved binding site for the chemokine receptor, evidence for a conformational change upon CD4 binding, the nature of a CD4-induced antibody epitope, and specific mechanisms for immune evasion. Our results provide a framework for understanding the complex biology of HIV entry into cells and should guide efforts to intervene.

Function of the chemokine receptor CXCR4 in haematopoiesis and in cerebellar development

Abstract: Chemokines and their receptors are important in cell migration during inflammation, in the establishment of functional lymphoid microenvironments, and in organogenesis. The chemokine receptor CXCR4 is broadly expressed in cells of both the immune and the central nervous systems and can mediate migration of resting leukocytes and haematopoietic progenitors in response to its ligand, SDF-1. CXCR4 is also a major receptor for strains of human immunodeficiency virus-1 (HIV-1) that arise during progression to immunodeficiency and AIDS dementia. Here we show that mice lacking CXCR4 exhibit haematopoietic and cardiac defects identical to those of SDF-1-deficient mice, indicating that CXCR4 may be the only receptor for SDF-1. Furthermore, fetal cerebellar development in mutant animals is markedly different from that in wild-type animals, with many proliferating granule cells invading the cerebellar anlage. This is, to our knowledge, the first demonstration of the involvement of a G-protein-coupled chemokine receptor in neuronal cell migration and patterning in the central nervous system. These results may be important for designing strategies to block HIV entry into cells and for understanding mechanisms of pathogenesis in AIDS dementia.

Stochastic Differential Equations

Abstract: We explore in this chapter questions of existence and uniqueness for solutions to stochastic differential equations and offer a study of their properties. This endeavor is really a study of diffusion processes. Loosely speaking, the term diffusion is attributed to a Markov process which has continuous sample paths and can be characterized in terms of its infinitesimal generator.

Geography and Economic Development

Abstract: This paper addresses the complex relationship between geography and macroeconomic growth. We investigate the ways in which geography may matter directly for growth, controlling for economic policies and institutions, as well as the effects of geography on policy choices and institutions. We find that location and climate have large effects on income levels and income growth, through their effects on transport costs, disease burdens, and agricultural productivity, among other channels. Furthermore, geography seems to be a factor in the choice of economic policy itself. When we identify geographical regions that are not conducive to modern economic growth, we find that many of these regions have high population density and rapid population increase. This is especially true of populations that are located far from the coast, and thus that face large transport costs for international trade, as well as populations in tropical regions of high disease burden. Furthermore, much of the population increase in the next thirty years is likely to take place in these geographically disadvantaged regions.

The role of feasibility and desirability considerations in near and distant future decisions: A test of temporal construal theory.

Abstract: Temporal construal theory states that distant future situations are construed on a higher level (i.e., using more abstract and central features) than near future situations. Accordingly, the theory suggests that the value associated with the high-level construal is enhanced over delay and that the value associated with the low-level construal is discounted over delay. In goal-directed activities, desirability of the activity's end state represents a high-level construal, whereas the feasibility of attaining this end state represents a low-level construal. Study 1 found that distant future activities were construed on a higher level than near future activities. Studies 2 and 3 showed that decisions regarding distant future activities, compared with decisions regarding near future activities, were more influenced by the desirability of the end state and less influenced by the feasibility of attaining the end state. Study 4 presented students with a real-life choice of academic assignments varying in difficulty (feasibility) and interest (desirability). In choosing a distant future assignment, students placed relatively more weight on the assignment's interest, whereas in choosing a near future assignment, they placed relatively more weight on difficulty. Study 5 found that distant future plans, compared with near future plans, were related to desirability of activities rather than to time constraints. In everyday life, people judge and make decisions about events that will take place either in the relatively near future or in the distant future. For example, one may need to decide whether to take a vacation, give a lecture, or attend a conference a few months in advance or just a few days in advance. This article addresses the question of how temporal distance from an event affects people's decisions regarding that event. We argue that distant future events are construed on a higher level (i.e., by using more central and abstract features of the event) than near future events. Therefore, decisions regarding distant future events are likely to be based on relatively central and

An Efficient Boosting Algorithm for Combining Preferences

Abstract: We study the problem of learning to accurately rank a set of objects by combining a given collection of ranking or preference functions. This problem of combining preferences arises in several applications, such as that of combining the results of different search engines, or the "collaborative-filtering" problem of ranking movies for a user based on the movie rankings provided by other users. In this work, we begin by presenting a formal framework for this general problem. We then describe and analyze an efficient algorithm called RankBoost for combining preferences based on the boosting approach to machine learning. We give theoretical results describing the algorithm's behavior both on the training data, and on new test data not seen during training. We also describe an efficient implementation of the algorithm for a particular restricted but common case. We next discuss two experiments we carried out to assess the performance of RankBoost. In the first experiment, we used the algorithm to combine different web search strategies, each of which is a query expansion for a given domain. The second experiment is a collaborative-filtering task for making movie recommendations.

Regulation of the Hedgehog and Wingless signalling pathways by the F-box/WD40-repeat protein Slimb

Abstract: Members of the Hedgehog (Hh) and Wnt/Wingless (Wg) families of secreted proteins control many aspects of growth and patterning during animal development1,2. Hh signal transduction leads to increased stability of a transcription factor, Cubitus interruptus (Ci)3,4, whereas Wg signal transduction causes increased stability of Armadillo (Arm/β-catenin)5, a possible co-factor for thetranscriptional regulator Lef1/TCF6. Here we describe a new gene, slimb (for supernumerary limbs), which negatively regulates both of these signal transduction pathways. Loss of function of slimb results in a cell-autonomous accumulation of high levels of both Ci and Arm, and the ectopic expression of both Hh− and Wg− responsive genes. The slimb gene encodes a conserved F-box/WD40-repeat protein related to Cdc4p, a protein in budding yeast that targets cell-cycle regulators for degradation by the ubiquitin/proteasome pathway7,8,9. We propose that Slimb protein normally targets Ci and Arm for processing or degradation by the ubiquitin/proteasomepathway, and that Hh and Wg regulate gene expression at least in part by inducing changes in Ci and Arm, which protect them from Slimb-mediated proteolysis.

Analyses of global sea surface temperature 1856–1991

Abstract: Global analyses of monthly sea surface temperature (SST) anomalies from 1856 to 1991 are produced using three statistically based methods: optimal smoothing (OS), the Kalrnan filter (KF) and optimal interpolation (OI). Each of these is accompanied by estimates of the error covariance of the analyzed fields. The spatial covariance function these methods require is estimated from the available data; the time-marching model is a first-order autoregressive model again estimated from data. The data input for the analyses are monthly anomalies from the United Kingdom Meteorological Office historical sea surface temperature data set (MOHSST5) (Parker et al., 1994) of the Global Ocean Surface Temperature Atlas (COSTA) (Bottoraley et al., 1990). These analyses are compared with each other, with COSTA, and with an analy- sis generated by projection (P) onto a set of empirical orthogonal functions (as in Smith et al. (1996)). In theory, the quality of the analyses should rank in the order OS, KF, OI, P, and COSTA. It is found that the first four give comparable results in the data-rich periods (1951-1991), but at times when data is sparse the first three differ significantly from P and COSTA. At these times the latter two often have extreme and fluctuating values, prima facie evidence of error. The statistical schemes are also verified against data not used in any of the analyses (proxy records derived from corals and air temperature records from coastal and island stations). We also present evidence that the analysis error estimates are indeed indicative of the quality of the products. At most times the OS and KF products are close to the OI product, but at times of especially poor coverage their use of information from other times is advantageous. The methods appear to reconstruct the major features of the global SST field from very sparse data. Comparison with other indications of the E1 Nifio - Southern Oscillation cycle show that the analyses provide usable information on interannual variability as far back as the 1860s.

Praise for Intelligence Can Undermine Children's Motivation and Performance

Abstract: Praise for ability is commonly considered to have beneficial effects on motivation. Contrary to this popular belief, six studies demonstrated that praise for intelligence had more negative consequences for students' achievement motivation than praise for effort. Fifth graders praised for intelligence were found to care more about performance goals relative to learning goals than children praised for effort. After failure, they also displayed less task persistence, less task enjoyment, more lowability attributions, and worse task performance than children praised for effort. Finally, children praised for intelligence described it as a fixed trait more than children praised for hard work, who believed it to be subject to improvement. These findings have important implications for how achievement is best encouraged, as well as for more theoretical issues, such as the potential cost of performance goals and the socialization of contingent self-worth.

The AMP-activated/SNF1 protein kinase subfamily: metabolic sensors of the eukaryotic cell?

Abstract: Mammalian AMP-activated protein kinase and yeast SNF1 protein kinase are the central components of kinase cascades that are highly conserved between animals, fungi, and plants. The AMP-activated protein kinase cascade acts as a metabolic sensor or "fuel gauge" that monitors cellular AMP and ATP levels because it is activated by increases in the AMP:ATP ratio. Once activated, the enzyme switches off ATP-consuming anabolic pathways and switches on ATP-producing catabolic pathways, such as fatty acid oxidation. The SNF1 complex in yeast is activated in response to the stress of glucose deprivation. In this case the intracellular signal or signals have not been identified; however, SNF1 activation is associated with depletion of ATP and elevation of AMP. The SNF1 complex acts primarily by inducing expression of genes required for catabolic pathways that generate glucose, probably by triggering phosphorylation of transcription factors. SNF1-related protein kinases in higher plants are likely to be involved in the response of plant cells to environmental and/or nutritional stress.

Social Memory Studies: From “Collective Memory” to the Historical Sociology of Mnemonic Practices

Abstract: Despite substantial work in a variety of disciplines, substantive areas, and geographical contexts, social memory studies is a nonparadigmatic, transdisciplinary, centerless enterprise. To remedy this relative disorganization, we (re-)construct out of the diversity of work addressing social memory a useful tradition, range of working definitions, and basis for future work. We trace lineages of the enterprise, review basic definitional disputes, outline a historical approach, and review sociological theories concerning the statics and dynamics of social memory.

Graph Theory and Its Applications

Abstract: INTRODUCTION TO GRAPH MODELS Graphs and Digraphs Common Families of Graphs Graph Modeling Applications Walks and Distance Paths, Cycles, and Trees Vertex and Edge Attributes: More Applications STRUCTURE AND REPRESENTATION Graph Isomorphism Revised! Automorphisms and Symmetry Moved and revised! Subgraphs Some Graph Operations Tests for Non-Isomorphism Matrix Representation More Graph Operations TREES Reorganized and revised! Characterizations and Properties of Trees Rooted Trees, Ordered Trees, and Binary Trees Binary-Tree Traversals Binary-Search Trees Huffman Trees and Optimal Prefix Codes Priority Trees Counting Labeled Trees: Prufer Encoding Counting Binary Trees: Catalan Recursion SPANNING TREES Reorganized and revised! Tree-Growing Depth-First and Breadth-First Search Minimum Spanning Trees and Shortest Paths Applications of Depth-First Search Cycles, Edge Cuts, and Spanning Trees Graphs and Vector Spaces Matroids and the Greedy Algorithm CONNECTIVITY Revised! Vertex- and Edge-Connectivity Constructing Reliable Networks Max-Min Duality and Menger's Theorems Block Decompositions OPTIMAL GRAPH TRAVERSALS Eulerian Trails and Tours DeBruijn Sequences and Postman Problems Hamiltonian Paths and Cycles Gray Codes and Traveling Salesman Problems PLANARITY AND KURATOWSKI'S THEOREM Reorganized and revised! Planar Drawings and Some Basic Surfaces Subdivision and Homeomorphism Extending Planar Drawings Kuratowski's Theorem Algebraic Tests for Planarity Planarity Algorithm Crossing Numbers and Thickness DRAWING GRAPHS AND MAPS Reorganized and revised! The Topology of Low Dimensions Higher-Order Surfaces Mathematical Model for Drawing Graphs Regular Maps on a Sphere Imbeddings on Higher-Order Surfaces Geometric Drawings of Graphs New! GRAPH COLORINGS Vertex-Colorings Map-Colorings Edge-Colorings Factorization New! MEASUREMENT AND MAPPINGS New Chapter! Distance in Graphs New! Domination in Graphs New! Bandwidth New! Intersection Graphs New! Linear Graph Mappings Moved and revised! Modeling Network Emulation Moved and revised! ANALYTIC GRAPH THEORY New Chapter! Ramsey Graph Theory New! Extremal Graph Theory New! Random Graphs New! SPECIAL DIGRAPH MODELS Reorganized and revised! Directed Paths and Mutual Reachability Digraphs as Models for Relations Tournaments Project Scheduling and Critical Paths Finding the Strong Components of a Digraph NETWORK FLOWS AND APPLICATIONS Flows and Cuts in Networks Solving the Maximum-Flow Problem Flows and Connectivity Matchings, Transversals, and Vertex Covers GRAPHICAL ENUMERATION Reorganized and revised! Automorphisms of Simple Graphs Graph Colorings and Symmetry Burnside's Lemma Cycle-Index Polynomial of a Permutation Group More Counting, Including Simple Graphs Polya-Burnside Enumeration ALGEBRAIC SPECIFICATION OF GRAPHS Cyclic Voltages Cayley Graphs and Regular Voltages Permutation Voltages Symmetric Graphs and Parallel Architectures Interconnection-Network Performance NON-PLANAR LAYOUTS Reorganized and revised! Representing Imbeddings by Rotations Genus Distribution of a Graph Voltage-Graph Specification of Graph Layouts Non KVL Imbedded Voltage Graphs Heawood Map-Coloring Problem APPENDIX Logic Fundamentals Relations and Functions Some Basic Combinatorics Algebraic Structures Algorithmic Complexity Supplementary Reading BIBLIOGRAPHY General Reading References SOLUTIONS AND HINTS New! INDEXES Index of Applications Index of Algorithms Index of Notations General Index

Data mining approaches for intrusion detection

Abstract: In this paper we discuss our research in developing general and systematic methods for intrusion detection. The key ideas are to use data mining techniques to discover consistent and useful patterns of system features that describe program and user behavior, and use the set of relevant system features to compute (inductively learned) classifiers that can recognize anomalies and known intrusions. Using experiments on the sendmail system call data and the network tcpdump data, we demonstrate that we can construct concise and accurate classifiers to detect anomalies. We provide an overview on two general data mining algorithms that we have implemented: the association rules algorithm and the frequent episodes algorithm. These algorithms can be used to compute the intra-and inter-audit record patterns, which are essential in describing program or user behavior. The discovered patterns can guide the audit data gathering process and facilitate feature selection. To meet the challenges of both efficient learning (mining) and real-time detection, we propose an agent-based architecture for intrusion detection systems where the learning agents continuously compute and provide the updated (detection) models to the detection agents.

Cadaver validation of skeletal muscle measurement by magnetic resonance imaging and computerized tomography

Abstract: Magnetic resonance imaging (MRI) and computerized tomography (CT) are promising reference methods for quantifying whole body and regional skeletal muscle mass. Earlier MRI and CT validation studies...

Bringing context back into epidemiology: variables and fallacies in multilevel analysis

Abstract: A large portion of current epidemiologic research is based on methodologic individualism: the notion that the distribution of health and disease in populations can be explained exclusively in terms of the characteristics of individuals. The present paper discusses the need to include group- or macro-level variables in epidemiologic studies, thus incorporating multiple levels of determination in the study of health outcomes. These types of analyses, which have been called contextual or multi-level analyses, challenge epidemiologists to develop theoretical models of disease causation that extend across levels and explain how group-level and individual-level variables interact in shaping health and disease. They also raise a series of methodological issues, including the need to select the appropriate contextual unit and contextual variables, to correctly specify the individual-level model, and, in some cases, to account for residual correlation between individuals within contexts. Despite its complexities, ...

Inhibition of cell migration, spreading, and focal adhesions by tumor suppressor PTEN

Abstract: The tumor suppressor PTEN is a phosphatase with sequence similarity to the cytoskeletal protein tensin. Here the cellular roles of PTEN were investigated. Overexpression of PTEN inhibited cell migration, whereas antisense PTEN enhanced migration. Integrin-mediated cell spreading and the formation of focal adhesions were down-regulated by wild-type PTEN but not by PTEN with an inactive phosphatase domain. PTEN interacted with the focal adhesion kinase FAK and reduced its tyrosine phosphorylation. Overexpression of FAK partially antagonized the effects of PTEN. Thus, PTEN phosphatase may function as a tumor suppressor by negatively regulating cell interactions with the extracellular matrix.

Suppression of accelerated diabetic atherosclerosis by the soluble receptor for advanced glycation endproducts.

Abstract: Accelerated atherosclerosis in patients with diabetes is a major cause of their morbidity and mortality, and it is unresponsive to therapy aimed at restoring relative euglycemia. In hyperglycemia, nonenzymatic glycation and oxidation of proteins and lipids results in the accumulation of irreversibly formed advanced glycation endproducts. These advanced glycation endproducts engage their receptor in cells of the blood vessel wall, thereby activating mechanisms linked to the development of vascular lesions. We report here a model of accelerated and advanced atherosclerosis in diabetic mice deficient for apolipoprotein E. Treatment of these mice with the soluble extracellular domain of the receptor for advanced glycation endproducts completely suppressed diabetic atherosclerosis in a glycemia- and lipid-independent manner. These findings indicate interaction between the advanced glycation endproducts and their receptor is involved in the development of accelerated atherosclerosis in diabetes, and identify this receptor as a new therapeutic target in diabetic macrovascular disease.

Protection against Fatal Sindbis Virus Encephalitis by Beclin, a Novel Bcl-2-Interacting Protein

Abstract: bcl-2, the prototypic cellular antiapoptotic gene, decreases Sindbis virus replication and Sindbis virus-induced apoptosis in mouse brains, resulting in protection against lethal encephalitis. To investigate potential mechanisms by which Bcl-2 protects against central nervous system Sindbis virus infection, we performed a yeast two-hybrid screen to identify Bcl-2-interacting gene products in an adult mouse brain library. We identified a novel 60-kDa coiled-coil protein, Beclin, which we confirmed interacts with Bcl-2 in mammalian cells, using fluorescence resonance energy transfer microscopy. To examine the role of Beclin in Sindbis virus pathogenesis, we constructed recombinant Sindbis virus chimeras that express full-length human Beclin (SIN/beclin), Beclin lacking the putative Bcl-2-binding domain (SIN/beclinDeltaBcl-2BD), or Beclin containing a premature stop codon near the 5' terminus (SIN/beclinstop). The survival of mice infected with SIN/beclin was significantly higher (71%) than the survival of mice infected with SIN/beclinDeltaBcl-2BD (9%) or SIN/beclinstop (7%) (P < 0.001). The brains of mice infected with SIN/beclin had fewer Sindbis virus RNA-positive cells, fewer apoptotic cells, and lower viral titers than the brains of mice infected with SIN/beclinDeltaBcl-2BD or SIN/beclinstop. These findings demonstrate that Beclin is a novel Bcl-2-interacting cellular protein that may play a role in antiviral host defense.

Biomimetic Reactions Catalyzed by Cyclodextrins and Their Derivatives.

Abstract: Cyclodextrins are extremely attractive components of artificial enzymes and other biomimetic materials. They are readily available, they bind hydrophobic substrates into their cavities in water solution, and they have two rims of hydroxyl groups (Figure 1) that can either react with substrates themselves or be used to attach other catalytic and functional groups. Of course, they have disadvantages. For one, unless they are extensively modified their complexes with substrates can be rather flexible and, perhaps, with unpredictable preferred geometry. They are also unstable to strong acid. Thus for some purposes such synthetic cavity species as calixerenes1 or synthetic macrocycles2-4 may have advantages. However, one of the chief advantages of cyclodextrins is highly attractivesthey are readily available, so it is possible to avoid the synthesis of a binding group and go directly to studies of what can be achieved with their use. Afterward, the lessons learned may be applied to other systems with advantage. This review will cover all the literature on reactions in which cyclodextrins bind substrates and then either catalyze their reactions or mimic a step in an enzymatic catalytic sequence. However, it will not describe work in which cyclodextrins simply change the course of a reaction without playing an obvious catalytic role involving substrate binding. For example, there are systems in which the main function of the cyclodextrin seems to be to complex a metal ion and keep it in solution.5-11 There are other studies in which binding into a cyclodextrin simply alters the selectivity of attack by an external reagent in some way12-24 or causes solubilization to facilitate phase transfer catalysis.12,25,26 Presumably such other areas are described elsewhere in this volume. While much work on artificial enzymes using cyclodextrins has been done in the author’s laboratory, and will be described, every effort is made to describe all the relevant work in the field. Several reviews of this subject already exist and should be consulted for further information.2,27-70 The readily Ronald Breslow, born in 1931 in Rahway, NJ, completed his B.A. in chemistry in1952, his M.A. in medical science in 1953, and his Ph.D. in chemistry in 1955 with R. B. Woodward, all at Harvard University. After a postdoctoral year with Alexander Todd in Cambridge, he came to Columbia University where he is now University Professor and Professor of Chemistry. His work on enzyme models, on novel conjugated aromatic and antiaromatic molecules, on electrochemical and hydrophobic methods in mechanistic chemistry, and on anticancer cytodifferentiating agents has been recognized by a number of awards, including the U.S. National Medal of Science. In 1996, he served as President of the American Chemical Society.

Transcription of IAP endogenous retroviruses is constrained by cytosine methylation

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Simulating normalizing constants: from importance sampling to bridge sampling to path sampling

Abstract: Computing (ratios of) normalizing constants of probability models is a fundamental computational problem for many statistical and scientific studies. Monte Carlo simulation is an effective technique, es- pecially with complex and high-dimensional models. This paper aims to bring to the attention of general statistical audiences of some effective methods originating from theoretical physics and at the same time to ex- plore these methods from a more statistical perspective, through estab- lishing theoretical connections and illustrating their uses with statistical problems. We show that the acceptance ratio method and thermodynamic integration are natural generalizations of importance sampling, which is most familiar to statistical audiences. The former generalizes importance sampling through the use of a single "bridge" density and is thus a case of bridge sampling in the sense of Meng and Wong. Thermodynamic integration, which is also known in the numerical analysis literature as Ogata's method for high-dimensional integration, corresponds to the use of infinitely many and continuously connected bridges (and thus a "path"). Our path sampling formulation offers more flexibility and thus potential efficiency to thermodynamic integration, and the search of op- timal paths turns out to have close connections with the Jeffreys prior density and the Rao and Hellinger distances between two densities. We provide an informative theoretical example as well as two empirical ex- amples (involving 17- to 70-dimensional integrations) to illustrate the potential and implementation of path sampling. We also discuss some open problems.

Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem

Abstract: The problem of merging multiple databases of information about common entities is frequently encountered in KDD and decision support applications in large commercial and government organizations. The problem we study is often called the Merge/Purge problem and is difficult to solve both in scale and accuracy. Large repositories of data typically have numerous duplicate information entries about the same entities that are difficult to cull together without an intelligent ’’equational theory‘‘ that identifies equivalent items by a complex, domain-dependent matching process. We have developed a system for accomplishing this Data Cleansing task and demonstrate its use for cleansing lists of names of potential customers in a direct marketing-type application. Our results for statistically generated data are shown to be accurate and effective when processing the data multiple times using different keys for sorting on each successive pass. Combing results of individual passes using transitive closure over the independent results, produces far more accurate results at lower cost. The system provides a rule programming module that is easy to program and quite good at finding duplicates especially in an environment with massive amounts of data. This paper details improvements in our system, and reports on the successful implementation for a real-world database that conclusively validates our results previously achieved for statistically generated data.