Twin Cities Orthopedics

Strategic Management: A Stakeholder Approach was first published in 1984 as a part of the Pitman series in Business and Public Policy. Its publication proved to be a landmark moment in the development of stakeholder theory. Widely acknowledged as a world leader in business ethics and strategic management, R. Edward Freeman's foundational work continues to inspire scholars and students concerned with a more practical view of how business and capitalism actually work. Business can be understood as a system of how we create value for stakeholders. This worldview connects business and capitalism with ethics once and for all. On the 25th anniversary of publication, Cambridge University Press are delighted to be able to offer a new print-on-demand edition of his work to a new generation of readers

Preface 1. Getting Started With R 2. Reading and Manipulating Data 3. Exploring and Transforming Data 4. Fitting Linear Models 5. Fitting Generalized Linear Models 6. Diagnosing Problems in Linear and Generalized Linear Models 7. Drawing Graphs 8. Writing Programs References Author Index Subject Index Command Index Data Set Index Package Index About the Authors

BACKGROUND: PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for faster and scalable implementations of key functions, such as logistic regression, linkage disequilibrium estimation, and genomic distance evaluation. In addition, GWAS and population-genetic data now frequently contain genotype likelihoods, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. FINDINGS: To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, [Formula: see text]-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. We have also developed an extension to the data format which adds low-overhead support for genotype likelihoods, phase, multiallelic variants, and reference vs. alternate alleles, which is the basis of our planned second release (PLINK 2.0). CONCLUSIONS: The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.

In Moral Boundaries Joan C. Tronto provides one of the most original responses to the controversial questions surrounding women and caring. Tronto demonstrates that feminist thinkers have failed to realise the political context which has shaped their debates about care. It is her belief that care cannot be a useful moral and political concept until its traditional and ideological associations as a women's morality are challenged.Moral Boundaries contests the association of care with women as empirically and historically inaccurate, as well as politically unwise. In our society, members of unprivileged groups such as the working classes and people of color also do disproportionate amounts of caring. Tronto presents care as one of the central activites of human life and illustrates the ways in which society degrades the importance of caring in order to maintain the power of those who are privileged.

Counseling psychologists often work with clients to increase their well-being as well as to decrease their distress. One important aspect of well-being, highlighted particularly in humanistic theories of the counseling process, is perceived meaning in life. However, poor measurement has hampered research on meaning in life. In 3 studies, evidence is provided for the internal consistency, temporal stability, factor structure, and validity of the Meaning in Life Questionnaire (MLQ), a new 10-item measure of the presence of, and the search for, meaning in life. A multitrait–multimethod matrix demonstrates the convergent and discriminant validity of the MLQ subscales across time and informants, in comparison with 2 other meaning scales. The MLQ offers several improvements over current meaning in life measures, including no item overlap with distress measures, a stable factor structure, better discriminant validity, a briefer format, and the ability to measure the search for meaning.

Eric Schiffman, DDS, MS/Richard Ohrbach, DDS, PhD/Edmond Truelove, DDS, MSD/John Look, DDS, PhD/Gary Anderson, DDS, MS/Jean-Paul Goulet, DDS, MSD/Thomas List, DDS, Odont Dr/Peter Svensson, DDS, PhD, Dr Odont/Yoly Gonzalez, DDS, MS, MPH/Frank Lobbezoo, DDS, PhD/Ambra Michelotti, DDS/Sharon L. Brooks, DDS, MS/Werner Ceusters, MD/Mark Drangsholt, DDS, PhD/Dominik Ettlin, MD, DDS/Charly Gaul, MD/Louis J. Goldberg, DDS, PhD/Jennifer A. Haythornthwaite, PhD/Lars Hollender, DDS, Odont Dr/Rigmor Jensen, MD, PhD/Mike T. John, DDS, PhD/Antoon De Laat, DDS, PhD/Reny de Leeuw, DDS, PhD/William Maixner, DDS, PhD/Marylee van der Meulen, PhD/Greg M. Murray, MDS, PhD/Donald R. Nixdorf, DDS, MS/Sandro Palla, Dr Med Dent/Arne Petersson, DDS, Odont Dr/Paul Pionchon, DDS, PhD/Barry Smith, PhD/Corine M. Visscher, PT, PhD/Joanna Zakrzewska, MD, FDSRCSI/Samuel F. Dworkin, DDS, PhD: Aims: The original Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD) Axis I diagnostic algorithms have been demonstrated to be reliable. However, the Validation Project determined that the RDC/TMD Axis I validity was below the target sensitivity of ≥ 0.70 and specificity of ≥ 0.95. Consequently, these empirical results supported the development of revised RDC/TMD Axis I diagnostic algorithms that were subsequently demonstrated to be valid for the most common pain-related TMD and for one temporomandibular joint (TMJ) intra-articular disorder. The original RDC/TMD Axis II instruments were shown to be both reliable and valid. Working from these findings and revisions, two international consensus workshops were convened, from which recommendations were obtained for the finalization of new Axis I diagnostic algorithms and new Axis II instruments. Methods: Through a series of workshops and symposia, a panel of clinical and basic science pain experts modified the revised RDC/TMD Axis I algorithms by using comprehensive searches of published TMD diagnostic literature followed by review and consensus via a formal structured process. The panel’s recommendations for further revision of the Axis I diagnostic algorithms were assessed for validity by using the Validation Project’s data set, and for reliability by using newly collected data from the ongoing TMJ Impact Project—the follow-up study to the Validation Project. New Axis II instruments were identified through a comprehensive search of the literature providing valid instruments that, relative to the RDC/TMD, are shorter in length, are available in the public domain, and currently are being used in medical settings. Results: The newly recommended Diagnostic Criteria for TMD (DC/TMD) Axis I protocol includes both a valid screener for detecting any pain-related TMD as well as valid diagnostic criteria for differentiating the most common pain-related TMD (sensitivity ≥ 0.86, specificity ≥ 0.98) and for one intra-articular disorder (sensitivity of 0.80 and specificity of 0.97). Diagnostic criteria for other common intra-articular disorders lack adequate validity for clinical diagnoses but can be used for screening purposes. Inter-examiner reliability for the clinical assessment associated with the validated DC/TMD criteria for pain-related TMD is excellent (kappa ≥ 0.85). Finally, a comprehensive classification system that includes both the common and less common TMD is also presented. The Axis II protocol retains selected original RDC/TMD screening instruments augmented with new instruments to assess jaw function as well as behavioral and additional psychosocial factors. The Axis II protocol is divided into screening and comprehensive selfreport instrument sets. The screening instruments’ 41 questions assess pain intensity, pain-related disability, psychological distress, jaw functional limitations, and parafunctional behaviors, and a pain drawing is used to assess locations of pain. The comprehensive instruments, composed of 81 questions, assess in further detail jaw functional limitations and psychological distress as well as additional constructs of anxiety and presence of comorbid pain conditions. Conclusion: The recommended evidence-based new DC/TMD protocol is appropriate for use in both clinical and research settings. More comprehensive instruments augment short and simple screening instruments for Axis I and Axis II. These validated instruments allow for identification of patients with a range of simple to complex TMD presentations. J Oral Facial Pain Headache 2014;28:6–27. doi: 10.11607/jop.1151

CONTEXT: The main threats to adolescents' health are the risk behaviors they choose. How their social context shapes their behaviors is poorly understood. OBJECTIVE: To identify risk and protective factors at the family, school, and individual levels as they relate to 4 domains of adolescent health and morbidity: emotional health, violence, substance use, and sexuality. DESIGN: Cross-sectional analysis of interview data from the National Longitudinal Study of Adolescent Health. PARTICIPANTS: A total of 12118 adolescents in grades 7 through 12 drawn from an initial national school survey of 90118 adolescents from 80 high schools plus their feeder middle schools. SETTING: The interview was completed in the subject's home. MAIN OUTCOME MEASURES: Eight areas were assessed: emotional distress; suicidal thoughts and behaviors; violence; use of 3 substances (cigarettes, alcohol, marijuana); and 2 types of sexual behaviors (age of sexual debut and pregnancy history). Independent variables included measures of family context, school context, and individual characteristics. RESULTS: Parent-family connectedness and perceived school connectedness were protective against every health risk behavior measure except history of pregnancy. Conversely, ease of access to guns at home was associated with suicidality (grades 9-12: P<.001) and violence (grades 7-8: P<.001; grades 9-12: P<.001). Access to substances in the home was associated with use of cigarettes (P<.001), alcohol (P<.001), and marijuana (P<.001) among all students. Working 20 or more hours a week was associated with emotional distress of high school students (P<.01), cigarette use (P<.001), alcohol use (P<.001), and marijuana use (P<.001). Appearing "older than most" in class was associated with emotional distress and suicidal thoughts and behaviors among high school students (P<.001); it was also associated with substance use and an earlier age of sexual debut among both junior and senior high students. Repeating a grade in school was associated with emotional distress among students in junior high (P<.001) and high school (P<.01) and with tobacco use among junior high students (P<.001). On the other hand, parental expectations regarding school achievement were associated with lower levels of health risk behaviors; parental disapproval of early sexual debut was associated with a later age of onset of intercourse (P<.001). CONCLUSIONS: Family and school contexts as well as individual characteristics are associated with health and risky behaviors in adolescents. The results should assist health and social service providers, educators, and others in taking the first steps to diminish risk factors and enhance protective factors for our young people.

Nonalcoholic fatty liver disease (NAFLD) is characterized by the accumulation of excess liver triacylglycerol (TAG), inflammation, and liver damage. The goal of the present study was to directly quantify the biological sources of hepatic and plasma lipoprotein TAG in NAFLD. Patients (5 male and 4 female; 44 +/- 10 years of age) scheduled for a medically indicated liver biopsy were infused with and orally fed stable isotopes for 4 days to label and track serum nonesterified fatty acids (NEFAs), dietary fatty acids, and those derived from the de novo lipogenesis (DNL) pathway, present in liver tissue and lipoprotein TAG. Hepatic and lipoprotein TAG fatty acids were analyzed by gas chromatography/mass spectrometry. NAFLD patients were obese, with fasting hypertriglyceridemia and hyperinsulinemia. Of the TAG accounted for in liver, 59.0% +/- 9.9% of TAG arose from NEFAs; 26.1% +/- 6.7%, from DNL; and 14.9% +/- 7.0%, from the diet. The pattern of labeling in VLDL was similar to that in liver, and throughout the 4 days of labeling, the liver demonstrated reciprocal use of adipose and dietary fatty acids. DNL was elevated in the fasting state and demonstrated no diurnal variation. These quantitative metabolic data document that both elevated peripheral fatty acids and DNL contribute to the accumulation of hepatic and lipoprotein fat in NAFLD.

Hamiltonian dynamics can be used to produce distant proposals for the Metropolis algorithm, thereby avoiding the slow exploration of the state space that results from the diffusive behaviour of simple random-walk proposals. Though originating in physics, Hamiltonian dynamics can be applied to most problems with continuous state spaces by simply introducing fictitious "momentum" variables. A key to its usefulness is that Hamiltonian dynamics preserves volume, and its trajectories can thus be used to define complex mappings without the need to account for a hard-to-compute Jacobian factor - a property that can be exactly maintained even when the dynamics is approximated by discretizing time. In this review, I discuss theoretical and practical aspects of Hamiltonian Monte Carlo, and present some of its variations, including using windows of states for deciding on acceptance or rejection, computing trajectories using fast approximations, tempering during the course of a trajectory to handle isolated modes, and short-cut methods that prevent useless trajectories from taking much computation time.

Information technology (IT) acceptance research has yielded many competing models, each with different sets of acceptance determinants. In this paper, we (1) review user acceptance literature and discuss eight prominent models, (2) empirically compare the eight models and their extensions, (3) formulate a unified theory that integrates elements across the eight models, and (4) empirically validate the unified model. The eight models reviewed are the theory of reasoned action, the technology acceptance model, a motivational model, the theory of planned behavior, a model combining the technology acceptance model and the theory of planned behavior, a model of PC utilization, innovation diffusion theory, and social cognitive theory. Using data from four organizations over a six-month period with three points of measurement, the eight models explained between 17 percent and 53 percent of the variance in user intentions to use information technology. Next, a unified theory, called the Unified Theory of Acceptance and Use of Technology (UTAUT), was formulated, with four core determinants of intention and usage, and up to four moderators of key relationships. UTAUT was then tested using the original data and found to outperform the eight individual models (69% adjusted-R2). UTAUT was then confirmed with data from two new organizations with similar results (70% adjusted-R2). UTAUT thus provides a useful tool for managers needing to assess the likelihood of success for new technology introductions and helps them understand the drivers of acceptance in order to proactively design interventions (including training, marketing, etc.) targeted at populations of users that may be less inclined to adopt and use new systems. The paper also makes several recommendations for future research including developing a deeper understanding of the dynamic influences studied here, refining measurement of the core constructs used in UTAUT, and understanding the organizational outcomes associated with new technology use.

Preface.1 Scatterplots and Regression.1.1 Scatterplots.1.2 Mean Functions.1.3 Variance Functions.1.4 Summary Graph.1.5 Tools for Looking at Scatterplots.1.5.1 Size.1.5.2 Transformations.1.5.3 Smoothers for the Mean Function.1.6 Scatterplot Matrices.Problems.2 Simple Linear Regression.2.1 Ordinary Least Squares Estimation.2.2 Least Squares Criterion.2.3 Estimating sigma 2.2.4 Properties of Least Squares Estimates.2.5 Estimated Variances.2.6 Comparing Models: The Analysis of Variance.2.6.1 The F-Test for Regression.2.6.2 Interpreting p-values.2.6.3 Power of Tests.2.7 The Coefficient of Determination, R2.2.8 Confidence Intervals and Tests.2.8.1 The Intercept.2.8.2 Slope.2.8.3 Prediction.2.8.4 Fitted Values.2.9 The Residuals.Problems.3 Multiple Regression.3.1 Adding a Term to a Simple Linear Regression Model.3.1.1 Explaining Variability.3.1.2 Added-Variable Plots.3.2 The Multiple Linear Regression Model.3.3 Terms and Predictors.3.4 Ordinary Least Squares.3.4.1 Data and Matrix Notation.3.4.2 Variance-Covariance Matrix of e.3.4.3 Ordinary Least Squares Estimators.3.4.4 Properties of the Estimates.3.4.5 Simple Regression in Matrix Terms.3.5 The Analysis of Variance.3.5.1 The Coefficient of Determination.3.5.2 Hypotheses Concerning One of the Terms.3.5.3 Relationship to the t -Statistic.3.5.4 t-Tests and Added-Variable Plots.3.5.5 Other Tests of Hypotheses.3.5.6 Sequential Analysis of Variance Tables.3.6 Predictions and Fitted Values.Problems.4 Drawing Conclusions.4.1 Understanding Parameter Estimates.4.1.1 Rate of Change.4.1.2 Signs of Estimates.4.1.3 Interpretation Depends on Other Terms in the Mean Function.4.1.4 Rank Deficient and Over-Parameterized Mean Functions.4.1.5 Tests.4.1.6 Dropping Terms.4.1.7 Logarithms.4.2 Experimentation Versus Observation.4.3 Sampling from a Normal Population.4.4 More on R2.4.4.1 Simple Linear Regression and R2.4.4.2 Multiple Linear Regression.4.4.3 Regression through the Origin.4.5 Missing Data.4.5.1 Missing at Random.4.5.2 Alternatives.4.6 Computationally Intensive Methods.4.6.1 Regression Inference without Normality.4.6.2 Nonlinear Functions of Parameters.4.6.3 Predictors Measured with Error.Problems.5 Weights, Lack of Fit, and More.5.1 Weighted Least Squares.5.1.1 Applications of Weighted Least Squares.5.1.2 Additional Comments.5.2 Testing for Lack of Fit, Variance Known.5.3 Testing for Lack of Fit, Variance Unknown.5.4 General F Testing.5.4.1 Non-null Distributions.5.4.2 Additional Comments.5.5 Joint Confidence Regions.Problems.6 Polynomials and Factors.6.1 Polynomial Regression.6.1.1 Polynomials with Several Predictors.6.1.2 Using the Delta Method to Estimate a Minimum or a Maximum.6.1.3 Fractional Polynomials.6.2 Factors.6.2.1 No Other Predictors.6.2.2 Adding a Predictor: Comparing Regression Lines.6.2.3 Additional Comments.6.3 Many Factors.6.4 Partial One-Dimensional Mean Functions.6.5 Random Coefficient Models.Problems.7 Transformations.7.1 Transformations and Scatterplots.7.1.1 Power Transformations.7.1.2 Transforming Only the Predictor Variable.7.1.3 Transforming the Response Only.7.1.4 The Box and Cox Method.7.2 Transformations and Scatterplot Matrices.7.2.1 The 1D Estimation Result and Linearly Related Predictors.7.2.2 Automatic Choice of Transformation of Predictors.7.3 Transforming the Response.7.4 Transformations of Nonpositive Variables.Problems.8 Regression Diagnostics: Residuals.8.1 The Residuals.8.1.1 Difference Between e and e.8.1.2 The Hat Matrix.8.1.3 Residuals and the Hat Matrix with Weights.8.1.4 The Residuals When the Model Is Correct.8.1.5 The Residuals When the Model Is Not Correct.8.1.6 Fuel Consumption Data.8.2 Testing for Curvature.8.3 Nonconstant Variance.8.3.1 Variance Stabilizing Transformations.8.3.2 A Diagnostic for Nonconstant Variance.8.3.3 Additional Comments.8.4 Graphs for Model Assessment.8.4.1 Checking Mean Functions.8.4.2 Checking Variance Functions.Problems.9 Outliers and Influence.9.1 Outliers.9.1.1 An Outlier Test.9.1.2 Weighted Least Squares.9.1.3 Significance Levels for the Outlier Test.9.1.4 Additional Comments.9.2 Influence of Cases.9.2.1 Cook's Distance.9.2.2 Magnitude of Di .9.2.3 Computing Di .9.2.4 Other Measures of Influence.9.3 Normality Assumption.Problems.10 Variable Selection.10.1 The Active Terms.10.1.1 Collinearity.10.1.2 Collinearity and Variances.10.2 Variable Selection.10.2.1 Information Criteria.10.2.2 Computationally Intensive Criteria.10.2.3 Using Subject-Matter Knowledge.10.3 Computational Methods.10.3.1 Subset Selection Overstates Significance.10.4 Windmills.10.4.1 Six Mean Functions.10.4.2 A Computationally Intensive Approach.Problems.11 Nonlinear Regression.11.1 Estimation for Nonlinear Mean Functions.11.2 Inference Assuming Large Samples.11.3 Bootstrap Inference.11.4 References.Problems.12 Logistic Regression.12.1 Binomial Regression.12.1.1 Mean Functions for Binomial Regression.12.2 Fitting Logistic Regression.12.2.1 One-Predictor Example.12.2.2 Many Terms.12.2.3 Deviance.12.2.4 Goodness-of-Fit Tests.12.3 Binomial Random Variables.12.3.1 Maximum Likelihood Estimation.12.3.2 The Log-Likelihood for Logistic Regression.12.4 Generalized Linear Models.Problems.Appendix.A.1 Web Site.A.2 Means and Variances of Random Variables.A.2.1 E Notation.A.2.2 Var Notation.A.2.3 Cov Notation.A.2.4 Conditional Moments.A.3 Least Squares for Simple Regression.A.4 Means and Variances of Least Squares Estimates.A.5 Estimating E(Y |X) Using a Smoother.A.6 A Brief Introduction to Matrices and Vectors.A.6.1 Addition and Subtraction.A.6.2 Multiplication by a Scalar.A.6.3 Matrix Multiplication.A.6.4 Transpose of a Matrix.A.6.5 Inverse of a Matrix.A.6.6 Orthogonality.A.6.7 Linear Dependence and Rank of a Matrix.A.7 Random Vectors.A.8 Least Squares Using Matrices.A.8.1 Properties of Estimates.A.8.2 The Residual Sum of Squares.A.8.3 Estimate of Variance.A.9 The QR Factorization.A.10 Maximum Likelihood Estimates.A.11 The Box-Cox Method for Transformations.A.11.1 Univariate Case.A.11.2 Multivariate Case.A.12 Case Deletion in Linear Regression.References.Author Index.Subject Index.

Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11-13% of the variance in educational attainment and 7-10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.

Conlputat ions E.ratnple 2.3.4Jer Jiyhters no.I 2.3.3Partial residual plots Esantple 2.3.5 Jef fiyhrers no. 2 2.3.4Probability plots E.ratrtpIe 2.3.6 Cloud seeditty no. 4 2.4 Transformations 2.4.1 Fanlilies of fratlsjorr~ratiot~s 2.4.2Selectb~g a trat~s/'orrtlatiott Itr~.ariattceNor~rrality Choice c?/'trtodel i ~t t c i scalirlg the predictors It~f'erettce E.uan~ple 2.4.1 Tree data 110.I 2.4.3Diagnostic ntetttods A tkinson 's method Andrew' nterhod Application to the power family Tukey's test E.rample 2.4.2Tree data no. 2 E.~antple 2.4.3Jet fiyhters no. 3 2.4.4Trarrsfor~r~ing the esplana~ory ~lariahles Esatrtple 2.4.4Tree data no. 3 Esatrtple 2.4.5 Cloud see(fittg no. 5 2.5 Residual analysis in two-way tables 2.5.1 Otitliers 2.5.2Nottaciditirity E.\-antple 2.5.1 A y ricultural e-rperintenl 3 Assessment of influence 3.1 Motivation 3.2 The influence curve E.uatrtple 3.2.1The sanlple auerage3.3 The influence curve in the linear model 3.4 Sample versions of the influence curve 3.4.1 E~rtpiricnl ittfl~iet~i-e curves 3.4.2Sotrtple it~fltrettce curres Esntilple 3.4.1 C l o ~i d seeding tlo.6 E.uittr~ple 3.4.2Partial Ffesrs 3.5 Applications of the sample influence curve 3.5.1 Estert~al scalit~g bI = XTX, c = p'62 Euatrtple 3.5.1 C'omhinafion.sqf r f , vii Esanlple 3.5.2Rat data Alrernatice full rank choices for M, c Lower dintensior~al trortrts E.ratiiple 3.5.3Cloud seeding 110.7

The authors applied functionalist theory to the question of the motivations underlying volunteerism, hypothesized 6 functions potentially served by volunteerism, and designed an instrument to assess these functions (Volunteer Functions Inventory; VFI). Exploratory and confirmatory factor analyses on diverse samples yielded factor solutions consistent with functionalist theorizing; each VFI motivation, loaded on a single factor, possessed substantial internal consistency and temporal stability and correlated only modestly with other VFI motivations (Studies 1, 2, and 3). Evidence for predictive validity is provided by a laboratory study in which VFI motivations predicted the persuasive appeal of messages better when message and motivation were matched than mismatched (Study 4), and by field studies in which the extent to which volunteers' experiences matched their motivations predicted satisfaction (Study 5) and future intentions (Study 6). Theoretical and practical implications are discussed.

Theory: Social capital is the web of cooperative relationships between citizens that facilitates resolution of collection action problems (Coleman 1990; Putnam 1993). Although normally conceived as a property of communities, the reciprocal relationship between community involvement and trust in others is a demonstration of social capital in individual behavior and attitudes. Hypotheses: Variation in social capital can be explained by citizens' psychological involvement with their communities, cognitive abilities, economic resources, and general life satisfaction. This variation affects citizens' confidence in national institutions, beyond specific controls for measures of actual performance. Methods: We analyze the pooled General Social Surveys from 1972 to 1994 in a latent variables framework incorporating aggregate contextual data. Results: Civic engagement and interpersonal trust are in a tight reciprocal relationship, where the connection is stronger from participation to interpersonal trust, rather than the reverse.

A graph labeling is an assignment of integers to the vertices or edges, or both, subject to certain conditions. Graph labelings were first introduced in the mid-1960s. In the intervening years over 200 graph labelings techniques have been studied in over 3000 papers. Finding out what has been done for any particular kind of labeling and keeping up with new discoveries is difficult because of the sheer number of papers and because many of the papers have appeared in journals that are not widely available. In this survey, I have collected everything I could find on graph labeling. For the convenience of the reader, the survey includes a detailed table of contents and index. This edition has 267 new references that are identified with the reference number and the word "new" in the right margin.

Markov chain Monte Carlo using the Metropolis-Hastings algorithm is a general method for the simulation of stochastic processes having probability densities known up to a constant of proportionality. Despite recent advances in its theory, the practice has remained controversial. This article makes the case for basing all inference on one long run of the Markov chain and estimating the Monte Carlo error by standard nonparametric methods well-known in the time-series and operations research literature. In passing it touches on the Kipnis-Varadhan central limit theorem for reversible Markov chains, on some new variance estimators, on judging the relative efficiency of competing Monte Carlo schemes, on methods for constructing more rapidly mixing Markov chains and on diagnostics for Markov chain Monte Carlo.

In spite of the fact that equal opportunities for men and women have been a priority in many countries, enormous gender differences prevail in most competitive high-ranking positions. We conduct a series of controlled experiments to investigate whether women might react differently than men to competitive incentive schemes commonly used in job evaluation and promotion. We observe no significant gender difference in mean performance when participants are paid proportional to their performance. But in the competitive environment with mixed gender groups we observe a significant gender difference: the mean performance of men has a large and significant, that of women is unchanged. This gap is not due to gender differences in risk aversion. We then run the same test with homogeneous groups, to investigate whether women under-perform only when competing against men. Women do indeed increase their performance and gender differences in mean performance are now insignificant. These results may be due to lower skill of women, or more likely to the fact that women dislike competition, or alternatively that they feel less competent than their male competitors, which depresses their performance in mixed tournaments. Our last experiment provides support for this hypothesis.

Bacteria able to transfer electrons to metals are key agents in biogeochemical metal cycling, subsurface bioremediation, and corrosion processes. More recently, these bacteria have gained attention as the transfer of electrons from the cell surface to conductive materials can be used in multiple applications. In this work, we adapted electrochemical techniques to probe intact biofilms of Shewanella oneidensis MR-1 and Shewanella sp. MR-4 grown by using a poised electrode as an electron acceptor. This approach detected redox-active molecules within biofilms, which were involved in electron transfer to the electrode. A combination of methods identified a mixture of riboflavin and riboflavin-5'-phosphate in supernatants from biofilm reactors, with riboflavin representing the dominant component during sustained incubations (>72 h). Removal of riboflavin from biofilms reduced the rate of electron transfer to electrodes by >70%, consistent with a role as a soluble redox shuttle carrying electrons from the cell surface to external acceptors. Differential pulse voltammetry and cyclic voltammetry revealed a layer of flavins adsorbed to electrodes, even after soluble components were removed, especially in older biofilms. Riboflavin adsorbed quickly to other surfaces of geochemical interest, such as Fe(III) and Mn(IV) oxy(hydr)oxides. This in situ demonstration of flavin production, and sequestration at surfaces, requires the paradigm of soluble redox shuttles in geochemistry to be adjusted to include binding and modification of surfaces. Moreover, the known ability of isoalloxazine rings to act as metal chelators, along with their electron shuttling capacity, suggests that extracellular respiration of minerals by Shewanella is more complex than originally conceived.

Americansnow face a caring deficit: there are simply too many demands on people’s timefor us to care adequately for our children, elderly people, and ourselves.At the same time, political involvement inthe United States is at an all-time low, and although political life shouldhelp us to care better, people see caring as unsupported by public life anddeem the concerns of politics as remote from their lives. Caring Democracy argues that we need to rethink American democracy,as well as our fundamental values and commitments, from a caring perspective. Whatit means to be a citizen is to be someone who takes up the challenge: howshould we best allocate care responsibilities in society?Joan Tronto argues that we need tolook again at how gender, race, class, and market forces misallocate caringresponsibilities and think about freedom and equality from the standpoint of makingcaring more just.The idea thatproduction and economic life are the most important political and humanconcerns ignores the reality that caring, for ourselves and others, should bethe highest value that shapes how we view the economy, politics, andinstitutions such as schools and the family. Care is at the center of our humanlives, but Tronto argues it is currently too far removed from the concerns ofpolitics. Caring Democracy traces the reasons for this disconnection andargues for the need to make care, not economics, the central concern ofdemocratic political life