Wild immunology: converging on the real world

Recently, the Centre for Immunity, Infection and Evolution sponsored a one-day symposium entitled "Wild Immunology." The CIIE is a new Wellcome Trust-funded initiative with the remit to connect evolutionary biology and ecology with research in immunology and infectious diseases in order to gain an interdisciplinary perspective on challenges to global health. The central question of the symposium was, "Why should we try to understand infection and immunity in wild systems?" Specifically, how does the immune response operate in the wild and how do multiple coinfections and commensalism affect immune responses and host health in these wild systems? The symposium brought together a broad program of speakers, ranging from laboratory immunologists to infectious disease ecologists, working on wild birds, unmanaged animals, wild and laboratory rodents, and on questions ranging from the dynamics of coinfection to how commensal bacteria affect the development of the immune system. The meeting on wild immunology, organized by Amy Pedersen, Simon Babayan, and Rick Maizels, was held at the University of Edinburgh on 30 June 2011.

Investigation of Rudimentary Meta-Cognition in Lion-Tailed Macaques and Tufted Capuchins

Meta-cognition, or "thinking about thinking," has been studied extensively in humans, but very little is known about the process in animals. Although great apes and rhesus macaques (Macaca mulatta) have demonstrated multiple apparently meta-cognitive abilities, other species have either been largely ignored or failed to convincingly display meta-cognitive traits. Recent work by Marsh, however, raised the possibility that some species may possess rudimentary or partial forms of meta-cognition. This thesis sought to further investigate this possibility by running multiple comparative experiments. The goal of the first study was to examine whether lion-tailed macaques, a species that may have a rudimentary form of meta-cognition, are able to use an uncertainty response adaptively, and if so, whether they could use the response flexibly when the stimuli for which the subjects should be uncertain changed. The macaques' acquisition of the initial discrimination task is ongoing, and as such there were not yet data to support a conclusion either way. In the second study, tufted capuchins were required to locate a food reward hidden beneath inverted cups that sat on a Plexiglas tray. In some conditions the capuchins were shown where the food was hidden, in others they could infer its location, and in yet others they were not given information about the location of the food. On all trials, however, capuchins could optionally seek additional information by looking up through the Plexiglas into the cups. In general, capuchins did this less often when they were shown the food reward, but not when they could infer the reward's location. These data suggest capuchins only meta-cognitively control their information seeking in some conditions, and thus, add support to the potential for a rudimentary form of meta-cognition. In convergence with other studies, these results may represent early models for rudimentary meta-cognition, although viable alternative explanations still remain.

Evaluation of flood risk in response to climate variability

Standard procedures for forecasting flood risk (Bulletin 17B) assume annual maximum flood (AMF) series are stationary, meaning the distribution of flood flows is not significantly affected by climatic trends/cycles, or anthropogenic activities within the watershed. Historical flood events are therefore considered representative of future flood occurrences, and the risk associated with a given flood magnitude is modeled as constant over time. However, in light of increasing evidence to the contrary, this assumption should be reconsidered, especially as the existence of nonstationarity in AMF series can have significant impacts on planning and management of water resources and relevant infrastructure. Research presented in this thesis quantifies the degree of nonstationarity evident in AMF series for unimpaired watersheds throughout the contiguous U.S., identifies meteorological, climatic, and anthropogenic causes of this nonstationarity, and proposes an extension of the Bulletin 17B methodology which yields forecasts of flood risk that reflect climatic influences on flood magnitude. To appropriately forecast flood risk, it is necessary to consider the driving causes of nonstationarity in AMF series. Herein, large-scale climate patterns—including El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), and Atlantic Multidecadal Oscillation (AMO)—are identified as influencing factors on flood magnitude at numerous stations across the U.S. Strong relationships between flood magnitude and associated precipitation series were also observed for the majority of sites analyzed in the Upper Midwest and Northeastern regions of the U.S. Although relationships between flood magnitude and associated temperature series are not apparent, results do indicate that temperature is highly correlated with the timing of flood peaks. Despite consideration of watersheds classified as unimpaired, analyses also suggest that identified change-points in AMF series are due to dam construction, and other types of regulation and diversion. Although not explored herein, trends in AMF series are also likely to be partially explained by changes in land use and land cover over time. Results obtained herein suggest that improved forecasts of flood risk may be obtained using a simple modification of the Bulletin 17B framework, wherein the mean and standard deviation of the log-transformed flows are modeled as functions of climate indices associated with oceanic-atmospheric patterns (e.g. AMO, ENSO, NAO, and PDO) with lead times between 3 and 9 months. Herein, one-year ahead forecasts of the mean and standard deviation, and subsequently flood risk, are obtained by applying site specific multivariate regression models, which reflect the phase and intensity of a given climate pattern, as well as possible impacts of coupling of the climate cycles. These forecasts of flood risk are compared with forecasts derived using the existing Bulletin 17B model; large differences in the one-year ahead forecasts are observed in some locations. The increased knowledge of the inherent structure of AMF series and an improved understanding of physical and/or climatic causes of nonstationarity gained from this research should serve as insight for the formulation of a physical-casual based statistical model, incorporating both climatic variations and human impacts, for flood risk over longer planning horizons (e.g., 10-, 50, 100-years) necessary for water resources design, planning, and management.

MODULARITY BASED FUZZY COMMUNITY DETECTION IN SOCIAL NETWORKS

Fuzzy community detection is to identify fuzzy communities in a network, which are groups of vertices in the network such that the membership of a vertex in one community is in [0,1] and that the sum of memberships of vertices in all communities equals to 1. Fuzzy communities are pervasive in social networks, but only a few works have been done for fuzzy community detection. Recently, a one-step forward extension of Newman’s Modularity, the most popular quality function for disjoint community detection, results into the Generalized Modularity (GM) that demonstrates good performance in finding well-known fuzzy communities. Thus, GMis chosen as the quality function in our research. We first propose a generalized fuzzy t-norm modularity to investigate the effect of different fuzzy intersection operators on fuzzy community detection, since the introduction of a fuzzy intersection operation is made feasible by GM. The experimental results show that the Yager operator with a proper parameter value performs better than the product operator in revealing community structure. Then, we focus on how to find optimal fuzzy communities in a network by directly maximizing GM, which we call it Fuzzy Modularity Maximization (FMM) problem. The effort on FMM problem results into the major contribution of this thesis, an efficient and effective GM-based fuzzy community detection method that could automatically discover a fuzzy partition of a network when it is appropriate, which is much better than fuzzy partitions found by existing fuzzy community detection methods, and a crisp partition of a network when appropriate, which is competitive with partitions resulted from the best disjoint community detections up to now. We address FMM problem by iteratively solving a sub-problem called One-Step Modularity Maximization (OSMM). We present two approaches for solving this iterative procedure: a tree-based global optimizer called Find Best Leaf Node (FBLN) and a heuristic-based local optimizer. The OSMM problem is based on a simplified quadratic knapsack problem that can be solved in linear time; thus, a solution of OSMM can be found in linear time. Since the OSMM algorithm is called within FBLN recursively and the structure of the search tree is non-deterministic, we can see that the FMM/FBLN algorithm runs in a time complexity of at least O (n2). So, we also propose several highly efficient and very effective heuristic algorithms namely FMM/H algorithms. We compared our proposed FMM/H algorithms with two state-of-the-art community detection methods, modified MULTICUT Spectral Fuzzy c-Means (MSFCM) and Genetic Algorithm with a Local Search strategy (GALS), on 10 real-world data sets. The experimental results suggest that the H2 variant of FMM/H is the best performing version. The H2 algorithm is very competitive with GALS in producing maximum modularity partitions and performs much better than MSFCM. On all the 10 data sets, H2 is also 2-3 orders of magnitude faster than GALS. Furthermore, by adopting a simply modified version of the H2 algorithm as a mutation operator, we designed a genetic algorithm for fuzzy community detection, namely GAFCD, where elite selection and early termination are applied. The crossover operator is designed to make GAFCD converge fast and to enhance GAFCD’s ability of jumping out of local minimums. Experimental results on all the data sets show that GAFCD uncovers better community structure than GALS.

A Study of the Physical Properties of Low Tin Solders

In 1947, out of the 100,000 long tons of tin con­sumed in the United States, 25,000 tons went into solder. Tin plate took 39,000 tons while babbit, bronze and coll­apsible tubes accounted for approximately 17,000 tons. Solder ranked second to tin plate and required more than the next three major uses combined.

Delay in diagnosis of eosinophilic esophagitis increases risk for stricture formation in a time-dependent manner

BACKGROUND & AIMS Development of strictures is a major concern for patients with eosinophilic esophagitis (EoE). At diagnosis, EoE can present with an inflammatory phenotype (characterized by whitish exudates, furrows, and edema), a stricturing phenotype (characterized by rings and stenosis), or a combination of these. Little is known about progression of stricture formation; we evaluated stricture development over time in the absence of treatment and investigated risk factors for stricture formation. METHODS We performed a retrospective study using the Swiss EoE Database, collecting data on 200 patients with symptomatic EoE (153 men; mean age at diagnosis, 39 ± 15 years old). Stricture severity was graded based on the degree of difficulty associated with passing of the standard adult endoscope. RESULTS The median delay in diagnosis of EoE was 6 years (interquartile range, 2-12 years). With increasing duration of delay in diagnosis, the prevalence of fibrotic features of EoE, based on endoscopy, increased from 46.5% (diagnostic delay, 0-2 years) to 87.5% (diagnostic delay, >20 years; P = .020). Similarly, the prevalence of esophageal strictures increased with duration of diagnostic delay, from 17.2% (diagnostic delay, 0-2 years) to 70.8% (diagnostic delay, >20 years; P < .001). Diagnostic delay was the only risk factor for strictures at the time of EoE diagnosis (odds ratio = 1.08; 95% confidence interval: 1.040-1.122; P < .001). CONCLUSIONS The prevalence of esophageal strictures correlates with the duration of untreated disease. These findings indicate the need to minimize delay in diagnosis of EoE.

Neighbourhood Diversity and Social Trust: An Empirical Analysis of Interethnic Contact and Group-specific Effects

To date, neighbourhood studies on ethnic diversity and social trust have revealed inconclusive findings. In this paper, three innovations are proposed in order to systemise the knowledge about neighbourhood ethnic diversity and the development of social trust. First, it is proposed to use a valid trust measure that is sensitive to the local neighbourhood context. Second, the paper argues for a conception of organically evolved neighbourhoods, rather than using local administrative units as readily available proxies for neighbourhood divisions. Thirdly, referring to intergroup contact theory and group-specific effects of diversity, the paper challenges the notion that ethnic diversity has overwhelmingly negative effects on social trust.

Spectral bounds and basis results for non-self-adjoint pencils, with an application to Hagen-Poiseuille flow

We obtain eigenvalue enclosures and basisness results for eigen- and associated functions of a non-self-adjoint unbounded linear operator pencil A−λBA−λB in which BB is uniformly positive and the essential spectrum of the pencil is empty. Both Riesz basisness and Bari basisness results are obtained. The results are applied to a system of singular differential equations arising in the study of Hagen–Poiseuille flow with non-axisymmetric disturbances.

CLINICAL TRIAL ENROLLMENT IN A MULTIDISCIPLINARY PROSTATE CANCER

Purpose: Clinical oncology trials are hampered by low accrual rates. Less than 5% of adult cancer patients are treated on a clinical trial. We aimed to evaluate clinical trial enrollment in our Multidisciplinary Prostate Cancer Clinic and to assess if a clinical trial initiative, introduced in 2006, increased our trial enrollment.Methods: Prostate cancer patients with non-metastatic disease who were seen in the clinic from 2004 to 2008 were included in the analysis. Men were categorized by whether they were seen before or after the clinical trial enrollment initiative started in 2006. The initiative included posting trial details in the clinic, educating patients about appropriate clinical trial options during the treatment recommendation discussion, and providing patients with documentation of trials offered to them. Univariate and multivariate (MVA) logistic regression analysis evaluated the impact of patient characteristics and the clinical trial initiative on clinical trial enrollment.Results: The majority of the 1,370 men were white (83%), and lived within the surrounding counties or state (69.4%). Median age was 64.2 years. Seventy-three point five percent enrolled in at least one trial and 28.5% enrolled in more than one trial. Sixty-seven percent enrolled in laboratory studies, 18% quality of life studies, 13% novel studies, and 3.7% procedural studies. On MVA, men seen in later years (p < 0.0001) were more likely to enroll in trials. The proportion of men enrolling increased from 38.9% to 84.3% (p<0.0001) after the clinical trial initiative. On MVA, older men (p < 0.0001) were less likely to enroll in clinical trials. There was a trend toward men in the high-risk group being more likely to participate in clinical trials (p = 0.056). There was a second trend for men of Hispanic, Asian, Native American and Indian decent being less likely to participate in clinical trials (p = 0.054).Conclusion: Clinical trial enrollment in the multidisciplinary clinic increased after introduction of a clinical trial initiative. Older men were less likely to enroll in trials. We speculate we achieved high enrollment rates because 1) specific trials are discussed at time of treatment recommendations, 2) we provide a letter documenting offered trials and 3) we introduce patients to the research team at the same clinic visit if they are interested in trial participation.

Self-control in social decision making: A neurobiological perspective

Self-control is defined as the process in which thoughts, emotions, or prepotent responses are inhibited to efficiently enact a more focal goal. Self-control not only allows for more adaptive individual decision making but also promotes adaptive social decision making. In this chapter, we examine a burgeoning area of interdisciplinary research: the neuroscience of self-control in social decision making. We examine research on self-control in complex social contexts examined from a social neuroscience perspective. We review correlational evidence from neuroimaging studies and causal evidence from neuromodulation studies (i.e., brain stimulation). We specifically highlight research that shows that self-control involves the lateral prefrontal cortex (PFC) across a number of social domains and behaviors. Research has also begun to directly integrate nonsocial with social forms of self-control, showing that the basic neurobiological processes involved in stopping a motor response appear to be involved in social contexts that require self-control. Further, neural traits, such as baseline activation in the lateral PFC, can explain sources of individual differences in self-control capacity. We explore whether techniques that change brain functioning could target neural mechanisms related to self-control capacity to potentially enhance self-control in social behavior. Finally, we discuss several research questions ripe for examination. We broadly suggest that future research can now turn to exploring how neural traits and situational affordances interact to impact self-control in social decision making in order to continue to elucidate the processes that allow people to maintain and realize stable goals in a dynamic and often uncertain social environment.