Maximal Clique Enumeration and Related Tools for Microarray Data Analysis

The purpose of this study was to investigate the utility of exact maximal clique enumeration in DNA microarray analysis, to analyze and improve upon existing exact maximal clique enumeration algorithms, and to develop new clique-based algorithms to assist in the analysis as indicated during the course of the study. As a first test, microarray data sets comprised of pre-classified human lung tissue samples were obtained through the Critical Assessment of Microarray Data Analysis (CAMDA) conference. A combination of exact maximal clique enumeration and approximate dominating set was used to attempt to classify the samples. In another test, maximal clique enumeration was used for a priori clustering of microarray data from Mus musculus (mouse). Cliques from this graph, though smaller than the anticipated groups of co-regulated genes, exhibited a high degree of overlap. Many genes within the overlap are either known or suspected to be involved in one or more gene regulatory networks. Experimental tests of four exact maximal clique enumeration algorithms on graphs derived from Mus musculus data normalized by either RMA or MAS 5.0 software were performed. A branch and bound Bron and Kerbosch algorithm was shown to perform the best on the widest range of inputs. A base Bron and Kerbosch algorithm was faster on very sparse graphs, but slowed considerably as edge density increased. Both the Kose and greedy algorithms were significantly slower than both Bron and Kerbosch algorithms on all inputs. Means to improve further the branch and bound Bron and Kerbosch algorithm were then considered. Two preprocessing rules and more exacting bounds were added to the algorithm both together and separately. The low degree preprocessing rule was found to improve performance most consistently, though significant improvement was only observed with the sparsest graphs, where improvement is least necessary. Finally, a first attempt at developing an algorithm that would integrate genes that were likely excluded from a clique as a result of noise into the appropriate group was made. Initial testing of the resulting paraclique algorithm revealed that the algorithm maintains the desired high level of inter-group edge density while expanding the core clique to a more acceptable size. Research in this area is ongoing

Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing

Motivation: Transcriptome-based computational drug repurposing has attracted considerable interest by bringing about faster and more cost-effective drug discovery. Nevertheless, key limitations of the current drug connectivity-mapping paradigm have been long overlooked, including the lack of effective means to determine optimal query gene signatures. Results: The novel approach Dr Insight implements a frame-breaking statistical model for the ‘hand-shake’ between disease and drug data. The genome-wide screening of concordantly expressed genes (CEGs) eliminates the need for subjective selection of query signatures, added to eliciting better proxy for potential disease-specific drug targets. Extensive comparisons on simulated and real cancer datasets have validated the superior performance of Dr Insight over several popular drug-repurposing methods to detect known cancer drugs and drug–target interactions. A proof-of-concept trial using the TCGA breast cancer dataset demonstrates the application of Dr Insight for a comprehensive analysis, from redirection of drug therapies, to a systematic construction of disease-specific drug-target networks

Computational, Integrative, and Comparative Methods for the Elucidation of Genetic Coexpression Networks

Gene expression microarray data can be used for the assembly of genetic coexpression network graphs. Using mRNA samples obtained from recombinant inbred Mus musculus strains, it is possible to integrate allelic variation with molecular and higher-order phenotypes. The depth of quantitative genetic analysis of microarray data can be vastly enhanced utilizing this mouse resource in combination with powerful computational algorithms, platforms, and data repositories. The resulting network graphs transect many levels of biological scale. This approach is illustrated with the extraction of cliques of putatively coregulated genes and their annotation using gene ontology analysis and cis-regulatory element discovery. The causal basis for coregulation is detected through the use of quantitative trait locus mapping

A shared fractal aesthetic across development

8 pagesFractal patterns that repeat at varying size scales comprise natural environments and are also present in artistic works deemed to be highly aesthetic. Observers’ aesthetic preferences vary in relation to fractal complexity. Previous work demonstrated that fractal preference consistently peaks at low-to-moderate complexity for patterns that repeat in a statistical manner across scale, whereas preference for exact repetition fractals peaks at a higher complexity due to the presence of order introduced by symmetry and exact recursion of features. However, these highly consistent preference trends have been demonstrated only in adult populations, and the extent to which exposure, development, or individual differences in perceptual strategies may impact preference has not yet been established. Here, we show differences in preference between fractal-type, but no differences between child and adult preferences, and no relationship between systemizing tendencies (demonstrated by the Systemizing Quotient and Ponzo task) and complexity preferences, further supporting the universality of fractal preference. Consistent preferences across development point toward shared general aesthetic experience of these complexities arising from a fluency of fractal processing established relatively early in development. This in part determines how humans experience natural patterns and interact with natural and built environments

Multidimensional Model of Racial Identity: A Reconceptualization of African American Racial Identity

Research on African American racial identity has utilized 2 distinct approaches. The mainstream approach has focused on universal properties associated with ethnic and racial identities. In contrast, the underground approach has focused on documenting the qualitative meaning of being African American, with an emphasis on the unique cultural and historical experiences of African Americans. The Multidimensional Model of Racial Identity (MMRI) represents a synthesis of the strengths of these two approaches. The underlying assumptions associated with the model are explored. The model proposes 4 dimensions of African American racial identity: salience, centrality, regard, and ideology. A description of these dimensions is provided along with a discussion of how they interact to influence behavior at the level of the event. We argue that the MMRI has the potential to make contributions to traditional research objectives of both approaches, as well as to provide the impetus to explore new questions.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68695/2/10.1207_s15327957pspr0201_2.pd

T cell receptor signal strength in Treg and iNKT cell development demonstrated by a novel fluorescent reporter mouse

Generation of a Nur77 reporter mouse is used to demonstrate TCR signal strength during thymic selection and peripheral maintenance of conventional and nonconventional T cell subsets and presents a novel tool for studying antigen receptor activation in vivo

Host Immune Transcriptional Profiles Reflect the Variability in Clinical Disease Manifestations in Patients with Staphylococcus aureus Infections

Staphylococcus aureus infections are associated with diverse clinical manifestations leading to significant morbidity and mortality. To define the role of the host response in the clinical manifestations of the disease, we characterized whole blood transcriptional profiles of children hospitalized with community-acquired S. aureus infection and phenotyped the bacterial strains isolated. The overall transcriptional response to S. aureus infection was characterized by over-expression of innate immunity and hematopoiesis related genes and under-expression of genes related to adaptive immunity. We assessed individual profiles using modular fingerprints combined with the molecular distance to health (MDTH), a numerical score of transcriptional perturbation as compared to healthy controls. We observed significant heterogeneity in the host signatures and MDTH, as they were influenced by the type of clinical presentation, the extent of bacterial dissemination, and time of blood sampling in the course of the infection, but not by the bacterial isolate. System analysis approaches provide a new understanding of disease pathogenesis and the relation/interaction between host response and clinical disease manifestations

Disparities in appendicitis rupture rate among mentally ill patients

<p>Abstract</p> <p>Background</p> <p>Many studies have been carried out that focus on mental patients' access to care for their mental illness, but very few pay attention on these same patients' access to care for their physical diseases. Acute appendicitis is a common surgical emergency. Our population-based study was to test for any possible association between mental illness and perforated appendicitis. We hypothesized that there are significant disparities in access to timely surgical care between appendicitis patients with and without mental illness, and more specifically, between patients with schizophrenia and those with another major mental illness.</p> <p>Methods</p> <p>Using the National Health Insurance (NHI) hospital-discharge data, we compared the likelihood of perforated appendix among 97,589 adults aged 15 and over who were hospitalized for acute appendicitis in Taiwan between the years 1997 to 2001. Among all the patients admitted for appendicitis, the outcome measure was the odds of appendiceal rupture vs. appendicitis that did not result in a ruptured appendix.</p> <p>Results</p> <p>After adjusting for age, gender, ethnicity, socioeconomic status (SES) and hospital characteristics, the presence of schizophrenia was associated with a 2.83 times higher risk of having a ruptured appendix (odds ratio [OR], 2.83; 95% confidence interval [CI], 2.20–3.64). However, the presence of affective psychoses (OR, 1.15; 95% CI: 0.77–1.73) or other mental disorders (OR, 1.58; 95% CI: 0.89–2.81) was not a significant predictor for a ruptured appendix.</p> <p>Conclusion</p> <p>These findings suggest that given the fact that the NHI program reduces financial barriers to care for mentally ill patients, they are still at a disadvantage for obtaining timely treatment for their physical diseases. Of patients with a major mental illness, schizophrenic patients may be the most vulnerable ones for obtaining timely surgical care.</p

Jasmonic Acid-Induced Changes in Brassica oleracea Affect Oviposition Preference of Two Specialist Herbivores

Jasmonic acid (JA) is a key hormone involved in plant defense responses. The effect of JA treatment of cabbage plants on their acceptability for oviposition by two species of cabbage white butterflies, Pieris rapae and P. brassicae, was investigated. Both butterfly species laid fewer eggs on leaves of JA-treated plants compared to control plants. We show that this is due to processes in the plant after JA treatment rather than an effect of JA itself. The oviposition preference for control plants is adaptive, as development time from larval hatch until pupation of P. rapae caterpillars was longer on JA-treated plants. Total glucosinolate content in leaf surface extracts was similar for control and treated plants; however, two of the five glucosinolates were present in lower amounts in leaf surface extracts of JA-treated plants. When the butterflies were offered a choice between the purified glucosinolate fraction isolated from leaf surface extracts of JA-treated plants and that from control plants, they did not discriminate. Changes in leaf surface glucosinolate profile, therefore, do not seem to explain the change in oviposition preference of the butterflies after JA treatment, suggesting that as yet unknown infochemicals are involved

The DOE E3SM Coupled Model Version 1: Overview and Evaluation at Standard Resolution

This work documents the first version of the U.S. Department of Energy (DOE) new Energy Exascale Earth System Model (E3SMv1). We focus on the standard resolution of the fully coupled physical model designed to address DOE mission-relevant water cycle questions. Its components include atmosphere and land (110-km grid spacing), ocean and sea ice (60 km in the midlatitudes and 30 km at the equator and poles), and river transport (55 km) models. This base configuration will also serve as a foundation for additional configurations exploring higher horizontal resolution as well as augmented capabilities in the form of biogeochemistry and cryosphere configurations. The performance of E3SMv1 is evaluated by means of a standard set of Coupled Model Intercomparison Project Phase 6 (CMIP6) Diagnosis, Evaluation, and Characterization of Klima simulations consisting of a long preindustrial control, historical simulations (ensembles of fully coupled and prescribed SSTs) as well as idealized CO2 forcing simulations. The model performs well overall with biases typical of other CMIP-class models, although the simulated Atlantic Meridional Overturning Circulation is weaker than many CMIP-class models. While the E3SMv1 historical ensemble captures the bulk of the observed warming between preindustrial (1850) and present day, the trajectory of the warming diverges from observations in the second half of the twentieth century with a period of delayed warming followed by an excessive warming trend. Using a two-layer energy balance model, we attribute this divergence to the model’s strong aerosol-related effective radiative forcing (ERFari+aci = -1.65 W/m2) and high equilibrium climate sensitivity (ECS = 5.3 K).Plain Language SummaryThe U.S. Department of Energy funded the development of a new state-of-the-art Earth system model for research and applications relevant to its mission. The Energy Exascale Earth System Model version 1 (E3SMv1) consists of five interacting components for the global atmosphere, land surface, ocean, sea ice, and rivers. Three of these components (ocean, sea ice, and river) are new and have not been coupled into an Earth system model previously. The atmosphere and land surface components were created by extending existing components part of the Community Earth System Model, Version 1. E3SMv1’s capabilities are demonstrated by performing a set of standardized simulation experiments described by the Coupled Model Intercomparison Project Phase 6 (CMIP6) Diagnosis, Evaluation, and Characterization of Klima protocol at standard horizontal spatial resolution of approximately 1° latitude and longitude. The model reproduces global and regional climate features well compared to observations. Simulated warming between 1850 and 2015 matches observations, but the model is too cold by about 0.5 °C between 1960 and 1990 and later warms at a rate greater than observed. A thermodynamic analysis of the model’s response to greenhouse gas and aerosol radiative affects may explain the reasons for the discrepancy.Key PointsThis work documents E3SMv1, the first version of the U.S. DOE Energy Exascale Earth System ModelThe performance of E3SMv1 is documented with a set of standard CMIP6 DECK and historical simulations comprising nearly 3,000 yearsE3SMv1 has a high equilibrium climate sensitivity (5.3 K) and strong aerosol-related effective radiative forcing (-1.65 W/m2)Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151288/1/jame20860_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151288/2/jame20860.pd