Accumulating data from genome-wide association studies (GWAS) have provided a collection of novel candidate genes associated with complex diseases, such as atherosclerosis. We identified an atherosclerosis-associated single-nucleotide polymorphism (SNP) located in the intron of the long noncoding RNA (lncRNA) LINC00305 by searching the GWAS database. Although the function of LINC00305 is unknown, we found that LINC00305 expression is enriched in atherosclerotic plaques and monocytes. Overexpression of LINC00305 promoted the expression of inflammation-associated genes in THP-1 cells and reduced the expression of contractile markers in co-cultured human aortic smooth muscle cells (HASMCs). We showed that overexpression of LINC00305 activated nuclear factor-kappa beta (NF-κB) and that inhibition of NF-κB abolished LINC00305-mediated activation of cytokine expression. Mechanistically, LINC00305 interacted with lipocalin-1 interacting membrane receptor (LIMR), enhanced the interaction of LIMR and aryl-hydrocarbon receptor repressor (AHRR), and promoted protein expression as well as nuclear localization of AHRR. Moreover, LINC00305 activated NF-κB exclusively in the presence of LIMR and AHRR. In light of these findings, we propose that LINC00305 promotes monocyte inflammation by facilitating LIMR and AHRR cooperation and the AHRR activation, which eventually activates NF-κB, thereby inducing HASMC phenotype switching.

In spite of the recent interest and advances in linear controllability of complex networks, controlling nonlinear network dynamics remains an outstanding problem. Here we develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another, assuming that the former is undesired and the latter is desired. To make our framework practically meaningful, we consider restricted parameter perturbation by imposing two constraints: it must be experimentally realizable and applied only temporarily. We introduce the concept of attractor network, which allows us to formulate a quantifiable controllability framework for nonlinear dynamical networks: a network is more controllable if the attractor network is more strongly connected. We test our control framework using examples from various models of experimental gene regulatory networks and demonstrate the beneficial role of noise in facilitating control.

A remarkable phenomenon in spatiotemporal dynamical systems is chimera state, where the structurally and dynamically identical oscillators in a coupled networked system spontaneously break into two groups, one exhibiting coherent motion and another incoherent. This phenomenon was typically studied in the setting of non-local coupling configurations. We ask what can happen to chimera states under systematic changes to the network structure when links are removed from the network in an orderly fashion but the local coupling topology remains invariant with respect to an index shift. We find the emergence of multicluster chimera states. Remarkably, as a parameter characterizing the amount of link removal is increased, chimera states of distinct numbers of clusters emerge and persist in different parameter regions. We develop a phenomenological theory, based on enhanced or reduced interactions among oscillators in different spatial groups, to explain why chimera states of certain numbers of clusters occur in certain parameter regions. The theoretical prediction agrees well with numerics.

Recently, the phenomenon of quantum-classical correspondence breakdown was uncovered in optomechanics, where in the classical regime the system exhibits chaos but in the corresponding quantum regime the motion is regular - there appears to be no signature of classical chaos whatsoever in the corresponding quantum system, generating a paradox. We find that transient chaos, besides being a physically meaningful phenomenon by itself, provides a resolution. Using the method of quantum state diffusion to simulate the system dynamics subject to continuous homodyne detection, we uncover transient chaos associated with quantum trajectories. The transient behavior is consistent with chaos in the classical limit, while the long term evolution of the quantum system is regular. Transient chaos thus serves as a bridge for the quantum-classical transition (QCT). Strikingly, as the system transitions from the quantum to the classical regime, the average chaotic transient lifetime increases dramatically (faster than the Ehrenfest time characterizing the QCT for isolated quantum systems). We develop a physical theory to explain the scaling law.

Network reconstruction is a fundamental problem for understanding many complex systems with unknown interaction structures. In many complex systems, there are indirect interactions between two individuals without immediate connection but with common neighbors. Despite recent advances in network reconstruction, we continue to lack an approach for reconstructing complex networks with indirect interactions. Here we introduce a two-step strategy to resolve the reconstruction problem, where in the first step, we recover both direct and indirect interactions by employing the Lasso to solve a sparse signal reconstruction problem, and in the second step, we use matrix transformation and optimization to distinguish between direct and indirect interactions. The network structure corresponding to direct interactions can be fully uncovered. We exploit the public goods game occurring on complex networks as a paradigm for characterizing indirect interactions and test our reconstruction approach. We find that high reconstruction accuracy can be achieved for both homogeneous and heterogeneous networks, and a number of empirical networks in spite of insufficient data measurement contaminated by noise. Although a general framework for reconstructing complex networks with arbitrary types of indirect interactions is yet lacking, our approach opens new routes to separate direct and indirect interactions in a representative complex system.

Background: Continuous monitoring technologies such as accelerometers and pedometers are the gold standard for physical activity (PA) measurement. However, inconsistencies in use, analysis, and reporting limit the understanding of dose–response relationships involving PA and the ability to make comparisons across studies and population subgroups. These issues are particularly detrimental to the study of PA across different ethnicities with different PA habits. This systematic review examined the inclusion of published guidelines involving data collection, processing, and reporting among articles using accelerometers or pedometers in Hispanic or Latino populations.

Methods: English (PubMed; EbscoHost) and Spanish (SCIELO; Biblioteca Virtual en Salud) articles published between 2000 and 2013 using accelerometers or pedometers to measure PA among Hispanics or Latinos were identified through systematic literature searches. Of the 253 abstracts which were initially reviewed, 57 met eligibility criteria (44 accelerometer, 13 pedometer). Articles were coded and reviewed to evaluate compliance with recommended guidelines (N = 20), and the percentage of accelerometer and pedometer articles following each guideline were computed and reported.

Results: On average, 57.1 % of accelerometer and 62.2 % of pedometer articles reported each recommended guideline for data collection. Device manufacturer and model were reported most frequently, and provision of instructions for device wear in Spanish was reported least frequently. On average, 29.6 % of accelerometer articles reported each guideline for data processing. Definitions of an acceptable day for inclusion in analyses were reported most frequently, and definitions of an acceptable hour for inclusion in analyses were reported least frequently. On average, 18.8 % of accelerometer and 85.7 % of pedometer articles included each guideline for data reporting. Accelerometer articles most frequently included average number of valid days and least frequently included percentage of wear time.

Discussion: Inclusion of standard collection and reporting procedures in studies using continuous monitoring devices in Hispanic or Latino population is generally low.

Background: The shift from solitary to social behavior is one of the major evolutionary transitions. Primitively eusocial bumblebees are uniquely placed to illuminate the evolution of highly eusocial insect societies. Bumblebees are also invaluable natural and agricultural pollinators, and there is widespread concern over recent population declines in some species. High-quality genomic data will inform key aspects of bumblebee biology, including susceptibility to implicated population viability threats.

Results: We report the high quality draft genome sequences of Bombus terrestris and Bombus impatiens, two ecologically dominant bumblebees and widely utilized study species. Comparing these new genomes to those of the highly eusocial honeybee Apis mellifera and other Hymenoptera, we identify deeply conserved similarities, as well as novelties key to the biology of these organisms. Some honeybee genome features thought to underpin advanced eusociality are also present in bumblebees, indicating an earlier evolution in the bee lineage. Xenobiotic detoxification and immune genes are similarly depauperate in bumblebees and honeybees, and multiple categories of genes linked to social organization, including development and behavior, show high conservation. Key differences identified include a bias in bumblebee chemoreception towards gustation from olfaction, and striking differences in microRNAs, potentially responsible for gene regulation underlying social and other traits.

Conclusions: These two bumblebee genomes provide a foundation for post-genomic research on these key pollinators and insect societies. Overall, gene repertoires suggest that the route to advanced eusociality in bees was mediated by many small changes in many genes and processes, and not by notable expansion or depauperation.

Resource-poor social environments predict poor health, but the mechanisms and processes linking the social environment to psychological health and well-being remain unclear. This study explored psychosocial mediators of the association between the social environment and mental health in African American adults. African American men and women (n = 1467) completed questionnaires on the social environment, psychosocial factors (stress, depressive symptoms, and racial discrimination), and mental health. Multiple-mediator models were used to assess direct and indirect effects of the social environment on mental health. Low social status in the community (p < .001) and U.S. (p < .001) and low social support (p < .001) were associated with poor mental health. Psychosocial factors significantly jointly mediated the relationship between the social environment and mental health in multiple-mediator models. Low social status and social support were associated with greater perceived stress, depressive symptoms, and perceived racial discrimination, which were associated with poor mental health. Results suggest the relationship between the social environment and mental health is mediated by psychosocial factors and revealed potential mechanisms through which social status and social support influence the mental health of African American men and women. Findings from this study provide insight into the differential effects of stress, depression and discrimination on mental health. Ecological approaches that aim to improve the social environment and psychosocial mediators may enhance health-related quality of life and reduce health disparities in African Americans.

In vitro rearing is an important and useful tool for honey bee (Apis mellifera L.) studies. However, it often results in intercastes between queens and workers, which are normally are not seen in hive-reared bees, except when larvae older than three days are grafted for queen rearing. Morphological classification (queen versus worker or intercastes) of bees produced by this method can be subjective and generally depends on size differences. Here, we propose an alternative method for caste classification of female honey bees reared in vitro, based on weight at emergence, ovariole number, spermatheca size and size and shape, and features of the head, mandible and basitarsus. Morphological measurements were made with both traditional morphometric and geometric morphometrics techniques. The classifications were performed by principal component analysis, using naturally developed queens and workers as controls. First, the analysis included all the characters. Subsequently, a new analysis was made without the information about ovariole number and spermatheca size. Geometric morphometrics was less dependent on ovariole number and spermatheca information for caste and intercaste identification. This is useful, since acquiring information concerning these reproductive structures requires time-consuming dissection and they are not accessible when abdomens have been removed for molecular assays or in dried specimens. Additionally, geometric morphometrics divided intercastes into more discrete phenotype subsets. We conclude that morphometric geometrics are superior to traditional morphometrics techniques for identification and classification of honey bee castes and intermediates.