Modeling the Adaptive Role of Negative Signaling in Honey Bee Intraspecific Competition

Collective decision making in the social insects often proceeds via feedback cycles based on positive signaling. Negative signals have, however, been found in a few contexts in which costs exist for paying attention to no longer useful information. Here we incorporate new research on the specificity and context of the negative stop signal into an agent based model of honey bee foraging to explore the adaptive basis of negative signaling in the dance language. Our work suggests that the stop signal, by acting as a counterbalance to the waggle dance, allows colonies to rapidly shut down attacks on other colonies. This could be a key adaptation, as the costs of attacking a colony strong enough to defend itself are significant

1H, 13C and 15N resonance assignments of the Calmodulin-Munc13-1 peptide complex

Ca2+-Calmodulin binding to the variable N-terminal region of the diacylglycerol/phorbol ester-binding UNC13/Munc13 family of proteins modulates the short-term synaptic plasticity characteristics in neurons. Here, we report the sequential backbone and side chain resonance assignment of the Ca2+-Calmodulin/Munc13-1458–492 peptide complex at pH 6.8 and 35°C (BMRB No. 15470)

The Waiting Time for Inter-Country Spread of Pandemic Influenza

BACKGROUND: The time delay between the start of an influenza pandemic and its subsequent initiation in other countries is highly relevant to preparedness planning. We quantify the distribution of this random time in terms of the separate components of this delay, and assess how the delay may be extended by non-pharmaceutical interventions. METHODS AND FINDINGS: The model constructed for this time delay accounts for: (i) epidemic growth in the source region, (ii) the delay until an infected individual from the source region seeks to travel to an at-risk country, (iii) the chance that infected travelers are detected by screening at exit and entry borders, (iv) the possibility of in-flight transmission, (v) the chance that an infected arrival might not initiate an epidemic, and (vi) the delay until infection in the at-risk country gathers momentum. Efforts that reduce the disease reproduction number in the source region below two and severe travel restrictions are most effective for delaying a local epidemic, and under favourable circumstances, could add several months to the delay. On the other hand, the model predicts that border screening for symptomatic infection, wearing a protective mask during travel, promoting early presentation of cases arising among arriving passengers and moderate reduction in travel volumes increase the delay only by a matter of days or weeks. Elevated in-flight transmission reduces the delay only minimally. CONCLUSIONS: The delay until an epidemic of pandemic strain influenza is imported into an at-risk country is largely determined by the course of the epidemic in the source region and the number of travelers attempting to enter the at-risk country, and is little affected by non-pharmaceutical interventions targeting these travelers. Short of preventing international travel altogether, eradicating a nascent pandemic in the source region appears to be the only reliable method of preventing country-to-country spread of a pandemic strain of influenza

MultiMetEval: comparative and multi-objective analysis of genome-scale metabolic models

Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEv​al/downloads

Interferon regulatory factor 8-deficiency determines massive neutrophil recruitment but T cell defect in fast growing granulomas during tuberculosis

Following Mycobacterium tuberculosis (Mtb) infection, immune cell recruitment in lungs is pivotal in establishing protective immunity through granuloma formation and neogenesis of lymphoid structures (LS). Interferon regulatory factor-8 (IRF-8) plays an important role in host defense against Mtb, although the mechanisms driving anti-mycobacterial immunity remain unclear. In this study, IRF-8 deficient mice (IRF-8−/−) were aerogenously infected with a low-dose Mtb Erdman virulent strain and the course of infection was compared with that induced in wild-type (WT-B6) counterparts. Tuberculosis (TB) progression was examined in both groups using pathological, microbiological and immunological parameters. Following Mtb exposure, the bacterial load in lungs and spleens progressed comparably in the two groups for two weeks, after which IRF-8−/− mice developed a fatal acute TB whereas in WT-B6 the disease reached a chronic stage. In lungs of IRF-8−/−, uncontrolled growth of pulmonary granulomas and impaired development of LS were observed, associated with unbalanced homeostatic chemokines, progressive loss of infiltrating T lymphocytes and massive prevalence of neutrophils at late infection stages. Our data define IRF-8 as an essential factor for the maintenance of proper immune cell recruitment in granulomas and LS required to restrain Mtb infection. Moreover, IRF-8−/− mice, relying on a common human and mouse genetic mutation linked to susceptibility/severity of mycobacterial diseases, represent a valuable model of acute TB for comparative studies with chronically-infected congenic WT-B6 for dissecting protective and pathological immune reactions

Evolutionary approaches to signal decomposition in an application service management system

The increased demand for autonomous control in enterprise information systems has generated interest on efficient global search methods for multivariate datasets in order to search for original elements in time-series patterns, and build causal models of systems interactions, utilization dependencies, and performance characteristics. In this context, activity signals deconvolution is a necessary step to achieve effective adaptive control in Application Service Management. The paper investigates the potential of population-based metaheuristic algorithms, particularly variants of particle swarm, genetic algorithms and differential evolution methods, for activity signals deconvolution when the application performance model is unknown a priori. In our approach, the Application Service Management System is treated as a black- or grey-box, and the activity signals deconvolution is formulated as a search problem, decomposing time-series that outline relations between action signals and utilization-execution time of resources. Experiments are conducted using a queue-based computing system model as a test-bed under different load conditions and search configurations. Special attention was put on high-dimensional scenarios, testing effectiveness for large-scale multivariate data analyses that can obtain a near-optimal signal decomposition solution in a short time. The experimental results reveal benefits, qualities and drawbacks of the various metaheuristic strategies selected for a given signal deconvolution problem, and confirm the potential of evolutionary-type search to effectively explore the search space even in high-dimensional cases. The approach and the algorithms investigated can be useful in support of human administrators, or in enhancing the effectiveness of feature extraction schemes that feed decision blocks of autonomous controllers

Exoplanet phase curves: observations and theory

Phase curves are the best technique to probe the three dimensional structure of exoplanets' atmospheres. In this chapter we first review current exoplanets phase curve observations and the particular challenges they face. We then describe the different physical mechanisms shaping the atmospheric phase curves of highly irradiated tidally locked exoplanets. Finally, we discuss the potential for future missions to further advance our understanding of these new worlds.Comment: Fig.5 has been updated. Table 1 and corresponding figures have been updated with new values for WASP-103b and WASP-18b. Contains a table sumarizing phase curve observation

Differential effects of cytokines and corticosteroids on Toll-like receptor 2 expression and activity in human airway epithelia

<p>Abstract</p> <p>Background</p> <p>The recognition of microbial molecular patterns via Toll-like receptors (TLRs) is critical for mucosal defenses.</p> <p>Methods</p> <p>Using well-differentiated primary cultures of human airway epithelia, we investigated the effects of exposure of the cells to cytokines (TNF-α and IFN-γ) and dexamethasone (dex) on responsiveness to the TLR2/TLR1 ligand Pam3CSK4. Production of IL-8, CCL20, and airway surface liquid antimicrobial activity were used as endpoints.</p> <p>Results</p> <p>Microarray expression profiling in human airway epithelia revealed that first response cytokines markedly induced TLR2 expression. Real-time PCR confirmed that cytokines (TNF-α and IFN-γ), dexamethasone (dex), or cytokines + dex increased TLR2 mRNA abundance. A synergistic increase was seen with cytokines + dex. To assess TLR2 function, epithelia pre-treated with cytokines ± dex were exposed to the TLR2/TLR1 ligand Pam3CSK4 for 24 hours. While cells pre-treated with cytokines alone exhibited significantly enhanced IL-8 and CCL20 secretion following Pam3CSK4, mean IL-8 and CCL20 release decreased in Pam3CSK4 stimulated cells following cytokines + dex pre-treatment. This marked increase in inflammatory gene expression seen after treatment with cytokines followed by the TLR2 ligand did not correlate well with NF-κB, Stat1, or p38 MAP kinase pathway activation. Cytokines also enhanced TLR2 agonist-induced beta-defensin 2 mRNA expression and increased the antimicrobial activity of airway surface liquid. Dex blocked these effects.</p> <p>Conclusion</p> <p>While dex treatment enhanced TLR2 expression, co-administration of dex with cytokines inhibited airway epithelial cell responsiveness to TLR2/TLR1 ligand over cytokines alone. Enhanced functional TLR2 expression following exposure to TNF-α and IFN-γ may serve as a dynamic means to amplify epithelial innate immune responses during infectious or inflammatory pulmonary diseases.</p

Electron spin coherence exceeding seconds in high purity silicon

Silicon is undoubtedly one of the most promising semiconductor materials for spin-based information processing devices. Its highly advanced fabrication technology facilitates the transition from individual devices to large-scale processors, and the availability of an isotopically-purified $^{28}$Si form with no magnetic nuclei overcomes what is a main source of spin decoherence in many other materials. Nevertheless, the coherence lifetimes of electron spins in the solid state have typically remained several orders of magnitude lower than what can be achieved in isolated high-vacuum systems such as trapped ions. Here we examine electron spin coherence of donors in very pure $^{28}$Si material, with a residual $^{29}$Si concentration of less than 50 ppm and donor densities of $10^{14-15}$ per cm$^3$. We elucidate three separate mechanisms for spin decoherence, active at different temperatures, and extract a coherence lifetime $T_2$ up to 2 seconds. In this regime, we find the electron spin is sensitive to interactions with other donor electron spins separated by ~200 nm. We apply a magnetic field gradient in order to suppress such interactions and obtain an extrapolated electron spin $T_2$ of 10 seconds at 1.8 K. These coherence lifetimes are without peer in the solid state by several orders of magnitude and comparable with high-vacuum qubits, making electron spins of donors in silicon ideal components of a quantum computer, or quantum memories for systems such as superconducting qubits.Comment: 18 pages, 4 figures, supplementary informatio