A single Streptomyces symbiont makes multiple antifungals to support the fungus farming ant Acromyrmex octospinosus

Attine ants are dependent on a cultivated fungus for food and use antibiotics produced by symbiotic Actinobacteria as weedkillers in their fungus gardens. Actinobacterial species belonging to the genera Pseudonocardia, Streptomyces and Amycolatopsis have been isolated from attine ant nests and shown to confer protection against a range of microfungal weeds. In previous work on the higher attine Acromyrmex octospinosus we isolated a Streptomyces strain that produces candicidin, consistent with another report that attine ants use Streptomyces-produced candicidin in their fungiculture. Here we report the genome analysis of this Streptomyces strain and identify multiple antibiotic biosynthetic pathways. We demonstrate, using gene disruptions and mass spectrometry, that this single strain has the capacity to make candicidin and multiple antimycin compounds. Although antimycins have been known for > 60 years we report the sequence of the biosynthetic gene cluster for the first time. Crucially, disrupting the candicidin and antimycin gene clusters in the same strain had no effect on bioactivity against a co-evolved nest pathogen called Escovopsis that has been identified in similar to 30% of attine ant nests. Since the Streptomyces strain has strong bioactivity against Escovopsis we conclude that it must make additional antifungal(s) to inhibit Escovopsis. However, candicidin and antimycins likely offer protection against other microfungal weeds that infect the attine fungal gardens. Thus, we propose that the selection of this biosynthetically prolific strain from the natural environment provides A. octospinosus with broad spectrum activity against Escovopsis and other microfungal weeds.Publisher PDFPeer reviewe

Industry-Based Competitive Strategies for Ohio: Managing Three Portfolios

Deloitte Consulting, LLP, Cleveland State University, OSA Strategy. Political and business leaders have recognized a need to chart a new economic course for Ohio’s future. This study represents a step toward determining effective uses for limited development dollars in the state and filling in Ohio’s economic development strategy. This statewide industry study has been designed to provide economic development officials with insight, analysis, and strategic tools to help businesses compete more efficiently in an increasingly global marketplace

Neural Level Set Topology Optimization Using Unfitted Finite Elements

To facilitate widespread adoption of automated engineering design techniques, existing methods must become more efficient and generalizable. In the field of topology optimization, this requires the coupling of modern optimization methods with solvers capable of handling arbitrary problems. In this work, a topology optimization method for general multiphysics problems is presented. We leverage a convolutional neural parameterization of a level set for a description of the geometry and use this in an unfitted finite element method that is differentiable with respect to the level set everywhere in the domain. We construct the parameter to objective map in such a way that the gradient can be computed entirely by automatic differentiation at roughly the cost of an objective function evaluation. The method produces optimized topologies that are similar in performance yet exhibit greater regularity than baseline approaches on standard benchmarks whilst having the ability to solve a more general class of problems, e.g., interface-coupled multiphysics.Comment: 16 pages + refs, 10 fig

Application of the adjoint approach to optimise the initial conditions of a turbidity current with the AdjointTurbidity 1.0 model

Turbidity currents are one of the main drivers of sediment transport from the continental shelf to the deep ocean. The resulting sediment deposits can reach hundreds of kilometres into the ocean. Computer models that simulate turbidity currents and the resulting sediment deposit can help us to understand their general behaviour. However, in order to recreate real-world scenarios, the challenge is to find the turbidity current parameters that reproduce the observations of sediment deposits. This paper demonstrates a solution to the inverse sediment transportation problem: for a known sedimentary deposit, the developed model reconstructs details about the turbidity current that produced the deposit. The reconstruction is constrained here by a shallow water sediment-laden density current model, which is discretised by the finite-element method and an adaptive time-stepping scheme. The model is differentiated using the adjoint approach, and an efficient gradient-based optimisation method is applied to identify the turbidity parameters which minimise the misfit between the modelled and the observed field sediment deposits. The capabilities of this approach are demonstrated using measurements taken in the Miocene Marnoso-arenacea Formation (Italy). We find that whilst the model cannot match the deposit exactly due to limitations in the physical processes simulated, it provides valuable insights into the depositional processes and represents a significant advance in our toolset for interpreting turbidity current deposits

Infrared regulators and SCETII

We consider matching from SCETI, which includes ultrasoft and collinear particles, onto SCETII with soft and collinear particles at one loop. Keeping the external fermions off their mass shell does not regulate all IR divergences in both theories. We give a new prescription to regulate infrared divergences in SCET. Using this regulator, we show that soft and collinear modes in SCETII are sufficient to reproduce all the infrared divergences of SCETI. We explain the relationship between IR regulators and an additional mode proposed for SCETII.Comment: 9 pages. Added discussion about relationship between IR regulators and messenger mode

Consequences of climate change on food-energy-water systems in arid regions without agricultural adaptation, analyzed using FEWCalc and DSSAT

Effects of a changing climate on agricultural system productivity are poorly understood, and likely to be met with as yet undefined agricultural adaptations by farmers and associated business and governmental entities. The continued vitality of agricultural systems depends on economic conditions that support farmers’ livelihoods. Exploring the long-term effects of adaptations requires modeling agricultural and economic conditions to engage stakeholders upon whom the burden of any adaptation will rest. Here, we use a new freeware model FEWCalc (Food-Energy-Water Calculator) to project farm incomes based on climate, crop selection, irrigation practices, water availability, and economic adaptation of adding renewable energy production. Thus, FEWCalc addresses United Nations Global Sustainability Goals No Hunger and Affordable and Clean Energy. Here, future climate scenario impacts on crop production and farm incomes are simulated when current agricultural practices continue so that no agricultural adaptations are enabled. The model Decision Support System for Agrotechnology Transfer (DSSAT) with added arid-region dynamics is used to simulate agricultural dynamics. Demonstrations at a site in the midwest USA with 2008–2017 historical data and two 2018–2098 RCP climate scenarios provide an initial quantification of increased agricultural challenges under climate change, such as reduced crop yields and increased financial losses. Results show how this finding is largely driven by increasing temperatures and changed distribution of precipitation throughout the year. Without effective technological advances and operational and policy changes, the simulations show how rural areas could increasingly depend economically on local renewable energy, while agricultural production from arid regions declines by 50% or more

AN APPROACH FOR THE EFFECTIVE UTILIZATION OF GP-GPUS IN PARALLEL COMBINED SIMULATION

A major challenge in the field of Modeling & Simulation is providing efficient parallel computation for a variety of algorithms. Algorithms that are described easily and computed efficiently for continuous simulation, may be complex to describe and/or efficiently execute in a discrete event context, and vice-versa. Real-world models often employ multiple algorithms that are optimally defined in one approach or the other. Parallel combined simulation addresses this problem by allowing models to define algorithmic components across multiple paradigms. In this paper, we illustrate the performance of parallel combined simulation, where the continuous component is executed across multiple graphical processing units (GPU) and the discrete event component is executed across multiple central processing units (CPU).