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Characterization of a Host-Specific Toxic Activity Produced by Bipolaris cookei , Causal Agent of Target Leaf Spot of Sorghum
Target leaf spot (TLS) of sorghum, caused by the necrotrophic fungus Bipolaris cookei, can cause severe yield loss in many parts of the world. We grew B. cookei in liquid culture and observed that the resulting culture filtrate (CF) was differentially toxic when infiltrated into the leaves of a population of 288 diverse sorghum lines. In this population, we found a significant correlation between high CF sensitivity and susceptibility to TLS. This suggests that the toxin produced in culture may play a role in the pathogenicity of B. cookei in the field. We demonstrated that the toxic activity is light sensitive and, surprisingly, insensitive to pronase, suggesting that it is not proteinaceous. We identified the two sorghum genetic loci most associated with the response to CF in this population. Screening seedlings with B. cookei CF could be a useful approach for prescreening germplasm for TLS resistance.
Calculating fluctuations and self-correlations numerically for causal charge diffusion in relativistic heavy-ion collisions
Here, we study the propagation and diffusion of electric charge fluctuations in the Bjorken hydrodynamic model with both white and Catteneo noise using purely numerical methods. We show that a lattice of noise fluctuations is required to fully calculate the two-point correlators of charge. We introduce a numerical procedure to solve the stochastic differential equations that arise from the charge conservation equation on the lattice event by event. We explicitly identify the self-correlation term in the case of Catteneo noise and provide a physical interpretation. We provide a numerical recipe to remove this contribution from the full two-point correlators. Finally, we calculate the balance functions for charged hadrons. By limiting the speed of signal propagation, we observe the expected narrowing of the balance functions after removing the self-correlations.
Path integrals for causal diamonds and the covariant entropy principle
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Graviton partial waves and causality in higher dimensions
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Causality and stability in first-order conformal anisotropic hydrodynamics
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Causal connectability between quantum systems and the black hole interior in holographic duality
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Relativistic hydrodynamic fluctuations from an effective action: Causality, stability, and the information current
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Consistent inclusion of fluctuations in first-order causal and stable relativistic hydrodynamics
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Hydrodynamic theories for a system of weakly self-interacting classical ultrarelativistic scalar particles: Causality and stability
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Assessing the Impact of Supplier Group Stability on Public Procurement Performance in Slovakia: A Causal Forest Analysis of Collusion Signals
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Discrete-Time Causal Control of a Wave Energy Converter With Finite Stroke in Stochastic Waves
We consider feedback control design for a wave energy converter (WEC) for which the power takeoff (PTO) system has a finite stroke limit. Stationary stochastic wave loading is assumed, with a known spectrum, and the plant dynamics are assumed to be linear. We develop a technique for the design of a discrete-time controller, which has three design stages. In the first stage, a linear controller is optimized while imposing a relaxation of the maximum stroke constraint on the design. In the second stage, a tandem nonlinear feedback loop is designed for the purpose of stroke protection. In the third stage, the two designs (linear and nonlinear) are fused in a manner that preserves the stability of the overall system. The technique is demonstrated in a simulation of a simple cylindrical buoy. We show that the controller may be tuned through the adjustment of scalar design parameters, which adjust the tradeoff between the mean generated power and the force levels required to protect the stroke.
Cross Modality Bias in Visual Question Answering: A Causal View With Possible Worlds VQA
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Apple Bitter Rot: Biology, Ecology, Omics, Virulence Factors, and Management of Causal Colletotrichum Species
ABSTRACT Apple bitter rot is caused by various Colletotrichum spp. that threaten apple production globally resulting in millions of dollars in damage annually. The fungus causes a decline in fruit quality and yield, eventually rotting the fruit and rendering it inedible. The pathogen is difficult to keep out of orchards because of its broad host range and transmissibility by rain splash and insects. Once the disease manifests, pathogen identification is difficult due to evolving taxonomy and similar morphology between species. Current management strategies are threatened by an increase in fungicide resistance and regulations on many multisite fungicides, leading to a pressing need for new management options for control. This review aims to summarise the most current knowledge regarding the biology, virulence factors, ecology, omics and emerging management strategies for Colletotrichum species that cause apple bitter rot. Taxonomy Colletotrichum species—Domain Eukaryota, Kingdom Fungi, Phylum Ascomycota, Class Sordariomycetes, Order Glomerellales, Family Glomerellaceae, Genus Colletotrichum . Biology Hemibiotrophic pathogen with a wide host range that establishes a biotrophic interaction where it penetrates host plants using appressoria followed by a switch to necrotrophy causing rot symptoms. Toxins Cercosporin, colletotrichins, colletotric acid, ferricrocin. Host Range The host range varies by species but largely occurs on dicotyledonous plants and is less prevalent on monocots as well as gymnosperms, ferns, mosses and animals (e.g., insects). Disease Symptoms Symptoms often manifest as flat to sunken necrotic areas on fruit. Lesions on leaves and fruit can have concentric rings with abundant pathogen sporulation. Disease Control Colletotrichum spp. are primarily managed by single‐site quinone outside inhibitor (Qol), methyl benzimidazole carbamate (MBC), demethylation inhibitor (DMI) fungicides, and multisite dithiocarbamate and phthalimide fungicides. Susceptibility may vary with species, strain specificity, or geographic region. Other management options include clean stock production, cultural practices, resistance breeding, and biological control through the introduction of protective or competing microorganisms.
Learning Flexible Time-windowed Granger Causality Integrating Heterogeneous Interventional Time Series Data
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Digital Analytics, Causal Knowledge Acquisition and Reasoning for Technical Language Processing
Complex engineering systems such as nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) data elements that contain information on the status of components, assets, and systems. Some of this information is textual in form and can be found in documents such as incident reports (IRs) and work orders (WOs). Analyses of textual data in current NPPs-using natural language processing (NLP) methods-have been expanded over the last decade, and it is only recently that the true potential of such analyses has emerged. So far, applications of NLP methods have mostly been limited to classification and prediction, the goal being to identify the nature of the textual element (e.g., safety or non-safety related). Here, we target a more complex problem: automatically extracting knowledge from a textual element in order to assist system engineers in conducting system health assessments. Knowledge extraction is a very broad concept, and its definition may vary depending on the application context. Our methods are a blend of both rule-based and machine learning (ML) algorithms. For our purposes, knowledge extraction means identifying the systems or assets mentioned in a given textual element, as well as the type of event described (e.g., component failure or maintenance activity). In addition, we want to capture details such as measured quantities and the temporal/cause-effect relations between events. In this tool, we also demonstrate how textual data elements are preprocessed in order to handle typos, acronyms, and abbreviations. One main feature of these methods is that they are not based solely on data, but are in fact model-based. In other words, they also rely on MBSE models that are designed to capture-from a functional point of view-the architecture of the systems/assets under consideration. The main purpose of such models is to digitally emulate system engineers' knowledge of system and asset architecture and to identify dependencies among systems, assets, and components. Provided these models, analyses of textual and numeric ER data can be performed by first identifying the OPM model elements to which the ER data elements are referring. The relationships between ER data elements are then identified by checking for any temporal or logical dependencies.