Inverse Modelling to Obtain Head Movement Controller Signal

Experimentally obtained dynamics of time-optimal, horizontal head rotations have previously been simulated by a sixth order, nonlinear model driven by rectangular control signals. Electromyography (EMG) recordings have spects which differ in detail from the theoretical rectangular pulsed control signal. Control signals for time-optimal as well as sub-optimal horizontal head rotations were obtained by means of an inverse modelling procedures. With experimentally measured dynamical data serving as the input, this procedure inverts the model to produce the neurological control signals driving muscles and plant. The relationships between these controller signals, and EMG records should contribute to the understanding of the neurological control of movements

Deep residual neural network for EMI event classification using bispectrum representation

This paper presents a novel method for condition monitoring of High Voltage (HV) power plant equipment through analysis of discharge signals. These discharge signals are measured using the Electromagnetic Interference (EMI) method and processed using third order Higher-Order Statistics (HOS) to obtain a Bispectrum representation. By mapping the time-domain signal to a Bispectrum image representations the problem can be approached as an image classification task. This allows for the novel application of a Deep Residual Neural Network (ResNet) to the classification of HV discharge signals. The network is trained on signals into 9 classes and achieves high classification accuracy in each category, improving upon our previous work on this task

Early warning signals in plant disease outbreaks

Infectious disease outbreaks in plants threaten ecosystems, agricultural crops and food trade. Currently, several fungal diseases are affecting forests worldwide, posing a major risk to tree species, habitats and consequently ecosystem decay. Prediction and control of disease spread are difficult, mainly due to the complexity of the interaction between individual components involved. In this work, we introduce a lattice-based epidemic model coupled with a stochastic process that mimics, in a very simplified way, the interaction between the hosts and pathogen. We studied the disease spread by measuring the propagation velocity of the pathogen on the susceptible hosts. Our quantitative results indicate the occurrence of a critical transition between two stable phases: local confinement and an extended epiphytotic outbreak that depends on the density of the susceptible individuals. Quantitative predictions of epiphytotics are performed using the framework early-warning indicators for impending regime shifts, widely applied on dynamical systems. These signals forecast successfully the outcome of the critical shift between the two stable phases before the system enters the epiphytotic regime. Our study demonstrates that early-warning indicators could be useful for the prediction of forest disease epidemics through mathematical and computational models suited to more specific pathogen–host-environmental interactions. Our results may also be useful to identify a suitable planting density to slow down disease spread and in the future, design highly resilient forests

GRP-3 and KAPP, encoding interactors of WAK1, negatively affect defense responses induced by oligogalacturonides and local response to wounding

Conserved microbe-associated molecular patterns (MAMPs) and damage-associated molecular patterns (DAMPs) act as danger signals to activate the plant immune response. These molecules are recognized by surface receptors that are referred to as pattern recognition receptors. Oligogalacturonides (OGs), DAMPs released from the plant cell wall homogalacturonan, have also been proposed to act as local signals in the response to wounding. The Arabidopsis Wall-Associated Kinase 1 (WAK1), a receptor of OGs, has been described to form a complex with a cytoplasmic plasma membrane-localized kinase-associated protein phosphatase (KAPP) and a glycine-rich protein (GRP-3) that we find localized mainly in the cell wall and, in a small part, on the plasma membrane. By using Arabidopsis plants overexpressing WAK1, and both grp-3 and kapp null insertional mutant and overexpressing plants, we demonstrate a positive function of WAK1 and a negative function of GRP-3 and KAPP in the OG-triggered expression of defence genes and the production of an oxidative burst. The three proteins also affect the local response to wounding and the basal resistance against the necrotrophic pathogen Botrytis cinerea. GRP-3 and KAPP are likely to function in the phasing out of the plant immune response

Classification of EMI discharge sources using time–frequency features and multi-class support vector machine

This paper introduces the first application of feature extraction and machine learning to Electromagnetic Interference (EMI) signals for discharge sources classification in high voltage power generating plants. This work presents an investigation on signals that represent different discharge sources, which are measured using EMI techniques from operating electrical machines within power plant. The analysis involves Time-Frequency image calculation of EMI signals using General Linear Chirplet Analysis (GLCT) which reveals both time and frequency varying characteristics. Histograms of uniform Local Binary Patterns (LBP) are implemented as a feature reduction and extraction technique for the classification of discharge sources using Multi-Class Support Vector Machine (MCSVM). The novelty that this paper introduces is the combination of GLCT and LBP applications to develop a new feature extraction algorithm applied to EMI signals classification. The proposed algorithm is demonstrated to be successful with excellent classification accuracy being achieved. For the first time, this work transfers expert's knowledge on EMI faults to an intelligent system which could potentially be exploited to develop an automatic condition monitoring system

Deciphering acoustic emission signals in drought stressed branches: the missing link between source and sensor

When drought occurs in plants, acoustic emission (AE) signals can be detected, but the actual causes of these signals are still unknown. By analyzing the waveforms of the measured signals, it should, however, be possible to trace the characteristics of the AE source and get information about the underlying physiological processes. A problem encountered during this analysis is that the waveform changes significantly from source to sensor and lack of knowledge on wave propagation impedes research progress made in this field. We used finite element modeling and the well-known pencil lead break source to investigate wave propagation in a branch. A cylindrical rod of polyvinyl chloride was first used to identify the theoretical propagation modes. Two wave propagation modes could be distinguished and we used the finite element model to interpret their behavior in terms of source position for both the PVC rod and a wooden rod. Both wave propagation modes were also identified in drying-induced signals from woody branches, and we used the obtained insights to provide recommendations for further AE research in plant science

Food for thought: how nutrients regulate root system architecture

The spatial arrangement of the plant root system (root system architecture, RSA) is very sensitive to edaphic and endogenous signals that report on the nutrient status of soil and plant. Signalling pathways underpinning RSA responses to individual nutrients, particularly nitrate and phosphate, have been unravelled. Researchers have now started to investigate interactive effects between two or more nutrients on RSA. Several proteins enabling crosstalk between signalling pathways have recently been identified. RSA is potentially an important trait for sustainable and/or marginal agriculture. It is generally assumed that RSA responses are adaptive and optimise nutrient uptake in a given environment, but hard evidence for this paradigm is still sparse. Here we summarize recent advances made in these areas of research

Open field study of some Zea mays hybrids, lipid compounds and fumonisins accumulation

Lipid molecules are increasingly recognized as signals exchanged by organisms interacting in pathogenic and/or symbiotic ways. Some classes of lipids actively determine the fate of the interactions. Host cuticle/cell wall/membrane components such as sphingolipids and oxylipins may contribute to determining the fate of host–pathogen interactions. In the present field study, we considered the relationship between specific sphingolipids and oxylipins of different hybrids of Zea mays and fumonisin by F. verticillioides, sampling ears at different growth stages from early dough to fully ripe. The amount of total and free fumonisin differed significantly between hybrids and increased significantly with maize ripening. Oxylipins and phytoceramides changed significantly within the hybrids and decreased with kernel maturation, starting from physiological maturity. Although the correlation between fumonisin accumulation and plant lipid profile is certain, the data collected so far cannot define a cause-effect relationship but open up new perspectives. Therefore, the question—“Does fumonisin alter plant lipidome or does plant lipidome modulate fumonisin accumulation?”—is still open

Characterizing Information Propagation in Plants

This paper considers an electro-chemical based communication model for intercellular communication in plants. Many plants, such as Mimosa pudica (the "sensitive plant"), employ electrochemical signals known as action potentials (APs) for communication purposes. In this paper we present a simple model for action potential generation. We make use of the concepts from molecular communication to explain the underlying process of information transfer in a plant. Using the information-theoretic analysis, we compute the mutual information between the input and output in this work. The key aim is to study the variations in the information propagation speed for varying number of plant cells for one simple case. Furthermore we study the impact of the AP signal on the mutual information and information propagation speed. We aim to explore further that how the growth rate in plants can impact the information transfer rate and vice versa.Comment: 6 pages, 5 Figures, Submitted to IEEE Conference, 201