New structure in cell puncture activities by aphid stylets: a dual-mode EPG study

Intracellular punctures by aphid stylets appear as potential drop (pd) waveforms in DC electrical penetration graph (EPG) recordings. We used a dual-EPG device that recorded in one channel the ‘full EPG’ with R-plus emf-components (i.e., the usual DC EPG) and concurrently in a second channel the ‘R-EPG’ with R-components only. The circuit of the latter channel was an optimised amplitude modulation (AM) version derived from early (before 1990) AC systems. We also made some ‘emf-EPG’ recordings using a separate high input resistance ‘emf-amplifier’ sensitive to emf-components only. The intracellular pd waveforms have previously been divided into three subphases, and we aimed to distinguish and separate these subphases more accurately by the dual-EPG recordings than with the normal full EPG only. In this study, we temporarily distinguished five subphases (a–e), but unequivocal distinction of only a few of these appeared possible, in spite of the information coming from the two signals. The lack of clearly separable features in R-EPG signals often provided serious difficulties in pd recognition without the concurrent full EPG, but once located, only subphase II-2 features were clear and supported the II-2 data from the full EPG. Consequently, we could not distinguish subphases of complete pd waveforms better with additional R-EPG information during cell punctures by Aphis gossypii Glover (Hemiptera: Aphididae). In Brevicoryne brassicae (L.) (Hemiptera: Aphididae), however, distinguishing II-2 subphases in the full EPG was sometimes a problem. Our detailed dual-EPG observations showed some waveform continuity from halfway into the II-1 subphase (start of the newly recognised subphase ß) until the end of the pd, with a strong but variable emf origin. This waveform tended to overrule other subphase waveforms in B. brassicae more than in A. gossypii and Myzus persicae (Sulzer) (Hemiptera: Aphididae). Subphase waveforms in full EPGs were especially difficult to recognise when pd periods had been interrupted in a virus inoculation experiment and additional R-EPG information could then be useful. This inoculation experiment showed again that only the first subphase (II-1) contributes to virus (Cucumber mosaic virus) inoculation by A. gossypii. In B. brassicae, the benefit of concurrent R-EPG information in such virus experiments is presently under further investigation. Apart from this special application to virus experiments, we do not recommend the routine use of the dual-EPG device. Furthermore, we do not advocate the distinction of more than the previously recognised three intracellular pd subphases as a feasible option in future studies. Analysis of EPGs with concurrent R-EPGs requires substantially more analysis work without yielding consistently useful additional insights. This confirms earlier dual-EPG results from thrip

PlantES: A plant electrophysiological multi-source data online analysis and sharing platform

At present, plant electrophysiological data volumes and complexity are increasing rapidly. It causes the demand for efficient management of big data, data sharing among research groups, and fast analysis. In this paper, we proposed PlantES (Plant Electrophysiological Data Sharing), a distributed computing-based prototype system that can be used to store, manage, visualize, analyze, and share plant electrophysiological data. We deliberately designed a storage schema to manage the multi-source plant electrophysiological data by integrating distributed storage systems HDFS and HBase to access all kinds of files efficiently. To improve the online analysis efficiency, parallel computing algorithms on Spark were proposed and implemented, e.g., plant electrical signals extraction method, the adaptive derivative threshold algorithm, and template matching algorithm. The experimental results indicated that Spark efficiently improves the online analysis. Meanwhile, the online visualization and sharing of multiple types of data in the web browser were implemented. Our prototype platform provides a solution for web-based sharing and analysis of plant electrophysiological multi-source data and improves the comprehension of plant electrical signals from a systemic perspective

The use of mechanical redundancy for fault detection in non-stationary machinery

The classical approach to machinery fault detection is one where a machinery’s condition is constantly compared to an established baseline with deviations indicating the occurrence of a fault. With the absence of a well-established baseline, fault detection for variable duty machinery requires the use of complex machine learning and signal processing tools. These tools require extensive data collection and expert knowledge which limits their use for industrial applications. The thesis at hand investigates the problem of fault detection for a specific class of variable duty machinery; parallel machines with simultaneously loaded subsystems. As an industrial case study, the parallel drive stations of a novel material haulage system have been instrumented to confirm the mechanical response similarity between simultaneously loaded machines. Using a table-top fault simulator, a preliminary statistical algorithm was then developed for fault detection in bearings under non-stationary operation. Unlike other state of the art fault detection techniques used in monitoring variable duty machinery, the proposed algorithm avoided the need for complex machine learning tools and required no previous training. The limitations of the initial experimental setup necessitated the development of a new machinery fault simulator to expand the investigation to include transmission systems. The design, manufacturing and setup of the various subsystems within the new simulator are covered in this manuscript including the mechanical, hydraulic and control subsystems. To ensure that the new simulator has successfully met its design objectives, extensive data collection and analysis has been completed and is presented in this thesis. The results confirmed that the developed machine truly represents the operation of a simultaneously loaded machine and as such would serve as a research tool for investigating the application of classical fault detection techniques to parallel machines in non-stationary operation.Master's These

Benchmarking organic electrochemical transistors for plant electrophysiology

Plants are able to sense and respond to a myriad of external stimuli, using different signal transduction pathways, including electrical signaling. The ability to monitor plant responses is essential not only for fundamental plant science, but also to gain knowledge on how to interface plants with technology. Still, the field of plant electrophysiology remains rather unexplored when compared to its animal counterpart. Indeed, most studies continue to rely on invasive techniques or on bulky inorganic electrodes that oftentimes are not ideal for stable integration with plant tissues. On the other hand, few studies have proposed novel approaches to monitor plant signals, based on non-invasive conformable electrodes or even organic transistors. Organic electrochemical transistors (OECTs) are particularly promising for electrophysiology as they are inherently amplification devices, they operate at low voltages, can be miniaturized, and be fabricated in flexible and conformable substrates. Thus, in this study, we characterize OECTs as viable tools to measure plant electrical signals, comparing them to the performance of the current standard, Ag/AgCl electrodes. For that, we focused on two widely studied plant signals: the Venus flytrap (VFT) action potentials elicited by mechanical stimulation of its sensitive trigger hairs, and the wound response of Arabidopsis thaliana. We found that OECTs are able to record these signals without distortion and with the same resolution as Ag/AgCl electrodes and that they offer a major advantage in terms of signal noise, which allow them to be used in field conditions. This work establishes these organic bioelectronic devices as non-invasive tools to monitor plant signaling that can provide insight into plant processes in their natural environment

Automated distribution network fault cause identification with advanced similarity metrics

Distribution network monitoring has the potential to improve service levels by reporting the origin of fault events and informing the nature of remedial action. To achieve this practically, intelligent systems to automatically recognize the cause of network faults could provide a data driven solution, however, these usually require a large amount of examples to learn from, making their implementation burdensome. Furthermore, the choice of input to such a system in order to make accurate classifications is not always clear. In response to this challenge, this paper contributes a means of using minimal amounts of historical fault data to infer fault cause from substation current data through a novel structural similarity metric applied to the associated power quality waveform. This approach is demonstrated along with disturbance context similarity assessment on an industrially relevant benchmark data set where it is shown to provide an improvement in classification accuracy over comparable techniques

Airborne LiDAR for DEM generation: some critical issues

Airborne LiDAR is one of the most effective and reliable means of terrain data collection. Using LiDAR data for DEM generation is becoming a standard practice in spatial related areas. However, the effective processing of the raw LiDAR data and the generation of an efficient and high-quality DEM remain big challenges. This paper reviews the recent advances of airborne LiDAR systems and the use of LiDAR data for DEM generation, with special focus on LiDAR data filters, interpolation methods, DEM resolution, and LiDAR data reduction. Separating LiDAR points into ground and non-ground is the most critical and difficult step for DEM generation from LiDAR data. Commonly used and most recently developed LiDAR filtering methods are presented. Interpolation methods and choices of suitable interpolator and DEM resolution for LiDAR DEM generation are discussed in detail. In order to reduce the data redundancy and increase the efficiency in terms of storage and manipulation, LiDAR data reduction is required in the process of DEM generation. Feature specific elements such as breaklines contribute significantly to DEM quality. Therefore, data reduction should be conducted in such a way that critical elements are kept while less important elements are removed. Given the highdensity characteristic of LiDAR data, breaklines can be directly extracted from LiDAR data. Extraction of breaklines and integration of the breaklines into DEM generation are presented

Modeling and Analysis of Neural Spike Trains

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Brown Planthopper (N. lugens Stal) Feeding Behaviour on Rice Germplasm as an Indicator of Resistance

BACKGROUND: The brown planthopper (BPH) Nilaparvata lugens (Stal) is a serious pest of rice in Asia. Development of novel control strategies can be facilitated by comparison of BPH feeding behaviour on varieties exhibiting natural genetic variation, and then elucidation of the underlying mechanisms of resistance. METHODOLOGY/PRINCIPAL FINDINGS: BPH feeding behaviour was compared on 12 rice varieties over a 12 h period using the electrical penetration graph (EPG) and honeydew clocks. Seven feeding behaviours (waveforms) were identified and could be classified into two phases. The first phase involved patterns of sieve element location including non penetration (NP), pathway (N1+N2+N3), xylem (N5) [21] and two new feeding waveforms, derailed stylet mechanics (N6) and cell penetration (N7). The second feeding phase consisted of salivation into the sieve element (N4-a) and sieve element sap ingestion (N4-b). Production of honeydew drops correlated with N4-b waveform patterns providing independent confirmation of this feeding behaviour. CONCLUSIONS/SIGNIFICANCE: Overall variation in feeding behaviour was highly correlated with previously published field resistance or susceptibility of the different rice varieties: BPH produced lower numbers of honeydew drops and had a shorter period of phloem feeding on resistant rice varieties, but there was no significant difference in the time to the first salivation (N4-b). These qualitative differences in behaviour suggest that resistance is caused by differences in sustained phloem ingestion, not by phloem location. Cluster analysis of the feeding and honeydew data split the 12 rice varieties into three groups: susceptible, moderately resistant and highly resistant. The screening methods that we have described uncover novel aspects of the resistance mechanism (or mechanisms) of rice to BPH and will in combination with molecular approaches allow identification and development of new control strategies