Application of logistic regression for fault analysis in an industrial printing process

A statistical technique for fault analysis in industrial printing is reported. The method specifically deals with binary data, for which the results of the production process fall into two categories, rejected or accepted. The method is referred to as logistic regression, and is capable of predicting future fault occurrences by the analysis of current measurements from machine parts sensors. Individual analysis of each type of fault can determine which parts of the plant have a significant influence on the occurrence of such faults; it is also possible to infer which measurable process parameters have no significant influence on the generation of these faults. Information derived from the analysis can be helpful in the operator's interpretation of the current state of the plant. Appropriate actions may then be taken to prevent potential faults from occurring. The algorithm is being implemented as part of an applied self-learning expert system.

Application of a hybrid tracking algorithm to motion analysis

An algorithm for tracking multiple feature positions in a dynamic image sequence is presented. This is achieved using a combination of two trajectory-based methods, with the resulting hybrid algorithm exhibiting the advantages of both. An optimizing exchange algorithm is described which enables short feature paths to be tracked without prior knowledge of the motion being studied. The resulting partial trajectories are then used to initialize a fast predictor algorithm which is capable of rapidly tracking multiple feature paths. As this predictor algorithm becomes tuned to the feature positions being tracked, it is shown how the location of occluded or poorly detected features can be predicted. The results of applying this tracking algorithm to data obtained from real-world scenes are then presented.

Techniques for image analysis of movement of juveniles of root-knot nematodes encumbered with Pasteuria penetrans spores

Root-knot nematodes (Meloidogyne spp.) are the most significant plant-parasitic nematodes that damage many crops all over the world. The free-living second stage juvenile (J2) is the infective stage that enters plants. The J2s move in the soil water films to reach the root zone. The bacterium Pasteuria penetrans is an obligate parasite of root-knot nematodes, is cosmopolitan, frequently encountered in many climates and environmental conditions and is considered promising for the control of Meloidogyne spp. The infection potential of P. penetrans to nematodes is well studied but not the attachment effects on the movement of root-knot nematode juveniles, image analysis techniques were used to characterize movement of individual juveniles with or without P. penetrans spores attached to their cuticles. Methods include the study of nematode locomotion based on (a) the centroid body point, (b) shape analysis and (c) image stack analysis. All methods proved that individual J2s without P. penetrans spores attached have a sinusoidal forward movement compared with those encumbered with spores. From these separate analytical studies of encumbered and unencumbered nematodes, it was possible to demonstrate how the presence of P. penetrans spores on a nematode body disrupted the normal movement of the nematode.

Evaluation of satellite-based and model re-analysis rainfall estimates for Uganda

The dependence of much of Africa on rain fed agriculture leads to a high vulnerability to fluctuations in rainfall amount. Hence, accurate monitoring of near-real time rainfall is particularly useful, for example in forewarning possible crop shortfalls in drought-prone areas. Unfortunately, ground based observations are often inadequate. Rainfall estimates from satellite-based algorithms and numerical model outputs can fill this data gap, however rigorous assessment of such estimates is required. In this case, three satellite based products (NOAA-RFE 2.0, GPCP-1DD and TAMSAT) and two numerical model outputs (ERA-40 and ERA-Interim) have been evaluated for Uganda in East Africa using a network of 27 rain gauges. The study focuses on the years 2001 to 2005 and considers the main rainy season (February to June). All data sets were converted to the same temporal and spatial scales. Kriging was used for the spatial interpolation of the gauge data. All three satellite products showed similar characteristics and had a high level of skill that exceeded both model outputs. ERA-Interim had a tendency to overestimate whilst ERA-40 consistently underestimated the Ugandan rainfall.

Single-cell proteomic analysis of glucosinolate-rich S-cells in Arabidopsis thaliana

Single-cell analysis is essential for understanding the processes of cell differentiation and metabolic specialisation in rare cell types. The amount of single proteins in single cells can be as low as one copy per cell and is for most proteins in the attomole range or below; usually considered as insufficient for proteomic analysis. The development of modern mass spectrometers possessing increased sensitivity and mass accuracy in combination with nano-LC-MS/MS now enables the analysis of single-cell contents. In Arabidopsis thaliana, we have successfully identified nine unique proteins in a single-cell sample and 56 proteins from a pool of 15 single-cell samples from glucosinolate-rich S-cells by nanoLC-MS/MS proteomic analysis, thus establishing the proof-of-concept for true single-cell proteomic analysis. Dehydrin (ERD14_ARATH), two myrosinases (BGL37_ARATH and BGL38_ARATH), annexin (ANXD1_ARATH), vegetative storage proteins (VSP1_ARATH and VSP2_ARATH) and four proteins belonging to the S-adenosyl-l-methionine cycle (METE_ARATH, SAHH1_ARATH, METK4_ARATH and METK1/3_ARATH) with associated adenosine kinase (ADK1_ARATH), were amongst the proteins identified in these single-S-cell samples. Comparison of the functional groups of proteins identified in S-cells with epidermal/cortical cells and whole tissue provided a unique insight into the metabolism of S-cells. We conclude that S-cells are metabolically active and contain the machinery for de novo biosynthesis of methionine, a precursor for the most abundant glucosinolate glucoraphanine in these cells. Moreover, since abundant TGG2 and TGG1 peptides were consistently found in single-S-cell samples, previously shown to have high amounts of glucosinolates, we suggest that both myrosinases and glucosinolates can be localised in the same cells, but in separate subcellular compartments. The complex membrane structure of S-cells was reflected by the presence of a number of proteins involved in membrane maintenance and cellular organisation.