Characterization of Azotobacter vinelandii aggregation in submerged culture by digital image analysis

A simple and accurate method for determining the distribution of sizes of single cells and aggregates of Azotobacter vinelandii by image analysis has been developed. A staining procedure using methylene blue helps to enhance the contrast between aggregates and background without altering aggregate size distribution. Sample dilution affected the distribution of the population and therefore should be avoided. Mixing and aeration conditions during culture play an important role in the aggregation of A. vinelandii. Cells grown under mild mixing conditions (unbaffled flasks) presented a thick slime layer and formed aggregates of up to 35 microm of average equivalent diameter. In contrast, under strong agitation conditions (baffled flasks) practically no aggregates were formed throughout cultivation. The method described can be used for the characterization of aggregation of other microbial cultures.     

An artificial neural network approach to identify fungal diseases of cucumber (Cucumis sativus L.) plants using digital image processing

Nowadays, artificial intelligence solutions such as digital image processing and artificial neural networks (ANN) have become important applicable techniques in phytomonitoring and plant health detection systems. In this research, an autonomous device was designed and developed for detecting two types of fungi (Pseudoperonospora cubensis, Sphaerotheca fuliginea) that infect the cucumber (Cucumis sativus L.) plant leaves. This device was able to recognise the fungal diseases of plants by detecting their symptoms on plant leaves (downy mildew and powdery mildew). For leaves of cucumber inoculated with different spores of the fungi, it was possible to estimate the amount of hour post inoculation (HPI) by extracting leaves’ image parameters. Device included a dark chamber, a CCD digital camera, a thermal camera, a light dependent resistor lightening module and a personal computer. The proposed programme for precise disease detection was based on an image processing algorithm and ANN. Three textural features and two thermal parameters from the obtained images were measured and normalised. Performance of ANN model was tested successfully for disease recognition and detecting HPI in images using back-propagation supervised learning method and inspection data. Such this machine vision system can be used in robotic intelligent systems to achieve a modern farmer’s assistant in agricultural crop fields.     

Quantitative analysis of phytopathogenic ascomycota on leaves of pedunculate oaks (Quercus robur L.) by real-time PCR

Leaves of oak trees are often infected by various pathogenic fungi. As it is difficult to remove such organisms quantitatively from the leaf surface and as it is often impossible to grow these organisms independently from their host, there are almost no molecular data available from these oak leaf specific pathogens. For the quantitative removal of the microorganisms a procedure was developed combining a wax and microorganism freezing method with a DNA extraction technique. For the development of a species specific detection, DNA of pathogenic filamentous fungi was isolated from hyphae of the upper leaf surface of Quercus robur. Three different species could be identified as (i) Cladosporium sp., (ii) Ramularia sp. and (iii) Microsphaera alphitoides by amplifying and sequencing an 18S-28S segment of their rDNA. For the final quantification a real-time PCR protocol was established allowing the species specific quantification of the three pathogenic filamentous fungi. The whole procedure was successfully applied to quantify the amount of the three species on oak leaves collected in autumn.     

A global genetic analysis of herbarium specimens reveals the invasion dynamics of an introduced plant pathogen

The introduction, spread, and impact of fungal plant pathogens is a critical concern in ecological systems. In this study, we were motivated by the rather sudden appearance of Acer macrophyllum heavily infected with powdery mildew. We used morphological and genetic analyses to confirm the pathogen causing the epidemic was Sawadaea bicornis. In subsequent field studies, this pathogen was found in several locations in western North America, and in greenhouse studies, A. macrophyllum was found to be significantly more susceptible to S. bicornis than nine other Acer species tested. A genetic analysis of 178 specimens of powdery mildew from freshly collected and old herbarium specimens from 15 countries revealed seven different haplotypes. The high diversity of haplotypes found in Europe coupled with sequence results from a specimen from 1864 provides evidence that S. bicornis has a European origin. Furthermore, sequence data from a specimen from 1938 in Canada show that the pathogen has been present in North America for at least 82 years revealing a considerable lag time between the introduction and current epidemic. This study used old herbarium specimens to genetically hypothesize the origin, the native host, and the invasion time of a detrimental fungal plant pathogen.     

Comparative analysis of 35S and lectin promoters in transgenic soybean tissue using an automated image acquisition system and image analysis

Expression of the green fluorescent protein (gfp) gene, under regulatory control of either the constitutive 35S promoter or the developmentally-regulated lectin promoter was monitored and quantified using a newly-developed automated tracking system. The automated system consisted of a computer-controlled two-dimensional robotics table and a programmable image acquisition system, which was used to semi-continuously monitor gfp gene expression during development of transgenic soybean [Glycine max (L.) Merrill] somatic embryos. Quantitative analysis of GFP expression showed that, during somatic embryo proliferation and early development, expression of lectin-GFP was not detected. During late embryo development, expression of lectin-GFP gradually increased until the levels were similar to those of 35S-GFP. The use of an automated image collection system and image analysis facilitated the frequent monitoring and quantification of gfp gene expression on a large number of samples over an extended period of time.Electronic Supplementary Material Supplementary material is available for this article at     

Estimating Culicoides sonorensis biting midge abundance using digital image analysis

ImageJ is an open‐source software tool used for a variety of scientific objectives including cell counting, shape analysis and image correction. This technology has previously been used to estimate mosquito abundance in surveillance efforts. However, the utility of this application for estimating abundance or parity in the surveillance of Culicoides spp. (Diptera: Ceratopogonidae) has not yet been tested. Culicoides sonorensis (Wirth and Jones), a biting midge often measuring 2.0–2.5 mm in length, is an economically important vector of ruminant arboviruses in California. Current surveillance methods use visual sorting for the characteristics of midges and are very time‐intensive for large studies. This project tested the utility of ImageJ as a tool to assist in gross trap enumeration as well as in parity analysis of C. sonorensis in comparison with traditional visual methods of enumeration using a dissecting microscope. Results confirmed that automated counting of midges is a reliable means of approximating midge numbers under certain conditions. Further evaluation confirmed accurate and time‐efficient parity analysis in comparison with hand sorting. The ImageJ software shows promise as a tool that can assist and expedite C. sonorensis surveillance. Further, these methods may be useful in other insect surveillance activities.     

Classification and measurement of fungal pellets by automated image analysis

An automated image analysis method for classifying and measuring pellets of filamentous fungi growing in submerged fermentations has been developed. The method discriminates between pelleted mycelial growth and loose aggregates of dispersed hyphae. Pellets are classified into smooth and hairy types. In both cases, the core of the pellet is identified and its shape and size characterized. For hairy pellets the annular region is also characterized. The method was tested on pellets of Aspergillus niger ATCC 11414 grown in a defined medium in shake flasks. This rapid method makes practical extensive studies on the morphology of pellets in submerged fermentations and the influence of fermentation conditions on that morphology.     

Accurate root length measurement by image analysis

Algorithms for estimating root length by image analysis should lead to results that have no systematic error (bias), be insensitive to preferential root orientation, valid across a wide range of sample sizes and adjust for overlap between roots in samples, to reduce the effort needed in spreading out root systems. We propose a new algorithm that forms a compromise between small bias and robustness (insensitivity to variation in sample size and preferential root orientation), and provide a simple way of dealing with root overlap. Image analysis was performed on a Macintosh computer using the public domain NIH Image program. The digital image of the root was processed to get the thinned image (skeleton). The numbers of orthogonally and diagonally connected pairs of pixels (N _o and N _d, respectively) in the skeleton were counted separately and used for length (L) calculation. A new length calculation equation was introduced so that the effect of orientation on length calculation was minimized; L=[N d ²+(N _d+N _o/2)²]^1/2+N _o/2. The maximum error due to orientation of a single line was evaluated for an ideal line, and the analysis revealed that the new equation was less affected by orientation than previous equations. Copper wire and rice (Oryza sativa L.) roots containing both primary and fine secondary root were measured manually and with image analysis. The two methods showed good agreement within 1.5%. The proposed image analysis method yielded length estimates with CV from 0.23 to 0.88%, which was lower than the CVs of the line-intersect method. Moreover, the lengths of overlapping samples were calculated correctly because the image analysis method distinguished an overlapping pixel from a thinned image, while the calculation with the line-intersect method showed underestimation because overlaps were not considered in that method. This revised version was published online in June 2006 with corrections to the Cover Date.     

Cell identification and sizing using digital image analysis for estimation of cell biomass in High Rate Algal Ponds

Current environmental concerns make estimation of microbial biomass apriority for monitoring purposes and to advance scientific understanding. Thispaper considers problems associated with algal cell imaging and measurement forcell biomass estimation in samples from high rate algal ponds. In a complexsystem, the only way of measuring microbial activity is to measure theindividual cells and estimate biovolumes. Accurate biomass determinationsdemanddirect microscopic counting and measurement of the sizes of individualmicrobialcells taken from known volumes of water. The system used for routinemeasurementat the laboratory where the images were generated, based on standard microscopeequipment, is only suitable for treatment of well dispersed specimens.Differential interference contrast (DIC) microscopy, on the other hand, offersthe best solution for optical enhancement of cell contrast, and produces animage with well defined edges, yet presents a great challenge to routine cellidentification by digital image analysis, owing to the bas-relief type imageproduced. The paper outlines several image analysis methods developedspecifically for this purpose, and presents illustrative results.     

Objective detection of apoptosis in rat renal tissue sections using light microscopy and free image analysis software with subsequent machine learning: Detection of apoptosis in renal tissue

Objective

The current study proposes an automated machine learning approach for the quantification of cells in cell death pathways according to DNA fragmentation.

Morphological quantification of filamentous fungal development using membrane immobilization and automatic image analysis

Mycelial morphology is a critically important process property in industrial fermentations of filamentous micro-organisms, as particular phenotypes are associated with maximum productivity. However, the accurate quantification of complex morphologies still represents a significant challenge in elucidating this relationship. A system has been developed for high-resolution characterisation of filamentous fungal growth on a solid substrate, using membrane immobilization and fully-automatic plug-ins developed for the public domain, Java-based, image-processing software, ImageJ. The system has been used to quantify the microscopic development of Aspergillus oryzae on malt agar, by measuring spore projected area and circularity, the total length of a hyphal element, the number of tips per element, and the hyphal growth unit. Two different stages of growth are described, from the swelling of a population of conidiospores up to fully developed, branched hyphae 24 h after inoculation. Spore swelling expressed as an increase in mean equivalent spore diameter was found to be approximately linear with time. Widespread germination of spores was observed by 8 h after inoculation. From approximately 12 h, the number of tips was found to increase exponentially. The specific growth rate of a population of hyphae was calculated as approximately 0.24–0.27 h⁻¹. A wide variation in growth kinetics was found within the population. The robustness of the image-analysis system was verified by testing the effect of small variations in the input data.     

Measurement of green fluorescent protein concentration in single cells by image analysis

The gene encoding the green fluorescent protein (GFP) has been widely used in studies of gene expression. The GFP can be detected nondestructively in living cells or tissues by the green fluorescence of the protein under blue light. Solutions of enhanced GFP (EGFP) of known concentration were filled in glass capillaries and used to calibrate a method for quantitative determination of EGFP or GFP-S65T in plant cells. Images captured by a digital camera were analyzed to determine the linear range for measurement of EGFP expression. The value of the method was illustrated by analysis of the relative levels of GFP expression under control of different promoters in aleurone cells of barley.     

Measurement of fluorine pollutant in plant leaves and soil using nuclear reaction analysis

In this article, the soil and the leaves of plants, parasol, cotton, and glossy privet around a fluorine-polluted area were taken as the samples, and fluorine concentration of the samples were studied using the nuclear reaction¹⁹F(P,α)¹⁶O, and some results were given.     

Starch determination in plant tissues using a computerized image analysis system

Starch is the main reserve compound in woody plant species. Changes in starch content are clear indicators of a variety of plant developmental processes. Thus, carbohydrate extraction and other analytical methods have been widely used to measure changes in starch content. However, the use of these methods can be limited by the fact that starch is often compartmentalized in very small portions of tissue. While changes in these small structures can be histochemically characterized and localized under the microscope, they cannot be quantified. As an alternative, an image analysis system attached to a microscope has been developed to detect quantitative variations in starch in particular tissues or cells. The system has been successfully used to study the differences in starch content of sections from pistillar structures in apricot ( Prunus armeniaca L.). The procedure is based on the measurement of the optical density of black and white images obtained from the microscope. Two staining methods, I₂KI (potassium iodide-iodine) and PAS (periodic acid Schiff's reagent), and two embedding techniques, paraffin and JB4 plastic resin, were compared. The best results were obtained using I₂KI-stained sections of paraffin-embedded material. Since the procedures used are non-destructive for the tissues studied, additional information can be obtained, on the same section, by the subsequent use of additional stains. The method described here can be used to detect quantitative variations in starch content under the microscope in different plant tissues and thus to follow changes in starch reserves in small structures.     

Linkage analysis of er-1, a recessive Pisum sativum gene for resistance to powdery mildew fungus (Erysiphe pisi D.C.)

Linkage analysis was used to determine the genetic map location of er-1, a recessive gene conditioning resistance to powdery mildew, on the Pisum sativum genome. Genetic linkage was demonstrated between er-1 and linkage group 6 markers after analyzing the progeny of two crosses, an F₂ population and a set of recombinant inbred lines. The classes of genetic markers surrounding er-1 include RFLP, RAPD and allozyme markers as well as the morphological marker Gty. A RAPD marker tightly linked to er-1 was identified by bulked segregant analysis. After DNA sequence characterization, specific PCR primers were designed to convert this RAPD marker into a sequence characterized amplified region (SCAR).