Going Dotty: The Distribution and Effects of Rust on Highbush Cranberry
Purpose Every fall, I collect highbush cranberries (Viburnum edule) to make jelly. In 2012, for the first time, I observed highbush cranberry leaves covered in striking patterns of raised purple dots (telia) caused by the pathogenic rust fungus Puccinia linkii. I investigated the distribution and effects of this rust for several reasons: little is known about P. linkii, highbush cranberries are an important food source for wildlife and people, and foliar pathogens may increase with climate change in sub-boreal forests. First, I investigated the patterns of telia within plants. Second, I compared the variation in infection severity among plants, hypothesising that younger plants, those in dense populations, and those in low-elevation riparian areas would be most infected. Finally, I studied the effects of P. linkii on its host, hypothesising that highly infected plants would produce fewer berries and produce berries with less sugar, and that infected leaves would die sooner. Procedures This study investigated P. linkii in mixed coniferous-deciduous forest sites near Smithers, British Columbia. I marked a total of 41 randomly-selected focal V. edule plants in four sites. To examine within-plant patterns of infection, I photographed four leaves of each plant and used a graphic analysis program to examine the size, density and coverage of telia. To assess among-plant patterns, I compared infection severity (5 classes of telia coverage), to three ecological variables: host density within 5m, position on a moisture gradient, and plant maturity. To investigate the effects of P. linkii on its host, I compared infection severity to the number of berries produced, the proportion of malformed and infected berries, and the sugar content of mature berries as measured with a handheld refractometer. I measured leaf mortality in fall. Results P. linkii produced a characteristic pattern within each plant: higher leaves consistently had fewer but bigger telia than lower leaves. Across sites, plants were significantly more infected in areas of high host density. Within sites, young plants and plants and in moister ecosystems were significantly more infected (increases of 1.1 ± 0.2; F1,128=44.8, P
New Screening Method for Early Pediatric Cancer Detection Through Automated Handwriting Analysis
Pediatric cancer has an incidence rate of more than 175,000 per year with a mortality rate of approximately 96,000 per year. One major cause of this problem is late diagnosis. A novel promising way of pediatric cancer screening is handwriting analysis. This method surpasses other methods by detecting pediatric cancer in a very early stage. However, studies are still limited to manual analysis which needs an expert and a long period of time. The aim of this project is to design a computer program to extract handwriting features and build a classification model to classify the user as patient or as control. Dataset was collected from schools and hospitals where all participants could read and write in English. After data cleansing, number of samples was 440 samples. MATLAB (Matrix Laboratory) program was used for extracting geometric features in handwriting. Program was validated using a subset of 50 samples of the dataset. WEKA Package was used to test and build the classifier. Experiments were done using classifiers: Logistic, Multilayer Perceptron, J48, LibSVM, AdaBoostM1 and Naïve Bayes. Best subset of attributes was evaluated and used for each classifier and all calculations were done as the average of cross validation operations of several folds assignments. Best performance was achieved by Logistic classifier with average accuracy of 80.15%, standard deviation of 0.43% and Matthews's correlation coefficient of 0.59. Finally, this project presents a new fast, free, ready, easy and psychologically comfortable method for pediatric cancer detection while keeping suitable accuracy for mass screening.