Daylength influences the response of three clover species (Trifolium spp.) to short-term ozone stress

-Long photoperiods characteristic of summers at high latitudes can increase ozone-induced foliar injury in subterranean clover (Trifolium subterraneum) This study compared the effects of long photoperiods on ozone injury in red and white clover cultivars adapted to shorter or longer daylengths of southern or northern Fennoscandia. Plants were exposed to 70 ppb ozone for six hours during the daytime for three consecutive days. Simultaneously, the daylength in the growth rooms was altered to long-day (10 h light; 14 h dim light) and short-day (10 h light; 14 h darkness) conditions. Thermal imaging showed that ozone disrupted leaf temperature and stomatal function, particularly in sensitive species, in which leaf temperature deviations persisted for several days after ozone exposure. Longday conditions increased visible foliar injury (30%–70%), characterized by chlorotic and necrotic areas, relative to short day conditions in all species and cultivars independently of the photoperiod in the region they were adapted to

Visible foliar injury and infrared imaging show that daylength affects short-term recovery after ozone stress in Trifolium subterraneum

Tropospheric ozone is a major air pollutant affecting plants worldwide. Plants in northern regions can display more ozone injury than plants at lower latitudes despite lower ozone levels. Larger ozone influx and shorter nights have been suggested as possible causes. However, the effects of the dim light present during northern summer nights have not been investigated. Young Trifolium subterraneum plants kept in environmentally controlled growth rooms under long day (10 h bright light, 14 h dim light) or short day (10 h bright light, 14 h darkness) conditions were exposed to 6 h of 70 ppb ozone during daytime for three consecutive days. Leaves were visually inspected and imaged in vivo using thermal imaging before and after the daily exposure. In long-day-treated plants, visible foliar injury within 1 week after exposure was more severe. Multivariate statistical analyses showed that the leaves of ozone-exposed long-day-treated plants were also warmer with more homogeneous temperature distributions than exposed short day and control plants, suggesting reduced transpiration. Temperature disruptions were not restricted to areas displaying visible damage and occurred even in leaves with only slight visible injury. Ozone did not affect the leaf temperature of short-day-treated plants. As all factors influencing ozone influx were the same for long- and short-day-treated plants, only the dim nocturnal light could account for the different ozone sensitivities. Thus, the twilight summer nights at high latitudes may have a negative effect on repair and defence processes activated after ozone exposure, thereby enhancing sensitivity

Automatic gross tumor segmentation of canine head and neck cancer using deep learning and cross-species transfer learning

BackgroundRadiotherapy (RT) is increasingly being used on dogs with spontaneous head and neck cancer (HNC), which account for a large percentage of veterinary patients treated with RT. Accurate definition of the gross tumor volume (GTV) is a vital part of RT planning, ensuring adequate dose coverage of the tumor while limiting the radiation dose to surrounding tissues. Currently the GTV is contoured manually in medical images, which is a time-consuming and challenging task.PurposeThe purpose of this study was to evaluate the applicability of deep learning-based automatic segmentation of the GTV in canine patients with HNC.Materials and methodsContrast-enhanced computed tomography (CT) images and corresponding manual GTV contours of 36 canine HNC patients and 197 human HNC patients were included. A 3D U-Net convolutional neural network (CNN) was trained to automatically segment the GTV in canine patients using two main approaches: (i) training models from scratch based solely on canine CT images, and (ii) using cross-species transfer learning where models were pretrained on CT images of human patients and then fine-tuned on CT images of canine patients. For the canine patients, automatic segmentations were assessed using the Dice similarity coefficient (Dice), the positive predictive value, the true positive rate, and surface distance metrics, calculated from a four-fold cross-validation strategy where each fold was used as a validation set and test set once in independent model runs.ResultsCNN models trained from scratch on canine data or by using transfer learning obtained mean test set Dice scores of 0.55 and 0.52, respectively, indicating acceptable auto-segmentations, similar to the mean Dice performances reported for CT-based automatic segmentation in human HNC studies. Automatic segmentation of nasal cavity tumors appeared particularly promising, resulting in mean test set Dice scores of 0.69 for both approaches.ConclusionIn conclusion, deep learning-based automatic segmentation of the GTV using CNN models based on canine data only or a cross-species transfer learning approach shows promise for future application in RT of canine HNC patients

Pixel classification methods for identifying and quantifying leaf surface injury from digital images

Plants exposed to stress due to pollution, disease or nutrient deficiency often develop visible symptoms on leaves such as spots, colour changes and necrotic regions. Early symptom detection is important for precision agriculture, environmental monitoring using bio-indicators and quality assessment of leafy vegetables. Leaf injury is usually assessed by visual inspection, which is labour-intensive and to a consid- erable extent subjective. In this study, methods for classifying individual pixels as healthy or injured from images of clover leaves exposed to the air pollutant ozone were tested and compared. RGB images of the leaves were acquired under controlled conditions in a laboratory using a standard digital SLR camera. Different feature vectors were extracted from the images by including different colour and texture (spa- tial) information. Four approaches to classification were evaluated: (1) Fit to a Pattern Multivariate Image Analysis (FPM) combined with T2 statistics (FPM-T2) or (2) Residual Sum of Squares statistics (FPM-RSS), (3) linear discriminant analysis (LDA) and (4) K-means clustering. The predicted leaf pixel classifications were trained from and compared to manually segmented images to evaluate classification performance. The LDA classifier outperformed the three other approaches in pixel identification with significantly higher accuracy, precision, true positive rate and F-score and significantly lower false positive rate and computation time. A feature vector of single pixel colour channel intensities was sufficient for capturing the information relevant for pixel identification. Including neighbourhood pixel information in the feature vector did not improve performance, but significantly increased the computation time. The LDA classifier was robust with 95% mean accuracy, 83% mean true positive rate and 2% mean false positive rate, indicating that it has potential for real-time applications.Opstad Kruse, OM.; Prats Montalbán, JM.; Indahl, UG.; Kvaal, K.; Ferrer Riquelme, AJ.; Futsaether, CM. (2014). Pixel classification methods for identifying and quantifying leaf surface injury from digital images. Computers and Electronics in Agriculture. 108:155-165. doi:10.1016/j.compag.2014.07.010S15516510