Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite Image

Powdery mildew, caused by the fungus Blumeria graminis, is a major winter wheat disease in China. Accurate delineation of powdery mildew infestations is necessary for site-specific disease management. In this study, high-resolution multispectral imagery of a 25 km2 typical outbreak site in Shaanxi, China, taken by a newly-launched satellite, SPOT-6, was analyzed for mapping powdery mildew disease. Two regions with high representation were selected for conducting a field survey of powdery mildew. Three supervised classification methods—artificial neural network, mahalanobis distance, and maximum likelihood classifier—were implemented and compared for their performance on disease detection. The accuracy assessment showed that the ANN has the highest overall accuracy of 89%, following by MD and MLC with overall accuracies of 84% and 79%, respectively. These results indicated that the high-resolution multispectral imagery with proper classification techniques incorporated with the field investigation can be a useful tool for mapping powdery mildew in winter wheat

Unsupervised Temporal Action Localization via Self-paced Incremental Learning

Recently, temporal action localization (TAL) has garnered significant interest in information retrieval community. However, existing supervised/weakly supervised methods are heavily dependent on extensive labeled temporal boundaries and action categories, which is labor-intensive and time-consuming. Although some unsupervised methods have utilized the ``iteratively clustering and localization'' paradigm for TAL, they still suffer from two pivotal impediments: 1) unsatisfactory video clustering confidence, and 2) unreliable video pseudolabels for model training. To address these limitations, we present a novel self-paced incremental learning model to enhance clustering and localization training simultaneously, thereby facilitating more effective unsupervised TAL. Concretely, we improve the clustering confidence through exploring the contextual feature-robust visual information. Thereafter, we design two (constant- and variable- speed) incremental instance learning strategies for easy-to-hard model training, thus ensuring the reliability of these video pseudolabels and further improving overall localization performance. Extensive experiments on two public datasets have substantiated the superiority of our model over several state-of-the-art competitors

Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects

Yellow rust (Puccinia striiformis f. sp. Tritici), powdery mildew (Blumeria graminis) and wheat aphid (Sitobion avenae F.) infestation are three serious conditions that have a severe impact on yield and grain quality of winter wheat worldwide. Discrimination among these three stressors is of practical importance, given that specific procedures (i.e. adoption of fungicide and insecticide) are needed to treat different diseases and insects. This study examines the potential of hyperspectral sensor systems in discriminating these three stressors at leaf level. Reflectance spectra of leaves infected with yellow rust, powdery mildew and aphids were measured at the early grain filling stage. Normalization was performed prior to spectral analysis on all three groups of samples for removing differences in the spectral baseline among different cultivars. To obtain appropriate bands and spectral features (SFs) for stressor discrimination and damage intensity estimation, a correlation analysis and an independent t-test were used jointly. Based on the most efficient bands/SFs, models for discriminating stressors and estimating stressor intensity were established by Fisher’s linear discriminant analysis (FLDA) and partial least square regression (PLSR), respectively. The results showed that the performance of the discrimination model was satisfactory in general, with an overall accuracy of 0.75. However, the discrimination model produced varied classification accuracies among different types of diseases and insects. The regression model produced reasonable estimates of stress intensity, with an R2 of 0.73 and a RMSE of 0.148. This study illustrates the potential use of hyperspectral information in discriminating yellow rust, powdery mildew and wheat aphid infestation in winter wheat. In practice, it is important to extend the discriminative analysis from leaf level to canopy level

Adaptive Response in Mice Exposed to 900 MHz Radiofrequency Fields: Primary DNA Damage

The phenomenon of adaptive response (AR) in animal and human cells exposed to ionizing radiation is well documented in scientific literature. We have examined whether such AR could be induced in mice exposed to non-ionizing radiofrequency fields (RF) used for wireless communications. Mice were pre-exposed to 900 MHz RF at 120 µW/cm2 power density for 4 hours/day for 1, 3, 5, 7 and 14 days and then subjected to an acute dose of 3 Gy γ-radiation. The primary DNA damage in the form of alkali labile base damage and single strand breaks in the DNA of peripheral blood leukocytes was determined using the alkaline comet assay. The results indicated that the extent of damage in mice which were pre-exposed to RF for 1 day and then subjected to γ-radiation was similar and not significantly different from those exposed to γ-radiation alone. However, mice which were pre-exposed to RF for 3, 5, 7 and 14 days showed progressively decreased damage and was significantly different from those exposed to γ-radiation alone. Thus, the data indicated that RF pre-exposure is capable of inducing AR and suggested that the pre-exposure for more than 4 hours for 1 day is necessary to elicit such AR

Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite Image

Powdery mildew, caused by the fungus Blumeria graminis, is a major winter wheat disease in China. Accurate delineation of powdery mildew infestations is necessary for site-specific disease management. In this study, high-resolution multispectral imagery of a 25 km2 typical outbreak site in Shaanxi, China, taken by a newly-launched satellite, SPOT-6, was analyzed for mapping powdery mildew disease. Two regions with high representation were selected for conducting a field survey of powdery mildew. Three supervised classification methods—artificial neural network, mahalanobis distance, and maximum likelihood classifier—were implemented and compared for their performance on disease detection. The accuracy assessment showed that the ANN has the highest overall accuracy of 89%, following by MD and MLC with overall accuracies of 84% and 79%, respectively. These results indicated that the high-resolution multispectral imagery with proper classification techniques incorporated with the field investigation can be a useful tool for mapping powdery mildew in winter wheat

Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects

Yellow rust (Puccinia striiformis f. sp. Tritici), powdery mildew (Blumeria graminis) and wheat aphid (Sitobion avenae F.) infestation are three serious conditions that have a severe impact on yield and grain quality of winter wheat worldwide. Discrimination among these three stressors is of practical importance, given that specific procedures (i.e. adoption of fungicide and insecticide) are needed to treat different diseases and insects. This study examines the potential of hyperspectral sensor systems in discriminating these three stressors at leaf level. Reflectance spectra of leaves infected with yellow rust, powdery mildew and aphids were measured at the early grain filling stage. Normalization was performed prior to spectral analysis on all three groups of samples for removing differences in the spectral baseline among different cultivars. To obtain appropriate bands and spectral features (SFs) for stressor discrimination and damage intensity estimation, a correlation analysis and an independent t-test were used jointly. Based on the most efficient bands/SFs, models for discriminating stressors and estimating stressor intensity were established by Fisher’s linear discriminant analysis (FLDA) and partial least square regression (PLSR), respectively. The results showed that the performance of the discrimination model was satisfactory in general, with an overall accuracy of 0.75. However, the discrimination model produced varied classification accuracies among different types of diseases and insects. The regression model produced reasonable estimates of stress intensity, with an R2 of 0.73 and a RMSE of 0.148. This study illustrates the potential use of hyperspectral information in discriminating yellow rust, powdery mildew and wheat aphid infestation in winter wheat. In practice, it is important to extend the discriminative analysis from leaf level to canopy level

Spartina alterniflora Invasion Enhances Dissimilatory Nitrate Reduction to Ammonium (DNRA) Rates in the Yangtze River Estuary, China

Dissimilatory nitrate reduction to ammonium (DNRA) can save N by converting nitrate into ammonium and avoiding nitrate leaching and runoff in saltmarshes. However, little is known about the effects of invasive plants on DNRA in the upper and deeper soil layers in salt marshes. Here, we investigated DNRA rates in the soils of six different depth layers (0–5, 5–10, 10–20, 20–30, 30–50, and 50–100 cm) from the invasive Spartina alterniflora marshland, two native plants Scirpus mariqueter and Phragmites australis marshlands, and bare mudflat on Chongming Island, located in the Yangtze River Estuary, China. Our results show that S. alterniflora significantly increased DNRA rates in both the upper 50 cm soil and deeper 50–100 cm soil layers. With respect to the entire soil profile, the NO3− reduction content calculated from DNRA in S. alterniflora marshland was 502.84 g N m−2 yr−1, increased by 47.10%, 49.42%, and 38.57% compared to bare mudflat, S. mariquete, and P. australis, respectively. Moreover, NO3− reduction content from the 50–100 cm soil layers was almost identical to that in the upper 50 cm of the soil. In the month of May, DNRA is primarily regulated by SO42− and pH in the upper and deeper soil layers, respectively, whereas, in the month of October, soil pH accounted for the most variables of DNRA in both the upper and deeper soil layers. Altogether, these results from a new perspective confirm that S. alterniflora invasion increases soil N pool and may further push its invasion in salt marshes, and the importance of deeper soil in nitrogen cycling cannot be ignored

<i>Spartina alterniflora</i> Invasion Enhances Dissimilatory Nitrate Reduction to Ammonium (DNRA) Rates in the Yangtze River Estuary, China

Dissimilatory nitrate reduction to ammonium (DNRA) can save N by converting nitrate into ammonium and avoiding nitrate leaching and runoff in saltmarshes. However, little is known about the effects of invasive plants on DNRA in the upper and deeper soil layers in salt marshes. Here, we investigated DNRA rates in the soils of six different depth layers (0–5, 5–10, 10–20, 20–30, 30–50, and 50–100 cm) from the invasive Spartina alterniflora marshland, two native plants Scirpus mariqueter and Phragmites australis marshlands, and bare mudflat on Chongming Island, located in the Yangtze River Estuary, China. Our results show that S. alterniflora significantly increased DNRA rates in both the upper 50 cm soil and deeper 50–100 cm soil layers. With respect to the entire soil profile, the NO3− reduction content calculated from DNRA in S. alterniflora marshland was 502.84 g N m−2 yr−1, increased by 47.10%, 49.42%, and 38.57% compared to bare mudflat, S. mariquete, and P. australis, respectively. Moreover, NO3− reduction content from the 50–100 cm soil layers was almost identical to that in the upper 50 cm of the soil. In the month of May, DNRA is primarily regulated by SO42− and pH in the upper and deeper soil layers, respectively, whereas, in the month of October, soil pH accounted for the most variables of DNRA in both the upper and deeper soil layers. Altogether, these results from a new perspective confirm that S. alterniflora invasion increases soil N pool and may further push its invasion in salt marshes, and the importance of deeper soil in nitrogen cycling cannot be ignored

Rac1 Promotes Cell Motility by Controlling Cell Mechanics in Human Glioblastoma

The failure of existing therapies in treating human glioblastoma (GBM) mostly is due to the ability of GBM to infiltrate into healthy regions of the brain; however, the relationship between cell motility and cell mechanics is not well understood. Here, we used atomic force microscopy (AFM), live-cell imaging, and biochemical tools to study the connection between motility and mechanics in human GBM cells. It was found thatRac1 inactivation by genomic silencing and inhibition with EHT 1864 reduced cell motility, inhibited cell ruffles, and disrupted the dynamics of cytoskeleton organization and cell adhesion. These changes were correlated with abnormal localization of myosin IIa and a rapid suppression of the phosphorylation of Erk1/2. At the same time, AFM measurements of the GBM cells revealed a significant increase in cell elasticity and viscosity following Rac1 inhibition. These results indicate that mechanical properties profoundly affect cell motility and may play an important role in the infiltration of GBM. It is conceivable that the mechanical characters might be used as markers for further surgical and therapeutical interventions