Heat stress during reproductive and grain filling phases adversely affects the growth of cereals through reduction in grain's number and size. However, exogenous application of antioxidants, plant growth regulators and osmoprotectants may be helpful to minimize these heat induced yield losses in cereals. This two year study was conducted to evaluate the role of exogenous application of ascorbic acid (AsA), salicylic acid (SA) and hydrogen peroxide (H 2 O 2 ) applied through seed priming or foliar spray on biochemical, physiological, morphological and yield related traits, grain yield and quality of late spring sown hybrid maize. The experiment was conducted in the spring season of 2007 and 2008. We observed that application of AsA, SA and H 2 O 2 applied through seed priming or foliar spray improved the physiological, biochemical, morphological and yield related traits, grain yield and grain quality of late spring sown maize in both years. In both years, we observed higher superoxide dismutase (SOD), catalase (CAT) and peroxidase (POD) activity in the plants where AsA, SA and H 2 O 2 were applied through seed priming or foliar spray than control. Membrane stability index (MSI), relative water contents (RWC), chlorophyll contents, grain yield and grain oil contents were also improved by exogenous application of AsA, SA and H 2 O 2 in both years. Seed priming of AsA, SA and H 2 O 2 was equally effective as the foliar application. In conclusion, seed priming with AsA, SA and H 2 O 2 may be opted to lessen the heat induced yield losses in late sown spring hybrid maize. Heat tolerance induced by ASA, SA and H 2 O 2 may be attributed to increase in antioxidant activities and MSI which maintained RWC and chlorophyll contents in maize resulting in better grain yield in heat stress conditions.
To understand how sulfur nutrition affects the quality and yield of vegetable plants, we have grown two cultivars of pakchoi (Brassica campestris L. ssp. chinensis var. communis cv. Shang Hai Qing and You Dong Er) hydroponically in nutrient solution supplied with two levels of sulfur (0.0558 mM as sulfur deficiency and 1.0058 mM as sulfur sufficiency, respectively) for three weeks and their growth, nutrient uptake and glucosinolate content under these two sulfur conditions were investigated. The results showed that plant growth of both the cultivars was inhibited by sulfur deficiency. The concentrations of nitrogen and magnesium in shoots of both the cultivars were increased notably under sulfur deficiency, but there was no significant change in concentrations of sulfur, potassium and calcium. Moreover, sulfur deficiency increased phosphorus uptake in You Dong Er but not in Shang Hai Qing. In Shang Hai Qing sulfur deficiency reduced the content of all individual and total glucosinolates, while in You Dong Er this was also the case for most individual and total glucosinolates. However, in You Dong Er the total aliphatic glucosinolate concentration was not significantly influenced but the concentrations of individual aliphatic glucosinolates-glucoalyssin and gluconapin were in contrast increased under sulfur deficiency. Our data show that sulfur deficiency will decrease the yield and deteriorate the quality of pakchoi vegetable by reducing its growth and the contents of nutrients and glucosinolates. In addition, there was a significant genotypic variation in the composition and content of glucosinolates between these two pakchoi cultivars when exposed to sulfur deficiency.Additional key words: aliphatic glucosinolate, genotype, nitrogen, plant growth, sulfur sufficiency Hort.
Ionizing radiation is necessary for diagnostic imaging and deciding the right radiation dose is extremely critical to obtain a decent quality image. However, increasing the dosage to improve the image quality has risks due to the potential harm from ionizing radiation. Thus, finding the optimal as low as diagnostically acceptable (ALADA) dosage is an open research problem that has yet to be tackled using artificial intelligence (AI) methods. This paper proposes a new multi-balancing 3D convolutional neural network methodology to build 3D multidetector computed tomography (MDCT) datasets and develop a 3D classifier model that can work properly with 3D CT scan images and balance itself over the heavy unbalanced multi-classes. The proposed models were exhaustively investigated through eighteen empirical experiments and three re-runs for clinical expert examination. As a result, it was possible to confirm that the proposed models improved the performance by an accuracy of 5% to 10% when compared to the baseline method. Furthermore, the resulting models were found to be consistent, and thus possibly applicable to different MDCT examinations and reconstruction techniques. The outcome of this paper can help radiologists to predict the suitability of CT dosages across different CT hardware devices and reconstruction algorithms. Moreover, the developed model is suitable for clinical application where the right dose needs to be predicted from numerous MDCT examinations using a certain MDCT device and reconstruction technique.
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