Genotypic variation in sorghum [Sorghum bicolor (L.) Moench] exotic germplasm collections for drought and disease tolerance

Citation: Kapanigowda, M., . . . & Little, C. (2013). Genotypic variation in sorghum [Sorghum bicolor (L.) Moench] exotic germplasm collections for drought and disease tolerance. SpringerPlus, 2, 650. https://doi.org/10.1186/2193-1801-2-650Sorghum [Sorghum bicolor (L.) Moench] grain yield is severely affected by abiotic and biotic stresses during post-flowering stages, which has been aggravated by climate change. New parental lines having genes for various biotic and abiotic stress tolerances have the potential to mitigate this negative effect. Field studies were conducted under irrigated and dryland conditions with 128 exotic germplasm and 12 adapted lines to evaluate and identify potential sources for post-flowering drought tolerance and stalk and charcoal rot tolerances. The various physiological and disease related traits were recorded under irrigated and dryland conditions. Under dryland conditions, chlorophyll content (SPAD), grain yield and HI were decreased by 9, 44 and 16%, respectively, compared to irrigated conditions. Genotype RTx7000 and PI475432 had higher leaf temperature and grain yield, however, genotype PI570895 had lower leaf temperature and higher grain yield under dryland conditions. Increased grain yield and optimum leaf temperature was observed in PI510898, IS1212 and PI533946 compared to BTx642 (B35). However, IS14290, IS12945 and IS1219 had decreased grain yield and optimum leaf temperature under dryland conditions. Under irrigated conditions, stalk and charcoal rot disease severity was higher than under dryland conditions. Genotypes IS30562 and 1790E R had tolerance to both stalk rot and charcoal rot respectively and IS12706 was the most susceptible to both diseases. PI510898 showed combined tolerance to drought and Fusarium stalk rot under dryland conditions. The genotypes identified in this study are potential sources of drought and disease tolerance and will be used to develop better adaptable parental lines followed by high yielding hybrids

An Unusual Cause of Elbow Pain – A Case Report

Giant cell tumours are common bone tumours usually benign which arise at the metaphysis and extend towards the epiphysis of bone. A case of giant cell tumour in the distal humerus which is a rare site is presented here. Radiological investigations and biopsy in a 20 year old male who presented to our orthopedic department with elbow pain, led to a diagnosis of giant cell tumour at the medial epicondyle of humerus. Literature is reviewed regarding the common sites of giant cell tumours along with the treatment modalities currently followed. Giant cell tumour should be kept in a mind as a rare cause of elbow pain

Validation of processing maps for a 15Cr-15Ni-2.2Mo-0.3Ti austenitic stainless steel using hot forging and rolling tests

The processing maps are being developed for use in optimising hot workability and controlling the microstructure of the product. The present investigation deals with the examination to assess the prediction of the processing maps for a 15Cr-15Ni-2.2Mo-0.3Ti austenitic stainless steel using forging and rolling tests at different temperatures in the range of 600–1200°C. The tensile properties of these deformed products were evaluated at room temperature. The influence of the processing conditions, i.e. strain rate and temperature on the tensile properties of the deformed product were analysed to identify the optimum processing parameters. The results have shown good agreement between the regimes exhibited by the map and the properties of the rolled or forged product. The optimum parameters for processing of this steel were identified as rolling or press forging at temperatures above 1050°C to obtain optimum product properties

Artificial neural network approach for estimating weld bead width and depth of penetration from infrared thermal image of weld pool

In this article an artificial neural network based system to predict weld bead geometry using features derived from the infrared thermal video of a welding process is proposed. The multilayer perceptron and radial basis function networks are used in the prediction model and an online feature selection technique prioritises the features used in the prediction model. The efficacy of the system is demonstrated with a number of welding experiments and using the leave one out cross-validation experiments