Do dholes (Cuon alpinus) live in packs in response to competition with or predation by large cats?

Interactions between carnivores during the defence of kills may be one reason why certain carnivores live in groups. This is especially true of lions, hyaenas and the African wild dog, The dhole or the Asiatic wild dog, primarily a pack living animal, has been observed to regularly interact with both tigers and leopards, Such interactions have taken place over kills and otherwise. In this report, five such interactions are described, It was found that the pack's behaviour of surrounding bushes acid trees on which the cat was confined precluded immediate escape. The presence of sentinels, while the pack was resting, warned the pack of the presence of a big cat and the pack grouped when a big cat appeared, Costs to both individuals within the dhole packs and the cats involved in the encounters were found to be slight, The reasons for such potentially costly encounters could be competition for finite food resources or thwarting predation, Dholes have a significant diet overlap with both leopards and tigers and aggressively encounter with leopards but not with tigers, Differences between diet overlaps may not be the basis behind the differences in aggression, It is more likely that, the small size of leopards and the fact that they predate more often on dholes, cause dhole packs to be more aggressive to leopards than to tigers, The size of carnivore groups may thus pose an advantage during competitive interactions among carnivore species.

Heat shock response in mulberry silkworm races with different thermotolerances

The thermal sensitivity and heat shock response of the different races of the mulberry silkworm Bombyx mori have been analysed. The multivoltine race, strains C. Nichi and Pure Mysore showed better survival rates than the bivoltine race, strain NB4D2 exposed to 41 degrees C and above. In general, the fifth instar larvae and the pupae exhibited maximum tolerance compared to the early larval instars, adult moths or the eggs. Exposure up to 39 degrees C for 1 or 2 h was tolerated equally whereas temperatures above 43 degrees C proved to be lethal for all. Treatment of larvae at 41 degrees C for Ih resulted in a variety of physiological alterations including increased heart beat rates, differential haemocyte counts, enlargement of granulocytes and the presence of additional protein species in the tissues and haemolymph. The appearance of a 93 kDa protein in the haemolymph, fat bodies and cuticle, following the heat shocking of larvae in vivo was a characteristic feature in all the three strains examined although the kinetics of their appearance itself was different. In haemolymph, the protein appeared immediately in response to heat shock in C. Nichi reaching the maximal levels in 2-4 h whereas its presence was noticeable only after 2-4 h recovery time in Pure Mysore and bivoltine races. The fat body from both C. Nichi and NB4D2 showed the presence of 93 kDa, 89 kDa and 70 kDa proteins on heat shock. The haemocytes, on the other hand, expressed only a 70 kDa protein consequent to heat shock. The 93 kDa protein in the haemolymph, therefore could have arisen from some other tissue, possibly the fat body. The 93 kDa protein was detected after heat shock in pupae and adult moths as well, although the presence of an additional (56 kDa) protein was also apparent in the adults. The presence of 46 kDa and 28 kDa bands in addition to the 93 kDa band in the cuticular proteins immediately following heat shock was clearly discernible. The 70 kDa band did not show much changes in the cuticular proteins on heat shock. In contrast to the changes in protein profiles seen in tissues and haemolymph following heat shock in vivo, the heat treatment of isolated fat body or haemolymph in vitro resulted in protein degradation.

Prediction of Candidate Primary Immunodeficiency Disease Genes Using a Support Vector Machine Learning Approach

Screening and early identification of primary immunodeficiency disease (PID) genes is a major challenge for physicians. Many resources have catalogued molecular alterations in known PID genes along with their associated clinical and immunological phenotypes. However, these resources do not assist in identifying candidate PID genes. We have recently developed a platform designated Resource of Asian PDIs, which hosts information pertaining to molecular alterations, protein-protein interaction networks, mouse studies and microarray gene expression profiling of all known PID genes. Using this resource as a discovery tool, we describe the development of an algorithm for prediction of candidate PID genes. Using a support vector machine learning approach, we have predicted 1442 candidate PID genes using 69 binary features of 148 known PID genes and 3162 non-PID genes as a training data set. The power of this approach is illustrated by the fact that six of the predicted genes have recently been experimentally confirmed to be PID genes. The remaining genes in this predicted data set represent attractive candidates for testing in patients where the etiology cannot be ascribed to any of the known PID genes.