Migratory birds need to undergo physiological changes during their preparation for migration. The current study characterized those changes in photoperiodic migratory black-headed buntings (Emberiza melanocephala), which initiate their northward spring migration in response to increasing day lengths. We measured differences in body mass, testis size and triglycerides levels in buntings between groups exposed to short (8 h light:16 h darkness, 8L:16D; SD) and long (16L:8D; LD) days, and identified proteins that showed significant differences between SD and LD in the flight muscle. To confirm that photostimulated changes were linked with migration, similar measurements were done on photoperiodic non-migratory Indian weaverbirds (Ploceus philippinus), which share the habitat with buntings for almost half-a-year. Buntings were fattened and gained weight and had elevated serum triglyceride levels and recrudesced testes under LD, but not SD. The SDS-polyacrylamide gel electrophoresis revealed differences between SD and LD conditions in the flight muscle protein profiles of buntings, but not of weaverbirds. Two-dimensional gel electrophoresis of flight muscle of bunting separated three proteins, of which two were upregulated under LD condition. Mass spectroscopic analysis and a protein database search identified them as the fatty acid binding protein (FABP), myoglobin and creatine kinase (CK). Further semi-quantitative and quantitative PCR assays revealed that FABP and myoglobin transcript levels in buntings, but not in weaverbirds, were upregulated under LD condition. However, there was no difference in CK mRNA levels between SD and LD in both the species. High FABP is perhaps linked with increased energy demands and high myoglobin with intense physical activity during migration. A difference in the CK protein, but not in mRNA levels between SD and LD may possibly indicate its photoperiodic regulation at the translational level.

Protein-protein interactions (PPI) plays considerable role in most of the cellular processes and study of PPI enhances understanding of molecular mechanism of the cells. After emergence of proteomics, huge amount of protein sequences were generated but there interaction patterns are still unrevealed. Traditionally various techniques were used to predict PPI but are deficient in terms of accuracy. To overcome the limitations of experimental approaches numerous computational approaches were developed to find PPI. However previous computational approaches were based on descriptors, various external factors and protein sequences. In this article, a sequence based prediction model is proposed by using various machine learning approaches. A comparative study was done to understand efficiency of various machine learning approaches. Large amount of yeast PPI data have been analyzed. Same data has been incorporated for different classification approach like Artificial Neural Network (ANN) and Support Vector Machine (SVM), and compared their results. Existing methods with additional features were implemented to enhance the accuracy of the result. Thus it was concluded that efficiency of this model was more admirable than those existing sequence-based methods; therefore it can be effective for future proteomics research work.

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With the remarkable success of the image captioning tasks, visual attention methods have become a vital part of captioning models. However, most attention-based image captioning methods do not consider any relationship among regions, which play a significant role in better image understanding. We proposed an image captioning method based on local relation network using a multilevel attention approach with graph neural network. It not only fully explores the relationship between the object and the image regions but also generates significant and contextbased features corresponding to every region in the image. The attention employed in our work enhances the image representation capability of our method by focusing on a given image region and its related image regions. Thus addressing the relevant contextual information, spatial locations, and deep visual features leads to improve caption generation. We verified the effectiveness of the proposed model by conducting extensive experiments on three benchmark datasets: Flickr30k, MSCOCO, and nocaps. The results show the superiority of the proposed method over the existing methods both in quantitative and qualitative manners. Detailed ablation studies are conducted to communicate how each part would contribute to the final performance.