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Introduction: Both cancer and cancer associated therapies (CAT; including chemotherapy or concurrent chemoradiation) disrupt cellular metabolism throughout the body, including the regulation of skeletal muscle mass and function. Adjunct testosterone therapy during standard of care chemotherapy and chemoradiation modulates CAT-induced dysregulation of skeletal muscle metabolism and protects lean body mass during CAT. However, the extent to which the skeletal muscle proteome is altered under these therapeutic conditions is unknown.Objective: We probed the skeletal muscle proteome of cancer patients as an ancillary analysis following a randomized, double-blind, placebo-controlled phase II trial investigating the effect of adjunct testosterone on body composition in men and women with advanced cancers undergoing CAT.Methods: Men and women diagnosed with late stage (≥IIB) or recurrent head and neck or cervical cancer who were scheduled to receive standard of care CAT were administered an adjunct 7 weeks treatment of weekly intramuscular injections of either 100 mg testosterone (CAT+T, n = 7; 2M/5F) or placebo/saline (CAT+P, n = 6; 4M/2F). Biopsies were performed on the vastus lateralis before (PRE) and after (POST) the 7 weeks treatment. Extracted proteins were separated with 2-dimensional gel electrophoresis (2DE), and subjected to analyses of total protein abundance, phosphorylation and S-nitrosylation. Proteoforms showing significant 1.5 fold differences (t-test p ≤ 0.05) between PRE and POST timepoints were identified by mass spectroscopy (MS), and lists of altered proteins were subjected to Gene Set Enrichment Analysis (GSEA) to identify affected pathways.Results: A total of 756 distinct protein spots were identified. Of those spots, 102 were found to be altered in terms of abundance, phosphorylation, or S-nitrosylation, and identified by mass spectroscopy analysis to represent 58 unique proteins. Among the biological processes and pathways identified, CAT+P predominantly impacted metabolic processes, cell assembly, oxygen transport, and apoptotic signaling, while CAT+T impacted transcription regulation, muscle differentiation, muscle development, and contraction.Conclusion: Cancer and CAT significantly altered the skeletal muscle proteome in a manner suggestive of loss of structural integrity, reduced contractile function, and disrupted metabolism. Proteomic analysis suggests that the addition of adjunct testosterone minimized the structural and contractile influence of cancer and its associated therapies.

In recent years, the multilingual content over the internet has grown exponentially together with the evolution of the internet. The usage of multilingual content is excluded from the regional language users because of the language barrier. So, machine translation between languages is the only possible solution to make these contents available for regional language users. Machine translation is the process of translating a text from one language to another. The machine translation system has been investigated well already in English and other European languages. However, it is still a nascent stage for Indian languages. This paper presents an overview of the Machine Translation in Indian Languages shared task conducted on September 7–8, 2017, at Amrita Vishwa Vidyapeetham, Coimbatore, India. This machine translation shared task in Indian languages is mainly focused on the development of English-Tamil, English-Hindi, English-Malayalam and English-Punjabi language pairs. This shared task aims at the following objectives: (a) to examine the state-of-the-art machine translation systems when translating from English to Indian languages; (b) to investigate the challenges faced in translating between English to Indian languages; (c) to create an open-source parallel corpus for Indian languages, which is lacking. Evaluating machine translation output is another challenging task especially for Indian languages. In this shared task, we have evaluated the participant’s outputs with the help of human annotators. As far as we know, this is the first shared task which depends completely on the human evaluation.

Robust and reliable recognition are indeed necessary requirements for optical character recognition systems. Distortions present in the document image and the pre-processing errors cause the optical character recognition system to apply rejection policies to achieve reliable recognition in computer assisted applications. The objective of this paper is to implement a robust and reliable character recognition system for Malayalam language. Random Fourier features classified with Regularized Least Square loss function based Regression classifier can approximate the non-linear kernel machines. Baseline Malayalam character recognition based on Random Fourier features and Regularized Least Square regression classifier is implemented in this paper. Up on this baseline character recognition system, rejection strategies are applied and are experimented with real world document images. An improvement in recognition accuracy is achieved with the simulated Malayalam character recognition system at the cost of rejecting character images having low classification score.