Evaluation of follistatin as a therapeutic in models of skeletal muscle atrophy associated with denervation and tenotomy

Follistatin is an inhibitor of TGF-β superfamily ligands that repress skeletal muscle growth and promote muscle wasting. Accordingly, follistatin has emerged as a potential therapeutic to ameliorate the deleterious effects of muscle atrophy. However, it remains unclear whether the anabolic effects of follistatin are conserved across different modes of non-degenerative muscle wasting. In this study, the delivery of a recombinant adeno-associated viral vector expressing follistatin (rAAV:Fst) to the hind-limb musculature of mice two weeks prior to denervation or tenotomy promoted muscle hypertrophy that was sufficient to preserve muscle mass comparable to that of untreated sham-operated muscles. However, administration of rAAV:Fst to muscles at the time of denervation or tenotomy did not prevent subsequent muscle wasting. Administration of rAAV:Fst to innervated or denervated muscles increased protein synthesis, but markedly reduced protein degradation only in innervated muscles. Phosphorylation of the signalling proteins mTOR and S6RP, which are associated with protein synthesis, was increased in innervated muscles administered rAAV:Fst, but not in treated denervated muscles. These results demonstrate that the anabolic effects of follistatin are influenced by the interaction between muscle fibres and motor nerves. These findings have important implications for understanding the potential efficacy of follistatin-based therapies for non-degenerative muscle wasting.

Stochastic optimization models for energy management in carbonization process of carbon fiber production

Industrial producers face the task of optimizing production process in an attempt to achieve the desired quality such as mechanical properties with the lowest energy consumption. In industrial carbon fiber production, the fibers are processed in bundles containing (batches) several thousand filaments and consequently the energy optimization will be a stochastic process as it involves uncertainty, imprecision or randomness. This paper presents a stochastic optimization model to reduce energy consumption a given range of desired mechanical properties. Several processing condition sets are developed and for each set of conditions, 50 samples of fiber are analyzed for their tensile strength and modulus. The energy consumption during production of the samples is carefully monitored on the processing equipment. Then, five standard distribution functions are examined to determine those which can best describe the distribution of mechanical properties of filaments. To verify the distribution goodness of fit and correlation statistics, the Kolmogorov-Smirnov test is used. In order to estimate the selected distribution (Weibull) parameters, the maximum likelihood, least square and genetic algorithm methods are compared. An array of factors including the sample size, the confidence level, and relative error of estimated parameters are used for evaluating the tensile strength and modulus properties. The energy consumption and N2 gas cost are modeled by Convex Hull method. Finally, in order to optimize the carbon fiber production quality and its energy consumption and total cost, mixed integer linear programming is utilized. The results show that using the stochastic optimization models, we are able to predict the production quality in a given range and minimize the energy consumption of its industrial process.

Online delivery models: students’ perspectives

The optimal delivery model for units always puzzle curriculum designers and lecturers, particularly when the unit is offered in the summer trimester and students have greater choice as to whether to enrol in a unit or not. An ongoing research project in the School of Architecture and Built Environment at Deakin University aims to understand students’ perceptions on unit delivery in the summer trimester in order to improve support for online delivery models. The five delivery models in the study ranged from ‘traditional’ i.e. on campus lectures and tutorials for each week of the trimester; to ‘wholly online’ i.e. learning materials and communications entirely through the web-based student portal. Students rated their preferences for the five delivery models with additional comments. Students overwhelmingly prefer wholly online delivery during the summer trimester despite the benefits of other delivery models and that wholly online delivery may not offer their preferred learning experience. The students’ primary need is for flexibility which can be at odds with their equal need for interaction with academics and peers. It is important that academics recognise students’ perspectives to ensure their design of online delivery models improves teaching and learning in the summer trimester.

Topic model kernel classification with probabilistically reduced features

Probabilistic topic models have become a standard in modern machine learning to deal with a wide range of applications. Representing data by dimensional reduction of mixture proportion extracted from topic models is not only richer in semantics interpretation, but could also be informative for classification tasks. In this paper, we describe the Topic Model Kernel (TMK), a topicbased kernel for Support Vector Machine classification on data being processed by probabilistic topic models. The applicability of our proposed kernel is demonstrated in several classification tasks with real world datasets. TMK outperforms existing kernels on the distributional features and give comparative results on nonprobabilistic data types.