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.

Maintenance of the gag/gag-pol ratio is important for human immunodeficiency virus type 1 RNA dimerization and viral infectivity

Production of the human immunodeficiency virus type 1 (HIV_-1) Gag-Pol precursor protein results from a −1 ribosomal frameshifting event. In infected cells, this generates Gag and Gag-Pol in a ratio that is estimated to be 20:1, a ratio that is conserved among retroviruses. To examine the impact of this ratio on HIV_-1 replication and viral assembly, we altered the Gag/Gag-Pol ratio in virus-producing cells by cotransfecting HIV_-1 proviral DNA with an HIV_-1 Gag-Pol expression vector. Two versions of the Gag-Pol expression vector were used; one contains an active protease [PR(+)], and the other contains an inactive protease [PR(−)]. In an attempt to produce viral particles with Gag/Gag-Pol ratios ranging from 20:21 to 20:1 (wild type), 293T cells were cotransfected with various ratios of wild-type proviral DNA and proviral DNA from either Gag-Pol expression vector. Viral particles derived from cells with altered Gag/Gag-Pol ratios via overexpression of PR(−) Gag-Pol showed a ratio-dependent defect in their virion protein profiles. However, the defects in virion infectivity were independent of the nature of the Gag-Pol expression vector, i.e., PR(+) or PR(−). Based on equivalent input of reverse transcriptase activity, we estimated that HIV_-1 infectivity was reduced 250- to 1,000-fold when the Gag/Gag-Pol ratio in the virion-producing cells was altered from 20:1 to 20:21. Although virion RNA packaging was not affected by altering Gag/Gag-Pol ratios, changing the ratio from 20:1 to 20:21 progressively reduced virion RNA dimer stability. The impact of the Gag/Gag-Pol ratio on virion RNA dimerization was amplified when the Gag-Pol PR(−) expression vector was expressed in virion-producing cells. Virions produced from cells expressing Gag and Gag-Pol PR(−) in a 20:21 ratio contained mainly monomeric RNA. Our observations provide the first direct evidence that, in addition to proteolytic processing, the ratio of Gag/Gag-Pol proteins is also important for RNA dimerization and that stable RNA dimers are not required for encapsidation of genomic RNA in HIV_-1.

Recombinant influenza viruses as a vaccine vector for HIV

 Live recombinant influenza viruses were successfully used as HIV vaccine vectors in a mouse model. Following intranasal prime-boost vaccination, HIV-specific CD8+ T cell responses were detected in the spleen, broncho-alveolar lavage, mediastinal and inguinal lymph nodes. HIV+α4β7+ CD8+ T cells contributed to protection in pseudo-challenge experiments using recombinant vaccinia virus expressing HIV antigens. This research highlights the importance of mucosal CD8+ T cells in viral immunity and emphasizes the need for additional studies to provide key insights to underpin future vaccine development.

Universal nonuniform random vector generator based on acceptance-rejection

The acceptance/rejection approach is widely used in universal nonuniform random number generators. Its key part is an accurate approximation of a given probability density from above by a hat function. This article uses a piecewise constant hat function, whose values are overestimates of the density on the elements of the partition of the domain. It uses a sawtooth overestimate of Lipschitz continuous densities, and then examines all local maximizers of such an overestimate. The method is applicable to multivariate multimodal distributions. It exhibits relatively short preprocessing time and fast generation of random variates from a very large class of distributions

Filtering mobile spam by support vector machine

Currently mobile spam has been a major menace to the development of wireless networks. In this paper, the mobile spam problem and its countermeasures are analysed. In particular, we propose a Support Vector Machine to filter mobile spam. This mobile spam filtering system can be deployed in current wireless networks and achieve good performance in protecting end users and operators from mobile spam. Legislation issues and challenges to defend mobile spam are also discussed in the latter part of this paper.

An innovative spam filtering model based on support vector machine

Spam is commonly defined as unsolicited email messages and the goal of spam categorization is to distinguish between spam and legitimate email messages. Many researchers have been trying to separate spam from legitimate emails using machine learning algorithms based on statistical learning methods. In this paper, an innovative and intelligent spam filtering model has been proposed based on support vector machine (SVM). This model combines both linear and nonlinear SVM techniques where linear SVM performs better for text based spam classification that share similar characteristics. The proposed model considers both text and image based email messages for classification by selecting an appropriate kernel function for information transformation.

Suppression of bovine viral diarrhea virus replication by small interfering RNA and short hairpin RNA-mediated RNA interference

Bovine viral diarrhea virus (BVDV) is a ubiquitous viral pathogen that affects cattle herds’ worldwide causing significant economic loss. The current strategies to control BVDV infection include vaccination (modified-live or killed) and control of virus spread by enhanced biosecurity management, however, the disease remains prevalent. With the discovery of the sequence-specific method of gene silencing known as RNA interference (RNAi), a new era in antiviral therapies has begun. Here we report the efficient inhibition of BVDV replication by small interfering (siRNA) and short hairpin RNA (shRNA)-mediated gene silencing. siRNAs were generated to target the 5′ non-translated (NTR) region and the regions encoding the C, NS4B and NS5A proteins of the BVDV genome. The siRNAs were first validated using an EGFP/BVDV reporter system and were then shown to suppress BVDV-induced cytopathic effects and viral titers in cell culture with surprisingly different activities compared to the reporter system. Efficient viral suppression was then achieved by bovine 7SK-expressed BVDV-specific shRNAs. Overall, our results demonstrated the use of siRNA and shRNA-mediated gene silencing to achieve efficient inhibition of the  replication of this virus in cell culture.

Dynamic feature selection for spam filtering using support vector machine

Spam is commonly defined as unsolicited email messages and the goal of spam filtering is to differentiate spam from legitimate email. Much work have been done to filter spam from legitimate emails using machine learning algorithm and substantial performance has been achieved with some amount of false positive (FP) tradeoffs. In this paper, architecture of spam filtering has been proposed based on support vector machine (SVM,) which will get better accuracy by reducing FP problems. In this architecture an innovative technique for feature selection called dynamic feature selection (DFS) has been proposed which is enhanced the overall performance of the architecture with reduction of FP problems. The experimental result shows that the proposed technique gives better performance compare to similar existing techniques.

A meta-learning approach to automatic kernel selection for support vector machines

Appropriate choice of a kernel is the most important ingredient of the kernel-based learning methods such as support vector machine (SVM). Automatic kernel selection is a key issue given the number of kernels available, and the current trial-and-error nature of selecting the best kernel for a given problem. This paper introduces a new method for automatic kernel selection, with empirical results based on classification. The empirical study has been conducted among five kernels with 112 different classification problems, using the popular kernel based statistical learning algorithm SVM. We evaluate the kernels’ performance in terms of accuracy measures. We then focus on answering the question: which kernel is best suited to which type of classification problem? Our meta-learning methodology involves measuring the problem characteristics using classical, distance and distribution-based statistical information. We then combine these measures with the empirical results to present a rule-based method to select the most appropriate kernel for a classification problem. The rules are generated by the decision tree algorithm C5.0 and are evaluated with 10 fold cross validation. All generated rules offer high accuracy ratings.

Improved support vector machine generalization using normalized input space

Data pre-processing always plays a key role in learning algorithm performance. In this research we consider data pre-processing by normalization for Support Vector Machines (SVMs). We examine the normalization affect across 112 classification problems with SVM using the rbf kernel. We observe a significant classification improvement due to normalization. Finally we suggest a rule based method to find when normalization is necessary for a specific classification problem. The best normalization method is also automatically selected by SVM itself.

On rapid vector alignment in permanent magnet brushless motor

A software replacement for the commutation signals of a permanent magnet brushless motor is presented. The feedback observed acceleration loop or equivalently the high-order position polynomial controller allows finding the initial relative orientation between the two magnetic fields of the motors within a fraction of a second. Also, using the proposed method allows a considerable cost saving, since the transducer that is usually used for this purpose can be eliminated. The cost saving is most obvious in the case of linear motors and angle motors with large diameters. The way the problem is posed is an essential part of this work and it is the reason behind the apparent simplicity of the solution. The method has been tested when a relative encoder was used and the motor current was regulated.

Nonlinear discrimination using support vector machine

Appropriate training data always play an important role in constructing an efficient classifier to solve the data mining classification problem. Support Vector Machine (SVM) is a comparatively new approach in constructing a model/classifier for data analysis, based on Statistical Learning Theory (SLT). SVM utilizes a transformation of the basic constrained optimization problem compared to that of a quadratic programming method, which can be solved parsimoniously through standard methods. Our research focuses on SVM to classify a number of different sizes of data sets. We found SVM to perform well in the case of discrimination compared to some other existing popular classifiers.

The cost of community-managed viral respiratory illnesses in a cohort of healthy preschool-aged children

Background : Acute respiratory illnesses (ARIs) during childhood are often caused by respiratory viruses, result in significant morbidity, and have associated costs for families and society. Despite their ubiquity, there is a lack of interdisciplinary epidemiologic and economic research that has collected primary impact data, particularly associated with indirect costs, from families during ARIs in children.
Methods : We conducted a 12-month cohort study in 234 preschool children with impact diary recording and PCR testing of nose-throat swabs for viruses during an ARI. We used applied values to estimate a virus-specific mean cost of ARIs.
Results : Impact diaries were available for 72% (523/725) of community-managed illnesses between January 2003 and January 2004. The mean cost of ARIs was AU$309 (95% confidence interval $263 to $354). Influenza illnesses had a mean cost of $904, compared with RSV, $304, the next most expensive single-virus illness, although confidence intervals overlapped. Mean carer time away from usual activity per day was two hours for influenza ARIs and between 30 and 45 minutes for all other ARI categories.
Conclusion : From a societal perspective, community-managed ARIs are a significant cost burden on families and society. The point estimate of the mean cost of community-managed influenza illnesses in healthy preschool aged children is three times greater than those illnesses caused by RSV and other respiratory viruses. Indirect costs, particularly carer time away from usual activity, are the key cost drivers for ARIs in children. The use of parent-collected specimens may enhance ARI surveillance and reduce any potential Hawthorne effect caused by compliance with study procedures. These findings reinforce the need for further integrated epidemiologic and economic research of ARIs in children to allow for comprehensive cost-effectiveness assessments of preventive and therapeutic options.

The effects of the social structure of digital networks on viral marketing performance

Viral marketing is a form of peer-to-peer communication in which individuals are encouraged to pass on promotional messages within their social networks. Conventional wisdom holds that the viral marketing process is both random and unmanageable. In this paper, we deconstruct the process and investigate the formation of the activated digital network as distinct from the underlying social network. We then consider the impact of the social structure of digital networks (random, scale free, and small world) and of the transmission behavior of individuals on campaign performance. Specifically, we identify alternative social network models to understand the mediating effects of the social structures of these models on viral marketing campaigns. Next, we analyse an actual viral marketing campaign and use the empirical data to develop and validate a computer simulation model for viral marketing. Finally, we conduct a number of simulation experiments to predict the spread of a viral message within different types of social network structures under different assumptions and scenarios. Our findings confirm that the social structure of digital networks play a critical role in the spread of a viral message. Managers seeking to optimize campaign performance should give consideration to these findings before designing and implementing viral marketing campaigns. We also demonstrate how a simulation model is used to quantify the impact of campaign management inputs and how these learnings can support managerial decision making.