Biosynthesis and expression of a disintegrin-like and metalloproteinase domain with thrombospondin-1 repeats-15

Background
ADAMTS proteoglycanases show proteolytic activity toward versican and other proteoglycans.

Results
ADAMTS15, which cleaves versican, is expressed during early cardiac development and during musculoskeletal development.

Conclusion
With unique and overlapping biological properties, ADAMTS15 is likely to have cooperative roles with other members of the ADAMTS proteoglycanase clade.

Significance
Versican cleavage has profound effects on developmental morphogenesis and regulates cancer cell behavior.

Potassium channel modulation by a toxin domain in matrix metalloprotease

Peptide toxins found in a wide array of venoms block K+ channels, causing profound physiological and pathological effects. Here we describe the first functional K+ channel-blocking toxin domain in a mammalian protein. MMP23 (matrix metalloprotease 23) contains a domain (MMP23TxD) that is evolutionarily related to peptide toxins from sea anemones. MMP23TxD shows close structural similarity to the sea anemone toxins BgK and ShK. Moreover, this domain blocks K+ channels in the nanomolar to low micromolar range (Kv1.6 > Kv1.3 > Kv1.1 = Kv3.2 > Kv1.4, in decreasing order of potency) while sparing other K+ channels (Kv1.2, Kv1.5, Kv1.7, and KCa3.1). Full-length MMP23 suppresses K+ channels by co-localizing with and trapping MMP23TxD-sensitive channels in the ER. Our results provide clues to the structure and function of the vast family of proteins that contain domains related to sea anemone toxins. Evolutionary pressure to maintain a channel-modulatory function may contribute to the conservation of this domain throughout the plant and animal kingdoms.

An ontology-driven topic mapping approach to multi-level management of e-learning resources

An appropriate use of various pedagogical strategies is fundamental for the effective transfer of knowledge in a flourishing e-learning environment. The resultant information superfluity, however, needs to be tackled for developing sustainable e-learning. This necessitates an effective representation and intelligent access to learning resources. Topic maps address these problems of representation and retrieval of information in a distributed environment. The former aspect is particularly relevant where the subject domain is complex and the later aspect is important where the amount of resources is abundant but not easily accessible. Conversely, effective presentation of learning resources based on various pedagogical strategies along with global capturing and authentication of learning resources are an intrinsic part of effective management of learning resources. Towards fulfilling this objective, this paper proposes a multi-level ontology-driven topic mapping approach to facilitate an effective visualization, classification and global authoring of learning resources in e-learning.

Acquisition of domain ontology from relational schemes based on reverse engineering

Acquisition of domain ontology from database has been of catholic concern. This paper, taking relational schemes as example, analyzes how to identify the information about the structure of relational schemes in legacy systems. Then, it presents twelve extraction rules, which facilitate the obtaining of terms and relations from the relational schemes. Finally, it uses the EER diagram to further obtain semantic information from relational schemes for refining ontology model. The development method of domain ontology based on reverse engineering is a supplement to forward engineering. The union of the two development methods is certainly beneficial for the designers of domain ontology. © 2009 IEEE.

Total and domain-specific sitting time among employees in desk-based work settings in Australia

Objective: To describe the total and domain-specific daily sitting time among a sample of Australian office-based employees. Methods: In April 2010, paper-based surveys were provided to desk-based employees (n=801) in Victoria, Australia. Total daily and domain-specific (work, leisure-time and transport-related) sitting time (minutes/day) were assessed by validated questionnaires. Differences in sitting time were examined across socio-demographic (age, sex, occupational status) and lifestyle characteristics (physical activity levels, body mass index [BMI]) using multiple linear regression analyses. Results: The median (95% confidence interval [CI]) of total daily sitting time was 540 (531-557) minutes/day. Insufficiently active adults (median=578 minutes/day, [95%CI: 564-602]), younger adults aged 18-29 years (median=561 minutes/day, [95%CI: 540-577]) reported the highest total daily sitting times. Occupational sitting time accounted for almost 60% of total daily sitting time. In multivariate analyses, total daily sitting time was negatively associated with age (unstandardised regression coefficient [B]=-1.58, p<0.001) and overall physical activity (minutes/week) (B=-0.03, p<0.001) and positively associated with BMI (B=1.53, p=0.038). Conclusions: Desk-based employees reported that more than half of their total daily sitting time was accrued in the work setting. Implications: Given the high contribution of occupational sitting to total daily sitting time among desk-based employees, interventions should focus on the work setting.

An online transfer learning RBF neural network for cross domain data classification

In this paper, a Radial Basis Function Network (RBFN) trained with the Dynamic Decay Adjustment (DDA) algorithm (i.e., RBFNDDA) is deployed as an incremental learning model for tackling transfer learning problems. An online learning strategy is exploited to allow the RBFNDDA model to transfer knowledge from one domain and applied to classification tasks in a different yet related domain. An experimental study is carried out to evaluate the effectiveness of the online RBFNDDA model using a benchmark data set obtained from a public domain. The results are analyzed and compared with those from other methods. The outcomes positively reveal the potentials of the online RBFNDDA model in handling transfer learning tasks. © 2014 The authors and IOS Press. All rights reserved.

Frequency-domain beamformers using conjugate gradient techniques for speech enhancement

A multiple-iteration constrained conjugate gradient (MICCG) algorithm and a single-iteration constrained conjugate gradient (SICCG) algorithm are proposed to realize the widely used frequency-domain minimum-variance-distortionless-response (MVDR) beamformers and the resulting algorithms are applied to speech enhancement. The algorithms are derived based on the Lagrange method and the conjugate gradient techniques. The implementations of the algorithms avoid any form of explicit or implicit autocorrelation matrix inversion. Theoretical analysis establishes formal convergence of the algorithms. Specifically, the MICCG algorithm is developed based on a block adaptation approach and it generates a finite sequence of estimates that converge to the MVDR solution. For limited data records, the estimates of the MICCG algorithm are better than the conventional estimators and equivalent to the auxiliary vector algorithms. The SICCG algorithm is developed based on a continuous adaptation approach with a sample-by-sample updating procedure and the estimates asymptotically converge to the MVDR solution. An illustrative example using synthetic data from a uniform linear array is studied and an evaluation on real data recorded by an acoustic vector sensor array is demonstrated. Performance of the MICCG algorithm and the SICCG algorithm are compared with the state-of-the-art approaches.

A stochastic time-domain model for burst data aggregation in IEEE 802.15.4 wireless sensor networks

In many network applications, the nature of traffic is of burst type. Often, the transient response of network to such traffics is the result of a series of interdependant events whose occurrence prediction is not a trivial task. The previous efforts in IEEE 802.15.4 networks often followed top-down approaches to model those sequences of events, i.e., through making top-view models of the whole network, they tried to track the transient response of network to burst packet arrivals. The problem with such approaches was that they were unable to give station-level views of network response and were usually complex. In this paper, we propose a non-stationary analytical model for the IEEE 802.15.4 slotted CSMA/CA medium access control (MAC) protocol under burst traffic arrival assumption and without the optional acknowledgements. We develop a station-level stochastic time-domain method from which the network-level metrics are extracted. Our bottom-up approach makes finding station-level details such as delay, collision and failure distributions possible. Moreover, network-level metrics like the average packet loss or transmission success rate can be extracted from the model. Compared to the previous models, our model is proven to be of lower memory and computational complexity order and also supports contention window sizes of greater than one. We have carried out extensive and comparative simulations to show the high accuracy of our model.

Ontology-based automated support for goal–use case model analysis

Combining goal-oriented and use case modeling has been proven to be an effective method in requirements elicitation and elaboration. To ensure the quality of such modeled artifacts, a detailed model analysis needs to be performed. However, current requirements engineering approaches generally lack reliable support for automated analysis of consistency, correctness and completeness (3Cs problems) between and within goal models and use case models. In this paper, we present a goal–use case integration framework with tool support to automatically identify such 3Cs problems. Our new framework relies on the use of ontologies of domain knowledge and semantics and our goal–use case integration meta-model. Moreover, functional grammar is employed to enable the semiautomated transformation of natural language specifications into Manchester OWL Syntax for automated reasoning. The evaluation of our tool support shows that for representative example requirements, our approach achieves over 85 % soundness and completeness rates and detects more problems than the benchmark applications.