Regulatory Light Chains in Cardiac Development and Disease

The role of regulatory light chains (RLCs) in cardiac muscle function has been elucidated progressively over the past decade. The RLCs are among the earliest expressed markers during cardiogenesis and persist through adulthood. Failing hearts have shown reduced RLC phosphorylation levels and that restoring baseline levels of RLC phosphorylation is necessary for generating optimal force of muscle contraction. The signalling mechanisms triggering changes in RLC phosphorylation levels during disease progression remain elusive. Uncovering this information may provide insights for better management of heart failure patients. Given the cardiac chamber-specific expression of RLC isoforms, ventricular RLCs have facilitated the identification of mature ventricular cardiomyocytes, opening up possibilities of regenerative medicine. This review consolidates the standing of RLCs in cardiac development and disease and highlights knowledge gaps and potential therapeutic advancements in targeting RLCs.     

Surface reconstruction techniques: a review

Surface reconstruction means that retrieve the data by scanning an object using a device such as laser scanner and construct it using the computer to gain back the soft copy of data on that particular object. It is a reverse process and is very useful especially when that particular object original data is missing without doing any backup. Hence, by doing so, the data can be recollected and can be stored for future purposes. The type of data can be in the form of structure or unstructured points. The accuracy of the reconstructed result should be concerned because if the result is incorrect, hence it will not exactly same like the original shape of the object. Therefore, suitable methods should be chosen based on the data used. Soft computing methods also have been used in the reconstruction field. This papers highlights the previous researches and methods that has been used in the surface reconstruction field.     

Redox and pH Dual Responsive Polymer Based Nanoparticles for In Vivo Drug Delivery

Responsive nanomaterials have emerged as promising candidates as drug delivery vehicles in order to address biomedical diseases such as cancer. In this work, polymer‐based responsive nanoparticles prepared by a supramolecular approach are loaded with doxorubicin (DOX) for the cancer therapy. The nanoparticles contain disulfide bonds within the polymer network, allowing the release of the DOX payload in a reducing environment within the endoplasm of cancer cells. In addition, the loaded drug can also be released under acidic environment. In vitro anticancer studies using redox and pH dual responsive nanoparticles show excellent performance in inducing cell death and apoptosis. Zebrafish larvae treated with DOX‐loaded nanoparticles exhibit an improved viability as compared with the cases treated with free DOX by the end of a 3 d treatment. Confocal imaging is utilized to provide the daily assessment of tumor size on zebrafish larva models treated with DOX‐loaded nanoparticles, presenting sustainable reduction of tumor. This work demonstrates the development of functional nanoparticles with dual responsive properties for both in vitro and in vivo drug delivery in the cancer therapy.     

An Intelligent Decision Support System for Investment Analysis

We present an intelligent decision support system that combines decision analysis and traditional investment evaluation and analysis. The system brings to the ordinary user expertise in both decision analysis and investment evaluation techniques, as well as domain knowledge about the market. The system supports the entire decision analysis cycle and provides facilities and tools for decision modeling, probability assessment, model evaluation, and sensitivity analysis. An example based on the Shanghai Stock Market is presented. We demonstrate how an investment decision model for the stock market is constructed by the system and how the optimal decision is obtained. Sensitivity and value of information analysis were also carried out by the system. Received 12 April 1999 / Revised 8 February 2000 / Accepted 2 March 2000     

Availability Modeling and Cost Optimization for the Grid Resource Management System

Grid computing is a recently developed technique for complex systems with large-scale resource sharing, wide-area communication, and multi-institutional collaboration. Although the development tools and techniques for the grid have been extensively investigated, the availability of the grid resource management system (RMS) has not been comprehensively studied. In order to contribute to this lacking but important field, this paper first models the grid RMS availability by considering both the failures of resource management (RM) servers and the length limitation of request queues. A hierarchical Markov reward model is implemented to evaluate the grid RMS availability. Based on the availability model, an optimization problem for designing the grid RMS is studied in order to minimize the cost by determining the best number of RM servers. Then, the sensitivity analysis is conducted, and a dynamic switching scheduling method is further presented based on the sensitivity analysis.     

Estimation of the inter-bar resistance of a cast cage rotor

This paper describes an experimental investigation of bar-to-bar resistance. It addresses the interpretation of experimental data, and the use of that data to develop a means of estimating bar-to-bar resistance at the design stage.     

Predicting the Academic Performance of Construction Engineering Students by Teaching and Learning Approaches: Case Study

There are various teaching approaches that instructors may adopt in their quest to teach effectively, and students can choose from a range of learning approaches to help them achieve good grades. This study investigates the effectiveness of personal learning approaches adopted by undergraduates and the teaching approaches employed by instructors in the context of construction engineering courses. The research questions are as follows. (1)?What are the students’ learning approaches that lead to better academic achievement? (2)?What are the instructors’ teaching approaches that give rise to better academic achievement? The study also aims to construct a model to predict a student’s likely academic performance in a construction engineering course. The research design is a self-administered survey. Using a structured questionnaire consisting of questions relating to learning strategies and teaching approaches, data were collected from undergraduates who majored in construction engineering. Statistical analyses undertaken include Pearson correlation and multiple linear regression modeling. Pearson correlation analysis shows that both the Growing Teaching approach and the students’ Achieving Motive learning approach are significantly correlated with academic performance. A robust grade prediction model was developed, whereby a student’s grade in a construction engineering course may be predicted using one teaching approach (Growing Teaching) and three learning approaches (Achieving Motive, Achieving Strategy, and Deep Motive). To help students obtain higher grades in a construction engineering course, instructors should adopt the Growing Teaching approach, and students should adopt the Achieving Motive learning approach. Because students who adopt the Deep Motive learning approach are penalized with lower grades, it is recommended that instructors modify their teaching methods and approaches so that students do not take shortcuts in their learning without deeper regard for higher-order learning outcomes.     

MicroRNAs as Potential Biomarkers in the Differential Diagnosis of Lipomatous Tumors and Their Mimics

Adipocytic tumors are the most common subtype of soft tissue tumors. In current clinical practice, distinguishing benign lipomas from well-differentiated liposarcomas (WDLPS), as well as dedifferentiated liposarcomas (DDLPS) from their morphologic mimics, remains a significant diagnostic challenge. This is especially so when examining small biopsy samples and without the aid of additional ancillary tests. Recognizing the important role that microRNAs (miRNAs) play in tumorigenesis and their potential utility in tumor classification, we analyzed routine clinical tissue samples of benign and malignant lipomatous tumors, as well as other sarcoma mimics, to identify distinguishing miRNA-based signatures that can aid in the differential diagnosis of these entities. We discovered a 6-miRNA signature that separated lipomas from WDLPS with high confidence (AUC of 0.963), as well as a separate 6-miRNA signature that distinguished DDLPS from their more aggressive histologic mimics (AUC of 0.740). Functional enrichment analysis unveiled possible mechanistic involvement of these predictive miRNAs in adipocytic cancer-related biological processes and pathways such as PI3K/AKT/mTOR and MAPK signaling, further supporting the relevance of these miRNAs as biomarkers for adipocytic tumors. Our results demonstrate that miRNA expression profiling may potentially be used as an adjunctive tool for the diagnosis of benign and malignant adipocytic tumors. Further validation studies are warranted.     

Postbuckling Formulation for Symmetric Laminated Panels with Various Edge Support Conditions

A study into the structural postbuckling response of symmetric laminated composite plates subjected to in-plane compressive load is presented in this contribution. Various cases of support conditions are investigated: loaded ends are either simply supported or clamped, while the unloaded edges are a combination of simple support, clamp, and free. The method of analysis used is the combination of the semienergy approach using the semianalytical finite-strip methodology and the well-known von Karman large-deflection equations. Extensive validation of the analytical solution with published theoretical solutions and experimental data are presented. The final form of the expression is quite versatile and can be readily used for design purposes.     

Determining the minimum grain size in severe plastic deformation process via first-principles calculations

Although the stacking fault energy (SFE) is a fundamental variable determining the minimum grain size (d_min) obtainable in severe plastic deformation (SPD) processes, its accurate measurement is difficult. Here we establish the SFEs of binary Pd–Ag, Pd–Cu, Pt–Cu and Ni–Cu solid solutions using the axial interaction model and the supercell model in combination with first-principles theory. The two models yield consistent formation energies. For Pd–Ag, Pd–Cu and Ni–Cu, the theoretical SFEs agree well with those from the experimental measurements. For Pt–Cu no experimental results are available, and thus our calculated SFEs represent the first reasonable predictions. We discuss the correlation of the SFE and d_min in SPD experiments and show that the d_min values can be evaluated from first-principles calculations.