Assessment of Bonelike® graft with a resorbable matrix using an animal model

Synthetic bone grafts have been developed to provide an alternative to autografts and allografts. Bonelike® is a patented synthetic osteoconductive bone graft that mimics the mineral composition of natural bone. In the present preliminary animal studies a user-friendly version of synthetic bone graft Bonelike® have been developed by using a resorbable matrix, Floseal®, as a vehicle and raloxifene hydrochloride as a therapeutic molecule, that is known to decrease osteoclast activity and therefore enhanced bone formation. From histological and scanning electron microscopy evaluations, the use of Bonelike® associated with Floseal® and raloxifene hydrochloride showed that new bone was rapidly apposed on implanted granules and also that the presence of the matrix and therapeutic molecule does not alter the proven highly osteoconductivity properties of Bonelike®. Therefore, this association may be one step-forward for the clinical applications of Bonelike® scaffolds since it is much more easy-to-handle when compared to granular materials.     

The influence of chloride-ion concentration on the Pitting of steel in simulated concrete environment

The influence of chloride-ion concentration on the pitting behaviour of steel in saturated Ca(OH)₂ solution was studied using the D.C. polarization technique. Results seem to indicate a threshold chloride level for the on-set of severe pitting around 3000 ppm, the pitting potential E_p exhibits dramatic drop and large hysteresis loops develop. The results provide insight into the pitting performance of steel in concrete and highlight the poor repassivation of this metal/environment system.     

Prostate cancer clinical trials of the Southwest Oncology Group

The changing clinical dynamics of prostate cancer have resulted in a broadening of the research focus of the Genitourinary (GU) Cancer Committee of the Southwest Oncology Group (SWOG). Beginning with an emphasis on hormone-refractory disease in its early years, SWOG prostate cancer trials now cover the entire spectrum of the disease: localized, locally advanced, metastatic and hormone-refractory disease. As the world's largest GU cancer research group, the GU committee of SWOG has pioneered studies in combined androgen therapy for metastatic disease, quality-of-life (QOL) assessments for patients with localized and advanced disease, adjuvant therapy models, and prostate cancer chemoprevention. The committee has also formed the GU Global Group, whose purpose is to convene the chairs of the GU committees of all the major national and international oncology cooperative groups. Meeting semiannually, this group discusses activities within their respective organizations, plans collaborative strategies and protocols, and establishes global strategy in prostate cancer clinical research. The future directions of national and international prostate cancer trials will build on this broad foundation of well-conceived, logically sequenced studies.     

Principles of high-resolution radar based on nonsinusoidal waves.II. Generalized ambiguity function

For pt.I see ibid., vol.31, no.4, p.359-68 (1989). A generalized ambiguity function for nonsinusoidal coded waveforms that are represented as a train of properly weighted Gaussian pulses is derived and analyzed to study its properties. Computer plots of generalized ambiguity functions of different binary codes, e.g. Barker, complementary, and pseudorandom codes, are presented. The various computer plots show that the desired thumbtack ambiguity function is achievable by nonsinusoidal coded waveforms     

Smart home security: challenges,issues and solutions at different IoT layers

The Journal of Supercomputing - The Internet of Things is a rapidly evolving technology in which interconnected computing devices and sensors share data over the network to decipher different...     

Hyperspectral imaging-based unsupervised adulterated red chili content transformation for classification: Identification of red chili adulterants

Neural Computing and Applications - Preserving red-chili quality is of utmost importance in which the authorities demand quality techniques to detect, classify, and prevent it from impurities. For...     

Feature Selection and Extraction Methods for Power Systems Transient Stability Assessment Employing Computational Intelligence Techniques

One of the important aspects in achieving better performance for transient stability assessment (TSA) of power systems employing computational intelligence (CI) techniques is by incorporating feature reduction techniques. For small power system the number of features may be small but when larger systems are considered the number of features increased as the size of the systems increases. Apart from employing faster CI techniques to achieve faster and accurate TSA of power system, feature reduction techniques are needed in reducing the input features while preserving the needed information so as to make faster training of the CI technique. This paper presents feature reductions techniques used, namely correlation analysis and principle component analysis, in reducing number of input features presented to two CI techniques for TSA, namely probabilistic neural network (PNN) and least squares support vector machines (LS-SVM). The proposed feature reduction techniques are implemented and tested on the IEEE 39-bus test system and 87-bus Malaysia’s power system. Numerical results are presented to demonstrate the performance of the feature reduction techniques and its effects on the accuracies and time taken for training the two CI techniques.     

Sentic Computing for social media marketing

In a world in which millions of people express their opinions about commercial products in blogs, wikis, fora, chats and social networks, the distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand or organization. Opinion mining for product positioning, in fact, is getting a more and more popular research field but the extraction of useful information from social media is not a simple task. In this work we merge AI and Semantic Web techniques to extract, encode and represent this unstructured information. In particular, we use Sentic Computing, a multi-disciplinary approach to opinion mining and sentiment analysis, to semantically and affectively analyze text and encode results in a semantic aware format according to different web ontologies. Eventually we represent this information as an interconnected knowledge base which is browsable through a multi-faceted classification website.     

Autonomous mapping of HL7 RIM and relational database schema

Healthcare systems need to share information within and across the boundaries in order to provide better care to the patients. For this purpose, they take advantage of the full potential of current state of the art in healthcare standards providing interoperable solutions. HL7 V3 specification is an international message exchange and interoperability standard. HL7 V3 messages exchanged between healthcare applications are ultimately recorded into local healthcare databases, mostly in relational databases. In order to bring these relational databases in compliance with HL7, mappings between HL7 RIM (Reference Information Model) and relational database schema are required. Currently, RIM and database mapping is largely performed manually, therefore it is tedious, time consuming, error prone and expensive process. It is a challenging task to determine all correspondences between RIM and schema automatically because of extreme heterogeneity issues in healthcare databases. To reduce the amount of manual efforts as much as possible, autonomous mapping approaches are required. This paper proposes a technique that addresses the aforementioned mapping issue and aligns healthcare databases to HL7 V3 RIM specifications. Furthermore, the proposed technique has been implemented as a working application and tested on real world healthcare systems. The application loads the target healthcare schema and then identifies the most appropriate match for tables and the associated fields in the schema by using domain knowledge and the matching rules defined in the Mapping Knowledge Repository. These rules are designed to handle the complexity of semantics found in healthcare databases. The GUI allows users to view and edit/re-map the correspondences. Once all the mappings are defined, the application generates Mapping Specification, which contains all the mapping information i.e. database tables and fields with associated RIM classes and attributes. In order to enable the transactions, the application is facilitated with the autonomous code generation from the Mapping Specification. The Code Generator component focuses primarily on generating custom classes and hibernate mapping files against the runtime system to retrieve and parse the data from the data source—thus allows bi-directional HL7 to database communication, with minimum programming required. Our experimental results show 35–65% accuracy on real laboratory systems, thus demonstrating the promise of the approach. The proposed scheme is an effective step in bringing the clinical databases in compliance with RIM, providing ease and flexibility.