Relevance of in vitro tests of adhesive and composite dental materials, a review in 3 parts. Part 1: Approval requirements and standardized testing of composite materials according to ISO specifications

The first part of this three-part review on the relevance of laboratory testing of composites and adhesives deals with approval requirements for composite materials. We compare the in vivo and in vitro literature data and discuss the relevance of in vitro analyses. The standardized ISO protocols are presented, with a focus on the evaluation of physical parameters. These tests all have a standardized protocol that describes the entire test set-up. The tests analyse flexural strength, depth of cure, susceptibility to ambient light, color stability, water sorption and solubility, and radiopacity. Some tests have a clinical correlation. A high flexural strength, for instance, decreases the risk of fractures of the marginal ridge in posterior restorations and incisal edge build-ups of restored anterior teeth. Other tests do not have a clinical correlation or the threshold values are too low, which results in an approval of materials that show inferior clinical properties (e.g., radiopacity). It is advantageous to know the test set-ups and the ideal threshold values to correctly interpret the material data. Overall, however, laboratory assessment alone cannot ensure the clinical success of a product.

Structural and resting state functional connectivity of the subthalamic nucleus: identification of motor STN parts and the hyperdirect pathway

Deep brain stimulation (DBS) for Parkinson's disease often alleviates the motor symptoms, but causes cognitive and emotional side effects in a substantial number of cases. Identification of the motor part of the subthalamic nucleus (STN) as part of the presurgical workup could minimize these adverse effects. In this study, we assessed the STN's connectivity to motor, associative, and limbic brain areas, based on structural and functional connectivity analysis of volunteer data. For the structural connectivity, we used streamline counts derived from HARDI fiber tracking. The resulting tracks supported the existence of the so-called "hyperdirect" pathway in humans. Furthermore, we determined the connectivity of each STN voxel with the motor cortical areas. Functional connectivity was calculated based on functional MRI, as the correlation of the signal within a given brain voxel with the signal in the STN. Also, the signal per STN voxel was explained in terms of the correlation with motor or limbic brain seed ROI areas. Both right and left STN ROIs appeared to be structurally and functionally connected to brain areas that are part of the motor, associative, and limbic circuit. Furthermore, this study enabled us to assess the level of segregation of the STN motor part, which is relevant for the planning of STN DBS procedures.

User and Machine Authentication and Authorization Infrastructure for Distributed Wireless Sensor Network Testbeds

The intention of an authentication and authorization infrastructure (AAI) is to simplify and unify access to different web resources. With a single login, a user can access web applications at multiple organizations. The Shibboleth authentication and authorization infrastructure is a standards-based, open source software package for web single sign-on (SSO) across or within organizational boundaries. It allows service providers to make fine-grained authorization decisions for individual access of protected online resources. The Shibboleth system is a widely used AAI, but only supports protection of browser-based web resources. We have implemented a Shibboleth AAI extension to protect web services using Simple Object Access Protocol (SOAP). Besides user authentication for browser-based web resources, this extension also provides user and machine authentication for web service-based resources. Although implemented for a Shibboleth AAI, the architecture can be easily adapted to other AAIs.

Combining Wireless Sensor Networks and Machine Learning for Flash Flood Nowcasting

This paper addresses an investigation with machine learning (ML) classification techniques to assist in the problem of flash flood now casting. We have been attempting to build a Wireless Sensor Network (WSN) to collect measurements from a river located in an urban area. The machine learning classification methods were investigated with the aim of allowing flash flood now casting, which in turn allows the WSN to give alerts to the local population. We have evaluated several types of ML taking account of the different now casting stages (i.e. Number of future time steps to forecast). We have also evaluated different data representation to be used as input of the ML techniques. The results show that different data representation can lead to results significantly better for different stages of now casting.

Simultaneous analysis of nuclear and mitochondrial DNA, mRNA and miRNA from backspatter from inside parts of firearms generated by shots at "triple contrast" doped ballistic models

When a firearm projectile hits a biological target a spray of biological material (e.g., blood and tissue fragments) can be propelled from the entrance wound back towards the firearm. This phenomenon has become known as "backspatter" and if caused by contact shots or shots from short distances traces of backspatter may reach, consolidate on, and be recovered from, the inside surfaces of the firearm. Thus, a comprehensive investigation of firearm-related crimes must not only comprise of wound ballistic assessment but also backspatter analysis, and may even take into account potential correlations between these emergences. The aim of the present study was to evaluate and expand the applicability of the "triple contrast" method by probing its compatibility with forensic analysis of nuclear and mitochondrial DNA and the simultaneous investigation of co-extracted mRNA and miRNA from backspatter collected from internal components of different types of firearms after experimental shootings. We demonstrate that "triple contrast" stained biological samples collected from the inside surfaces of firearms are amenable to forensic co-analysis of DNA and RNA and permit sequence analysis of the entire mtDNA displacement-loop, even for "low template" DNA amounts that preclude standard short tandem repeat DNA analysis. Our findings underscore the "triple contrast" method's usefulness as a research tool in experimental forensic ballistics.

Fast Prediction of Femoral Biomechanics Using Supervised Machine Learning and Statistical Shape Modeling

Finite element (FE) analysis is an important computational tool in biomechanics. However, its adoption into clinical practice has been hampered by its computational complexity and required high technical competences for clinicians. In this paper we propose a supervised learning approach to predict the outcome of the FE analysis. We demonstrate our approach on clinical CT and X-ray femur images for FE predictions ( FEP), with features extracted, respectively, from a statistical shape model and from 2D-based morphometric and density information. Using leave-one-out experiments and sensitivity analysis, comprising a database of 89 clinical cases, our method is capable of predicting the distribution of stress values for a walking loading condition with an average correlation coefficient of 0.984 and 0.976, for CT and X-ray images, respectively. These findings suggest that supervised learning approaches have the potential to leverage the clinical integration of mechanical simulations for the treatment of musculoskeletal conditions.