Mechanical testing of internal fixation devices: A theoretical and practical examination of current methods

Successful healing of long bone fractures is dependent on the mechanical environment created within the fracture, which in turn is dependent on the fixation strategy. Recent literature reports have suggested that locked plating devices are too stiff to reliably promote healing. However, in vitro testing of these devices has been inconsistent in both method of constraint and reported outcomes, making comparisons between studies and the assessment of construct stiffness problematic. Each of the methods previously used in the literature were assessed for their effect on the bending of the sample and concordant stiffness. The choice of outcome measures used in in vitro fracture studies was also assessed. Mechanical testing was conducted on seven hole locked plated constructs in each method for comparison. Based on the assessment of each method the use of spherical bearings, ball joints or similar is suggested at both ends of the sample. The use of near and far cortex movement was found to be more comprehensive and more accurate than traditional centrally calculated inter fragmentary movement values; stiffness was found to be highly susceptible to the accuracy of deformation measurements and constraint method, and should only be used as a within study comparison method. The reported stiffness values of locked plate constructs from in vitro mechanical testing is highly susceptible to testing constraints and output measures, with many standard techniques overestimating the stiffness of the construct. This raises the need for further investigation into the actual mechanical behaviour within the fracture gap of these devices.

Multiple instance dictionary learning for activity representation

This paper presents an effective feature representation method in the context of activity recognition. Efficient and effective feature representation plays a crucial role not only in activity recognition, but also in a wide range of applications such as motion analysis, tracking, 3D scene understanding etc. In the context of activity recognition, local features are increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational requirements, their performance is still limited for real world applications due to a lack of contextual information and models not being tailored to specific activities. We propose a new activity representation framework to address the shortcomings of the popular, but simple bag-of-words approach. In our framework, first multiple instance SVM (mi-SVM) is used to identify positive features for each action category and the k-means algorithm is used to generate a codebook. Then locality-constrained linear coding is used to encode the features into the generated codebook, followed by spatio-temporal pyramid pooling to convey the spatio-temporal statistics. Finally, an SVM is used to classify the videos. Experiments carried out on two popular datasets with varying complexity demonstrate significant performance improvement over the base-line bag-of-feature method.

The effect of internal audit function quality and internal audit contribution to external audit on audit fees

Corporate governance mandates and listing rules identify internal audit functions (IAF) as a central internal control mechanism. External audits are expected to assess the quality of IAF before placing reliance on its work. We provide evidence on the effect of IAF quality and IAF contribution to external audit on audit fees. Using data from a matched survey of both external and internal audits, we extend prior research which is based mainly on internal audits' assessment and conducted predominantly in highly developed markets. We find a positive relationship between IAF quality and audit fees as well as a reduction in audit fees as a result of external auditors' reliance on IAF. The interaction between IAF quality and IAF contribution to external audit suggests that high quality IAF induces greater external auditor reliance on internal auditors' work and thus result in lower external audit fees.

A Distributed Robotic Vision Service

Robotic vision is limited by line of sight and onboard camera capabilities. Robots can acquire video or images from remote cameras, but processing additional data has a computational burden. This paper applies the Distributed Robotic Vision Service, DRVS, to robot path planning using data outside line-of-sight of the robot. DRVS implements a distributed visual object detection service to distributes the computation to remote camera nodes with processing capabilities. Robots request task-specific object detection from DRVS by specifying a geographic region of interest and object type. The remote camera nodes perform the visual processing and send the high-level object information to the robot. Additionally, DRVS relieves robots of sensor discovery by dynamically distributing object detection requests to remote camera nodes. Tested over two different indoor path planning tasks DRVS showed dramatic reduction in mobile robot compute load and wireless network utilization.

Visual detection of occluded crop: For automated harvesting

This paper presents a novel crop detection system applied to the challenging task of field sweet pepper (capsicum) detection. The field-grown sweet pepper crop presents several challenges for robotic systems such as the high degree of occlusion and the fact that the crop can have a similar colour to the background (green on green). To overcome these issues, we propose a two-stage system that performs per-pixel segmentation followed by region detection. The output of the segmentation is used to search for highly probable regions and declares these to be sweet pepper. We propose the novel use of the local binary pattern (LBP) to perform crop segmentation. This feature improves the accuracy of crop segmentation from an AUC of 0.10, for previously proposed features, to 0.56. Using the LBP feature as the basis for our two-stage algorithm, we are able to detect 69.2% of field grown sweet peppers in three sites. This is an impressive result given that the average detection accuracy of people viewing the same colour imagery is 66.8%.

The oxygen migration in the apatite-type lanthanum silicate with the cation substitution

A theoretical model is proposed to determine the effects of Si substitution with Al on the oxygen diffusion in apatite-type lanthanum silicates based on density-functional theory (DFT) calculations for La10(SiO 4)4(AlO4)2O2. Substitution changes the stable configuration for excess oxygen from the split interstitial to a new cluster form with the original cluster. Al doping completely changes the migration mechanism from the interstitialcy one, which was proposed for the La9.33(SiO4)6O2 starting material, to a mechanism which contains an interstitial process. Nevertheless, the migration barrier is calculated to be 0.81 eV, which indicates small changes in oxygen conduction and is consistent with the observations. The present study indicates that the cation substitution on silicon site alone does not promise the improvement of the oxide ion conduction in the lanthanum silicate.

Elevated CDCP1 predicts poor patient outcome and mediates ovarian clear cell carcinoma by promoting tumor spheroid formation, cell migration and chemoresistance

Hematogenous metastases are rarely present at diagnosis of ovarian clear cell carcinoma (OCC). Instead dissemination of these tumors is characteristically via direct extension of the primary tumor into nearby organs and the spread of exfoliated tumor cells throughout the peritoneum, initially via the peritoneal fluid, and later via ascites that accumulates as a result of disruption of the lymphatic system. The molecular mechanisms orchestrating these processes are uncertain. In particular, the signaling pathways used by malignant cells to survive the stresses of anchorage-free growth in peritoneal fluid and ascites, and to colonize remote sites, are poorly defined. We demonstrate that the transmembrane glycoprotein CUB-domain-containing protein 1 (CDCP1) has important and inhibitable roles in these processes. In vitro assays indicate that CDCP1 mediates formation and survival of OCC spheroids, as well as cell migration and chemoresistance. Disruption of CDCP1 via silencing and antibody-mediated inhibition markedly reduce the ability of TOV21G OCC cells to form intraperitoneal tumors and induce accumulation of ascites in mice. Mechanistically our data suggest that CDCP1 effects are mediated via a novel mechanism of protein kinase B (Akt) activation. Immunohistochemical analysis also suggested that CDCP1 is functionally important in OCC, with its expression elevated in 90% of 198 OCC tumors and increased CDCP1 expression correlating with poor patient disease-free and overall survival. This analysis also showed that CDCP1 is largely restricted to the surface of malignant cells where it is accessible to therapeutic antibodies. Importantly, antibody-mediated blockade of CDCP1 in vivo significantly increased the anti-tumor efficacy of carboplatin, the chemotherapy most commonly used to treat OCC. In summary, our data indicate that CDCP1 is important in the progression of OCC and that targeting pathways mediated by this protein may be useful for the management of OCC, potentially in combination with chemotherapies and agents targeting the Akt pathway.

Place categorization and semantic mapping on a mobile robot

In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot with-out environment-specific training. Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network. We overcome its closed-set limitations by complementing the network with a series of one-vs-all classifiers that can learn to recognize new semantic classes online. Prior domain knowledge is incorporated by embedding the classification system into a Bayesian filter framework that also ensures temporal coherence. We evaluate the classification accuracy of the system on a robot that maps a variety of places on our campus in real-time. We show how semantic information can boost robotic object detection performance and how the semantic map can be used to modulate the robot’s behaviour during navigation tasks. The system is made available to the community as a ROS module.