Static load bearing exercises during rehabilitation of individuals with transfemoral amputation fitted with osseointegrated implant: Kinetic analysis

The desire to solve problems caused by socket prostheses in transfemoral amputees and the acquired success of osseointegration in the dental application has led to the introduction of osseointegration in the orthopedic surgery. Since its first introduction in 1990 in Gothenburg Sweden the osseointegrated (OI) orthopedic fixation has proven several benefits[1]. The surgery consists of two surgical procedures followed by a lengthy rehabilitation program. The rehabilitation program after an OI implant includes a specific training period with a short training prosthesis. Since mechanical loading is considered to be one of the key factors that influence bone mass and the osseointegration of bone-anchored implants, the rehabilitation program will also need to include some form of load bearing exercises (LBE). To date there are two frequently used commercially available human implants. We can find proof in the literature that load bearing exercises are performed by patients with both types of OI implants. We refer to two articles, a first one written by Dr. Aschoff and all and published in 2010 in the Journal of Bone and Joint Surgery.[2] The second one presented by Hagberg et al in 2009 gives a very thorough description of the rehabilitation program of TFA fitted with an OPRA implant. The progression of the load however is determined individually according to the residual skeleton’s quality, pain level and body weight of the participant.[1] Patients are using a classical bathroom weighing scale to control the load on the implant during the course of their rehabilitation. The bathroom scale is an affordable and easy-to-use device but it has some important shortcomings. The scale provides instantaneous feedback to the patient only on the magnitude of the vertical component of the applied force. The forces and moments applied along and around the three axes of the implant are unknown. Although there are different ways to assess the load on the implant for instance through inverse dynamics in a motion analysis laboratory [3-6] this assessment is challenging. A recent proof- of-concept study by Frossard et al (2009) showed that the shortcomings of the weighing scale can be overcome by a portable kinetic system based on a commercial transducer[7].

Static load bearing exercises of individuals with transfemoral amputation fitted with an osseointegrated implant: Reliability of kinetic data

This study aimed at presenting the intra-tester reliability of the static load bearing exercises (LBEs) performed by individuals with transfemoral amputation (TFA) fitted with an osseointegrated implant to stimulate the bone remodelling process. There is a need for a better understanding of the implementation of these exercises particularly the reliability. The intra-tester reliability is discussed with a particular emphasis on inter-load prescribed, inter-axis and inter-component reliabilities as well as the effect of body weight normalisation. Eleven unilateral TFAs fitted with an OPRA implant performed five trials in four loading conditions. The forces and moments on the three axes of the implant were measured directly with an instrumented pylon including a six-channel transducer. Reliability of loading variables was assessed using intraclass correlation coefficients (ICCs) and percentage standard error of measurement values (%SEMs). The ICCs of all variables were above 0.9 and the %SEM values ranged between 0 and 87%. This study showed a high between-participants’ variance highlighting the lack of loading consistency typical of symptomatic population as well as a high reliability between the loading sessions indicating a plausible correct repetition of the LBE by the participants. However, these outcomes must be understood within the framework of the proposed experimental protocol.

Searching for people using semantic soft biometric descriptions

It is not uncommon to hear a person of interest described by their height, build, and clothing (i.e. type and colour). These semantic descriptions are commonly used by people to describe others, as they are quick to communicate and easy to understand. However such queries are not easily utilised within intelligent video surveillance systems, as they are difficult to transform into a representation that can be utilised by computer vision algorithms. In this paper we propose a novel approach that transforms such a semantic query into an avatar in the form of a channel representation that is searchable within a video stream. We show how spatial, colour and prior information (person shape) can be incorporated into the channel representation to locate a target using a particle-filter like approach. We demonstrate state-of-the-art performance for locating a subject in video based on a description, achieving a relative performance improvement of 46.7% over the baseline. We also apply this approach to person re-detection, and show that the approach can be used to re-detect a person in a video steam without the use of person detection.

Making the profitable transition towards sustainable business practice

The last three decades have been difficult for companies and industry. In an increasingly competitive international business climate with shifting national environmental regulations, higher standards are being demanded by the consumer and community groups, not-to-mention the escalating cost of primary resources such as water, steel and minerals. The cause of these pressures is the traditional notion held by business executives and engineers that there is an inherent trade off between eco-efficiency and improving the economic bottom line. However there is significant evidence and examples of best practice to show that there is in fact no trade-off between the environment and the economy if sustainable development through continual improvement is adopted. It is highly possible therefore for companies to make a profitable transition towards sustainable business practice, where along the transition significant business opportunities can be taken advantage of. Companies are by their very nature dynamic, influential and highly capable of adapting to change. Making an organisational transformation to a sustainable business is not outside the capacity of the typical company, who know much of what is needed already to change their activities to satisfy current market demands while achieving competitiveness. However in order to make the transition towards sustainable business practice companies require some key mechanisms such as accurate information on methodologies and opportunities, understanding of the financial and non-financial incentives, permission from stakeholders and shareholders, understanding of the emerging market opportunities, a critical mass of leaders in their sector and demonstrated case studies, and awarding appropriate risk-taking activities undertaken by engineers and CEOs. Satisfying these requirements will adopt an innovative culture within the company that strives for continual improvement and successfully transforms itself to achieve competitiveness in the 21st Century. This paper will summarise the experiences of The Natural Edge Project (TNEP) and its partners in assisting organisations to make a profitable transition towards sustainable business practice through several initiatives. The Natural Advantage of Nations publication provides the critical information required by business leaders and engineers to set the context of sustainable business practice. The Profiting in a Carbon Constrained World report, developed with Natural Capitalism Inc led by Hunter Lovins, summarises the opportunities available to companies to take advantage of the carbon trading market mechanisms such as the Chicago Climate Exchange and European Climate Exchange. The Sustainability Helix then guides the company through the transition by identifying the key tools and methodologies required by companies to reduce environmental loading while dramatically improving resource productivity and achieving competitiveness. Finally, the Engineering Sustainable Solutions Program delivers the key engineering information required by companies and university departments to deliver sustainable engineering solutions. The initiatives are of varying complexity and level of application, however all are designed to provide key staff the critical information required to make a profitable transition towards sustainable business practice. It is then their responsibility to apply and teach their knowledge to the rest of the organisation.

Frequency response function based damage identification using principal component analysis and pattern recognition technique

Pattern recognition is a promising approach for the identification of structural damage using measured dynamic data. Much of the research on pattern recognition has employed artificial neural networks (ANNs) and genetic algorithms as systematic ways of matching pattern features. The selection of a damage-sensitive and noise-insensitive pattern feature is important for all structural damage identification methods. Accordingly, a neural networks-based damage detection method using frequency response function (FRF) data is presented in this paper. This method can effectively consider uncertainties of measured data from which training patterns are generated. The proposed method reduces the dimension of the initial FRF data and transforms it into new damage indices and employs an ANN method for the actual damage localization and quantification using recognized damage patterns from the algorithm. In civil engineering applications, the measurement of dynamic response under field conditions always contains noise components from environmental factors. In order to evaluate the performance of the proposed strategy with noise polluted data, noise contaminated measurements are also introduced to the proposed algorithm. ANNs with optimal architecture give minimum training and testing errors and provide precise damage detection results. In order to maximize damage detection results, the optimal architecture of ANN is identified by defining the number of hidden layers and the number of neurons per hidden layer by a trial and error method. In real testing, the number of measurement points and the measurement locations to obtain the structure response are critical for damage detection. Therefore, optimal sensor placement to improve damage identification is also investigated herein. A finite element model of a two storey framed structure is used to train the neural network. It shows accurate performance and gives low error with simulated and noise-contaminated data for single and multiple damage cases. As a result, the proposed method can be used for structural health monitoring and damage detection, particularly for cases where the measurement data is very large. Furthermore, it is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy under varying levels of damage.

Loading compliance of static load bearing exercises performed by transfemoral amputees fitted with an osseointegrated implant

Background To date bone-anchored prostheses are used to alleviate the concerns caused by socket suspended prostheses and to improve the quality of life of transfemoral amputees (TFA). Currently, two implants are commercially available (i.e., OPRA (Integrum AB, Sweden), ILP (Orthodynamics GmbH, Germany)). [1-17]The success of the OPRA technique is codetermined by the rehabilitation program. TFA fitted with an osseointegrated implant perform progressive mechanical loading (i.e. static load bearing exercises (LBE)) to facilitate bone remodelling around the implant.[18, 19] Aim This study investigated the trustworthiness of monitoring the load prescribed (LP) during experimental static LBEs using the vertical force provided by a mechanical bathroom scale that is considered a surrogate of the actual load applied. Method Eleven unilateral TFAs fitted with an OPRA implant performed five trials in four loading conditions. The forces and moments on the three axes of the implant were measured directly with an instrumented pylon including a six-channel transducer. The “axial” and “vectorial” comparisons corresponding to the difference between the force applied on the long axis of the fixation and LP as well as the resultant of the three components of the load applied and LP, respectively were analysed Results For each loading condition, Wilcoxon One-Sample Signed Rank Tests were used to investigate if significant differences (p<0.05) could be demonstrated between the force applied on the long axis and LP, and between the resultant of the force and LP. The results demonstrated that the raw axial and vectorial differences were significantly different from zero in all conditions (p<0.05), except for the vectorial difference for the 40 kg loading condition (p=0.182). The raw axial difference was negative for all the participants in every loading condition, except for TFA03 in the 10 kg condition (11.17 N). Discussion & Conclusion This study showed a significant lack of axial compliance. The load applied on the long axis was significantly smaller than LP in every loading condition. This led to a systematic underloading of the long axis of the implant during the proposed experimental LBE. Monitoring the vertical force might be only partially reflective of the actual load applied, particularly on the long axis of the implant.

Genetic studies of body mass index yield new insights for obesity biology

Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 × 10−8), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ~2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.

The neuroscience of inspirational leadership: the importance of collective-oriented language and shared group membership

Effective leaders are believed to inspire followers by providing inclusive visions of the future that followers can identify with. In the present study, we examined the neural mechanisms underlying this process, testing key hypotheses derived from transformational and social identity approaches to leadership. While undergoing functional MRI, supporters from the two major Australian political parties (Liberal vs. Labor) were presented with inspirational collective-oriented and noninspirational personal-oriented statements made by in-group and out-group leaders. Imaging data revealed that inspirational (rather than noninspirational) statements from in-group leaders were associated with increased activation in the bilateral rostral inferior parietal lobule, pars opercularis, and posterior midcingulate cortex: brain areas that are typically implicated in controlling semantic information processing. In contrast, for out-group leaders, greater activation in these areas was associated with noninspirational statements. In addition, noninspirational statements by in-group (but not out-group) leaders resulted in increased activation in the medial prefrontal cortex, an area typically associated with reasoning about a person’s mental state. These results show that followers processed identical statements qualitatively differently as a function of leaders’ group membership, thus demonstrating that shared identity acts as an amplifier for inspirational leadership communication.

Steady-state dendritic cells expressing cognate antigen terminate memory CD8+ T-cell responses

Antigen stimulation of naive T cells in conjunction with strong costimulatory signals elicits the generation of effector and memory populations. Such terminal differentiation transforms naive T cells capable of differentiating along several terminal pathways in response to pertinent environmental cues into cells that have lost developmental plasticity and exhibit heightened responsiveness. Because these cells exhibit little or no need for the strong costimulatory signals required for full activation of naive T cells, it is generally considered memory and effector T cells are released from the capacity to be inactivated. Here, we show that steadystate dendritic cells constitutively presenting an endogenously expressed antigen inactivate fully differentiated memory and effector CD8+ T cells in vivo through deletion and inactivation. These findings indicate that fully differentiated effector and memory T cells exhibit a previously unappreciated level of plasticity and provide insight into how memory and effector T-cell populations may be regulated. © 2008 by The American Society of Hematology.

Deeper and wider fully convolutional network coupled with conditional random fields for scene labeling

Deep convolutional neural networks (DCNNs) have been employed in many computer vision tasks with great success due to their robustness in feature learning. One of the advantages of DCNNs is their representation robustness to object locations, which is useful for object recognition tasks. However, this also discards spatial information, which is useful when dealing with topological information of the image (e.g. scene labeling, face recognition). In this paper, we propose a deeper and wider network architecture to tackle the scene labeling task. The depth is achieved by incorporating predictions from multiple early layers of the DCNN. The width is achieved by combining multiple outputs of the network. We then further refine the parsing task by adopting graphical models (GMs) as a post-processing step to incorporate spatial and contextual information into the network. The new strategy for a deeper, wider convolutional network coupled with graphical models has shown promising results on the PASCAL-Context dataset.