Bone-to-bone and implant-to-bone impingement : a novel graphical representation for hip replacement planning

Bone-to-bone impingement (BTBI) and implant-to-bone impingement (ITBI) risk assessment is generally performed intra-operatively by surgeons, which is entirely subjective and qualitative, and therefore, lead to sub-optimal results and recurrent dislocation in some cases. Therefore, a method was developed for identifying subject-specific BTBI and ITBI, and subsequently, visualising the impingement area on native bone anatomy to highlight where prominent bone should be resected. Activity definitions and subject-specific bone geometries, with planned implants were used as inputs for the method. The ITBI and BTBI boundary and area were automatically identified using ray intersection and region growing algorithm respectively to retain the same ‘conical clearance angle’ obtained to avoid prosthetic impingement (PI). The ITBI and BTBI area was then presented with different colours to highlight the risk of impingement, and importance of resection. A clinical study with five patients after 2 years of THA was performed to validate the method. The results supported the study hypothesis, in that the predicted highest risk area (red coloured zone) was completely/majorly resected during the surgery. Therefore, this method could potentially be used to examine the effect of different pre-operative plans and hip motions on BTBI, ITBI, and PI, and to guide bony resection during THA surgery

Anatomy of Motor Axons to Direct Flight Muscles in \u3ci\u3eDrosophila\u3c/i\u3e

The direct flight muscles of Drosophila melanogaster are innervated by the anterior dorsal mesothoracic (ADM) nerve and the mesothoracic accessory (MAC) nerve. Each of the four conspicuously large axons in the ADM nerve serves one of the muscles designated pal, pa3, pa4 and pa5. Muscle pa4 is additionally innervated by a very small neurosecretory axon. Muscle pa6, also innervated by the ADM nerve, receives at least one small nerve fibre but no large axon. Muscle pa2 is innervated by a large axon from the MAC nerve. Large motor axons, identified by serial section tracing from their respective muscles, are consistent among different individuals in both relative positions and relative diameters within the ADM nerve

Wildlife Carcass Disposal

Many wildlife management situations require the disposal of animal carcasses. These can include the lethal removal of wildlife to resolve damage or conflicts, as well as clean-up after mortalities caused by vehicle collisions, disease, oil spills (Figure 1) or other natural disasters. Carcasses must be disposed of properly to protect public sensitivities, the environment, and public health. Improper disposal of carcasses can result in public outrage, site contamination, injury to animals and people, and the attraction of other animals that may lead to wildlife damage issues. Concern over ground water contamination and disease transmission from improper carcass disposal has resulted in increased regulation. Successful carcass disposal programs are cost-effective, environmentally sound, and protective of public health. In addition, disposal practices must demonstrate sensitivity to public perception while adhering to state and local guidelines. This publication discusses the range of options available for the responsible disposal of animal carcasses. Proper disposal of carcasses protects the sensitivities of the public, reduces the potential for the spread of zoonotic diseases, prevents nutrient losses to surrounding soils and ultimately, groundwater, and reduces human-wildlife conflicts. Failure to dispose of carcasses appropriately can cause unwanted media attention and public outrage. The overall goal of any animal carcass management plan is to ensure clean, safe disposal of all materials in a manner that protects human, animal, and environmental health

Endogenous Growth and the role of History

This paper presents a model in which the realizations of stochastic tax and depreciation rates determine both the level and growth rate of output: externalities to investment - learning by watching - are characterized by diminishing returns, yielding a nonlinear "technical progress function". This results in multiple steady-state growth rates. History matters. It is possible that two economies with identical "deep" parameters and initial capital stocks may cycle around different trend growth rates, depending upon the historical path of fiscal shocks. Growth and cycles interact, and the nonlinearity means that output changes cannot be decomposed into a stochastic trend and a trend-stationary process.

COORDINATION IN DYNAMIC JUMPING

This study investigated coordination in dynamic jumping using a forward dynamics computer simulation model. A planar eight-segment torque-driven model was used to match the takeoff phase in a recorded running jump for height and recorded jump for distance by varying the torque generator activation timings. Two optimisations were then carried out to maximise height reached and distance travelled for each set of initial conditions used in the matching simulations. Although for each set of initial conditions, the order of activation onset timing was different for the two optimisations, the timing of activation onset in the optimisations for height and distance using the same initial conditions was very similar. This study has shown that the optimal activations are more a function of the initial conditions than the selection of maximal height or maximal distance

PERFORMANCE SENSITIVITY TO PERTURBATIONS IN ACTIVATION TIMING

This study investigated the sensitivity of optimum jumping performances to perturbations in activation timing. A planar eight-segment computer simulation model was used to simulate the takeoff phase in a high jumping performance. The model was evaluated and subsequently used to produce an optimum performance with a jump height of 2.63 m. The mLJscle activation onset timings at the knee were then varied by ± 5 ms and the effect on the simulated performance was determined. By simply varying the knee activation onset timings the performance did not change in terms of jump height, but the simulations included penalties which indicated that anatomical constraints had been violated. Reoptimisation with a measure of robustness included resulted in an optimum simulated jump of 2.32 m with no penalties which was unaffected by 5 ms perturbations

OPTIMISATION OF PERFORMANCE IN RUNNING JUMPS FOR HEIGHT

This study investigates the effect of approach conditions and takeoff technique on optimum performance. A planar eight-segment computer simulation model was used to simulate the takeoff phase in high jumping. Optimisations based on performances in the laboratory and at an athletics track were carried out to maximise the height reached by the mass centre in the flight phase. Three pairs of optimisations were performed: (i) optimisation of technique, (ii) optimisation of technique and initial conditions, (iii) optimisation of technique, initial conditions and approach velocity. In the first pair of optimisations the increases in height were 0.12 m and 0.17 m respectively. In the second pair of optimisations the additional increases in height were 0.09 m and 0.19 m and in the third pair further increases of 0.42 m and 0.02 m were obtained