Evaluation of a surrogate contact model of TKA

INTRODUCTION: Simultaneous prediction of body-level dynamics and detailed joint mechanics in the frame of musculoskeletal (MS) modeling represents still a highly computationally demanding task. Marra et al. (2014) recently presented and validated a MS model capable of concurrent prediction of muscle forces, knee ligament forces, tibiofemoral (TF) and patellofemoral (PF) contact forces in a MS model of Total Knee Arthroplasty (TKA) [1]. Simulation time for one complete gait cycle was in the order of 3 hours, and the iterative process that solved the equilibrium in the knee joint was thought to be the main source of overhead. Surrogate modeling techniques were suggested [2]. In this study, we develop a surrogate contact model of TKA to decrease the simulation time in the MS simulation. We hypothesize that the algorithm that allows the muscle fibers to wrap around the bones constitutes another source of overhead in the MS model. Therefore, we will also evaluate the performances of the surrogate model with and without muscle wrapping. METHODS: The original tibial component from our TKA model [1] was split in a medial and lateral hemi-part and fixed to the ground, whereas the femoral part was left with 6 degrees of freedom (DOF). The contacting pairs exchanged three forces and three moments, which were assumed functions of the relative pose only. Translations (X, Y, Z) were defined relative to the tibial component frame and rotations of the femoral component (RotX, RotY, RotZ) were described with Cardan angles, using the z-y-x rotation sequence. Similarly to Lin et al (2010) [3] we identified two sensitive directions, Y and RotX and, therefore, we defined a sample point as composed by four pose parameters and the two loads in the sensitive directions: X, Fy, Z, Tx, RotY, RotZ. Reference load-pose data were obtained from four simulations of gait, squat, chair-rise, and right-turn trials using the original contact model. The design space was populated using the Hammersley quasi-random sequence and adopting a multi-domain approach, as proposed by Eskinazi and Fregly (2015) [2]. One domain consisted of 20 data points per each frame of the four dynamic simulations, spanning the boundaries of ± 1 standard deviation from the time-varying reference envelopes. A second domain of 2500 points was generated in the principal component space of the reference load-pose data of each dynamic simulation, with boundaries enlarged by 50%. A third domain of 1000 data points represented one-side-contact situations, in which Tx was bounded to ± 4 Nm. In total, 36000 data points were sampled in the three different domains. Data points were evaluated using the original contact model (Fig. 1) by repeated Force-Dependent Kinematics (FDK) analyses. Data points which did not lead to equilibrium were discarded. The remaining 27620 points were randomly subdivided into a training (70%) and testing (30%) group. Three separate Feed-Forward Artificial Neural Networks (FFANN), consisting of four inner layers of 20 hyperbolic tangent sigmoid neurons each, were configured within the Neural Network Toolbox in MATLAB 8.1 (The MathWorks Inc., Natick, MA, 2013). The first network was trained to learn the relationships between the four –two medial and two lateral– sensitive loads (output) and the six pose parameters (input). Two other networks –one medial and one lateral– were trained separately to learn the relationships between the remaining loads of each side (output) and all the pose parameters plus the two sensitive loads from each side (input). We used the popular Levenberg-Marquardt training algorithm in conjunction with Bayesian regularization to avoid over-fitting. Stopping criterion was a training time of two hours for each network. The trained networks were translated to custom C++ DLL functions for successive inclusion in our MS model. The surrogate contact model replaced the original contact model and one gait trial was simulated with 4 different combination of the following model settings: original versus surrogate contact model, wrapping enabled versus disabled. RESULTS: The contact sampling model required 238 hours to evaluate the 36000 data points. Predicted tibiofemoral compressive forces under all simulated cases are shown in Fig. 2. A comparison with experimental measurements (eTibia line) is also shown. Surrogate model predictions showed a very good agreement with the original model counterparts. Fig. 3 summarizes the computation times: simulations took the longest when muscle wrapping was enabled and the benefits of using the surrogate model became evident only when the wrapping algorithm was switched off, leading to a 6x speed-up. Simulation time with the original contact model decreased by a factor of 8 by switching off the wrapping algorithm. DISCUSSION: The use of FFANN-based surrogate contact model, in place of the original rigid contact model, could substantially reduce the simulation time of a full gait cycle down to 3 minutes, when the wrapping algorithm was turned off. Such improvement could not be achieved when using the wrapping algorithm. This enlightens another important source of overhead in MS modeling –the muscle wrapping algorithm– which unexpectedly was found to dominate the simulation time. At each FDK iteration, the wrapping algorithm needs to be solved as well, introducing overhead. If the wrapping algorithm is slower than the contact algorithm, then the computation time of each step will be dominated by the former, leaving only a small fraction to be gained from the latter. SIGNIFICANCE: We showed that surrogate contact model could reduce the simulation time in a MS model of TKA down to a level which allows parametric studies and/or optimization to be feasible. We also discovered that the muscle wrapping algorithm constituted an unexpectedly large source of overhead during dynamic simulations. These represent new and important findings for the MS modeling community. REFERENCES: [1] M. A. Marra, V. Vanheule, R. Fluit, B. H. F. J. M. Koopman, J. Rasmussen, N. J. J. Verdonschot, and M. S. Andersen, “A Subject-Specific Musculoskeletal Modeling Framework to Predict in Vivo Mechanics of Total Knee Arthroplasty.,” J. Biomech. Eng., Nov. 2014. [2] I. Eskinazi and B. J. Fregly, “Surrogate modeling of deformable joint contact using artificial neural networks.,” Med. Eng. Phys., Jul. 2015. [3] Y.-C. Lin, R. T. Haftka, N. V Queipo, and B. J. Fregly, “Surrogate articular contact models for computationally efficient multibody dynamic simulations.,” Med. Eng. Phys., vol. 32, no. 6, pp. 584–94, Jul. 2010. ACKNOWLEDGEMENTS: This study was conducted within the ERC ‘BioMechTools’ project, funded by the European Research Council

Evaluation of laxity tests with a musculoskeletal model of total knee arthroplasty

Introduction Musculoskeletal models are emerging as potential tools for the use in many clinical applications. One important example is aid to the clinical decision in the orthopaedic field. Recently, a patient-specific model of Cruciate-Retaining Total Knee Arthroplasty (CR-TKA) was presented and validated with respect to knee joint forces and kinematics [1]. However, the ligament restraints were not calibrated and inaccuracies in knee kinematic predictions were present. The objective of this study was to evaluate the effect of ligament calibration on the performance of simulated laxity tests. Methods A musculoskeletal model of CR-TKA was previously described [1]. The model comprised the musculoskeletal architecture of a TKA patient and a force-dependent model of the prosthetic knee and patellofemoral joint. Ligament restraints were modelled using non-linear springs and contact was solved using a rigid formulation. To calibrate the ligament parameters we simulated anterior/posterior, valgus/varus and endo-/exorotation laxity tests. Each test was performed at four different knee flexion angles (0, 30, 60, 90 deg). The anterior (respectively posterior) laxity load consisted of a 35 N force applied on the tibia at a distance of approximately 15 cm from the surface of the tibial component, pointing anteriorly (respectively posteriorly). Valgus (respectively varus) test was simulated by applying a force on the tibia at a distance of approximately 15 cm from the ankle joint, pointing laterally (respectively medially) so that the resulting moment was equal to 10 Nm. For the endo- (respectively exo-) rotation a 1.5 Nm torque was applied to the longitudinal axis of the tibia. Laxity envelopes for each test were calculated as the difference between the values obtained in the two opposite directions of the test. Manual changes to ligament insertion site, stiffness, and reference strain were made iteratively in order to obtain laxity envelopes close to those reported in the literature for cadaveric tests on a CR-TKA [2]. All the laxity tests were eventually simulated with the same ligament configuration. Results The results for all simulated laxity tests and the reference values from the literature are summarized in Table 1. 0° 30° 60° 90° AP (M) 3.5mm 4.2mm 1.0mm 1.0mm AP (L) 1.5mm 5mm 4mm 4.5mm VV (M) 0.9° 4.3° 2.6° 1.5° VV (L) 3.0° 6.0° 7.0° 7.0° EE (M) 7.0° 16.5° 4.0° 5.5° EE (L) 6.5° 22.0° 21.0° 23.0° Table 1: AP: Anterior/Posterior, VV: Valgus/Varus, EE: Endo-/Exorotation, M: Model prediction, L: Literature value Discussion The laxity envelopes predicted by the model were in partial agreement with those reported in the literature. The largest differences were noted for 60-90 degrees of knee flexion for all laxity tests, where the model showed considerably less laxity. These deviations may be attributable to actual differences between the implant design and subject geometry currently simulated and those used in the cadaveric tests. In future studies we aim to simulate surgical variations such as implant size and positioning, joint line elevation and ligament restraint. This musculoskeletal model of TKA has potential as a pre-operative planning tool for orthopaedic interventions. References Marra et al, J Biomech Eng, 137, 2015 Saeki et al, Clin Orthop Relat Res, 392:184-189, 200

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Yersinia effectors target mammalian signalling pathways

Animals have an immune system to fight off challenges from both viruses and bacteria. The first line of defence is innate immunity, which is composed of cells that engulf pathogens as well as cells that release potent signalling molecules to activate an inflammatory response and the adaptive immune system. Pathogenic bacteria have evolved a set of weapons, or effectors, to ensure survival in the host. Yersinia spp. use a type III secretion system to translocate these effector proteins, called Yops, into the host. This report outlines how Yops thwart the signalling machinery of the host immune system.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73466/1/j.1462-5822.2002.00182.x.pd

State of the climate in 2013

In 2013, the vast majority of the monitored climate variables reported here maintained trends established in recent decades. ENSO was in a neutral state during the entire year, remaining mostly on the cool side of neutral with modest impacts on regional weather patterns around the world. This follows several years dominated by the effects of either La Niña or El Niño events. According to several independent analyses, 2013 was again among the 10 warmest years on record at the global scale, both at the Earths surface and through the troposphere. Some regions in the Southern Hemisphere had record or near-record high temperatures for the year. Australia observed its hottest year on record, while Argentina and New Zealand reported their second and third hottest years, respectively. In Antarctica, Amundsen-Scott South Pole Station reported its highest annual temperature since records began in 1957. At the opposite pole, the Arctic observed its seventh warmest year since records began in the early 20th century. At 20-m depth, record high temperatures were measured at some permafrost stations on the North Slope of Alaska and in the Brooks Range. In the Northern Hemisphere extratropics, anomalous meridional atmospheric circulation occurred throughout much of the year, leading to marked regional extremes of both temperature and precipitation. Cold temperature anomalies during winter across Eurasia were followed by warm spring temperature anomalies, which were linked to a new record low Eurasian snow cover extent in May. Minimum sea ice extent in the Arctic was the sixth lowest since satellite observations began in 1979. Including 2013, all seven lowest extents on record have occurred in the past seven years. Antarctica, on the other hand, had above-average sea ice extent throughout 2013, with 116 days of new daily high extent records, including a new daily maximum sea ice area of 19.57 million km2 reached on 1 October. ENSO-neutral conditions in the eastern central Pacific Ocean and a negative Pacific decadal oscillation pattern in the North Pacific had the largest impacts on the global sea surface temperature in 2013. The North Pacific reached a historic high temperature in 2013 and on balance the globally-averaged sea surface temperature was among the 10 highest on record. Overall, the salt content in nearsurface ocean waters increased while in intermediate waters it decreased. Global mean sea level continued to rise during 2013, on pace with a trend of 3.2 mm yr-1 over the past two decades. A portion of this trend (0.5 mm yr-1) has been attributed to natural variability associated with the Pacific decadal oscillation as well as to ongoing contributions from the melting of glaciers and ice sheets and ocean warming. Global tropical cyclone frequency during 2013 was slightly above average with a total of 94 storms, although the North Atlantic Basin had its quietest hurricane season since 1994. In the Western North Pacific Basin, Super Typhoon Haiyan, the deadliest tropical cyclone of 2013, had 1-minute sustained winds estimated to be 170 kt (87.5 m s-1) on 7 November, the highest wind speed ever assigned to a tropical cyclone. High storm surge was also associated with Haiyan as it made landfall over the central Philippines, an area where sea level is currently at historic highs, increasing by 200 mm since 1970. In the atmosphere, carbon dioxide, methane, and nitrous oxide all continued to increase in 2013. As in previous years, each of these major greenhouse gases once again reached historic high concentrations. In the Arctic, carbon dioxide and methane increased at the same rate as the global increase. These increases are likely due to export from lower latitudes rather than a consequence of increases in Arctic sources, such as thawing permafrost. At Mauna Loa, Hawaii, for the first time since measurements began in 1958, the daily average mixing ratio of carbon dioxide exceeded 400 ppm on 9 May. The state of these variables, along with dozens of others, and the 2013 climate conditions of regions around the world are discussed in further detail in this 24th edition of the State of the Climate series. © 2014, American Meteorological Society. All rights reserved

Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy