Musculoskeletal models permit the determination of internal forces acting during dynamic movement, which is clinically useful, but traditional methods may suffer from slowness and a need for extensive input data. Recently, there has been interest in the use of supervised learning to build approximate models for computationally demanding processes, with benefits in speed and flexibility. Here, we use a deep neural networks to learn the mapping from movement space to muscle space. Trained on a set of kinematic, kinetic and electromyographic measurements from 156 subjects during gait, the network’s predictions of internal force magnitudes show good concordance with those derived by musculoskeletal modelling. In a separate set of experiments, training on data from the most widely known benchmarks of modelling performance, the international Grand Challenge competitions, generates predictions that better those of the winning submissions in four of the six competitions. Computational speedup facilitates incorporation into a lab-based system permitting real-time estimation of forces, and interrogation of the trained neural networks provides novel insights into population-level relationships between kinematic and kinetic factors.

2Segment-based musculoskeletal models allow the prediction of muscle, ligament and joint forces without making assumptions regarding joint degrees of freedom. The 4 dataset published for the "Grand Challenge Competition to Predict In Vivo Knee Loads" provides directly-measured tibiofemoral contact forces for activities of daily living. For 6 the "Sixth Grand Challenge Competition to Predict In Vivo Knee Loads", blinded results for "smooth" and "bouncy" gait trials were predicted using a customised patient-specific 8 musculoskeletal model. For an unblinded comparison the following modifications were made to improve the predictions: 10  further customisations, including modifications to the knee centre of rotation;  reductions to the maximum allowable muscle forces to represent known loss of 12 strength in knee arthroplasty patients; and  a kinematic constraint to the hip joint to address the sensitivity of the segment-14 based approach to motion tracking artefact.For validation, the improved model was applied to normal gait, squat and sit-to-stand 16 for three subjects. Comparisons of the predictions with measured contact forces showed that segment-based musculoskeletal models using patient-specific input data 18 can estimate tibiofemoral contact forces with root mean square errors (RMSEs)

Protein-protein interactions (PPIs) play essential roles in many biological processes. A PPI network provides crucial information on how biological pathways are structured and coordinated from individual protein functions. In the past two decades, large-scale PPI networks of a handful of organisms were determined by experimental techniques. However, these experimental methods are time-consuming, expensive, and are not easy to perform on new target organisms. Large-scale PPI data is particularly sparse in plant organisms. Here, we developed a computational approach for detecting PPIs trained and tested on known PPIs of Arabidopsis thaliana and applied to three plants, Arabidopsis thaliana , Glycine max (soybean), and Zea mays (maize) to discover new PPIs on a genome-scale. Our method considers a variety of features including protein sequences, gene co-expression, functional association, and phylogenetic profiles. This is the first work where a PPI prediction method was developed for is the first PPI prediction method applied on benchmark datasets of Arabidopsis . The method showed a high prediction accuracy of over 90% and very high precision of close to 1.0. We predicted 50,220 PPIs in Arabidopsis thaliana , 13,175,414 PPIs in corn, and 13,527,834 PPIs in soybean. Newly predicted PPIs were classified into three confidence levels according to the availability of existing supporting evidence and discussed. Predicted PPIs in the three plant genomes are made available for future reference.

BackgroundThe number of genomics and proteomics experiments is growing rapidly, producing an ever-increasing amount of data that are awaiting functional interpretation. A number of function prediction algorithms were developed and improved to enable fast and automatic function annotation. With the well-defined structure and manual curation, Gene Ontology (GO) is the most frequently used vocabulary for representing gene functions. To understand relationship and similarity between GO annotations of genes, it is important to have a convenient pipeline that quantifies and visualizes the GO function analyses in a systematic fashion.ResultsNaviGO is a web-based tool for interactive visualization, retrieval, and computation of functional similarity and associations of GO terms and genes. Similarity of GO terms and gene functions is quantified with six different scores including protein-protein interaction and context based association scores we have developed in our previous works. Interactive navigation of the GO function space provides intuitive and effective real-time visualization of functional groupings of GO terms and genes as well as statistical analysis of enriched functions.ConclusionsWe developed NaviGO, which visualizes and analyses functional similarity and associations of GO terms and genes. The NaviGO webserver is freely available at: http://kiharalab.org/web/navigo.

Accurate muscle geometry for musculoskeletal models is important to enable accurate subject-specific simulations. Commonly, linear scaling is used to obtain individualised muscle geometry. More advanced methods include non-linear scaling using segmented bone surfaces and manual or semi-automatic digitisation of muscle paths from medical images. In this study, a new scaling method combining non-linear scaling with reconstructions of bone surfaces using statistical shape modelling is presented. Statistical Shape Models (SSMs) of femur and tibia/fibula were used to reconstruct bone surfaces of nine subjects. Reference models were created by morphing manually digitised muscle paths to mean shapes of the SSMs using non-linear transformations and inter-subject variability was calculated. Subject-specific models of muscle attachment and via points were created from three reference models. The accuracy was evaluated by calculating the differences between the scaled and manually digitised models. The points defining the muscle paths showed large inter-subject variability at the thigh and shank - up to 26mm; this was found to limit the accuracy of all studied scaling methods. Errors for the subject-specific muscle point reconstructions of the thigh could be decreased by 9% to 20% by using the non-linear scaling compared to a typical linear scaling method. We conclude that the proposed non-linear scaling method is more accurate than linear scaling methods. Thus, when combined with the ability to reconstruct bone surfaces from incomplete or scattered geometry data using statistical shape models our proposed method is an alternative to linear scaling methods.

Understanding protein-protein interactions (PPIs) in a cell is essential for learning protein functions, pathways, and mechanism of diseases. PPIs are also important targets for developing drugs. Experimental methods, both small-scale and large-scale, have identified PPIs in several model organisms. However, results cover only a part of PPIs of organisms; moreover, there are many organisms whose PPIs have not yet been investigated. To complement experimental methods, many computational methods have been developed that predict PPIs from various characteristics of proteins. Here we provide an overview of literature reports to classify computational PPI prediction methods that consider different features of proteins, including protein sequence, genomes, protein structure, function, PPI network topology, and those which integrate multiple methods. © 2018 by John Wiley & Sons, Inc.

The anterior cruciate ligament (ACL) provides resistance to tibial internal rotation torque and anterior shear at the knee. ACL deficiency results in knee instability. Optimisation of muscle contraction through functional electrical stimulation (FES) offers the prospect of mitigating the destabilising effects of ACL deficiency. The hypothesis of this study is that activation of the biceps femoris long head (BFLH) reduces the tibial internal rotation torque and the anterior shear force at the knee. Gait data of twelve healthy subjects were measured with and without the application of FES and taken as inputs to a computational musculoskeletal model. The model was used to investigate the optimum levels of BFLH activation during FES gait in reducing the anterior shear force to zero. This study found that FES significantly reduced the tibial internal rotation torque at the knee during the stance phase of gait (p = 0.0322) and the computational musculoskeletal modelling revealed that a mean BFLH activation of 20.8% (±8.4%) could reduce the anterior shear force to zero. At the time frame when the anterior shear force was zero, the internal rotation torque was reduced by 0.023 ± 0.0167 Nm/BW, with a mean 188% reduction across subjects (p = 0.0002). In conclusion, activation of the BFLH is able to reduce the tibial internal rotation torque and the anterior shear force at the knee in healthy control subjects. This should be tested on ACL deficient subject to consider its effect in mitigating instability due to ligament deficiency. In future clinical practice, activating the BFLH may be used to protect ACL reconstructions during post-operative rehabilitation, assist with residual instabilities post reconstruction, and reduce the need for ACL reconstruction surgery in some cases.

The accuracy of a musculoskeletal model relies heavily on the implementation of the underlying anatomical dataset. Linear scaling of a generic model, despite being time and cost efficient, produces substantial errors as it does not account for gender differences and inter-individual anatomical variations. The hypothesis of this study is that linear scaling to a musculoskeletal model with gender and anthropometric similarity to the individual subject produces similar results to the ones that can be obtained from a subject-specific model. Methods: A lower limb musculoskeletal anatomical atlas was developed consisting of ten datasets derived from magnetic resonance imaging of healthy subjects and an additional generic dataset from the literature. Predicted muscle activation and joint reaction force were compared with electromyography and literature data. Regressions based on gender and anthropometry were used to identify the use of atlas. Results: Primary predictors of differences for the joint reaction force predictions were mass difference for the ankle (p < 0.001) and length difference for the knee and hip (p ࣘ 0.017). Gender difference accounted for an additional 3% of the variance (p ࣘ 0.039). Joint reaction force differences at the ankle, knee, and hip were reduced by between 50% and 67% (p = 0.005) when using a musculoskeletal model with the same gender and similar anthropometry in comparison with a generic model. Conclusion: Linear scaling with gender and anthropometric similarity can improve joint reaction force predictions in comparison with a scaled generic model. Significance: The presented scaling approach and atlas can improve the fidelity and utility of musculoskeletal models for subjectspecific applications.