Anterior cruciate ligament (ACL) rupture is a common, debilitating condition that leads to early-onset osteoarthritis and reduced quality of human life. ACL rupture is a complex disease with both genetic and environmental risk factors. Characterizing the genetic basis of ACL rupture would provide the ability to identify individuals that have high genetic risk and allow the opportunity for preventative management. Spontaneous ACL rupture is also common in dogs and shows a similar clinical presentation and progression. Thus, the dog has emerged as an excellent genomic model for human ACL rupture. Genome-wide association studies (GWAS) in the dog have identified a number of candidate genetic variants, but research in genomic prediction has been limited. In this analysis, we explore several Bayesian and machine learning models for genomic prediction of ACL rupture in the Labrador Retriever dog. Our work demonstrates the feasibility of predicting ACL rupture from SNPs in the Labrador Retriever model with and without consideration of non-genetic risk factors. Genomic prediction including non-genetic risk factors approached clinical relevance using multiple linear Bayesian and non-linear models. This analysis represents the first steps towards development of a predictive algorithm for ACL rupture in the Labrador Retriever model. Future work may extend this algorithm to other high-risk breeds of dog. The ability to accurately predict individual dogs at high risk for ACL rupture would identify candidates for clinical trials that would benefit both veterinary and human medicine.

Introduction and backgroundIn the past, a large number of garment manufacturing models [1][2][3][4][5] have been developed to model garment production behaviour for improving productivity, reducing costs and improving quality. These models range from full deterministic to full stochastic models. Representation of uncertainty in these models is often necessary because, in the garment industry, uncertainty arises not only in the marketplace but also during the production cycle. It is especially true when predicting the complexity of production orders.The complexity of production orders, when properly measured, can reflect the degree of difficulty in handling an order. Knowing how difficult it is to handle an order allows us to predict accurately the requirement for resources (such as operators, equipment and materials) and to prepare for an effective order scheduling.Under normal circumstances, an order could be divided into three main operations -cutting, assembling, and finishing (Figure 1). Within each operation, a series of jobs such as cuff making, placket making and sleeve making could be determined by customer requirements.When a complicated production job is encountered, the apparel manufacturer should plan to allocate the appropriate resources very carefully prior to carrying out real production in order to be able to deliver on time, minimize cost and achieve good quality. A field expert is normally required to produce the capacity plan based on his or her experience and expertise. The capacity plan includes a production schedule, manpower allocation, machinery utilization and materials requirements. If the degree of complexity of a production job is high, it will put a lot of effort on some operations during the production. For instance, the fashioned style men's shirts may require a lot of effort on colour/pattern matching during assembling resulting in it becoming the bottleneck for the production job.