Is anterior cruciate reconstruction superior to conservative treatment?

Not all patients who have a rupture of the anterior cruciate ligament (ACL) elect to have surgical reconstruction. The aim of this study was to assess the short-to-medium-term results of patients who chose conservative management in comparison to patients who had reconstructive surgery within the same time period. Sixty-three patients with an ACL injury were retrospectively studied. Forty patients were managed, according to patient choice, with ACL reconstruction and 23 conservatively. Four validated questionnaires were used to assess general and knee-specific function in a cohort with a median age of 32 years and a median follow-up period of 38 months. Patients were matched on demographic variables except for gender. There were no statistically significant differences in the outcome measures, and the majority of patients would proceed with the same treatment in the event the control leg became injured. Patients who elect to have conservative management of an ACL rupture can achieve similar function and satisfaction to those who elect to have reconstruction. Until a large randomized controlled trial is conducted, patients need to be made aware of the merits of both management strategies and the lack of evidence of superiority of one over the other.

Sparse coding for improved signal-to-noise ratio in MRI

Magnetic Resonance images (MRI) do not only exhibit sparsity but their sparsity take a certain predictable shape which is common for all kinds of images. That region based localised sparsity can be used to de-noise MR images from random thermal noise. This paper present a simple framework to exploit sparsity of MR images for image de-noising. As, noise in MR images tends to change its shape based on contrast level and signal itself, the proposed method is independent of noise shape and type and it can be used in combination with other methods.

Roles of civil society in nation-building and post-conflict reconstruction in Iraq

 This thesis aims to provide a nuanced typology of post-2003-war Iraqi CSOs that reflect their functions, rather their manifestations, by analysing and examining their roles in socio-economic service provisions and active citizenship; the impact of their roles in nation-building; and the geographic field (rural or urban) of their activities.

Improved risk predictions via sparse imputation of patient conditions in electronic medical records

Electronic Medical Records (EMR) are increasingly used for risk prediction. EMR analysis is complicated by missing entries. There are two reasons - the “primary reason for admission” is included in EMR, but the co-morbidities (other chronic diseases) are left uncoded, and, many zero values in the data are accurate, reflecting that a patient has not accessed medical facilities. A key challenge is to deal with the peculiarities of this data - unlike many other datasets, EMR is sparse, reflecting the fact that patients have some, but not all diseases. We propose a novel model to fill-in these missing values, and use the new representation for prediction of key hospital events. To “fill-in” missing values, we represent the feature-patient matrix as a product of two low rank factors, preserving the sparsity property in the product. Intuitively, the product regularization allows sparse imputation of patient conditions reflecting common comorbidities across patients. We develop a scalable optimization algorithm based on Block coordinate descent method to find an optimal solution. We evaluate the proposed framework on two real world EMR cohorts: Cancer (7000 admissions) and Acute Myocardial Infarction (2652 admissions). Our result shows that the AUC for 3 months admission prediction is improved significantly from (0.741 to 0.786) for Cancer data and (0.678 to 0.724) for AMI data. We also extend the proposed method to a supervised model for predicting of multiple related risk outcomes (e.g. emergency presentations and admissions in hospital over 3, 6 and 12 months period) in an integrated framework. For this model, the AUC averaged over outcomes is improved significantly from (0.768 to 0.806) for Cancer data and (0.685 to 0.748) for AMI data.

Persisting side-to-side differences in bone mineral content, but not in muscle strength and tendon stiffness after anterior cruciate ligament reconstruction

Tendon stiffness may be involved in limiting peak musculoskeletal forces and thus may constitute an upper limit for bone strength. The patellar tendon bone (PTB) graft, which is harvested from the patellar tendon during surgical reconstruction of the anterior cruciate ligament (ACL), is an ideal scenario to test this hypothesis. Eleven participants were recruited who had undergone surgical reconstruction of the ACL with a PTB graft 1-10 years prior to study inclusion. As previously reported, there was no side-to-side difference in thigh muscle cross-sectional area, in maximum voluntary knee extension torque, or in patellar tendon stiffness, suggesting full recovery of musculature and tendon. However, in the present study bone mineral content (BMC), assessed by peripheral quantitative computed tomography, was lower on the operated side than on the control side in four regions studied (P = 0·0019). Differences were less pronounced in the two sites directly affected by the operation (patella and tibia epiphysis) when compared to the more remote sites. Moreover, significant side-to-side differences were found in BMC in the trabecular compartment in the femoral and tibial epiphysis (P = 0·004 and P = 0·047, respectively) with reductions on the operated side, but increased in the patella (P = 0·00016). Cortical BMC, by contrast, was lower on the operated side at all sites except the tibia epiphysis (P = 0·09). These findings suggest that impaired recovery of BMC following ACL reconstruction is not because of lack of recovery of knee extensor strength or patellar tendon stiffness. The responsible mechanisms still remain to be determined.

Mixed-norm sparse representation for multi view face recognition

Face recognition with multiple views is a challenging research problem. Most of the existing works have focused on extracting shared information among multiple views to improve recognition. However, when the pose variation is too large or missing, 'shared information' may not be properly extracted, leading to poor recognition results. In this paper, we propose a novel method for face recognition with multiple view images to overcome the large pose variation and missing pose issue. By introducing a novel mixed norm, the proposed method automatically selects candidates from the gallery to best represent a group of highly correlated face images in a query set to improve classification accuracy. This mixed norm combines the advantages of both sparse representation based classification (SRC) and joint sparse representation based classification (JSRC). A trade off between the ℓ1-norm from SRC and ℓ2,1-norm from JSRC is introduced to achieve this goal. Due to this property, the proposed method decreases the influence when a face image is unseen and has large pose variation in the recognition process. And when some face images with a certain degree of unseen pose variation appear, this mixed norm will find an optimal representation for these query images based on the shared information induced from multiple views. Moreover, we also address an open problem in robust sparse representation and classification which is using ℓ1-norm on the loss function to achieve a robust solution. To solve this formulation, we derive a simple, yet provably convergent algorithm based on the powerful alternative directions method of multipliers (ADMM) framework. We provide extensive comparisons which demonstrate that our method outperforms other state-of-the-arts algorithms on CMU-PIE, Yale B and Multi-PIE databases for multi-view face recognition.

Stabilizing sparse cox model using statistic and semantic structures in electronic medical records

Stability in clinical prediction models is crucial for transferability between studies, yet has received little attention. The problem is paramount in high dimensional data, which invites sparse models with feature selection capability. We introduce an effective method to stabilize sparse Cox model of time-to-events using statistical and semantic structures inherent in Electronic Medical Records (EMR). Model estimation is stabilized using three feature graphs built from (i) Jaccard similarity among features (ii) aggregation of Jaccard similarity graph and a recently introduced semantic EMR graph (iii) Jaccard similarity among features transferred from a related cohort. Our experiments are conducted on two real world hospital datasets: a heart failure cohort and a diabetes cohort. On two stability measures – the Consistency index and signal-to-noise ratio (SNR) – the use of our proposed methods significantly increased feature stability when compared with the baselines.

Sparse adaptive multi-hyperplane machine

The Adaptive Multiple-hyperplane Machine (AMM) was recently proposed to deal with large-scale datasets. However, it has no principle to tune the complexity and sparsity levels of the solution. Addressing the sparsity is important to improve learning generalization, prediction accuracy and computational speedup. In this paper, we employ the max-margin principle and sparse approach to propose a new Sparse AMM (SAMM). We solve the new optimization objective function with stochastic gradient descent (SGD). Besides inheriting the good features of SGD-based learning method and the original AMM, our proposed Sparse AMM provides machinery and flexibility to tune the complexity and sparsity of the solution, making it possible to avoid overfitting and underfitting. We validate our approach on several large benchmark datasets. We show that with the ability to control sparsity, the proposed Sparse AMM yields superior classification accuracy to the original AMM while simultaneously achieving computational speedup.

Resistance and reconstruction: older women talk about childhood sexual abuse

 This research explores personal stories of 16 Australian women aged 57 years and older who have experienced childhood sexual abuse. It privileges their views and examines how they managed the impact during their lives. The project contributes to professional knowledge by developing anti-ageist practices for social work and human services.