University of Queensland

About: University of Queensland is a education organization based out in Brisbane, Queensland, Australia. It is known for research contribution in the topics: Population & Poison control. The organization has 51138 authors who have published 155721 publications receiving 5717659 citations. The organization is also known as: UQ & The University of Queensland.

Internal governance structures and earnings management

Abstract: This paper investigates the role of a firm's internal governance structure in constraining earnings management. It is hypothesized that the practice of earnings management is systematically related to the strength of internal corporate governance mechanisms, including the board of directors, the audit committee, the internal audit function and the choice of external auditor. Based on a broad cross-sectional sample of 434 listed Australian firms, for the financial year ending in 2000, a majority of non-executive directors on the board and on the audit committee are found to be significantly associated with a lower likelihood of earnings management, as measured by the absolute level of discretionary accruals. The voluntary establishment of an internal audit function and the choice of auditor are not significantly related to a reduction in the level of discretionary accruals. Our additional analysis, using small increases in earnings as a measure of earnings management, also found a negative association between this measure and the existence of an audit committee.

A randomized controlled trial of exercise and manipulative therapy for cervicogenic headache

Abstract: STUDY DESIGN: A multicenter, randomized controlled trial with unblinded treatment and blinded outcome assessment was conducted. The treatment period was 6 weeks with follow-up assessment after treatment, then at 3, 6, and 12 months. OBJECTIVES: To determine the effectiveness of manipulative therapy and a low-load exercise program for cervicogenic headache when used alone and in combination, as compared with a control group. SUMMARY OF BACKGROUND DATA: Headaches arising from cervical musculoskeletal disorders are common. Conservative therapies are recommended as the first treatment of choice. Evidence for the effectiveness of manipulative therapy is inconclusive and available only for the short term. There is no evidence for exercise, and no study has investigated the effect of combined therapies for cervicogenic headache. METHODS: In this study, 200 participants who met the diagnostic criteria for cervicogenic headache were randomized into four groups: manipulative therapy group, exercise therapy group, combined therapy group, and a control group. The primary outcome was a change in headache frequency. Other outcomes included changes in headache intensity and duration, the Northwick Park Neck Pain Index, medication intake, and patient satisfaction. Physical outcomes included pain on neck movement, upper cervical joint tenderness, a craniocervical flexion muscle test, and a photographic measure of posture. RESULTS: There were no differences in headache-related and demographic characteristics between the groups at baseline. The loss to follow-up evaluation was 3.5%. At the 12-month follow-up assessment, both manipulative therapy and specific exercise had significantly reduced headache frequency and intensity, and the neck pain and effects were maintained (P < 0.05 for all). The combined therapies was not significantly superior to either therapy alone, but 10% more patients gained relief with the combination. Effect sizes were at least moderate and clinically relevant. CONCLUSION: Manipulative therapy and exercise can reduce the symptoms of cervicogenic headache, and the effects are maintained.

Pitfalls of predicting complex traits from SNPs

Abstract: The success of genome-wide association studies (GWASs) has led to increasing interest in making predictions of complex trait phenotypes, including disease, from genotype data. Rigorous assessment of the value of predictors is crucial before implementation. Here we discuss some of the limitations and pitfalls of prediction analysis and show how naive implementations can lead to severe bias and misinterpretation of results.

Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels

Abstract: Deep learning with noisy labels is practically challenging, as the capacity of deep models is so high that they can totally memorize these noisy labels sooner or later during training. Nonetheless, recent studies on the memorization effects of deep neural networks show that they would first memorize training data of clean labels and then those of noisy labels. Therefore in this paper, we propose a new deep learning paradigm called ''Co-teaching'' for combating with noisy labels. Namely, we train two deep neural networks simultaneously, and let them teach each other given every mini-batch: firstly, each network feeds forward all data and selects some data of possibly clean labels; secondly, two networks communicate with each other what data in this mini-batch should be used for training; finally, each network back propagates the data selected by its peer network and updates itself. Empirical results on noisy versions of MNIST, CIFAR-10 and CIFAR-100 demonstrate that Co-teaching is much superior to the state-of-the-art methods in the robustness of trained deep models.

Aberrant mucin assembly in mice causes endoplasmic reticulum stress and spontaneous inflammation resembling ulcerative colitis.

Abstract: Background MUC2 mucin produced by intestinal goblet cells is the major component of the intestinal mucus barrier. The inflammatory bowel disease ulcerative colitis is characterized by depleted goblet cells and a reduced mucus layer, but the aetiology remains obscure. In this study we used random mutagenesis to produce two murine models of inflammatory bowel disease, characterised the basis and nature of the inflammation in these mice, and compared the pathology with human ulcerative colitis.