Deakin University

About: Deakin University is a education organization based out in Burwood, Victoria, Australia. It is known for research contribution in the topics: Population & Poison control. The organization has 12118 authors who have published 46470 publications receiving 1188841 citations. The organization is also known as: Deakin.

Nutritional status, nutrition practices and post-operative complications in patients with gastrointestinal cancer

Abstract: Background: Malnutrition and its associated complications are a considerable issue for surgical patients with upper gastrointestinal and colorectal cancer. The present study aimed to determine whether specific perioperative nutritional practices and protocols are associated with improved patient outcomes in this group. Methods: Patients admitted for elective upper gastrointestinal or colorectal cancer surgery (n = 95) over a 19-month period underwent a medical history audit assessing weight changes, nutritional intake, biochemistry, post-operative complications and length of stay. A subset of patients (n = 25) underwent nutritional assessment by subjective global assessment prior to surgery in addition to assessment of post-operative medical outcomes, nutritional intake and timing of dietetic intervention. Results: Mean (SD) length of stay for patients was 14.0 (12.2) days, with complication rates at 35%. Length of stay was significantly longer in patients who experienced significant preoperative weight loss compared to those who did not [17.0 (15.8) days versus 10.0 (6.8) days, respectively; P < 0.05]. Low albumin and post-operative weight loss were also predictive of increased length of stay. Of patients who underwent nutritional assessment, 32% were classified as mild–moderately malnourished and 16% severely malnourished. Malnourished patients were hospitalised twice as long as well-nourished patients [15.8 (12.8) days versus 7.6 (3.5) days; P < 0.05]. Time taken [6.9 (3.6) days] to achieve adequate nutrition post surgery was a factor in post-operative outcomes, with a positive correlation with length of stay (r = 0.493; P < 0.01), a negative correlation with post-operative weight change (r = −0.417; P < 0.05) and a greater risk of complications (52% versus 13%; P < 0.01). Conclusions: Malnutrition is prevalent among surgical patients with gastrointestinal cancer. Poor nutritional status coupled with delayed and inadequate post-operative nutrition practices are associated with worse clinical outcomes.

JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds With Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields

Abstract: Deep learning techniques have become the to-go models for most vision-related tasks on 2D images. However, their power has not been fully realised on several tasks in 3D space, e.g., 3D scene understanding. In this work, we jointly address the problems of semantic and instance segmentation of 3D point clouds. Specifically, we develop a multi-task pointwise network that simultaneously performs two tasks: predicting the semantic classes of 3D points and embedding the points into high-dimensional vectors so that points of the same object instance are represented by similar embeddings. We then propose a multi-value conditional random field model to incorporate the semantic and instance labels and formulate the problem of semantic and instance segmentation as jointly optimising labels in the field model. The proposed method is thoroughly evaluated and compared with existing methods on different indoor scene datasets including S3DIS and SceneNN. Experimental results showed the robustness of the proposed joint semantic-instance segmentation scheme over its single components. Our method also achieved state-of-the-art performance on semantic segmentation.

An evaluation of the construct of body image.

Abstract: The two studies reported in this paper were designed to evaluate the efficacy of a multidimensional model of body image that incorporated the dimensions of perception, affect, cognition, and behavior. Study 1 selected items from established measures that were judged to reflect these four dimensions. This four-factor model was then tested in Study 2. The participants for Study 2 were 175 females. The results did not support the hypothesized four-factor model. An exploratory factor analysis revealed a model that consisted of three factors: Cognitions and Affect Regarding Body, Body Importance and Dieting Behavior, and Perceptual Body Image. Below-average-weight respondents rated the Cognitions and Affect Regarding Body factor as more important than did above-average-weight respondents. Below-average-weight respondents overestimated their body size, whereas average-weight and above-average-weight respondents made underestimates, with above-average-weight respondents underestimating their body size to a greater extent than average-weight respondents. The results highlight the multidimensionality of the body image construct and the difficulty in attempting to simplify this construct. Implications of these findings for better understanding problems among people with disturbed body image are discussed.

A global map of mangrove forest soil carbon at 30 m spatial resolution

Abstract: With the growing recognition that effective action on climate change will require a combination of emissions reductions and carbon sequestration, protecting, enhancing and restoring natural carbon sinks have become political priorities. Mangrove forests are considered some of the most carbon-dense ecosystems in the world with most of the carbon stored in the soil. In order for mangrove forests to be included in climate mitigation efforts, knowledge of the spatial distribution of mangrove soil carbon stocks are critical. Current global estimates do not capture enough of the finer scale variability that would be required to inform local decisions on siting protection and restoration projects. To close this knowledge gap, we have compiled a large georeferenced database of mangrove soil carbon measurements and developed a novel machine-learning based statistical model of the distribution of carbon density using spatially comprehensive data at a 30 m resolution. This model, which included a prior estimate of soil carbon from the global SoilGrids 250 m model, was able to capture 63% of the vertical and horizontal variability in soil organic carbon density (RMSE of 10.9 kg m−3). Of the local variables, total suspended sediment load and Landsat imagery were the most important variable explaining soil carbon density. Projecting this model across the global mangrove forest distribution for the year 2000 yielded an estimate of 6.4 Pg C for the top meter of soil with an 86–729 Mg C ha−1 range across all pixels. By utilizing remotely-sensed mangrove forest cover change data, loss of soil carbon due to mangrove habitat loss between 2000 and 2015 was 30–122 Tg C with >75% of this loss attributable to Indonesia, Malaysia and Myanmar. The resulting map products from this work are intended to serve nations seeking to include mangrove habitats in payment-for- ecosystem services projects and in designing effective mangrove conservation strategies.