Flinders University

About: Flinders University is a education organization based out in Adelaide, South Australia, Australia. It is known for research contribution in the topics: Population & Health care. The organization has 12033 authors who have published 32831 publications receiving 973172 citations. The organization is also known as: Flinders University of South Australia.

An evaluation of sequence tagged microsatellite site markers for genetic analysis within Citrus and related species

Abstract: Microsatellites, also called sequence tagged microsatellite sites (STMSs), have become important markers for genome analysis but are currently little studied in plants. To assess the value of STMSs for analysis within the Citrus plant species, two example STMSs were isolated from an intergeneric cross between rangpur lime (Citrus x limonia Osbeck) and trifoliate orange (Poncirus trifoliata (L.) Raf.). Unique flanking primers were constructed for polymerase chain reaction amplification both within the test cross and across a broad range of citrus and related species. Both loci showed length variation between test cross parents with alleles segregating in a Mendelian fashion to progeny. Amplification across species showed the STMS flanking primers to be conserved in every genome tested. The traits of polymorphism, inheritance, and conservation across species mean that STMS markers are ideal for genome mapping within Citrus, which contains high levels of genetic variability.

A renaissance of neural networks in drug discovery

Abstract: Introduction: Neural networks are becoming a very popular method for solving machine learning and artificial intelligence problems. The variety of neural network types and their application to drug discovery requires expert knowledge to choose the most appropriate approach.Areas covered: In this review, the authors discuss traditional and newly emerging neural network approaches to drug discovery. Their focus is on backpropagation neural networks and their variants, self-organizing maps and associated methods, and a relatively new technique, deep learning. The most important technical issues are discussed including overfitting and its prevention through regularization, ensemble and multitask modeling, model interpretation, and estimation of applicability domain. Different aspects of using neural networks in drug discovery are considered: building structure-activity models with respect to various targets; predicting drug selectivity, toxicity profiles, ADMET and physicochemical properties; characte...

Optimization and uncertainty assessment of strongly nonlinear groundwater models with high parameter dimensionality

Abstract: [1] Highly parameterized and CPU-intensive groundwater models are increasingly being used to understand and predict flow and transport through aquifers. Despite their frequent use, these models pose significant challenges for parameter estimation and predictive uncertainty analysis algorithms, particularly global methods which usually require very large numbers of forward runs. Here we present a general methodology for parameter estimation and uncertainty analysis that can be utilized in these situations. Our proposed method includes extraction of a surrogate model that mimics key characteristics of a full process model, followed by testing and implementation of a pragmatic uncertainty analysis technique, called null-space Monte Carlo (NSMC), that merges the strengths of gradient-based search and parameter dimensionality reduction. As part of the surrogate model analysis, the results of NSMC are compared with a formal Bayesian approach using the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm. Such a comparison has never been accomplished before, especially in the context of high parameter dimensionality. Despite the highly nonlinear nature of the inverse problem, the existence of multiple local minima, and the relatively large parameter dimensionality, both methods performed well and results compare favorably with each other. Experiences gained from the surrogate model analysis are then transferred to calibrate the full highly parameterized and CPU intensive groundwater model and to explore predictive uncertainty of predictions made by that model. The methodology presented here is generally applicable to any highly parameterized and CPU-intensive environmental model, where efficient methods such as NSMC provide the only practical means for conducting predictive uncertainty analysis.

A Randomized Controlled Trial of Cognitive-Behavior Therapy Plus Bright Light Therapy for Adolescent Delayed Sleep Phase Disorder

Abstract: Objective: To evaluate cognitive-behavior therapy plus bright light therapy (CBT plus BLT) for adolescents diagnosed with delayed sleep phase disorder (DSPD). Design: Randomized controlled trial of CBT plus BLT vs. waitlist (WL) control with comparisons at pre- and post-treatment. There was 6-month follow-up for the CBT plus BLT group only. Setting: Flinders University Child & Adolescent Sleep Clinic, Adelaide, South Australia. Patients: 49 adolescents (mean age 14.6 ± 1.0 y, 53% males) diagnosed with DSPD; mean chronicity 4 y 8 months; 16% not attending school. Eighteen percent of adolescents dropped out of the study (CBT plus BLT: N = 23 vs WL: N = 17). Interventions: CBT plus BLT consisted of 6 individual sessions, including morning bright light therapy to advance adolescents' circadian rhythms, and cognitive restructuring and sleep education to target associated insomnia and sleep hygiene. Measurements and Results: DSPD diagnosis was performed via a clinical interview and 7-day sleep diary. Measurements at each time-point included online sleep diaries and scales measuring sleepiness, fatigue, and depression symptoms. Compared to WL, moderate-to-large improvements (d = 0.65-1.24) were found at post-treatment for CBT plus BLT adolescents, including reduced sleep latency, earlier sleep onset and rise times, total sleep time (school nights), wake after sleep onset, sleepiness, and fatigue. At 6-month follow-up (N = 15), small-to-large improvements (d = 0.24-1.53) continued for CBT plus BLT adolescents, with effects found for all measures. Significantly fewer adolescents receiving CBT plus BLT met DPSD criteria at post-treatment (WL = 82% vs. CBT plus BLT = 13%, P < 0.0001), yet 13% still met DSPD criteria at the 6-month follow-up. Conclusions: CBT plus BLT for adolescent DSPD is effective for improving multiple sleep and daytime impairments in the immediate and long-term. Studies evaluating the treatment effectiveness of each treatment component are needed. Clinical Trial Information: Australia & New Zealand Trials Registry Number: ACTRN12610001041044.

Measuring semantic similarity in the taxonomy of WordNet

Abstract: This paper presents a new model to measure semantic similarity in the taxonomy of WordNet, using edge-counting techniques We weigh up our model against a benchmark set by human similarity judgment, and achieve a much improved result compared with other methods: the correlation with average human judgment on a standard 28 word pair dataset is 0921, which is better than anything reported in the literature and also significantly better than average individual human judgments As this set has been effectively used for algorithm selection and tuning, we also cross-validate an independent 37 word pair test set (0876) and present results for the full 65 word pair superset (0897)