Corner inlet and Nooramunga habitat mapping project

Deakin University and the University of Tasmania were commissioned by Parks Victoria (PV) to create two updated habitat maps for areas within the Corner Inlet and Nooramunga Marine and Coastal Park and Ramsar area. The team obtained a ground-truth data set using in situ video and still photographs. This dataset was used to develop and assess predictive models of benthic marine habitat distributions incorporating data from both ALOS (Advanced Land Observation Satellite) imagery atmospherically corrected by CSIRO and LiDAR (Light Detection and Ranging) bathymetry. This report describes the results of the mapping effort as well as the methodology used to produce these habitat maps.

Overall accuracies of habitat classifications were good, returning overall accuracies >73 % and kappa values > 0.62 for both study localities. Habitats predicted with highest accuracies included Zosteraceae in Nooramunga (91 %), reef in Corner Inlet (80 %), and bare sediment (no-visible macrobiota/no-visible seagrass classes; both > 76 %). The majority of classification errors were due to the misclassification of areas of sparse seagrass as bare sediment. For the Corner Inlet study locality the no-visible macrobiota (10,698 ha), Posidonia (4,608 ha) and Zosteraceae (4,229 ha) habitat classes covered the most area. In Nooramunga no-visible seagrass (5,538 ha), Zosteraceae (4,060 ha) and wet saltmarsh (1,562 ha) habitat classes were most dominant.

In addition to the commissioned work preliminary change detection analyses were undertaken as part of this project. These analyses indicated shifts in habitat extents in both study localities since the late 1990s/2000. In particular, a post-classification analysis highlighted that there were considerable increases in seagrass habitat (primarily Zosteraceae) throughout the littoral zones and river/creek mouths of both study localities. Further, the numerous channel systems remained stable and were free of seagrass at both times. A substantial net loss of Posidonia in the Corner Inlet locality is likely but requires further investigation due to potential misclassifications between habitats in both the 1998 map (Roob et al. 1998) and the current mapping. While the unsupervised Independent Components Analysis (ICA) change detection technique indicated some changes in habitat extent and distribution, considerable areas of habitat change observed in the post-classification approach are questionable, and may reflect misclassifications rather than real change. A particular example of this is an apparent large decrease in Zosteraceae and increase in Posidonia being related to the classification of Posidonia beds as Zosteraceae in the 1998 mapping. Despite this, we believe that changes indicated by both the ICA and post-classification approaches have a high likelihood of being ‘actual’ change. A pattern of gains and losses of Zosteraceae in the region north of Stockyard channel is an example of this. Further analyses and refinements of approaches in change detection analyses such as would improve confidence in the location and extent of habitat changes over this time period.

This work has been successful in providing new baseline maps using a repeatable method meaning that any future changes in intertidal and shallow water marine habitats may be assessed in a consistent way with quantitative error assessments. In wider use, these maps should also allow improved conservation planning, advance fisheries and catchment management, and progress infrastructure planning to limit impacts on the Inlet environment.

Dimensionality reduction of protein mass spectrometry data using random projection

Protein mass spectrometry (MS) pattern recognition has recently emerged as a new method for cancer diagnosis. Unfortunately, classification performance may degrade owing to the enormously high dimensionality of the data. This paper investigates the use of Random Projection in protein MS data dimensionality reduction. The effectiveness of Random Projection (RP) is analyzed and compared against Principal Component Analysis (PCA) by using three classification algorithms, namely Support Vector Machine, Feed-forward Neural Networks and K-Nearest Neighbour. Three real-world cancer data sets are employed to evaluate the performances of RP and PCA. Through the investigations, RP method demonstrated better or at least comparable classification performance as PCA if the dimensionality of the projection matrix is sufficiently large. This paper also explores the use of RP as a pre-processing step prior to PCA. The results show that without sacrificing classification accuracy, performing RP prior to PCA significantly improves the computational time.

Visual mouse : SIFT detection and PCA recognition

The paper presents the Visual Mouse (VM), a novel and simple system for interaction with displays via hand gestures. Our method includes detecting bare hands using the fast SIFT (Scale-Invariant Feature Transform) algorithm saving long training time of the Adaboost algorithm, tracking hands based on the CAMShift algorithm, recognizing hand gestures in cluttered background via Principle Components Analysis (PCA) without extracting clear-cut hand contour, and defining simple and robustly interpretable vocabularies of hand gestures, which are subsequently used to control a computer mouse. The system provides a fast and simple interaction experience without the need for more expensive hardware and software.

Use of circle-segments as a data visualization technique for feature selection in pattern classification

One of the issues associated with pattern classification using data based machine learning systems is the “curse of dimensionality”. In this paper, the circle-segments method is proposed as a feature selection method to identify important input features before the entire data set is provided for learning with machine learning systems. Specifically, four machine learning systems are deployed for classification, viz. Multilayer Perceptron (MLP), Support Vector Machine (SVM), Fuzzy ARTMAP (FAM), and k-Nearest Neighbour (kNN). The integration between the circle-segments method and the machine learning systems has been applied to two case studies comprising one benchmark and one real data sets. Overall, the results after feature selection using the circle segments method demonstrate improvements in performance even with more than 50% of the input features eliminated from the original data sets.

Association between maternal education and diet of children at 9 months is partially explained by mothers' diet

Infants of mothers of low educational background display consistently poorer outcomes, including suboptimal weaning diets. Less is known about the different causal pathways that relate maternal education to infants' diet. The present study aimed to test the hypothesis that the relationship between maternal education and infants' diet is mediated by mothers' diet. The analyses included 421 mother–infant pairs from the Melbourne Infant Feeding Activity and Nutrition Trial (InFANT) Program. Dietary intakes were collected from mothers when infants were aged 3 months, using a validated food frequency questionnaire relating to the past year, and in infants aged 9 months using 3 × 24-h recalls. Principal component analysis was used to derive dietary pattern scores, based on frequencies of 55 food groups in mothers, and intakes of 23 food groups in infants. Associations were assessed with multivariable linear regression. We tested the product ‘ab’ to address the mediation hypothesis, where ‘a’ refers to the relationship between the predictor variable (education) and the mediator variable (mothers' diet), and ‘b’ refers to the association between the mediator variable and the outcome variable (infants' diet), controlling for the predictor variable. Maternal scores on the ‘Fruit and vegetables’ dietary pattern partially mediated the relationships between maternal education and two infant dietary patterns, namely ‘Balanced weaning diet’ [ab = 0.11; 95% confidence interval (CI): 0.04; 0.18] and ‘Formula’ (ab = −0.08; 95%CI: −0.15; −0.02). These findings suggest that targeting pregnant mothers of low education level with the aim of improving their own diet may also promote better weaning diets in their infants.

Estimating the intensity of ward admission and its effect on emergency department access block

Emergency department access block is an urgent problem faced by many public hospitals today. When access block occurs, patients in need of acute care cannot access inpatient wards within an optimal time frame. A widely held belief is that access block is the end product of a long causal chain, which involves poor discharge planning, insufficient bed capacity, and inadequate admission intensity to the wards. This paper studies the last link of the causal chain-the effect of admission intensity on access block, using data from a metropolitan hospital in Australia. We applied several modern statistical methods to analyze the data. First, we modeled the admission events as a nonhomogeneous Poisson process and estimated time-varying admission intensity with penalized regression splines. Next, we established a functional linear model to investigate the effect of the time-varying admission intensity on emergency department access block. Finally, we used functional principal component analysis to explore the variation in the daily time-varying admission intensities. The analyses suggest that improving admission practice during off-peak hours may have most impact on reducing the number of ED access blocks.

Mixing Matrix Estimation From Sparse Mixtures With Unknown Number of Sources

In blind source separation, many methods have been proposed to estimate the mixing matrix by exploiting sparsity. However, they often need to know the source number a priori, which is very inconvenient in practice. In this paper, a new method, namely nonlinear projection and column masking (NPCM), is proposed to estimate the mixing matrix. A major advantage of NPCM is that it does not need any knowledge of the source number. In NPCM, the objective function is based on a nonlinear projection and its maxima just correspond to the columns of the mixing matrix. Thus a column can be estimated first by locating a maximum and then deflated by a masking operation. This procedure is repeated until the evaluation of the objective function decreases to zero dramatically. Thus the mixing matrix and the number of sources are estimated simultaneously. Because the masking procedure may result in some small and useless local maxima, particle swarm optimization (PSO) is introduced to optimize the objective function. Feasibility and efficiency of PSO are also discussed. Comparative experimental results show the efficiency of NPCM, especially in the cases where the number of sources is unknown and the sources are relatively less sparse.

The effect of an early childhood obesity intervention on father's obesity risk behaviors: the Melbourne InFANT program

Background
To investigate the effect of an early childhood obesity prevention intervention, incorporating a parent modelling component, on fathers’ obesity risk-related behaviours.

Methods
Cluster randomized-controlled trial in the setting of pre-existing first-time parents groups organised by Maternal and Child Health Nurses in Victoria, Australia. Participants were 460 first-time fathers mean age = 34.2 (s.d.4.90) years. Dietary pattern scores of fathers were derived using principal component analysis, total physical activity and total television viewing time were assessed at baseline (infant aged three to four months) and after 15 months.

Results
No significant beneficial intervention effect was observed on fathers’ dietary pattern scores, total physical activity or total television viewing time.

Conclusion
Despite a strong focus on parent modelling (targeting parents own diet, physical activity and television viewing behaviours), and beneficial impact on mothers’ obesity risk behaviours, this intervention, with mothers as the point of contact, had no effect on fathers’ obesity risk-related behaviours. Based on the established links between children’s obesity risk-related behaviors and that of their fathers, a need exists for research testing the effectiveness of interventions with a stronger engagement of fathers.

Evaluation of community participation for the development of culture-based fisheries in village reservoirs of Sri Lanka

In this paper, an attempt is made to identify the socioeconomic characteristics of a community that influence the development and management of culture-based fisheries in village reservoirs of Sri Lanka. Socioeconomic data were collected from 46 agricultural farming communities associated with 47 village reservoirs in Sri Lanka. Principal component analysis indicated that scores of the first principal component were positively influenced by socioeconomic characteristics that are favorable for making collective decisions. These included leadership of the officers, age of the group, percentage of active members of the group, percentage of kinship of the group, percentage of common interest of the group, and percentage of participation of the group. The size of the group had a negative effect on the first principal component. The principal component scores of communities were positively related to willingness to pay (P < 0.001). The communities with socioeconomic characteristics favoring collective decision making were in favor of culture-based fisheries. Homogeneity of group characteristics facilitated successful development of culture-based fisheries.

Gambling motives: application of the reasons for gambling questionnaire in an Australian population survey

The Reasons for Gambling Questionnaire (RGQ) consist of 15 items forming five factors: enhancement, social, money, recreation and coping. The RGQ was developed for use in the 2010 British Gambling Prevalence Survey (BGPS) and has now been employed in the second Social and Economic Impact Study (SEIS) of Gambling in Tasmania study conducted in 2011 in Australia. Given differences between Britain and Australia in terms of socio-demographic profiles, gambling cultures and attitudes, gambling access and availability, gambling regulation, and rates and patterns of gambling participation, the aims of this study were to analyse the RGQ data from the SEIS to: (1) determine the most commonly endorsed gambling motives in an Australian jurisdiction, (2) explore the factor structure of the RGQ in an Australian sample, and (3) explore how motives for gambling vary among different Australian population sub-groups. A representative sample of the Tasmanian population who had gambled in the previous 12 months (n = 2,796) were administered the RGQ via computer-assisted telephone interviewing. The five most commonly endorsed reasons for gambling were for fun (62 %), followed by the chance of winning big money (52 %), it being something to do with friends and family (48 %), to be sociable (40 %), and excitement (38 %). A principal component analysis revealed a five-factor structure that is slightly different from that derived in the BGPS: money, regulate internal state, positive feelings, social, and challenge reasons. Finally, gambling motives varied according to socio-demographic factors, number of gambling activities, problem gambling severity, and participation on different gambling activities. Although some of these findings are consistent with those from the BGPS, there are also some slight differences, suggesting that there may be regional-specific variations in gambling motives.

Testing the validity, reliability and utility of the Self-Administration of Medication (SAM) tool in patients undergoing rehabilitation.

Determination of patients' ability to self-administer medications in the hospital has largely been determined using the subjective judgment of health professionals.

Atmospheric quality and distribution of heavy metals in Argentina employing Tillandsia capillaris as a biomonitor.

The atmospheric quality and distribution of heavy metals were evaluated throughout a wide region of Argentina. In addition, the biomonitor performance of Tillandsia capillaris Ruiz & Pav. f. capillaris was studied in relation to the accumulation of heavy metals and to its physiologic response to air pollutants. A sampling area of 50,000 km2 was selected in the central region of the Argentine Republic. This area was subdivided into grids of 25 x 25 km. Pools of T. capillaris, where present, were collected at each intersection point. From each pool three sub-samples were analyzed independently. Furthermore, five replicates were collected at 20% of the points in order to analyze the variability within the site. The content of Co, Cu, Fe, Ni, Mn, Pb and Zn was determined by Atomic Absorption Spectrometry. Chemical-physiological parameters were also determined to detect symptoms of foliar damage. Chlorophylls, phaeophytins, hydroperoxy conjugated dienes, malondialdehyde and sulfur were quantified in T. capillaris. Some of these parameters were used to calculate a foliar damage index. Data sets were evaluated by one-way ANOVA, correlation analysis, principal component analysis and mapping. Geographical distribution patterns were obtained for the different metals reflecting the contribution of natural and anthropogenic emission sources. According to our results it can be inferred that Fe, Mn and Co probably originated in the soil. For Pb, the highest values were found in the mountainous area, which can be attributed to the presence of Pb in the granitic rocks. Ni showed mainly an anthropogenic origin, with higher values found in places next to industrial centers. For Zn the highest values were in areas of agricultural development. The same was observed for Cu, whose presence could be related to the employment of pesticides. The foliar damage index distribution map showed that the central and southeastern zones were the ones where the major damage in the bioindicator was found. The central zone coincides with the city of Córdoba whereas the southeastern area is strictly agricultural, so the high values found there could be related to the use of pesticides.

Parents' food choice motives and their associations with children's food preferences

Objective The objective was to investigate parents' motives for selecting foods for their children and the associations between these motives and children's food preferences. Design Cross-sectional survey. A modified version of the Food Choice Questionnaire was used to assess parents' food choice motives. Parents also reported children's liking/disliking of 176 food and beverage items on 5-point Likert scales. Patterns of food choice motives were examined with exploratory principal component analysis. Associations between motives and children's food preferences were assessed with linear regression while one-way and two-way ANOVA were used to test for sociodemographic differences. Setting Two Australian cities. Subjects Parents (n 371) of 2-5-year-old children. Results Health, nutrition and taste were key motivators for parents, whereas price, political concerns and advertising were among the motives considered least important. The more parents' food choice for their children was driven by what their children wanted, the less children liked vegetables (β =-0·27, P<0·01), fruit (β=-0·19, P<0·01) and cereals (β=-0·28, P<0·01) and the higher the number of untried foods (r=0·17, P<0·01). The reverse was found for parents' focus on natural/ethical motives (vegetables β=0·17, P<0·01; fruit β=0·17, P<0·01; cereals β=0·14, P=0·01). Health and nutrition motives bordered on statistical significance as predictors of children's fruit and vegetable preferences. Conclusions Although parents appear well intentioned in their motives for selecting children's foods, there are gaps to be addressed in the nature of such motives (e.g. selecting foods in line with the child's desires) or the translation of health motives into healthy food choices.

Geographical variation and population structure in the White-rumped Sandpiper Calidris fuscicollis as shown by morphology, mitochondrial DNA and carbon isotope ratios

We studied the population structure of a high arctic breeding wader bird species, the White-rumped Sandpiper Calidris fuscicollis. Breeding adults, chicks and juveniles were sampled at seven localities throughout the species' breeding range in arctic Canada in 1999. The mitochondrial control region was analysed by DNA sequencing, feathers were analysed for carbon isotope ratios (C13/C12) by isotope ratio mass spectrometry, and morphological measurements were analysed using principal component analyses, taking the effect of sex into account (identified by molecular genetic methods). In general, our results support the notion that the White-rumped Sandpiper is a monotypic species with no subspecies, and most of the morphological and genetic variation occurs within sites. Nevertheless, some differences between sites were found. Birds from the two northernmost sites (Ellesmere and Devon Islands) had relatively longer bill and wing and shorter tarsus than birds sampled further south, possibly reflecting genetic differences between populations. The carbon isotope ratios were higher at the easternmost site (Baffin Island), revealing differences in the isotope content of the food. The mtDNA sequences showed no significant differentiation between sites and no pattern of isolation-by-distance was found. Based on the mtDNA variation, the species was estimated to have a long-term effective population size of approximately 9,000 females. The species shows no clear evidence of any population expansion or decline. Our results indicate that carbon isotope ratios, and possibly also certain mtDNA haplotypes, may be useful as tools for identifying the breeding origin of White-rumped Sandpipers on migration and wintering sites.

The control of epidermal growth factor grafted on mesoporous silica nanoparticles for targeted delivery

The performance of biomaterials in a biological environment is largely influenced by the surface properties of the biomaterials. In particular, grafted targeting ligands significantly impact the subsequent cellular interactions. The utilisation of a grafted epidermal growth factor (EGF) is effective for targeted delivery of drugs to tumours, but the amount of these biological attachments cannot be easily quantified as most characterization methods could not detect the extremely low amount of EGF ligands grafted on the surface of nanoparticles. In this study, hollow mesoporous silica nanoparticles (HMSNs) were functionalized with amine groups to conjugate with EGFs via carbodiimide chemistry. Time of flight secondary ion mass spectrometry (ToF-SIMS), a very surface specific technique (penetration depth <1.5 nm), was employed to study the binding efficiency of the EGF to the nanoparticles. Principal component analysis (PCA) was implemented to track the relative surface concentrations of EGFs on HMSNs. It was found that ToF-SIMS combined with the PCA technique is an effective method to evaluate the immobilization efficiency of EGFs. Based on this useful technique, the quantity and density of the EGF attachments that grafted on nanoparticles can be effectively controlled by varying the EGF concentration at grafting stages. Cell experiments demonstrated that the targeting performance of EGFR positive cells was affected by the number of EGFs attached on HMSNs.