Pubertal stage and deliberate self-harm in adolescents

Objective: To ascertain the association between pubertal stage and deliberate self-harm.

Method: Cross-sectional survey of 12- to 15-year-olds in 300 secondary schools in the U.S. state of Washington in February-April 2002 and the Australian state of Victoria in June-August 2002. A total of 3,332 students in grades 7 and 9 provided complete data on episodes of deliberate self-harm in the previous 12 months and pubertal stage. Pubertal stage was assessed with the Pubertal Development Scale.

Results: The prevalence of deliberate self-harm was 3.7% with a more than twofold higher rate in females. Late puberty was associated with a more than fourfold higher rate of self-harm (odds ratio 4.6, 95% confidence interval 1.5-14) after adjustment for age and school grade level. In contrast age had a protective association (odds ratio 0.7, confidence interval 0.4-1.0). The sharpest rises in prevalence across puberty were for self-laceration and self-poisoning in females. Higher rates of depressive symptoms, frequent alcohol use, and initiation of sexual activity largely accounted for the association between self-harm and pubertal stage in multivariate models.

Conclusions: Puberty is associated with changes in the form and frequency of self-harm. For adolescents with a gap between puberty and brain development, risk factors such as early sexual activity and substance abuse may be particularly potent.

Regional faunal decline – reptile occurrence in fragmented rural landscapes of south-eastern Australia

Many species of reptiles are sedentary and depend on ground-layer habitats, suggesting that they may be particularly vulnerable to landscape changes that result in isolation or degradation of native vegetation. We investigated patterns of reptile distribution and abundance in remnant woodland across the Victorian Riverina, south-eastern Australia, a bioregion highly modified (>90%) by clearing for agriculture. Reptiles were intensively surveyed by pitfall trapping and censuses at 60 sites, stratified to sample small (<30 ha) and large (>30 ha) remnants, and linear strips of roadside and streamside vegetation, across the regional environmental gradient. The recorded assemblage of 21 species was characterised by low abundance and patchy distribution of species. Reptiles were not recorded by either survey technique at 22% of sites and at a further 10% only a single individual was detected. More than half (53%) of all records were of two widespread, generalist skink species. Multivariate models showed that the distribution of reptiles is influenced by factors operating at several levels. The environmental gradient exerts a strong influence, with increasing species richness and numbers of individuals from east (moister, higher elevation) to west (drier, lower elevation). Differences existed between types of remnants, with roadside vegetation standing out as important; this probably reflects greater structural heterogeneity of ground and shrub strata than in remnants subject to grazing by stock. Although comparative historical data are lacking, we argue that there has been a region-wide decline in the status of reptiles in the Victorian Riverina involving: (1) overall population decline commensurate with loss of >90% of native vegetation; (2) disproportionate decline of grassy dry woodlands and their fauna (cf. floodplains); and (3) changes to populations and assemblages in surviving remnants due to effects of land-use on reptile habitats. Many species now occur as disjunct populations, vulnerable to changing land-use. The status of reptiles in rural Australia warrants greater attention than has been given to date. Effective conservation of this component of the biota requires better understanding of the population dynamics, habitat use and dispersal capacity of species; and a commitment to landscape restoration coupled with effective ecological monitoring.

Accounting for the multidimensional nature of the relationship between adult obesity and socio-economic status : the French second National Individual Survey on Food Consumption (INCA 2) dietary survey (2006–07)

The objective was to study the multidimensional nature of the relationship between adult obesity (OB) and socio-economic status (SES),
using comprehensive indices of SES taken separately or synthesised in an overall index. A nationally representative sample of adults aged
18–79 years was taken from the French second National Individual Survey on Food Consumption (INCA 2) dietary survey (2006–07).
Weight and height were measured and OB defined as BMI $ 30 kg/m². SES variables were reported in questionnaires and included
occupation, education and characteristics of household wealth. Composite indices of SES (household wealth and overall SES indices)
were computed by correspondence analysis, and relationships with OB were investigated with logistic regression analysis. In total, 11·8
(95% CI 10·1, 13·4) % of French adults were obese, without significant difference by sex. While no significant relationship was observed
in men, all SES indicators were inversely correlated to OB in women. Both education and the household wealth index were retained in the
stepwise multivariate model, confirming that different socio-economic variables are not necessarily proxies of each other regarding the OB
issue. On the other hand, ‘controlling for SES’ while including several measures of SES in multivariate models may lead to collinearity, and
thus over-adjustment. A more integrative approach may be to derive a synthetic index by including the SES factors available in a given
study. Beyond this methodological perspective, understanding how OB is related to the different dimensions of SES should help to
target the more vulnerable groups and increase the effectiveness of prevention.

Psychosocial and other working conditions in relation to body mass index in a representative sample of Australian workers

Background The aim of the study was to examine the relationship between psychosocial and other working conditions and body-mass index (BMI) in a working population. This study contributes to the approximately dozen investigations of job stress, which have demonstrated mixed positive and negative results in relation to obesity, overweight and BMI. Methods A cross-sectional population-based survey was conducted among working Australians in the state of Victoria. Participants were contacted by telephone from a random sample of phone book listings. Information on body mass index was self-reported as were psychosocial work conditions assessed using the demand/control and effort/reward imbalance models. Other working conditions measured included working hours, shift work, and physical demand. Separate linear regression analyses were undertaken for males and females, with adjustment for potential confounders. Results A total of 1101 interviews (526 men and 575 women) were completed. Multivariate models (adjusted for socio-demographics) demonstrated no associations between job strain, as measured using the demand/control model, or ERI using the effort/reward imbalance model (after further adjustment for over commitment) and BMI among men and women. Multivariate models demonstrated a negative association between low reward and BMI among women. Among men, multivariate models demonstrated positive associations between high effort, high psychological demand, long working hours and BMI and a negative association between high physical demand and BMI. After controlling for the effort/reward imbalance or the demand/control model, the association between physical demand and working longer hours and BMI remained. Conclusion Among men and women the were differing patterns of both exposures to psychosocial working conditions and associations with BMI. Among men, working long hours was positively associated with higher BMI and this association was partly independent of job stress. Among men physical demand was negatively associated with BMI and this association was independent of job stress.

An analysis of sectoral equity and CDS spreads

In this paper, we find that CDS return shocks are important in explaining the forecast error variance of sectoral equity returns for the USA. The CDS return shocks have different effects on equity returns and return volatility in the pre-crisis and crisis periods. It is the post-Lehman crisis period in which the effects of CDS return shocks are the most dominant. Finally, we construct a spillover index and find that it is time-varying and explains a larger share of total forecast error variance of sectoral equity and CDS returns for some sectors than for others.

Parenting style and behavior as longitudinal predictors of adolescent alcohol use

OBJECTIVE: Adolescent alcohol use is a serious problem in Australia and other nations. Longitudinal data on family predictors are valuable to guide parental education efforts. The present study tested Baumrind's proposal that parenting styles are direct predictors of adolescent alcohol use.

METHOD: Latent class modeling was used to investigate adolescent perceptions of parenting styles and multivariate regression to examine their predictive effect on the development of adolescent alcohol use. The data set comprised 2,081 secondary school students (55.9% female) from metropolitan Melbourne, Australia, who completed three waves of annual longitudinal data starting in 2004.

RESULTS: Baumrind's parenting styles were significant predictors in unadjusted analyses, but these effects were not maintained in multivariate models that also included parenting behavior dimensions.

CONCLUSIONS: Family influences on the development of adolescent alcohol use appear to operate more directly through specific family management behaviors rather than through more global parenting styles.

Maternal correlates of young children's physical activity across periods of the day

OBJECTIVES: Associations between maternal correlates and young children's physical activity levels across the whole day and the segmented day were examined.
DESIGN: Cross-sectional study
METHODS: Participants were 136 mothers and their 1-3 year old children recruited between July 2013 and March 2014. Mothers reported time spent providing physical activity opportunities for their child, co-participating in and modelling physical activity and sedentary behaviours during the morning, afternoon and evening. Participants wore ActiGraph GT3X (mothers) and GT3X+ (children) accelerometers concurrently for seven consecutive days and the time spent in light- (LPA), moderate- to vigorous- (MVPA) and total (LMVPA) physical activity were assessed. Two-level (family; recruitment group) multivariate models examined associations between maternal correlates (including maternal objectively-assessed sedentary time [ST] and physical activity) and children's physical activity.
RESULTS: Maternal self-reported co-participation in sedentary behaviour and provision of child opportunities for physical activity were associated with children's physical activity; associations varied by period and physical activity intensity. During the morning period, mothers' objectively assessed ST was negatively associated with children's MVPA and LMVPA while her LPA was positively associated with children's LPA, MVPA and LMVPA. Mothers' MVPA was negatively associated with children's LPA and LMVPA during the evening period.
CONCLUSIONS: Maternal correlates of young children's physical activity may be period- and intensity-specific. Programmes promoting physical activity for families may need to consider incorporating strategies to reduce mother-child co-participation in sedentary behaviour, increase mothers' provision of opportunities to be active and increase mothers' own LPA over ST during certain periods of the day.

Factors contributing to depressive mood states in everyday life: A systematic review

BACKGROUND: Although accumulated evidence suggests that fluctuations in depressed mood are common among individuals with depression, and may be associated with onset, duration, and severity of illness, a systematic appraisal of putative predictors of depressed mood is lacking. METHODS: A systematic search for relevant studies in the literature was conducted using PsycInfo and PubMed databases via EbscoHost in February 2016. The search was limited to articles using the experience sampling method, an approach suitable for capturing in situ fluctuations in mood states. RESULTS: Forty-two studies met inclusion criteria for the review, from which three key risk factors (poor sleep, stress, and significant life events) and two protective factors (physical activity and quality of social interactions) were identified. The majority of papers supported concurrent and lagged associations between these putative protective/risk factors and depressed mood. LIMITATIONS: Despite support for each of the proposed protective/risk factors, few studies evaluated multiple factors in the same study. Moreover, the time course for the effects of these predictors on depressed mood remains largely unknown. CONCLUSIONS: The present review identified several putative risk and protective factors for depressed mood. A review of the literature suggests that poor sleep, negative social interactions, and stressful negative events may temporally precede spikes in depressed mood. In contrast, exercise and positive social interactions have been shown to predict subsequent declines in depressed mood. However, the lack of multivariate models in which the unique contributions of various predictors could be evaluated means that the current state of knowledge prevents firm conclusions about which factors are most predictive of depressed mood. More complex modeling of these effects is necessary in order to provide insights useful for clinical treatment in daily life of the depressed mood component of depressive disorders.

Financial fraud and the use of ratios in corporate collapse prediction models

This paper highlights the prevalence and extent of financial fraud amongst collapsed corporations. In doing so, it examines the recent spectacular corporate collapses of Parmalat in Europe, Enron and WoridCom in the USA and HIH in Australia. A new methodology that provides empirical evidence to the financial fraud claims found in the literature, is then put forward. The proposed methodology argues that if financial fraud was a possibility amongst collapsed corporations, then two premises ought to be observed in the literature on ratio based multivariate modelling for predicting corporate collapse. First, in the absence of financial fraud, we expect the models to consistently predict corporate collapse with a high degree of accuracy; particularly, as one approaches the incident of collapse. Second, if financial fraud takes place and statement figures are distorted, then we expect the financial ratios, which are the predictor variables in these models, to lose relevance and therefore their use in models will be short-lived. Empirical support from Hossari and Rahman (2004) and Hossari and Rahman (2005) is presented as evidence to the two premises.

Predictive distribution models and their application in wildlife conservation

Wildlife managers are often faced with the difficult task of determining the distribution of species, and their preferred habitats, at large spatial scales. This task is even more challenging when the species of concern is in low abundance and/or the terrain is largely inaccessible. Spatially explicit distribution models, derived from multivariate statistical analyses and implemented in a geographic information system (GIS), can be used to predict the distributions of species and their habitats, thus making them a useful conservation tool. We present two such models: one for a dasyurid, the Swamp Antechinus (Antechinus minimus), and the other for a ground-dwelling bird, the Rufous Bristlebird (Dasyornis broadbenti), both of which are rare species occurring in the coastal heathlands of south-western Victoria. Models were generated using generalized linear modelling (GLM) techniques with species presence or absence as the independent variable and a series of landscape variables derived from GIS layers and high-resolution imagery as the predictors. The most parsimonious model, based on the Akaike Information Criterion, for each species then was extrapolated spatially in a GIS. Probability of species presence was used as an index of habitat suitability. Because habitat fragmentation is thought to be one of the major threats to these species, an assessment of the spatial distribution of suitable habitat across the landscape is vital in prescribing management actions to prevent further habitat fragmentation.

An extended multivariate autoregressive framework for EEG-based information flow analysis of a brain network

Recently effective connectivity studies have gained significant attention among the neuroscience community as Electroencephalography (EEG) data with a high time resolution can give us a wider understanding of the information flow within the brain. Among other tools used in effective connectivity analysis Granger Causality (GC) has found a prominent place. The GC analysis, based on strictly causal multivariate autoregressive (MVAR) models does not account for the instantaneous interactions among the sources. If instantaneous interactions are present, GC based on strictly causal MVAR will lead to erroneous conclusions on the underlying information flow. Thus, the work presented in this paper applies an extended MVAR (eMVAR) model that accounts for the zero lag interactions. We propose a constrained adaptive Kalman filter (CAKF) approach for the eMVAR model identification and demonstrate that this approach performs better than the short time windowing-based adaptive estimation when applied to information flow analysis.

Market effects on forecasting construction prices using vector error correction models

Construction price forecasting is an essential component to facilitate decision-making for construction contractors, investors and related financial institutions. Construction economists are increasingly interested in seeking a more analytical method to forecast construction prices. Although many studies have focused on construction price modelling and forecasting, few have considered the impacts of large-scale economic events and seasonality. In this study, an advanced multivariate modelling technique, namely the vector correction (VEC) model with dummy variables, was employed. The impacts of global economic events and seasonality are factored into the model to forecast the construction price in the Australian construction market. Research findings suggest that both long-run and dynamic short-term causal relationships exist among the price and levels of supply and demand in the construction market. These relationships drive the construction price and supply and demand, which interact with one another as a loop system. The reliability of forecasting models was examined by the mean absolute percentage error (MAPE) and the Theil's inequality coefficient U tests. The test results suggest that the conventional VEC model and the VEC model with dummy variable are both acceptable for forecasting the construction price, while the VEC model considering external impacts achieves higher prediction accuracy than the conventional VEC model. © 2014 © 2014 Taylor & Francis.

Rapid discrimination and determination of polyunsaturated fatty acid composition in marine oils by FTIR Spectroscopy and Multivariate Data Analysis

A rapid analytical approach for discrimination and quantitative determination of polyunsaturated fatty acid (PUFA) contents, particularly eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), in a range of oils extracted from marine resources has been developed by using attenuated total reflection Fourier transform infrared spectroscopy and multivariate data analysis. The spectral data were collected without any sample preparation; thus, no chemical preparation was involved, but data were rather processed directly using the developed spectral analysis platform, making it fast, very cost effective, and suitable for routine use in various biotechnological and food research and related industries. Unsupervised pattern recognition techniques, including principal component analysis and unsupervised hierarchical cluster analysis, discriminated the marine oils into groups by correlating similarities and differences in their fatty acid (FA) compositions that corresponded well to the FA profiles obtained from traditional lipid analysis based on gas chromatography (GC). Furthermore, quantitative determination of unsaturated fatty acids, PUFAs, EPA and DHA, by partial least square regression analysis through which calibration models were optimized specifically for each targeted FA, was performed in both known marine oils and totally independent unknown n - 3 oil samples obtained from an actual commercial product in order to provide prospective testing of the developed models towards actual applications. The resultant predicted FAs were achieved at a good accuracy compared to their reference GC values as evidenced through (1) low root mean square error of prediction, (2) good coefficient of determination close to 1 (i.e., R 2≥ 0.96), and (3) the residual predictive deviation values that indicated the predictive power at good and higher levels for all the target FAs. © 2014 Springer Science+Business Media New York.

Application of extended multivariate modeling for information flow analysis of event related responses

Event related potential (ERP) analysis is one of the most widely used methods in cognitive neuroscience research to study the physiological correlates of sensory, perceptual and cognitive activity associated with processing information. To this end information flow or dynamic effective connectivity analysis is a vital technique to understand the higher cognitive processing under different events. In this paper we present a Granger causality (GC)-based connectivity estimation applied to ERP data analysis. In contrast to the generally used strictly causal multivariate autoregressive model, we use an extended multivariate autoregressive model (eMVAR) which also accounts for any instantaneous interaction among variables under consideration. The experimental data used in the paper is based on a single subject data set for erroneous button press response from a two-back with feedback continuous performance task (CPT). In order to demonstrate the feasibility of application of eMVAR models in source space connectivity studies, we use cortical source time series data estimated using blind source separation or independent component analysis (ICA) for this data set.

Multivariate adaptive autoregressive modeling and kalman filtering for motor imagery BCI

Adaptive autoregressive (AAR) modeling of the EEG time series and the AAR parameters has been widely used in Brain computer interface (BCI) systems as input features for the classification stage. Multivariate adaptive autoregressive modeling (MVAAR) also has been used in literature. This paper revisits the use of MVAAR models and propose the use of adaptive Kalman filter (AKF) for estimating the MVAAR parameters as features in a motor imagery BCI application. The AKF approach is compared to the alternative short time moving window (STMW) MVAAR parameter estimation approach. Though the two MVAAR methods show a nearly equal classification accuracy, the AKF possess the advantage of higher estimation update rates making it easily adoptable for on-line BCI systems.