Covitality constructs as predictors of psychological well-being and depression for secondary school students'

This study was an examination of the strength of relations among covitality, and its underlying constructs of belief in self, emotional competence, belief in others, and engaged living, and two outcome variables; subjective well-being and depression. Participants included 361 Australian secondary school students (75 males and 286 females) who completed a series of online questionnaires related to positive psychological well-being in adolescents. The results from the first standard multiple regression analysis indicated that higher levels of belief in self, belief in others, and engaged living were significant predictors of increased subjective well-being. The results from the second standard multiple regression showed that higher levels of belief in self, belief in others, and engaged living were significant predictors of decreased feelings of depression. In both standard multiple regression models, the combined effect of the traits that comprise covitality was greater than the effect of each individual positive psychological trait.

Early detection of melanoma metastases using microRNAs as novel biomarkers

Early detection of melanoma skin cancer, prior to metastatic spread, is critical to improve survival outcomes in patients. This study identified a melanoma-related panel of blood markers that can detect the presence of melanoma with high sensitivity and accuracy which is superior to currently used markers for melanoma progression, recurrence, and survival. Overall, the findings discussed in this thesis may lead to more precise measurement of disease progression allowing for better treatments and an increase in overall survival.

Improving spoken term detection using complementary information

This research has made contributions to the area of spoken term detection (STD), defined as the process of finding all occurrences of a specified search term in a large collection of speech segments. The use of visual information in the form of lip movements of the speaker in addition to audio and the use of topic of the speech segments, and the expected frequency of words in the target speech domain, are proposed. By using these complementary information, improvement in the performance of STD has been achieved which enables efficient search of key words in large collection of multimedia documents.

A Bayesian Framework for the Assessment of Vision-based Weed and Fruit Detection and Classification Algorithms

This paper proposes new metrics and a performance-assessment framework for vision-based weed and fruit detection and classification algorithms. In order to compare algorithms, and make a decision on which one to use fora particular application, it is necessary to take into account that the performance obtained in a series of tests is subject to uncertainty. Such characterisation of uncertainty seems not to be captured by the performance metrics currently reported in the literature. Therefore, we pose the problem as a general problem of scientific inference, which arises out of incomplete information, and propose as a metric of performance the(posterior) predictive probabilities that the algorithms will provide a correct outcome for target and background detection. We detail the framework through which these predicted probabilities can be obtained, which is Bayesian in nature. As an illustration example, we apply the framework to the assessment of performance of four algorithms that could potentially be used in the detection of capsicums (peppers).

On Visual Detection of Highly-occluded Objects for Harvesting Automation in Horticulture

Developing accurate and reliable crop detection algorithms is an important step for harvesting automation in horticulture. This paper presents a novel approach to visual detection of highly-occluded fruits. We use a conditional random field (CRF) on multi-spectral image data (colour and Near-Infrared Reflectance, NIR) to model two classes: crop and background. To describe these two classes, we explore a range of visual-texture features including local binary pattern, histogram of oriented gradients, and learn auto-encoder features. The pro-posed methods are evaluated using hand-labelled images from a dataset captured on a commercial capsicum farm. Experimental results are presented, and performance is evaluated in terms of the Area Under the Curve (AUC) of the precision-recall curves.Our current results achieve a maximum performance of 0.81AUC when combining all of the texture features in conjunction with colour information.

Association of NOS1AP variants and depression phenotypes in schizophrenia

Background The nitric oxide synthase 1 adaptor protein gene (NOS1AP) has previously been recognised as a schizophrenia susceptibility gene due to its role in glutamate neurotransmission. The gene is believed to inhibit nitric oxide (NO) production activated by the N-methyl-d-aspartate (NMDA) receptor and reduced NO levels have been observed in schizophrenia patients. However, association studies investigating NOS1AP and schizophrenia have produced inconsistent results, most likely because schizophrenia is a clinically heterogeneous disorder. This study aims to investigate the association between NOS1AP variants and defined depression phenotypes of schizophrenia. Methods Nine NOS1AP SNPs, rs1415259, rs1415263, rs1858232, rs386231, rs4531275, rs4656355, rs4657178, rs6683968 and rs6704393 were genotyped in 235 schizophrenia subjects screened for various phenotypes of depression. Result One NOS1AP SNP (rs1858232) was associated with the broad diagnosis of schizophrenia and eight SNPs were associated with depression related phenotypes within schizophrenia. The rs1415259 SNP showed strong association with sleep dysregulation phenotypes of depression. Conclusion Results suggest that NOS1AP variants are associated with various forms of depression in schizophrenia and are more prevalent in males. Limitation Schizophrenia is a clinically heterogeneous disease that can vary greatly between different ethnic and geographic populations so our observations should be viewed with caution until they are independently replicated, particularly in larger patient cohorts.

Nonparametric rank regression for analyzing water quality concentration data with multiple detection limits

Environmental data usually include measurements, such as water quality data, which fall below detection limits, because of limitations of the instruments or of certain analytical methods used. The fact that some responses are not detected needs to be properly taken into account in statistical analysis of such data. However, it is well-known that it is challenging to analyze a data set with detection limits, and we often have to rely on the traditional parametric methods or simple imputation methods. Distributional assumptions can lead to biased inference and justification of distributions is often not possible when the data are correlated and there is a large proportion of data below detection limits. The extent of bias is usually unknown. To draw valid conclusions and hence provide useful advice for environmental management authorities, it is essential to develop and apply an appropriate statistical methodology. This paper proposes rank-based procedures for analyzing non-normally distributed data collected at different sites over a period of time in the presence of multiple detection limits. To take account of temporal correlations within each site, we propose an optimal linear combination of estimating functions and apply the induced smoothing method to reduce the computational burden. Finally, we apply the proposed method to the water quality data collected at Susquehanna River Basin in United States of America, which dearly demonstrates the advantages of the rank regression models.

Statistical power calculation and sample size determination for environmental studies with data below detection limits

Power calculation and sample size determination are critical in designing environmental monitoring programs. The traditional approach based on comparing the mean values may become statistically inappropriate and even invalid when substantial proportions of the response values are below the detection limits or censored because strong distributional assumptions have to be made on the censored observations when implementing the traditional procedures. In this paper, we propose a quantile methodology that is robust to outliers and can also handle data with a substantial proportion of below-detection-limit observations without the need of imputing the censored values. As a demonstration, we applied the methods to a nutrient monitoring project, which is a part of the Perth Long-Term Ocean Outlet Monitoring Program. In this example, the sample size required by our quantile methodology is, in fact, smaller than that by the traditional t-test, illustrating the merit of our method.

Learning Temporal Alignment Uncertainty for Efficient Event Detection

In this paper we tackle the problem of efficient video event detection. We argue that linear detection functions should be preferred in this regard due to their scalability and efficiency during estimation and evaluation. A popular approach in this regard is to represent a sequence using a bag of words (BOW) representation due to its: (i) fixed dimensionality irrespective of the sequence length, and (ii) its ability to compactly model the statistics in the sequence. A drawback to the BOW representation, however, is the intrinsic destruction of the temporal ordering information. In this paper we propose a new representation that leverages the uncertainty in relative temporal alignments between pairs of sequences while not destroying temporal ordering. Our representation, like BOW, is of a fixed dimensionality making it easily integrated with a linear detection function. Extensive experiments on CK+, 6DMG, and UvA-NEMO databases show significant performance improvements across both isolated and continuous event detection tasks.

Family type and depression in pregnancy: Factors mediating risk in a community sample

The rate of severe depression among women in single-parent and biological families and in a variety of stepfamilies was examined in a large community sample of 13,088 pregnant women in the United Kingdom. Compared with women in biological families and published population rates, women in single-parent families and step-families reported significantly elevated rates of depression. Family-type differences in several risk factors were examined, including cohabiting (vs. married) status, relationship history, and socioeconomic and psychosocial risks, such as crowding, social support, and stressful life events. Family-type differences in depression were mediated partly by differences in social support, stressful life events, and crowding, but a main effect of family type in predicting depression remained after statistically controlling for these risks.

Changes in depression during and following pregnancy

Rates of depression were studied in a sample of over 9000 women who were participants in the Avon Longitudinal Study of Pregnancy and Childhood. Assessments of depression were made at 18 and 32 weeks gestation, and at 8 and 32 weeks postpartum. Changes in depressive status across time were modelled using latent Markov modelling methods. This analysis showed that when classification errors were taken into account there was relatively high stability in diagnostic status during pregnancy and after pregnancy. However, the transition from late pregnancy to the early postnatal period showed evidence of increased instability and remission of depression. The net effects of this were that rates of depression tended to decline following childbirth. The implications of these results for a series of issues including measurement errors in depression reports and the prevalence of depression before and after childbirth are discussed.

A study of the use of the Edinburgh postnatal depression scale with parent groups outside the postpartum period

The Edinburgh Postnatal Depression Scale (EPDS) was sent by post to 206 mothers and 201 fathers of toddlers (aged between 19 and 22 months). At the same time these parents also completed subscales of the Crown—Crisp Experiential Index (CCEI). The responses were used to assess the feasibility of postal completion of the EPDS and its acceptability to parents outside the postpartum year, particularly fathers for whom there have been no previous reports of its use. On a small sub-group, the sensitivity, specificity and predictive values of the measures were assessed using the Present. State Examination. Answers to the depression subscale of the CCEI to the EPDS and to the Present State Examination were compared to assess validity. Completion of the postally-administered EPDS was satisfactory, though some difficulties were experienced in a second postal administration to a subsample. The scale was completed without obvious error or omission and this, combined with positive comments from parents, suggests the acceptability of the scale to both mothers and fathers. The mean scores were higher for mothers than for fathers, but the pattern of distribution was similar with a marked positive skew and a distinct decline in scores above 10. Because the subsample of parents interviewed was small the calculation of sensitivity and specificity has to be treated with caution. However, the results for mothers suggest that the EPDS has satisfactory validity for this group and one superior to the depression subscale of the CCEI. Among the fathers interviewed there were insufficient cases to enable calculation of sensitivity and specificity. Other results were encouraging, however, and suggest the merit of further studies of the application and validity of the EPDS with fathers.

Comparison of prevalence of depression in mothers of twins and mothers of singletons

OBJECTIVE To determine whether the apparent additional and exceptional stresses associated with bearing and parenting twins affect the emotional wellbeing of mothers. SETTING--Great Britain, 1970-5. DESIGN Cohort study of 13,135 children born between 4 April and 11 April 1970. Mothers of all children, both singletons and twins, were interviewed by health visitors (providing demographic data) and completed a self report measure of emotional well-being (the Rutter malaise inventory) when the child was 5 years of age. The malaise scores of mothers of twins were compared with those of all mothers of singletons and then with those of mothers categorised by the age spacing of their children (only one child, widely spaced, or closely spaced), taking account of maternal age, social class, and whether the study child had a disability, by using logistic regression. SUBJECTS 139 mothers of twins--122 pairs of twins and 17 twins whose cotwin had died--and 12,573 controls, who were mothers of singletons. RESULTS A significantly higher proportion of mothers of twins at 5 years had malaise scores indicative of depression than mothers of singletons at the same age. Mothers who had borne twins, one of whom had subsequently died, had the highest malaise scores and were three times more likely than mothers of singletons to experience depression. Both mothers of twin pairs and mothers of singletons closely spaced in age were at significantly higher risk of experiencing depression than mothers of children widely spaced in age or mothers of only one child (p less than 0.0001). Odds ratios indicated that the risk of depression in mothers of twins was higher than that in mothers of closely spaced singletons. CONCLUSION Mothers of twins are more likely to experience depression. This suggests a relation between the additional and exceptional stresses that twins present and the mother's emotional wellbeing.

Rapid isolation and detection of erythropoietin in blood plasma by magnetic core gold nanoparticles and portable Raman spectroscopy

Isolating, purifying, and identifying proteins in complex biological matrices is often difficult, time consuming, and unreliable. Herein we describe a rapid screening technique for proteins in biological matrices that combines selective protein isolation with direct surface enhanced Raman spectroscopy (SERS) detection. Magnetic core gold nanoparticles were synthesised, characterised, and subsequently functionalized with recombinant human erythropoietin (rHuEPO)-specific antibody. The functionalized nanoparticles were used to capture rHuEPO from horse blood plasma within 15 minutes. The selective binding between the protein and the functionalized nanoparticles was monitored by SERS. The purified protein was then released from the nanoparticles’ surface and directly spectroscopically identified on a commercial nanopillar SERS substrate. ELISA independently confirmed the SERS identification and quantified the released rHuEPO. Finally, the direct SERS detection of the extracted protein was successfully demonstrated for in-field screening by a handheld Raman spectrometer within 1 minute sample measurement time.