An investigation of schizotypy in injecting amphetamine users

A fully dimensional view of psychiatric disorder conceptualises schizotypy as both a continuous personality trait and an underlying vulnerability to the development of psychotic illness. Such a model would predict that the structure of schizotypal traits would closely parallel the structure of schizophrenia or psychosis. This was investigated in injecting amphetamine users (N = 322), a clinical population who have high rates of acute psychotic episodes and subclinical schizotypal experiences. Schizotypy was assessed using the Oxford-Liverpool Inventory of Feelings and Experiences (O-LIFE), and psychotic symptoms were assessed using the Brief Psychiatric Rating Scale (BPRS). Using confirmatory factor analysis, O-LIFE subscale scores were mapped onto latent variables with their more clinical counterparts from the BPRS. A four-factor model comprising positive schizotypy, disorganisation, negative schizotypy, and disinhibition provided the best model fit, consistent with prior research into the structure of schizotypy. The model provided a good fit to the data, lending support to the theory that schizotypy and psychotic symptoms map onto common underlying dimensions.

The relationship between cannabis outcome expectancies and cannabis refusal self-efficacy in a treatment population

Background and aims Self-efficacy beliefs and outcome expectancies are central to Social Cognitive Theory (SCT). Alcohol studies demonstrate the theoretical and clinical utility of applying both SCT constructs. This study examined the relationship between refusal self-efficacy and outcome expectancies in a sample of cannabis users, and tested formal mediational models. Design Patients referred for cannabis treatment completed a comprehensive clinical assessment, including recently validated cannabis expectancy and refusal self-efficacy scales. Setting A hospital alcohol and drug out-patient clinic. Participants Patients referred for a cannabis treatment [n = 1115, mean age 26.29, standard deviation (SD) 9.39]. Measurements The Cannabis Expectancy Questionnaire (CEQ) and Cannabis Refusal Self-Efficacy Questionnaire (CRSEQ) were completed, along with measures of cannabis severity [Severity of Dependence Scale (SDS)] and cannabis consumption. Findings Positive (β = −0.29, P < 0.001) and negative (β = −0.19, P < 0.001) cannabis outcome expectancies were associated significantly with refusal self-efficacy. Refusal self-efficacy, in turn, fully mediated the association between negative expectancy and weekly consumption [95% confidence interval (CI) = 0.03, 0.17] and partially mediated the effect of positive expectancy on weekly consumption (95% CI = 0.06, 0.17). Conclusions Consistent with Social Cognitive Theory, refusal self-efficacy (a person's belief that he or she can abstain from cannabis use) mediates part of the association between cannabis outcome expectancies (perceived consequences of cannabis use) and cannabis use.

Comparing methods for modelling spreading cell fronts

Spreading cell fronts play an essential role in many physiological processes. Classically, models of this process are based on the Fisher-Kolmogorov equation; however, such continuum representations are not always suitable as they do not explicitly represent behaviour at the level of individual cells. Additionally, many models examine only the large time asymptotic behaviour, where a travelling wave front with a constant speed has been established. Many experiments, such as a scratch assay, never display this asymptotic behaviour, and in these cases the transient behaviour must be taken into account. We examine the transient and asymptotic behaviour of moving cell fronts using techniques that go beyond the continuum approximation via a volume-excluding birth-migration process on a regular one-dimensional lattice. We approximate the averaged discrete results using three methods: (i) mean-field, (ii) pair-wise, and (iii) one-hole approximations. We discuss the performace of these methods, in comparison to the averaged discrete results, for a range of parameter space, examining both the transient and asymptotic behaviours. The one-hole approximation, based on techniques from statistical physics, is not capable of predicting transient behaviour but provides excellent agreement with the asymptotic behaviour of the averaged discrete results, provided that cells are proliferating fast enough relative to their rate of migration. The mean-field and pair-wise approximations give indistinguishable asymptotic results, which agree with the averaged discrete results when cells are migrating much more rapidly than they are proliferating. The pair-wise approximation performs better in the transient region than does the mean-field, despite having the same asymptotic behaviour. Our results show that each approximation only works in specific situations, thus we must be careful to use a suitable approximation for a given system, otherwise inaccurate predictions could be made.

Constructing a clinical decision-making framework for image-guided radiotherapy using a Bayesian Network

A decision-making framework for image-guided radiotherapy (IGRT) is being developed using a Bayesian Network (BN) to graphically describe, and probabilistically quantify, the many interacting factors that are involved in this complex clinical process. Outputs of the BN will provide decision-support for radiation therapists to assist them to make correct inferences relating to the likelihood of treatment delivery accuracy for a given image-guided set-up correction. The framework is being developed as a dynamic object-oriented BN, allowing for complex modelling with specific sub-regions, as well as representation of the sequential decision-making and belief updating associated with IGRT. A prototype graphic structure for the BN was developed by analysing IGRT practices at a local radiotherapy department and incorporating results obtained from a literature review. Clinical stakeholders reviewed the BN to validate its structure. The BN consists of a sub-network for evaluating the accuracy of IGRT practices and technology. The directed acyclic graph (DAG) contains nodes and directional arcs representing the causal relationship between the many interacting factors such as tumour site and its associated critical organs, technology and technique, and inter-user variability. The BN was extended to support on-line and off-line decision-making with respect to treatment plan compliance. Following conceptualisation of the framework, the BN will be quantified. It is anticipated that the finalised decision-making framework will provide a foundation to develop better decision-support strategies and automated correction algorithms for IGRT.

Posttraumatic stress disorder and traumatic stress : from bench to bedside, from war to disaster

War is a tragic event and its mental health consequences can be profound. Recent studies indicate substantial rates of posttraumatic stress disorder and other behavioral alterations because of war exposure. Understanding the psychological, behavioral, and neurobiological mechanism of mental health and behavioral changes related to war exposure is critical to helping those in need of care. Substantial work to encourage bench to bedside to community knowledge and communication is a core component of addressing this world health need.

Engraftment outcomes after HPC co-culture with mesenchymal stromal cells and osteoblasts

Haematopoietic stem cell (HSC) transplantation is an established cell-based therapy for a number of haematological diseases. To enhance this therapy, there is considerable interest in expanding HSCs in artificial niches prior to transplantation. This study compared murine HSC expansion supported through co-culture on monolayers of either undifferentiated mesenchymal stromal cells (MSCs) or osteoblasts. Sorted Lineage− Sca-1+ c-kit+ (LSK) haematopoietic stem/progenitor cells (HPC) demonstrated proliferative capacity on both stromal monolayers with the greatest expansion of LSK shown in cultures supported by osteoblast monolayers. After transplantation, both types of bulk-expanded cultures were capable of engrafting and repopulating lethally irradiated primary and secondary murine recipients. LSKs co-cultured on MSCs showed comparable, but not superior, reconstitution ability to that of freshly isolated LSKs. Surprisingly, however, osteoblast co-cultured LSKs showed significantly poorer haematopoietic reconstitution compared to LSKs co-cultured on MSCs, likely due to a delay in short-term reconstitution. We demonstrated that stromal monolayers can be used to maintain, but not expand, functional HSCs without a need for additional haematopoietic growth factors. We also demonstrated that despite apparently superior in vitro performance, co-injection of bulk cultures of osteoblasts and LSKs in vivo was detrimental to recipient survival and should be avoided in translation to clinical practice.

Exact series solutions of reactive transport models with general initial conditions

Exact solutions of partial differential equation models describing the transport and decay of single and coupled multispecies problems can provide insight into the fate and transport of solutes in saturated aquifers. Most previous analytical solutions are based on integral transform techniques, meaning that the initial condition is restricted in the sense that the choice of initial condition has an important impact on whether or not the inverse transform can be calculated exactly. In this work we describe and implement a technique that produces exact solutions for single and multispecies reactive transport problems with more general, smooth initial conditions. We achieve this by using a different method to invert a Laplace transform which produces a power series solution. To demonstrate the utility of this technique, we apply it to two example problems with initial conditions that cannot be solved exactly using traditional transform techniques.

Towards robotic visual and acoustic stealth for outdoor dynamic target tracking

Covertly tracking mobile targets, either animal or human, in previously unmapped outdoor natural environments using off-road robotic platforms requires both visual and acoustic stealth. Whilst the use of robots for stealthy surveillance is not new, the majority only consider navigation for visual covertness. However, most fielded robotic systems have a non-negligible acoustic footprint arising from the onboard sensors, motors, computers and cooling systems, and also from the wheels interacting with the terrain during motion. This time-varying acoustic signature can jeopardise any visual covertness and needs to be addressed in any stealthy navigation strategy. In previous work, we addressed the initial concepts for acoustically masking a tracking robot’s movements as it travels between observation locations selected to minimise its detectability by a dynamic natural target and ensuring con- tinuous visual tracking of the target. This work extends the overall concept by examining the utility of real-time acoustic signature self-assessment and exploiting shadows as hiding locations for use in a combined visual and acoustic stealth framework.

Benthic shift in a Solomon Islands' lagoon : corals to cyanobacteria

In June 2011 a large phytoplankton bloom resulted in a catastrophic mortality event that affected a large coastal embayment in the Solomon Islands. This consisted of an area in excess of 20 km2 of reef and soft sandy habitats in Marovo Lagoon, the largest double barrier lagoon in the world. This embayment is home to over 1200 people leading largely subsistence lifestyles depending on the impacted reefs for majority of their protein needs. A toxic diatom (Psuedo-nitzchia spp.) and toxic dinoflagellate (Pyrodinium bahamense var. compressum) reached concentrations of millions of cells per litre. The senescent phytoplankton bloom led to complete de-oxygenation of the water column that reportedly caused substantial mortality of marine animal life in the immediate area within a rapid timeframe (24 h). Groups affected included holothurians, crabs and reef and pelagic fish species. Dolphins, reptiles and birds were also found dead within the area, indicating algal toxin accumulation in the food chain. Deep reefs and sediments, whilst initially unaffected, have now been blanketed in large cyanobacterial mats which have negatively impacted live coral cover especially within the deep reef zone (> 6 m depth). Reef recovery within the deep zone has been extremely slow and may indicate an alternative state for the system.

Acoustic Masking of a Stealthy Outdoor Robot Tracking a Dynamic Target

This work is motivated by the desire to covertly track mobile targets, either animal or human, in previously unmapped outdoor natural environments using off-road robotic platforms with a non-negligible acoustic signature. The use of robots for stealthy surveillance is not new. Many studies exist but only consider the navigation problem to maintain visual covertness. However, robotic systems also have a significant acoustic footprint from the onboard sensors, motors, computers and cooling systems, and also from the wheels interacting with the terrain during motion. All these can jepordise any visual covertness. In this work, we experimentally explore the concepts of opportunistically utilizing naturally occurring sounds within outdoor environments to mask the motion of a robot, and being visually covert whilst maintaining constant observation of the target. Our experiments in a constrained outdoor built environment demonstrate the effectiveness of the concept by showing a reduced acoustic signature as perceived by a mobile target allowing the robot to covertly navigate to opportunistic vantage points for observation.

Real-time image classification for adaptive mission planning using an Autonomous Underwater Vehicle

Real-time image analysis and classification onboard robotic marine vehicles, such as AUVs, is a key step in the realisation of adaptive mission planning for large-scale habitat mapping in previously unexplored environments. This paper describes a novel technique to train, process, and classify images collected onboard an AUV used in relatively shallow waters with poor visibility and non-uniform lighting. The approach utilises Förstner feature detectors and Laws texture energy masks for image characterisation, and a bag of words approach for feature recognition. To improve classification performance we propose a usefulness gain to learn the importance of each histogram component for each class. Experimental results illustrate the performance of the system in characterisation of a variety of marine habitats and its ability to operate onboard an AUV's main processor suitable for real-time mission planning.

Kelp detection in highly dynamic environments using texture recognition

This paper describes a texture recognition based method for segmenting kelp from images collected in highly dynamic shallow water environments by an Autonomous Underwater Vehicle (AUV). A particular challenge is image quality that is affected by uncontrolled lighting, reduced visibility, significantly varying perspective due to platform egomotion, and kelp sway from wave action. The kelp segmentation approach uses the Mahalanobis distance as a way to classify Haralick texture features from sub-regions within an image. The results illustrate the applicability of the method to classify kelp allowing construction of probability maps of kelp masses across a sequence of images.

Experimental evaluation of an autonomous surface vehicle for water quality and greenhouse gas emission monitoring

This paper describes the experimental evaluation of a novel Autonomous Surface Vehicle capable of navigating complex inland water reservoirs and measuring a range of water quality properties and greenhouse gas emissions. The 16 ft long solar powered catamaran is capable of collecting water column profiles whilst in motion. It is also directly integrated with a reservoir scale floating sensor network to allow remote mission uploads, data download and adaptive sampling strategies. This paper describes the onboard vehicle navigation and control algorithms as well as obstacle avoidance strategies. Experimental results are shown demonstrating its ability to maintain track and avoid obstacles on a variety of large-scale missions and under differing weather conditions, as well as its ability to continuously collect various water quality parameters complimenting traditional manual monitoring campaigns.

An autonomous surface vehicle for water quality monitoring

This paper describes a novel Autonomous Surface Vehicle capable of navigating throughout complex inland water storages and measuring a range of water quality properties and greenhouse gas emissions. The 16 ft long solar powered catamaran can collect this information throughout the water column whilst the vehicle is moving. A unique feature of this ASV is its integration into a storage scale floating sensor network to allow remote mission uploads, data download and adaptive sampling strategies. This paper provides an overview of the vehicle design and operation including control, laser-based obstacle avoidance, and vision-based inspection capabilities. Experimental results are shown illustrating its ability to continuously collect key water quality parameters and compliment intensive manual monitoring campaigns.