Simplification of arboreal marsupial assemblages in response to increasing urbanization

Arboreal marsupials play an essential role in ecosystem function including regulating insect and plant populations, facilitating pollen and seed dispersal and acting as a prey source for higher-order carnivores in Australian environments. Primarily, research has focused on their biology, ecology and response to disturbance in forested and urban environments. We used presence-only species distribution modelling to understand the relationship between occurrences of arboreal marsupials and eco-geographical variables, and to infer habitat suitability across an urban gradient. We used post-proportional analysis to determine whether increasing urbanization affected potential habitat for arboreal marsupials. The key eco-geographical variables that influenced disturbance intolerant species and those with moderate tolerance to disturbance were natural features such as tree cover and proximity to rivers and to riparian vegetation, whereas variables for disturbance tolerant species were anthropogenic-based (e.g., road density) but also included some natural characteristics such as proximity to riparian vegetation, elevation and tree cover. Arboreal marsupial diversity was subject to substantial change along the gradient, with potential habitat for disturbance-tolerant marsupials distributed across the complete gradient and potential habitat for less tolerant species being restricted to the natural portion of the gradient. This resulted in highly-urbanized environments being inhabited by a few generalist arboreal marsupial species. Increasing urbanization therefore leads to functional simplification of arboreal marsupial assemblages, thus impacting on the ecosystem services they provide.

From knowledge acquisition to knowledge production: Issues with Australian honours curricula

Although there have been increasing attempts to involve undergraduate students in conducting research, a pivotal moment when students engage in knowledge production is during honours programmes. Honours programmes, particularly those in Australia, seek to develop students' capacity to engage in higher order thinking that may lead to knowledge production. This transition is facilitated through advanced disciplinary knowledge, research training and a research project. However, there is a pedagogical tension between requiring students to engage in this deeper level of inquiry at the same time as they complete a heavy knowledge acquisition load. This paper explores how a number of disciplines in Australia balance these elements of the honours curricula. It argues that the combination of these curriculum goals can make it difficult for students to apply the knowledge they have gained in advanced disciplinary and research training courses to their research project work. This has serious implications for honours programmes. © 2012 Copyright Taylor and Francis Group, LLC.

Development of a compact rectenna for wireless powering of a head-mountable deep brain stimulation device

Design of a rectangular spiral planar inverted-F antenna (PIFA) at 915 MHz for wireless power transmission applications is proposed. The antenna and rectifying circuitry form a rectenna, which can produce dc power from a distant radio frequency energy transmitter. The generated dc power is used to operate a low-power deep brain stimulation pulse generator. The proposed antenna has the dimensions of 10 mm × 12.5 mm × 1.5 mm and resonance frequency of 915 MHz with a measured bandwidth of 15 MHz at return loss of -10 dB. A dielectric substrate of FR-4 of εr = 4.8 and δ = 0.015 with thickness of 1.5 mm is used for both antenna and rectifier circuit simulation and fabrication because of its availability and low cost. An L-section impedance matching circuit is used between the PIFA and voltage doubler rectifier. The impedance matching circuit also works as a low-pass filter for elimination of higher order harmonics. Maximum dc voltage at the rectenna output is 7.5 V in free space and this rectenna can drive a deep brain stimulation pulse generator at a distance of 30 cm from a radio frequency energy transmitter, which transmits power of 26.77 dBm.

Ordinal random fields for recommender systems

Recommender Systems heavily rely on numerical preferences, whereas the importance of ordinal preferences has only been recognised in recent works of Ordinal Matrix Factorisation (OMF). Although the OMF can effectively exploit ordinal properties, it captures only the higher-order interactions among users and items, without considering the localised interactions properly. This paper employs Markov Random Fields (MRF) to investigate the localised interactions, and proposes a unified model called Ordinal Random Fields (ORF) to take advantages of both the representational power of the MRF and the ease of modelling ordinal preferences by the OMF. Experimental result on public datasets demonstrates that the proposed ORF model can capture both types of interactions, resulting in improved recommendation accuracy.

Mechanisms underlying neurocognitive dysfunctions in recurrent major depression

Recent work shows that depression is intimately associated with changes in cognitive functioning, including memory, attention, verbal fluency, and other aspects of higher-order cognitive processing. Changes in cognitive functioning are more likely to occur when depressive episodes are recurrent and to abate to some degree during periods of remission. However, with accumulating frequency and duration of depressive episodes, cognitive deficits can become enduring, being evident even when mood improves. Such changes in cognitive functioning give depression links to mild cognitive impairment and thereby with neurodegenerative conditions, including Alzheimer's disease, Parkinson's disease, schizophrenia, and multiple sclerosis. Depression may then be conceptualized on a dimension of depression - mild cognitive impairment - dementia. The biological underpinnings of depression have substantial overlaps with those of neurodegenerative conditions, including reduced neurogenesis, increased apoptosis, reactive oxygen species, tryptophan catabolites, autoimmunity, and immune-inflammatory processes, as well as decreased antioxidant defenses. These evolving changes over the course of depressive episodes drive the association of depression with neurodegenerative conditions. As such, the changes in cognitive functioning in depression have important consequences for the treatment of depression and in reconceptualizing the role of depression in wider neuroprogressive conditions. Here we review the data on changes in cognitive functioning in recurrent major depression and their association with other central conditions.

Self-assembled peptide nanostructures for the fabrication of cell scaffolds

The fabrication of artificial scaffolds that effectively mimic the host environment of the cell have exciting potential for the treatment of many diseases in regenerative medicine. In particular, appropriately designed scaffolds have the capacity to support, replace, and mediate the transplantation of therapeutic cells in order to regenerate damaged or diseased tissues. To achieve these goals for regeneration, the engineering of an environment structurally similar to the native extracellular matrix (ECM) is necessary in order to closely match the chemical and physical conditions found within the extracellular niche. Recently, self-assembled peptide (SAP) hydrogels have shown great potential for such biological applications due to their inherent biocompatibility, propensity to form higher order structures, rich chemical functionality and ease of synthesis. Importantly, it is possible to control the organization and properties of the target materials as the chemical structure is determined by amino acid sequence. Here, the development of SAP hydrogels as functional cell scaffolds and useful tools in tissue engineering is reviewed.

Tensor-variate restricted Boltzmann machines

Restricted Boltzmann Machines (RBMs) are an important class of latent variable models for representing vector data. An under-explored area is multimode data, where each data point is a matrix or a tensor. Standard RBMs applying to such data would require vectorizing matrices and tensors, thus resulting in unnecessarily high dimensionality and at the same time, destroying the inherent higher-order interaction structures. This paper introduces Tensor-variate Restricted Boltzmann Machines (TvRBMs) which generalize RBMs to capture the multiplicative interaction between data modes and the latent variables. TvRBMs are highly compact in that the number of free parameters grows only linear with the number of modes. We demonstrate the capacity of TvRBMs on three real-world applications: handwritten digit classification, face recognition and EEG-based alcoholic diagnosis. The learnt features of the model are more discriminative than the rivals, resulting in better classification performance.

Preference relation-based markov random fields

A preference relation-based Top-N recommendation approach, PrefMRF, is proposed to capture both the second-order and the higher-order interactions among users and items. Traditionally Top-N recommendation was achieved by predicting the item ratings fi rst, and then inferring the item rankings, based on the assumption of availability of explicit feed-backs such as ratings, and the assumption that optimizing the ratings is equivalent to optimizing the item rankings. Nevertheless, both assumptions are not always true in real world applications. The proposed PrefMRF approach drops these assumptions by explicitly exploiting the preference relations, a more practical user feedback. Comparing to related work, the proposed PrefMRF approach has the unique property of modeling both the second-order and the higher-order interactions among users and items. To the best of our knowledge, this is the first time both types of interactions have been captured in preference relation-based method. Experiment results on public datasets demonstrate that both types of interactions have been properly captured, and signifi cantly improved Top-N recommendation performance has been achieved.