Myosin light chain kinase regulates synaptic plasticity and fear learning in the lateral amygdala

Learning and memory depend on signaling mole- cules that affect synaptic efficacy. The cytoskeleton has been implicated in regulating synaptic transmission but its role in learning and memory is poorly understood. Fear learning depends on plasticity in the lateral nucleus of the amygdala. We therefore examined whether the cytoskeletal-regulatory protein, myosin light chain kinase, might contribute to fear learning in the rat lateral amygdala. Microinjection of ML-7, a specific inhibitor of myosin light chain kinase, into the lateral nucleus of the amygdala before fear conditioning, but not immediately afterward, enhanced both short-term memory and long-term memory, suggesting that myosin light chain kinase is involved specifically in memory acquisition rather than in posttraining consolidation of memory. Myosin light chain kinase inhibitor had no effect on memory retrieval. Furthermore, ML-7 had no effect on behavior when the train- ing stimuli were presented in a non-associative manner. An- atomical studies showed that myosin light chain kinase is present in cells throughout lateral nucleus of the amygdala and is localized to dendritic shafts and spines that are postsynaptic to the projections from the auditory thalamus to lateral nucleus of the amygdala, a pathway specifically impli- cated in fear learning. Inhibition of myosin light chain kinase enhanced long-term potentiation, a physiological model of learning, in the auditory thalamic pathway to the lateral nu- cleus of the amygdala. When ML-7 was applied without as- sociative tetanic stimulation it had no effect on synaptic responses in lateral nucleus of the amygdala. Thus, myosin light chain kinase activity in lateral nucleus of the amygdala appears to normally suppress synaptic plasticity in the cir- cuits underlying fear learning, suggesting that myosin light chain kinase may help prevent the acquisition of irrelevant fears. Impairment of this mechanism could contribute to pathological fear learning.

Privacy enhancements for hardware-based security modules

The increasing growth in the use of Hardware Security Modules (HSMs) towards identification and authentication of a security endpoint have raised numerous privacy and security concerns. HSMs have the ability to tie a system or an object, along with its users to the physical world. However, this enables tracking of the user and/or an object associated with the HSM. Current systems do not adequately address the privacy needs and as such are susceptible to various attacks. In this work, we analyse various security and privacy concerns that arise when deploying such hardware security modules and propose a system that allow users to create pseudonyms from a trusted master public-secret key pair. The proposed system is based on the intractability of factoring and finding square roots of a quadratic residue modulo a composite number, where the composite number is a product of two large primes. Along with the standard notion of protecting privacy of an user, the proposed system offers colligation between seemingly independent pseudonyms. This new property when combined with HSMs that store the master secret key is extremely beneficial to a user, as it offers a convenient way to generate a large number of pseudonyms using relatively small storage requirements.

Regional trade-offs between mine water and energy use : a water treatment case study

The mining industry faces concurrent pressures of reducing water use, energy consumption and greenhouse gas (GHG) emissions in coming years. However, the interactions between water and energy use, as well as GHG e missions have largely been neglected in modelling studies to date. In addition, investigations tend to focus on the unit operation scale, with little consideration of whole-of-site or regional scale effects. This paper presents an application of a hierarchical systems model (HSM) developed to represent water, energy and GHG emissions fluxes at scales ranging from the unit operation, to the site level, to the regional level. The model allows for the linkages between water use, energy use and GHG emissions to be examined in a fl exible and intuitive way, so that mine sites can predict energy and emissions impacts of water use reduction schemes and vice versa. This paper examines whether this approach can also be applied to the regional scale with multiple mine sites. The model is used to conduct a case study of several coal mines in the Bowen Basin, Australia, to compare the utility of centralised and decentralised mine water treatment schemes. The case study takes into account geographical factors (such as water pumping distances and elevations), economic factors (such as capital and operating cost curves for desalination treatment plants) and regional factors (such as regionally varying climates and associated variance in mine water volumes and quality). The case study results indicate that treatment of saline mine water incurs a trade-off between water and energy use in all cases. However, significant cost differences between centralised and decentralised schemes can be observed in a simple economic analysis. Further research will examine the possibility for deriving model up-scaling algorithms to reduce computational requirements.

Modelling the water, energy and economic nexus

The mining industry faces three long term strategic risks in relation to its water and energy use: 1) securing enough water and energy to meet increased production; 2) reducing water use, energy consumption and emissions due to social, environmental and economic pressures; and 3) understanding the links between water and energy, so that an improvement in one area does not create an adverse effect in another. This project helps the industry analyse these risks by creating a hierarchical systems model (HSM) that represents the water and energy interactions on a sub-site, site and regional scales; which is coupled with a flexible risk framework. The HSM consists of: components that represent sources of water and energy; activities that use water and energy and off-site destinations of water and produced emissions. It can also represent more complex components on a site, with inbuilt examples including tailings dams and water treatment plants. The HSM also allows multiple sites and other infrastructure to be connected together to explore regional water and energy interactions. By representing water and energy as a single interconnected system the HSM can explore tradeoffs and synergies. For example, on a synthetic case study, which represents a typical site, simulations suggested that while a synergy in terms of water use and energy use could be made when chemical additives were used to enhance dust suppression, there were trade-offs when either thickened tailings or dry processing were used. On a regional scale, the HSM was used to simulate various scenarios, including: mines only withdrawing water when needed; achieving economics-of-scale through use of a single centralised treatment plant rather than smaller decentralised treatment plants; and capturing of fugitive emissions for energy generation. The HSM also includes an integrated risk framework for interpreting model output, so that onsite and off-site impacts of various water and energy management strategies can be compared in a managerial context. The case studies in this report explored company, social and environmental risks for scenarios of regional water scarcity, unregulated saline discharge, and the use of plantation forestry to offset carbon emissions. The HSM was able to represent the non-linear causal relationship at the regional scale, such as the forestry scheme offsetting a small percentage of carbon emissions but causing severe regional water shortages. The HSM software developed in this project will be released as an open source tool to allow industry personnel to easily and inexpensively quantify and explore the links between water use, energy use, and carbon emissions. The tool can be easily adapted to represent specific sites or regions. Case studies conducted in this project highlighted the potential complexity of these links between water, energy, and carbon emissions, as well as the significance of the cumulative effects of these links over time. A deeper understanding of these links is vital for the mining industry in order to progress to more sustainable operations, and the HSM provides an accessible, robust framework for investigating these links.

Adaptive estimation of hidden semi-Markov chains with parameterised transition probabilities and exponential decaying states

This paper develops maximum likelihood (ML) estimation schemes for finite-state semi-Markov chains in white Gaussian noise. We assume that the semi-Markov chain is characterised by transition probabilities of known parametric from with unknown parameters. We reformulate this hidden semi-Markov model (HSM) problem in the scalar case as a two-vector homogeneous hidden Markov model (HMM) problem in which the state consist of the signal augmented by the time to last transition. With this reformulation we apply the expectation Maximumisation (EM ) algorithm to obtain ML estimates of the transition probabilities parameters, Markov state levels and noise variance. To demonstrate our proposed schemes, motivated by neuro-biological applications, we use a damped sinusoidal parameterised function for the transition probabilities.