Studies of the chemistry of dithiocarbamates and their metal complexes : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science with Honours in Chemistry at Massey University, New Zealand

The studies of the chemistry of dithiocarbamates and its related compounds have been undertaken. It is hoped that such studies would shed light on the interaction betwe n such compounds and the thiol-enzyme, aldehyde dehydrogenase. The facts of previous publication revealed that metabolism of alcohol occurs chiefly in the liver and involves several different enzyme systems. The major pathway, however, is oxidation of ethanol to acetaldehyde, catalysed by alcohol dehydrogenase, followed by oxidation of acetaldehyde to acetate, catalysed by aldehyde dehydrogenase. This normal pathway can be disrupted by the ingestion of certain compound, the famous of which is disulfiram or Antabuse, prior to the drinking of alcohol. The compound 4-nitro-phenyl di-methyldithiocarbamate has a close structural similarity to both the inactivator, disulfiram, and the substrate, 4-nitro-phenyl acetate. It turns out that the dithiocarbamate ester is in fact an inactivator of aldehyde dehydrogenase. The chemical reaction resulted in the formation of an inactivated enzyme. The extent of the inhibition can be measured by the release of 4-nitrothiophenoxide ion and upon the treatment of di-methyldithiocarbamate ion with acid to form carbon disulphide gas. However, the analysis is not fully understood, due to further complex reactions occured in the system. Two other suggestions have been put forward to account for the gap. Nevertheless, the study of the chemistry of dithiocarbamate is a step further towards understanding. The study of metal complexes of some substituted dithiocarbamates has found considerable use in analytical methods for heavy metals. The complexes gave approximately the correct metal analysis based on the expected stoichiometry of a 2:1 ratio of dithiocarbamate to metal

Co-expression of Gbeta 5 Enhances the Function of Two Ggamma Subunit-like Domain-containing Regulators of G Protein Signaling Proteins

Regulators of G protein signaling (RGS) stimulate the GTPase activity of G protein Galpha subunits and probably play additional roles. Some RGS proteins contain a Ggamma subunit-like (GGL) domain, which mediates a specific interaction with Gbeta 5. The role of such interactions in RGS function is unclear. RGS proteins can accelerate the kinetics of coupling of G protein-coupled receptors to G-protein-gated inwardly rectifying K+ (GIRK) channels. Therefore, we coupled m2-muscarinic acetylcholine receptors to GIRK channels in Xenopus oocytes to evaluate the effect of Gbeta 5 on RGS function. Co-expression of either RGS7 or RGS9 modestly accelerated GIRK channel kinetics. When Gbeta 5 was co-expressed with either RGS7 or RGS9, the acceleration of GIRK channel kinetics was strongly increased over that produced by RGS7 or RGS9 alone. RGS function was not enhanced by co-expression of Gbeta 1, and co-expression of Gbeta 5 alone had no effect on GIRK channel kinetics. Gbeta 5 did not modulate the function either of RGS4, an RGS protein that lacks a GGL domain, or of a functional RGS7 construct in which the GGL domain was omitted. Enhancement of RGS7 function by Gbeta 5 was not a consequence of an increase in the amount of plasma membrane or cytosolic RGS7 protein

Algorithm Choice For Multiple-Query Evaluation

Traditional query optimization concentrates on the optimization of the execution of each individual query. More recently, it has been observed that by considering a sequence of multiple queries some additional high-level optimizations can be performed. Once these optimizations have been performed, each operation is translated into executable code. The fundamental insight in this paper is that significant improvements can be gained by careful choice of the algorithm to be used for each operation. This choice is not merely based on efficiency of algorithms for individual operations, but rather on the efficiency of the algorithm choices for the entire multiple-query evaluation. An efficient procedure for automatically optimizing these algorithm choices is given

Automatic Parallelization of Database Queries

Although automatic parallelization of conventional language programs is now widely accepted, relatively little emphasis has been placed on automatic parallelization of database query programs (sometimes referred to as “multiple queries” ). In this paper, we discuss the unique problems associated with automatic parallelization of database programs. From this discussion, we derive a complete approach to automatic parallelization of database programs. Beside integrating a number of existing techniques, our approach relies heavily on several new concepts, including the concepts of “algorithm-level” analysis and hybrid static/dynamic scheduling

Data processing of physiological sensor data and alarm determination utilising activity recognition

Current physiological sensors are passive and transmit sensed data to Monitoring centre (MC) through wireless body area network (WBAN) without processing data intelligently. We propose a solution to discern data requestors for prioritising and inferring data to reduce transactions and conserve battery power, which is important requirements of mobile health (mHealth). However, there is a problem for alarm determination without knowing the activity of the user. For example, 170 beats per minute of heart rate can be normal during exercising, however an alarm should be raised if this figure has been sensed during sleep. To solve this problem, we suggest utilising the existing activity recognition (AR) applications. Most of health related wearable devices include accelerometers along with physiological sensors. This paper presents a novel approach and solution to utilise physiological data with AR so that they can provide not only improved and efficient services such as alarm determination but also provide richer health information which may provide content for new markets as well as additional application services such as converged mobile health with aged care services. This has been verified by experimented tests using vital signs such as heart pulse rate, respiration rate and body temperature with a demonstrated outcome of AR accelerometer sensors integrated with an Android app

Direct Numerical Simulations of Electrophoresis of Charged Colloids

We propose a numerical method to simulate electrohydrodynamic phenomena in charged colloidal dispersions. This method enables us to compute the time evolutions of colloidal particles, ions, and host fluids simultaneously by solving Newton, advection-diffusion, and Navier--Stokes equations so that the electrohydrodynamic couplings can be fully taken into account. The electrophoretic mobilities of charged spherical particles are calculated in several situations. The comparisons with approximation theories show quantitative agreements for dilute dispersions without any empirical parameters, however, our simulation predicts notable deviations in the case of dense dispersions.Comment: 4pages, 3figures, to appear in Phys. Rev. Let

Event-Driven Tactile Learning with Location Spiking Neurons

The sense of touch is essential for a variety of daily tasks. New advances in event-based tactile sensors and Spiking Neural Networks (SNNs) spur the research in event-driven tactile learning. However, SNN-enabled event-driven tactile learning is still in its infancy due to the limited representative abilities of existing spiking neurons and high spatio-temporal complexity in the data. In this paper, to improve the representative capabilities of existing spiking neurons, we propose a novel neuron model called "location spiking neuron", which enables us to extract features of event-based data in a novel way. Moreover, based on the classical Time Spike Response Model (TSRM), we develop a specific location spiking neuron model - Location Spike Response Model (LSRM) that serves as a new building block of SNNs. Furthermore, we propose a hybrid model which combines an SNN with TSRM neurons and an SNN with LSRM neurons to capture the complex spatio-temporal dependencies in the data. Extensive experiments demonstrate the significant improvements of our models over other works on event-driven tactile learning and show the superior energy efficiency of our models and location spiking neurons, which may unlock their potential on neuromorphic hardware.Comment: accepted by IJCNN 2022 (oral), the source code is available at https://github.com/pkang2017/TactileLocNeuron

SUNLAB: a Functional-Structral Model for Genotypic and Phenotypic Characterization of the Sunflower Crop

International audienceA new functional-structural model SUNLAB for the crop sunflower (Helianthus annuus L.) is developed. It is dedicated to simulate the organogenesis, morphogenesis, biomass accumulation and biomass partitioning to organs in sunflower growth. It is adapted to model phenotypic response to diverse environment factors including temperature stress and water deficiency, and adapted to different genotypic variants. The model is confronted to experimental data and estimated parameter values of two genotypes "Melody" and "Prodisol" are presented. SUNLAB parameters seem to show genotypic variability, which potentially makes the model an interesting intermediate to discriminate between genotypes. Statistical tests on estimated parameter values suggest that some parameters are common between genotypes and others are genotypic specific. Since SUNLAB simulate individual leaf area and biomass as two state variables, an interesting corollary is that it also simulates dynamically the specific leaf area (SLA) variable. Further studies are performed to evaluate model performances with more genotypes and more discriminating environments to test and expand model's adaptability and usabilit

D₂ Dopamine Receptors Colocalize Regulator of G-Protein Signaling 9-2 (RGS9-2) via the RGS9 DEP Domain, and RGS9 Knock-Out Mice Develop Dyskinesias Associated with Dopamine Pathways

Regulator of G-protein signaling 9-2 (RGS9-2), a member of the RGS family of Gα GTPase accelerating proteins, is expressed specifically in the striatum, which participates in antipsychotic-induced tardive dyskinesia and in levodopa-induced dyskinesia. We report that RGS9 knock-out mice develop abnormal involuntary movements when inhibition of dopaminergic transmission is followed by activation of D₂-like dopamine receptors (DRs). These abnormal movements resemble drug-induced dyskinesia more closely than other rodent models. Recordings from striatal neurons of these mice establish that activation of D₂-like DRs abnormally inhibits glutamate-elicited currents. We show that RGS9-2, via its DEP domain (for Disheveled, EGL-10, Pleckstrin homology), colocalizes with D₂DRs when coexpressed in mammalian cells. Recordings from oocytes coexpressing D₂DR or the m2 muscarinic receptor and G-protein-gated inward rectifier potassium channels show that RGS9-2, via its DEP domain, preferentially accelerates the termination of D₂DR signals. Thus, alterations in RGS9-2 may be a key factor in the pathway leading from D₂DRs to the side effects associated with the treatment both of psychoses and Parkinson's disease