α-Conotoxins targeting neuronal nAChRs: understanding molecular pharmacology and potential therapeutics.

α-Conotoxins, a new class of peptides that act as nicotinic acetylcholine receptor (nAChR) antagonists have been identified from the venom of predatory marine cone snails. α-Conotoxins specifically target various nAChRs subtypes and are excellent molecular probes for identifying the physiological role of nAChR subtypes in both normal and disease states. nAChRs are ligand-gated ion channels expressed in both central nervous system (CNS) and peripheral nervous system (PNS) and are shown to contribute to the physiological roles of neurotransmitter release and synaptic plasticity. Further, they are also implicated in various conditions including Alzheimer’s disease and schizophrenia. The α3β4 subtype is shown to be involved in lung cancer and nicotine addiction. Despite this, the knowledge of the pathophysiological role of α3β4 subtypes is limited by the lack of adequate subtype specific probes. I report the discovery of new α4/7-conotoxin RegIIA which was isolated from Conus regius. Using alanine scanning mutagenesis, I report the synthesis of [N11A,N12A]RegIIA, a selective α3β4 nAChR antagonist (IC50 of 370 nM) that could potentially be used in the treatment of lung cancer and nicotine addiction. In this study, I also identified critical residues of α-conotoxin RegIIA that interact with the acetylcholine-binding site of α3β2, α3β4 and α7 nAChRs. My research also describes the pharmacological properties of two novel conotoxins: LsIA and GeXXA. α-Conotoxin LsIA is the first peptide isolated from Conus limpusi, a species of worm-hunting cone snail. LsIA exhibited selective and potent α7 and α3β2 nAChR subtype antagonism. These subtypes play vital roles in various functions, such as neuronal plasticity and synaptic transmission. In this report, I examined the structure–function relationship of a unique N-terminal serine and C-terminal carboxylation of LsIA. Furthermore, I also investigated the effect of α5 subunit incorporation, towards the inhibition of α3β2 nAChR subtype by LsIA. GeXXA is a novel αD-conotoxin isolated from the venom of Conus generalis. This toxin is a disulfide-linked homodimer of a 10-cysteine-containing peptide with each peptide chain made of 50 amino acid residues. αD-GeXXA is a non-selective inhibitor of muscle and neuronal nAChRs. Here I describe the functional characterization of monomeric αD–conotoxin which shows selective and reversible inhibition of α9α10 nAChR subtype. These results provide insight into the novel blocking mechanism of α-D conotoxins. α-Conotoxins inhibiting nAChRs have potential therapeutic applications. However, peptidic nature of α-conotoxins affects their stability and bioavailability. Various strategies to improve α-conotoxin stability have been implemented. Here, I explore the functional implications of dicarba modified cysteine bridges on novel analgesic α-conotoxins Vc1.1 and RgIA, which inhibit HVA calcium channel currents via GABAB receptor activation and α9α10 nAChR subtypes. My results revealed disulphide stacking interaction between the Cys2–Cys8 bond and disulphide of the C-loop of the principal subunit of nAChRs. My research describes the discovery, characterization and development of a novel α3β4 antagonist as neurophysiological and potential therapeutic tool for lung cancer. Further, dicarba modification of α-conotoxins and characterization of new class of α- and αD-conotoxins provide future insights towards drug development

Learning the optimal synchronization rates in distributed SDN control architectures

Since the early development of Software-DefinedNetwork (SDN) technology, researchers have been concernedwith the idea of physical distribution of the control plane to ad-dress scalability and reliability challenges of centralized designs.However, having multiple controllers managing the networkwhile maintaining a “logically-centralized” network view bringsadditional challenges. One such challenge is how to coordinatethe management decisions made by the controllers which isusually achieved by disseminating synchronization messages ina peer-to-peer manner. While there exist many architecturesand protocols to ensure synchronized network views and drivecoordination among controllers, there is no systematic method-ology for deciding the optimal frequency (or rate) of messagedissemination. In this paper, we fill this gap by introducingthe SDN synchronization problem: how often to synchronize thenetwork views for each controller pair. We consider two differentobjectives; first, the maximization of the number of controllerpairs that are synchronized, and second, the maximization of theperformance of applications of interest which may be affectedby the synchronization rate. Using techniques from knapsackoptimization and learning theory, we derive algorithms withprovable performance guarantees for each objective. Evaluationresults demonstrate significant benefits over baseline schemes thatsynchronize all controller pairs at equal rate

Agent-Based Markov Modeling for Improved COVID-19 Mitigation Policies

The year 2020 saw the covid-19 virus lead to one of the worst global pandemics in history. As a result, governments around the world have been faced with the challenge of protecting public health while keeping the economy running to the greatest extent possible. Epidemiological models provide insight into the spread of these types of diseases and predict the e_ects of possible intervention policies. However, to date, even the most data-driven intervention policies rely on heuristics. In this paper, we study how reinforcement learning (RL) and Bayesian inference can be used to optimize mitigation policies that minimize economic impact without overwhelming hospital capacity. Our main contributions are (1) a novel agent-based pandemic simulator which, unlike traditional models, is able to model _ne-grained interactions among people at speci_c locations in a community; (2) an RL- based methodology for optimizing _ne-grained mitigation policies within this simulator; and (3) a Hidden Markov Model for predicting infected individuals based on partial observations regarding test results, presence of symptoms, and past physical contacts

LEDGF1-326 Decreases P23H and Wild Type Rhodopsin Aggregates and P23H Rhodopsin Mediated Cell Damage in Human Retinal Pigment Epithelial Cells

P23H rhodopsin, a mutant rhodopsin, is known to aggregate and cause retinal degeneration. However, its effects on retinal pigment epithelial (RPE) cells are unknown. The purpose of this study was to determine the effect of P23H rhodopsin in RPE cells and further assess whether LEDGF(1-326), a protein devoid of heat shock elements of LEDGF, a cell survival factor, reduces P23H rhodopsin aggregates and any associated cellular damage.ARPE-19 cells were transiently transfected/cotransfected with pLEDGF(1-326) and/or pWT-Rho (wild type)/pP23H-Rho. Rhodopsin mediated cellular damage and rescue by LEDGF(1-326) was assessed using cell viability, cell proliferation, and confocal microscopy assays. Rhodopsin monomers, oligomers, and their reduction in the presence of LEDGF(1-326) were quantified by western blot analysis. P23H rhodopsin mRNA levels in the presence and absence of LEDGF(1-326) was determined by real time quantitative PCR.P23H rhodopsin reduced RPE cell viability and cell proliferation in a dose dependent manner, and disrupted the nuclear material. LEDGF(1-326) did not alter P23H rhodopsin mRNA levels, reduced its oligomers, and significantly increased RPE cell viability as well as proliferation, while reducing nuclear damage. WT rhodopsin formed oligomers, although to a smaller extent than P23H rhodopsin. Further, LEDGF(1-326) decreased WT rhodopsin aggregates.P23H rhodopsin as well as WT rhodopsin form aggregates in RPE cells and LEDGF(1-326) decreases these aggregates. Further, LEDGF(1-326) reduces the RPE cell damage caused by P23H rhodopsin. LEDGF(1-326) might be useful in treating cellular damage associated with protein aggregation diseases such as retinitis pigmentosa

A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems

This paper presents a new hybrid evolutionary algorithm to solve multi-objective multicast routing problems in telecommunication networks. The algorithm combines simulated annealing based strategies and a genetic local search, aiming at a more flexible and effective exploration and exploitation in the search space of the complex problem to find more non-dominated solutions in the Pareto Front. Due to the complex structure of the multicast tree, crossover and mutation operators have been specifically devised concerning the features and constraints in the problem. A new adaptive mutation probability based on simulated annealing is proposed in the hybrid algorithm to adaptively adjust the mutation rate according to the fitness of the new solution against the average quality of the current population during the evolution procedure. Two simulated annealing based search direction tuning strategies are applied to improve the efficiency and effectiveness of the hybrid evolutionary algorithm. Simulations have been carried out on some benchmark multi-objective multicast routing instances and a large amount of random networks with five real world objectives including cost, delay, link utilisations, average delay and delay variation in telecommunication networks. Experimental results demonstrate that both the simulated annealing based strategies and the genetic local search within the proposed multi-objective algorithm, compared with other multi-objective evolutionary algorithms, can efficiently identify high quality non-dominated solution set for multi-objective multicast routing problems and outperform other conventional multi-objective evolutionary algorithms in the literature

Particle swarm optimization for the Steiner tree in graph and delay-constrained multicast routing problems

This paper presents the first investigation on applying a particle swarm optimization (PSO) algorithm to both the Steiner tree problem and the delay constrained multicast routing problem. Steiner tree problems, being the underlining models of many applications, have received significant research attention within the meta-heuristics community. The literature on the application of meta-heuristics to multicast routing problems is less extensive but includes several promising approaches. Many interesting research issues still remain to be investigated, for example, the inclusion of different constraints, such as delay bounds, when finding multicast trees with minimum cost. In this paper, we develop a novel PSO algorithm based on the jumping PSO (JPSO) algorithm recently developed by Moreno-Perez et al. (Proc. of the 7th Metaheuristics International Conference, 2007), and also propose two novel local search heuristics within our JPSO framework. A path replacement operator has been used in particle moves to improve the positions of the particle with regard to the structure of the tree. We test the performance of our JPSO algorithm, and the effect of the integrated local search heuristics by an extensive set of experiments on multicast routing benchmark problems and Steiner tree problems from the OR library. The experimental results show the superior performance of the proposed JPSO algorithm over a number of other state-of-the-art approaches