Dynamic and Quantitative Method of Analyzing Service Consistency Evolution Based on Extended Hierarchical Finite State Automata

This paper is concerned with the dynamic evolution analysis and quantitative measurement of primary factors that cause service inconsistency in service-oriented distributed simulation applications (SODSA). Traditional methods are mostly qualitative and empirical, and they do not consider the dynamic disturbances among factors in service’s evolution behaviors such as producing, publishing, calling, and maintenance. Moreover, SODSA are rapidly evolving in terms of large-scale, reusable, compositional, pervasive, and flexible features, which presents difficulties in the usage of traditional analysis methods. To resolve these problems, a novel dynamic evolution model extended hierarchical service-finite state automata (EHS-FSA) is constructed based on finite state automata (FSA), which formally depict overall changing processes of service consistency states. And also the service consistency evolution algorithms (SCEAs) based on EHS-FSA are developed to quantitatively assess these impact factors. Experimental results show that the bad reusability (17.93% on average) is the biggest influential factor, the noncomposition of atomic services (13.12%) is the second biggest one, and the service version’s confusion (1.2%) is the smallest one. Compared with previous qualitative analysis, SCEAs present good effectiveness and feasibility. This research can guide the engineers of service consistency technologies toward obtaining a higher level of consistency in SODSA

Pharmaceutical Optimization of Peptide Toxins for Ion Channel Targets: Potent, Selective, and Long-Lived Antagonists of Kv1.3

To realize the medicinal potential of peptide toxins, naturally occurring disulfide-rich peptides, as ion channel antagonists, more efficient pharmaceutical optimization technologies must be developed. Here, we show that the therapeutic properties of multiple cysteine toxin peptides can be rapidly and substantially improved by combining direct chemical strategies with high-throughput electrophysiology. We applied whole-molecule, brute-force, structure–activity analoging to ShK, a peptide toxin from the sea anemone Stichodactyla helianthus that inhibits the voltage-gated potassium ion channel Kv1.3, to effectively discover critical structural changes for 15× selectivity against the closely related neuronal ion channel Kv1.1. Subsequent site-specific polymer conjugation resulted in an exquisitely selective Kv1.3 antagonist (>1000× over Kv1.1) with picomolar functional activity in whole blood and a pharmacokinetic profile suitable for weekly administration in primates. The pharmacological potential of the optimized toxin peptide was demonstrated by potent and sustained inhibition of cytokine secretion from T cells, a therapeutic target for autoimmune diseases, in cynomolgus monkeys