The medium chain acyl-CoA dehydrogenase is rapidly inhibited by racemic 3,4-dienoyl-CoA derivatives with a stoichiometry of two molecules of racemate per enzyme flavin. Synthesis of R- and S-3,4-decadienoyl-CoA shows that the R-enantiomer is a potent, stoichiometric, inhibitor of the enzyme. alpha-Proton abstraction yields an enolate to oxidized flavin charge-transfer intermediate prior to adduct formation. The crystal structure of the reduced, inactive enzyme shows a single covalent bond linking the C-4 carbon of the 2,4-dienoyl-CoA moiety and the N5 locus of reduced flavin. The kinetics of reversal of adduct formation by release of the conjugated 2,4-diene were evaluated as a function of both acyl chain length and truncation of the CoA moiety. The adduct is most stable with medium chain length allenic inhibitors. However, the adducts with R-3,4-decadienoyl-pantetheine and -N-acetylcysteamine are some 9- and >100-fold more kinetically stable than the full-length CoA thioester. Crystal structures of these reduced enzyme species, determined to 2.4 A, suggest that the placement of H-bonds to the inhibitor carbonyl oxygen and the positioning of the catalytic base are important determinants of adduct stability. The S-3,4-decadienoyl-CoA is not a significant inhibitor of the medium chain dehydrogenase and does not form a detectable flavin adduct. However, the S-isomer is rapidly isomerized to the trans-trans-2,4-conjugated diene. Protein modeling studies suggest that the S-enantiomer cannot approach close enough to the isoalloxazine ring to form a flavin adduct, but can be facilely reprotonated by the catalytic base. These studies show that truncation of CoA thioesters may allow the design of unexpectedly potent lipophilic inhibitors of fatty acid oxidation.

Remotely-sensed satellite image fusion is indispensable for the generation of long-term gap-free Earth observation data. While cloud computing (CC) provides the big picture for RS big data (RSBD), the fundamental question of the efficient fusion of RSBD on CC platforms has not yet been settled. To this end, we propose a lightweight cloud-native framework for the elastic processing of RSBD in this study. With the scaling mechanisms provided by both the Infrastructure as a Service (IaaS) and Platform as a Services (PaaS) of CC, the Spark-on-Kubernetes operator model running in the framework can enhance the efficiency of Spark-based algorithms without considering bottlenecks such as task latency caused by an unbalanced workload, and can ease the burden to tune the performance parameters for their parallel algorithms. Internally, we propose a task scheduling mechanism (TSM) to dynamically change the Spark executor pods’ affinities to the computing hosts. The TSM learns the workload of a computing host. Learning from the ratio between the number of completed and failed tasks on a computing host, the TSM dispatches Spark executor pods to newer and less-overwhelmed computing hosts. In order to illustrate the advantage, we implement a parallel enhanced spatial and temporal adaptive reflectance fusion model (PESTARFM) to enable the efficient fusion of big RS images with a Spark aggregation function. We construct an OpenStack cloud computing environment to test the usability of the framework. According to the experiments, TSM can improve the performance of the PESTARFM using only PaaS scaling to about 11.7%. When using both the IaaS and PaaS scaling, the maximum performance gain with the TSM can be even greater than 13.6%. The fusion of such big Sentinel and PlanetScope images requires less than 4 min in the experimental environment.

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