Expression, Purification, and Comparative Inhibition of Helicobacter pylori Urease by Regio-Selectively Alkylated Benzimidazole 2-Thione Derivatives.

The urease enzyme has been an important target for the discovery of effective pharmacological and agricultural products. Thirteen regio-selectively alkylated benzimidazole-2-thione derivatives have been designed to carry the essential features of urease inhibitors. The urease enzyme was isolated from Helicobacter pylori as a recombinant urease utilizing the His-tag method. The isolated enzyme was purified and characterized using chromatographic and FPLC techniques showing a maximal activity of 200 mg/mL. Additionally, the commercial Jack bean urease was purchased and included in this study for comparative and mechanistic investigations. The designed compounds were synthesized and screened for their inhibitory activity against the two ureases. Compound 2 inhibited H. pylori and Jack bean ureases with IC50 values of 0.11; and 0.26 mM; respectively. While compound 5 showed IC50 values of 0.01; and 0.29 mM; respectively. Compounds 2 and 5 were docked against Helicobacter pylori urease (PDB ID: 1E9Y; resolution: 3.00 Å) and exhibited correct binding modes with free energy (ΔG) values of -9.74 and -13.82 kcal mol-1; respectively. Further; the in silico ADMET and toxicity properties of 2 and 5 indicated their general safeties and likeness to be used as drugs. Finally, the compounds' safety was authenticated by an in vitro cytotoxicity assay against fibroblast cells.

Sum Product Networks for Activity Recognition.

This paper addresses detection and localization of human activities in videos. We focus on activities that may have variable spatiotemporal arrangements of parts, and numbers of actors. Such activities are represented by a sum-product network (SPN). A product node in SPN represents a particular arrangement of parts, and a sum node represents alternative arrangements. The sums and products are hierarchically organized, and grounded onto space-time windows covering the video. The windows provide evidence about the activity classes based on the Counting Grid (CG) model of visual words. This evidence is propagated bottom-up and top-down to parse the SPN graph for the explanation of the video. The node connectivity and model parameters of SPN and CG are jointly learned under two settings, weakly supervised, and supervised. For evaluation, we use our new Volleyball dataset, along with the benchmark datasets VIRAT, UT-Interactions, KTH, and TRECVID MED 2011. Our video classification and activity localization are superior to those of the state of the art on these datasets.