No abstract

Reaction of 4-(1-adamantyl)-3-thiosemicarbazide (1) with numerous substituted acetophenones and benzaldehydes yielded the corresponding thiosemicarbazones containing adamantane skeletons. The synthesized compounds were evaluated for their in vitro activities against some Gram-positive and Gram-negative bacteria, and the fungus Candida albicans, and cytotoxicity against four cancer cell lines (Hep3B, HeLa, A549, and MCF-7). All of them showed good antifungal activity against Candida albicans. Compounds 2c, 2d, 2g, 2j and 3a, 3e, 3g displayed significant inhibitory activity against Enterococcus faecalis. Compounds 2a, 2e, 2h, 2k and 3j had moderate inhibitory potency against Staphylococcus aureus. Compounds 2a, 2e and 2g found so good inhibitory effect on Bacillus cereus. Compounds 2d and 2h, which contain (ortho) hydroxyl groups on the phenyl ring, were shown to be good candidates as potential agents for killing the tested cancer cell lines, i.e., Hep3B, A549, and MCF-7. Compounds 2a–c, 2f, 2g, 2j, 2k, 3g, and 3i were moderate inhibitors against MCF-7.

Major societal and environmental challenges involve complex systems that have diverse multi-scale interacting processes. Consider, for example, how droughts and water reserves affect crop production and how agriculture and industrial needs affect water quality and availability. Preventive measures, such as delaying planting dates and adopting new agricultural practices in response to changing weather patterns, can reduce the damage caused by natural processes. Understanding how these natural and human processes affect one another allows forecasting the effects of undesirable situations and study interventions to take preventive measures. For many of these processes, there are expert models that incorporate state-of-the-art theories and knowledge to quantify a system's response to a diversity of conditions. A major challenge for efficient modeling is the diversity of modeling approaches across disciplines and the wide variety of data sources available only in formats that require complex conversions. Using expert models for particular problems requires integration of models with third-party data as well as integration of models across disciplines. Modelers face significant heterogeneity that requires resolving semantic, spatiotemporal, and execution mismatches, which are largely done by hand today and may take more than 2 years of effort. We are developing a modeling framework that uses artificial intelligence (AI) techniques to reduce modeling effort while ensuring utility for decision making. Our work to date makes several innovative contributions: (1) an intelligent user interface that guides analysts to frame their modeling problem and assists them by suggesting relevant choices and automating steps along the way; (2) semantic metadata for models, including their modeling variables and constraints, that ensures model relevance and proper use for a given decision-making problem; and (3) semantic representations of datasets in terms of modeling variables that enable automated data selection and data transformations. This framework is implemented in the MINT (Model INTegration) framework, and currently includes data and models to analyze the interactions between natural and human systems involving climate, water availability, agricultural production, and markets. Our work to date demonstrates the utility of AI techniques to accelerate modeling to support decision-making and uncovers several challenging directions for future work.

Reaction of 1-adamantyl carbohydrazide (1) with various substituted benzaldehydes and acetophenones yielded the corresponding hydrazide-hydrazones with a 1-adamantane carbonyl moiety. The new synthesized compounds were tested for activities against some Gram-negative and Gram-positive bacteria, and the fungus Candida albicans. Compounds 4a, 4b, 5a, and 5c displayed potential antibacterial activity against tested Gram-positive bacteria and C. albicans, while compounds 4e and 5e possessed cytotoxicity against tested human cancer cell lines.

No abstract