Over 80 taxa of mostly agriculturally important legumes were surveyed as sources of the metabolites, genistein and daidzein. Remarkably high concentrations (over 2 g · kg^–1 dry weight) of the anticancer metabolite, genistein, were found in the leaves of Psoralea corylifolia (Indian bread root). All other legumes, with the exception of fermented soybean miso, had genistein levels <400 mg · kg^–1 dry weight. Concentrations of over 1 g · kg^–1 dry weight and 0.95 g · kg^–1 dry weight of the anticancer metabolite, daidzein, were found in the stems of the fava bean (Vicia faba) and roots of kudzu vine (Pueraria lobata)' respectively. From this survey, our results indicate that the legumes, lupine (Lupinus spp.), fava bean, (Vicia faha), soybeans (Glycine max), kudzu (Pueraria lobata), and psoralea (Psoralea corylifolia), are excellent food sources for both genistein and daidzein. Miso, a fermented soybean product, is also a rich source of both isoflavones.

One of the key enabling features of coherent Raman scattering (CRS) techniques is the dramatically improved imaging speed over conventional vibrational imaging methods. It is this enhanced imaging acquisition rate that has guided the field of vibrational microscopy into the territory of real‐time imaging of live tissues. In this feature article, we review several aspects of fast vibrational imaging and discuss new applications made possible by the improved CRS imaging capabilities. In addition, we reflect on the current limitations of CRS microscopy and look ahead at several new developments towards real‐time, hyperspectral vibrational imaging of biological tissues.

Quantitative structure-activity relationship (QSAR) models have become popular tools to help identify promising lead compounds in anticancer drug development. Few QSAR studies have investigated multitask learning, however. Multitask learning is an approach that allows distinct but related data sets to be used in training. In this paper, a suite of three QSAR models is developed to identify compounds that are likely to (a) exhibit cytotoxic behavior against cancer cells, (b) exhibit high rat LD50 values (low systemic toxicity), and (c) exhibit low to modest human oral clearance (favorable pharmacokinetic characteristics). Models were constructed using Kernel Multitask Latent Analysis (KMLA), an approach that can effectively handle a large number of correlated data features, nonlinear relationships between features and responses, and multitask learning. Multitask learning is particularly useful when the number of available training records is small relative to the number of features, as was the case with the oral clearance data.

An R Package for Assessing Drug Synergism/Antagonism

Humanity faces serious social and environmental problems, including climate change and biodiversity loss. Increasingly, scientists, global policy experts, and the general public conclude that incremental approaches to reduce risk are insufficient and transformative change is needed across all sectors of society. However, the meaning of transformation is still unsettled in the literature, as is the proper role of science in fostering it. This paper is the first in a three-part series that adds to the discussion by proposing a novel science-driven research-and-development program aimed at societal transformation. More than a proposal, it offers a perspective and conceptual framework from which societal transformation might be approached. As part of this, it advances a formal mechanics with which to model and understand self-organizing societies of individuals. While acknowledging the necessity of reform to existing societal systems (e.g., governance, economic, and financial systems), the focus of the series is on transformation understood as systems change or systems migration—the de novo development of and migration to new societal systems. The series provides definitions, aims, reasoning, worldview, and a theory of change, and discusses fitness metrics and design principles for new systems. This first paper proposes a worldview, built using ideas from evolutionary biology, complex systems science, cognitive sciences, and information theory, which is intended to serve as the foundation for the R&D program. Subsequent papers in the series build on the worldview to address fitness metrics, system design, and other topics.