Advancing Alternative Analysis: Integration of Decision Science.

Decision analysis-a systematic approach to solving complex problems-offers tools and frameworks to support decision making that are increasingly being applied to environmental challenges. Alternatives analysis is a method used in regulation and product design to identify, compare, and evaluate the safety and viability of potential substitutes for hazardous chemicals.Assess whether decision science may assist the alternatives analysis decision maker in comparing alternatives across a range of metrics.A workshop was convened that included representatives from government, academia, business, and civil society and included experts in toxicology, decision science, alternatives assessment, engineering, and law and policy. Participants were divided into two groups and prompted with targeted questions. Throughout the workshop, the groups periodically came together in plenary sessions to reflect on other groups' findings.We conclude the further incorporation of decision science into alternatives analysis would advance the ability of companies and regulators to select alternatives to harmful ingredients, and would also advance the science of decision analysis.We advance four recommendations: (1) engaging the systematic development and evaluation of decision approaches and tools; (2) using case studies to advance the integration of decision analysis into alternatives analysis; (3) supporting transdisciplinary research; and (4) supporting education and outreach efforts

Theory and research in strategic management: Swings of a pendulum

The development of the field of strategic management within the last two decades has been dramatic. While its roots have been in a more applied area, often referred to as business policy, the current field of strategic management is strongly theory based, with substantial empirical research, and is eclectic in nature. This review of the development of the field and its current position examines the field’s early development and the primary theoretical and methodological bases through its history. Early developments include Chandler’s (1962) Strategy and Structure and Ansoff’s (1965) Corporate Strategy. These early works took on a contingency perspective (fit between strategy and structure) and a resource-based framework emphasizing internal strengths and weaknesses. Perhaps, one of the more significant contributions to the development of strategic management came from industrial organization (IO) economics, specifically the work of Michael Porter. The structure-conduct-performance framework and the notion of strategic groups, as well as providing a foundation for research on competitive dynamics, are flourishing currently. The IO paradigm also brought econometric tools to the research on strategic management. Building on the IO economics framework, the organizational economics perspective contributed transaction costs economics and agency theory to strategic management. More recent theoretical contributions focus on the resource-based view of the firm. While it has its roots in Edith Penrose’s work in the late 1950s, the resource-based view was largely introduced to the field of strategic management in the 1980s and became a dominant framework in the 1990s. Based on the resource-based view or developing concurrently were research on strategic leadership, strategic decision theory (process research) and knowledge-based view of the firm. The research methodologies are becoming increasingly sophisticated and now frequently combine both quantitative and qualitative approaches and unique and new statistical tools. Finally, this review examines the future directions, both in terms of theory and methodologies, as the study of strategic management evolves.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Gleason Probability Maps: A Radiomics Tool for Mapping Prostate Cancer Likelihood in MRI Space

Prostate cancer is the most common noncutaneous cancer in men in the United States. The current paradigm for screening and diagnosis is imperfect, with relatively low specificity, high cost, and high morbidity. This study aims to generate new image contrasts by learning a distribution of unique image signatures associated with prostate cancer. In total, 48 patients were prospectively recruited for this institutional review board–approved study. Patients underwent multiparametric magnetic resonance imaging 2 weeks before surgery. Postsurgical tissues were annotated by a pathologist and aligned to the in vivo imaging. Radiomic profiles were generated by linearly combining 4 image contrasts (T2, apparent diffusion coefficient [ADC] 0-1000, ADC 50-2000, and dynamic contrast-enhanced) segmented using global thresholds. The distribution of radiomic profiles in high-grade cancer, low-grade cancer, and normal tissues was recorded, and the generated probability values were applied to a naive test set. The resulting Gleason probability maps were stable regardless of training cohort, functioned independent of prostate zone, and outperformed conventional clinical imaging (area under the curve [AUC] = 0.79). Extensive overlap was seen in the most common image signatures associated with high- and low-grade cancer, indicating that low- and high-grade tumors present similarly on conventional imaging