Biocatalytic Synthesis of Stereospecific Triketide Lactones using Polyketide Synthases

Polyketide synthases are modular enzymes that create and modify large acyl chains. The domains and modules of polyketide synthases allow us to create molecules that resemble naturally occurring products by applying a biocatalytic in vitro in vivo approach to a diketide acyl chain. We showed that a triketide lactone of desired stereochemistry could be made using a domain and module from the polyketide synthase found in Saccharopolyspora erythraea, 6-Deoxyerythronolide B Synthase. Future projects will explore this approach using different domains and modules.Biochemistr

Interactive situation modelling in knowledge intensive domains

Interactive Situation Modelling (ISM) method, a semi-methodological approach, is proposed to tackle issues associated with modelling complex knowledge intensive domains, which cannot be easily modelled using traditional approaches. This paper presents the background and implementation of ISM within a complex domain, where synthesizing knowledge from various sources is critical, and is based on the principles of ethnography within a constructivist framework. Although the motivation for the reported work comes from the application presented in the paper, the actual scope of the paper covers a wide range of issues related to modelling complex systems. The author firstly reviews approaches used for modelling knowledge intensive domains, preceded by a brief discussion about two main issues: symmetry of ignorance and system behaviour, which are often confronted when applying modelling approaches to business domains. The ISM process is then characterized and critiqued with lessons from an exemplar presented to illustrate its effectiveness

Improving clerkship preparedness: a hospital medicine elective for pre-clerkship students.

BackgroundMedical students often struggle to apply their nascent clinical skills in clerkships. While transitional clerkships can orient students to new roles and logistics, students may benefit from developing clinical skills in inpatient environments earlier in their curriculum to improve readiness for clerkships.InterventionOur four- to six-session elective provides pre-clerkship students with individualized learning in the inpatient setting with the aim of improving clerkship preparedness. Students work one-on-one with faculty who facilitate individualized learning through mentoring, deliberate practice, and directed feedback. Second-year medical students are placed on an attending-only, traditionally 'non-teaching' service in the hospital medicine division of a Veterans Affairs (VA) hospital for half-day sessions. Most students self-select into the elective following a class-wide advertisement. The elective also accepts students who are referred for remediation of their clinical skills.OutcomeIn the elective's first two years, 25 students participated and 47 students were waitlisted. We compared participant and waitlisted (non-participant) students' self-efficacy in several clinical and professional domains during their first clerkship. Elective participants reported significantly higher clerkship preparedness compared to non-participants in the areas of physical exam, oral presentation, and formulation of assessments and plans.ConclusionsStudents found the one-on-one feedback and personalized attention from attending physicians to be a particularly useful aspect of the course. This frequently cited benefit points to students' perceived needs and the value they place on individualized feedback. Our innovation harnesses an untapped resource - the hospital medicine 'non-teaching' service - and serves as an attainable option for schools interested in enhancing early clinical skill-building for all students, including those recommended for remediation.AbbreviationsA&P: Assessment and plan; H&P: History and physical; ILP: Individual learning plan

Planning and scheduling research at NASA Ames Research Center

Planning and scheduling is the area of artificial intelligence research that focuses on the determination of a series of operations to achieve some set of (possibly) interacting goals and the placement of those operations in a timeline that allows them to be accomplished given available resources. Work in this area at the NASA Ames Research Center ranging from basic research in constrain-based reasoning and machine learning, to the development of efficient scheduling tools, to the application of such tools to complex agency problems is described