Efficient and Accurate Characterization of the Bergman Cyclization for an Expanded Substructure of Esperamicin A1

Incorporation of enediynes into anticancer drugs remains an intriguing yet elusive strategy for the design of therapeutically active agents. Density functional theory was used to locate reactants, products, and transition states along the Bergman cyclization pathways connecting enediynes to reactive para-biradicals. Sum method correction to low-level calculations confirmed B3LYP/6-31G(d,p) as the method of choice in investigating enediynes. Herein described as MI:Sum, calculated reaction enthalpies differed from experiment by an average of 2.1 kcal·mol−1 (mean unsigned error). A combination of strain energy released across the reaction coordinate and the critical intramolecular distance between reacting diynes explains reactivity differences. Where experimental and calculated barrier heights are in disagreement, higher level multireference treatment of the enediynes confirms lower level estimates. Previous work concerning the chemically reactive fragment of esperamcin, MTC, is expanded to our model system MTC2.

Must We Forgive?: Exploring the Limits and Possibilities of Forgiveness and Resentment

Conflict has marked civilization from Biblical times to the present day. Each of us, with our different and competing interests, and our desires to pursue those interests, have over time wronged another person. Not surprisingly then, forgiveness is a concern of individuals and groups¿communities, countries, religious groups, races¿yet it is a complex idea that philosophers, theologians, political scientists, and psychologists have grappled with. Some have argued that forgiveness is a therapeutic means for overcoming guilt, pain, and anger. Forgiveness is often portrayed as a coping mechanism¿how often we hear the phrase, ¿forgive and forget,¿ as an arrangement to help two parties surmount the complications of disagreement. But forgiveness is not simply a modus vivendi; the ability to forgive and conversely to ask for forgiveness, is counted as an admirable trait and virtue. This essay will explore the nature of forgiveness, which in Christian dogma is often posited as an unqualified virtue. The secular world has appropriated the Christian notion of forgiveness as such a virtue¿but are there instances wherein offering forgiveness is morally inappropriate or dangerous? I will consider the situations in which forgiveness, understood in this essay as the overcoming of resentment, may not be a virtue¿when perhaps maintaining resentment is as virtuous, if not more virtuous, than forgiving. I will explain the various ethical frameworks involved in understanding forgiveness as a virtue, and the relationship between them. I will argue that within Divine Command Theory forgiveness is a virtue¿and thus morally right¿because God commands it. This ethical system has established forgiveness as unconditional, an idea which has been adopted into popular culture. With virtue ethics in mind, which holds virtues to be those traits which benefit the person who possesses them, contributing to the good life, I will argue unqualified forgiveness is not always a virtue, as it will not always benefit the victim. Because there is no way to avoid wrongdoing, humans are confronted with the question of forgiveness with every indiscretion. Its limits, its possibilities, its relationship to one¿s character¿forgiveness is a concern of all people at some time if for no other reason than the plain fact that the past cannot be undone. I will be evaluating the idea of forgiveness as a virtue, in contrast to its counterpart, resentment. How can forgiveness be a response to evil, a way to renounce resentment, and a means of creating a positive self-narrative? And what happens when a sense of moral responsibility is impossible to reconcile with the Christian (and now, secularized imperative of) forgiveness? Is it ever not virtuous to forgive? In an attempt to answer that question I will argue that there are indeed times when forgiveness is not a virtue, specifically: when forgiveness compromises one¿s own self-respect; when it is not compatible with respect for the moral community; and when the offender is unapologetic. The kind of offense I have in mind is a dehumanizing one, one that intends to diminish another person¿s worth or humanity. These are moral injuries, to which I will argue resentment is a better response than forgiveness when the three qualifications cannot be met.

Advantages and Applications of Transforming Empirical Model Input Space with Dimensional Models

Model-based calibration of steady-state engine operation is commonly performed with highly parameterized empirical models that are accurate but not very robust, particularly when predicting highly nonlinear responses such as diesel smoke emissions. To address this problem, and to boost the accuracy of more robust non-parametric methods to the same level, GT-Power was used to transform the empirical model input space into multiple input spaces that simplified the input-output relationship and improved the accuracy and robustness of smoke predictions made by three commonly used empirical modeling methods: Multivariate Regression, Neural Networks and the k-Nearest Neighbor method. The availability of multiple input spaces allowed the development of two committee techniques: a 'Simple Committee' technique that used averaged predictions from a set of 10 pre-selected input spaces chosen by the training data and the "Minimum Variance Committee" technique where the input spaces for each prediction were chosen on the basis of disagreement between the three modeling methods. This latter technique equalized the performance of the three modeling methods. The successively increasing improvements resulting from the use of a single best transformed input space (Best Combination Technique), Simple Committee Technique and Minimum Variance Committee Technique were verified with hypothesis testing. The transformed input spaces were also shown to improve outlier detection and to improve k-Nearest Neighbor performance when predicting dynamic emissions with steady-state training data. An unexpected finding was that the benefits of input space transformation were unaffected by changes in the hardware or the calibration of the underlying GT-Power model.