Reactions of C2_2({\it a}3Piu^3Pi_u) with selected saturated alkanes: A temperature dependence study

We present a temperature dependence study on the gas phase reactions of the C$_2$({\it a}$^3Pi_u$) radical with a selected series of saturated alkanes (C$_2$H$_6$, C$_3$H$_8$, n-C$_4$H$_10$, i-C$_4$H$_10$, and n-C$_6$H$_14$) by means of pulsed laser photolysis/laser-induced fluorescence technique. The bimolecular rate constants for these reactions were obtained between 298 and 673 K. A pronounced negative temperature effect was observed for n-C$_4$H$_10$, i-C$_4$H$_10$, and n-C$_6$H$_14$ and interpreted in terms of steric hindrance of the more reactive secondary or tertiary C-H bonds by less reactive CH$_3$ groups. Detailed analysis of our experimental results reveals quantitatively the temperature dependence of reactivities for the primary, secondary, and tertiary C-H bonds in these saturated alkanes and further lends support to a mechanism of hydrogen abstraction.Comment: 26 pages, 8 figures, 1 table, 30 references; accepted to JC

Actions and Potential Therapeutic Applications of Growth Hormone-Releasing Hormone Agonists.

Abstract In this article, we briefly review the identification of GHRH, provide an abridged overview of GHRH antagonists, and focus on studies with GHRH agonists. Potent GHRH agonists of JI and MR class were synthesized and evaluated biologically. Besides the induction of the release of pituitary GH, GHRH analogs promote cell proliferation and exert stimulatory effects on various tissues, which express GHRH receptors (GHRH-Rs). A large body of work shows that GHRH agonists, such as MR-409, improve pancreatic β-cell proliferation and metabolic functions and facilitate engraftment of islets after transplantation in rodents. Accordingly, GHRH agonists offer a new therapeutic approach to treating diabetes. Various studies demonstrate that GHRH agonists promote repair of cardiac tissue, producing improvement of ejection fraction and reduction of infarct size in rats, reduction of infarct scar in swine, and attenuation of cardiac hypertrophy in mice, suggesting clinical applications. The presence of GHRH-Rs in ocular tissues and neuroprotective effects of GHRH analogs in experimental diabetic retinopathy indicates their possible therapeutic applications for eye diseases. Other effects of GHRH agonists, include acceleration of wound healing, activation of immune cells, and action on the central nervous system. As GHRH might function as a growth factor, we examined effects of GHRH agonists on tumors. In vitro, GHRH agonists stimulate growth of human cancer cells and upregulate GHRH-Rs. However, in vivo, GHRH agonists inhibit growth of human cancers xenografted into nude mice and downregulate pituitary and tumoral GHRH-Rs. Therapeutic applications of GHRH analogs are discussed. The development of GHRH analogs should lead to their clinical use

Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions

Machine learning (ML) applications have been thriving recently, largely attributed to the increasing availability of data. However, inconsistency and incomplete information are ubiquitous in real-world datasets, and their impact on ML applications remains elusive. In this paper, we present a formal study of this impact by extending the notion of Certain Answers for Codd tables, which has been explored by the database research community for decades, into the field of machine learning. Specifically, we focus on classification problems and propose the notion of "Certain Predictions" (CP) -- a test data example can be certainly predicted (CP'ed) if all possible classifiers trained on top of all possible worlds induced by the incompleteness of data would yield the same prediction. We study two fundamental CP queries: (Q1) checking query that determines whether a data example can be CP'ed; and (Q2) counting query that computes the number of classifiers that support a particular prediction (i.e., label). Given that general solutions to CP queries are, not surprisingly, hard without assumption over the type of classifier, we further present a case study in the context of nearest neighbor (NN) classifiers, where efficient solutions to CP queries can be developed -- we show that it is possible to answer both queries in linear or polynomial time over exponentially many possible worlds. We demonstrate one example use case of CP in the important application of "data cleaning for machine learning (DC for ML)." We show that our proposed CPClean approach built based on CP can often significantly outperform existing techniques in terms of classification accuracy with mild manual cleaning effort