Chance-lowering causes

Philosophers have long been fascinated by the connection between cause and effect: are 'causes' things we can experience, or are they concepts provided by our minds? The study of causation goes back to Aristotle, but resurged with David Hume and Immanuel Kant, and is now one of the most important topics in metaphysics. Most of the recent work done in this area has attempted to place causation in a deterministic, scientific, worldview. But what about the unpredictable and chancey world we actually live in: can one theory of causation cover all instances of cause and effect?Cause and Chance: Causation in an Indeterministic Worldis a collection of specially written papers by world-class metaphysicians. Its focus is the problem facing the 'reductionist' approach to causation: the attempt to cover all types of causation, deterministic and indeterministic, with one basic theory.

Application of recombinant antibody library for screening specific antigens in a bovine sperm cell subpopulation

Antibody phage display libraries are a useful tool in proteomic analyses. This study evaluated an antibody recombinant library for identification of sex-specific proteins on the sperm cell surface. The Griffin.1 library was used to produce phage antibodies capable of recognizing membrane proteins from Nelore sperm cells. After producing soluble monoclonal scFv, clones were screened on Simental sperm cells by flow cytometry and those that bound to 40-60% of cells were selected. These clones were re-analyzed using Nelore sperm cells and all clones bound to 40-60% of cells. Positive clones were submitted to a binding assay against male and female bovine leukocytes by flow cytometry and one clone preferentially bound to male cells. The results indicate that phage display antibodies are an alternative method for identification of molecules markers on sperm cells. (C) 2007 Elsevier B.V. All rights reserved.

The application of adaptive partitioned random search in feature selection problem

Feature selection is one of important and frequently used techniques in data preprocessing. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. Feature selection can be viewed as a global optimization problem of finding a minimum set of M relevant features that describes the dataset as well as the original N attributes. In this paper, we apply the adaptive partitioned random search strategy into our feature selection algorithm. Under this search strategy, the partition structure and evaluation function is proposed for feature selection problem. This algorithm ensures the global optimal solution in theory and avoids complete randomness in search direction. The good property of our algorithm is shown through the theoretical analysis.