Membrane fluidity, capping of cell-surface antigens and immune response in mouse leukaemia cells.

Transplantation of primary GRSL cells in the ascitic form led to a decrease in membrane microviscosity as measured by the fluorescence polarization technique. The transplanted GRSL ascitic cells showed a markedly lower ability to form caps with respect to both virus-related (MLr, GIX) and normal (H-2.7(G), H-2.8(K) and TL1.2) cell-surface antigens and their appropriate antisera in the indirect membrane immunofluorescence tests, than did primary GRSL cells, transplanted GRSL cells growing in solid form, and thymocytes, which all exhibited significantly higher membrane microviscosities. Transplantation of primary GRSL cells into syngeneic mice pre-irradiated with 400 rad did not lead to a fall in membrane microviscosity. It is suggested that the host immune response in intact mice leads to a selective survival of ascitic tumour cells with low membrane microviscosity

Learning Opportunities And Learning Behaviours Of Small Business Starters: Relations With Goal Achievement, Skill Development, And Satisfaction

Learning is a vital issue for small business starters, contributing to short term and long term business performance, as well as to personal development. This study investigates when and how small business starters learn. It specifies the situations that offer learning opportunities, as well as the learning behaviours that small business starters can employ in order to learn from these opportunities. In a cross-sectional, quantitative study of recently started small business founders, learning opportunities and learning behaviours are related to three outcome measures: a performance outcome (goal achievement), a personal growth outcome (skill development), and an affective evaluation outcome (satisfaction). The results show the importance of learning opportunities and learning behaviours in influencing these outcome variables, albeit not always in the directions we hypothesized

Simple and Nearly Optimal Polynomial Root-finding by Means of Root Radii Approximation

We propose a new simple but nearly optimal algorithm for the approximation of all sufficiently well isolated complex roots and root clusters of a univariate polynomial. Quite typically the known root-finders at first compute some crude but reasonably good approximations to well-conditioned roots (that is, those isolated from the other roots) and then refine the approximations very fast, by using Boolean time which is nearly optimal, up to a polylogarithmic factor. By combining and extending some old root-finding techniques, the geometry of the complex plane, and randomized parametrization, we accelerate the initial stage of obtaining crude to all well-conditioned simple and multiple roots as well as isolated root clusters. Our algorithm performs this stage at a Boolean cost dominated by the nearly optimal cost of subsequent refinement of these approximations, which we can perform concurrently, with minimum processor communication and synchronization. Our techniques are quite simple and elementary; their power and application range may increase in their combination with the known efficient root-finding methods.Comment: 12 pages, 1 figur