University of California

About: University of California is a education organization based out in Oakland, California, United States. It is known for research contribution in the topics: Population & Layer (electronics). The organization has 55175 authors who have published 52933 publications receiving 1491169 citations. The organization is also known as: UC & University of California System.

Measurement of imposed voltage gradient adequate to modulate neuronal firing.

Abstract: Many authors1–31 have described the effects of polarization by imposed electric current upon nerve cells. We have not seen in the literature, however, a quantitative evaluation of the sensitivity of nerve cells to electric fields in terms of voltage gradient across some appropriate dimension of the neuron. We have undertaken to estimate the threshold value as being the unique value of greatest interest and have found this to be far lower for modulation of the frequency of an already active neuron than for the excitation of a silent one.

Quantitative DNA fiber mapping

Abstract: The present invention relates generally to the DNA mapping and sequencing technologies. In particular, the present invention provides enhanced methods and compositions for the physical mapping and positional cloning of genomic DNA. The present invention also provides a useful analytical technique to directly map cloned DNA sequences onto individual stretched DNA molecules.

The geometry and structure of isotropy irreducible homogeneous spaces

Abstract: Professors McKenzie Wang and Wolfgang Ziller pointed out to me that Theorem 4.1 omits the spaces Sp (n)/Sp (1) x SO (n) and SO (4n)/Sp (1) x Sp (n), which are isotropy irreducible for n> 1. The gap in the proof is in the argument of Case 2 on page 69, where it is assumed that the representation ql is nontrivial, which is the case only for p l> 1. Since Sp (2)/Sp (1)xSO (2)=Sp (2)/U(2), which is hermitian symmetric, the correct statement is:

Photosynthetic biomanufacturing in green algae; production of recombinant proteins for industrial, nutritional, and medical uses

Abstract: Recombinant proteins are widely used for industrial, nutritional, and medical applications. Green microalgae have attracted considerable attention recently as a biomanufacturing platform for the production of recombinant proteins for a number of reasons. These photosynthetic eukaryotic microorganisms are safe, scalable, easy to genetically modify through transformation, mutagenesis, or breeding, and inexpensive to grow. Many microalgae species are genetically transformable, but the green alga Chlamydomonas reinhardtii is the most widely used host for recombinant protein expression. An extensive suite of molecular genetic tools has been developed for C. reinhardtii over the last 25 years, including a fully sequenced genome, well-established methods for transformation, mutagenesis and breeding, and transformation vectors for high levels of recombinant protein accumulation and secretion. Here, we review recent successes in the development of C. reinhardtii as a biomanufacturing host for recombinant proteins, including antibodies and immunotoxins, hormones, industrial enzymes, an orally-active colostral protein for gastrointestinal health, and subunit vaccines. In addition, we review the biomanufacturing potential of other green algae from the genera Dunaliella and Chlorella.

Dynamic graph algorithms

Abstract: In many applications of graph algorithms, including communication networks, graphics, assembly planning, and VLSI design, graphs are subject to discrete changes, such as additions or deletions of edges or vertices. In the last decades there has been a growing interest in such dynamically changing graphs, and a whole body of algorithms and data structures for dynamic graphs has been discovered. This chapter is intended as an overview of this field. In a typical dynamicgraphproblemonewould like to answerqueries ongraphs that areundergoinga sequence of updates, for instance, insertions and deletions of edges and vertices. The goal of a dynamic graph algorithm is to update efficiently the solution of a problem after dynamic changes, rather than having to recompute it from scratch each time. Given their powerful versatility, it is not surprising that dynamic algorithms and dynamic data structures are often more difficult to design and analyze than their static counterparts. We can classify dynamic graph problems according to the types of updates allowed. A problemis said to be fully dynamic if the update operations include unrestricted insertions and deletions of edges. A problem is called partially dynamic if only one type of update, either insertions or deletions, is allowed. If only insertions are allowed, the problem is called incremental; if only deletions are allowed it is called decremental. In this chapter, we describe fully dynamic algorithms for graph problems. We present dynamicalgorithms for undirected graphs in Section 9.2. Dynamic algorithms for directed graphs are next described in Section 9.3. Finally, in Section 9.4 we describe some open problems.