Community Structure and Seasonal Dynamics of Dactylogyrus spp. (Monogenea) on the Fathead Minnow (Pimephales promelas) from the Salt Valley Watershed, Lancaster County, Nebraska

The gill monogene communities of Pimephales promelas (fathead minnow) in three distinct sites on converging streams were investigated from 2004 to 2006 in three different seasons. Thirty collections of P. promelas were made in southeastern Nebraska along three converging tributaries: Elk Creek (40.88534°N, 96.83366°W), West Oak Creek (40.9082°N, 96.81432°W), and Oak Creek (40.91402°N, 96.770583°W), Lancaster County, Nebraska. In all, 103 P. promelas were collected from Elk Creek, 115 from West Oak Creek, and 78 from Oak Creek and examined for gill monogenes. Among the P. promelas collected, 93.5% were infected with up to three species of Dactylogyrus, including Dactylogyrus simplex Mizelle, 1937, Dactylogyrus bychowskyi Mizelle, 1937, and Dactylogyrus pectenatus Mayes, 1977. Mean intensities at Elk Creek, West Oak Creek, and Oak Creek were 17.6, 22.8, and 25.1, and prevalences 88, 95, and 97%, respectively. At these three sites: (1) P. promelas does not share Dactylogyrus species with Semotilus atromaculatus (creek chub) or Notropis stramineus (sand shiner); (2) fish size and sex are not predictive of Dactylogyrus infection; (3) Dactylogyrus spp. vary (not always predictably) in their seasonal occurrence; (4) populations of Dactylogyrus spp. respond to environmental differences among sites; and (5) the community structure of Dactylogyrus spp. (order of abundance) is independent of environment.

Applications of Linear Programming to Coding Theory

Maximum-likelihood decoding is often the optimal decoding rule one can use, but it is very costly to implement in a general setting. Much effort has therefore been dedicated to find efficient decoding algorithms that either achieve or approximate the error-correcting performance of the maximum-likelihood decoder. This dissertation examines two approaches to this problem. In 2003 Feldman and his collaborators defined the linear programming decoder, which operates by solving a linear programming relaxation of the maximum-likelihood decoding problem. As with many modern decoding algorithms, is possible for the linear programming decoder to output vectors that do not correspond to codewords; such vectors are known as pseudocodewords. In this work, we completely classify the set of linear programming pseudocodewords for the family of cycle codes. For the case of the binary symmetric channel, another approximation of maximum-likelihood decoding was introduced by Omura in 1972. This decoder employs an iterative algorithm whose behavior closely mimics that of the simplex algorithm. We generalize Omura's decoder to operate on any binary-input memoryless channel, thus obtaining a soft-decision decoding algorithm. Further, we prove that the probability of the generalized algorithm returning the maximum-likelihood codeword approaches 1 as the number of iterations goes to infinity.