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When an essential nutrient is depleted from the medium, cultures of wildtype E. coli cells enter a period called stationary phase. The transition into stationary phase is marked by distinct changes in cell physiology, gene expression, and morphology. Pr???? and Matsumura (18) found a mutant strain of E. coli that was able to continue growing exponentially at a time when wild-type cells had stopped growing and entered stationary phase. They concluded that FlhD, a transcriptional activator of flagellar genes, was responsible for this growth phenotype and that it is a regulator of cell division (17, 18). Contrary to the findings of Pr???? and Matsumura, research in our lab has shown that the mutant growth phenotype observed in the strain used by Matsumura and Pr???? is flhD independent. This study sought to identify the second mutation, which we call cdr (cell division regulator) in the strain used by Matsumura and Pr????. We used Hfr mapping and P1 transduction to localize the mutation to a specific region of the chromosome. We also sought to determine if this growth phenotype was due to loss of function or gain of function and whether the mutation in the cdr gene was sufficient to cause the observed growth phenotype in other strain backgrounds. In addition the growth phenotype of these two strains was compared to that of other wild-type and standard laboratory E. coli strains. Our results indicate that the cdr mutation is located in the 88.5. region of the chromosome and is due to loss of Cdr function. We also discovered that the growth phenotype assigned to the mutant strain more closely reflects that of other wild-type laboratory strains as did the morphology of cells in stationary phase. This evidence suggests that the actual mutant strain might be the one that was designated as the wild-type strain by Matsumura and Pr???? and both strains may contain mutations that actually cause a decrease in cell number instead of an increase as previously reported.

Source: Masters Abstracts International, Volume: 47-06, page: 3550. Adviser: Maria E. Fernandez. Includes bibliographical references.

Beethoven wrote his earliest piano concerto, the Piano Concerto in E-Flat Major WoO 4, in 1784-85. The surviving manuscript copy contains the solo part complete and a piano reduction of all orchestral passages (Tutti) whenever the soloist is not playing. That manuscript also includes Beethoven's cues for an instrumentation consisting of strings, horns and flutes. Eminent Beethoven scholar Willy Hess completed his own reconstruction of the concerto in 1943. His version has been recorded three times, but only one is currently available on the Philips label (442580-2). The newest reconstruction of the concerto, created by Professor Jon Ceander Mitchell in 2003, is presented in this study in the form of a piano reduction (as a two-piano critical edition). This present edition, edited by Dr. Mitchell and the author of this essay, retains Beethoven's instrumentation and restores the endings of the second and third movements (which were changed by Willy Hess). This study also includes a piano cadenza for the first movement, which is a free composition by the author. It also discusses both available restorations of this work and some of the concerto's interpretative issues.

The hsDC2, an unknown gene, was located on chromosome 4q25. Its genetic protein product contains 149 amino acids with the molecular weight of 16.8 kDa approximately. Predicted by bioinformatics, hsDC2 might be a transmembrane protein with three transmembrane helices on the endoplasmic reticulum. Based on the results of reverse transcription-polymerase chain reaction, it revealed that hsDC2 was expressed in many tissues. It was showed that there were more mRNA expression in the cancer tissue. Using real-time quantitative PCR to analyze cancer cell lines, we found that hsDC2 might be related to differentiation status of cells. Among the well-differentiated cells such as nasopharyngeal carcinoma cell line NPC TW01, hepatoma cell line Hep3B and temperature-induced differentiated human fetal osteoblast cell line (hFOB), there were more hsDC2 mRNA expression. We also obtained some useful information from bioinformatics databases. To further elucidate the biological functions of the gene, glutathion S-transferase-hsDC2 fusion protein was used to generate anti-hsDC2 polyclonal antibody. However, we still need further research to clarify the biological function of hsDC2.

The hsFLJ22386 gene, an unknown gene, was located on chromosome 16p13.3. Its protein product contains 287 amino acids with the molecular weight of 32.2 kDa approximately. Predicted by bioinformatics, hsFLJ22386 might be a protein containing a leucine zipper domain. Based on the results of reverse transcriptase polymerase chain reaction, it revealed that FLJ22386 was expressed in several nervous system tissues and several organ tissues (liver, spleen, small intestine and kidney). In human cancer cell lines, the RT-PCR results showed that FLJ22386 was expressed in brain tumor cell lines (T98G, U87MG, U251, GBM8401), nasopharyngeal epithelial cell line (NNE-3)and carcinoma cell lines (NPCTW01, NPCTW04), hepatoma cell lines (J5, Hep3B, SK-Hep-1) and lymphoma cell lines (RPMI, P3HR1, Raji, U937). Human FLJ22386 coding sequence was inserted into pEGFP-C2 plasmid, and the tag-fused gene was transfected into NIH3T3 cells to see if it has the ability to promote cell proliferation. To further investigate the protein level expression and biological functions of the gene, glutathione S-transferase-hsFLJ22386 fusion protein was expressed and used to generate anti-FLJ22386 polyclonal antibody. According to the results of RT-PCR and anchorage dependent growth assay, it is presumed that FLJ22386 may play a role in cell proliferation.

There are over 250,000 reinforced concrete bridges in the U.S. many of which do not have a load rating on record nor the plans required to perform the calculations. The U.S. Army owns and maintains hundreds of these bridges throughout the U.S. This dissertation describes the development of multiple regression models to estimate the load rating of reinforced concrete bridges. An exploratory data analysis of the 2017 NBI data was performed for the selection of a representative data sample. The data was found to have multiple errors and required significant processing in order to extract a reliable sample for modeling. After processing, a data sample of 31,112 bridges remained, providing sufficient sample for model training and testing. A six-variable model (Model A) was determined to provide the best performance while maintaining a desired low level of complexity. The model was tested by comparing the percentage of cases that fell within its 95% prediction interval, which resulted in 94.9% of the real values falling within the prediction interval. Given the concerns that arose of the quality of the 2017 NBI data during its exploration, as built-drawings from 50 slab bridges throughout the U.S. were collected. With these drawings a new data sample was generated by calculating the load rating of each bridge. Availability of the as-built drawings provided the opportunity to investigate other variables not available in the 2017 NBI, most notably the slab thickness. This data sample was significantly smaller than the previous one, therefore a repeated 10old cross-validation approach was taken to evaluate model performance. It was determined that a five-variable model (Model B) provided the best trade-off between complexity and performance. Model B performed significantly better than Model A due to the inclusion of the slab thickness variable. The models presented in this dissertation provide a valuable tool for reinforced concrete bridge owners tasked with the assigning a load rating when no structural plans are available helping.

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<p> In this thesis, we present three algorithms for blind estimation of the finite impulse response (FIR) channels in the presence of unknown noise. The algorithms are developed considering different available system resources: 1) If only one receiving antenna is available, based on the single-input-single-output (SISO) system model, with the output being up-sampled, we develop the maximum a posteriori (MAP) algorithm for Gaussian distributed noise. With large enough samples being collected, during which the channel keeps invariant, an efficient implementation of the MAP algorithm is also obtained; 2) If two receiving antennae can be affordable, based on the singleinput-multiple-output (SIMO) system model and up-sampling both the outputs, we develop a subspace based algorithm utilizing Canonical Correlation Decomposition (CCD) to obtain the subspaces, and a maximum likelihood (ML) based algorithm which starts from the Gaussian distributed projection error from the noise subspace onto the COD-estimated signal subspace. The developed channel estimators achieve superior performance measured by the normalized root mean square error (NRMSE), compared with some existing second-order-statistics (SOS) based methods while keeping the computation complexity comparable. When more than two receiving antennae are available, by treating them as one group and applying the MAP algorithm or separating them into two groups and applying the CCD based algorithms, the channels can still be blindly estimated with or without up-sampling the outputs. </p> / Thesis / Master of Applied Science (MASc)

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