The SasS -SasR two-component signal transduction system required for early Myxococcus xanthus multicellular *development

Initiation of Myxococcus xanthus multicellular development requires both nutrient limitation and high cell density. The extracellular signal, A signal, which consists of a set of amino acids at specific concentrations, serves as a cell density signal in M. xanthus early development. A reporter gene, designated 4521, that requires both starvation and A signal for developmental expression was used to identify mutations in the signal transduction pathways. A group of point mutations located in the chromosomal sasB locus that bypasses both requirements was previously isolated. One of these point mutations, sasB7, was mapped to the sasS gene, which is predicted to encode a transmembrane histidine protein kinase required for normal development. SasS is a positive regulator of 4521 and a candidate A signal sensor. This dissertation continues the characterization of the sasB locus, focusing on the sasR gene and the functional relationship of SasS and SasR. ^ The sasR gene is located 2.2-kb downstream of sasS. It is predicted to encode an NtrC-like response regulator, which belongs to the family of sigma54 transcriptional activators. SasR is a positive regulator of 4521 gene and is required for normal development. The sasR mutant displays phenotypes similar to that of sasS mutant. Both SasS and SasR are required for the A-signal-dependent 4521 expression. Genetic epistasis analysis indicates that SasR functions downstream of SasS. Biochemical studies show that SasS has autokinase activity, and phosphorylated SasS is able to transfer its phosphate to SasR. We propose that SasS and SasR form a two-component signal transduction system in the A signal transduction pathway. ^ To search for the genes regulated by SasS and SasR, expression patterns of a group of developmental genes were compared in wild-type and sasS null mutant backgrounds. SasS and SasR were found to positively regulate sasN and 4521. The sasN gene was previously identified as a negative regulator of 4521, located at about 170-bp downstream of sasR. It is required for normal fruiting body development. Based on the above data, a regulatory network consisting of sasS, sasR, sasN, and 4521 is hypothesized, and the interactions of the components in this network can now be further studied. ^

Point of decision signs and stair use in a university worksite setting: General versus specific messages

Point-of-decision signs to promote stair use have been found to be effective in various environments. However, these signs have been more consistently successful in public access settings that use escalators, such as shopping centers and transportation stations, compared to worksite settings, which are more likely to contain elevators that are not directly adjacent to the stairs. Therefore, this study tested the effectiveness of two point-of-decision sign prompts to increase stair use in a university worksite setting. Also, this study investigated the importance of the message content of the signs. One sign displayed a general health promotion message, while the other sign presented more specific information. Overall, this project examined whether the presence of the point-of-decision signs increases stair use. In addition, this research determined whether the general or specific sign promotes greater stair use. ^ Inconspicuous observers measured stair use both before the signs were present and while they were posted. The study setting was the University of Texas School of Nursing, and the target population was anyone who entered the building, including employees, students, and visitors. The study was conducted over six weeks and included two weeks of baseline measurement, two weeks with the general sign posted, and two weeks with the specific sign posted. Each sign was displayed on a stand in the decision point area near the stairs and the elevator. Logistic regression was used to analyze the data. ^ After adjustment for covariates, the odds of stair use were significantly greater during the intervention period than the baseline period. Furthermore, the specific sign period showed significantly greater odds of stair use than the general sign period. These results indicate that a point-of-decision sign intervention can be effective at promoting stair use in a university worksite setting and that a sign with a specific health information message may be more effective at promoting stair use than a sign with a general health promotion message. These findings can be considered when planning future worksite and university based stair promotion interventions.^

Robust effect sizes and their confidence intervals for group difference between trajectories in hierarchical linear growth model

Hierarchical linear growth model (HLGM), as a flexible and powerful analytic method, has played an increased important role in psychology, public health and medical sciences in recent decades. Mostly, researchers who conduct HLGM are interested in the treatment effect on individual trajectories, which can be indicated by the cross-level interaction effects. However, the statistical hypothesis test for the effect of cross-level interaction in HLGM only show us whether there is a significant group difference in the average rate of change, rate of acceleration or higher polynomial effect; it fails to convey information about the magnitude of the difference between the group trajectories at specific time point. Thus, reporting and interpreting effect sizes have been increased emphases in HLGM in recent years, due to the limitations and increased criticisms for statistical hypothesis testing. However, most researchers fail to report these model-implied effect sizes for group trajectories comparison and their corresponding confidence intervals in HLGM analysis, since lack of appropriate and standard functions to estimate effect sizes associated with the model-implied difference between grouping trajectories in HLGM, and also lack of computing packages in the popular statistical software to automatically calculate them. ^ The present project is the first to establish the appropriate computing functions to assess the standard difference between grouping trajectories in HLGM. We proposed the two functions to estimate effect sizes on model-based grouping trajectories difference at specific time, we also suggested the robust effect sizes to reduce the bias of estimated effect sizes. Then, we applied the proposed functions to estimate the population effect sizes (d ) and robust effect sizes (du) on the cross-level interaction in HLGM by using the three simulated datasets, and also we compared the three methods of constructing confidence intervals around d and du recommended the best one for application. At the end, we constructed 95% confidence intervals with the suitable method for the effect sizes what we obtained with the three simulated datasets. ^ The effect sizes between grouping trajectories for the three simulated longitudinal datasets indicated that even though the statistical hypothesis test shows no significant difference between grouping trajectories, effect sizes between these grouping trajectories can still be large at some time points. Therefore, effect sizes between grouping trajectories in HLGM analysis provide us additional and meaningful information to assess group effect on individual trajectories. In addition, we also compared the three methods to construct 95% confident intervals around corresponding effect sizes in this project, which handled with the uncertainty of effect sizes to population parameter. We suggested the noncentral t-distribution based method when the assumptions held, and the bootstrap bias-corrected and accelerated method when the assumptions are not met.^