Fluorine-18-labeled estrogens, progestins and corticosteroids for receptor-based imaging of breast tumors and target areas of the brain

Estrogens can be labeled with the positron-emitting radionuclide fluorine-18 (t$\sb{1/2}$ = 110 min) by fluoride ion (n-Bu$\sb4$N$\sp{18}$F) displacement of a 16$\beta$-trifluoromethanesulfonate (triflate) derivative of the corresponding estrone 3-triflate, and purification by HPLC. That sequence has been used to synthesize the 11$\beta$-methoxy 1 and 11$\beta$-ethyl 2 analogues of the breast tumor imaging agent, 16$\alpha$-($\sp{18}$F) fluoro-17$\beta$-estradiol (FES). Tissue distribution studies of 1 and 2 in immature female rats show high selectivity for target tissue (T, uterus) vs non-target (NT, muscle and lung), with T/NT ratios being 43 and 17 at one hour after injection for 1 and 2, respectively. The parent estrogen FES has previously been shown to display an intermediate value for tissue selectivity.

Scheduling of track inspection and maintenance activities in railroad networks

The U.S. railroad companies spend billions of dollars every year on railroad track maintenance in order to ensure safety and operational efficiency of their railroad networks. Besides maintenance costs, other costs such as train accident costs, train and shipment delay costs and rolling stock maintenance costs are also closely related to track maintenance activities. Optimizing the track maintenance process on the extensive railroad networks is a very complex problem with major cost implications. Currently, the decision making process for track maintenance planning is largely manual and primarily relies on the knowledge and judgment of experts. There is considerable potential to improve the process by using operations research techniques to develop solutions to the optimization problems on track maintenance. In this dissertation study, we propose a range of mathematical models and solution algorithms for three network-level scheduling problems on track maintenance: track inspection scheduling problem (TISP), production team scheduling problem (PTSP) and job-to-project clustering problem (JTPCP). TISP involves a set of inspection teams which travel over the railroad network to identify track defects. It is a large-scale routing and scheduling problem where thousands of tasks are to be scheduled subject to many difficult side constraints such as periodicity constraints and discrete working time constraints. A vehicle routing problem formulation was proposed for TISP, and a customized heuristic algorithm was developed to solve the model. The algorithm iteratively applies a constructive heuristic and a local search algorithm in an incremental scheduling horizon framework. The proposed model and algorithm have been adopted by a Class I railroad in its decision making process. Real-world case studies show the proposed approach outperforms the manual approach in short-term scheduling and can be used to conduct long-term what-if analyses to yield managerial insights. PTSP schedules capital track maintenance projects, which are the largest track maintenance activities and account for the majority of railroad capital spending. A time-space network model was proposed to formulate PTSP. More than ten types of side constraints were considered in the model, including very complex constraints such as mutual exclusion constraints and consecution constraints. A multiple neighborhood search algorithm, including a decomposition and restriction search and a block-interchange search, was developed to solve the model. Various performance enhancement techniques, such as data reduction, augmented cost function and subproblem prioritization, were developed to improve the algorithm. The proposed approach has been adopted by a Class I railroad for two years. Our numerical results show the model solutions are able to satisfy all hard constraints and most soft constraints. Compared with the existing manual procedure, the proposed approach is able to bring significant cost savings and operational efficiency improvement. JTPCP is an intermediate problem between TISP and PTSP. It focuses on clustering thousands of capital track maintenance jobs (based on the defects identified in track inspection) into projects so that the projects can be scheduled in PTSP. A vehicle routing problem based model and a multiple-step heuristic algorithm were developed to solve this problem. Various side constraints such as mutual exclusion constraints and rounding constraints were considered. The proposed approach has been applied in practice and has shown good performance in both solution quality and efficiency.

The identification and characterization of novel antibacterial compounds via target-based and whole cell screening approaches

The emergence of multidrug-resistant bacterial infections in both the clinical setting and the community has created an environment in which the development of novel antibacterial compounds is necessary to keep dangerous infections at bay. While the derivatization of existing antibiotics by pharmaceutical companies has so far been successful at achieving this end, this strategy is short-term, and the discovery of antibacterials with novel scaffolds would be a greater contribution to the fight of multidrug-resistant infections. Described herein is the application of both target-based and whole cell screening strategies to identify novel antibacterial compounds. In a target-based approach, we sought small-molecule disruptors of the MazEF toxin-antitoxin protein complex. A lack of facile, continuous assays for this target required the development of a fluorometric assay for MazF ribonuclease activity. This assay was employed to further characterize the activity of the MazF enzyme and was used in a screening effort to identify disruptors of the MazEF complex. In addition, by employing a whole cell screening approach, we identified two compounds with potent antibacterial activity. Efforts to characterize the in vitro antibacterial activities displayed by these compounds and to identify their modes of action are described.

Hand detection on images based on deformable part models and additional features

Hand detection on images has important applications on person activities recognition. This thesis focuses on PASCAL Visual Object Classes (VOC) system for hand detection. VOC has become a popular system for object detection, based on twenty common objects, and has been released with a successful deformable parts model in VOC2007. A hand detection on an image is made when the system gets a bounding box which overlaps with at least 50% of any ground truth bounding box for a hand on the image. The initial average precision of this detector is around 0.215 compared with a state-of-art of 0.104; however, color and frequency features for detected bounding boxes contain important information for re-scoring, and the average precision can be improved to 0.218 with these features. Results show that these features help on getting higher precision for low recall, even though the average precision is similar.