Responsiveness of voltage-gated calcium channels in SH-SY5Y human neuroblastoma cells on quasi-three-dimensional micropatterns formed with poly (l-lactic acid)

Introduction: In this study, quasi-three-dimensional (3D) microwell patterns were fabricated with poly (l-lactic acid) for the development of cell-based assays, targeting voltage-gated calcium channels (VGCCs). Methods and materials: SH-SY5Y human neuroblastoma cells were interfaced with the microwell patterns and found to grow as two dimensional (2D), 3D, and near two dimensional (N2D), categorized on the basis of the cells’ location in the pattern. The capability of the microwell patterns to support 3D cell growth was evaluated in terms of the percentage of the cells in each growth category. Cell spreading was analyzed in terms of projection areas under light microscopy. SH-SY5Y cells’ VGCC responsiveness was evaluated with confocal microscopy and a calcium fluorescent indicator, Calcium GreenTM-1. The expression of L-type calcium channels was evaluated using immunofluorescence staining with DM-BODIPY. Results: It was found that cells within the microwells, either N2D or 3D, showed more rounded shapes and less projection areas than 2D cells on flat poly (l-lactic acid) substrates. Also, cells in microwells showed a significantly lower VGCC responsiveness than cells on flat substrates, in terms of both response magnitudes and percentages of responsive cells, upon depolarization with 50 mM K+. This lower VGCC responsiveness could not be explained by the difference in L-type calcium channel expression. For the two patterns addressed in this study, N2D cells consistently exhibited an intermediate value of either projection areas or VGCC responsiveness between those for 2D and 3D cells, suggesting a correlative relation between cell morphology and VGCC responsiveness. Conclusion: These results suggest that the pattern structure and therefore the cell growth characteristics were critical factors in determining cell VGCC responsiveness and thus provide an approach for engineering cell functionality in cell-based assay systems and tissue engineering scaffolds.

The Application of Fluorescent Nanocrystals to Study the Distribution of Neuropeptides in the Aedes Aegypti Mosquito

Semiconductor nanocrystals, also known as quantum dots (QDs), have been used in studies involving mice and human tissues, but never before in research on insects. We used QDs to study the distribution of two neuropeptides in the Aedes aegypti mosquito, the vector of both dengue and yellow fever. These neuropeptides play a significant role in the production of juvenile hormone, a hormone that controls biting behavior, metamorphosis, and reproduction throughout the life of the mosquito. The two neuropeptides allatostatin-C (AS-C) and allatotropin (AT) function as inhibitory (AS-C) and stimulatory (AT) regulators of juvenile hormone synthesis in the corpus allatum gland. In other insects, they also affect heart rate, gut movement, and nutrient uptake. Conjugating these neuropeptides to quantum dots via a streptavidinlbiotin link, we were able to expose the mosquito corpus allatum and abdomen to allatostatin-C and allatotropin and then to visualize their distribution under UV light using confocal and compound light microscopy. Histological sections of the whole mosquito, incubations of tissues with conjugates (in vitro), and microinjections of conjugates into the mosquito (in vivo) were performed. The results showed that quantum dots can be used to detect neuropeptide distribution in the mosquito. The more we understand about these neuropeptides and juvenile hormone, the more we can contribute to stopping the spread of infectious diseases, such as dengue and yellow fever.

The development, application, and implications of a strategy for reflective learning from experience

The problem on which this study focused was individuals' reduced capacity to respond to change and to engage in innovative learning when their reflective learning skills are limited. In this study, the preceding problem was addressed by two primary questions: To what degree can mastery of a strategy for reflective learning be facilitated as a part of an academic curriculum for professional practitioners? What impact will mastery of this strategy have on the learning style and adaptive flexibility of adult learners? The focus of the study was a direct application of human resource development technology in the professional preparation of teachers. The background of the problem in light of changing global paradigms and educational action orientations was outlined and a review of the literature was provided. Roots of thought for two key concepts (i.e., learning to learn from experience and meaningful reflection in learning) were traced. Reflective perspectives from the work of eight researchers were compared. A meta-model of learning from experience drawn from the literature served as a conceptual framework for the study. A strategy for reflective learning developed from this meta-model was taught to 109 teachers-in-training at Florida International University in Miami, Florida. Kolb's Adaptive Style Inventory and Learning Style Inventory were administered to the treatment group and to two control groups taught by the same professor. Three research questions and fourteen hypotheses guided data analysis. Qualitative review of 1565 personal documents generated by the treatment group indicated that 77 students demonstrated "double-loop" learning, going beyond previously established limits to perception, understanding, or action. The mean score for depth of reflection indicated "single-loop" learning with "reflection-in-action" present. The change in the mean score for depth of reflection from the beginning to end of the study was statistically significant (p $<$.05). On quantitative measures of adaptive flexibility and learning style, with two exceptions, there were no significant differences noted between treatment and control groups on pre-test to post-test differences and on post-test mean scores adjusted for pre-test responses and demographic variables. Conclusions were drawn regarding treatment, instrumentation, and application of the strategy and the meta-model. Implications of the strategy and the meta-model for research, for education, for human resource development, for professional practice, and for personal growth were suggested. Qualitative training materials and Kolb's instruments were provided in the appendices.

A framework for automatic feature extraction from airborne light detection and ranging data

Recent advances in airborne Light Detection and Ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. Airborne LIDAR systems usually return a 3-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. This technology is becoming a primary method for extracting information of different kinds of geometrical objects, such as high-resolution digital terrain models (DTMs), buildings and trees, etc. In the past decade, LIDAR gets more and more interest from researchers in the field of remote sensing and GIS. Compared to the traditional data sources, such as aerial photography and satellite images, LIDAR measurements are not influenced by sun shadow and relief displacement. However, voluminous data pose a new challenge for automated extraction the geometrical information from LIDAR measurements because many raster image processing techniques cannot be directly applied to irregularly spaced LIDAR points. ^ In this dissertation, a framework is proposed to filter out information about different kinds of geometrical objects, such as terrain and buildings from LIDAR automatically. They are essential to numerous applications such as flood modeling, landslide prediction and hurricane animation. The framework consists of several intuitive algorithms. Firstly, a progressive morphological filter was developed to detect non-ground LIDAR measurements. By gradually increasing the window size and elevation difference threshold of the filter, the measurements of vehicles, vegetation, and buildings are removed, while ground data are preserved. Then, building measurements are identified from no-ground measurements using a region growing algorithm based on the plane-fitting technique. Raw footprints for segmented building measurements are derived by connecting boundary points and are further simplified and adjusted by several proposed operations to remove noise, which is caused by irregularly spaced LIDAR measurements. To reconstruct 3D building models, the raw 2D topology of each building is first extracted and then further adjusted. Since the adjusting operations for simple building models do not work well on 2D topology, 2D snake algorithm is proposed to adjust 2D topology. The 2D snake algorithm consists of newly defined energy functions for topology adjusting and a linear algorithm to find the minimal energy value of 2D snake problems. Data sets from urbanized areas including large institutional, commercial, and small residential buildings were employed to test the proposed framework. The results demonstrated that the proposed framework achieves a very good performance. ^