Incorporating an Ipad to Facilitate Letter Recognition and Enhance Academic Achievement

The Annual South Florida Education Research Conference is a presentation of scholarly work by students and faculty of member institutions and the community.

Effects of Increased Levels of Prenatal Mesotocin on Postnatal Individual Recognition and Stress Responsiveness in Northern bobwhite quail (Colinus Virginianus)

Oxytocin (OT) plays a key role in the mediation of social and stress behaviors across many species; however, the mechanism is still unclear. The present study investigated the influence of prenatal levels of mesotocin (MT; avian homologue of OT) on postnatal social and stress behavior in Northern bobwhite quail. Experiment one determined endogenous levels of MT during prenatal development using an enzyme-linked immunoassay kit. Experiment two examined the influence of increased MT during prenatal development on chicks' individual recognition ability and stress response to a novel environment. Experiment one showed MT levels increased significantly throughout embryonic development. Experiment two showed significant differences in stress behavior for chicks with increased MT during prenatal development; however, no significant differences were found for social behavior. This study suggests MT serves different functions depending on the stage of embryonic development and that increasing MT levels affects postnatal stress behavior, but not social behavior.

Consistency and accuracy in memory: Effects of schema-typicality, retention interval and question-format

This study examined the effect of schemas on consistency and accuracy of memory across interviews, providing theoretical hypotheses explaining why inconsistencies may occur. The design manipulated schema-typicality of items (schema-typical and atypical), question format (free-recall, cued-recall and recognition) and retention interval (immediate/2 week and 2 week/4 week). Consistency, accuracy and experiential quality of memory were measured. ^ All independent variables affected accuracy and experiential quality of memory while question format was the only variable affecting consistency. These results challenge the commonly held notion in the legal arena that consistency is a proxy for accuracy. The study also demonstrates that other variables, such as item-typicality and retention interval have different effects on consistency and accuracy in memory. ^

Multidimensional classification and diagnosis of leukemia in flow cytometry

This research is to establish new optimization methods for pattern recognition and classification of different white blood cells in actual patient data to enhance the process of diagnosis. Beckman-Coulter Corporation supplied flow cytometry data of numerous patients that are used as training sets to exploit the different physiological characteristics of the different samples provided. The methods of Support Vector Machines (SVM) and Artificial Neural Networks (ANN) were used as promising pattern classification techniques to identify different white blood cell samples and provide information to medical doctors in the form of diagnostic references for the specific disease states, leukemia. The obtained results prove that when a neural network classifier is well configured and trained with cross-validation, it can perform better than support vector classifiers alone for this type of data. Furthermore, a new unsupervised learning algorithm---Density based Adaptive Window Clustering algorithm (DAWC) was designed to process large volumes of data for finding location of high data cluster in real-time. It reduces the computational load to ∼O(N) number of computations, and thus making the algorithm more attractive and faster than current hierarchical algorithms.

Estrogen and lithium: Facilitating factors involved in brain cell signaling pathways

Learning and memory in adult females decline during menopause and estrogen replacement therapy is commonly prescribed during menopause. Post-menopausal women tend to suffer from depression and are prescribed antidepressants – in addition to hormone therapy. Estrogen replacement therapy is a topic that engenders debate since several studies contradict its efficacy as a palliative therapy for cognitive decline and neurodegenerative diseases. Signaling transduction pathways can alter brain cell activity, survival, and morphology by facilitating transcription factor DNA binding and protein production. The steroidal hormone estrogen and the anti-depressant drug lithium interact through these signaling transduction pathways facilitating transcription factor activation. The paucity of data on how combined hormones and antidepressants interact in regulating gene expression led me to hypothesize that in primary mixed brain cell cultures, combined 17β-estradiol (E2) and lithium chloride (LiCl) (E2/LiCl) will alter genetic expression of markers involved in synaptic plasticity and neuroprotection. Results from these studies indicated that a 48 h treatment of E2/LiCl reduced glutamate receptor subunit genetic expression, but increased neurotrophic factor and estrogen receptor genetic expression. Combined treatment also failed to protect brain cell cultures from glutamate excitotoxicity. If lithium facilitates protein signaling pathways mediated by estrogen, can lithium alone serve as a palliative treatment for post-menopause? This question led me to hypothesize that in estrogen-deficient mice, lithium alone will increase episodic memory (tested via object recognition), and enhance expression in the brain of factors involved in anti-apoptosis, learning and memory. I used bilaterally ovariectomized (bOVX) C57BL/6J mice treated with LiCl for one month. Results indicated that LiCl-treated bOVX mice increased performance in object recognition compared with non-treated bOVX. Increased performance in LiCl-treated bOVX mice coincided with augmented genetic and protein expression in the brain. Understanding the molecular pathways of estrogen will assist in identifying a palliative therapy for menopause-related dementia, and lithium may serve this purpose by acting as a selective estrogen-mediated signaling modulator.

Neural network based off-line handwritten text recognition system

This dissertation introduces a new system for handwritten text recognition based on an improved neural network design. Most of the existing neural networks treat mean square error function as the standard error function. The system as proposed in this dissertation utilizes the mean quartic error function, where the third and fourth derivatives are non-zero. Consequently, many improvements on the training methods were achieved. The training results are carefully assessed before and after the update. To evaluate the performance of a training system, there are three essential factors to be considered, and they are from high to low importance priority: (1) error rate on testing set, (2) processing time needed to recognize a segmented character and (3) the total training time and subsequently the total testing time. It is observed that bounded training methods accelerate the training process, while semi-third order training methods, next-minimal training methods, and preprocessing operations reduce the error rate on the testing set. Empirical observations suggest that two combinations of training methods are needed for different case character recognition. Since character segmentation is required for word and sentence recognition, this dissertation provides also an effective rule-based segmentation method, which is different from the conventional adaptive segmentation methods. Dictionary-based correction is utilized to correct mistakes resulting from the recognition and segmentation phases. The integration of the segmentation methods with the handwritten character recognition algorithm yielded an accuracy of 92% for lower case characters and 97% for upper case characters. In the testing phase, the database consists of 20,000 handwritten characters, with 10,000 for each case. The testing phase on the recognition 10,000 handwritten characters required 8.5 seconds in processing time.

Color-based surface reflectance separation for scene illumination estimation and rendering

Given the importance of color processing in computer vision and computer graphics, estimating and rendering illumination spectral reflectance of image scenes is important to advance the capability of a large class of applications such as scene reconstruction, rendering, surface segmentation, object recognition, and reflectance estimation. Consequently, this dissertation proposes effective methods for reflection components separation and rendering in single scene images. Based on the dichromatic reflectance model, a novel decomposition technique, named the Mean-Shift Decomposition (MSD) method, is introduced to separate the specular from diffuse reflectance components. This technique provides a direct access to surface shape information through diffuse shading pixel isolation. More importantly, this process does not require any local color segmentation process, which differs from the traditional methods that operate by aggregating color information along each image plane. ^ Exploiting the merits of the MSD method, a scene illumination rendering technique is designed to estimate the relative contributing specular reflectance attributes of a scene image. The image feature subset targeted provides a direct access to the surface illumination information, while a newly introduced efficient rendering method reshapes the dynamic range distribution of the specular reflectance components over each image color channel. This image enhancement technique renders the scene illumination reflection effectively without altering the scene’s surface diffuse attributes contributing to realistic rendering effects. ^ As an ancillary contribution, an effective color constancy algorithm based on the dichromatic reflectance model was also developed. This algorithm selects image highlights in order to extract the prominent surface reflectance that reproduces the exact illumination chromaticity. This evaluation is presented using a novel voting scheme technique based on histogram analysis. ^ In each of the three main contributions, empirical evaluations were performed on synthetic and real-world image scenes taken from three different color image datasets. The experimental results show over 90% accuracy in illumination estimation contributing to near real world illumination rendering effects. ^

Estrogen and Lithium: Facilitating Factors Involved in Brain Cell Signaling Pathways

Learning and memory in adult females decline during menopause and estrogen replacement therapy is commonly prescribed during menopause. Post-menopausal women tend to suffer from depression and are prescribed antidepressants – in addition to hormone therapy. Estrogen replacement therapy is a topic that engenders debate since several studies contradict its efficacy as a palliative therapy for cognitive decline and neurodegenerative diseases. Signaling transduction pathways can alter brain cell activity, survival, and morphology by facilitating transcription factor DNA binding and protein production. The steroidal hormone estrogen and the anti-depressant drug lithium interact through these signaling transduction pathways facilitating transcription factor activation. The paucity of data on how combined hormones and antidepressants interact in regulating gene expression led me to hypothesize that in primary mixed brain cell cultures, combined 17beta-estradiol (E2) and lithium chloride (LiCl) (E2/LiCl) will alter genetic expression of markers involved in synaptic plasticity and neuroprotection. Results from these studies indicated that a 48 h treatment of E2/LiCl reduced glutamate receptor subunit genetic expression, but increased neurotrophic factor and estrogen receptor genetic expression. Combined treatment also failed to protect brain cell cultures from glutamate excitotoxicity. If lithium facilitates protein signaling pathways mediated by estrogen, can lithium alone serve as a palliative treatment for post-menopause? This question led me to hypothesize that in estrogen-deficient mice, lithium alone will increase episodic memory (tested via object recognition), and enhance expression in the brain of factors involved in anti-apoptosis, learning and memory. I used bilaterally ovariectomized (bOVX) C57BL/6J mice treated with LiCl for one month. Results indicated that LiCl-treated bOVX mice increased performance in object recognition compared with non-treated bOVX. Increased performance in LiCl-treated bOVX mice coincided with augmented genetic and protein expression in the brain. Understanding the molecular pathways of estrogen will assist in identifying a palliative therapy for menopause-related dementia, and lithium may serve this purpose by acting as a selective estrogen-mediated signaling modulator.