Data mining based learning algorithms for semi-supervised object identification and tracking

Sensor exploitation (SE) is the crucial step in surveillance applications such as airport security and search and rescue operations. It allows localization and identification of movement in urban settings and can significantly boost knowledge gathering, interpretation and action. Data mining techniques offer the promise of precise and accurate knowledge acquisition techniques in high-dimensional data domains (and diminishing the “curse of dimensionality” prevalent in such datasets), coupled by algorithmic design in feature extraction, discriminative ranking, feature fusion and supervised learning (classification). Consequently, data mining techniques and algorithms can be used to refine and process captured data and to detect, recognize, classify, and track objects with predictable high degrees of specificity and sensitivity. Automatic object detection and tracking algorithms face several obstacles, such as large and incomplete datasets, ill-defined regions of interest (ROIs), variable scalability, lack of compactness, angular regions, partial occlusions, environmental variables, and unknown potential object classes, which work against their ability to achieve accurate real-time results. Methods must produce fast and accurate results by streamlining image processing, data compression and reduction, feature extraction, classification, and tracking algorithms. Data mining techniques can sufficiently address these challenges by implementing efficient and accurate dimensionality reduction with feature extraction to refine incomplete (ill-partitioning) data-space and addressing challenges related to object classification, intra-class variability, and inter-class dependencies. A series of methods have been developed to combat many of the challenges for the purpose of creating a sensor exploitation and tracking framework for real time image sensor inputs. The framework has been broken down into a series of sub-routines, which work in both series and parallel to accomplish tasks such as image pre-processing, data reduction, segmentation, object detection, tracking, and classification. These methods can be implemented either independently or together to form a synergistic solution to object detection and tracking. The main contributions to the SE field include novel feature extraction methods for highly discriminative object detection, classification, and tracking. Also, a new supervised classification scheme is presented for detecting objects in urban environments. This scheme incorporates both novel features and non-maximal suppression to reduce false alarms, which can be abundant in cluttered environments such as cities. Lastly, a performance evaluation of Graphical Processing Unit (GPU) implementations of the subtask algorithms is presented, which provides insight into speed-up gains throughout the SE framework to improve design for real time applications. The overall framework provides a comprehensive SE system, which can be tailored for integration into a layered sensing scheme to provide the war fighter with automated assistance and support. As more sensor technology and integration continues to advance, this SE framework can provide faster and more accurate decision support for both intelligence and civilian applications

Identification of Novel Adenylyl Cyclase 5 (AC5) Signaling Networks in D1 and D2 Medium Spiny Neurons using Bimolecular Fluorescence Complementation Screening

Adenylyl cyclase type 5 (AC5), as the principal isoform expressed in striatal medium spiny neurons (MSNs), is essential for the integration of both stimulatory and inhibitory midbrain signals that initiate from dopaminergic G protein-coupled receptor (GPCR) activation. The spatial and temporal control of cAMP signaling is dependent upon the composition of local regulatory protein networks. However, there is little understanding of how adenylyl cyclase protein interaction networks adapt to the multifarious pressures of integrating acute versus chronic and inhibitory vs. stimulatory receptor signaling in striatal MSNs. Here, we presented the development of a novel bimolecular fluorescence complementation (BiFC)-based protein-protein interaction screening methodology to further identify and characterize elements important for homeostatic control of dopamine-modulated AC5 signaling in a neuronal model cell line and striatal MSNs. We identified two novel AC5 modulators: the protein phosphatase 2A (PP2A) catalytic subunit (PPP2CB) and the intracellular trafficking associated protein—NSF (N-ethylmaleimide-sensitive factor) attachment protein alpha (NAPA). The effects of genetic knockdown (KD) of each gene were evaluated in several cellular models, including D1- and D2-dopamine receptor-expressing MSNs from CAMPER mice. The knockdown of PPP2CB was associated with a reduction in acute and sensitized adenylyl cyclase activity, implicating PP2A is an important and persistent regulator of adenylyl cyclase activity. In contrast, the effects of NAPA knockdown were more nuanced and appeared to involve an activity-dependent protein interaction network. Taken together, these data represent a novel screening method and workflow for the identification and validation of adenylyl cyclase protein-protein interaction networks under diverse cAMP signaling paradigms