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

Studies of the dose-effect relation

Dose-effect relations and, specifically, cell survival curves are surveyed with emphasis on the interplay of the random factors — biological variability, stochastic reaction of the cell, and the statistics of energy deposition —that co-determine their shape. The global parameters mean inactivation dose, , and coefficient of variance, V, represent this interplay better than conventional parameters. Mechanisms such as lesion interaction, misrepair, repair overload, or repair depletion have been invoked to explain sigmoid dose dependencies, but these notions are partly synonymous and are largely undistinguishable on the basis of observed dose dependencies. All dose dependencies reflect, to varying degree, the microdosimetric fluctuations of energy deposition, and these have certain implications, e.g. the linearity of the dose dependence at small doses, that apply regardless of unresolved molecular mechanisms of cellular radiation action

Why Current Publication Practices May Distort Science

John Ioannidis and colleagues argue that the current system of publication in biomedical research provides a distorted view of the reality of scientific data

The Concise Guide to PHARMACOLOGY 2023/24: Enzymes

The Concise Guide to PHARMACOLOGY 2023/24 is the sixth in this series of biennial publications. The Concise Guide provides concise overviews, mostly in tabular format, of the key properties of approximately 1800 drug targets, and about 6000 interactions with about 3900 ligands. There is an emphasis on selective pharmacology (where available), plus links to the open access knowledgebase source of drug targets and their ligands (www.guidetopharmacology.org), which provides more detailed views of target and ligand properties. Although the Concise Guide constitutes almost 500 pages, the material presented is substantially reduced compared to information and links presented on the website. It provides a permanent, citable, point-in-time record that will survive database updates. The full contents of this section can be found at http://onlinelibrary.wiley.com/doi/10.1111/bph.16176. In addition to this overview, in which are identified ‘Other protein targets’ which fall outside of the subsequent categorisation, there are six areas of focus: G protein-coupled receptors, ion channels, nuclear hormone receptors, catalytic receptors, enzymes and transporters. These are presented with nomenclature guidance and summary information on the best available pharmacological tools, alongside key references and suggestions for further reading. The landscape format of the Concise Guide is designed to facilitate comparison of related targets from material contemporary to mid-2023, and supersedes data presented in the 2021/22, 2019/20, 2017/18, 2015/16 and 2013/14 Concise Guides and previous Guides to Receptors and Channels. It is produced in close conjunction with the Nomenclature and Standards Committee of the International Union of Basic and Clinical Pharmacology (NC-IUPHAR), therefore, providing official IUPHAR classification and nomenclature for human drug targets, where appropriate

The Circadian Neuropeptide PDF Signals Preferentially through a Specific Adenylate Cyclase Isoform AC3 in M Pacemakers of Drosophila

To synchronize a network of pacemakers in the Drosophila brain, a neuropeptide receptor specifically associates with adenylate cyclase 3 to create a “circadian signalosome.

PKA regulatory subunits mediate synergy among conserved G-protein-coupled receptor cascades

G-protein-coupled receptors sense extracellular chemical or physical stimuli and transmit these signals to distinct trimeric G-proteins. Activated Gα-proteins route signals to interconnected effector cascades, thus regulating thresholds, amplitudes and durations of signalling. Gαs- or Gαi-coupled receptor cascades are mechanistically conserved and mediate many sensory processes, including synaptic transmission, cell proliferation and chemotaxis. Here we show that a central, conserved component of Gαs-coupled receptor cascades, the regulatory subunit type-II (RII) of protein kinase A undergoes adenosine 3′-5′-cyclic monophosphate (cAMP)-dependent binding to Gαi. Stimulation of a mammalian Gαi-coupled receptor and concomitant cAMP-RII binding to Gαi, augments the sensitivity, amplitude and duration of Gαi:βγ activity and downstream mitogen-activated protein kinase signalling, independent of protein kinase A kinase activity. The mechanism is conserved in budding yeast, causing nutrient-dependent modulation of a pheromone response. These findings suggest a direct mechanism by which coincident activation of Gαs-coupled receptors controls the precision of adaptive responses of activated Gαi-coupled receptor cascades

Colocalization of Protein Kinase A with Adenylyl Cyclase Enhances Protein Kinase A Activity during Induction of Long-Lasting Long-Term-Potentiation

The ability of neurons to differentially respond to specific temporal and spatial input patterns underlies information storage in neural circuits. One means of achieving spatial specificity is to restrict signaling molecules to particular subcellular compartments using anchoring molecules such as A-Kinase Anchoring Proteins (AKAPs). Disruption of protein kinase A (PKA) anchoring to AKAPs impairs a PKA-dependent form of long term potentiation (LTP) in the hippocampus. To investigate the role of localized PKA signaling in LTP, we developed a stochastic reaction-diffusion model of the signaling pathways leading to PKA activation in CA1 pyramidal neurons. Simulations investigated whether the role of anchoring is to locate kinases near molecules that activate them, or near their target molecules. The results show that anchoring PKA with adenylyl cyclase (which produces cAMP that activates PKA) produces significantly greater PKA activity, and phosphorylation of both inhibitor-1 and AMPA receptor GluR1 subunit on S845, than when PKA is anchored apart from adenylyl cyclase. The spatial microdomain of cAMP was smaller than that of PKA suggesting that anchoring PKA near its source of cAMP is critical because inactivation by phosphodiesterase limits diffusion of cAMP. The prediction that the role of anchoring is to colocalize PKA near adenylyl cyclase was confirmed by experimentally rescuing the deficit in LTP produced by disruption of PKA anchoring using phosphodiesterase inhibitors. Additional experiments confirm the model prediction that disruption of anchoring impairs S845 phosphorylation produced by forskolin-induced synaptic potentiation. Collectively, these results show that locating PKA near adenylyl cyclase is a critical function of anchoring

Quantitative Modeling of GRK-Mediated β2AR Regulation

We developed a unified model of the GRK-mediated β2 adrenergic receptor (β2AR) regulation that simultaneously accounts for six different biochemical measurements of the system obtained over a wide range of agonist concentrations. Using a single deterministic model we accounted for (1) GRK phosphorylation in response to various full and partial agonists; (2) dephosphorylation of the GRK site on the β2AR; (3) β2AR internalization; (4) recycling of the β2AR post isoproterenol treatment; (5) β2AR desensitization; and (6) β2AR resensitization. Simulations of our model show that plasma membrane dephosphorylation and recycling of the phosphorylated receptor are necessary to adequately account for the measured dephosphorylation kinetics. We further used the model to predict the consequences of (1) modifying rates such as GRK phosphorylation of the receptor, arrestin binding and dissociation from the receptor, and receptor dephosphorylation that should reflect effects of knockdowns and overexpressions of these components; and (2) varying concentration and frequency of agonist stimulation “seen” by the β2AR to better mimic hormonal, neurophysiological and pharmacological stimulations of the β2AR. Exploring the consequences of rapid pulsatile agonist stimulation, we found that although resensitization was rapid, the β2AR system retained the memory of the previous stimuli and desensitized faster and much more strongly in response to subsequent stimuli. The latent memory that we predict is due to slower membrane dephosphorylation, which allows for progressive accumulation of phosphorylated receptor on the surface. This primes the receptor for faster arrestin binding on subsequent agonist activation leading to a greater extent of desensitization. In summary, the model is unique in accounting for the behavior of the β2AR system across multiple types of biochemical measurements using a single set of experimentally constrained parameters. It also provides insight into how the signaling machinery can retain memory of prior stimulation long after near complete resensitization has been achieved

Subcellular Location of PKA Controls Striatal Plasticity: Stochastic Simulations in Spiny Dendrites

Dopamine release in the striatum has been implicated in various forms of reward dependent learning. Dopamine leads to production of cAMP and activation of protein kinase A (PKA), which are involved in striatal synaptic plasticity and learning. PKA and its protein targets are not diffusely located throughout the neuron, but are confined to various subcellular compartments by anchoring molecules such as A-Kinase Anchoring Proteins (AKAPs). Experiments have shown that blocking the interaction of PKA with AKAPs disrupts its subcellular location and prevents LTP in the hippocampus and striatum; however, these experiments have not revealed whether the critical function of anchoring is to locate PKA near the cAMP that activates it or near its targets, such as AMPA receptors located in the post-synaptic density. We have developed a large scale stochastic reaction-diffusion model of signaling pathways in a medium spiny projection neuron dendrite with spines, based on published biochemical measurements, to investigate this question and to evaluate whether dopamine signaling exhibits spatial specificity post-synaptically. The model was stimulated with dopamine pulses mimicking those recorded in response to reward. Simulations show that PKA colocalization with adenylate cyclase, either in the spine head or in the dendrite, leads to greater phosphorylation of DARPP-32 Thr34 and AMPA receptor GluA1 Ser845 than when PKA is anchored away from adenylate cyclase. Simulations further demonstrate that though cAMP exhibits a strong spatial gradient, diffusible DARPP-32 facilitates the spread of PKA activity, suggesting that additional inactivation mechanisms are required to produce spatial specificity of PKA activity