Protein-Ligand Interactions and Allosteric Regulation of Activity in DREAM Protein

Downstream regulatory antagonist modulator (DREAM) is a calcium sensing protein that co-assembles with KV4 potassium channels to regulate ion currents as well as with DNA in the nucleus, where it regulates gene expression. The interaction of DREAM with A-type KV4 channels and DNA has been shown to regulate neuronal signaling, pain sensing, and memory retention. The role of DREAM in modulation of pain, onset of Alzheimer’s disease, and cardiac pacemaking has set this protein as a novel therapeutic target. Moreover, previous results have shown a Ca2+ dependent interaction between DREAM and KV4/DNA involving surface contacts at the N-terminus of DREAM. However, the mechanisms by which Ca2+ binding at the C-terminus of DREAM induces structural changes at the C- and N-terminus remain unknown. Here, we present the use of biophysics and biochemistry techniques in order to map the interactions of DREAM and numerous small synthetic ligands as well as KV channels. We further demonstrate that a highly conserved network of aromatic residues spanning the C- and N-terminus domains control protein dynamics and the pathways of signal transduction on DREAM. Using molecular dynamics simulations, site directed mutagenesis, and fluorescence spectroscopy we provide strong evidence in support of a highly dynamic mechanism of signal transduction and regulation. A set of aromatic amino acids including Trp169, Phe171, Tyr174, Phe218, Phe235, Phe219, and Phe252 are identified to form a dynamic network involved in propagation of Ca2+ induced structural changes. These amino acids form a hydrophobic network connecting the N- and C-terminus domains of DREAM and are well conserved in other neuronal calcium sensors. In addition, we show evidence in support of a mechanism in which Ca2+ signals are propagated towards the N-terminus and ultimately lead to the rearrangement of the inactive EF-hand 1. The observed structural motions provide a novel mechanism involved in control of the calcium dependent KV4 and DNA binding. Altogether, we provide the first mechanism of intramolecular and intermolecular signal transduction in a Ca2+ binding protein of the neuronal calcium sensor family

Persistence of neuronal representations through time and damage in the hippocampus

How do neurons encode long-term memories? Bilateral imaging of neuronal activity in the mouse hippocampus reveals that, from one day to the next, ~40% of neurons change their responsiveness to cues, but thereafter only 1% of cells change per day. Despite these changes, neuronal responses are resilient to a lack of exposure to a previously completed task or to hippocampus lesions. Unlike individual neurons, the responses of which change after a few days, groups of neurons with inter- and intrahemispheric synchronous activity show stable responses for several weeks. The likelihood that a neuron maintains its responsiveness across days is proportional to the number of neurons with which its activity is synchronous. Information stored in individual neurons is relatively labile, but it can be reliably stored in networks of synchronously active neurons

Neutron-proton analyzing power at 12 MeV and inconsistencies in parametrizations of nucleon-nucleon data

We present the most accurate and complete data set for the analyzing power Ay(theta) in neutron-proton scattering. The experimental data were corrected for the effects of multiple scattering, both in the center detector and in the neutron detectors. The final data at En = 12.0 MeV deviate considerably from the predictions of nucleon-nucleon phase-shift analyses and potential models. The impact of the new data on the value of the charged pion-nucleon coupling constant is discussed in a model study.Comment: Six pages, four figures, one table, to be published in Physics Letters

Noise-tolerant Modular Neural Network System for Classifying ECG Signal

Millions of electrocardiograms (ECG) are interpreted every year, requiring specialized training for accurate interpretation. Because automated and accurate classification ECG signals will improve early diagnosis of heart condition, several neural network (NN) approaches have been proposed for classifying ECG signals. Current strategies for a critical step, the preprocessing for noise removal, are still unsatisfactory. We propose a modular NN approach based on artificial noise injection, to improve the generalization capability of the resulting model. The NN classifier initially performed a fairly accurate recognition of four types of cardiac anomalies in simulated ECG signals with minor, moderate, severe, and extreme noise, with an average accuracy of 99.2%, 95.1%, 91.4%, and 85.2% respectively. Ultimately we discriminated normal and abnormal heartbeat patterns for single lead of raw ECG signals, obtained 95.7% of overall accuracy and 99.5% of Precision. Therefore, the propose approach is a useful tool for the detection and diagnosis of cardiac abnormalities

Persistence of neuronal representations through time and damage in the hippocampus

How do neurons encode long-term memories? Bilateral imaging of neuronal activity in the mouse hippocampus reveals that, from one day to the next, ~40% of neurons change their responsiveness to cues, but thereafter only 1% of cells change per day. Despite these changes, neuronal responses are resilient to a lack of exposure to a previously completed task or to hippocampus lesions. Unlike individual neurons, the responses of which change after a few days, groups of neurons with inter- and intrahemispheric synchronous activity show stable responses for several weeks. The likelihood that a neuron maintains its responsiveness across days is proportional to the number of neurons with which its activity is synchronous. Information stored in individual neurons is relatively labile, but it can be reliably stored in networks of synchronously active neurons

Towards an understanding of the rapid decline of the cosmic star formation rate

We present a first analysis of deep 24 micron observations with the Spitzer Space Telescope of a sample of nearly 1500 galaxies in a thin redshift slice, 0.65<z<0.75. We combine the infrared data with redshifts, rest-frame luminosities, and colors from COMBO-17, and with morphologies from Hubble Space Telescope images collected by the GEMS and GOODS projects. To characterize the decline in star-formation rate (SFR) since z~0.7, we estimate the total thermal infrared (IR) luminosities, SFRs, and stellar masses for the galaxies in this sample. At z~0.7, nearly 40% of intermediate and high-mass galaxies (with stellar masses >2x10^10 solar masses) are undergoing a period of intense star formation above their past-averaged SFR. In contrast, less than 1% of equally-massive galaxies in the local universe have similarly intense star formation activity. Morphologically-undisturbed galaxies dominate the total infrared luminosity density and SFR density: at z~0.7, more than half of the intensely star-forming galaxies have spiral morphologies, whereas less than \~30% are strongly interacting. Thus, a decline in major-merger rate is not the underlying cause of the rapid decline in cosmic SFR since z~0.7. Physical properties that do not strongly affect galaxy morphology - for example, gas consumption and weak interactions with small satellite galaxies - appear to be responsible.Comment: To appear in the Astrophysical Journal 1 June 2005. 14 pages with 8 embedded figure

Invariant Representative Cocycles of Cohomology Generators using Irregular Graph Pyramids

Structural pattern recognition describes and classifies data based on the relationships of features and parts. Topological invariants, like the Euler number, characterize the structure of objects of any dimension. Cohomology can provide more refined algebraic invariants to a topological space than does homology. It assigns `quantities' to the chains used in homology to characterize holes of any dimension. Graph pyramids can be used to describe subdivisions of the same object at multiple levels of detail. This paper presents cohomology in the context of structural pattern recognition and introduces an algorithm to efficiently compute representative cocycles (the basic elements of cohomology) in 2D using a graph pyramid. An extension to obtain scanning and rotation invariant cocycles is given.Comment: Special issue on Graph-Based Representations in Computer Visio