Absolute normalization for uncorrelated background with the event mixing technique

We present a method to determine the magnitude of the uncorrelated background distribution obtained with the event mixing technique, through the simultaneous observation of the projectile elastic scattering in different detectors, which correspond to random coincidences. The procedure is tested with alpha-d angular correlation data from the (6)Li + (59)Co reaction at E(lab) = 29.6 MeV. We also show that the method can be applied using the product of singles events, when singles measurements are available. (C) 2009 Elsevier B.V. All rights reserved.

Separating the wheat from the chaff: unbiased filtering of background tandem mass spectra improves protein identification

Only a small fraction of spectra acquired in LC-MS/MS runs matches peptides from target proteins upon database searches. The remaining, operationally termed background, spectra originate from a variety of poorly controlled sources and affect the throughput and confidence of database searches. Here, we report an algorithm and its software implementation that rapidly removes background spectra, regardless of their precise origin. The method estimates the dissimilarity distance between screened MS/MS spectra and unannotated spectra from a partially redundant background library compiled from several control and blank runs. Filtering MS/MS queries enhanced the protein identification capacity when searches lacked spectrum to sequence matching specificity. In sequence-similarity searches it reduced by, on average, 30-fold the number of orphan hits, which were not explicitly related to background protein contaminants and required manual validation. Removing high quality background MS/MS spectra, while preserving in the data set the genuine spectra from target proteins, decreased the false positive rate of stringent database searches and improved the identification of low-abundance proteins.

Anesthetic Activation of Central Respiratory Chemoreceptor Neurons Involves Inhibition of a THIK-1-Like Background K(+) Current

At surgical depths of anesthesia, inhalational anesthetics cause a loss of motor response to painful stimuli (i.e., immobilization) that is characterized by profound inhibition of spinal motor circuits. Yet, although clearly depressed, the respiratory motor system continues to provide adequate ventilation under these same conditions. Here, we show that isoflurane causes robust activation of CO(2)/pH-sensitive, Phox2b-expressing neurons located in the retrotrapezoid nucleus (RTN) of the rodent brainstem, in vitro and in vivo. In brainstem slices from Phox2b-eGFP mice, the firing of pH-sensitive RTN neurons was strongly increased by isoflurane, independent of prevailing pH conditions. At least two ionic mechanisms contributed to anesthetic activation of RTN neurons: activation of an Na(+)-dependent cationic current and inhibition of a background K(+) current. Single-cell reverse transcription-PCR analysis of dissociated green fluorescent protein-labeled RTN neurons revealed expression of THIK-1 (TWIK-related halothane-inhibited K(+) channel, K(2P)13.1), a channel that shares key properties with the native RTN current (i.e., suppression by inhalational anesthetics, weak rectification, inhibition by extracellular Na(+), and pH-insensitivity). Isoflurane also increased firing rate of RTN chemosensitive neurons in urethane-anesthetized rats, again independent of CO(2) levels. In these animals, isoflurane transiently enhanced activity of the respiratory system, an effect that was most prominent at low levels of respiratory drive and mediated primarily by an increase in respiratory frequency. These data indicate that inhalational anesthetics cause activation of RTN neurons, which serve an important integrative role in respiratory control; the increased drive provided by enhanced RTN neuronal activity may contribute, in part, to maintaining respiratory motor activity under immobilizing anesthetic conditions.

Chaotic phase synchronization and desynchronization in an oscillator network for object selection

Object selection refers to the mechanism of extracting objects of interest while ignoring other objects and background in a given visual scene. It is a fundamental issue for many computer vision and image analysis techniques and it is still a challenging task to artificial Visual systems. Chaotic phase synchronization takes place in cases involving almost identical dynamical systems and it means that the phase difference between the systems is kept bounded over the time, while their amplitudes remain chaotic and may be uncorrelated. Instead of complete synchronization, phase synchronization is believed to be a mechanism for neural integration in brain. In this paper, an object selection model is proposed. Oscillators in the network representing the salient object in a given scene are phase synchronized, while no phase synchronization occurs for background objects. In this way, the salient object can be extracted. In this model, a shift mechanism is also introduced to change attention from one object to another. Computer simulations show that the model produces some results similar to those observed in natural vision systems.

Pseudoclassical description of scalar particle in non-abelian background and path-integral representations

Path-integral representations for a scalar particle propagator in non-Abelian external backgrounds are derived. To this aim, we generalize the procedure proposed by Gitman and Schvartsman of path-integral construction to any representation of SU(N) given in terms of antisymmetric generators. And for arbitrary representations of SU(N), we present an alternative construction by means of fermionic coherent states. From the path-integral representations we derive pseudoclassical actions for a scalar particle placed in non-Abelian backgrounds. These actions are classically analyzed and then quantized to prove their consistency.