Modulation of arginine decarboxylase activity in cucumber (Cucumis sativus) cotyledons in short-term organ culture

Among the various amines administered to excisedCucumis sativus cotyledons in short-term organ culture, agmatine (AGM) inhibited arginine decarboxylase (ADC) activity to around 50%, and putrescine was the most potent entity in this regard. Homoarginine (HARG) dramatically stimulated (3- to 4-fold) the enzyme activity. Both AGM inhibition and HARG stimulation of ADC were transient, the maximum response being elicited at 12 h of culture. Mixing experiments ruled out involvement of a macromolecular effector in the observed modulation of ADC. HARG-stimulated ADC activity was completely abolished by cycloheximide, whereas AGM-mediated inhibition was unaffected. Half-life of the enzyme did not alter on treatment with either HARG or AGM. The observed alterations in ADC activity are accompanied by change in Km of the enzyme. HARG-stimulated ADC activity is additive to that induced by benzyladenine (BA) whereas in presence of KCl, HARG failed to enhance ADC activity, thus demonstrating the overriding influence of K+ on amine metabolism.

Noise measurements of a phase fluorometric instrument

We present noise measurements of a phase fluorometric oxygen sensor that sets the limits of accuracy for this instrument. We analyze the phase sensitive detection measurement system with the signal ''shot'' noise being the only significant contribution to the system noise. Based on the modulated optical power received by the photomultiplier, the analysis predicts a noise spectral power density that was within 3 dB of the measured power spectral noise density. Our results demonstrate that at a received optical power of 20 fW the noise level was low enough to permit the detection of a change oxygen concentration of 1% at the sensor. We also present noise measurements of a new low-cost version of this instrument that uses a photodiode instead of a photomultiplier. These measurements show that the noise for this instrument was limited by noise generated in the preamplifier following the photodiode. (C) 1996 Society of Photo-Optical Instrumentation Engineers.

A high precision instrument to measure angular and binocular deviation introduced by aircraft windscreens by using a shadow casting technique

Objects viewed through transparent sheets with residual non-parallelism and irregularity appear shifted and distorted. This distortion is measured in terms of angular and binocular deviation of an object viewed through the transparent sheet. The angular and binocular deviations introduced are particularly important in the context of aircraft windscreens and canopies as they can interfere with decision making of pilots especially while landing, leading to accidents. In this work, we have developed an instrument to measure both the angular and binocular deviations introduced by transparent sheets. This instrument is especially useful in the qualification of aircraft windscreens and canopies. It measures the deviation in the geometrical shadow cast by a periodic dot pattern trans-illuminated by the distorted light beam from the transparent test specimen compared to the reference pattern. Accurate quantification of the shift in the pattern is obtained by cross-correlating the reference shadow pattern with the specimen shadow pattern and measuring the location of the correlation peak. The developed instrument is handy to use and computes both angular and binocular deviation with an accuracy of less than +/- 0.1 mrad (approximate to 0.036 mrad) and has an excellent repeatability with an error of less than 2%. (C) 2012 American Institute of Physics. http://dx.doi.org/10.1063/1.4769756]

Sensory-organ-like response determines the magnetism of zigzag-edged honeycomb nanoribbons

We present an analytical effective theory for the magnetic phase diagram for zigzag-edge terminated honeycomb nanoribbons described by a Hubbard model with an interaction parameter U. We show that the edge magnetic moment varies as ln U and uncover its dependence on the width W of the ribbon. The physics of this owes its origin to the sensory-organ-like response of the nanoribbons, demonstrating that considerations beyond the usual Stoner-Landau theory are necessary to understand the magnetism of these systems. A first-order magnetic transition from an antiparallel orientation of the moments on opposite edges to a parallel orientation occurs upon doping with holes or electrons. The critical doping for this transition is shown to depend inversely on the width of the ribbon. Using variational Monte Carlo calculations, we show that magnetism is robust to fluctuations. Additionally, we show that the magnetic phase diagram is generic to zigzag-edge terminated nanostructures such as nanodots. Furthermore, we perform first-principles modeling to show how such magnetic transitions can be realized in substituted graphene nanoribbons. DOI: 10.1103/PhysRevB.87.085412

STUDENT'S-t MIXTURE MODEL BASED MULTI-INSTRUMENT RECOGNITION IN POLYPHONIC MUSIC

We address the problem of multi-instrument recognition in polyphonic music signals. Individual instruments are modeled within a stochastic framework using Student's-t Mixture Models (tMMs). We impose a mixture of these instrument models on the polyphonic signal model. No a priori knowledge is assumed about the number of instruments in the polyphony. The mixture weights are estimated in a latent variable framework from the polyphonic data using an Expectation Maximization (EM) algorithm, derived for the proposed approach. The weights are shown to indicate instrument activity. The output of the algorithm is an Instrument Activity Graph (IAG), using which, it is possible to find out the instruments that are active at a given time. An average F-ratio of 0 : 7 5 is obtained for polyphonies containing 2-5 instruments, on a experimental test set of 8 instruments: clarinet, flute, guitar, harp, mandolin, piano, trombone and violin.

MULTI-INSTRUMENT DETECTION IN POLYPHONIC MUSIC USING GAUSSIAN MIXTURE BASED FACTORIAL HMM

We formulate the problem of detecting the constituent instruments in a polyphonic music piece as a joint decoding problem. From monophonic data, parametric Gaussian Mixture Hidden Markov Models (GM-HMM) are obtained for each instrument. We propose a method to use the above models in a factorial framework, termed as Factorial GM-HMM (F-GM-HMM). The states are jointly inferred to explain the evolution of each instrument in the mixture observation sequence. The dependencies are decoupled using variational inference technique. We show that the joint time evolution of all instruments' states can be captured using F-GM-HMM. We compare performance of proposed method with that of Student's-t mixture model (tMM) and GM-HMM in an existing latent variable framework. Experiments on two to five polyphony with 8 instrument models trained on the RWC dataset, tested on RWC and TRIOS datasets show that F-GM-HMM gives an advantage over the other considered models in segments containing co-occurring instruments.