Allelopathic Effects of Water Hyacinth [Eichhornia crassipes]

Eichhornia crassipes (Mart) Solms is an invasive weed known to out-compete native plants and negatively affect microbes including phytoplankton. The spread and population density of E. crassipes will be favored by global warming. The aim here was to identify compounds that underlie the effects on microbes. The entire plant of E. crassipes was collected from El Zomor canal, River Nile (Egypt), washed clean, then air dried. Plant tissue was extracted three times with methanol and fractionated by thin layer chromatography (TLC). The crude methanolic extract and five fractions from TLC (A–E) were tested for antimicrobial (bacteria and fungal) and anti-algal activities (green microalgae and cyanobacteria) using paper disc diffusion bioassay. The crude extract as well as all five TLC fractions exhibited antibacterial activities against both the Gram positive bacteria; Bacillus subtilis and Streptococcus faecalis; and the Gram negative bacteria; Escherichia coli and Staphylococcus aureus. Growth of Aspergillus flavus and Aspergillus niger were not inhibited by either E. crassipes crude extract nor its five fractions. In contrast, Candida albicans (yeast) was inhibited by all. Some antialgal activity of the crude extract and its fractions was manifest against the green microalgae; Chlorella vulgaris and Dictyochloropsis splendida as well as the cyanobacteria; Spirulina platensis and Nostoc piscinale. High antialgal activity was only recorded against Chlorella vulgaris. Identifications of the active antimicrobial and antialgal compounds of the crude extract as well as the five TLC fractions were carried out using gas chromatography combined with mass spectroscopy. The analyses showed the presence of an alkaloid (fraction A) and four phthalate derivatives (Fractions B–E) that exhibited the antimicrobial and antialgal activities

T7 RNA Polymerase Functions In Vitro without Clustering

Many nucleic acid polymerases function in clusters known as factories. We investigate whether the RNA polymerase (RNAP) of phage T7 also clusters when active. Using ‘pulldowns’ and fluorescence correlation spectroscopy we find that elongation complexes do not interact in vitro with a Kd<1 µM. Chromosome conformation capture also reveals that genes located 100 kb apart on the E. coli chromosome do not associate more frequently when transcribed by T7 RNAP. We conclude that if clustering does occur in vivo, it must be driven by weak interactions, or mediated by a phage-encoded protein

Review of Coronal Oscillations - An Observer's View

Recent observations show a variety of oscillation modes in the corona. Early non-imaging observations in radio wavelengths showed a number of fast-period oscillations in the order of seconds, which have been interpreted as fast sausage mode oscillations. TRACE observations from 1998 have for the first time revealed the lateral displacements of fast kink mode oscillations, with periods of ~3-5 minutes, apparently triggered by nearby flares and destabilizing filaments. Recently, SUMER discovered with Doppler shift measurements loop oscillations with longer periods (10-30 minutes) and relatively short damping times in hot (7 MK) loops, which seem to correspond to longitudinal slow magnetoacoustic waves. In addition, propagating longitudinal waves have also been detected with EIT and TRACE in the lowest density scale height of loops near sunspots. All these new observations seem to confirm the theoretically predicted oscillation modes and can now be used as a powerful tool for ``coronal seismology'' diagnostic.Comment: 5 Figure

Conditional Probability-Based Significance Tests for Sequential Patterns in Multineuronal Spike Trains

We consider the problem of detecting statistically significant sequential patterns in multineuronal spike trains. These patterns are characterized by ordered sequences of spikes from different neurons with specific delays between spikes. We have previously proposed a data-mining scheme to efficiently discover such patterns, which occur often enough in the data. Here we propose a method to determine the statistical significance of such repeating patterns. The novelty of our approach is that we use a compound null hypothesis that not only includes models of independent neurons but also models where neurons have weak dependencies. The strength of interaction among the neurons is represented in terms of certain pair-wise conditional probabilities. We specify our null hypothesis by putting an upper bound on all such conditional probabilities. We construct a probabilistic model that captures the counting process and use this to derive a test of significance for rejecting such a compound null hypothesis. The structure of our null hypothesis also allows us to rank-order different significant patterns. We illustrate the effectiveness of our approach using spike trains generated with a simulator

Memory Neuron Networks for Identification and Control of Dynamical Systems

This paper discusses memory neuron networks as models for identification and adaptive control of nonlinear dynamical systems. These are a class of recurrent networks obtained by adding trainable temporal elements to feedforward networks that makes the output history-sensitive. By virtue of this capability, these networks can identify dynamical systems without having to be explicitly fed with past inputs and outputs. Thus, they can identify systems whose order is unknown or systems with unknown delay. It is argued that for satisfactory modeling of dynamical systems, neural networks should be endowed with such internal memory. The paper presents a preliminary analysis of the learning algorithm, providing theoretical justification for the identification method. Methods for adaptive control of nonlinear systems using these networks are presented. Through extensive simulations, these models are shown to be effective both for identification and model reference adaptive control of nonlinear systems

Generalized frequent episodes in Event Sequences

Discovering patterns in temporal data is an important task in Data Mining. A successful method for this was proposed by Mannila et al. [1] in 1997. In their framework, mining for temporal patterns in a database of sequences of events is done by discovering the so called frequent episodes. These episodes characterize interesting collections of events occurring relatively close to each other in some partial order. However, in this framework(and in many others for finding patterns in event sequences), the ordering of events in an event sequence is the only allowed temporal information. But there are many applications where the events are not instantaneous; they have time durations. Interesting episodesthat we want to discover may need to contain information regarding event durations etc. In this paper we extend Mannila et al.’s framework to tackle such issues. In our generalized formulation, episodes are defined so that much more temporal information about events can be incorporated into the structure of an episode. This significantly enhances the expressive capability of the rules that can be discovered in the frequent episode framework. We also present algorithms for discovering such generalized frequent episodes