The Io sulfur torus in 1981

A Fabry-Perot spectrometer was used to obtain images of the Io torus in emission lines of S II (wavelength 6716 and 6731) and S III (wavelength 9531) in February and March 1981, on the 2.1 meter telescope at KPNO. The S II and S III images showed a large variation in brightness and radial extent. There is an indication the S II and S III emissions in the warm torus are correlated. The S II and S III emissions in the warm torus also have similar scale heights along the magnetic field lines of approximately 0.6 to 0.72 R sub J. The east-west asymmetry in the S II images taken at similar magnetic longitudes, but 2.5 Jovian rotations apart, supports the theory of convective motions suggested by others. In addition to the images, simultaneous measurements of the S II (6731 wavelength) line profile were also made on one night using a Fabry-Perot scanning spectrometer on the 4 meter at KPNO. The S II spectral scans implied ion temperatures of 52 (+ or - 10) x 10 to the 3rd at 5.2 to 5.6 R sub J from Jupiter and a minimum temperature of at least 3 x 10 to the 5th K at 6 R sub J from Jupiter

SWAS observations of comet 9P/Tempel 1 and Deep Impact

On 4 July 2005 at 1:52 UT the Deep Impact mission successfully completed its goal to hit the nucleus of 9P/Tempel 1 with an impactor, forming a crater on the nucleus and ejecting material into the coma of the comet. The 370 kg impactor collided with the sunlit side of the nucleus with a relative velocity of 10.2 km/s. NASA's Submillimeter Wave Astronomy Satellite (SWAS) observed the 1(10)-1(01) ortho-water ground-state rotational transition in comet 9P/Tempel 1 before, during, and after the impact. No excess emission from the impact was detected by SWAS. However, the water production rate of the comet showed large natural variations of more than a factor of three during the weeks before the impact.Comment: to appear in the proceedings of the IAU Symposium No. 231: "Astrochemistry - Recent Successes and Current Callenges". Typo corrected in author affiliation lis

Inference of Genetic Regulatory Networks with Recurrent Neural Network Models using Particle Swarm Optimization

Genetic regulatory network inference is critically important for revealing fundamental cellular processes, investigating gene functions, and understanding their relations. The availability of time series gene expression data makes it possible to investigate the gene activities of whole genomes, rather than those of only a pair of genes or among several genes. However, current computational methods do not sufficiently consider the temporal behavior of this type of data and lack the capability to capture the complex nonlinear system dynamics. We propose a recurrent neural network (RNN) and particle swarm optimization (PSO) approach to infer genetic regulatory networks from time series gene expression data. Under this framework, gene interaction is explained through a connection weight matrix. Based on the fact that the measured time points are limited and the assumption that the genetic networks are usually sparsely connected, we present a PSO-based search algorithm to unveil potential genetic network constructions that fit well with the time series data and explore possible gene interactions. Furthermore, PSO is used to train the RNN and determine the network parameters. Our approach has been applied to both synthetic and real data sets. The results demonstrate that the RNN/PSO can provide meaningful insights in understanding the nonlinear dynamics of the gene expression time series and revealing potential regulatory interactions between genes

Characterization of the IS200/IS605 Insertion Sequence Family in Halanaerobium Hydrogeniformans

Mobile DNA elements play a significant evolutionary role by promoting genome plasticity. Insertion sequences are the smallest prokaryotic transposable elements. They are highly diverse elements, and the ability to accurately identify, annotate, and infer the full genomic impact of insertion sequences is lacking. Halanaerobium hydrogeniformans is a haloalkaliphilic bacterium with an abnormally high number of insertion sequences. One family, IS200/IS605, showed several interesting features distinct from other elements in this genome. Twenty-three loci harbor elements of this family in varying stages of decay, from nearly intact to an ends-only sequence. The loci were characterized with respect to two divergent open reading frames (ORF), tnpA and tnpB, and left and right ends of the elements. The tnpB ORF contains two nearly identical insert sequences that suggest recombination between tnpB ORF is occurring. From these results, insertion sequence activity can be inferred, including transposition capability and element interaction

A Combined Digital-Analog Tracker for Terrestrial Applications

A combined Digital-Analog Tracker is suggested to allow maximum efficiency in a solar-electrical energy converter, utilizing a twelve-foot parabolic collector. The analog tracker compares solar beam radiation to ambient (diffuse) light to obtain optimum placement of the collector when the sun is visible. The digital portion of the tracker utilizes a wired program which derives information on solar position from a non-volatile random-access semiconductor memory. This arrangement allows accurate mapping of the sun even when the sun is obscured by atmospheric phenomena which would make mapping impossible

Protein Secondary Structure Prediction using Parallelized Rule Induction from Coverings

Protein 3D structure prediction has always been an important research area in bioinformatics. In particular, the prediction of secondary structure has been a well-studied research topic. Despite the recent breakthrough of combining multiple sequence alignment information and artificial intelligence algorithms to predict protein secondary structure, the Q3 accuracy of various computational prediction algorithms rarely has exceeded 75%. In a previous paper [1], this research team presented a rule-based method called RT-RICO (Relaxed Threshold Rule Induction from Coverings) to predict protein secondary structure. The average Q3 accuracy on the sample datasets using RT-RICO was 80.3%, an improvement over comparable computational methods. Although this demonstrated that RT-RICO might be a promising approach for predicting secondary structure, the algorithm\u27s computational complexity and program running time limited its use. Herein a parallelized implementation of a slightly modified RT-RICO approach is presented. This new version of the algorithm facilitated the testing of a much larger dataset of 396 protein domains [2]. Parallelized RTRICO achieved a Q3 score of 74.6%, which is higher than the consensus prediction accuracy of 72.9% that was achieved for the same test dataset by a combination of four secondary structure prediction methods [2]

Intelligent Tool for Determining the True Harmonic Current Contribution of a Customer in a Power Distribution Network

Customer loads connected to power distribution network may be broadly categorized as either linear or nonlinear. Nonlinear loads inject harmonics into the power network. Harmonics in a power system are classified as either load harmonics or as supply harmonics depending on their origin. The source impedance also impacts the harmonic current flowing in the network. Hence, any change in the source impedance is reflected in the harmonic spectrum of the current. This paper proposes a novel method based on artificial neural networks to isolate and evaluate the impact of the source impedance change without disrupting the operation of any load, by using actual field data. The test site chosen for this paper has a significant amount of triplen harmonics in the current. by processing the acquired data with the proposed algorithm, the actual load harmonic contribution of the customer is predicted. Experimental results confirm that attempting to predict the total harmonic distortion of a customer by simply measuring the customer\u27s current may not be accurate. The main advantage of this method is that only waveforms of voltages and currents at the point of common coupling have to be measured. This method is applicable for both single- and three-phase loads

Intelligent Tool for Determining the True Harmonic Current Contribution of a Customer in a Power Distribution Network

Customer loads connected to electricity supply systems may be broadly categorized as either linear or nonlinear. Nonlinear loads inject harmonics into the power network. Harmonics in a power system are classified as either load harmonics or as supply harmonics depending on their origin. The source impedance also impacts the harmonic current flowing in the network. Hence any change in the source impedance is reflected in the harmonic spectrum of the current. This paper proposes a novel method based on Artificial Neural Networks to isolate and evaluate the impact of the source impedance change without disrupting the operation of any load, by using actual field data. The test site chosen for this study has a significant amount of triplen harmonics in the current. By processing the acquired data with the proposed algorithm, the actual load harmonic contribution of the customer is predicted. Experimental results confirm that attempting to predict the total harmonic distortion (THD) of a customer by simply measuring the customer\u27s current may not be accurate. The main advantage of this method is that only waveforms of voltages and currents at the point of common coupling have to be measured. This method is applicable for both single and three phase loads

Developmental appearance of factors that bind specifically to cis-regulatory sequences of a gene expressed in the sea urchin embryo

Previous gene-transfer experiments have identified a 2500-nucleotide 5' domain of the CyIIIa cytoskeletal actin gene, which contains cis-regulatory sequences that are necessary and sufficient for spatial and temporal control of CyIIIa gene expression during embryogenesis. This gene is activated in late cleavage, exclusively in aboral ectoderm cell lineages. In this study, we focus on interactions demonstrated in vitro between sequences of the regulatory domain and proteins present in crude extracts derived from sea urchin embryo nuclei and from unfertilized eggs. Quantitative gel-shift measurements are utilized to estimate minimum numbers of factor molecules per embryo at 24 hr postfertilization, when the CyIIIa gene is active, at 7 hr, when it is still silent, and in the unfertilized egg. We also estimate the binding affinity preferences (K_r) of the various factors for their respective sites, relative to their affinity for synthetic DNA competitors. At least 14 different specific interactions occur within the regulatory regions, some of which produce multiple DNA-protein complexes. Values of K_r range from approximately 2 x 10^4 to approximately 2 x 10^6 for these factors under the conditions applied. With one exception, the minimum factor prevalences that we measured in the 400-cell 24-hr embryo nuclear extracts fell within the range of 2 x 10^5 to 2 x 10^6 molecules per embryo, i.e., a few hundred to a few thousand molecules per nucleus. Three developmental patterns were observed with respect to factor prevalence: Factors reacting at one site were found in unfertilized egg cytoplasm at about the same level per egg or embryo as in 24-hr embryo nuclei; factors reacting with five other regions of the regulatory domain are not detectable in egg cytoplasm but in 7-hr mid-cleavage-stage embryo, nuclei are already at or close to their concentrations in the 24-hr embryo nuclei; and factors reacting with five additional regions are not detectable in egg cytoplasm and are low in 7-hr embryo nuclei, i.e., ⩽10% per embryo of the level they attain in 24-hr embryo nuclei. The rise in concentration of factors of the latter class could provide the proximal cause for the temporal activation of the CyIIIa gene at the early blastula stage