Clustering with neural networks

Partitioning a set ofN patterns in ad-dimensional metric space intoK clusters — in a way that those in a given cluster are more similar to each other than the rest — is a problem of interest in many fields, such as, image analysis, taxonomy, astrophysics, etc. As there are approximatelyK ^N/K! possible ways of partitioning the patterns amongK clusters, finding the best solution is beyond exhaustive search whenN is large. We show that this problem, in spite of its exponential complexity, can be formulated as an optimization problem for which very good, but not necessarily optimal, solutions can be found by using a Hopfield model of neural networks. To obtain a very good solution, the network must start from many randomly selected initial states. The network is simulated on the MPP, a 128 × 128 SIMD array machine, where we use the massive parallelism not only in solving the differential equations that govern the evolution of the network, but also in starting the network from many initial states at once thus obtaining many solutions in one run. We achieve speedups of two to three orders of magnitude over serial implementations and the promise through Analog VLSI implementations of further speedups of three to six orders of magnitude.Supported by a National Research Council-NASA Research Associatship     

Assessment of Morpho-Physiological and Biochemical Responses of Perennial Ryegrass to Gamma-Aminobutyric Acid (GABA) Application Under Salinity Stress Using Multivariate Analyses Techniques

Journal of Plant Growth Regulation - Salinity stress is one of the most important global problems that afflicts and limits the growth and development of turfgrass in arid and semi-arid areas....     

Using Immunoinformatics and Structural Approaches to Design a Novel HHV8 Vaccine

International Journal of Peptide Research and Therapeutics - Kaposi’s sarcoma (KS)-associated herpesvirus (KSHV) or human herpesvirus 8 (HHV8) has caused infection in different parts of the...     

Expression of recombinant human IFN-γ protein in soybean (Glycine max L.)

Plant Cell, Tissue and Organ Culture (PCTOC) - Globular androgenic haploid embryos of TV21 and TV19 cultivars of Camellia ssp., obtained on embryo induction medium (EIM), Murashige and Skoog medium...     

MicroRNA-424-5p enhances chemosensitivity of breast cancer cells to Taxol and regulates cell cycle,apoptosis, and proliferation

Molecular Biology Reports - Combination therapy has been considered as a potential method to overcome the BC chemoresistance. MicroRNAs (miRs) have been suggested as a therapeutic factor in the...     

Comparison of PCR-RFLP with 21-plex PCR and rDNA Sequencing for Identification of Clinical Yeast Isolates

Non-albicans Candida species and other rare yeasts have emerged as major opportunistic pathogens in fungal infections. Identification of opportunistic yeasts in developing countries is mainly performed by phenotypic assay, which are time-consuming and prone to errors. The aim of the present study was to evaluate PCR-RFLP as a routinely used identification technique for the most clinically important Candida species in Iran and make a comparison with a novel multiplex PCR, called 21-plex PCR. One hundred and seventy-three yeast isolates from clinical sources were selected and identified with sequence analysis of the D1/D2 domains of rDNA (LSU rDNA) sequencing as the gold standard method. The results were compared with those obtained by PCR-RFLP using MspI restriction enzyme and the 21-plex PCR. PCR-RFLP correctly identified 93.4% of common pathogenic Candida species (C. albicans, C. glabrata, C. parapsilosis, C. tropicalis, and P. kudriavsevii (=?C. krusei)) and was able to identify 45.5% of isolates of the uncommon yeast species compared to the D1/D2 rDNA sequencing. Compared with PCR-RFLP, all common Candida species and 72.7% of uncommon yeast species were correctly identified by the 21-plex PCR. The application of the 21-plex PCR assay as a non-sequence-based molecular method for the identification of common and rare yeasts can reduce turnaround time and costs for the identification of clinically important yeasts and can be applied in resource-limited settings.

     

Molecular phylogeny and historical biogeography of the Anatolian lizard Apathya (Squamata,Lacertidae)

Apathya is a lacertid genus occurring mainly in south-east Turkey and its adjacent regions (part of Iran and Iraq). So far two morphological species have been attributed to the genus; A. cappadocica (with five subspecies, A. c. cappadocica, A. c. muhtari, A. c. schmidtlerorum, A. c. urmiana and A. c. wolteri) and A. yassujica. The first species occupies most of the genus’ distribution range, while A. yassujica is endemic of the Zagros Mountains. Here, we explored Apathya’s taxonomy and investigated the evolutionary history of the species by employing phylogenetic and phylogeographic approaches and using both mitochondrial (mtDNA) and nuclear markers. The phylogenetic relationships and the genetic distances retrieved, revealed that Apathya is a highly variable genus, which parallels its high morphological variation. Such levels of morphological and genetic differentiation often exceed those between species of other Lacertini genera that are already treated as full species, suggesting the necessity for a taxonomic revision of Apathya. The phylogeographical scenario emerging from the genetic data suggests that the present distribution of the genus was determined by a combination of dispersal and vicariance events between Anatolia and Southwest Asia dating back to the Miocene and continuing up to the Pleistocene. Key geological events for the understanding of the phylogeography of the genus are the movement of the Arabian plate that led to the configuration of Middle East (orogenesis of the mountain ranges of Turkey and Iran) and the formation of Anatolian Diagonal.     

Hydrogen dissociation on diene-functionalized carbon nanotubes

Chemical functionalization of a zigzag carbon nanotube (CNT) with 1, 3-cyclohexadiene (CHD), previously reported by experimentalists, has been investigated in the present study using density functional theory in terms of energetic, geometric, and electronic properties. Then, the thermodynamic and kinetic feasibility of H₂ dissociation on the pristine and functionalized CNTs have been compared. The dissociation energy of the H₂ molecule on the pristine and functionalized CNT has been calculated to be about ?1.00 and ?1.55 eV, while the barrier energy is found to be about 3.70 and 3.51 eV, respectively. Therefore, H₂ dissociation is thermodynamically more favorable on the CNT-CHD system than on the pristine tube, while the favorability of the dissociation on the pristine tube is higher in term of kinetics.     

Fluorination of BC3 nanotubes: DFT studies

We have studied the adsorption of atomic and molecular fluorines on a BC₃ nanotube by using density functional calculations. It was found that the adsorption of atomic fluorine on a C atom of the tube surface is energetically more favorable than that on a B atom by about 0.97 eV. The adsorption of atomic fluorine on both C and B atoms significantly affects the electronic properties of the BC₃ tube. The HOMO-LUMO energy gap is considerably reduced from 2.37 to 1.50 and 1.14 eV upon atomic F adsorption on B and C atoms, respectively. Molecular fluorine energetically tends to be dissociated on B atoms of the tube surface. The associative and dissociative adsorption energies of F₂ were calculated to be about ?0.42 and ?4.79 eV, respectively. Electron emission density from BC₃ nanotube surface will be increased upon both atomic and molecular fluorine adsorptions due to work function decrement.