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     

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...     

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.     

Elevated nutrient inputs to marshes differentially impact carbon and nitrogen cycling in two northern Gulf of Mexico saltmarsh plants

Peatlands have accumulated vast quantities of organic carbon over thousands of years but it is unclear how these sensitive ecosystems will respond to future climate change. If emissions of methane from peatlands increase, then they may contribute increasingly towards climatic warming due to the higher greenhouse warming potential of this gas. We investigated the radiocarbon concentration of methane emissions from a temperate bog over 1.5 years, which we supported with measurements of the surface flux of methane and carbon dioxide. The radiocarbon content of methane emissions varied greatly, from modern (i.e. fixed from the atmosphere within recent decades) to ~ 1400 years BP. Flux rates of methane were spatially and temporally highly variable. A vegetation clipping experiment showed that plants had a great influence on the carbon isotope composition and flux of methane emitted from the peat surface, consistent with earlier studies showing the key role of plants in peatland methane emissions. When plants were absent, emission rates were 70–94% lower and the radiocarbon age of methane emissions was much younger and less variable. Our radiocarbon measurements show that at this peatland, plant-associated methane emissions contain carbon originally fixed from the atmosphere up to hundreds of years earlier, consistent with a contribution from plant mediated transport of methane sourced from sub-surface layers.

     

CD133 suppression increases the sensitivity of prostate cancer cells to paclitaxel

Molecular Biology Reports - One of the major barriers in cancer therapy is the resistance to conventional therapies and cancer stem cells (CSCs) are among the main causes of this problem. CD133 as...     

Colon cancer therapy by focusing on colon cancer stem cells and their tumor microenvironment

Despite many advances and optimization in colon cancer treatment, tumor recurrence and metastases make the development of new therapies necessary. Colon cancer stem cells (CCSCs) are considered as the main triggering factor of cancer progression, recurrence, and metastasis. CCSCs as a result of accumulated genetic and epigenetic alterations and also complex interconnection with the tumor microenvironment (TME) can evolve and convert to full malignant cells. Mounting evidence suggests that in cancer therapy both CCSCs and non-CCSCs in TME have to be regarded to break through the limitation of current therapies. In this regard, stem cell capabilities of some non-CCSCs may arise inside the TME condition. Therefore, a deep knowledge of regulatory mechanisms, heterogeneity, specific markers, and signaling pathways of CCSCs and their interconnection with TME components is needed to improve the treatment of colorectal cancer and the patient's life quality. In this review, we address current different targeted therapeutic options that target cell surface markers and signaling pathways of CCSCs and other components of TME. Current challenges and future perspectives of colon cancer personalized therapy are also provided here. Taken together, based on the deep understanding of biology of CCSCs and using three-dimensional culture technologies, it can be possible to reach successful colon cancer eradication and improvise combination targeted therapies against CCSCs and TME.