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     

Medicago truncatula,a model plant for studying the molecular genetics of theRhizobium-legume symbiosis

Medicago truncatula has all the characteristics required for a concerted analysis of nitrogen-fixing symbiosis withRhizobium using the tools of molecular biology, cellular biology and genetics.M. truncatula is a diploid and autogamous plant has a relatively small genome, and preliminary molecular analysis suggests that allelic heterozygosity is minimal compared with the cross-fertilising tetraploid alfalfa (Medicago sativa). TheM. truncatula cultivar Jemalong is nodulated by theRhizobium meliloti strain 2011, which has already served to define many of the bacterial genes involved in symbiosis with alfalfa. A genotype of Jemalong has been identified which can be regenerated after transformation byAgrobacterium, thus allowing the analysis ofin-vitro-modified genes in an homologous transgenic system. Finally, by virtue of the diploid, self-fertilising and genetically homogeneous character ofM. truncatula, it should be relatively straightforward to screen for recessive mutations in symbiotic genes, to carry out genetic analysis, and to construct an RFLP map for this plant.     

Cell line selection through gamma irradiation combined with multi-walled carbon nanotubes elicitation enhanced phenolic compounds accumulation in Salvia nemorosa cell culture

The current study focused on improving the production of phenolic acids in the Woodland Sage cell suspension culture (CSC) through attaining high-yielding cell lines and carboxyl functionalized multi-walled carbon nanotubes (MWCNT-COOH) elicitation. The leaf-derived callus was irradiated at different doses of gamma irradiation 10 to 100 Gy. The maximum content of rosmarinic acid (RA), salvianolic acid B (SAB), ferulic acid (FA), and cinnamic acid (CA) was recorded in callus cultures irradiated with 70 Gy, which was 18.53, 5.21, 1.9, and 7.59 mg/g DW, respectively. The CSC that established from 70 Gy γ-irradiated calli accumulated 1.7-fold RA more higher irradiated callus culture. The CSC elicited with various concentrations of MWCNT-COOH in range 25 to 100 mg/l significantly increased fresh weight (FW), dry weight (DW), and phenolic acid contents of cells. The highest FW with 268.47 g/l and DW with 22.17 g/l was obtained in 100 mg/l MWCNT-COOH treatment. The RA, SAB, CA and FA content of CSC treated with 100 mg/l MWCNT-COOH were 13-fold, 14.2-fold, 20-fold, and 3- fold higher than wild S. nemorosa plant at flowering stage, respectively. The antioxidant activity of cultures significantly enhanced with both gamma and MWCNT-COOH based on DPPH and FRAP assay. Our results showed that the combination of cell line selection and MWCNT-COOH elicitation significantly improved the production of secondary metabolites in Woodland Sage, which is useful for large-scale production of phenolic compounds.

     

Effects of Pro-Tex® on the expression of Hsp70 gene and immune response parameters in the Persian sturgeon fingerlings,Acipenser persicus,infected with Aeromonas hydrophila

The induction of Hsps (heat shock protein) recognized as a promising approach to limiting disease and improving health in aquaculture. This investigation aimed to study the impacts of Pro-Tex®, an extract from the prickly pear cactus (Opuntia ficus indica), on the expression of Hsp70 gene and induction of immune response parameters in Acipenser persicus infected with Aeromonas hydrophila ATCC^®7966^TM. Fish were pretreated with 25, 50 and 100 mg/L of Pro-Tex and then injected in the intra-peritoneal cavity with A. hydrophila. The expression level of Hsp70 gene, lysozyme activity (LYZ) and complement C3 (C3), and immunoglobulin M (IgM) levels were assessed in liver, gill, and intestine on the days 3 and 7 post-infection. Tex-OE® increased expression of Hsp70 in a dose-dependent way in A. persicus, but this expression significantly reduced on the 7-days post-injection. The Hsp70 expression pattern was variable in each tissue, also, LYZ activity, C3, and IgM increased, depending on the concentration, and showed a decreasing trend in a time-dependent way. In conclusion, our data indicated that Pro-Tex as an Hsp70 inducer increases the resistance of sturgeon fry against fish pathogens by induction of different immunity factors.     

Robust algorithm for brain magnetic resonance image (MRI) classification based on GARCH variances series

In this paper, a robust algorithm for disease type determination in brain magnetic resonance image (MRI) is presented. The proposed method classifies MRI into normal or one of the seven different diseases. At first two-level two-dimensional discrete wavelet transform (2D DWT) of input image is calculated. Our analysis show that the wavelet coefficients of detail sub-bands can be modeled by generalized autoregressive conditional heteroscedasticity (GARCH) statistical model. The parameters of GARCH model are considered as the primary feature vector. After feature vector normalization, principal component analysis (PCA) and linear discriminant analysis (LDA) are used to extract the proper features and remove the redundancy from the primary feature vector. Finally, the extracted features are applied to the K-nearest neighbor (KNN) and support vector machine (SVM) classifiers separately to determine the normal image or disease type. Experimental results indicate that the proposed algorithm achieves high classification rate and outperforms recently introduced methods while it needs less number of features for classification.     

Optimising the duration of floating row cover period to minimise the incidence of tomato yellow leaf curl disease and maximise yield of tomato

The influence of polyester cover on tomato crops was evaluated to determine its effectiveness in reducing the incidence of tomato yellow leaf curl disease and on tomato yield. Uncovered plots were compared in a randomised block design with 14 treatments having different durations of cover. The identification of begomovirus was carried out by polymerase chain reaction, cloning and complete nucleotide sequencing of viruses from infected plants. Disease incidence was high in plots covered for less than 3 weeks with no significant differences among these treatments and those that were left uncovered. In the three treatments with the longest duration of cover, no disease was recorded but yield was significantly decreased. The optimum covering period was 6–7 weeks, providing protection against disease (10% incidence) and maintaining high yields (3 kg per plant). Floating row covers can play a major role in reducing begomovirus‐induced diseases by reducing exposure to whiteflies which transmit these viruses. The current work appears to be the first attempt to optimise the cover period by sequentially removing covers from crops to analyse the consequences in terms of crop biomass, yield and disease and to explore the interactions between these parameters.     

Nanoformulation of curcumin protects HUVEC endothelial cells against ionizing radiation and suppresses their adhesion to monocytes: potential in prevention of radiation-induced atherosclerosis

Objectives

To investigated the potential of a novel dendrosomal nanoformulation of curcumin (DNC) in blocking radiation-induced changes in irradiated human umbilical vein endothelial cells (HUVECs), and their adhesion to human THP-1 monocytoid cells.

Results

Co⁶⁰ gamma rays reduced viability, raised the expression of adhesion molecules, ICAM-1, VCAM-1 and E-selectin (mRNA and protein), augmented the adhesion of THP-1 cells to HUVECs, activated NF-κB binding, increased the release of pro-inflammatory cytokines (IL-6, IL-8 and MCP-1) and induced oxidative damage (reduced glutathione declined, while 8-OHdG and TBARS increased). 5 µM DNC significantly inhibited these radiation-induced changes, activated the Nrf-2 pathway, and effectively suppressed THP-1 adhesion to HUVECs, implicating p38 MAPK signaling.

Conclusion

DNC treatment is a potential preventive method against inflammation and vascular damage from ionizing radiation.

     

Nanocomposite scaffold seeded with mesenchymal stem cells for bone repair

The mechanical property of bone tissue scaffolds is one of the most important aspects in bone tissue engineering that has remained problematic. In our previous study, we fabricated a three‐dimensional scaffold from nano‐hydroxyapatite/gelatin (nHA/Gel) and investigated its efficiency in promoting bone regeneration both in vitro and in vivo. In the present study, the effect of adding silicon carbide (SiC) on the mechanical and biological behaviors of the nHA/Gel/SiC and bone regeneration in vivo were determined. nHA and SiC were synthesized and characterized by the X‐ray diffraction pattern and transmission electron microscope image. Layer solvent casting, freeze drying, and lamination techniques were applied to prepare these scaffolds. Then, the biocompatibility and cell adhesion behavior of the synthesized nHA/Gel/SiC scaffolds were investigated. For in vivo studies, rats were categorized into three groups: blank defect, blank scaffold, and rat bone marrow mesenchymal stem cells (rBM‐MSCs)/scaffold. After 1, 4, and 12 weeks post‐injury, the rats were sacrificed and the calvaria were harvested. Sections with a thickness of 5 µm thickness were prepared and stained with hematoxylin–eosin and Masson's Trichrome, and immunohistochemistry was performed. Our results showed that SiC effectively increased the mechanical properties of the nHA/Gel/SiC scaffold. No significant differences were observed in biocompatibility, cell adhesion, and cytotoxicity of the nHA/Gel/SiC in comparison with the nHA/Gel nanocomposite. Based on histological and immunohistochemical studies, both osteogenesis and collagenization were significantly higher in the rBM‐MSCs/scaffold group, quantitatively and qualitatively. The present study strongly suggests the potential of SiC as an alternative strategy to improve the mechanical and biological properties of bone tissue engineering scaffolds, and shows that the pre‐seeded nHA/Gel/SiC scaffold with rBM‐MSCs improves osteogenesis in the engineered bone implant.     

Methods to produce marker-free transgenic plants

Selectable marker genes (SMGs) have been extraordinarily useful in enabling plant transformation because of the low efficiency of transgene integration. The most used SMGs encode proteins resistant to antibiotics or herbicides and use negative selection, i.e., by killing nontransgenic tissue. However, there are perceived risks in wide-scale deployment of SMG-transgenic plants, and therefore research has recently been performed to develop marker-free systems. In this review, transformation using markers not based on antibiotic or herbicide resistance genes, as well as different systems of marker gene deletion, are discussed.