Histone acetylation dynamics regulating plant development and stress responses

Cellular and Molecular Life Sciences - Crop productivity is directly dependent on the growth and development of plants and their adaptation during different environmental stresses. Histone...     

Adelfa： a futuristic biodiesel fuel for DI diesel engines： a comparative experimental investigation

Biodiesel fuel is found to be a promising alter- native for the petroleum diesel based on the results published by the researchers for a decade. Biodiesel fuel is renewable and non degradable fuel. Many countries use biodiesel fuel for automotives to meet the crisis due to the depletion of the petroleum fuel and to meet the stringent emission norms. Various researches have been carried out with different bio- diesel fuels with vegetable oil as the source and appreciable results were reported. Few biodiesel fuels which have been already tested are Jatropha, Pongamia, Mahua, neem, cotton seed, etc. In this experimental work, Adelfa biodiesel blend is used as the test fuel. The emission and performance charac- teristics were compared with three other different biodiesel fuel blends. Appreciable results imply that Adelfa biodiesel （Nerium oil methyl ester） can be a futuristic biodiesel fuel, which has a good compatibility with the direct injection （DI） diesel engine without any major modification. Moreover, Adelfa can be cultivated in a non agricultural land with fewer sources of water. It is widely spread over all major countries of Asia. Experimental investigations have been carried out on a single cylinder DI diesel engine with standard engine speci- fications. In this experimental work, various Adelfa biodiesel blends is compared with reference fuel （diesel） to choose the best blend which gives a closer performance to diesel. The comparative analysis with other biodiesel fuels has also been done and results have been discussed.     

Effect of molasses binder on the physical and mechanical properties

Molasses was used as an alternative binder to the bentonite binder. The change in moisture absorption by pellets prepared with different iron ores and different molasses contents were investigated. Iron ore properties exerted the major effect on pellet behavior and final pellet quality. The absorbed moisture content of pellets prepared without binder, bentonite-added pellets, and molasses-added pellets were in the range of 7.72%–9.95%, 9.62%–10.84%, and 6.14%-6.69%, respectively. The wet pellet compressive strength of molasses-added pellets(43–230 N/pellet) was superior to that of bentonite-added pellets(9.47–11.92 N/pellet). The compressive strength of dried molasses-modified pellets increased to 222–394 N/pellet, which is currently the highest value achieved for dried pellets.     

Formalising and Detecting Community Structures in Real World Complex Networks

Community structure is an integral characteristic of real world networks whichever processes or areas they emerge from. This paper addresses the problem of community structure detection theoretically as well as computationally. The authors introduce a number of concepts such as the neighbourhood and strength of a subgraph, p-community, local maximal p-community, hubs, and outliers that play elemental role in formalising the concept of community structure in complex networks. A few preliminary results have been derived that lead to the development of an algorithm for community structure detection in undirected unweighted networks. The algorithm is based on a local seed expansion strategy that uses the concept of interaction coefficient. The authors have analysed the algorithm on a number of parameters such as accuracy, stability, and quality on synthetic and real world networks from different areas.     

miR-1, miR-10b,miR-155, and miR-191 are novel regulators of BDNF

Brain-derived neurotrophic factor (BDNF) is a secreted protein of the neurotrophin family that regulates brain development, synaptogenesis, memory and learning, as well as development of peripheral organs, such as angiogenesis in the heart and postnatal growth and repair of skeletal muscle. However, while precise regulation of BDNF levels is an important determinant in defining the biological outcome, the role of microRNAs (miRs) in modulating BDNF expression has not been extensively analyzed. Using in silico approaches, reporter systems, and analysis of endogenous BDNF, we show that miR-1, miR-10b, miR-155, and miR-191 directly repress BDNF through binding to their predicted sites in BDNF 3′UTR. We find that the overexpression of miR-1 and miR-10b suppresses endogenous BDNF protein levels and that silencing endogenous miR-10b increases BDNF mRNA and protein levels. Furthermore, we show that miR-1/206 binding sites within BDNF 3′UTR are used in differentiated myotubes but not in undifferentiated myoblasts. Finally, our data from two cell lines suggest that endogenous miR-1/206 and miR-10 family miRs act cooperatively in suppressing BDNF through their predicted sites in BDNF 3′UTR. In conclusion, our results highlight miR-1, miR-10b, miR-155, and miR-191 as novel regulators of BDNF long and short 3′UTR isoforms, supporting future research in different physiological and pathological contexts.     

Business failure prediction using decision trees

Accurate business failure prediction models would be extremely valuable to many industry sectors, particularly financial investment and lending. The potential value of such models is emphasised by the extremely costly failure of high‐profile companies in the recent past. Consequently, a significant interest has been generated in business failure prediction within academia as well as in the finance industry. Statistical business failure prediction models attempt to predict the failure or success of a business. Discriminant and logit analyses have traditionally been the most popular approaches, but there are also a range of promising non‐parametric techniques that can alternatively be applied. In this paper, the relatively new technique of decision trees is applied to business failure prediction. The numerical results suggest that decision trees could be superior predictors of business failure as compared to discriminant analysis. Copyright © 2009 John Wiley & Sons, Ltd.     

Mutations in CTC1, encoding conserved telomere maintenance component 1, cause Coats plus

Coats plus is a highly pleiotropic disorder particularly affecting the eye, brain, bone and gastrointestinal tract. Here, we show that Coats plus results from mutations in CTC1, encoding conserved telomere maintenance component 1, a member of the mammalian homolog of the yeast heterotrimeric CST telomeric capping complex. Consistent with the observation of shortened telomeres in an Arabidopsis CTC1 mutant and the phenotypic overlap of Coats plus with the telomeric maintenance disorders comprising dyskeratosis congenita, we observed shortened telomeres in three individuals with Coats plus and an increase in spontaneous γH2AX-positive cells in cell lines derived from two affected individuals. CTC1 is also a subunit of the α-accessory factor (AAF) complex, stimulating the activity of DNA polymerase-α primase, the only enzyme known to initiate DNA replication in eukaryotic cells. Thus, CTC1 may have a function in DNA metabolism that is necessary for but not specific to telomeric integrity.     

Optimal sampling frequency for volatility forecast models for the Indian stock markets

This paper evaluates the performance of conditional variance models using high‐frequency data of the National Stock Index (S&P CNX NIFTY) and attempts to determine the optimal sampling frequency for the best daily volatility forecast. A linear combination of the realized volatilities calculated at two different frequencies is used as benchmark to evaluate the volatility forecasting ability of the conditional variance models (GARCH (1, 1)) at different sampling frequencies. From the analysis, it is found that sampling at 30 minutes gives the best forecast for daily volatility. The forecasting ability of these models is deteriorated, however, by the non‐normal property of mean adjusted returns, which is an assumption in conditional variance models. Nevertheless, the optimum frequency remained the same even in the case of different models (EGARCH and PARCH) and different error distribution (generalized error distribution, GED) where the error is reduced to a certain extent by incorporating the asymmetric effect on volatility. Our analysis also suggests that GARCH models with GED innovations or EGRACH and PARCH models would give better estimates of volatility with lower forecast error estimates. Copyright © 2008 John Wiley & Sons, Ltd.     

Rolling of Al-SiC_p Composites

Aluminum based metal matrix composites are offering o utstanding properties in a number of automotive and aircraft components and body structures. The major advantages of these composite materials are their high st rength to weight ratio, high stiffness, high hardness, wear resistance, low coef ficient of thermal expansion, superior dimensional stability and versatility to designer. In addition to these their isotropic properties, good forming characte ristics, easy availability of cheaper reinforcements...