Application of integrated data mining techniques in stock market forecasting

Stock market is considered too uncertain to be predictable. Many individuals have developed methodologies or models to increase the probability of making a profit in their stock investment. The overall hit rates of these methodologies and models are generally too low to be practical for real-world application. One of the major reasons is the huge fluctuation of the market. Therefore, the current research focuses in the stock forecasting area is to improve the accuracy of stock trading forecast. This paper introduces a system that addresses the particular need. The system integrates various data mining techniques and supports the decision-making for stock trades. The proposed system embeds the top-down trading theory, artificial neural network theory, technical analysis, dynamic time series theory, and Bayesian probability theory. To experimentally examine the trading return of the presented system, two examples are studied. The first uses the Taiwan Semiconductor Manufacturing Company (TSMC) data-set that covers an investment horizon of 240 trading days from 16 February 2011 to 23 January 2013. Eighty four transactions were made using the proposed approach and the investment return of the portfolio was 54% with an 80.4% hit rate during a 12-month period in which the TSMC stock price increased by 25% (from $NT 78.5 to$NT 101.5). The second example examines the stock data of Evergreen Marine Corporation, an international marine shipping company. Sixty four transactions were made and the investment return of the portfolio was 128% in 12 months. Given the remarkable investment returns in trading the example TSMC and Evergreen stocks, the proposed system demonstrates promising potentials as a viable tool for stock market forecasting

Amplicon sequencing based profiling of bacterial diversity from Krossfjorden, Arctic

In this study, Illumina Miseq sequencing of 16S rRNA gene amplicon was performed on sediments collected from Krossfjorden, Arctic for analyzing the bacterial community structure. Metagenome contained 15,936 sequences with 5,809,491 bp size and 53% G+C content. Metagenome sequence information are now available at NCBI under the Sequence Read Archive (SRA) database with accession no. SRP159159. Taxonomic hits distribution from MG-RAST analysis revealed the dominance of Alpha- and Gamma-subdivisions of Proteobacteria (88.89%) along with Bacteriodetes (8.89%) and Firmicutes (2.22%). Predominant species were Alteromonadales bacterium TW-7 (24%), Pseudoalteromonas haloplanktis (20%) and Pseudoalteromonas spp. SM9913 (18%). MG-RAST assisted analysis also detected the presence of a variety of marine taxa like Bacteriodes, Pseudovibrio, Marinobacter, Idiomarina, Teredinibacter, etc. which take part in key ecological functions and biogeochemical activities of Arctic fjord ecosystems. Keywords: Arctic, Bacterial diversity, Metagenome, Amplicon sequencing, Illumin

Characterization of the gene encoding the polymorphic immunodominant molecule, a neutralizing antigen of Theileria parva

Theileria parva, a tick-transmitted protozoan parasite related to Plasmodium spp., causes the disease East Coast fever, an acute and usually fatal lymphoproliferative disorder of cattle in Africa. Previous studies using sera from cattle that have survived infection identified a polymorphic immunodominant molecule (PIM) that is expressed by both the infective sporozoite stage of the parasite and the intracellular schizont. Here we show that mAb specific for the PIM Ag can inhibit sporozoite invasion of lymphocytes in vitro. A cDNA clone encoding the PIM Ag of the T. parva (Muguga) stock was obtained by using these mAb in a novel eukaryotic expression cloning system that allows isolation of cDNA encoding cytoplasmic or surface Ags. To establish the molecular basis of the polymorphism of PIM, the cDNA of the PIM Ag from a buffalo-derived T. parva stock was isolated and its sequence was compared with that of the cattle-derived Muguga PIM. The two cDNAs showed considerable identity in both the 5' and 3' regions, but there was substantial sequence divergence in the central regions. Several types of repeated sequences were identified in the variant regions. In the Muguga form of the molecule, there were five tandem repeats of the tetrapeptide, QPEP, that were shown, by transfection of a deleted version of the PIM gene, not to react with several anti-PIM mAbs. By isolating and sequencing the genomic version of the gene, we identified two small introns in the 3' region of the gene. Finally, we showed that polyclonal rat Abs against recombinant PIM neutralize sporozoite infectivity in vitro, suggesting that the PIM Ag should be evaluated for its capacity to immunize cattle against East Coast fever

Modelling length-at-age variability under irreversible growth

In this paper, we describe a discrete-time formalism for describing the dynamics of the size-at-age distribution of a cohort of individuals exhibiting irreversible von Bertalanffy growth in a statistically uniform random environment. This formalism yields a highly efficient numerical implementation, which is particularly suited to automatic optimization. In the special case where mortality is sufficiently size-independent not to vary substantially across the bulk of the size distribution at any given age, we can further increase this efficiency by deriving compact update rules for the mean and coefficient of variation of size-at-age. In this case, we also demonstrate that the depensatory effect of random growth variability and the compensatory effect of deterministic von Bertalanffy growth balance to yield an attracting (initial condition independent) trajectory of mean length and length coefficient of variation against age. We demonstrate the applicability and extensibility of this formalism by two exemplary applications - juvenile salmonids and demersal cod

Importance of soils, topography and geographic distance in structuring central Amazonian tree communities

Question: What is the relative contribution of geographic distance, soil and topographic variables in determining the community floristic patterns and individual tree species abundances in the nutrient-poor soils of central Amazonia? Location: Central Amazonia near Manaus, Brazil. Methods: Our analysis was based on data for 1105 tree species (≥ 10 cm dbh) within 40 1-ha plots over a ca. 1000-km2 area. Slope and 26 soil-surface parameters were measured for each plot. A main soil-fertility gradient (encompassing soil texture, cation content, nitrogen and carbon) and five other uncorrelated soil and topographic variables were used as potential predictors of plant-community composition. Mantel tests and multiple regressions on distance matrices were used to detect relationships at the community level, and ordinary least square (OLS) and conditional autoregressive (CAR) models were used to detect relationships for individual species abundances. Results: Floristic similarity declined rapidly with distance over small spatial scales (0-5 km), but remained constant (ca. 44%) over distances of 5 to 30 km, which indicates lower beta diversity than in western Amazonian forests. Distance explained 1/3 to 1/2 more variance in floristics measures than environmental variables. Community composition was most strongly related to the main soil-fertility gradient and C:N ratio. The main fertility gradient and pH had the greatest impact of species abundances. About 30% of individual tree species were significantly related to one or more soil/topographic parameters. Conclusions: Geographic distance and the main fertility gradient are the best predictors of community floristic composition, but other soil variables, particularly C:N ratio, pH, and slope, have strong relationships with a significant portion of the tree community