Statistical significance of variables driving systematic variation

There are a number of well-established methods such as principal components analysis (PCA) for automatically capturing systematic variation due to latent variables in large-scale genomic data. PCA and related methods may directly provide a quantitative characterization of a complex biological variable that is otherwise difficult to precisely define or model. An unsolved problem in this context is how to systematically identify the genomic variables that are drivers of systematic variation captured by PCA. Principal components (and other estimates of systematic variation) are directly constructed from the genomic variables themselves, making measures of statistical significance artificially inflated when using conventional methods due to over-fitting. We introduce a new approach called the jackstraw that allows one to accurately identify genomic variables that are statistically significantly associated with any subset or linear combination of principal components (PCs). The proposed method can greatly simplify complex significance testing problems encountered in genomics and can be utilized to identify the genomic variables significantly associated with latent variables. Using simulation, we demonstrate that our method attains accurate measures of statistical significance over a range of relevant scenarios. We consider yeast cell-cycle gene expression data, and show that the proposed method can be used to straightforwardly identify statistically significant genes that are cell-cycle regulated. We also analyze gene expression data from post-trauma patients, allowing the gene expression data to provide a molecularly-driven phenotype. We find a greater enrichment for inflammatory-related gene sets compared to using a clinically defined phenotype. The proposed method provides a useful bridge between large-scale quantifications of systematic variation and gene-level significance analyses.Comment: 35 pages, 1 table, 6 main figures, 7 supplementary figure

Interactive multimedia education: Using Authorware as an instructional tool to enhance teaching and learning in the Malaysian classroom

The infusion of multimedia technology into the teaching and learning process is changing the way teachers teach and students learn. It is providing powerful tools for accessing, storing and disseminating information and re-shaping the delivery methodology of our educational content. This paper discusses the use of multimedia as an enabler for educators to become developers of their educational content, focussing on the creation of an interactive multimedia learning (IML) module using Authorware. A survey was carried out to assess students' response toward the module. Results showed a favourable trend towards using authoring technology in the classroom.The infusion of multimedia technology into the teaching and learning process is changing the way teachers teach and students learn. It is providing powerful tools for accessing, storing and disseminating information and re-shaping the delivery methodology of our educational content. This paper discusses the use of multimedia as an enabler for educators to become developers of their educational content, focussing on the creation of an interactive multimedia learning (IML) module using Authorware. A survey was carried out to assess students' response toward the module. Results showed a favourable trend towards using authoring technology in the classroom

Task decomposition using pattern distributor

In this paper, we propose a new task decomposition method for multilayered feedforward neural networks, namely Task Decomposition with Pattern Distributor in order to shorten the training time and improve the generalization accuracy of a network under training. This new method uses the combination of modules (small-size feedforward network) in parallel and series, to produce the overall solution for a complex problem. Based on a “divide-and-conquer” technique, the original problem is decomposed into several simpler sub-problems by a pattern distributor module in the network, where each sub-problem is composed of the whole input vector and a fraction of the output vector of the original problem. These sub-problems are then solved by the corresponding groups of modules, where each group of modules is connected in series with the pattern distributor module and the modules in each group are connected in parallel. The design details and implementation of this new method are introduced in this paper. Several benchmark classification problems are used to test this new method. The analysis and experimental results show that this new method could reduce training time and improve generalization accuracy

Jaccard/Tanimoto similarity test and estimation methods

Binary data are used in a broad area of biological sciences. Using binary presence-absence data, we can evaluate species co-occurrences that help elucidate relationships among organisms and environments. To summarize similarity between occurrences of species, we routinely use the Jaccard/Tanimoto coefficient, which is the ratio of their intersection to their union. It is natural, then, to identify statistically significant Jaccard/Tanimoto coefficients, which suggest non-random co-occurrences of species. However, statistical hypothesis testing using this similarity coefficient has been seldom used or studied. We introduce a hypothesis test for similarity for biological presence-absence data, using the Jaccard/Tanimoto coefficient. Several key improvements are presented including unbiased estimation of expectation and centered Jaccard/Tanimoto coefficients, that account for occurrence probabilities. We derived the exact and asymptotic solutions and developed the bootstrap and measurement concentration algorithms to compute statistical significance of binary similarity. Comprehensive simulation studies demonstrate that our proposed methods produce accurate p-values and false discovery rates. The proposed estimation methods are orders of magnitude faster than the exact solution. The proposed methods are implemented in an open source R package called jaccard (https://cran.r-project.org/package=jaccard). We introduce a suite of statistical methods for the Jaccard/Tanimoto similarity coefficient, that enable straightforward incorporation of probabilistic measures in analysis for species co-occurrences. Due to their generality, the proposed methods and implementations are applicable to a wide range of binary data arising from genomics, biochemistry, and other areas of science

Effect of long-term starvation on the survival, recovery, and carbon utilization profiles of a bovine Escherichia coli O157:H7 isolate from New Zealand

The ability to maintain a dual lifestyle of colonizing the ruminant gut and surviving in nonhost environments once shed is key to the success of Escherichia coli O157:H7 as a zoonotic pathogen. Both physical and biological conditions encountered by the bacteria are likely to change during the transition between host and nonhost environments. In this study, carbon starvation at suboptimal temperatures in nonhost environments was simulated by starving a New Zealand bovine E. coli O157:H7 isolate in phosphate-buffered saline at 4 and 15°C for 84 days. Recovery of starved cells on media with different nutrient availabilities was monitored under aerobic and anaerobic conditions. We found that the New Zealand bovine E. coli O157:H7 isolate was able to maintain membrane integrity and viability over 84 days and that the level of recovery depended on the nutrient level of the recovery medium as well as the starvation temperature. In addition, a significant difference in carbon utilization was observed between starved and nonstarved cells

Reduced pattern training based on task decomposition using pattern distributor

Task Decomposition with Pattern Distributor (PD) is a new task decomposition method for multilayered feedforward neural networks. Pattern distributor network is proposed that implements this new task decomposition method. We propose a theoretical model to analyze the performance of pattern distributor network. A method named Reduced Pattern Training is also introduced, aiming to improve the performance of pattern distribution. Our analysis and the experimental results show that reduced pattern training improves the performance of pattern distributor network significantly. The distributor module’s classification accuracy dominates the whole network’s performance. Two combination methods, namely Cross-talk based combination and Genetic Algorithm based combination, are presented to find suitable grouping for the distributor module. Experimental results show that this new method can reduce training time and improve network generalization accuracy when compared to a conventional method such as constructive backpropagation or a task decomposition method such as Output Parallelism

Nutrient Uptake by Rice Plant Inoculated with Microaerophilic Rhizobacteria Isolated from Selected Rice Soils

The major problem of rice cultivation is low efficiency of N fertilizer and increasing production costs due to the rising price of N fertilizer. Nitrogen fertilizer inputs constitute a high proportion of the price of production. The biological nitrogen fixation technology by inoculating plants with diazotrophic rhizobacteria is an alternative that would subsequently reduces cereal production cost. The study consisted of two experiments. Experiment I was the isolation of microaerophilic rhizobacterial strains from rice soil. Experiment II was a glasshouse experiment to study the effect of selected microaerophilic rhizobacteria on rice nutrient uptake under glasshouse condition. From the isolation procedures, 62 rhizobacterial isolates were collected from different rice soils. Twenty-six or 41.93% isolates showed positive results in nitrogen-free media test. Among the positive strains, six isolates (designated as E 1 8, E23, E38, E40, E44 and E47) with comparatively the highest c.f.u. were studied under the microscope and applied as inoculant in subsequent glasshouse experiment. In the glasshouse experiment, a factorial experiment comprising 6 strains X 5 concentrations of nitrogen input with 5 replications were set up giving a total of 1 50 pots. Rice plants inoculated with rhizobacteria E44 showed the tendency to increase nitrogen content, rhizobacteria E38 tends to increase plant phosphorus content, and rhizobacteria E40 inoculation tends to increase dry weight of rice plants. Meanwhile, inoculation of rhizobacteria E40 showed the tendency to increase shoot magnesium content while rhizobacteria E23 tends to increase root magnesium content. There was no obvious result that shows either potassium and calcium uptake of rice plant were promoted by inoculation with the isolated rhizobacteria. In conclusion, rhizospheres of rice cultivation areas were found to have high populations of microaerophilic rhizobacteria that have the potential to be diazotrophs. The selected rhizobacterial strains respectively showed the trend to promote plant growth (in term of total dry weight) and nutrient (nitrogen, phosphorus, potassium, calcium and magnesium) uptake of rice plants. Therefore, they could have the potential to be applied as biofertilizer or bioenhancer

Characterization and Antioxidant Activity of Phenolic Extracts from Oil Palm (Elaeis Guineensis) Fruits

The extracted oil palm fruit phenolics were analysed using spectrophotometry methods to obtain information on the different types of oil palm phenolics and their antioxidative activities. Different methods were used to extract soluble free (SFP), insoluble-bound (ISBP) and esterified (EFP) phenolics for a better understanding of the types of phenolics present. TPC, TFC, ODPI and DPPH of oil palm phenolics were also monitored to investigate the possible relationships between these variables and the degree of maturity/ripeness of the oil palm fruit from 16 to 24 weeks. The antioxidant activities of oil palm phenolic extracts were analysed using different antioxidant assays, namely the 2,2-azino-bis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) assay, 2,2-diphenyl-2-picrylhydrazyl (DPPH) assay, the ferric-reducing ability (FRAP) assay, β-carotene bleaching assay (BCB) and the oxidative stability index (OSI). Results showed that oil palm phenolic extracts contained high antioxidant activities in the order of ISBP > EFP > SFP. Eight different phenolic acids were identified and quantified using a simple reversed-phase high performance liquid chromatography (HPLC) with a diode array detector (DAD) and liquid chromatography/ tandem mass spectrometry (LC/MS/MS). Gallic, protocatechuic, p-hydroxybenzoic, vanillic, caffeic, syringic, p-coumaric and ferulic acids were detected in oil palm phenolic extracts. Ferulic, p-hydroxybenzoic and p-coumaric acid were the dominant phenolic acids found in oil palm fruit extracts and ranged from 55 - 376 μg/g of DW. The results suggested the potent antioxidant activities of oil palm phenolic extracts and the presence of phenolic acids in palm fruits