Hydrogen peroxide regulates the Osa-miR156-OsSPL2/OsTIFY11b module in rice.

Field-grown rice (Oryza sativa L.), when exposed to various environmental stresses, produces high amounts of reactive oxygen species, such as H2 O2 . MicroRNAs (miRNAs) play crucial roles in plant stress responses. This study characterized the functions of H2 O2 -regulated miRNAs in rice. Small RNA deep sequencing revealed that miR156 levels decreased following H2 O2 treatment. Searches of the rice transcriptome and degradome databases indicated that OsSPL2 and OsTIFY11b are miR156-target genes. Interactions between miR156 and OsSPL2 and OsTIFY11b were confirmed using transient expression assays through agroinfiltration. In addition, the levels of OsSPL2 and OsTIFY11b transcripts were lower in transgenic rice plants overexpressing miR156 than in wild-type plants. The OsSPL2-GFP and OsTIFY11b-GFP proteins were localized to the nucleus. Yeast two-hybrid and bimolecular fluorescence complementation assays indicated interactions between OsSPL2 and OsTIFY11b. Furthermore, OsTIFY11b interacted with OsMYC2 to regulate the expression of OsRBBI3-3, which encodes a proteinase inhibitor. The results suggested that H2 O2 accumulation in rice suppresses the expression of miR156, and induces the expression of its target genes, OsSPL2 and OsTIFY11b, whose proteins interact in the nucleus to regulate the expression of OsRBBI3-3, which is involved in plant defense.

Conical photonic crystals for enhancing light extraction efficiency from high refractive index materials.

We propose, analyze and optimize a two-dimensional conical photonic crystal geometry to enhance light extraction from a high refractive index material, such as an inorganic scintillator. The conical geometry suppresses Fresnel reflections at an optical interface due to adiabatic impedance matching from a gradient index effect. The periodic array of cone structures with a pitch larger than the wavelength of light diffracts light into higher-order modes with different propagating angles, enabling certain photons to overcome total internal reflection (TIR). The numerical simulation shows simultaneous light yield gains relative to a flat surface both below and above the critical angle and how key parameters affect the light extraction efficiency. Our optimized design provides a 46% gain in light yield when the conical photonic crystals are coated on an LSO (cerium-doped lutetium oxyorthosilicate) scintillator.

Compressive holographic two-dimensional localization with 1/30(2) subpixel accuracy.

We propose the use of compressive holography for two-dimensional (2D) subpixel motion localization. Our approach is based on computational implementation of edge-extraction using a Fourier-plane spiral phase mask, followed by compressive reconstruction of the edge of the object. Using this technique and relatively low-cost computer and piezo motion stage to establish ground truth for the motion, we demonstrated localization within 1/30th of a camera pixel in each linear dimension.

A cycle-based data aggregation scheme for grid-based wireless sensor networks.

In a wireless sensor network (WSN), a great number of sensor nodes are deployed to gather sensed data. These sensor nodes are typically powered by batteries so their energy is restricted. Sensor nodes mainly consume energy consumption in data transmission, especially for a long distance. Since the location of the base station (BS) is remote, the energy consumed by each node to directly transmit its data to the BS is considerable and the node will die very soon. A well-designed routing protocol is thus essential to reduce the energy consumption. In this paper, we propose a Cycle-Based Data Aggregation Scheme (CBDAS) for grid-based WSNs. In CBDAS, the whole sensor field is divided into a grid of cells, each with a head. We prolong the network lifetime by linking all cell heads together to form a cyclic chain so that the gathered data can move in two directions. For data gathering in each round, the gathered data moves from node to node along the chain, getting aggregated. Finally, a designated cell head, the cycle leader, directly transmits to the BS. CBDAS performs data aggregation at every cell head so as to substantially reduce the amount of data that must be transmitted to the BS. Only cell heads need disseminate data so that the number of data transmissions is greatly diminished. Sensor nodes of each cell take turns as the cell head, and all cell heads on the cyclic chain also take turns being cycle leader. The energy depletion is evenly distributed so that the nodes' lifetime is extended. As a result, the lifetime of the whole sensor network is extended. Simulation results show that CBDAS outperforms protocols like Direct, PEGASIS, and PBDAS.

Comparisons within the Rice GA 2-Oxidase Gene Family Revealed Three Dominant Paralogs and a Functional Attenuated Gene that Led to the Identification of Four Amino Acid Variants Associated with GA Deactivation Capability.

BACKGROUND: GA 2-oxidases (GA2oxs) are involved in regulating GA homeostasis in plants by inactivating bioactive GAs through 2ß-hydroxylation. Rice GA2oxs are encoded by a family of 10 genes; some of them have been characterized, but no comprehensive comparisons for all these genes have been conducted. RESULTS: Rice plants with nine functional GA2oxs were demonstrated in the present study, and these genes not only were differentially expressed but also revealed various capabilities for GA deactivation based on their height-reducing effects in transgenic plants. Compared to that of wild-type plants, the relative plant height (RPH) of transgenic plants was scored to estimate their reducing effects, and 8.3% to 59.5% RPH was observed. Phylogenetic analysis of class I GA2ox genes revealed two functionally distinct clades in the Poaceae. The OsGA2ox3, 4, and 8 genes belonging to clade A showed the most severe effect (8.3% to 8.7% RPH) on plant height reduction, whereas the OsGA2ox7 gene belonging to clade B showed the least severe effect (59.5% RPH). The clade A OsGA2ox3 gene contained two conserved C186/C194 amino acids that were crucial for enzymatic activity. In the present study, these amino acids were replaced with OsGA2ox7-conserved arginine (C186R) and proline (C194P), respectively, or simultaneously (C186R/C194P) to demonstrate their importance in planta. Another two amino acids, Q220 and Y274, conserved in OsGA2ox3 were substituted with glutamic acid (E) and phenylalanine (F), respectively, or simultaneously to show their significance in planta. In addition, through sequence divergence, RNA expression profile and GA deactivation capability analyses, we proposed that OsGA2ox1, OsGA2ox3 and OsGA2ox6 function as the predominant paralogs in each of their respective classes. CONCLUSIONS: This study demonstrates rice has nine functional GA2oxs and the class I GA2ox genes are divided into two functionally distinct clades. Among them, the OsGA2ox7 of clade B is a functional attenuated gene and the OsGA2ox1, OsGA2ox3 and OsGA2ox6 are the three predominant paralogs in the family.

Predicting microRNA precursors with a generalized Gaussian components based density estimation algorithm.

BACKGROUND: MicroRNAs (miRNAs) are short non-coding RNA molecules, which play an important role in post-transcriptional regulation of gene expression. There have been many efforts to discover miRNA precursors (pre-miRNAs) over the years. Recently, ab initio approaches have attracted more attention because they do not depend on homology information and provide broader applications than comparative approaches. Kernel based classifiers such as support vector machine (SVM) are extensively adopted in these ab initio approaches due to the prediction performance they achieved. On the other hand, logic based classifiers such as decision tree, of which the constructed model is interpretable, have attracted less attention. RESULTS: This article reports the design of a predictor of pre-miRNAs with a novel kernel based classifier named the generalized Gaussian density estimator (G2DE) based classifier. The G2DE is a kernel based algorithm designed to provide interpretability by utilizing a few but representative kernels for constructing the classification model. The performance of the proposed predictor has been evaluated with 692 human pre-miRNAs and has been compared with two kernel based and two logic based classifiers. The experimental results show that the proposed predictor is capable of achieving prediction performance comparable to those delivered by the prevailing kernel based classification algorithms, while providing the user with an overall picture of the distribution of the data set. CONCLUSION: Software predictors that identify pre-miRNAs in genomic sequences have been exploited by biologists to facilitate molecular biology research in recent years. The G2DE employed in this study can deliver prediction accuracy comparable with the state-of-the-art kernel based machine learning algorithms. Furthermore, biologists can obtain valuable insights about the different characteristics of the sequences of pre-miRNAs with the models generated by the G2DE based predictor.

An intelligent two-stage evolutionary algorithm for dynamic pathway identification from gene expression profiles.

From gene expression profiles, it is desirable to rebuild cellular dynamic regulation networks to discover more delicate and substantial functions in molecular biology, biochemistry, bioengineering and pharmaceutics. S-system model is suitable to characterize biochemical network systems and capable to analyze the regulatory system dynamics. However, inference of an S-system model of N-gene genetic networks has 2N(N+1) parameters in a set of non-linear differential equations to be optimized. This paper proposes an intelligent two-stage evolutionary algorithm (iTEA) to efficiently infer the S-system models of genetic networks from time-series data of gene expression. To cope with curse of dimensionality, the proposed algorithm consists of two stages where each uses a divide-and-conquer strategy. The optimization problem is first decomposed into N subproblems having 2(N+1) parameters each. At the first stage, each subproblem is solved using a novel intelligent genetic algorithm (IGA) with intelligent crossover based on orthogonal experimental design (OED). At the second stage, the obtained N solutions to the N subproblems are combined and refined using an OED-based simulated annealing algorithm for handling noisy gene expression profiles. The effectiveness of iTEA is evaluated using simulated expression patterns with and without noise running on a single-processor PC. It is shown that 1) IGA is efficient enough to solve subproblems; 2) IGA is significantly superior to the existing method SPXGA; and 3) iTEA performs well in inferring S-system models for dynamic pathway identification.

Interpretable gene expression classifier with an accurate and compact fuzzy rule base for microarray data analysis.

An accurate classifier with linguistic interpretability using a small number of relevant genes is beneficial to microarray data analysis and development of inexpensive diagnostic tests. Several frequently used techniques for designing classifiers of microarray data, such as support vector machine, neural networks, k-nearest neighbor, and logistic regression model, suffer from low interpretabilities. This paper proposes an interpretable gene expression classifier (named iGEC) with an accurate and compact fuzzy rule base for microarray data analysis. The design of iGEC has three objectives to be simultaneously optimized: maximal classification accuracy, minimal number of rules, and minimal number of used genes. An "intelligent" genetic algorithm IGA is used to efficiently solve the design problem with a large number of tuning parameters. The performance of iGEC is evaluated using eight commonly-used data sets. It is shown that iGEC has an accurate, concise, and interpretable rule base (1.1 rules per class) on average in terms of test classification accuracy (87.9%), rule number (3.9), and used gene number (5.0). Moreover, iGEC not only has better performance than the existing fuzzy rule-based classifier in terms of the above-mentioned objectives, but also is more accurate than some existing non-rule-based classifiers.

Cultivation of microalgae for oil production with a cultivation strategy of urea limitation.

The microalgae, Chlorella sp., were cultivated in various culture modes to assess biomass and lipid productivity in this study. In the batch mode, the biomass concentrations and lipid content of Chlorella sp. cultivated in a medium containing 0.025-0.200 g L(-1) urea were 0.464-2.027 g L(-1) and 0.661-0.326 g g(-1), respectively. The maximum lipid productivity of 0.124 g d(-1)L(-1) occurred in a medium containing 0.100 g L(-1) urea. In the fed-batch cultivation, the highest lipid content was obtained by feeding 0.025 g L(-1) of urea during the stationary phase, but the lipid productivity was not significantly increased. However, a semi-continuous process was carried out by harvesting the culture and renewing urea at 0.025 g L(-1) each time when the cultivation achieved the early stationary phase. The maximum lipid productivity of 0.139 g d(-1)L(-1) in the semi-continuous culture was highest in comparison with those in the batch and fed-batch cultivations.

Effects of biomass weight and light intensity on the performance of photosynthetic microbial fuel cells with Spirulina platensis.

Microalgae Spirulina platensis were attached to the anode of a membrane-free and mediator-free microbial fuel cell (MFC) to produce electricity through the consumption of biochemical compounds inside the microalgae. An increase in open circuit voltage (OCV) was observed with decreasing light intensity and optimal biomass area density. The highest OCV observation for the MFC was 0.39 V in the dark with a biomass area density on the anode surface of 1.2 g cm(-2). Additionally, it was observed that the MFC with 0.75 g cm(-2) of biomass area density produced 1.64 mW m(-2) of electrical power in the dark, which is superior to the 0.132 mW m(-2) produced in the light. Which also means the MFC can be applied to generate electrical power under both day and night conditions.