Transcriptome provides potential insights into how calcium affects the formation of stone cell in Pyrus.

BACKGROUND: The content of stone cells in pears has a great influence on taste. Stone cells are formed by the accumulation of lignin. The treatment of exogenous calcium can affect the lignin synthesis, but this Ca-mediated mechanism is still unclear. In this study, the author performed a comparative transcriptomic analysis of callus of pears (Pyrus x bretschneideri) treated with calcium nitrate Ca (NO3)2 to investigate the role of calcium in lignin synthesis. RESULTS: There were 2889 differentially expressed genes (DEGs) detected between the Control and Ca (NO3)2 treatment in total. Among these 2889 DEGs, not only a large number of genes related to Ca single were found, but also many genes were enriched in secondary metabolic pathway, especially in lignin synthesis. Most of them were up-regulated during the development of callus after Ca (NO3)2 treatment. In order to further explore how calcium nitrate treatment affects lignin synthesis, the author screened genes associated with transduction of calcium signal in DEGs, and finally found CAM, CML, CDPK, CBL and CIPK. Then the author identified the PbCML3 in pears and conducted relevant experiments finding the overexpression of PbCML3 would increase the content of pear stone cells, providing potential insights into how Ca treatment enhances the stone cell in pears. CONCLUSIONS: Our deep analysis reveals the effects of exogenous calcium on calcium signal and lignin biosynthesis pathway. The function of PbCML3 on stone cells formation was verified in pear.

Analysis of PRX Gene Family and Its Function on Cell Lignification in Pears (Pyrus bretschneideri).

Class III peroxidases (PRXs) are plant-specific enzymes that play key roles in the responses to biotic and abiotic stress during plant growth and development. In addition, some peroxidases also play roles in plant lignification. In this study, a total of 114 PRX (designated PbPRXs) genes were identified in the pear (Pyrus bretschneideri Rehd) genome based on systematic analysis. These PRX genes were divided into 12 groups based on their phylogenetic relationships. We performed systematic bioinformatics analysis of the PRX genes, including analysis of gene structures, conserved motifs, phylogenetic relationships, and gene expression patterns during pear fruit growth. The PbPRXs are unevenly distributed on the 17 pear chromosomes and some of them on other scaffolds. Gene duplication event analysis indicated that whole-genome duplication (WGD) and segmental duplication play key roles in PRX gene amplification. Ka/Ks analysis suggested that most duplicated PbPRXs experienced purifying selection, with limited functional divergence during the duplication events. Furthermore, the analysis indicated that those highly expressed genes might play significant roles in the lignification of cells to form stone cells in pear fruit. We examined the expression of those highly expressed genes during fruit growth using quantitative real-time PCR (qRT-PCR), verifying differential expression patterns at different stages of fruit. This study provides useful information for further functional analysis of the PRX gene family in pears.

CAD Genes: Genome-Wide Identification, Evolution, and Their Contribution to Lignin Biosynthesis in Pear (Pyrus bretschneideri).

The synthetic enzyme cinnamyl alcohol dehydrogenase (CAD) is involved in responses to various stresses during plant growth. It regulates the monolignol biosynthesis and catalyzes hydroxyl cinnamaldehyde reduction to the corresponding alcohols. Although the CAD gene families have been explored in some species, little known is in Rosaceae. In this study, we identified 149 genes in Pyrus bretschneideri (PbrCAD), Malus domestica (MDPCAD), Prunus mume (PmCAD) and Fragaria vesca (mrnaCAD). They were phylogenetically clustered into six subgroups. All CAD genes contained ADH-N and ADH-zinc-N domains and were distributed on chromosomes unevenly. Dispersed and WGD/segmental duplications accounted the highest number of evolutionary events. Eight collinear gene pairs were identified among the four Rosaceae species, and the highest number was recorded in pear as five pairs. The five PbrCAD gene pairs had undergone purifying selection under Ka/Ks analysis. Furthermore, nine genes were identified based on transcriptomic and stone cell content in pear fruit. In qRT-PCR, the expression patterns of PbrCAD1, PbrCAD20, PbrCAD27, and PbrCAD31 were consistent with variation in stone cell content during pear fruit development. These results will provide valuable information for understanding the relationship between gene expressions and stone cell number in fruit.

Multi-Sensor Perception Strategy to Enhance Autonomy of Robotic Operation for Uncertain Peg-in-Hole Task.

The peg-in-hole task with object feature uncertain is a typical case of robotic operation in the real-world unstructured environment. It is nontrivial to realize object perception and operational decisions autonomously, under the usual visual occlusion and real-time constraints of such tasks. In this paper, a Bayesian networks-based strategy is presented in order to seamlessly combine multiple heterogeneous senses data like humans. In the proposed strategy, an interactive exploration method implemented by hybrid Monte Carlo sampling algorithms and particle filtering is designed to identify the features' estimated starting value, and the memory adjustment method and the inertial thinking method are introduced to correct the target position and shape features of the object respectively. Based on the Dempster-Shafer evidence theory (D-S theory), a fusion decision strategy is designed using probabilistic models of forces and positions, which guided the robot motion after each acquisition of the estimated features of the object. It also enables the robot to judge whether the desired operation target is achieved or the feature estimate needs to be updated. Meanwhile, the pliability model is introduced into repeatedly perform exploration, planning and execution steps to reduce interaction forces, the number of exploration. The effectiveness of the strategy is validated in simulations and in a physical robot task.

PbMC1a/1b regulates lignification during stone cell development in pear (Pyrus bretschneideri) fruit.

Programmed cell death (PCD) and secondary cell wall (SCW) thickening in pear fruit are accompanied by the deposition of cellulose and lignin to form stone cells. Metacaspase is an important protease for development, tissue renewal and PCD. The understanding of the molecular mechanism whereby pear (Pyrus) metacaspase promotes PCD and cell wall lignification is still limited. In this study, the Metacaspases gene family (PbMCs) from P. bretschneideri was identified. PbMC1a/1b was associated with lignin deposition and stone cell formation by physiological data, semiquantitative real-time polymerase chain reaction (RT-PCR) and quantitative RT-PCR (qRT-PCR). Relative to wild-type (WT) Arabidopsis, the overexpression of PbMC1a/1b increased lignin deposition and delayed growth, thickened the cell walls of vessels, xylary fibers and interfascicular fibers, and increased the expression of lignin biosynthetic genes. Yeast two-hybrid (Y2H), bimolecular fluorescence complementation (BiFC) and GST pull-down assays indicated that the PbMC1a/1b protein physically interacted with PbRD21. Simultaneously, the transient expression of PbMC1a/1b and PbRD21 led to significant changes in the expression of genes and lignin contents in pear fruits and flesh calli. These results indicate that PbMC1a/1b plays an important role in cell wall lignification, possibly by interacting with PbRD21 to increase the mRNA levels of some lignin synthesis-associated genes and promote the formation of stone cells in pear fruit.

A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks.

In this paper, a mobile anchor node assisted RSSI localization scheme in underwater wireless sensor networks (UWSNs) is proposed, which aims to improve location accuracy and shorten location time. First, to improve location accuracy, we design a support vector regression (SVR) based interpolation method to estimate the projection of sensor nodes on the linear trajectory of the mobile anchor node. The proposed method increases the accuracy of the nonlinear regression model of noisy measured data and synchronously decreases the estimation error caused by the discreteness of measured data. Second, to shorten location time, we develop a curve matching method to obtain the perpendicular distance from sensor nodes to the linear trajectory of the mobile anchor node. The location of the sensor node can be calculated based on the projection and the perpendicular distance. Compared with existing schemes that require the anchor node to travel at least two trajectories, the proposed scheme only needs one-time trajectory to locate sensor nodes, and the location time is shortened with the reduction in the number of trajectories. Finally, simulation results prove that the proposed scheme can obtain more accurate sensor node location in less time compared with the existing schemes.

Combining pseudo dinucleotide composition with the Z curve method to improve the accuracy of predicting DNA elements: a case study in recombination spots.

Pseudo dinucleotide composition (PseDNC) and Z curve showed excellent performance in the classification issues of nucleotide sequences in bioinformatics. Inspired by the principle of Z curve theory, we improved PseDNC to give the phase-specific PseDNC (psPseDNC). In this study, we used the prediction of recombination spots as a case to illustrate the capability of psPseDNC and also PseDNC fused with Z curve theory based on a novel machine learning method named large margin distribution machine (LDM). We verified that combining the two widely used approaches could generate better performance compared to only using PseDNC with a support vector machine based (SVM-based) model. The best Mathew's correlation coefficient (MCC) achieved by our LDM-based model was 0.7037 through the rigorous jackknife test and improved by ∼6.6%, ∼3.2%, and ∼2.4% compared with three previous studies. Similarly, the accuracy was improved by 3.2% compared with our previous iRSpot-PseDNC web server through an independent data test. These results demonstrate that the joint use of PseDNC and Z curve enhances performance and can extract more information from a biological sequence. To facilitate research in this area, we constructed a user-friendly web server for predicting hot/cold spots, HcsPredictor, which can be freely accessed from . In summary, we provided a united algorithm by integrating Z curve with PseDNC. We hope this united algorithm could be extended to other classification issues in DNA elements.

ZCURVE 3.0: identify prokaryotic genes with higher accuracy as well as automatically and accurately select essential genes.

In 2003, we developed an ab initio program, ZCURVE 1.0, to find genes in bacterial and archaeal genomes. In this work, we present the updated version (i.e. ZCURVE 3.0). Using 422 prokaryotic genomes, the average accuracy was 93.7% with the updated version, compared with 88.7% with the original version. Such results also demonstrate that ZCURVE 3.0 is comparable with Glimmer 3.02 and may provide complementary predictions to it. In fact, the joint application of the two programs generated better results by correctly finding more annotated genes while also containing fewer false-positive predictions. As the exclusive function, ZCURVE 3.0 contains one post-processing program that can identify essential genes with high accuracy (generally >90%). We hope ZCURVE 3.0 will receive wide use with the web-based running mode. The updated ZCURVE can be freely accessed from http://cefg.uestc.edu.cn/zcurve/ or http://tubic.tju.edu.cn/zcurveb/ without any restrictions.

An energy-efficient underground localization system based on heterogeneous wireless networks.

A precision positioning system with energy efficiency is of great necessity for guaranteeing personnel safety in underground mines. The location information of the miners' should be transmitted to the control center timely and reliably; therefore, a heterogeneous network with the backbone based on high speed Industrial Ethernet is deployed. Since the mobile wireless nodes are working in an irregular tunnel, a specific wireless propagation model cannot fit all situations. In this paper, an underground localization system is designed to enable the adaptation to kinds of harsh tunnel environments, but also to reduce the energy consumption and thus prolong the lifetime of the network. Three key techniques are developed and implemented to improve the system performance, including a step counting algorithm with accelerometers, a power control algorithm and an adaptive packets scheduling scheme. The simulation study and experimental results show the effectiveness of the proposed algorithms and the implementation.