Intercellular Communication during Stomatal Development with a Focus on the Role of Symplastic Connection.

Stomata are microscopic pores on the plant epidermis that serve as a major passage for the gas and water exchange between a plant and the atmosphere. The formation of stomata requires a series of cell division and cell-fate transitions and some key regulators including transcription factors and peptides. Monocots have different stomatal patterning and a specific subsidiary cell formation process compared with dicots. Cell-to-cell symplastic trafficking mediated by plasmodesmata (PD) allows molecules including proteins, RNAs and hormones to function in neighboring cells by moving through the channels. During stomatal developmental process, the intercellular communication between stomata complex and adjacent epidermal cells are finely controlled at different stages. Thus, the stomata cells are isolated or connected with others to facilitate their formation or movement. In the review, we summarize the main regulation mechanism underlying stomata development in both dicots and monocots and especially the specific regulation of subsidiary cell formation in monocots. We aim to highlight the important role of symplastic connection modulation during stomata development, including the status of PD presence at different cell-cell interfaces and the function of relevant mobile factors in both dicots and monocots.

Effects of Fatty Alcohols with Different Chain Lengths on the Performance of Low pH Biomass-Based Foams for Radioactive Decontamination.

Compared with polymers and nanoparticles, fatty alcohols can not only increase the stability of foam, but also maintain better foamability at pH < 2, which is beneficial to reduce waste liquid and increase decontamination efficiency for radioactive surface pollution. However, different fatty alcohols have different hydrophobic chain lengths. The effects of fatty alcohols with different chain lengths on the performance of decontamination foam were studied at pH < 2, to assist in the selection of suitable fatty alcohols as foam stabilizers. Combined with betaine surfactant and phytic acid, biomass-based foams were synthesized using fatty alcohols with different chain lengths. When the hydrophobic tail groups of the fatty alcohol and the surfactant were the same, the foam showed the best performance, including the lowest surface tension, the highest liquid film strength, the greatest sag-resistance and the best stability. However, when the hydrophobic tail groups were different, the space between adjacent surface active molecules was increased by thermal motion of the excess terminal tail segments (a tail-wagging effect), and the adsorption density reduced on the gas-liquid interface, leading to increased surface tension and decreased liquid film strength, sag-resistance and stability. The use of decontamination foam stabilized by fatty alcohols with the same hydrophobic group as the surfactant was found to increase the decontamination rate of radioactive uranium pollution from 64 to over 90% on a vertical surface.

Collective Anomalies Detection for Sensing Series of Spacecraft Telemetry with the Fusion of Probability Prediction and Markov Chain Model.

Telemetry series, generally acquired from sensors, are the only basis for the ground management system to judge the working performance and health status of orbiting spacecraft. In particular, anomalies within telemetry can reflect sensor failure, transmission errors, and the major faults of the related subsystem. Therefore, anomaly detection for telemetry series has drawn great attention from the aerospace area, where probability prediction methods, e.g., Gaussian process regression and relevance vector machine, have an inherent advantage for anomaly detection in time series with uncertainty presentation. However, labelling a single point with probability prediction faces many isolated false alarms, as well as a lower detection rate for collective anomalies that significantly limits its practical application. Simple sliding window fusion can decrease the false positives, but the support number of anomalies within the sliding window is difficult to set effectively for different series. Therefore, in this work, fused with the probability prediction-based method, the Markov chain is designed to compute the support probability of each testing series to realize the improvement on collective anomaly mode. The experiments on simulated data sets and the actual telemetry series validated the effectiveness and applicability of our proposed method.

UAV Sensor Fault Detection Using a Classifier without Negative Samples: A Local Density Regulated Optimization Algorithm.

Fault detection for sensors of unmanned aerial vehicles is essential for ensuring flight security, in which the flight control system conducts real-time control for the vehicles relying on the sensing information from sensors, and erroneous sensor data will lead to false flight control commands, causing undesirable consequences. However, because of the scarcity of faulty instances, it still remains a challenging issue for flight sensor fault detection. The one-class support vector machine approach is a favorable classifier without negative samples, however, it is sensitive to outliers that deviate from the center and lacks a mechanism for coping with them. The compactness of its decision boundary is influenced, leading to the degradation of detection rate. To deal with this issue, an optimized one-class support vector machine approach regulated by local density is proposed in this paper, which regulates the tolerance extents of its decision boundary to the outliers according to their extent of abnormality indicated by their local densities. The application scope of the local density theory is narrowed to keep the internal instances unchanged and a rule for assigning the outliers continuous density coefficients is raised. Simulation results on a real flight control system model have proved its effectiveness and superiority.

An Electro-Mechanical Actuator Motor Voltage Estimation Method with a Feature-Aided Kalman Filter.

Electro-Mechanical Actuators (EMA) have attracted growing attention with their increasing incorporation in More Electric Aircraft. The performance degradation assessment of EMA needs to be studied, in which EMA motor voltage is an essential parameter, to ensure its reliability and safety of EMA. However, deviation exists between motor voltage monitoring data and real motor voltage due to electromagnetic interference. To reduce the deviation, EMA motor voltage estimation generally requires an accurate voltage state equation which is difficult to obtain due to the complexity of EMA. To address this problem, a Feature-aided Kalman Filter (FAKF) method is proposed, in which the state equation is substituted by a physical model of current and voltage. Consequently, voltage state data can be obtained through current monitoring data and a current⁻voltage model. Furthermore, voltage estimation can be implemented by utilizing voltage state data and voltage monitoring data. To validate the effectiveness of the FAKF-based estimation method, experiments have been conducted based on the published data set from NASA's Flyable Electro-Mechanical Actuator (FLEA) test stand. The experiment results demonstrate that the proposed method has good performance in EMA motor voltage estimation.

Optimize the Coverage Probability of Prediction Interval for Anomaly Detection of Sensor-Based Monitoring Series.

Effective anomaly detection of sensing data is essential for identifying potential system failures. Because they require no prior knowledge or accumulated labels, and provide uncertainty presentation, the probability prediction methods (e.g., Gaussian process regression (GPR) and relevance vector machine (RVM)) are especially adaptable to perform anomaly detection for sensing series. Generally, one key parameter of prediction models is coverage probability (CP), which controls the judging threshold of the testing sample and is generally set to a default value (e.g., 90% or 95%). There are few criteria to determine the optimal CP for anomaly detection. Therefore, this paper designs a graphic indicator of the receiver operating characteristic curve of prediction interval (ROC-PI) based on the definition of the ROC curve which can depict the trade-off between the PI width and PI coverage probability across a series of cut-off points. Furthermore, the Youden index is modified to assess the performance of different CPs, by the minimization of which the optimal CP is derived by the simulated annealing (SA) algorithm. Experiments conducted on two simulation datasets demonstrate the validity of the proposed method. Especially, an actual case study on sensing series from an on-orbit satellite illustrates its significant performance in practical application.

Effective Sensor Selection and Data Anomaly Detection for Condition Monitoring of Aircraft Engines.

In a complex system, condition monitoring (CM) can collect the system working status. The condition is mainly sensed by the pre-deployed sensors in/on the system. Most existing works study how to utilize the condition information to predict the upcoming anomalies, faults, or failures. There is also some research which focuses on the faults or anomalies of the sensing element (i.e., sensor) to enhance the system reliability. However, existing approaches ignore the correlation between sensor selecting strategy and data anomaly detection, which can also improve the system reliability. To address this issue, we study a new scheme which includes sensor selection strategy and data anomaly detection by utilizing information theory and Gaussian Process Regression (GPR). The sensors that are more appropriate for the system CM are first selected. Then, mutual information is utilized to weight the correlation among different sensors. The anomaly detection is carried out by using the correlation of sensor data. The sensor data sets that are utilized to carry out the evaluation are provided by National Aeronautics and Space Administration (NASA) Ames Research Center and have been used as Prognostics and Health Management (PHM) challenge data in 2008. By comparing the two different sensor selection strategies, the effectiveness of selection method on data anomaly detection is proved.

A multifunctional joint angle sensor with measurement adaptability.

The paper presents a multifunctional joint sensor with measurement adaptability for biological engineering applications, such as gait analysis, gesture recognition, etc. The adaptability is embodied in both static and dynamic environment measurements, both of body pose and in motion capture. Its multifunctional capabilities lay in its ability of simultaneous measurement of multiple degrees of freedom (MDOF) with a single sensor to reduce system complexity. The basic working mode enables 2DOF spatial angle measurement over big ranges and stands out for its applications on different joints of different individuals without recalibration. The optional advanced working mode enables an additional DOF measurement for various applications. By employing corrugated tube as the main body, the sensor is also characterized as flexible and wearable with less restraints. MDOF variations are converted to linear displacements of the sensing elements. The simple reconstruction algorithm and small outputs volume are capable of providing real-time angles and long-term monitoring. The performance assessment of the built prototype is promising enough to indicate the feasibility of the sensor.

Temporal dependence Mahalanobis distance for anomaly detection in multivariate spacecraft telemetry series.

Spacecraft telemetry data are real-time data as the only basis for ground operation station and management system to judge the working performance and health status of spacecrafts in orbit. Telemetry data are high dimension, strong-dependent, and pseudo-periodic series, which bring great challenges to traditional anomaly detection methods of multivariate parameters. In this case, with the advantages of strong feature extraction and space injection ability, Mahalanobis distance (MD)-based approach has been a strong foundation for industrial system health monitoring. However, the typical MD-based method performs anomaly detection with a fixed threshold for MD series without capturing temporal evolution which cause high false alarms or missing alarms for complex abnormal modes. In this work, the temporal dependence Mahalanobis distance (TDMD) is realized based on multi-factors prediction which can effectively detect contextual and collective anomalies in multivariate telemetry series. Upper and lower limits with time series correlation and dynamic characteristics for the MD of each arriving multivariate point are constructed for online testing. Adequate experiments on simulated and real telemetry series verify the effectiveness and applicability of the proposed method.

Secretory expression and characterization of a soluble laccase from the Ganoderma lucidum strain 7071-9 in Pichia pastoris.

Laccases are strong oxidizing enzymes that oxidize chlorinated phenols, synthetic dyes, pesticides, polycyclic aromatic hydrocarbons as well as a very wide range of other compounds with high redox potential. Based on the bias of genetic codons between fungus and yeast, we synthesized a laccase gene GlLCCI, originated from Ganoderma lucidum using optimized codons and a PCR-based two-step DNA synthesis method. The recombinant laccase, GlLCCI was successfully over-expressed in yeast, Pichia pastoris, with an alcohol oxidase1 promoter. The recombinant GlLCCI has a molecular mass of approximately 58 kDa. The K (m) values of GlLCCI for 2-2'-azino-bis-(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS) and guaiacol were 0.9665, and 1.1122 mM, respectively. The V (max) of GlLCCI for both substrates was 3,024 and 82.13 µM mg(-1 )min(-1). When ABTS was used as a substrate, the enzyme had an optimal temperature of approximately 55°C. The enzyme was detected over pH values from 2 to 8. The enzyme was strongly activated by K(+), Na(+), Cu(2+) and mannitol. Six amino acids (alanine, histidine, glycine, arginine, aspartate and phenylalanine) increased the catalytic ability of the enzyme. The activity of laccase was obviously inhibited by Fe(2+), Fe(3+), sodium hydrosulphite, and sodium azide. Additionally, under optimal conditions, GlLCCI decolorized 37.62 mg l(-1) of azo dye methyl orange (MO) in cultural medium. With a high MO degradation ability, GlLCCI may have potential in the treatment of industrial effluent containing azo dye MO.

Genome-Wide Identification and Expression Profiles of Late Embryogenesis-Abundant (LEA) Genes during Grain Maturation in Wheat (Triticum aestivum L.).

Late embryogenesis-abundant (LEA) genes play important roles in plant growth and development, especially the cellular dehydration tolerance during seed maturation. In order to comprehensively understand the roles of LEA family members in wheat, we carried out a series of analyses based on the latest genome sequence of the bread wheat Chinese Spring. 121 Triticum aestivum L. LEA (TaLEA) genes, classified as 8 groups, were identified and characterized. TaLEA genes are distributed in all chromosomes, most of them with a low number of introns (≤3). Expression profiles showed that most TaLEA genes expressed specifically in grains. By qRT-PCR analysis, we confirmed that 12 genes among them showed high expression levels during late stage grain maturation in two spring wheat cultivars, Yangmai16 and Yangmai15. For most genes, the peak of expression appeared earlier in Yangmai16. Statistical analysis indicated that expression level of 8 genes in Yangmai 16 were significantly higher than Yangmai 15 at 25 days after anthesis. Taken together, our results provide more knowledge for future functional analysis and potential utilization of TaLEA genes in wheat breeding.