Detection of Rice Phenological Variations under Heavy Metal Stress by Means of Blended Landsat and MODIS Image Time Series

Monitoring phenological changes of crops through remote sensing methods is becoming a new perspective in assessing heavy metal contamination in agricultural farmlands. This paper proposes a method that combines the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI) to detect heavy metal stress-induced variations in satellite-derived rice phenology. First, we applied the enhanced spatial and temporal adaptive reflectance fusion model to obtain the NDVI and NDWI time series for the NDVI–NDWI phase–space construction. Then, six specific rice phenometrics were derived from the NDVI and the phase–space, respectively. Last, we introduced a relative phenophase index (RPI), which characterizes the relative change of the phenometrics to identify the rice paddies under heavy metal stress. The results indicated that satellite-derived rice phenometrics are generally influenced by human and natural factors (e.g., transplanting date, air temperature, and solar radiation), while the RPI showed weak correlations with all of these variables. In the determination of heavy metal stress, the NDVI- and phase–space-based RPIs of unstressed rice both show significantly (p < 0.001) higher values than those of stressed rice, while the phase–space-based RPI shows more apparent statistical difference between the stressed and unstressed rice compared to the NDVI-based one. Our work proved the capability of the phase–space-based method as well as the RPI in the discrimination of regional heavy metal pollution in rice fields

Hopf Bifurcation, Periodic Solutions, and Control of a New 4D Hyperchaotic System

In this paper, a new four-dimensional (4D) hyperchaotic biplane system is designed and presented. The dynamical properties of this new system are studied by means of tools such as bifurcation diagrams, Lyapunov exponents and phase diagrams. The Hopf bifurcation and periodic solutions of this hyperchaotic system are solved analytically. In addition, a new hyperchaotic control strategy is applied, and a comparative analysis of the controlled system is performed

Divergence of Desiccation-Related Traits in Sitobion avenae from Northwestern China

The impact of drought on insects has become increasingly evident in the context of global climate change, but the physiological mechanisms of aphids’ responses to desiccating environments are still not well understood. We sampled the wheat aphid Sitobion avenae (Fabricius) (Hemiptera: Aphididae) from arid areas of northwestern China. Both desiccation-resistant and -nonresistant genotypes were identified, providing direct evidence of genetic divergence in desiccation resistance of S. avenae. Resistant genotypes of wingless S. avenae showed longer survival time and LT50 under the desiccation stress (i.e., 10% relative humidity) than nonresistant genotypes, and wingless individuals tended to have higher desiccation resistance than winged ones. Both absolute and relative water contents did not differ between the two kinds of genotypes. Resistant genotypes had lower water loss rates than nonresistant genotypes for both winged and wingless individuals, suggesting that modulation of water loss rates could be the primary strategy in resistance of this aphid against desiccation stress. Contents of cuticular hydrocarbons (CHC) (especially methyl-branched alkanes) showed significant increase for both resistant and nonresistant genotypes after exposure to the desiccation stress for 24 h. Under desiccation stress, survival time was positively correlated with contents of methyl-branched alkanes for resistant genotypes. Thus, the content of methyl-branched alkanes and their high plasticity could be closely linked to water loss rate and desiccation resistance in S. avenae. Our results provide insights into fundamental aspects and underlying mechanisms of desiccation resistance in aphids, and have significant implications for the evolution of aphid populations in the context of global warming

A Framework for Rice Heavy Metal Stress Monitoring Based on Phenological Phase Space and Temporal Profile Analysis

Previous studies make it possible to use remote sensing techniques to monitor heavy metal stress of rice synchronously and continuously. However, most studies mainly focus on the analysis of rice’s visual symptoms and physiological functions rather than temporal information during the growth period, which may reflect significant changes of rice under heavy metal stress. In this paper, an enhanced spatial and temporal adaptive reflectance fusion model was used to generate synthetic Landsat time series. A normalized difference water index and an enhanced vegetation index were employed to build phenological phase space. Then, the ratio of the rice growth rate fluctuation (GRFI Ratio) was constructed for discriminating the different heavy metal stress levels on rice. Results suggested that the trajectories of rice growth in phenological phase space can depict the similarities and differences of rice growth under different heavy metal stress levels. The most common phenological parameters in the phase space cannot accurately discriminate the heavy metal stress level. However, the GRFI Ratio that we proposed outperformed in discriminating different levels of heavy metal stress. This study suggests that this framework of detecting the heavy metal pollution in paddy filed based on phenological phase space and temporal profile analysis is promising

Extraction of Rice Heavy Metal Stress Signal Features Based on Long Time Series Leaf Area Index Data Using Ensemble Empirical Mode Decomposition

The use of remote sensing technology to diagnose heavy metal stress in crops is of great significance for environmental protection and food security. However, in the natural farmland ecosystem, various stressors could have a similar influence on crop growth, therefore making heavy metal stress difficult to identify accurately, so this is still not a well resolved scientific problem and a hot topic in the field of agricultural remote sensing. This study proposes a method that uses Ensemble Empirical Mode Decomposition (EEMD) to obtain the heavy metal stress signal features on a long time scale. The method operates based on the Leaf Area Index (LAI) simulated by the Enhanced World Food Studies (WOFOST) model, assimilated with remotely sensed data. The following results were obtained: (i) the use of EEMD was effective in the extraction of heavy metal stress signals by eliminating the intra-annual and annual components; (ii) LAIdf (The first derivative of the sum of the interannual component and residual) can preferably reflect the stable feature responses to rice heavy metal stress. LAIdf showed stability with an R2 of greater than 0.9 in three growing stages, and the stability is optimal in June. This study combines the spectral characteristics of the stress effect with the time characteristics, and confirms the potential of long-term remotely sensed data for improving the accuracy of crop heavy metal stress identification

A Novel Nanowire Assembly Process for the Fabrication of CO Sensor

Nanowires have been widely studied due to their outstanding mechanical and electrical properties; however, their practical applications are limited to the lack of an effective technique for controlled assembly. In the present work, zinc oxide (ZnO) nanowire arrays were assembled via a combing process using a makeup brush and the nanodevice was fabricated. The current–voltage (I–V) and ultraviolet (UV) characteristics of the device indicate stable and repeatable electrical properties. The carbon monoxide (CO) sensing properties were tested at operating temperatures of 200, 300 and 400 °C. It was found that ZnO based sensor exhibited the highest sensitivity to CO at 300 °C due to the change of dominant oxygen species. Comparing with others result, the sensitivity of the fabricated sensor exhibits higher sensing performance. The sensing mechanism of the CO sensor is also discussed

Assessing cyber-user awareness of an emerging infectious disease: evidence from human infections with avian influenza A H7N9 in Zhejiang, China

Objectives: The aim of this study was to assess cyber-user awareness of human infections with avian influenza A H7N9 in Zhejiang, China. Methods: Daily Baidu index values were compared for different keywords, different periods (epidemic and non-epidemic), different levels of epidemic publicity (whether new cases were publicized), and different cities (divided into high, medium, low, and zero groups according to the number of cases). Furthermore, the correlation between the daily Baidu index values and the daily number of new cases was analyzed. Results: Three epidemic periods (periods A/C/E) and three non-epidemic periods (periods B/D/F) were identified from April 2013 to May 2015 according to the curves of daily new cases. Each epidemic period was followed by a non-epidemic period. Baidu index values using ‘H7N9’ as a keyword were higher than the values using the keyword ‘fx1’ (avian influenza in Chinese) in earlier periods, but the situation reversed in later periods. Index values for ‘H7N9’ in the epidemic periods were higher than in the non-epidemic periods. In the first epidemic period (period A), the Baidu index values for ‘H7N9’ showed no difference between the different levels of epidemic publicity and had no correlation with the daily number of new cases. The index values in cities without reported cases showed no difference from the values recorded in the medium and low groups. However, a difference and a correlation were found in a later epidemic period. Conclusions: The Baidu index would be a useful tool for assessing cyber-user awareness of an emerging infectious disease

Genome-Wide Identification of the CER Gene Family and Significant Features in Climate Adaptation of Castanea mollissima

The plant cuticle is the outermost layer of the aerial organs and an important barrier against biotic and abiotic stresses. The climate varies greatly between the north and south of China, with large differences in temperature and humidity, but Chinese chestnut is found in both regions. This study investigated the relationship between the wax layer of chestnut leaves and environmental adaptation. Firstly, semi-thin sections were used to verify that there is a significant difference in the thickness of the epicuticular wax layer between wild chestnut leaves in northwest and southeast China. Secondly, a whole-genome selective sweep was used to resequence wild chestnut samples from two typical regional populations, and significant genetic divergence was identified between the two populations in the CmCER1-1, CmCER1-5 and CmCER3 genes. Thirty-four CER genes were identified in the whole chestnut genome, and a series of predictive analyses were performed on the identified CmCER genes. The expression patterns of CmCER genes were classified into three trends—upregulation, upregulation followed by downregulation and continuous downregulation—when chestnut seedlings were treated with drought stress. Analysis of cultivars from two resource beds in Beijing and Liyang showed that the wax layer of the northern variety was thicker than that of the southern variety. For the Y-2 (Castanea mollissima genome sequencing material) cultivar, there were significant differences in the expression of CmCER1-1, CmCER1-5 and CmCER3 between the southern variety and the northern one-year-grafted variety. Therefore, this study suggests that the CER family genes play a role in environmental adaptations in chestnut, laying the foundation for further exploration of CmCER genes. It also demonstrates the importance of studying the adaptation of Chinese chestnut wax biosynthesis to the southern and northern environments