Identification of candidate genes and clarification of the maintenance of the green pericarp of weedy rice grains

The weedy rice (Oryza sativa f. spontanea) pericarp has diverse colors (e.g., purple, red, light-red, and white). However, research on pericarp colors has focused on red and purple, but not green. Unlike many other common weedy rice resources, LM8 has a green pericarp at maturity. In this study, the coloration of the LM8 pericarp was evaluated at the cellular and genetic levels. First, an examination of their ultrastructure indicated that LM8 chloroplasts were normal regarding plastid development and they contained many plastoglobules from the early immature stage to maturity. Analyses of transcriptome profiles and differentially expressed genes revealed that most chlorophyll (Chl) degradation-related genes in LM8 were expressed at lower levels than Chl a/b cycle-related genes in mature pericarps, suggesting that the green LM8 pericarp was associated with inhibited Chl degradation in intact chloroplasts. Second, the F2 generation derived from a cross between LM8 (green pericarp) and SLG (white pericarp) had a pericarp color segregation ratio of 9:3:4 (green:brown:white). The bulked segregant analysis of the F2 populations resulted in the identification of 12 known genes in the chromosome 3 and 4 hotspot regions as candidate genes related to Chl metabolism in the rice pericarp. The RNA-seq and sqRT-PCR assays indicated that the expression of the Chl a/b cycle-related structural gene DVR (encoding divinyl reductase) was sharply up-regulated. Moreover, genes encoding magnesium-chelatase subunit D and the light-harvesting Chl a/b-binding protein were transcriptionally active in the fully ripened dry pericarp. Regarding the ethylene signal transduction pathway, the CTR (encoding an ethylene-responsive protein kinase) and ERF (encoding an ethylene-responsive factor) genes expression profiles were determined. The findings of this study highlight the regulatory roles of Chl biosynthesis- and degradation-related genes influencing Chl accumulation during the maturation of the LM8 pericarp

Reflectance Spectroscopy with Multivariate Methods for Non-Destructive Discrimination of Edible Oil Adulteration

The visible and near-infrared (Vis-NIR) reflectance spectroscopy was utilized for the rapid and nondestructive discrimination of edible oil adulteration. In total, 110 samples of sesame oil and rapeseed oil adulterated with soybean oil in different levels were produced to obtain the reflectance spectra of 350–2500 nm. A set of multivariant methods was applied to identify adulteration types and adulteration rates. In the qualitative analysis of adulteration type, the support vector machine (SVM) method yielded high overall accuracy with multiple spectra pretreatments. In the quantitative analysis of adulteration rate, the random forest (RF) combined with multivariate scattering correction (MSC) achieved the highest identification accuracy of adulteration rate with the full wavelengths of Vis-NIR spectra. The effective wavelengths of the Vis-NIR spectra were screened to improve the robustness of the multivariant methods. The analysis results suggested that the competitive adaptive reweighted sampling (CARS) was helpful for removing the redundant information from the spectral data and improving the prediction accuracy. The PLSR + MSC + CARS model achieved the best prediction performance in the two adulteration cases of sesame oil and rapeseed oil. The coefficient of determination (RPcv2) and the root mean square error (RMSEPcv) of the prediction set were 0.99656 and 0.01832 in sesame oil adulterated with soybean oil, and the RPcv2 and RMSEPcv were 0.99675 and 0.01685 in rapeseed oil adulterated with soybean oil, respectively. The Vis-NIR reflectance spectroscopy with the assistance of multivariant analysis can effectively discriminate the different adulteration rates of edible oils

Rapid Detection of Fatty Acids in Edible Oils Using Vis-NIR Reflectance Spectroscopy with Multivariate Methods

The composition and content of fatty acids are critical indicators to identify the quality of edible oils. This study was undertaken to establish a rapid determination method for quality detection of edible oils based on quantitative analysis of palmitic acid, stearic acid, arachidic acid, and behenic acid. Seven kinds of oils were measured to obtain Vis-NIR spectra. Multivariate methods combined with pretreatment methods were adopted to establish quantitative analysis models for the four fatty acids. The model of support vector machine (SVM) with standard normal variate (SNV) pretreatment showed the best predictive performance for the four fatty acids. For the palmitic acid, the determination coefficient of prediction (RP2) was 0.9504 and the root mean square error of prediction (RMSEP) was 0.8181. For the stearic acid, RP2 and RMSEP were 0.9636 and 0.2965. In the prediction of arachidic acid, RP2 and RMSEP were 0.9576 and 0.0577. In the prediction of behenic acid, the RP2 and RMSEP were 0.9521 and 0.1486. Furthermore, the effective wavelengths selected by successive projections algorithm (SPA) were useful for establishing simplified prediction models. The results demonstrate that Vis-NIR spectroscopy combined with multivariate methods can provide a rapid and accurate approach for fatty acids detection of edible oils

Fault Detection and Exclusion for Tightly Coupled GNSS/INS System Considering Fault in State Prediction

To ensure navigation integrity for safety-critical applications, this paper proposes an efficient Fault Detection and Exclusion (FDE) scheme for tightly coupled navigation system of Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS). Special emphasis is placed on the potential faults in the Kalman Filter state prediction step (defined as “filter fault”), which could be caused by the undetected faults occurring previously or the Inertial Measurement Unit (IMU) failures. The integration model is derived first to capture the features and impacts of GNSS faults and filter fault. To accommodate various fault conditions, two independent detectors, which are respectively designated for GNSS fault and filter fault, are rigorously established based on hypothesis-test methods. Following a detection event, the newly-designed exclusion function enables (a) identifying and removing the faulty measurements and (b) eliminating the effect of filter fault through filter recovery. Moreover, we also attempt to avoid wrong exclusion events by analyzing the underlying causes and optimizing the decision strategy for GNSS fault exclusion accordingly. The FDE scheme is validated through multiple simulations, where high efficiency and effectiveness have been achieved in various fault scenarios

Rapid Detection of Pesticide Residues in Paddy Water Using Surface-Enhanced Raman Spectroscopy

Pesticide residue in paddy water is one of the main factors affecting the quality and safety of rice, however, the negative effect of this residue can be effectively prevented and reduced through early detection. This study developed a rapid detection method for fonofos, phosmet, and sulfoxaflor in paddy water through chemometric methods and surface-enhanced Raman spectroscopy (SERS). Residue from paddy water samples was directly used for SERS measurement. The obtained spectra from the SERS can detect 0.5 mg/L fonofos, 0.25 mg/L phosmet, and 1 mg/L sulfoxaflor through the appearance of major characteristic peaks. Then, we used chemometric methods to develop models for the intelligent analysis of pesticides, alongside the SERS spectra. The classification models developed by K-nearest neighbor identified all of the samples, with an accuracy of 100%. For the quantitative analysis, the partial least squares regression models obtained the best predicted performance for fonofos and sulfoxaflor, and the support vector machine model provided optimal results, with a root-mean-square error of validation of 0.207 and a coefficient of determination of validation of 0.99952, for phosmet. Experiments for actual contaminated samples also showed that the above models predicted the pesticide residue values with high accuracy. Overall, using SERS with chemometric methods provided a simple and convenient approach for the detection of pesticide residues in paddy water

Spatial and Temporal Distribution Characteristics of Landslide Surge Based on Large-Scale Physical Modeling Experiment

Surge is a common secondary disaster caused by reservoir landslides. The study of its spatial and temporal distribution characteristics is important since it affects not only the normal operation of reservoirs but also the safety of people residing along the river. This paper presents a large-scale three-dimensional physical modeling experiment using a near-dam high-position landslide project as a prototype. It investigated the relationships between the river course characteristics, the landslide volume, the head wave velocity of the landslide surge, the waveform of surges, and the head wave crest of the wave along the course in depth. The results indicate that the head wave velocity of the landslide surge is basically unchanged during the propagation process, and it is minimally affected by the landslide volume. The waveform distribution characteristics and head wave crests change considerably in the diversion area and the curved areas but remain mostly unchanged in the topographic similarity area. In addition, there is a negative correlation between the head wave crest and the cross-sectional area of the river course. Furthermore, under conditions of a large landslide volume, the influence of the cross-sectional area of the river channel on the wave height of landslide surges becomes more significant. Finally, the maximum wave height along the course may not necessarily occur in the head wave crest; it could occur in the second wave or even the subsequent ones

Three-Dimensional Surface-Enhanced Raman Scattering Hotspots in Spherical Colloidal Superstructure for Identification and Detection of Drugs in Human Urine

Rapid component separation and robust surface-enhanced Raman scattering (SERS) identification of drugs in real human urine remain an attractive challenge because of the sample complexity, low molecular affinity for metal surface, and inefficient use of hotspots in one- or two-dimensional (2D) geometries. Here, we developed a 5 min strategy of cyclohexane (CYH) extraction for separating amphetamines from human urine. Simultaneously, an oil-in-water emulsion method is used to assemble monodisperse Ag nanoparticles in the CYH phase into spherical colloidal superstructures in the aqueous phase. These superstructures create three-dimensional (3D) SERS hotspots which exist between every two adjacent particles in 3D space, break the traditional 2D limitation, and extend the hotspots into the third dimension along the <i>z</i>-axis. In this platform, a conservative estimate of Raman enhancement factor is larger than 10⁷, and the same CYH extraction processing results in a high acceptability and enrichment of drug molecules in 3D hotspots which demonstrates excellent stability and reproducibility and is suitable for the quantitative examination of amphetamines in both aqueous and organic phases. Parallel ultraperformance liquid chromatography (UPLC) examinations corroborate an excellent performance of our SERS platform for the quantitative analysis of methamphetamine (MA) in both aqueous solution and real human urine, of which the detection limits reach 1 and 10 ppb, respectively, with tolerable signal-to-noise ratios. Moreover, SERS examinations on different proportions of MA and 3,4-methylenedioxymethamphetamine (MDMA) in human urine demonstrate an excellent capability of multiplex quantification of ultratrace analytes. By virtue of a spectral classification algorithm, we realize the rapid and accurate recognition of weak Raman signals of amphetamines at trace levels and also clearly distinguish various proportions of multiplex components. Our platform for detecting drugs promises to be a great prospect for a rapid, reliable, and on-spot analyzer