Janus plasmonic-aggregation induced emission nanobeads as high-performance colorimetric-fluorescent probe of immunochromatographic assay for the ultrasensitive detection of staphylococcal enterotoxin B in milk.

Herein, a colorimetric-fluorescent hybrid bifunctional nanobead with Janus structure (J-cf-HBN) was synthesized via one-pot microemulsification. Oleylamine-coated AuNPs and aggregation-induced emission luminogens (AIEgens) were suggested as building blocks to obtain high-performance colorimetric-fluorescent signals. The as-prepared J-cf-HBNs were used as a signal amplification probe to construct an immunochromatographic assay (J-cf-HBNs-ICA) platform for the ultrasensitive detection of staphylococcal enterotoxin B (SEB) in milk samples. Owing to the rational spatial distribution of AuNPs and AIEgens, the J-cf-HBNs present a highly retained photoluminescence and enhanced colorimetric signals. Combined with a pair of highly affinitive anti-SEB antibodies, the J-cf-HBN-ICA platform enabled the fast naked-eye visualization and fluorescent quantitative detection of SEB in various milk matrices. Given the advantages of the dual-mode high-performance J-cf-HBNs, the proposed strip achieved a high sensitivity for SEB qualitative determination with a visual limit of detection (LOD) of 1.56 ng mL-1 and exhibited ultrasensitivity for SEB quantitative detection with a LOD of 0.09 ng mL-1, which is 139-fold lower than that of ELISA using same antibodies. In conclusion, this work provides new insights into the construction of multimode immunochromatographic methods for food safety detection in the field.

Determination of corn protein content using near-infrared spectroscopy combined with A-CARS-PLS.

In order to quickly and accurately determine the protein content of corn, a new characteristic wavelength selection algorithm called anchor competitive adaptive reweighted sampling (A-CARS) was proposed in this paper. This method first lets Monte Carlo synergy interval PLS (MC-siPLS) to select the sub-intervals where the characteristic variables exist and then uses CARS to screen the variables further. A-CARS-PLS was compared with 6 methods, including 3 feature variable selection methods (GA-PLS, random frog PLS, and CARS-PLS) and 2 interval partial least squares methods (siPLS and MWPLS). The results showed that A-CARS-PLS was significantly better than other methods with the results: RMSECV = 0.0336, R2 c = 0.9951 in the calibration set; RMSEP = 0.0688, R2 p = 0.9820 in the prediction set. Furthermore, A-CARS reduced the original 700-dimensional variable to 23 variables. The results showed that A-CARS-PLS was better than some wavelength selection methods, and it has great application potential in the non-destructive detection of protein content in corn.

Interval Dominance-Based Feature Selection for Interval-Valued Ordered Data.

Dominance-based rough approximation discovers inconsistencies from ordered criteria and satisfies the requirement of the dominance principle between single-valued domains of condition attributes and decision classes. When the ordered decision system (ODS) is no longer single-valued, how to utilize the dominance principle to deal with multivalued ordered data is a promising research direction, and it is the most challenging step to design a feature selection algorithm in interval-valued ODS (IV-ODS). In this article, we first present novel thresholds of interval dominance degree (IDD) and interval overlap degree (IOD) between interval values to make the dominance principle applicable to an IV-ODS, and then, the interval-valued dominance relation in the IV-ODS is constructed by utilizing the above two developed parameters. Based on the proposed interval-valued dominance relation, the interval-valued dominance-based rough set approach (IV-DRSA) and their corresponding properties are investigated. Moreover, the interval dominance-based feature selection rules based on IV-DRSA are provided, and the relevant algorithms for deriving the interval-valued dominance relation and the feature selection methods are established in IV-ODS. To illustrate the effectiveness of the parameters variation on feature selection rules, experimental evaluation is performed using 12 datasets coming from the University of California-Irvine (UCI) repository.

Rapid determination of lambda-cyhalothrin residues on Chinese cabbage based on MIR spectroscopy and a Gustafson-Kessel noise clustering algorithm.

Pesticide residues exceeding the standard in Chinese cabbage is harmful to human health. In order to quickly, non-destructively and effectively qualitatively analyze lambda-cyhalothrin residues on Chinese cabbage, a method involving a Gustafson-Kessel noise clustering (GKNC) algorithm was proposed to cluster the mid-infrared (MIR) spectra. A total of 120 Chinese cabbage samples with three different lambda-cyhalothrin residue levels (no lambda-cyhalothrin, and cases where the ratios of lambda-cyhalothrin and water were 1 : 500 and 1 : 100) were scanned using an Agilent Cary 630 FTIR spectrometer for collecting the MIR spectra. Next, multiple scatter correction (MSC) was employed to eliminate the effects of light scattering. Furthermore, principal component analysis (PCA) and linear discriminant analysis (LDA) were utilized to reduce the dimensionality and extract the feature information from the MIR spectra. Finally, fuzzy c-means (FCM) clustering, Gustafson-Kessel (GK) clustering, noise clustering (NC) and the GKNC algorithm were applied to cluster the MIR spectral data, respectively. The experimental results showed that the GKNC algorithm gave the best classification performance compared against the other three fuzzy clustering algorithms, and its highest clustering accuracy reached 93.3%. Therefore, the GKNC algorithm coupled with MIR spectroscopy is an effective method for detecting lambda-cyhalothrin residues on Chinese cabbage.

Nitrogen Addition Changes the Stoichiometry and Growth Rate of Different Organs in Pinus tabuliformis Seedlings.

Background: Nitrogen (N) deposition could influence plant stoichiometry and growth rate and thus alter the structure and function of the ecosystem. However, the mechanism by which N deposition changes the stoichiometry and relative growth rate (RGR) of plant organs, especially roots with different diameters, is unclear. Methods: We created a gradient of N availability (0-22.4 g N m-2 year-1) for Pinus tabuliformis seedlings for 3 years and examined changes in the carbon (C):N:phosphorus (P) ratios and RGRs of the leaves, stems, and roots with four diameter classes (finest roots, <0.5 mm; finer roots, 0.5-1 mm; middle roots, 1-2 mm; and coarse roots, >2 mm). Results: (1) N addition significantly increased the C and N contents of the leaves and whole roots, the C content of the stems, the N:P ratios of the leaves and stems, and the C:P ratio of the whole roots. (2) In the root system, the C:N ratio of the finest roots and the C:P ratios of the finest and finer roots significantly changed with N addition. The N:P ratios of the finest, finer, and middle roots significantly increased with increasing amount of N added. The stoichiometric responses of the roots were more sensitive to N addition than those of the other organs (3) The RGR of all the organs significantly increased at low N addition levels (2.8-11.2 g N m-2 year-1) but decreased at high N addition levels (22.4 g N m-2 year-1). (4) The RGRs of the whole seedlings and leaves were not significantly correlated with their N:P ratios at low and high N addition levels. By contrast, the RGRs of the stems and roots showed a significantly positive correlation with their own N:P ratio only at low N addition level. Conclusion: Addition of N affected plant growth by altering the contents of C and N; the ratios of C, N, and P; and the RGRs of the organs. RGR is correlated with the N:P ratios of the stems and roots at low N addition level but not at high N addition level. This finding is inconsistent with the growth rate hypothesis.