Antibiotic abuse is becoming increasingly serious and the potential for harm to human health and the environment has aroused widespread social concern. Aminoglycoside antibiotics (AGs) are broad-spectrum antibiotics that have been widely used in clinical and animal medicine. Consequently, their residues are commonly found in animal-derived food items and the environment. A simple, rapid, and sensitive detection method for on-site screening and detection of AGs is urgently required. In recent years, with the development of molecular detection technology, nucleic acid aptamers have been successfully used as recognition molecules for the identification and detection of AGs in food and the environment. These aptamers have high affinities, selectivities, and specificities, are inexpensive, and can be produced with small batch-to-batch differences. This paper reviews the applications of aptamers for AG detection in colorimetric, fluorescent, chemiluminescent, surface plasmon resonance, and electrochemical sensors for the analysis in food and environmental samples. This study provides useful references for future research.

A modeling method based on discrete wavelet transform (DWT) was introduced to analyze the concentration of chromium, copper, zinc, arsenic and lead in soil with a portable X-ray fluorescence (XRF) spectrometer. A total of 111 soil samples were collected and observed. Denoising and baseline correction were performed on each spectrum before modeling. The optimum conditions for pre-processing were denoising with Coiflet 3 on the 3rd level and baseline correction with Coiflet 3 on the 9th level. Calibration curves were established for the five heavy metals (HMs). The detection limits were compared before and after the application of DWT, the qualitative detection limits and the quantitative detection limits were calculated to be three and ten times as high as the standard deviation with silicon dioxide (blank), respectively. The results showed that the detection limits of the instrument using DWT were lower, and that they were below national soil standards; the determination coefficients (R²) based on DWT-processed spectra were higher, and ranged from 0.990 to 0.996, indicating a high degree of linearity between the contents of the HMs in soil and the XRF spectral characteristic peak intensity with the instrument measurement.

Chloropicrin (CP) can effectively combat soil-borne diseases but has significant side effects on nontarget microorganisms. The rhizosphere microflora play a crucial role in promoting plant growth and protecting plants from infection by soil-borne pathogens. We conducted a laboratory pot experiment to evaluate the effect of CP on the rhizosphere soil bacterial flora and the effect of biochar amendments on the reconstruction of microbial communities. Our results show that CP fumigation and biochar additions promoted the growth of cucumber plants in the later stage of the pot experiment. CP significantly inhibited the rhizobacterial diversity and changed the community composition. Biochar amendments after CP fumigation shortened the time for the rhizobacterial diversity to recover to unfumigated levels. Biochar amendments promoted the transplantation of new populations to empty microbiome niches that were caused by CP and, in particular, stimulated many beneficial microorganisms to become the predominant flora. The relative abundances of many functional taxa related to plant-disease suppressiveness and pollutant bioremediation increased, including Pseudomonas, Stenotrophomonas, Bacillus, Massilia, Acinetobacter, Delftia, Micromonospora, Cytophagaceae, and Flavisolibacter. These changes stimulated by biochar amendments would promote multifunctionality in the soil rhizosphere and benefit plant growth and disease resistance.

Fast and accurate acquisition of the outline of rural buildings on remote sensing images is an efficient method to monitor illegal rural buildings. The traditional object detection method produces useless background information when detecting rural buildings; the semantic segmentation method cannot accurately segment the contours between buildings; the instance segmentation method cannot obtain regular building contours. The rotated object detection methods can effectively solve the problem that the traditional artificial intelligence method cannot accurately extract the outline of buildings. However, the rotated object detection methods are easy to lose location information of small objects in advanced feature maps and are sensitive to noise. To resolve these problems, this paper proposes a two-stage rotated object detection network for rural buildings (TRDet) by using a deep feature fusion network (DFF-Net) and a pixel attention module (PAM). Specifically, TRDet first fuses low-level location and high-level semantic information through the DFF-Net and then reduces the interference of noise information to the network through the PAM. The experimental results show that the mean average precession (mAP), precision, recall rate, and F1 score of the proposed TRDet are 83.57%, 91.11%, 86.5%, and 88.74%, respectively, which outperform the R2CNN model by 15%, 15.54%, 4.01%, and 9.87%. The results demonstrate that the TRDet can achieve better detection in small rural buildings and dense rural buildings.

The purpose of this study is to investigate the feasibility of anNIR hyperspectral imaging (HSI) technique coupled with machinelearning methods for the classification of cooked meat.

In this paper, the near-infrared spectroscopy is used to obtainthe near-infrared spectra data of tea for the detection of WestLake Longjing tea and the general Longjing tea.