Smartphone-assisted carbon dots fluorescent sensing platform for visual detection of Thiophanate-methyl in fruits and vegetables.

Trimesic acid and o-phenylenediamine (OPD) were employed as precursors to synthesize yellow-green fluorescent carbon dots (Y-G-CDs) by solvothermal synthesis for the sensitive detection of Thiophanate-methyl (TM) in real agricultural products. The Y-G-CDs probe could specifically recognize the TM primarily through π-π stacking. Moreover, the fluorescence quenching of the probe was ultimately dominated by the PET effect, based on the interaction between the abundant carboxyl groups on the surface of the Y-G-CDs and the amino group of TM. A strong linear relationship between the fluorescence quenching of the probe and TM concentration in the range of 0-10 µmol/L was observed and the limit of detection (LOD) was calculated to be 50.7 nmol/L. Compared to the interference pesticides, the Y-G-CDs probe demonstrated exceptional selectivity toward TM, with satisfactory recoveries of 96.3 % - 104.2 % in spiked food samples. The Y-G-CDs probe enables simple pretreatment, cost-effective, and on-site detection of TM in fruits and vegetables with visual detection of the TM employing a smartphone-assisted sensing platform.

Metabolic Resistance of Sogatella furcifera (Hemiptera: Delphacidae) toward Pymetrozine Involves the Overexpression of CYP6FJ3.

Sogatella furcifera (Horváth), which mainly threatens rice, shows various levels of pesticide resistance due to long-term overuse of pesticides. Our resistance monitoring of 20 field populations in Sichuan, China, revealed that they were susceptible to highly resistant toward pymetrozine (0.4-142.2 RR), and JL21 reached the highest level of resistance. The JL21 population exhibited cross-resistance to triflumezopyrim and dinotefuran but sensitivity to sulfoxaflor, acetamiprid, clothianidin, and nitenpyram. The increased P450 activity were support to involve in pymetrozine resistance by detoxification enzyme activities and synergist determination. Among 16 candidate P450 genes, CYP6FJ3 (5.25-fold) was the most up-regulated in JL21, while no significant change was found after LC25 pymetrozine treatment. Furthermore, the knockdown by RNAi and heterologous overexpression by the GAL4/UAS system confirmed that the CYP6FJ3 overexpression was involved in the pymetrozine resistance, and recombination in vitro confirmed that CYP6FJ3 could hydroxylate pymetrozine. Therefore, the overexpression of CYP6FJ3 promotes pymetrozine metabolic resistance in S. furcifera.

GFI-Net: Global Feature Interaction Network for Monocular Depth Estimation.

Monocular depth estimation techniques are used to recover the distance from the target to the camera plane in an image scene. However, there are still several problems, such as insufficient estimation accuracy, the inaccurate localization of details, and depth discontinuity in planes parallel to the camera plane. To solve these problems, we propose the Global Feature Interaction Network (GFI-Net), which aims to utilize geometric features, such as object locations and vanishing points, on a global scale. In order to capture the interactive information of the width, height, and channel of the feature graph and expand the global information in the network, we designed a global interactive attention mechanism. The global interactive attention mechanism reduces the loss of pixel information and improves the performance of depth estimation. Furthermore, the encoder uses the Transformer to reduce coding losses and improve the accuracy of depth estimation. Finally, a local-global feature fusion module is designed to improve the depth map's representation of detailed areas. The experimental results on the NYU-Depth-v2 dataset and the KITTI dataset showed that our model achieved state-of-the-art performance with full detail recovery and depth continuation on the same plane.

3D-GIoU: 3D Generalized Intersection over Union for Object Detection in Point Cloud.

Three-dimensional (3D) object detection is an important research in 3D computer vision with significant applications in many fields, such as automatic driving, robotics, and human-computer interaction. However, the low precision is an urgent problem in the field of 3D object detection. To solve it, we present a framework for 3D object detection in point cloud. To be specific, a designed Backbone Network is used to make fusion of low-level features and high-level features, which makes full use of various information advantages. Moreover, the two-dimensional (2D) Generalized Intersection over Union is extended to 3D use as part of the loss function in our framework. Empirical experiments of Car, Cyclist, and Pedestrian detection have been conducted respectively on the KITTI benchmark. Experimental results with average precision (AP) have shown the effectiveness of the proposed network.

MicroRNA-106a confers cisplatin resistance in non-small cell lung cancer A549 cells by targeting adenosine triphosphatase-binding cassette A1.

MicroRNAs (miRNAs) have been discovered to have pivotal roles in regulating the drug resistance of various types of human cancer, including cisplatin (DDP) resistance in non-small cell lung cancer (NSCLC). Fewer studies have explored the roles of miR-106a in NSCLC-cell resistance to DDP and its precise molecular mechanism has remained elusive. In the present study, whether miR-106a was able to mediate resistance of the lung cancer cell line A549 to DDP was investigated. Reverse transcription quantitative polymerase chain reaction was used to analyze miR-106a mRNA expression levels. miR-106a expression levels were upregulated in the DDP-resistant cell line A549/DDP compared with its parental cell line, A549. miR-106a-transfection induced DDP-resistance in A549 cells, while repression of miR-106a by anti-miR-106a in A549/DDP resulted in enhanced DDP cytotoxicity. Furthermore, it was discovered that the mechanism of miR-106a-induced DDP resistance involved the expression of adenosine triphosphatase-binding cassette, sub-family A, member 1 (ABCA1), as indicated by transfection of cells with short interfering RNA-ABCA1. The results of the present study suggested a novel mechanism underlying DDP-resistance in NSCLC.

Recognizing objects in 3D point clouds with multi-scale local features.

Recognizing 3D objects from point clouds in the presence of significant clutter and occlusion is a highly challenging task. In this paper, we present a coarse-to-fine 3D object recognition algorithm. During the phase of offline training, each model is represented with a set of multi-scale local surface features. During the phase of online recognition, a set of keypoints are first detected from each scene. The local surfaces around these keypoints are further encoded with multi-scale feature descriptors. These scene features are then matched against all model features to generate recognition hypotheses, which include model hypotheses and pose hypotheses. Finally, these hypotheses are verified to produce recognition results. The proposed algorithm was tested on two standard datasets, with rigorous comparisons to the state-of-the-art algorithms. Experimental results show that our algorithm was fully automatic and highly effective. It was also very robust to occlusion and clutter. It achieved the best recognition performance on all of these datasets, showing its superiority compared to existing algorithms.