Development of a TaqMan probe-based multiplex real-time PCR for the simultaneous detection of four clinically important filamentous fungi.

Filamentous fungi present significant health hazards to immunocompromised individuals globally; however, the prompt and precise identification of them during infection remains challenging. In this study, a TaqMan probe-based multiplex real-time PCR (M-qPCR) assay was developed to detect simultaneously the target genes of four important pathogenic filamentous fungi: ANXC4 gene of Aspergillus fumigatus, EF1-α gene of Fusarium spp., mitochondrial rnl gene of Mucorales, and hcp100 gene of Histoplasma capsulatum. In this M-qPCR assay, the limit of detection (LoD) to all four kinds of fungi was 100 copies and the correlation coefficients (R2) were above 0.99. The specificity of this assay is 100%, and the minimum detection limit is 100 copies/reaction. In conclusion, an M-qPCR detection assay was well established with high specificity and sensitivity for rapid and simultaneous detection on four important filamentous fungi in the clinic. IMPORTANCE: World Health Organization developed the first fungal priority pathogens list (WHO FPPL) in 2022. Aspergillus fumigatus, Mucorales, Fusarium spp., and Histoplasma spp. are the four types of pathogenic fungi with filamentous morphology in the critical priority group and high priority group of WHO FPPL. These four filamentous fungal infections have become more common and severe in immunocompromised patients with the increase in susceptible populations in recent decades, which resulted in a substantial burden on the public health system. However, prompt and precise identification of them during infection remains challenging. Our study established successfully a TaqMan probe-based multiplex real-time qPCR assay for four clinically important filamentous fungi, A. fumigatus, Fusarium spp., Mucorales, and Histoplasma capsulatum, with high sensitivity and specificity, which shows promising potential for prompt and precise diagnosis against fungal infection.

Detection of Small-Sized Insects in Sticky Trapping Images Using Spectral Residual Model and Machine Learning.

One fundamental component of Integrated pest management (IPM) is field monitoring and growers use information gathered from scouting to make an appropriate control tactics. Whitefly (Bemisia tabaci) and thrips (Frankliniella occidentalis) are two most prominent pests in greenhouses of northern China. Traditionally, growers estimate the population of these pests by counting insects caught on sticky traps, which is not only a challenging task but also an extremely time-consuming one. To alleviate this situation, this study proposed an automated detection approach to meet the need for continuous monitoring of pests in greenhouse conditions. Candidate targets were firstly located using a spectral residual model and then different color features were extracted. Ultimately, Whitefly and thrips were identified using a support vector machine classifier with an accuracy of 93.9 and 89.9%, a true positive rate of 93.1 and 80.1%, and a false positive rate of 9.9 and 12.3%, respectively. Identification performance was further tested via comparison between manual and automatic counting with a coefficient of determination, R 2, of 0.9785 and 0.9582. The results show that the proposed method can provide a comparable performance with previous handcrafted feature-based methods, furthermore, it does not require the support of high-performance hardware compare with deep learning-based method. This study demonstrates the potential of developing a vision-based identification system to facilitate rapid gathering of information pertaining to numbers of small-sized pests in greenhouse agriculture and make a reliable estimation of overall population density.

Detecting Pests From Light-Trapping Images Based on Improved YOLOv3 Model and Instance Augmentation.

Light traps have been widely used as effective tools to monitor multiple agricultural and forest insect pests simultaneously. However, the current detection methods of pests from light trapping images have several limitations, such as exhibiting extremely imbalanced class distribution, occlusion among multiple pest targets, and inter-species similarity. To address the problems, this study proposes an improved YOLOv3 model in combination with image enhancement to better detect crop pests in real agricultural environments. First, a dataset containing nine common maize pests is constructed after an image augmentation based on image cropping. Then, a linear transformation method is proposed to optimize the anchors generated by the k-means clustering algorithm, which can improve the matching accuracy between anchors and ground truths. In addition, two residual units are added to the second residual block of the original YOLOv3 network to obtain more information about the location of the underlying small targets, and one ResNet unit is used in the feature pyramid network structure to replace two DBL(Conv+BN+LeakyReLU) structures to enhance the reuse of pest features. Experiment results show that the mAP and mRecall of our proposed method are improved by 6.3% and 4.61%, respectively, compared with the original YOLOv3. The proposed method outperforms other state-of-the-art methods (SSD, Faster-rcnn, and YOLOv4), indicating that the proposed method achieves the best detection performance, which can provide an effective model for the realization of intelligent monitoring of maize pests.