The implementation of robotic dogs in automatic detection and surveillance of red imported fire ant nests.

BACKGROUND: The Red Imported Fire Ant (RIFA), scientifically known as Solenopsis invicta, is a destructive invasive species causing considerable harm to ecosystems and generating substantial economic costs globally. Traditional methods for RIFA nests detection are labor-intensive and may not be scalable to larger field areas. This study aimed to develop an innovative surveillance system that leverages artificial intelligence (AI) and robotic dogs to automate the detection and geolocation of RIFA nests, thereby improving monitoring and control strategies. RESULTS: The designed surveillance system, through integrating the CyberDog robotic platform with a YOLOX AI model, demonstrated RIFA nest detection precision rates of >90%. The YOLOX model was trained on a dataset containing 1118 images and achieved a final precision rate of 0.95, with an inference time of 20.16 ms per image, indicating real-time operational suitability. Field tests revealed that the CyberDog system identified three times more nests than trained human inspectors, with significantly lower rates of missed detections and false positives. CONCLUSION: The findings underscore the potential of AI-driven robotic systems in advancing pest management. The CyberDog/YOLOX system not only matched human inspectors in speed, but also exceeded them in accuracy and efficiency. This study's results are significant as they highlight how technology can be harnessed to address biological invasions, offering a more effective, ecologically friendly, and scalable solution for RIFA detection. The successful implementation of this system could pave the way for broader applications in environmental monitoring and pest control, ultimately contributing to the preservation of biodiversity and economic stability. © 2024 Society of Chemical Industry.

Bioactivities of scent gland chemicals from Mictis fuscipes Hsiao (Hemiptera: Coreidae) on Solenopsis invicta Buren (Hymenoptera: Formicidae).

BACKGROUND: Given the chemical diversity within stink bugs scent glands, they can be convenient models for bioprospecting novel pest control products. Preliminary behaviour observations indicated that adult Mictis fuscipes stink bugs secrete liquid droplets when defending against Solenopsis invicta fire ants, killing them within minutes. Hence, this study aimed to analyse the chemical composition of the metathoracic scent gland secretions of M. fuscipes adults, as well as assess their biological activities against fire ants. RESULTS: Bioassaying fire ants against secretions of several local stink bugs confirmed that the defensive secretions of two Mictis species are significantly more lethal, where M. fuscipes was the most lethal. Volatiles chromatography analysis indicated the secretions of female and male M. fuscipes stink bugs contains 20 and 26 components, respectively, chiefly hexanoic acid and hexyl hexanoate. Five compounds were consistently present in the secretion of female adults: hexyl hexanoate, hexanoic acid, hexyl acetate, hexyl butyrate, and eugenol. These yielded a strong electrophysiological antennal (EAD) response from S. invicta workers, female alates and males, where hexyl acetate showed the strongest response. The combination of these five compounds proved strongly repellent to S. invicta. When tested singly, hexanoic acid, hexyl butyrate, hexyl hexanoate, and eugenol were repellent to S. invicta, but hexyl acetate seemed slightly attractive. Additionally, the same mixture of five components exhibited strong contact and fumigant toxicity towards S. invicta workers, eugenol being the strongest. CONCLUSION: Defensive chemicals of M. fuscipes exhibit robust biological activity against S. invicta and could inspire the development of biopesticides. © 2024 Society of Chemical Industry.

Diagnostic Performance of Diffusion-Weighted Imaging for Colorectal Cancer Detection: An Updated Systematic Review and Meta-Analysis.

Background: Magnetic resonance imaging (MRI), which uses strong magnetic fields and radio waves (radiofrequency energy) to make images, is one of the best imaging methods for soft tissues and can clearly display unique anatomical structures. Diffusion-weighted imaging (DWI) has been developed for identifying various malignant tumors. Aim: To investigate the diagnostic value of DWI-MRI quantitative analysis in colorectal cancer detection. Methods: The PubMed, Cochrane Library, and Embase databases were searched from inception to May 29, 2020. Studies published in English that used DWI-MRI for diagnosing colorectal cancer were included. Case reports, letters, reviews, and studies conducted in non-humans or in-vitro experiments were excluded. The pooled diagnostic odds ratio (DOR) and hierarchical summary receiver operating characteristic (HSROC) curves were computed for DWI, and the area under the curve (AUC) and associated standard error (SE) and 95% confidence intervals (CIs) were also used. Results: In total, 15 studies with 1,655 participants were finally included in this meta-analysis. There were four prospective studies and 11 retrospective studies. Eight studies focused on rectal cancer, six on colorectal cancer, and one on colonic cancer. The performance of DWI-MRI for diagnosing colorectal cancer was accurate, with pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of 0.88 (95% CI = 0.85-0.91), 0.92 (95% CI = 0.91-0.94), 30.36 (95% CI = 11.05-83.43), and 0.44 (95% CI = 0.30-0.64), respectively. The DOR and HSROC curves were 121 (95% CI = 56-261) and 0.92 (λ: 4.79), respectively. Conclusion: DWI showed high diagnostic accuracy for colorectal cancer detection. Further studies with large sample sizes and prospective design are needed to confirm these results.