ABSTRACT
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.
ABSTRACT
MicroRNAs (miRNAs) are endogenous ~23 nt RNAs which regulate message RNA (mRNA) targets mainly through perfect pairing with their seed region (positions 2-7). Several instances of UTR sequence with an additional nucleotide that might form a bulge within the pairing region, can also be recognized by miRNA as their target (bugle-target). But the prevalence of such imperfect base pairings in human and their roles in the evolution are incompletely understood. We found that human miRNAs with the CG dinucleotides (CG dimer) in their seed region have a significant low mutation rate than their putative binding sites in mRNA targets. Interspecific comparation shows that these miRNAs had very few conservative targets with the perfect seed-pairing, while potentially having a subclass of bulge-targets. Compared with the canonical target (perfect seed-pairing), these bulge-targets had a lower negative correlation with the miRNA expression, and either were down-regulated in the miRNA overexpression experiment or up-regulated in the miRNA knock-down experiment. Our results show that the bulge-targets are widespread in the miRNAs with CG dinucleotide within their seed regions, which could in part explain the rare conserved targets of these miRNAs based on seed rule. Incorporating these bulge-targets, together with conservation information, could more accurately predict the entire targets of these miRNAs.
ABSTRACT
Large amount SO2 emission caused serious damage of forest ecosystem in China and calculation of the damage cost is an important issue for policy-making. However, no applicable method was developed to estimate forest damage under different SO2 emission scenarios. Basing on previous field researches on sulfur-related forest impact in China and recent critical load mapping research, this paper presented a model for forest damage calculation by developing a dose-response function that related the damage to cumulative sulfur critical loads. This model was applied to the forests in Hunan, a province in acid rain control zone in China. Results showed that in the business-as-usual case, SO2 emission in Hunan will increase by 120% from 1995 (8.82 mil. ton) to 2020 (19.56 mil. ton), but damage cost will increase by 4.3 times, reaching 6.19 billion RMB in 2020. Results also showed the measures for SO2 control were cost-effective because the marginal damage cost will be about 6000 RMB per ton SO2 in 2020 in BAU case. At current SO2 emission level, marginal benefit will be about 1500 RMB per ton. Uncertainty analysis demonstrated that this model provides reasonable damage estimates and would therefore be applicable in a broad range of policy settings.
