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The synchronous-like flash behavior was observed in five male Sclerotia aquatilis, the x-axis represents time, and the y-axis represents the flash area (flash illuminance), the temperature was 25 degrees Celsius
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Previous methods for detecting the flashing behavior of fireflies were using either a photomultiplier tube, a stopwatch, or videography. Limitations and problems are associated with these methods, i.e., errors in data collection and analysis, and it is time-consuming. This study aims to applied a computer vision approach to reduce the time of data...
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... we found the synchronous-like flash behavior of Sclerotia aquatilis males, which were placed in the same box (2-6). This behavior has not been reported in previous studies of this species (Fig. 6). The synchronous-like flashing behavior of Sclerotia aquatilis may be performed for the sympatric speciation of competing males. The synchronous flash was reported in some species of Pteroptix, Photinus, and Luciola genera, but there are no other reports in S. aquatilis [31] [32]. Genetically, the S. aquatilis was thought to be ...
Citations
... Recently, improvements in photosensitivity and digital video recording technologies have allowed the luminous signaling to be recorded and stored as digital information. Hence, the spatiotemporal image analysis of flash patterns from a single individual or a population can be directly processed using a personal computer [27,28]. Flash patterns are thought to be a luminous marker for firefly species identification and classification, but this theory has only been investigated in a few species, such as members of the genus Photinus [13,14]. ...
... To process and analyze firefly flash signals, various imaging software have been or are being developed, such as TILIA [27,28,46]. Unlike other imaging software, FIJI / ImageJ is highly compatible with almost all computer operation systems and all video-imagery file formats [39]. ...
... In recent years, artificial intelligence (AI) on image pattern recognition has been intensively developed for various academic and industrial applications [47,48]. The use of AI in flash signal analysis and firefly species recognition is in development [27], and we anticipate that it will become a future trend. Until then, a complete digital database of firefly species-specific luminous features must be established. ...
Glycosylation occurring at either lipids, proteins, or sugars plays important roles in many biological systems. In nature, enzymatic glycosylation is the formation of a glycosidic bond between the anomeric carbon of the donor sugar and the functional group of the sugar acceptor. This study found novel glycoside anomers without an anomeric carbon linkage of the sugar donor. A glycoside hydrolase (GH) enzyme, amylosucrase from Deinococcus geothermalis (DgAS), was evaluated to glycosylate ganoderic acid F (GAF), a lanostane triterpenoid from medicinal fungus Ganoderma lucidum, at different pH levels. The results showed that GAF was glycosylated by DgAS at acidic conditions pH 5 and pH 6, whereas the activity dramatically decreased to be undetectable at pH 7 or pH 8. The biotransformation product was purified by preparative high-performance liquid chromatography and identified as unusual α-glucosyl-(2→26)-GAF and β-glucosyl-(2→26)-GAF anomers by mass and nucleic magnetic resonance (NMR) spectroscopy. We further used DgAS to catalyze another six triterpenoids. Under the acidic conditions, two of six compounds, ganoderic acid A (GAA) and ganoderic acid G (GAG), could be converted to α–glucosyl-(2→26)-GAA and β–glucosyl-(2→26)-GAA anomers and α-glucosyl-(2→26)-GAG and β-glucosyl-(2→26)-GAG anomers, respectively. The glycosylation of triterpenoid aglycones was first confirmed to be converted via a GH enzyme, DgAS. The novel enzymatic glycosylation-formed glycoside anomers opens a new bioreaction in the pharmaceutical industry and in the biotechnology sector.
... Recently, improvements in photosensitivity and digital video recording technologies have allowed the luminous signaling to be recorded and stored as digital information. Hence, the spatiotemporal image analysis of flash patterns from a single individual or a population can be directly processed using a personal computer [27,28]. Flash patterns are thought to be a luminous marker for firefly species identification and classification, but this theory has only been investigated in a few species, such as members of the genus Photinus [13,14]. ...
... To process and analyze firefly flash signals, various imaging software have been or are being developed, such as TILIA [27,28,46]. Unlike other imaging software, FIJI / ImageJ is highly compatible with almost all computer operation systems and all video-imagery file formats [39]. ...
... In recent years, artificial intelligence (AI) on image pattern recognition has been intensively developed for various academic and industrial applications [47,48]. The use of AI in flash signal analysis and firefly species recognition is in development [27], and we anticipate that it will become a future trend. Until then, a complete digital database of firefly species-specific luminous features must be established. ...
It is highly challenging to evaluate the species’ content and behavior changes in wild fireflies, especially for a sympatric population. Here, the flash interval (FI) and flash duration (FD) of flying males from three sympatric species (Abscondita cerata, Luciola kagiana, and Luciola curtithorax) were investigated for their potentials in assessing species composition and nocturnal behaviors during the A. cerata mating season. Both FI and FD were quantified from the continuous flashes of adult fireflies (lasting 5–30 s) via spatiotemporal analyses of video recorded along the Genliao hiking trail in Taipei, Taiwan. Compared to FD patterns and flash colors, FI patterns exhibited the highest species specificity, making them a suitable reference for differentiating firefly species. Through the case study of a massive occurrence of A. cerata (21 April 2018), the species contents (~85% of the flying population) and active periods of a sympatric population comprising A. cerata and L. kagiana were successfully evaluated by FI pattern matching, as well as field specimen collections. Our study suggests that FI patterns may be a reliable species-specific luminous marker for monitoring the behavioral changes in a sympatric firefly population in the field, and has implication values for firefly conservation.
Artificial intelligence approaches, such as computer vision, can help better understand the behavior of bees and management. However, the accurate detection and tracking of bee species in the field remain challenging for traditional methods. In this study, we compared YOLOv7 and YOLOv8, two state-of-the-art object detection models, aiming to detect and classify Jataí Brazilian native bees using a custom dataset. Also, we integrated two tracking algorithms (Tracking based on Euclidean distance and ByteTrack) with YOLOv8, yielding a mean average precision (mAP50) of 0.969 and mAP50–95 of 0.682. Additionally, we introduced an optical flow algorithm to monitor beehive entries and exits. We evaluated our approach by comparing it to human performance benchmarks for the same task with and without the aid of technology. Our findings highlight occlusions and outliers (anomalies) as the primary sources of errors in the system. We must consider a coupling of both systems in practical applications because ByteTrack counts bees with an average relative error of 11%, EuclidianTrack monitors incoming bees with 9% (21% if there are outliers), both monitor bees that leave, ByteTrack with 18% if there are outliers, and EuclidianTrack with 33% otherwise. In this way, it is possible to reduce errors of human origin.
