Search Results (5)

Following the detection of highly pathogenic avian influenza (HPAI) virus in countries bordering Kenya to the west, we conducted surveillance among domestic and wild birds along the shores of Lake Victoria. In addition, between 2018 and 2020, we conducted surveillance among poultry and poultry workers in live bird markets and among wild migratory birds in various lakes that are resting sites during migration to assess introduction and circulation of avian influenza viruses in these populations. We tested 7464 specimens (oropharyngeal (OP) and cloacal specimens) from poultry and 6531 fresh fecal specimens from wild birds for influenza A viruses by real-time RT-PCR. Influenza was detected in 3.9% (n = 292) of specimens collected from poultry and 0.2% (n = 10) of fecal specimens from wild birds. On hemagglutinin subtyping, most of the influenza A positives from poultry (274/292, 93.8%) were H9. Of 34 H9 specimens randomly selected for further subtyping, all were H9N2. On phylogenetic analysis, these viruses were genetically similar to other H9 viruses detected in East Africa. Only two of the ten influenza A-positive specimens from the wild bird fecal specimens were successfully subtyped; sequencing analysis of one specimen collected in 2018 was identified as a low-pathogenicity avian influenza H5N2 virus of the Eurasian lineage, and the second specimen, collected in 2020, was subtyped as H11. A total of 18 OP and nasal specimens from poultry workers with acute respiratory illness (12%) were collected; none were positive for influenza A virus. We observed significant circulation of H9N2 influenza viruses in poultry in live bird markets in Kenya. During the same period, low-pathogenic H5N2 virus was detected in a fecal specimen collected in a site hosting a variety of migratory and resident birds. Although HPAI H5N8 was not detected in this survey, these results highlight the potential for the introduction and establishment of highly pathogenic avian influenza viruses in poultry populations and the associated risk of spillover to human populations. Full article

Hand gesture recognition, which is one of the fields of human–computer interaction (HCI) research, extracts the user’s pattern using sensors. Radio detection and ranging (RADAR) sensors are robust under severe environments and convenient to use for hand gestures. The existing studies mostly adopted continuous-wave (CW) radar, which only shows a good performance at a fixed distance, which is due to its limitation of not seeing the distance. This paper proposes a hand gesture recognition system that utilizes frequency-shift keying (FSK) radar, allowing for a recognition method that can work at the various distances between a radar sensor and a user. The proposed system adopts a convolutional neural network (CNN) model for the recognition. From the experimental results, the proposed recognition system covers the range from 30 cm to 180 cm and shows an accuracy of 93.67% over the entire range. Full article

A novel design of a portable funnel light trap (PFLT) was presented for collecting insects in ecological studies. The trap consists of a compact plastic box equipped with a light source and power source, along with two plastic polypropylene interception vanes. The PFLT costs 18.3 USD per unit and weighs approximately 300 g. A maximum of six PFLT units can be packed in one medium-sized backpack (32 cm × 45 cm × 15 cm, 20 L), making it easier to set up multiple units in remote areas wherein biodiversity research is needed. The low cost and weight of the trap also allows for large-scale deployment. The design is customizable and can be easily manufactured to fit various research needs. To validate the PFLT’s efficacy in collecting insects across different habitat types, a series of field experiments were conducted in South Korea and Laos, where 37 trials were carried out. The PFLT successfully collected 7497 insects without experiencing battery issues or damage by rain or wind. Insect compositions and abundances differed across the three sampled habitat types: forests, grasslands, and watersides. This new FLT trap is an important tool for studying and protecting insect biodiversity, particularly in areas wherein conventional light traps cannot be deployed. Full article

Despite all the expectations for photoacoustic endoscopy (PAE), there are still several technical issues that must be resolved before the technique can be successfully translated into clinics. Among these, electromagnetic interference (EMI) noise, in addition to the limited signal-to-noise ratio (SNR), have hindered the rapid development of related technologies. Unlike endoscopic ultrasound, in which the SNR can be increased by simply applying a higher pulsing voltage, there is a fundamental limitation in leveraging the SNR of PAE signals because they are mostly determined by the optical pulse energy applied, which must be within the safety limits. Moreover, a typical PAE hardware situation requires a wide separation between the ultrasonic sensor and the amplifier, meaning that it is not easy to build an ideal PAE system that would be unaffected by EMI noise. With the intention of expediting the progress of related research, in this study, we investigated the feasibility of deep-learning-based EMI noise removal involved in PAE image processing. In particular, we selected four fully convolutional neural network architectures, U-Net, Segnet, FCN-16s, and FCN-8s, and observed that a modified U-Net architecture outperformed the other architectures in the EMI noise removal. Classical filter methods were also compared to confirm the superiority of the deep-learning-based approach. Still, it was by the U-Net architecture that we were able to successfully produce a denoised 3D vasculature map that could even depict the mesh-like capillary networks distributed in the wall of a rat colorectum. As the development of a low-cost laser diode or LED-based photoacoustic tomography (PAT) system is now emerging as one of the important topics in PAT, we expect that the presented AI strategy for the removal of EMI noise could be broadly applicable to many areas of PAT, in which the ability to apply a hardware-based prevention method is limited and thus EMI noise appears more prominently due to poor SNR. Full article

Maize weevils, Sitophilus zeamais, are stored product pests mostly found in warm and humid regions around the globe. In the present study, acute toxicity via contact and residual bioassay and fumigant bioassay of 28 essential oils as well as their attraction–inhibitory activity against the adults of S. zeamais were evaluated. Chemical composition of the essential oils was analyzed by gas chromatography-mass spectrometry, and a compound elimination assay was conducted on the four most active oils (cinnamon, tea tree, ylang ylang, and marjoram oils) to identify major active constituents. Amongst the oils examined, cinnamon oil was the most active in both contact/residual and fumigant bioassays, and exhibited strong behavioral inhibitory activity. Based on the compound elimination assay and chemical analyses, trans-cinnamaldehyde in cinnamon oil, and terpinen-4-ol in tea tree and marjoram oils were identified as the major active components. Although cinnamon oil seemed promising in the lab-scale bioassay without rice grains, it failed to exhibit strong insecticidal activity when the container was filled with rice. When a cinnamon oil-based product was applied both in an empty glass jar and a rice-filled container, all weevils in the empty jar were killed, whereas fewer than 15% died in the rice-filled container. Full article