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Haemaphysalis longicornis is one of the most medically important carriers of various pathogens. Although H. longicornis is an important vector, only basic ecological and biological information has been obtained, primarily focusing on its abundance and distribution. This study determined the most active time and meteorological conditions for the diel activity of H. longicornis. The diel activity pattern of H. longicornis was the highest between 10:00 and 14:00, and the lowest between 22:00 and 02:00. The major activity temperature of H. longicornis was between 25 °C and 40 °C, with the highest activity at 35 °C. The relative humidity was between 30% and 70% during the active period. Temperature had the highest correlation with diel activity (R = 0.679), followed by humidity (R = -0.649) and light intensity (R = 0.572). Our results provide basic information for the development of tick-borne disease vector control programs and tick surveillance.

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Objective.This paper proposes a new objective function to improve the quality of synthesized breast CT images generated by the GAN and compares the GAN performances on transfer learning datasets from different image domains.Approach.The proposed objective function, named beta loss function, is based on the fact that x-ray-based breast images follow the power-law spectrum. Accordingly, the exponent of the power-law spectrum (beta value) for breast CT images is approximately two. The beta loss function is defined in terms of L1 distance between the beta value of synthetic images and validation samples. To compare the GAN performances for transfer learning datasets from different image domains, ImageNet and anatomical noise images are used in the transfer learning dataset. We employ styleGAN2 as the backbone network and add the proposed beta loss function. The patient-derived breast CT dataset is used as the training and validation dataset; 7355 and 212 images are used for network training and validation, respectively. We use the beta value evaluation and Fréchet inception distance (FID) score for quantitative evaluation.Main results.For qualitative assessment, we attempt to replicate the images from the validation dataset using the trained GAN. Our results show that the proposed beta loss function achieves a more similar beta value to real images and a lower FID score. Moreover, we observe that the GAN pretrained with anatomical noise images achieves better equality than ImageNet for beta value evaluation and FID score. Finally, the beta loss function with anatomical noise as the transfer learning dataset achieves the lowest FID score.Significance.Overall, the GAN using the proposed beta loss function with anatomical noise images as the transfer learning dataset provides the lowest FID score among all tested cases. Hence, this work has implications for developing GAN-based breast image synthesis methods for medical imaging applications.

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Objective.In this work, we propose a convolutional neural network (CNN)-based multi-slice ideal model observer using transfer learning (TL-CNN) to reduce the required number of training samples.Approach.To train model observers, we generate simulated breast CT image volumes that are reconstructed using the FeldkampDavisKress algorithm with a ramp and Hanning-weighted ramp filter. The observer performance is evaluated on the background-known-statistically (BKS)/signal-known-exactly task with a spherical signal, and the BKS/signal-known-statistically task with random signal generated by the stochastic grown method. We compare the detectability of the CNN-based model observer with that of conventional linear model observers for multi-slice images (i.e. a multi-slice channelized Hotelling observer (CHO) and volumetric CHO). We also analyze the detectability of the TL-CNN for different numbers of training samples to examine its performance robustness to a limited number of training samples. To further analyze the effectiveness of transfer learning, we calculate the correlation coefficients of filter weights in the CNN-based multi-slice model observer.Main results.When using transfer learning for the CNN-based multi-slice ideal model observer, the TL-CNN provides the same performance with a 91.7% reduction in the number of training samples compared to that when transfer learning is not used. Moreover, compared to the conventional linear model observer, the proposed CNN-based multi-slice model observers achieve 45% higher detectability in the signal-known-statistically detection tasks and 13% higher detectability in the SKE detection tasks. In correlation coefficient analysis, it is observed that the filters in most of the layers are highly correlated, demonstrating the effectiveness of the transfer learning for multi-slice model observer training.Significance.Deep learning-based model observers require large numbers of training samples, and the required number of training samples increases as the dimensions of the image (i.e. the number of slices) increase. With applying transfer learning, the required number of training samples is significantly reduced without performance drop.

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Can we recognize intraoperative real-time stress of orthopedic surgeons and which factors affect the stress of intraoperative orthopedic surgeons with EEG and HRV? From June 2018 to November 2018, 265 consecutive records of intraoperative stress measures for orthopedic surgeons were compared. Intraoperative EEG waves and HRV, comprising beats per minute (BPM) and low frequency (LF)/high frequency (HF) ratio were gathered for stress-associated parameters. Differences in stress parameters according to the experience of surgeons, intraoperative blood loss, and operation time depending on whether or not a tourniquet were investigated. Stress-associated EEG signals including beta 3 waves were significantly higher compared to EEG at rest for novice surgeons as the procedure progressed. Among senior surgeons, the LF/HF ratio reflecting the physical demands of stress was higher than that of novice surgeons at all stages. In surgeries including tourniquets, operation time was positively correlated with stress parameters including beta 1, beta 2, beta 3 waves and BPM. In non-tourniquet orthopedic surgeries, intraoperative blood loss was positively correlated with beta 1, beta 2, and beta 3 waves. Among orthopedic surgeons, those with less experience demonstrated relatively higher levels of stress during surgery. Prolonged operation time or excessive intraoperative blood loss appear to be contributing factors that increase stress.

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Surgeon-dependent factors such as optimal implant alignment of the tibial component are thought to play a significant role in the outcome following primary total knee arthroplasty (TKA). In addition, tibial component malrotation is associated with pain, stiffness, and altered patellofemoral kinematics in TKA. However, measuring tibial component rotation after TKA is difficult. Therefore, the purpose of this study was to find a reliable method for positioning the tibial component in TKA. To investigate the morphology of the tibial plateau, 977 patients' knees (829 females and 148 males) were evaluated using MRI. The relationships between the femoral transepicondylar axis (TEA), Akagi line, posterior tibial margin (PTM), medial third of the tibial tubercle (MTT), and anatomical tibial axis (ATS) were investigated in this study. In addition, gender difference in tibial rotational alignment were evaluated. Relative to the TEA, the MTT and ATS were externally rotated by 0.5° ± 4.4° and 0.5° ± 5.4°, respectively, while Akagi line and PTM were internally rotated by 3.7° ± 4.5° and 9.9° ± 6.1°, respectively. Gender differences were found in MTT, Akagi line and ATS (P < 0.05). Our result showed that the rotational alignment led to notable variance between femoral and tibial components using fixed bone landmarks. The MTT and ATS axes showed the closest perpendicular aspect with projected TEA. And the MTT and Akagi axes showed the reduced variance. In addition, PTM is not a reliable landmark for rotation of the tibial component. Based on the results of this study, surgeons may choose the proper anteroposterior axis of the tibial component in order to reduce rotational mismatch and improve clinical outcomes.

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PURPOSE: In this paper, we propose a convolutional neural network (CNN)-based efficient model observer for breast computed tomography (CT) images. METHODS: We first showed that the CNN-based model observer provided similar detection performance to the ideal observer (IO) for signal-known-exactly and background-known-exactly detection tasks with an uncorrelated Gaussian background noise image. We then demonstrated that a single-layer CNN without a nonlinear activation function provided similar detection performance in breast CT images to the Hotelling observer (HO). To train the CNN-based model observer, we generated simulated breast CT images to produce a training dataset in which different background noise structures were generated using filtered back projection with a ramp, or a Hanning weighted ramp, filter. Circular, elliptical, and spiculated signals were used for the detection tasks. The optimal depth and the number of channels for the CNN-based model observer were determined for each task. The detection performances of the HO and a channelized Hotelling observer (CHO) with Laguerre-Gauss (LG) and partial least squares (PLS) channels were also estimated for comparison. RESULTS: The results showed that the CNN-based model observer provided higher detection performance than the HO, LG-CHO, and PLS-CHO for all tasks. In addition, it was shown that the proposed CNN-based model observer provided higher detection performance than the HO using a smaller training dataset. CONCLUSIONS: In the presence of nonlinearity in the CNN, the proposed CNN-based model observer showed better performance than other linear observers.

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Zika virus (ZIKV) is an arthropod-borne virus mainly transmitted by Aedes species. A total of nine of the 16 imported ZIKV reported cases during the mosquito season in the Republic of Korea (ROK), following the return of local nationals from foreign ZIKV endemic countries, were surveyed for Aedes albopictus. Surveillance and vector control of Ae. albopictus, a potential vector of ZIKV, and related species are critical for reducing the potential for autochthonous transmission in the ROK. Surveillance and vector control were coordinated by Korean Centers for Disease Control & Prevention (KCDC) and conducted by local health authorities within 200 m of imported ZIKV patients' residences. After diagnosis, thermal fogging (3 × week × 3 weeks), residual spray for homes and nearby structures (1 × week × 3 weeks), and larval control (3 × week × 3 weeks) were conducted in accordance with national guidelines developed by KCDC in early 2016. Of the nine residences surveyed using BG Sentinel traps, Ae. albopictus trap indices (TIs) for the three (3) patients' residences located near/in forested areas were significantly higher than the six patients' residences located inside villages/urban areas or low-lying farmland without trees. Overall, Ae. albopictus TIs in forested areas decreased by 90.4% after adult and larval control, whereas TIs decreased by 75.8% for residences in nonforested areas. A total of 3,216 Aedes and Ochlerotatus spp. were assayed by real-time polymerase chain reaction for ZIKV, dengue, and chikungunya virus. Both species collected before and after vector control were negative for all viruses. Vector control within 200 m of residences of imported ZIKV patients, conducted in accordance with established guidelines, may have effectively reduced human-mosquito-human transmission cycle by competent vectors in South Korea.

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The compounds 1,8-cineole and zerumbone (ZER) from the Cyperus rotundus rhizome along with another 11 previously identified rhizome essential oil constituents and α-humulene, which lacks the only carbonyl group present in ZER, as well as binary mixtures of ZER and seven active compounds were tested for repellency to male B. germanica. The results were compared to N,N-diethyl-3-methylbenzamide (deet). In filter-paper choice tests, ZER was the most repellent compound, and α-humulene was ineffective, which indicates that the α,ß-unsaturated carbonyl group of ZER is a prerequisite component for repellency. At 81.5 µg cm-2, enhanced repellency was produced by binary mixtures of ZER and 1,8-cineole, (+)-dihydrocarvone or (R)-(+)-limonene (70:30, 50:50 and 30:70 ratios by weight). These mixtures were very effective against male B. germanica within 24 h and were more repellent than a single compound or deet alone. The optimum ZER content was determined to be more than 50%. In Ebeling choice box tests at 652.4 µg cm-2, these compounds and deet resulted in complete repellency to intact male B. germanica, while they exhibited 35-47% repellency to antennectomized male one. Mixtures formulated from the active constituents of the C. rotundus rhizome could be useful as potential repellents for controlling B. germanica.

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