Search

Cephalometric landmark detection performs an important role in a diagnostic measurements for orthodontic treatment plans. As manual depiction of landmarks is a time-consuming and tedious task, the development of an automated detection algorithm for daily clinics is in high demand. In this study, we propose a single-passing convolutional neural network that performs an accurate landmark detection in a hierarchical fashion. The proposed network first extracts global contexts by regressing initial positions of all the landmarks. Subsequently, local features are extracted from landmark-centered patches, which are obtained through global regression. The encoded global and local features are concatenated and weighed through a novel patch-wise attention module to weigh the relative importance. The experimental results demonstrate that our proposed local patch-wise attention mechanism performs a significant role in accurate detection. The proposed method outperformed other state-of-the-art methods by improving the successful detection rate by approximately 1 ∼ 2 % . The proposed method suggests that a structured attention, which is developed in a patch-wise fashion, significantly enhances the local feature encoders to further improve the final accuracy.

The major pathologies of Alzheimer's disease (AD) are amyloid plaques and hyperphosphorylated tau. The deposition of amyloid plaques leads to synaptic dysfunction, neuronal cell death, and cognitive impairment. Among the neurotransmitters, glutamate is the most abundant in the mammalian brain and plays an important role in synaptic plasticity. With respect to synaptic transmission, metabotropic glutamate receptor 5 (mGluR5) is highly affected by amyloid pathology. However, the neuropathologic changes in the protein expression of mGluR5 in AD remain unclear. Therefore, to elucidate the alteration in mGluR5 expression with the progression of AD, we performed serial behavioral tests, longitudinal imaging studies, and histopathological immunoassay for both 5xFAD (n = 14) mice and age-matched wild-type mice (n = 14). The 5xFAD mice started showing severe hyperactivity and memory impairment from 7 months of age. In addition, mGluR5 positron emission tomography revealed that while the binding values in the wild-type mice were similar over time, those in 5xFAD mice fluctuated from 5 months of age. Furthermore, the 5xFAD mice presented a 35% decrease in the binding values of their cortical and subcortical areas at 9 months of age compared with those at 3 months of age. Magnetic resonance spectroscopy and histopathological studies showed similar changes. In conclusion, mGluR5 availability changes with age, and mGluR5 positron emission tomography could successfully detect this synaptic change in the 5xFAD mice.

Conductive self-healing polymer hydrogel and related soft sensor devices are receiving considerable attention from academia to industry because of their impacts on the lifetime and ergonomic design of soft robotics, prosthesis, and health monitoring systems. However, the development of such a material has thus far been limited considering performances and accessibility. Herein, robustness, self-healing, and conductivity for soft electronic skin are realized by an interpenetrating polymer network (IPN) system based on chemical/ionic cross-linked poly(acrylic acid) containing ferric ions, intercalated with physically cross-linked poly(vinyl alcohol). This IPN hydrogel successfully satisfies all three aforementioned capabilities; elongation at break greater than 1400%; recovery to original mechanical properties greater than 80% after 24 h; and 0.14 S m−1 of ionic conductivity, which is electrically healable. Such ionic conductivity of hydrogels enables multimodal sensing capabilities, i.e., for strain, pressure, and temperature. Particularly, a uniquely designed dual sensor attached to a finger simultaneously detects mechanical folding and pressure changes independently and can undergo large deformation 1000 times repeated and heating up to 90 °C.

An efficient method for the formation of hexagonal disk shape C60 crystals at desired positions on a solid substrate has been achieved. The detailed growth behavior was studied through in-situ observation and series of control experiments. Our position selective growth technique enabled a fabrication of bottom electrode type C60 single crystal devices which exhibited n-type field-effect transistor behavior and photo response. Moreover, the C60 single crystal devices were further exploited to study alkali metal doped C60 crystal and its superconductivity.

This study examines the impact of an ex-ante control mechanisms on opportunistic behavior and ex-post transaction costs, as well as how this leads to relationship termination intention. Data gathered from 211 companies in South Korea that have installed and run outsourced vending machines is analyzed and used to validate the article's theoretical and empirical contributions. The findings show that contractual completeness has a negative and direct effect on both active and passive opportunism. The study also discovers that active opportunism has a positive effect on both bargaining costs and monitoring costs, whereas passive opportunism has a positive and direct effect on maladaption costs but a negative effect on monitoring costs. Both bargaining and maladaption costs have a positive and direct effect on relationship termination intention, while monitoring costs have a negative effect on same. Furthermore, alternative attractiveness moderates the relationships between bargaining costs and relationship termination intention as well as maladaption costs and relationship termination intention. The implication is that contract completeness can function as an important ex-ante control mechanism, whereas the two types of opportunism can raise transaction costs. Alternative attractiveness is identified as a driver of the impact of transaction costs on relationship termination intention.

Here, we report the persistent photoconductance (PPC) and transient photo-response characteristics of photochemically activated and thermally annealed indium‑gallium‑zinc-oxide (IGZO) thin-film transistors (TFTs). From various analyses on the time-variant photo-current change in IGZO TFTs, it was found that photochemically activated IGZO TFTs exhibited significantly weak PPC behavior and rapid photo-decaying characteristics compared to thermally annealed IGZO TFTs. It is claimed that such behaviors of photochemically activated IGZO TFTs are due to the low activation energy for the neutralization of ionized oxygen vacancies, which can be beneficial in achieving highly photo-stable electronic devices. Furthermore, we report the influence of pulsed gate bias on the photo-decaying characteristics of photochemically activated and thermally annealed IGZO TFTs.

An optimal model to schedule fuel regeneration is developed for the Tokamak Cryopumps and Cryogenic Viscous Compressors (CVCs) of the fuel cycle of ITER. The objective is to minimize the tritium inventory inside the Isotope Separation Systems (ISS) while supplying the tokamak’s required amount of fuel considering fueling stages: ramp-up, flat-top, and ramp-down for each tokamak operation mode (Inductive, Hybrid, and Non-Inductive). Case studies of CVC regeneration times are presented to illustrate how CVC regeneration affects the ISS tritium working inventory. Results showed that nine iterations of CVC regeneration are enough to maintain ISS working tritium inventory management during Non-Inductive tokamak operation mode. The developed model uses mixed integer linear programming of the state-task network.

Owing to the structural resemblance of synthetic hydrogels to biological tissues, numerous self-healing wet materials have recently emerged, based on either chemical or physical motifs. With a plethora of novel synthetic hydrogels, a reliable characterisation strategy to assess self-healing is necessary. Herein, we therefore propose rheological criteria to distinguish self-healing hydrogels by comparing two types of poly (vinyl alcohol) hydrogels prepared via freezing/thawing (FX) or borax addition (BX). Non-self-healing FX and self-healing BX hydrogels exhibit incompatible features for three rheological characteristics: 1) Bingham behaviour without a lower Newtonian flow region (LNFR) (FX) versus pseudoplastic behaviour with a LNFR (BX), 2) infinite chain flow relaxation time (τf) versus finite τf on a reasonable time scale, and 3) drastic gel-sol transition with heating versus negligible transition. The plotting of modulus master curves confirms the terminal flow behaviour of the self-healing BX hydrogels, in contrast to the rubbery nature of the FX gels. The flowability of the self-healing hydrogels is ascribed to the pseudo-state of the gel network, due to the dynamic nature of the borate-diol bonds. Thus, the rheological criteria provide a sound theoretical background for self-healing material characterisations.

The goal of this study was to provide a discrete-continuous scheduling model for optimal operation of the fuel cycle of ITER. The model is designed to minimize the tritium working inventory in the Isotope Separation System (ISS) of the fuel cycle by adopting a two-stage scheduling approach. To consider the effect of pump regeneration schedule on tritium fluctuation in the ISS, the suggested solving algorithm is presented as a two-stage scheduling models: Stage I obtains the optimal pump-regeneration schedule, and Stage II suggests the optimal fuel-transition timing from the ISS to the Storage and Delivery System to minimize tritium working inventory in the ISS. The scheduling model uses discrete-time representation in Stage I and continuous-time representation in Stage II.

Utilization of industrial off-gases to produce valuable chemicals has the advantages of reducing dependence on fossil fuels. Despite many studies of off-gas utilization, production of secondary chemicals including detailed kinetic models integrated with a power-generation system to fully utilize byproducts has not been considered. The goal of this study was to techno-economically evaluate a polygeneration system to produce olefins and power from steel-mill off-gases. Two process cases were evaluated; they use carbon monoxide-rich Linz-Donawitz converter gas with either hydrogen or coke oven gas as feed. The hydrogen was concentrated from coke oven gas by using membrane-separation technology. These gases reacted with Linz-Donawitz converter gas to yield methanol, which was then converted to olefins. Kinetic models were applied for methanol synthesis and methanol-to-olefin reaction. Heat exchanger networks are designed to minimize utility usage, and a power generation system that uses the Rankine cycle to fully utilize byproduct stream. Calculated minimum selling prices of ethylene by discounted cash flow analysis were US$ 2030∙tonne−1 when hydrogen was the feed gas, and US$ 1064∙tonne−1 when coke oven gas was the feed gas; both were higher than the current minimum selling price (~US$ 720∙tonne−1) of ethylene derived from naphtha. The reduction in minimum selling price by use of coke oven gas is due to the revenue of power generation from coke oven gas retentate. Linz-Donawitz converter gas production with hydrogen is the most sensitive to feed price, whereas Linz-Donawitz converter gas-with- coke oven gas is the most sensitive to capital investment. The analysis demonstrates that neither system is economically viable under the current condition. The findings of this study represent a basic analysis for future improvements of the off-gas to olefins process, especially highly integrated system with power generation.

Objective: Herein, a neural network-based liver segmentation algorithm is proposed, and its performance was evaluated using abdominal computed tomography (CT) images. Methods: A fully convolutional network was developed to overcome the volumetric image segmentation problem. To guide a neural network to accurately delineate a target liver object, the network was deeply supervised by applying the adaptive self-supervision scheme to derive the essential contour, which acted as a complement with the global shape. The discriminative contour, shape, and deep features were internally merged for the segmentation results. Results and Conclusion: 160 abdominal CT images were used for training and validation. The quantitative evaluation of the proposed network was performed through an eight-fold cross-validation. The result showed that the method, which uses the contour feature, segmented the liver more accurately than the state-of-the-art with a 2.13% improvement in the dice score. Significance: In this study, a new framework was introduced to guide a neural network and learn complementary contour features. The proposed neural network demonstrates that the guided contour features can significantly improve the performance of the segmentation task., Comment: 10 pages, 9 figures

Individual tooth segmentation from cone beam computed tomography (CBCT) images is an essential prerequisite for an anatomical understanding of orthodontic structures in several applications, such as tooth reformation planning and implant guide simulations. However, the presence of severe metal artifacts in CBCT images hinders the accurate segmentation of each individual tooth. In this study, we propose a neural network for pixel-wise labeling to exploit an instance segmentation framework that is robust to metal artifacts. Our method comprises of three steps: 1) image cropping and realignment by pose regressions, 2) metal-robust individual tooth detection, and 3) segmentation. We first extract the alignment information of the patient by pose regression neural networks to attain a volume-of-interest (VOI) region and realign the input image, which reduces the inter-overlapping area between tooth bounding boxes. Then, individual tooth regions are localized within a VOI realigned image using a convolutional detector. We improved the accuracy of the detector by employing non-maximum suppression and multiclass classification metrics in the region proposal network. Finally, we apply a convolutional neural network (CNN) to perform individual tooth segmentation by converting the pixel-wise labeling task to a distance regression task. Metal-intensive image augmentation is also employed for a robust segmentation of metal artifacts. The result shows that our proposed method outperforms other state-of-the-art methods, especially for teeth with metal artifacts. The primary significance of the proposed method is two-fold: 1) an introduction of pose-aware VOI realignment followed by a robust tooth detection and 2) a metal-robust CNN framework for accurate tooth segmentation., Comment: 10 pages, 10 figures

Background Hypertriglyceridemic waist phenotype is a simple screening parameter to identify people at increased risk for cardiovascular disease. We evaluated whether hypertriglyceridemic waist (HTGW) phenotype increases the risk for coronary artery calcification (CAC) in apparently healthy Korean adults. Methods A total of 32,186 participants (mean age 41.3, 80.2% men) in a health screening program, in whom the coronary artery calcium score (CACS) was measured, were analyzed. Subjects were divided into four groups: 1) normal waist circumference (WC)–normal triglyceride (TG) (NWNT), 2) normal WC–high TG (NWHT), 3) enlarged WC–normal TG (EWNT), and 4) enlarged WC–high TG (EWHT). Enlarged WC was defined as WC ≥90cm for men and ≥85cm for women; high serum TG was defined as TG ≥150mg/dL. The presence of CAC was defined by CACS >0, and CACS was analyzed in a logarithmized form of CACS plus 1 {ln(CACS+1)}. Results A total of 14.9% of the participants had CAC. The EWHT group showed the highest mean value for ln(CACS+1) among the four groups. The EWHT group showed the highest odds ratio for CAC, with NWHT group the second, and with EWNT group the third compared with the NWNT group after adjusting for confounding variables (1.579, 1.302, and 1.266 vs. NWNT). Conclusions The EWHT group showed the highest association for CAC, suggesting this HTGW phenotype as a useful marker for the detection of subjects with high cardiometabolic risk in healthy Korean adults.

The purpose of the present study is to investigate the relationship between dopaminergic neuron destruction and 5-HT system changes in a hemiparkinsonian rat model. We performed PET imaging studies with trans -[ 18 F]Mefway in a hemiparkinsonian model of unilateral 6-hydroxydopamine (6-OHDA) rats. Region-of-interests (ROIs) were drawn in the hippocampus (HP) and cerebellum (CB). HP uptake, the ratios of specific binding to non-specific binding in the HP, and non-displaceable binding potential (BP ND ) in the HP were compared between 6-OHDA and control rats. As a result, unilateral 6-OHDA-lesioned rats exhibited significant bilateral reduction of HP uptake and trans -[ 18 F]Mefway BP ND compared to the intact control group. Therefore, the results demonstrate that destruction of the dopaminergic system causes the reduction of the serotonergic system.