Fatigue in PD is due to decreased efficiency of the frontal network: quantitative EEG analysis.

Objective: Fatigue is a common, debilitating non-motor symptom of Parkinson's disease (PD), but its mechanism is poorly understood. We aimed to determine whether electroencephalography (EEG) could measure fatigue objectively and to expound on the pathophysiology of fatigue in PD. Methods: We studied 32 de novo PD patients who underwent electroencephalography (EEG). We compared brain activity between 19 PD patients without fatigue and 13 PD patients with fatigue via EEG power spectrum and graph including global efficiency (GE), characteristic path length (CPL), clustering coefficient (CCO), small worldness (SW), local efficiency (LE), degree centrality (DC), closeness centrality (CCE), and betweenness centrality (BC). Results: No significant differences in absolute and relative powers were seen between PD without and with fatigue (all ps > 0.02, Bonferroni-corrected). In network analysis, the brain network efficiency differed by frequency band. Generally, the brain network in the frontal area for theta and delta bands showed greater efficiency, and in the temporal area, the alpha1 band was less efficient in PD without fatigue (p= 0.0000, p = 0.0011, ps ≤ 0.0007, respectively, Bonferroni-corrected). Conclusions: Our study suggests that PD patients with fatigue have less efficient networks in the frontal area compared with networks of those with PD without fatigue. These findings may explain why fatigue is common in PD, a frontostriatal disorder. Increased efficiency in the temporal area in PD with fatigue is assumed to be compensation. Brain network analysis using graph theory is more valuable than power spectrum analysis in revealing the brain mechanism related to fatigue.

Surgical Results of the Superior Vena Cava Intimal Layer-Only Suture Technique in Heart Transplantation.

Background: Superior vena cava (SVC) stenosis during follow-up is a major concern after heart transplantation, and many technical modifications have been introduced. We analyzed the surgical results of the SVC intima layer-only suture technique in heart transplantation. Methods: We performed SVC anastomosis with sutures placed only in the intima during heart transplantation. We measured the area of the SVC at 3 different points (above the anastomosis, at the anastomosis, and below the anastomosis) in an axial view by freely drawing regions of interest, and then evaluated the degree of stenosis. Patients who underwent cardiac computed tomography (CT) at 2 years postoperatively between June 2017 and May 2020 were included in this study. Results: We performed heart transplantation in 41 patients. Among them, 24 patients (16 males and 8 females) underwent follow-up cardiac CT at 2 years postoperatively. The mean age at operation was 49.4±4.9 years. The diagnoses at time of operation were dilated cardiomyopathy (n=12), ischemic heart disease (n=8), valvular heart disease (n=2), hypertrophic cardiomyopathy (n=1), and congenital heart disease (n=1). No cases of postoperative bleeding requiring intervention occurred. The mean CT follow-up duration was 1.9±0.7 years. At follow-up, the mean areas at the 3 key points were 2.7±0.8 cm2, 2.7±0.8 cm2, and 2.7±1.0 cm2 (p=0.996). There were no SVC stenosis-related symptoms during follow-up. Conclusion: The suture technique using only the SVC intimal layer is a safe and effective method for use in heart transplantation.

Surgical results of only antegrade del Nido cardioplegia infusion in conventional coronary artery bypass grafting: a retrospective study.

BACKGROUND: Additional retrograde cardioplegia infusion in conventional coronary artery bypass grafting (CABG) was introduced to address the concern of inappropriate cardioplegia delivery through the stenotic coronary artery. However, this method is complex and requires repeated infusions. Therefore, we investigated the surgical outcomes of only antegrade cardioplegia infusion in conventional CABG. METHODS: We included 224 patients who underwent isolated CABG between 2017 and 2019. The patients were divided into two groups according to the cardioplegia infusion method: antegrade cardioplegia infusion with del Nido solution (n=111, group I) and antegrade+retrograde cardioplegia infusion with blood cardioplegia solution (n=113, group II). RESULTS: The sinus recovery time after release of the aorta cross-clamp was shorter in group I (3.8±7.1 minutes, n=98) than in group II (5.8±4.1 minutes, n=73) (p=0.033). The total cardioplegia infusion volume was lower in group I (1,998.6±668.6 mL) than in group II (7,321.0±2,865.3 mL) (p<0.001). Creatine kinase-MB levels were significantly lower in group I than in group II (p=0.039). Newly developed regional wall motion abnormalities on follow-up echocardiography were detected in two patients (1.8%) in group I and five patients (4.4%) in group II (p=0.233). There was no significant difference in ejection fraction improvement between the two groups (3.3%±9.3% in group I and 3.3%±8.7% in group II, p=0.990). CONCLUSION: The only antegrade cardioplegia infusion strategy in conventional CABG is safe and has no harmful effects.

Emerging role for R-loop formation in hepatocellular carcinoma.

The pathophysiological characteristics of hepatocellular carcinoma (HCC) is closely associated with genomic instability. Genomic instability has long been considered to be a hallmark of both human genetic disease and cancers. It is now well accepted that regulating R-loop formation to minimized levels is one of critical modulation to maintain genome integrity, and that improper regulation of R-loop metabolism causes genomic instability via DNA breakage, ultimately resulting in replicative senescence and even tumorigenesis. Given that R-loop is natural by-product formed during normal transcription condition, and that several types of cancer have defense mechanism against the genomic instability resulted from R-loop formation, modulating functional implication of proteins involved in the intrinsic and specific mechanisms of abnormal R-loop formation in cancers therefore could play an important part in appropriated therapeutic strategies for HCC cohorts. In this review, we highlight the latest understanding on how R-loops promote genomic instability and address how alterations in these pathways link to human HCC.

Cardiac segmentation on CT Images through shape-aware contour attentions.

BACKGROUND AND OBJECTIVE: Cardiac segmentation of atriums, ventricles, and myocardium in computed tomography (CT) images is an important first-line task for presymptomatic cardiovascular disease diagnosis. In several recent studies, deep learning models have shown significant breakthroughs in medical image segmentation tasks. Unlike other organs such as the lungs and liver, the cardiac organ consists of multiple substructures, i.e., ventricles, atriums, aortas, arteries, veins, and myocardium. These cardiac substructures are proximate to each other and have indiscernible boundaries (i.e., homogeneous intensity values), making it difficult for the segmentation network focus on the boundaries between the substructures. METHODS: In this paper, to improve the segmentation accuracy between proximate organs, we introduce a novel model to exploit shape and boundary-aware features. We primarily propose a shape-aware attention module, that exploits distance regression, which can guide the model to focus on the edges between substructures so that it can outperform the conventional contour-based attention method. RESULTS: In the experiments, we used the Multi-Modality Whole Heart Segmentation dataset that has 20 CT cardiac images for training and validation, and 40 CT cardiac images for testing. The experimental results show that the proposed network produces more accurate results than state-of-the-art networks by improving the Dice similarity coefficient score by 4.97%. CONCLUSION: Our proposed shape-aware contour attention mechanism demonstrates that distance transformation and boundary features improve the actual attention map to strengthen the responses in the boundary area. Moreover, our proposed method significantly reduces the false-positive responses of the final output, resulting in accurate segmentation.

Distributed Energy IoT-Based Real-Time Virtual Energy Prosumer Business Model for Distributed Power Resource.

Smart energy technologies, services, and business models are being developed to reduce energy consumption and emissions of CO2 and greenhouse gases and to build a sustainable environment. Renewable energy is being actively developed throughout the world, and many intelligent service models related to renewable energy are being proposed. One of the representative service models is the energy prosumer. Through energy trading, the demand for renewable energy and distributed power is efficiently managed, and insufficient energy is covered through energy transaction. Moreover, various incentives can be provided, such as reduced electricity bills. However, despite such a smart service, the energy prosumer model is difficult to expand into a practical business model for application in real life. This is because the production price of renewable energy is higher than that of the actual grid, and it is difficult to accurately set the selling price, restricting the formation of the actual market between sellers and consumers. To solve this problem, this paper proposes a small-scale energy transaction model between a seller and a buyer on a peer-to-peer (P2P) basis. This model employs a virtual prosumer management system that utilizes the existing grid and realizes the power system in real time without using an energy storage system (ESS). Thus, the profits of sellers and consumers of energy transactions are maximized with an improved return on investment (ROI), and an intelligent demand management system can be established.

Liver segmentation in abdominal CT images via auto-context neural network and self-supervised contour attention.

OBJECTIVE: Accurate image segmentation of the liver is a challenging problem owing to its large shape variability and unclear boundaries. Although the applications of fully convolutional neural networks (CNNs) have shown groundbreaking results, limited studies have focused on the performance of generalization. In this study, we introduce a CNN for liver segmentation on abdominal computed tomography (CT) images that focus on the performance of generalization and accuracy. METHODS: To improve the generalization performance, we initially propose an auto-context algorithm in a single CNN. The proposed auto-context neural network exploits an effective high-level residual estimation to obtain the shape prior. Identical dual paths are effectively trained to represent mutual complementary features for an accurate posterior analysis of a liver. Further, we extend our network by employing a self-supervised contour scheme. We trained sparse contour features by penalizing the ground-truth contour to focus more contour attentions on the failures. RESULTS: We used 180 abdominal CT images for training and validation. Two-fold cross-validation is presented for a comparison with the state-of-the-art neural networks. The experimental results show that the proposed network results in better accuracy when compared to the state-of-the-art networks by reducing 10.31% of the Hausdorff distance. Novel multiple N-fold cross-validations are conducted to show the best performance of generalization of the proposed network. CONCLUSION AND SIGNIFICANCE: The proposed method minimized the error between training and test images more than any other modern neural networks. Moreover, the contour scheme was successfully employed in the network by introducing a self-supervising metric.

Individual tooth detection and identification from dental panoramic X-ray images via point-wise localization and distance regularization.

Dental panoramic X-ray imaging is a popular diagnostic method owing to its very small dose of radiation. For an automated computer-aided diagnosis system in dental clinics, automatic detection and identification of individual teeth from panoramic X-ray images are critical prerequisites. In this study, we propose a point-wise tooth localization neural network by introducing a spatial distance regularization loss. The proposed network initially performs center point regression for all the anatomical teeth (i.e., 32 points), which automatically identifies each tooth. A novel distance regularization penalty is employed on the 32 points by considering L2 regularization loss of Laplacian on spatial distances. Subsequently, teeth boxes are individually localized using a multitask neural network on a patch basis. A multitask offset training is employed on the final output to improve the localization accuracy. Our method successfully localizes not only the existing teeth but also missing teeth; consequently, highly accurate detection and identification are achieved. The experimental results demonstrate that the proposed algorithm outperforms state-of-the-art approaches by increasing the average precision of teeth detection by 15.71 % compared to the best performing method. The accuracy of identification achieved a precision of 0.997 and recall value of 0.972. Moreover, the proposed network does not require any additional identification algorithm owing to the preceding regression of the fixed 32 points regardless of the existence of the teeth.

Reinforcement Learning-Based BEMS Architecture for Energy Usage Optimization.

Currently, many intelligent building energy management systems (BEMSs) are emerging for saving energy in new and existing buildings and realizing a sustainable society worldwide. However, installing an intelligent BEMS in existing buildings does not realize an innovative and advanced society because it only involves simple equipment replacement (i.e., replacement of old equipment or LED (Light Emitting Diode) lamps) and energy savings based on a stand-alone system. Therefore, artificial intelligence (AI) is applied to a BEMS to implement intelligent energy optimization based on the latest ICT (Information and Communications Technologies) technology. AI can analyze energy usage data, predict future energy requirements, and establish an appropriate energy saving policy. In this paper, we present a dynamic heating, ventilation, and air conditioning (HVAC) scheduling method that collects, analyzes, and infers energy usage data to intelligently save energy in buildings based on reinforcement learning (RL). In this regard, a hotel is used as the testbed in this study. The proposed method collects, analyzes, and infers IoT data from a building to provide an energy saving policy to realize a futuristic HVAC (heating system) system based on RL. Through this process, a purpose-oriented energy saving methodology to achieve energy saving goals is proposed.

Pose-aware instance segmentation framework from cone beam CT images for tooth segmentation.

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. Our method demonstrated 5.68% and 30.30% better accuracy in the F1 score and aggregated Jaccard index, respectively, when compared to the best performing state-of-the-art algorithms. The major implication 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.

Large-aperture ground glass surface profile measurement using coherence scanning interferometry.

We present a coherence scanning interferometer configured to deal with rough glass surfaces exhibiting very low reflectance due to severe sub-surface light scattering. A compound light source is prepared by combining a superluminescent light-emitting diode with an ytterbium-doped fiber amplifier. The light source is attuned to offer a short temporal coherence length of 15 µm but with high spatial coherence to secure an adequate correlogram contrast by delivering strongly unbalanced optical power to the low reflectance target. In addition, the infrared spectral range of the light source is shifted close to the visible side at a 1,038 nm center wavelength, so a digital camera of multi-mega pixels available for industrial machine vision can be used to improve the correlogram contrast further with better lateral image resolutions. Experimental results obtained from a ground Zerodur mirror of 200 mm aperture size and 0.9 µm rms roughness are discussed to validate the proposed interferometer system.

Intravitreal anti-vascular endothelial growth factor versus observation in acute central serous chorioretinopathy: one-year results.

PURPOSE: To evaluate the efficacy of anti-vascular endothelial growth factor (VEGF) compared with observation for treating acute central serous chorioretinopathy (CSC). METHODS: A retrospective study of 36 patients with acute CSC, including 21 patients treated with anti-VEGF (anti-VEGF group) and 15 patients with observation (observation group). Patients in the anti-VEGF group received a single dose of bevacizumab or ranibizumab at baseline. Best-corrected visual acuity (BCVA), central foveal thickness (CFT) and resolution of subretinal fluid (SRF) on optical coherence tomography (OCT) were assessed. The integrity of the foveal inner segment/outer segment (IS/OS) line at 12 months was also analyzed. RESULTS: Resolution of SRF was achieved in 20 of 21 eyes in the anti-VEGF group and in 12 of 15 eyes in the observation group (p = 0.151). Mean BCVA and CFT were not different between the two groups at 12 months (p > 0.05). The amount of change in BCVA, however, differed significantly between the groups (p = 0.044). Final OCT more frequently detected the foveal IS/OS line in the anti-VEGF group than in the observation group (p = 0.012). CONCLUSIONS: In terms of BCVA, anti-VEGF and observation only had similar therapeutic effects in acute CSC patients. In some patients, however, the rapid resolution of SRF by anti-VEGF might reduce the risk of photoreceptor degeneration and improve long-term visual acuity.

Near infrared ray annealing effects on the properties of Al-doped ZnO thin films prepared by spin-coating method.

In this research, we will present Al doped ZnO thin films for transparent conducting oxide applications. Aluminum doped zinc oxide (AZO) thin films have been deposited on the glass substrates by sol-gel spin-coating method using zinc acetate dehydrate (Zn(CH3COO)2 2H2O) and aluminum chloride hexahydrate (AlCl3 x 6H2O) as cation sources. In this study, we investigated the effects of near infrared ray (NIR) annealing on the structural, optical and electrical characteristics of the AZO thin films. The experimental results showed that AZO thin films have a hexagonal wurtzite crystal structure and had a good transmittance higher than 85% within the visible wavelength region. It was also found that the additional energy of NIR helps to improve the electrical properties of Al doped ZnO transparent conducting oxides.

Comparative studies of Al-doped ZnO and Ga-doped ZnO transparent conducting oxide thin films.

We have investigated the influences of aluminum and gallium dopants (0 to 2.0 mol%) on zinc oxide (ZnO) thin films regarding crystallization and electrical and optical properties for application in transparent conducting oxide devices. Al- and Ga-doped ZnO thin films were deposited on glass substrates (corning 1737) by sol-gel spin-coating process. As a starting material, AlCl3⋅6H2O, Ga(NO3)2, and Zn(CH3COO)2⋅2H2O were used. A lowest sheet resistance of 3.3 × 103 Ω/□ was obtained for the GZO thin film doped with 1.5 mol% of Ga after post-annealing at 650°C for 60 min in air. All the films showed more than 85% transparency in the visible region. We have studied the structural and microstructural properties as a function of Al and Ga concentrations through X-ray diffraction and scanning electron microscopy analysis. In addition, the optical bandgap and photoluminescence were estimated.

Hybrid mode-locked Er-doped fiber femtosecond oscillator with 156 mW output power.

We report on an Er-doped fiber oscillator that produces 146 fs pulses with 156 mW average power at a repetition rate of 49.9 MHz. The pulse energy reaches 3.13 nJ, surpassing the conventional power limit in the dispersion-managed soliton regime. Such high pulse power is obtained by devising a hybrid mode-locking scheme that combines saturable absorption with nonlinear polarization evolution. The oscillator also offers excellent temporal purity in the generated pulses with high power, providing a robust fiber-based frequency comb well suited for industrial uses.