先発スタチン使用者と後発スタチン使用者における服薬アドヒアランス、継続率および臨床的アウトカムの比較：レセプトデータベースを用いた過去起点コホート研究

京都大学新制・課程博士博士(医学)甲第24188号医博第4882号京都大学大学院医学研究科医学専攻(主査)教授 古川 壽亮, 教授 中山 健夫, 教授 寺田 智祐学位規則第4条第1項該当Doctor of Medical ScienceKyoto UniversityDFA

Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation

Convolutional neural networks have been widely deployed in various application scenarios. In order to extend the applications' boundaries to some accuracy-crucial domains, researchers have been investigating approaches to boost accuracy through either deeper or wider network structures, which brings with them the exponential increment of the computational and storage cost, delaying the responding time. In this paper, we propose a general training framework named self distillation, which notably enhances the performance (accuracy) of convolutional neural networks through shrinking the size of the network rather than aggrandizing it. Different from traditional knowledge distillation - a knowledge transformation methodology among networks, which forces student neural networks to approximate the softmax layer outputs of pre-trained teacher neural networks, the proposed self distillation framework distills knowledge within network itself. The networks are firstly divided into several sections. Then the knowledge in the deeper portion of the networks is squeezed into the shallow ones. Experiments further prove the generalization of the proposed self distillation framework: enhancement of accuracy at average level is 2.65%, varying from 0.61% in ResNeXt as minimum to 4.07% in VGG19 as maximum. In addition, it can also provide flexibility of depth-wise scalable inference on resource-limited edge devices.Our codes will be released on github soon.Comment: 10page

Downregulated serum 14, 15-epoxyeicosatrienoic acid is associated with abdominal aortic calcification in patients with primary aldosteronism

Patients with primary aldosteronism (PA) have increased risk of target-organ damage, among which vascular calcification is an important indicator of cardiovascular mortality. 14, 15-Epoxyeicosatrienoic acid (14, 15-EET) has been shown to have beneficial effects in vascular remodeling. However, whether 14, 15-EET associates with vascular calcification in PA is unknown. Thus, we aimed to investigate the association between 14, 15-EET and abdominal aortic calcification (AAC) in patients with PA. Sixty-nine patients with PA and 69 controls with essential hypertension, matched for age, sex, and blood pressure, were studied. 14, 15-Dihydroxyeicosatrienoic acid (14, 15-DHET), the inactive metabolite from 14, 15-EET, was estimated to reflect serum 14, 15-EET levels. AAC was assessed by computed tomographic scanning. Compared with matched controls, the AAC prevalence was almost 1-fold higher in patients with PA (27 [39.1%] versus 14 [20.3%]; P=0.023), accompanied by significantly higher serum 14, 15-DHET levels (7.18±4.98 versus 3.50±2.07 ng/mL; P<0.001). Plasma aldosterone concentration was positively associated with 14, 15-DHET (β=0.444; P<0.001). Multivariable logistic analysis revealed that lower 14, 15-DHET was an independent risk factor for AAC in PA (odds ratio, 1.371; 95% confidence interval, 1.145–1.640; P<0.001), especially in young patients with mild hypertension and normal body mass index. In conclusion, PA patients exibited more severe AAC, accompanied by higher serum 14, 15-DHET levels. On the contrary, decreased 14, 15-EET was significantly associated with AAC prevalence in PA patients, especially in those at low cardiovascular risk

Video Face Super-Resolution with Motion-Adaptive Feedback Cell

Video super-resolution (VSR) methods have recently achieved a remarkable success due to the development of deep convolutional neural networks (CNN). Current state-of-the-art CNN methods usually treat the VSR problem as a large number of separate multi-frame super-resolution tasks, at which a batch of low resolution (LR) frames is utilized to generate a single high resolution (HR) frame, and running a slide window to select LR frames over the entire video would obtain a series of HR frames. However, duo to the complex temporal dependency between frames, with the number of LR input frames increase, the performance of the reconstructed HR frames become worse. The reason is in that these methods lack the ability to model complex temporal dependencies and hard to give an accurate motion estimation and compensation for VSR process. Which makes the performance degrade drastically when the motion in frames is complex. In this paper, we propose a Motion-Adaptive Feedback Cell (MAFC), a simple but effective block, which can efficiently capture the motion compensation and feed it back to the network in an adaptive way. Our approach efficiently utilizes the information of the inter-frame motion, the dependence of the network on motion estimation and compensation method can be avoid. In addition, benefiting from the excellent nature of MAFC, the network can achieve better performance in the case of extremely complex motion scenarios. Extensive evaluations and comparisons validate the strengths of our approach, and the experimental results demonstrated that the proposed framework is outperform the state-of-the-art methods.Comment: To appear in AAAI 202

Facial Attribute Capsules for Noise Face Super Resolution

Existing face super-resolution (SR) methods mainly assume the input image to be noise-free. Their performance degrades drastically when applied to real-world scenarios where the input image is always contaminated by noise. In this paper, we propose a Facial Attribute Capsules Network (FACN) to deal with the problem of high-scale super-resolution of noisy face image. Capsule is a group of neurons whose activity vector models different properties of the same entity. Inspired by the concept of capsule, we propose an integrated representation model of facial information, which named Facial Attribute Capsule (FAC). In the SR processing, we first generated a group of FACs from the input LR face, and then reconstructed the HR face from this group of FACs. Aiming to effectively improve the robustness of FAC to noise, we generate FAC in semantic, probabilistic and facial attributes manners by means of integrated learning strategy. Each FAC can be divided into two sub-capsules: Semantic Capsule (SC) and Probabilistic Capsule (PC). Them describe an explicit facial attribute in detail from two aspects of semantic representation and probability distribution. The group of FACs model an image as a combination of facial attribute information in the semantic space and probabilistic space by an attribute-disentangling way. The diverse FACs could better combine the face prior information to generate the face images with fine-grained semantic attributes. Extensive benchmark experiments show that our method achieves superior hallucination results and outperforms state-of-the-art for very low resolution (LR) noise face image super resolution.Comment: To appear in AAAI 202

RNA sequencing analysis to capture the transcriptome landscape during skin ulceration syndrome progression in sea cucumber Apostichopus japonicus

Complement and coagulation cascades pathways (tif). Red boxes represent up-regulated genes, and green boxes represent down-regulated genes. (TIF 627 kb

Electrically empowered microcomb laser

Optical frequency comb underpins a wide range of applications from communication, metrology, to sensing. Its development on a chip-scale platform -- so called soliton microcomb -- provides a promising path towards system miniaturization and functionality integration via photonic integrated circuit (PIC) technology. Although extensively explored in recent years, challenges remain in key aspects of microcomb such as complex soliton initialization, high threshold, low power efficiency, and limited comb reconfigurability. Here we present an on-chip laser that directly outputs microcomb and resolves all these challenges, with a distinctive mechanism created from synergetic interaction among resonant electro-optic effect, optical Kerr effect, and optical gain inside the laser cavity. Realized with integration between a III-V gain chip and a thin-film lithium niobate (TFLN) PIC, the laser is able to directly emit mode-locked microcomb on demand with robust turnkey operation inherently built in, with individual comb linewidth down to 600 Hz, whole-comb frequency tuning rate exceeding $\rm 2.4\times10^{17}$ Hz/s, and 100% utilization of optical power fully contributing to comb generation. The demonstrated approach unifies architecture and operation simplicity, high-speed reconfigurability, and multifunctional capability enabled by TFLN PIC, opening up a great avenue towards on-demand generation of mode-locked microcomb that is expected to have profound impact on broad applications