Longitudinal evolution of cortical thickness signature reflecting Lewy body dementia in isolated REM sleep behavior disorder: a prospective cohort study

Background The isolated rapid-eye-movement sleep behavior disorder (iRBD) is a prodromal condition of Lewy body disease including Parkinson's disease and dementia with Lewy bodies (DLB). We aim to investigate the longitudinal evolution of DLB-related cortical thickness signature in a prospective iRBD cohort and evaluate the possible predictive value of the cortical signature index in predicting dementia-first phenoconversion in individuals with iRBD. Methods We enrolled 22 DLB patients, 44 healthy controls, and 50 video polysomnography-proven iRBD patients. Participants underwent 3-T magnetic resonance imaging (MRI) and clinical/neuropsychological evaluations. We characterized DLB-related whole-brain cortical thickness spatial covariance pattern (DLB-pattern) using scaled subprofile model of principal components analysis that best differentiated DLB patients from age-matched controls. We analyzed clinical and neuropsychological correlates of the DLB-pattern expression scores and the mean values of the whole-brain cortical thickness in DLB and iRBD patients. With repeated MRI data during the follow-up in our prospective iRBD cohort, we investigated the longitudinal evolution of the cortical thickness signature toward Lewy body dementia. Finally, we analyzed the potential predictive value of cortical thickness signature as a biomarker of phenoconversion in iRBD cohort. Results The DLB-pattern was characterized by thinning of the temporal, orbitofrontal, and insular cortices and relative preservation of the precentral and inferior parietal cortices. The DLB-pattern expression scores correlated with attentional and frontal executive dysfunction (Trail Making Test-A and B: R = − 0.55, P = 0.024 and R = − 0.56, P = 0.036, respectively) as well as visuospatial impairment (Rey-figure copy test: R = − 0.54, P = 0.0047). The longitudinal trajectory of DLB-pattern revealed an increasing pattern above the cut-off in the dementia-first phenoconverters (Pearsons correlation, R = 0.74, P = 6.8 × 10−4) but no significant change in parkinsonism-first phenoconverters (R = 0.0063, P = 0.98). The mean value of the whole-brain cortical thickness predicted phenoconversion in iRBD patients with hazard ratio of 9.33 [1.16–74.12]. The increase in DLB-pattern expression score discriminated dementia-first from parkinsonism-first phenoconversions with 88.2% accuracy. Conclusion Cortical thickness signature can effectively reflect the longitudinal evolution of Lewy body dementia in the iRBD population. Replication studies would further validate the utility of this imaging marker in iRBD

The Sedimentary records of the Hapcheon impact crater basin in Korea over the past 1.3 Ma

The Hapcheon impact crater is the only meteorite impact crater identified on the Korean peninsula. However, the morphology of the impact crater and the nature of the meteorite collision are unknown. In this study, we analyzed the sedimentary facies using grain size data; computed tomography images, 14C, 10Be, and optically stimulated luminescence dating on a >66-m-long sediment core (20HCL04) recovered from the Hapcheon Basin. Four sedimentary units and 10 types of facies were documented in the Hapcheon Basin sediment core. The sedimentary units comprise 1) a lower part (unit 1) that is dominated by moderately to well-sorted coarse gravel, which contains some impact-related sediments; 2) a middle part (units 2 and 3) dominated by well-laminated mud; and 3) an upper part (Unit 4) that is dominated by poorly sorted coarse gravel supplied from the surrounding mountain slopes by alluvial and fluvial processes. After the meteorite impact, the Hapcheon impact crater was filled with deposits from the crater wall after ca. 1.3 Ma and the Hapcheon Basin became a deep lake environment. After ca. 0.5 Ma, sediments were supplied from the surrounding mountains until the lake was filled. Finally, sediments were deposited in an alluvial fan setting. In addition, the Hapcheon Basin sedimentary cores contain a tephra layer and deformed soft sediments that can be used to investigate volcanic and seismic events on the Korean Peninsula over the past 1.3 Ma

Validation of the REM behaviour disorder phenoconversion-related pattern in an independent cohort

Background: A brain glucose metabolism pattern related to phenoconversion in patients with idiopathic/isolated REM sleep behaviour disorder (iRBDconvRP) was recently identified. However, the validation of the iRBDconvRP in an external, independent group of iRBD patients is needed to verify the reproducibility of such pattern, so to increase its importance in clinical and research settings. The aim of this work was to validate the iRBDconvRP in an independent group of iRBD patients. Methods: Forty iRBD patients (70 ± 5.59 years, 19 females) underwent brain [18F]FDG-PET in Seoul National University. Thirteen patients phenoconverted at follow-up (7 Parkinson disease, 5 Dementia with Lewy bodies, 1 Multiple system atrophy; follow-up time 35 ± 20.56 months) and 27 patients were still free from parkinsonism/dementia after 62 ± 29.49 months from baseline. We applied the previously identified iRBDconvRP to validate its phenoconversion prediction power. Results: The iRBDconvRP significantly discriminated converters from non-converters iRBD patients (p = 0.016; Area under the Curve 0.74, Sensitivity 0.69, Specificity 0.78), and it significantly predicted phenoconversion (Hazard ratio 4.26, C.I.95%: 1.18–15.39). Conclusions: The iRBDconvRP confirmed its robustness in predicting phenoconversion in an independent group of iRBD patients, suggesting its potential role as a stratification biomarker for disease-modifying trials.</p

Fast Monocular Depth Estimation with Neural Network Compression for Collision Avoidance

Department of Mechanical EngineeringThis paper introduces monocular depth estimation leveraging neural network compression algorithms which make a deep neural network smaller and faster for embedded systems and a collision avoidance algorithm with deep reinforcement learning. In this study, we employ an auto-encoder architecture for depth estimation, with ResNet-50 as the encoder and multiple convolutional layers as the decoder. As the network is too heavy to operate on embedded systems in real-time, knowledge distillation and neural network quantization and tensor decomposition, especially tucker-2 decomposition, are employed to reduce the number of parameters and latency. We employ knowledge distillation to initialize the weights of our depth estimator to improve the accuracy of depth estimation. We use tucker-2 decomposition to reduce the number of parameters and variational bayesian matrix factorization to estimate the ranks for the tucker decomposition, yielding multiple tensors that approximate the original weight. As a result of the decomposition, the accuracy of the network may degenerate, so we fine-tune the neural network to recover the accuracy. We also apply neural network quantization which converts computational structure from floating point representation to fixed point representation to accelerate the neural network on embedded systems. As a result of the neural network compression, an embedded computer board can execute the deep neural network faster with less memory footprint while maintaining reasonable accuracy. We integrate the network into a collision avoidance algorithm based on deep reinforcement learning whose states are depth information from our depth estimator. We demonstrate that our depth estimator can operate at 17 frames per second on NVIDIA Jetson TX2 CPU and that the agent can avoid obstacles at a high speed.ope

"ImagiNATION is my nation."

"Imagination is my nation, and that is where I want to live and die."- Guillermo Gómez-PeñaIntroIn this paper, I discuss artistic subjects and themes that were featured in my project, 호올 (Whore/Hole/Whole) (2017), and more recent work, Free Fish”es” (2018), that stemmed from my three-years residence practice at the Visual Arts Department, the University of California, San Diego. A Korean transcription of multiple English terms whose sounds are clearly distinguishable to English speakers but suggest little distinctions to Korean speakers, 호올 (Whore/Hole/Whole) is an attempt to defy the hierarchy of languages and gender inequality by adopting forms of rap music video, dance and vernacular swear words. In the exhibition, viewers are embedded in the counter-representational space against patriarchal, white-supremacist, hetero-normative institutions of language, gender, and race that marginalize women of color and hyphenated Americans. Free Fish”es” imagines the world of immigration and border policing as the vast body of water in which the migrant body as fish has to navigate and confront obstacles, which endlessly confines migrants into the water. Why the sea and the sky both have the same color—BLUE—but are not traversable? Let’s make fish fly. Let’s break the horizon, the borderline

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This paper introduces model compression algorithms which make a deep neural network smaller and faster for embedded systems. The model compression algorithms can be largely categorized into pruning, quantization and knowledge distillation. In this study, gradual pruning, quantization aware training, and knowledge distillation which learns the activation boundary in the hidden layer of the teacher neural network are integrated. As a large deep neural network is compressed and accelerated by these algorithms, embedded computing boards can run the deep neural network much faster with less memory usage while preserving the reasonable accuracy. To evaluate the performance of the compressed neural networks, we evaluate the size, latency and accuracy of the deep neural network, DenseNet201, for image classification with CIFAR-10 dataset on the NVIDIA Jetson Xavier

Development Pattern of Medical Device Technology and Regulatory Evolution of Cataract Treatment

To prevent regulation from becoming an obstacle to healthcare technological innovation, regulation should evolve as new healthcare technologies are developed. Although regulation is closely related to healthcare technology development, there are few studies that view healthcare technological advances from the multi-layered perspective of papers, patents, and clinical research and link this with regulatory evolution. Therefore, this study tried to develop a new method from a multi-layer perspective and draw regulatory implications based on it. This study applied this method to intraocular lens (IOLs) for cataract treatment and detected four major healthcare technologies and two recent healthcare technologies. Moreover, it discussed how current regulations evaluate these technologies. The findings provide implications for healthcare technological advances and the evolutionary direction of regulation through the example of IOLs for cataract treatment. This study contributes to the development of theoretical methods for co-evolution with regulations based on healthcare technology innovation