Huntingtin’s spherical solenoid structure enables polyglutamine tract-dependent modulation of its structure and function

The polyglutamine expansion in huntingtin protein causes Huntington’s disease. Here, we investigated structural and biochemical properties of huntingtin and the effect of the polyglutamine expansion using various biophysical experiments including circular dichroism, single-particle electron microscopy and cross-linking mass spectrometry. Huntingtin is likely composed of five distinct domains and adopts a spherical α-helical solenoid where the amino-terminal and carboxyl-terminal regions fold to contain a circumscribed central cavity. Interestingly, we showed that the polyglutamine expansion increases α-helical properties of huntingtin and affects the intramolecular interactions among the domains. Our work delineates the structural characteristics of full-length huntingtin, which are affected by the polyglutamine expansion, and provides an elegant solution to the apparent conundrum of how the extreme amino-terminal polyglutamine tract confers a novel property on huntingtin, causing the disease. DOI: http://dx.doi.org/10.7554/eLife.11184.00

Elevated IFNA1 and suppressed IL12p40 associated with persistent hyperinflammation in COVID-19 pneumonia

IntroductionDespite of massive endeavors to characterize inflammation in COVID-19 patients, the core network of inflammatory mediators responsible for severe pneumonia stillremain remains elusive. MethodsHere, we performed quantitative and kinetic analysis of 191 inflammatory factors in 955 plasma samples from 80 normal controls (sample n = 80) and 347 confirmed COVID-19 pneumonia patients (sample n = 875), including 8 deceased patients. ResultsDifferential expression analysis showed that 76% of plasmaproteins (145 factors) were upregulated in severe COVID-19 patients comparedwith moderate patients, confirming overt inflammatory responses in severe COVID-19 pneumonia patients. Global correlation analysis of the plasma factorsrevealed two core inflammatory modules, core I and II, comprising mainly myeloid cell and lymphoid cell compartments, respectively, with enhanced impact in a severity-dependent manner. We observed elevated IFNA1 and suppressed IL12p40, presenting a robust inverse correlation in severe patients, which was strongly associated with persistent hyperinflammation in 8.3% of moderate pneumonia patients and 59.4% of severe patients. DiscussionAberrant persistence of pulmonary and systemic inflammation might be associated with long COVID-19 sequelae. Our comprehensive analysis of inflammatory mediators in plasmarevealed the complexity of pneumonic inflammation in COVID-19 patients anddefined critical modules responsible for severe pneumonic progression

Fast non-blind deconvolution via regularized residual networks with long/short skip-connections

This paper proposes a novel framework for non-blind de-convolution using deep convolutional network. To deal with various blur kernels, we reduce the training complexity using Wiener filter as a preprocessing step in our framework. This step generates amplified noise and ringing artifacts, but the artifacts are little correlated with the shapes of blur kernels, making the input of our network independent of the blur kernel shape. Our network is trained to effectively remove those artifacts via a residual network with long/short skip-connections. We also add a regularization to help our network robustly process untrained and inaccurate blur kernels by suppressing abnormal weights of convolutional layers that may incur overfitting. Our postprocessing step can further improve the deconvolution quality. Experimental results demonstrate that our framework can process images blurred by a variety of blur kernels with faster speed and comparable image quality to the state-of-the-art methods.1

Single Image Defocus Deblurring Using Kernel-Sharing Parallel Atrous Convolutions

This paper proposes a novel deep learning approach for single image defocus deblurring based on inverse kernels. In a defocused image, the blur shapes are similar among pixels although the blur sizes can spatially vary. To utilize the property with inverse kernels, we exploit the observation that when only the size of a defocus blur changes while keeping the shape, the shape of the corresponding inverse kernel remains the same and only the scale changes. Based on the observation, we propose a kernel-sharing parallel atrous convolutional (KPAC) block specifically designed by incorporating the property of inverse kernels for single image defocus deblurring. To effectively simulate the invariant shapes of inverse kernels with different scales, KPAC shares the same convolutional weights among multiple atrous convolution layers. To efficiently simulate the varying scales of inverse kernels, KPAC consists of only a few atrous convolution layers with different dilations and learns per-pixel scale attentions to aggregate the outputs of the layers. KPAC also utilizes the shape attention to combine the outputs of multiple convolution filters in each atrous convolution layer, to deal with defocus blur with a slightly varying shape. We demonstrate that our approach achieves state-of-the-art performance with a much smaller number of parameters than previous methods1

Real-Time Video Deblurring via Lightweight Motion Compensation

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Recurrent Video Deblurring with Blur-Invariant Motion Estimation and Pixel Volumes

For the success of video deblurring, it is essential to utilize information from neighboring frames. Most state-of-the-art video deblurring methods adopt motion compensation between video frames to aggregate information from multiple frames that can help deblur a target frame. However, the motion compensation methods adopted by previous deblurring methods are not blur-invariant, and consequently, their accuracy is limited for blurry frames with different blur amounts. To alleviate this problem, we propose two novel approaches to deblur videos by effectively aggregating information from multiple video frames. First, we present blur-invariant motion estimation learning to improve motion estimation accuracy between blurry frames. Second, for motion compensation, instead of aligning frames by warping with estimated motions, we use a pixel volume that contains candidate sharp pixels to resolve motion estimation errors. We combine these two processes to propose an effective recurrent video deblurring network that fully exploits deblurred previous frames. Experiments show that our method achieves the state-of-the-art performance both quantitatively and qualitatively compared to recent methods that use deep learning.11Nsciescopu

Improved thermoelectric properties of silicon nanowire with silicide layer

열전 효율은 성능지수 ZT에 비례하며, ZT 는 제백 계수와 전기전도도, 열전도도로 구성되어 있다. 실리콘 나노선은 벌크 실리콘의 높은 열전도도를 상당히 억제함으로써 열전효율을 크게 향상시킬 수 있는 구조이다. 이는 나노선 구조에 의해 발생한 포논-경계 산란의 결과이다. 또한, 나노선의 표면이 거칠고, 직경이 작을수록 포논의 산란이 증가하기 때문에 나노선의 표면을 거칠게 제작할 필요가 있다. 불순물의 농도 역시 열전도도와 전기전도도, 제백 계수에 영향을 주기 때문에 최적의 농도 (1019~1021 cm-3)가 존재한다. 한편, 이종접합 구조는 포논 산란, 음향 임피던스 부조화, 전자 여과 효과와 같은 현상들에 의해 ZT 의 향상을 기대할 수 있다. 본 논문에서 실리콘 반도체 공정을 이용하여 금속 실리사이드 층을 포함한 200, 350, 500 nm 직경의 고농도 도핑된 실리콘 나노선을 제작하고, 열전 특성을 조사하였다. CoSi2는 열 안정성이 뛰어나며, 선폭 감소에 따른 면 저항의 변화가 작기 때문에 나노선과 같이 작은 면적을 갖는 구조체에 적용함에도 균일한 특성을 기대할 수 있다. 차등 3ω 방법과 포논-볼츠만 전송식을 기반으로 CoSi2 유무에 따른 나노선의 열전도도의 측정과 분석을 진행하였다. 나노선의 전기전도도와 제백 계수의 측정을 통해 실리사이드 층을 포함한 나노선의 ZT 가 향상됨을 실험적으로 검증하였다.2

Iterative Filter Adaptive Network for Single Image Defocus Deblurring

We propose a novel end-to-end learning-based approach for single image defocus deblurring. The proposed approach is equipped with a novel Iterative Filter Adaptive Network (IFAN) that is specifically designed to handle spatially-varying and large defocus blur. For adaptively handling spatially-varying blur, IFAN predicts pixel-wise deblurring filters, which are applied to defocused features of an input image to generate deblurred features. For effectively managing large blur, IFAN models deblurring filters as stacks of small-sized separable filters. Predicted separable deblurring filters are applied to defocused features using a novel Iterative Adaptive Convolution (IAC) layer. We also propose a training scheme based on defocus disparity estimation and reblurring, which significantly boosts the deblurring quality. We demonstrate that our method achieves state-of-the-art performance both quantitatively and qualitatively on real-world images1

Independently Responsive Stretchable Electroluminescent Platform for Electronic Skin under Multi-Stimulation

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